From 0137b8fd8f2e81c84e5e909fc9c2ca897b5b5b2c Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Mon, 29 Apr 2024 17:00:19 +0200 Subject: [PATCH 01/56] chore: update requirements.txt --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 67955c4..efbf9b5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,5 @@ scipy>=1.10.0 bitsandbytes>=0.42.0 nltk>=3.8.1 luxonis-ml[all]>=0.1.0 -python-box>=7.1.1 \ No newline at end of file +python-box>=7.1.1 +gcsfs>=2023.1.0 From 15fe38a4a1831dff22589085efba5b615678a55c Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Wed, 8 May 2024 15:12:50 +0000 Subject: [PATCH 02/56] feature: add dataset_plugin argument --- .../generate_dataset_from_scratch.py | 19 +++++++++++++++++++ datadreamer/utils/config.py | 1 + datadreamer/utils/convert_dataset.py | 18 ++++++++++++++++-- .../utils/luxonis_dataset_converter.py | 16 +++++++++++----- 4 files changed, 47 insertions(+), 7 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index cc5750e..609af9d 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -10,6 +10,7 @@ import numpy as np import torch from box import Box +from luxonis_ml.data import DATASETS_REGISTRY from PIL import Image from tqdm import tqdm @@ -227,6 +228,12 @@ def parse_args(): help="Path to the configuration file", ) + parser.add_argument( + "--dataset_plugin", + type=str, + help="Dataset plugin for luxonis-dataset", + ) + parser.add_argument( "--seed", type=int, @@ -345,6 +352,17 @@ def check_args(args): "--split_ratios must be a list of three floats that sum up to 1" ) + # Check if dataset_plugin is valid + if args.dataset_plugin is not None: + if args.dataset_format != "luxonis-dataset": + raise ValueError( + "--dataset_format must be 'luxonis-dataset' if --dataset_plugin is specified" + ) + if args.dataset_plugin not in DATASETS_REGISTRY.module_dict: + raise ValueError( + f"Invalid dataset plugin: {args.dataset_plugin}. Available plugins: {list(DATASETS_REGISTRY.module_dict.keys())}" + ) + def main(): args = parse_args() @@ -590,6 +608,7 @@ def main(): args.save_dir, "luxonis-dataset", args.split_ratios, + dataset_plugin=args.dataset_plugin, copy_files=False, seed=args.seed, ) diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index 2e87832..2ff15e8 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -44,3 +44,4 @@ class Config(LuxonisConfig): use_tta: bool = False annotator_size: Literal["base", "large"] = "base" batch_size_annotation: int = 1 + dataset_plugin: str = None diff --git a/datadreamer/utils/convert_dataset.py b/datadreamer/utils/convert_dataset.py index 7b028e4..8a9a140 100644 --- a/datadreamer/utils/convert_dataset.py +++ b/datadreamer/utils/convert_dataset.py @@ -11,14 +11,20 @@ def convert_dataset( - input_dir, output_dir, dataset_format, split_ratios, copy_files=True, seed=42 + input_dir, + output_dir, + dataset_format, + split_ratios, + dataset_plugin=None, + copy_files=True, + seed=42, ): if dataset_format == "yolo": converter = YOLOConverter(seed=seed) elif dataset_format == "coco": converter = COCOConverter(seed=seed) elif dataset_format == "luxonis-dataset": - converter = LuxonisDatasetConverter(seed=seed) + converter = LuxonisDatasetConverter(dataset_plugin=dataset_plugin, seed=seed) elif dataset_format == "cls-single": converter = SingleLabelClsConverter(seed=seed) else: @@ -52,6 +58,12 @@ def main(): default=[0.8, 0.1, 0.1], help="Train-validation-test split ratios (default: 0.8, 0.1, 0.1).", ) + parser.add_argument( + "--dataset_plugin", + type=str, + default=None, + help="Dataset plugin to use for luxonis-dataset format.", + ) parser.add_argument( "--copy_files", type=bool, @@ -72,7 +84,9 @@ def main(): args.output_dir, args.dataset_format, args.split_ratios, + args.dataset_plugin, args.copy_files, + args.seed, ) diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index d9cb5a5..2115656 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -2,7 +2,7 @@ import os -from luxonis_ml.data import LuxonisDataset +from luxonis_ml.data import DATASETS_REGISTRY, LuxonisDataset from luxonis_ml.data.utils.enums import BucketStorage from PIL import Image @@ -12,8 +12,9 @@ class LuxonisDatasetConverter(BaseConverter): """Class for converting a dataset to LuxonisDataset format.""" - def __init__(self, seed=42): + def __init__(self, dataset_plugin=None, seed=42): super().__init__(seed) + self.dataset_plugin = dataset_plugin def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): """Converts a dataset into a LuxonisDataset format. @@ -66,16 +67,21 @@ def dataset_generator(): dataset = LuxonisDataset(dataset_name) dataset.delete_dataset() + # if dataset_plugin is set, use that + if self.dataset_plugin: + print(f"Using {self.dataset_plugin} dataset") + dataset_constructor = DATASETS_REGISTRY.get(self.dataset_plugin) + dataset = dataset_constructor(dataset_name) # if LUXONISML_BUCKET and GOOGLE_APPLICATION_CREDENTIALS are set, use GCS bucket - if ( + elif ( "LUXONISML_BUCKET" in os.environ and "GOOGLE_APPLICATION_CREDENTIALS" in os.environ ): - dataset = LuxonisDataset(dataset_name, bucket_storage=BucketStorage.GCS) print("Using GCS bucket") + dataset = LuxonisDataset(dataset_name, bucket_storage=BucketStorage.GCS) else: - dataset = LuxonisDataset(dataset_name) print("Using local dataset") + dataset = LuxonisDataset(dataset_name) dataset.set_classes(class_names) dataset.add(dataset_generator) From a8f12dd0d9d5097d0ab7c764dc9f41f5d0cfaf8c Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Wed, 8 May 2024 21:36:02 +0200 Subject: [PATCH 03/56] docs: improve dataset_plugin description Co-authored-by: conorsim <60359299+conorsim@users.noreply.github.com> --- datadreamer/pipelines/generate_dataset_from_scratch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 609af9d..1acd229 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -231,7 +231,7 @@ def parse_args(): parser.add_argument( "--dataset_plugin", type=str, - help="Dataset plugin for luxonis-dataset", + help="LuxonisDataset plugin for the luxonis-dataset format", ) parser.add_argument( From 0ada3cc5ea99d377dde31b6926e06442cca0699c Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Wed, 26 Jun 2024 16:00:05 +0000 Subject: [PATCH 04/56] fix: dataset plugin default value --- datadreamer/pipelines/generate_dataset_from_scratch.py | 2 +- datadreamer/utils/config.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 1acd229..3a24382 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -353,7 +353,7 @@ def check_args(args): ) # Check if dataset_plugin is valid - if args.dataset_plugin is not None: + if args.dataset_plugin: if args.dataset_format != "luxonis-dataset": raise ValueError( "--dataset_format must be 'luxonis-dataset' if --dataset_plugin is specified" diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index 2ff15e8..8bf4f93 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -44,4 +44,4 @@ class Config(LuxonisConfig): use_tta: bool = False annotator_size: Literal["base", "large"] = "base" batch_size_annotation: int = 1 - dataset_plugin: str = None + dataset_plugin: str = "" From 872d001ef31c5457d1ccb4f9142940d2b5c607d6 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Mon, 5 Aug 2024 11:24:09 +0200 Subject: [PATCH 05/56] Feature/luxonis loader dataset (#57) * fix: replace typing with typing_extensions * fix: remove deprecated truncation argument * feature: add luxonis loader with plugin * feature: modify luxonis dataset with plugin * style: formatting * [Automated] Updated coverage badge * style: remove commented lines * fix: remove dataset_id from luxonis dataset args * fix: remove redundant env var check * [Automated] Updated coverage badge * fix: empty image_paths with luxonis dataset plugin * style: formatting * [Automated] Updated coverage badge * test: simple luxonis dataset test * fix: luxonis dataset converter missing attr * style: formatting * fix: luxonis dataset converter parent * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions --- .../dataset_annotation/owlv2_annotator.py | 1 - .../generate_dataset_from_scratch.py | 98 ++++++++++++++----- datadreamer/utils/config.py | 6 +- datadreamer/utils/convert_dataset.py | 13 ++- .../utils/luxonis_dataset_converter.py | 44 ++++++--- media/coverage_badge.svg | 4 +- tests/unittests/test_converters.py | 41 ++++++++ 7 files changed, 166 insertions(+), 41 deletions(-) diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index 1d4243c..25f247f 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -104,7 +104,6 @@ def _generate_annotations( images=images, return_tensors="pt", padding="max_length", - truncation=True, ).to(self.device) with torch.no_grad(): outputs = self.model(**inputs) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 3a24382..8812a5d 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -10,7 +10,7 @@ import numpy as np import torch from box import Box -from luxonis_ml.data import DATASETS_REGISTRY +from luxonis_ml.data import DATASETS_REGISTRY, LOADERS_REGISTRY from PIL import Image from tqdm import tqdm @@ -234,6 +234,18 @@ def parse_args(): help="LuxonisDataset plugin for the luxonis-dataset format", ) + parser.add_argument( + "--loader_plugin", + type=str, + help="Loader plugin for the LuxonisLoader", + ) + + parser.add_argument( + "--dataset_name", + type=str, + help="Name of the dataset to create if dataset_plugin or loader_plugin is used", + ) + parser.add_argument( "--seed", type=int, @@ -391,6 +403,12 @@ def main(): generated_prompts = None image_paths = [] + def split_image_paths(image_paths, batch_size): + return [ + image_paths[i : i + batch_size] + for i in range(0, len(image_paths), batch_size) + ] + if not args.annotate_only: # Prompt generation prompt_generator_class = prompt_generators[args.prompt_generator] @@ -439,12 +457,30 @@ def main(): image_generator.release(empty_cuda_cache=True) + # Split image_paths into batches + image_batches = split_image_paths(image_paths, args.batch_size_annotation) + else: - # Load image paths for annotation - for image_path in os.listdir(save_dir): - # Check file extension: jpg, png, jpeg - if image_path.lower().endswith((".jpg", ".png", ".jpeg", ".bmp", "webp")): - image_paths.append(os.path.join(save_dir, image_path)) + if args.loader_plugin: + if "DATASET_ID" in os.environ: + image_batches = LOADERS_REGISTRY.get(args.loader_plugin)( + view="all", dataset_id=os.getenv("DATASET_ID") + ) + else: + raise ValueError( + "DATASET_ID environment variable is not set for using the loader plugin" + ) + + else: + # Load image paths for annotation + for image_path in os.listdir(save_dir): + # Check file extension: jpg, png, jpeg + if image_path.lower().endswith( + (".jpg", ".png", ".jpeg", ".bmp", "webp") + ): + image_paths.append(os.path.join(save_dir, image_path)) + # Split image_paths into batches + image_batches = split_image_paths(image_paths, args.batch_size_annotation) # Synonym generation synonym_dict = None @@ -457,27 +493,45 @@ def main(): synonym_dict, os.path.join(save_dir, "synonyms.json") ) + def read_image_batch(image_batch, batch_num, batch_size): + if type(image_batch[0]) == np.ndarray: + images = [] + batch_image_paths = [] + for i, image in enumerate(image_batch[:-1]): + image = Image.fromarray(image) + unique_id = uuid.uuid4().hex + image_path = os.path.join( + save_dir, f"image_{batch_num * batch_size + i}_{unique_id}.jpg" + ) + image.save(image_path) + images.append(image) + batch_image_paths.append(image_path) + + else: + images = [Image.open(image_path) for image_path in image_batch] + batch_image_paths = image_batch + return images, batch_image_paths + boxes_list = [] scores_list = [] labels_list = [] + image_paths = [] if args.task == "classification": # Classification annotation annotator_class = clf_annotators[args.image_annotator] annotator = annotator_class(device=args.device, size=args.annotator_size) - # Split image_paths into batches - image_batches = [ - image_paths[i : i + args.batch_size_annotation] - for i in range(0, len(image_paths), args.batch_size_annotation) - ] - - for image_batch in tqdm( - image_batches, + for i, image_batch in tqdm( + enumerate(image_batches), desc="Annotating images", total=len(image_batches), ): - images = [Image.open(image_path) for image_path in image_batch] + images, batch_image_paths = read_image_batch( + image_batch, i, args.batch_size_annotation + ) + image_paths.extend(batch_image_paths) + batch_labels = annotator.annotate_batch( images, args.class_names, @@ -503,21 +557,20 @@ def main(): seed=args.seed, ) else: - # Annotation + # Detection annotation annotator_class = det_annotators[args.image_annotator] annotator = annotator_class(device=args.device, size=args.annotator_size) - # Split image_paths into batches - image_batches = [ - image_paths[i : i + args.batch_size_annotation] - for i in range(0, len(image_paths), args.batch_size_annotation) - ] for i, image_batch in tqdm( enumerate(image_batches), desc="Annotating images", total=len(image_batches), ): - images = [Image.open(image_path) for image_path in image_batch] + images, batch_image_paths = read_image_batch( + image_batch, i, args.batch_size_annotation + ) + image_paths.extend(batch_image_paths) + boxes_batch, scores_batch, local_labels_batch = annotator.annotate_batch( images, args.class_names, @@ -609,6 +662,7 @@ def main(): "luxonis-dataset", args.split_ratios, dataset_plugin=args.dataset_plugin, + dataset_name=args.dataset_name, copy_files=False, seed=args.seed, ) diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index 8bf4f93..bf0c5cb 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -1,9 +1,8 @@ from __future__ import annotations -from typing import Annotated, List, Literal - from luxonis_ml.utils import LuxonisConfig from pydantic import Field +from typing_extensions import Annotated, List, Literal class Config(LuxonisConfig): @@ -45,3 +44,6 @@ class Config(LuxonisConfig): annotator_size: Literal["base", "large"] = "base" batch_size_annotation: int = 1 dataset_plugin: str = "" + loader_plugin: str = "" + dataset_name: str = "" + dataset_id: str = "" diff --git a/datadreamer/utils/convert_dataset.py b/datadreamer/utils/convert_dataset.py index 8a9a140..21f1159 100644 --- a/datadreamer/utils/convert_dataset.py +++ b/datadreamer/utils/convert_dataset.py @@ -16,6 +16,7 @@ def convert_dataset( dataset_format, split_ratios, dataset_plugin=None, + dataset_name=None, copy_files=True, seed=42, ): @@ -24,7 +25,11 @@ def convert_dataset( elif dataset_format == "coco": converter = COCOConverter(seed=seed) elif dataset_format == "luxonis-dataset": - converter = LuxonisDatasetConverter(dataset_plugin=dataset_plugin, seed=seed) + converter = LuxonisDatasetConverter( + dataset_plugin=dataset_plugin, + dataset_name=dataset_name, + seed=seed, + ) elif dataset_format == "cls-single": converter = SingleLabelClsConverter(seed=seed) else: @@ -64,6 +69,11 @@ def main(): default=None, help="Dataset plugin to use for luxonis-dataset format.", ) + parser.add_argument( + "--dataset_name", + type=str, + help="Name of the dataset to create if dataset_plugin is used", + ) parser.add_argument( "--copy_files", type=bool, @@ -85,6 +95,7 @@ def main(): args.dataset_format, args.split_ratios, args.dataset_plugin, + args.dataset_name, args.copy_files, args.seed, ) diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 2115656..657c115 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -12,9 +12,10 @@ class LuxonisDatasetConverter(BaseConverter): """Class for converting a dataset to LuxonisDataset format.""" - def __init__(self, dataset_plugin=None, seed=42): + def __init__(self, dataset_plugin=None, dataset_name=None, seed=42): super().__init__(seed) self.dataset_plugin = dataset_plugin + self.dataset_name = dataset_name def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): """Converts a dataset into a LuxonisDataset format. @@ -46,32 +47,50 @@ def dataset_generator(): for label in labels: yield { "file": image_full_path, - "class": class_names[label], - "type": "classification", - "value": True, + "annotation": { + "class": class_names[label], + "type": "classification", + # "value": True, + }, } if "boxes" in data[image_path]: boxes = data[image_path]["boxes"] for box in boxes: x, y, w, h = box[0], box[1], box[2] - box[0], box[3] - box[1] + x = max(0, x) + y = max(0, y) yield { "file": image_full_path, - "class": class_names[label], - "type": "box", - "value": (x / width, y / height, w / width, h / height), + "annotation": { + "class": class_names[label], + "type": "boundingbox", + "x": x / width, + "y": y / height, + "w": w / width, + "h": h / height, + }, } - dataset_name = os.path.basename(output_dir) + dataset_name = ( + os.path.basename(output_dir) + if self.dataset_name is None + else self.dataset_name + ) if LuxonisDataset.exists(dataset_name): dataset = LuxonisDataset(dataset_name) dataset.delete_dataset() # if dataset_plugin is set, use that if self.dataset_plugin: - print(f"Using {self.dataset_plugin} dataset") - dataset_constructor = DATASETS_REGISTRY.get(self.dataset_plugin) - dataset = dataset_constructor(dataset_name) + if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ: + print(f"Using {self.dataset_plugin} dataset") + dataset_constructor = DATASETS_REGISTRY.get(self.dataset_plugin) + dataset = dataset_constructor(dataset_name) + else: + raise ValueError( + "GOOGLE_APPLICATION_CREDENTIALS environment variable is not set for using the dataset plugin." + ) # if LUXONISML_BUCKET and GOOGLE_APPLICATION_CREDENTIALS are set, use GCS bucket elif ( "LUXONISML_BUCKET" in os.environ @@ -82,8 +101,7 @@ def dataset_generator(): else: print("Using local dataset") dataset = LuxonisDataset(dataset_name) - dataset.set_classes(class_names) - dataset.add(dataset_generator) + dataset.add(dataset_generator()) dataset.make_splits(split_ratios) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index f9eb6b4..9d027c7 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -15,7 +15,7 @@ coverage coverage - 55% - 55% + 49% + 49% diff --git a/tests/unittests/test_converters.py b/tests/unittests/test_converters.py index f56ba71..776baea 100644 --- a/tests/unittests/test_converters.py +++ b/tests/unittests/test_converters.py @@ -3,11 +3,13 @@ import shutil import unittest +from luxonis_ml.data import LuxonisDataset from PIL import Image from datadreamer.utils import ( BaseConverter, COCOConverter, + LuxonisDatasetConverter, SingleLabelClsConverter, YOLOConverter, ) @@ -248,6 +250,45 @@ def test_create_data_yaml(self): self.assertIn("names: ['cat', 'dog']", content) +class TestLuxonisDatasetConverter(unittest.TestCase): + def setUp(self): + self.test_dir = "test_dataset" + os.makedirs(self.test_dir, exist_ok=True) + + # Create sample images + self.image_size = (100, 100) + self.create_sample_image("0.jpg") + self.create_sample_image("1.jpg") + + # Create sample labels + self.labels = { + "class_names": ["cat", "dog"], + "0.jpg": {"boxes": [(10, 10, 50, 50)], "labels": [0]}, + "1.jpg": {"boxes": [(20, 20, 70, 70)], "labels": [1]}, + } + with open(os.path.join(self.test_dir, "annotations.json"), "w") as f: + json.dump(self.labels, f) + + def tearDown(self): + shutil.rmtree(self.test_dir) + if hasattr(self, self.dataset_name) and LuxonisDataset.exists( + self.dataset_name + ): + dataset = LuxonisDataset(self.dataset_name) + dataset.delete_dataset() + + def create_sample_image(self, filename): + image = Image.new("RGB", self.image_size, color="white") + image.save(os.path.join(self.test_dir, filename)) + + def test_convert(self): + self.dataset_name = "test_dataset" + converter = LuxonisDatasetConverter(dataset_name=self.dataset_name) + split_ratios = [1, 0, 0] + converter.convert(self.test_dir, self.dataset_name, split_ratios) + self.assertTrue(LuxonisDataset.exists(self.dataset_name)) + + class TestSingleLabelClsConverter(unittest.TestCase): def setUp(self): self.converter = SingleLabelClsConverter() From 931390d617b4edab9aab40b165b8ac782f491450 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Fri, 9 Aug 2024 00:18:32 +0200 Subject: [PATCH 06/56] Merge main to dev (#58) * chore: add gcsfs to requirements.txt * chore: fix dev image tag --- .github/workflows/gar-publish-dev.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gar-publish-dev.yaml b/.github/workflows/gar-publish-dev.yaml index 8693392..cc1646e 100644 --- a/.github/workflows/gar-publish-dev.yaml +++ b/.github/workflows/gar-publish-dev.yaml @@ -34,5 +34,5 @@ jobs: - name: 'Build Inventory Image' working-directory: . run: | - docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} . --tag dev + docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} . --tag $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer:dev docker push $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer --all-tags From a1ea12b887baa7177eb7e39f692545abbfefcaca Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Fri, 9 Aug 2024 13:23:26 +0200 Subject: [PATCH 07/56] chore: fix gar publish dev action --- .github/workflows/gar-publish-dev.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/gar-publish-dev.yaml b/.github/workflows/gar-publish-dev.yaml index cc1646e..dd70fa9 100644 --- a/.github/workflows/gar-publish-dev.yaml +++ b/.github/workflows/gar-publish-dev.yaml @@ -16,6 +16,8 @@ jobs: steps: - name: 'Checkout GitHub Action' uses: actions/checkout@main + with: + ref: dev # Checkout the dev branch - id: 'auth' name: 'Authenticate to Google Cloud' From 6b580baa9b65876e160b94a8e5c1995c2df9887b Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Fri, 9 Aug 2024 15:35:33 +0200 Subject: [PATCH 08/56] feature: add branch arg to Dockerfile --- Dockerfile | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index 20f3905..94eacea 100644 --- a/Dockerfile +++ b/Dockerfile @@ -7,9 +7,14 @@ WORKDIR /app ## instal RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y RUN apt-get install -y git -RUN git clone https://github.com/luxonis/datadreamer.git -b main + +## Define a build argument for the branch, defaulting to "main" +ARG BRANCH=main + +## Clone the repository with the specified branch +RUN git clone --branch ${BRANCH} https://github.com/luxonis/datadreamer.git RUN cd datadreamer && pip install . ## define image execution -ENTRYPOINT ["datadreamer"] \ No newline at end of file +ENTRYPOINT ["datadreamer"] From 307102c520db5675c9f6ceae76014a60f30d0b65 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Fri, 9 Aug 2024 15:38:57 +0200 Subject: [PATCH 09/56] fix: build dev docker image from dev branch --- .github/workflows/gar-publish-dev.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gar-publish-dev.yaml b/.github/workflows/gar-publish-dev.yaml index dd70fa9..9f81624 100644 --- a/.github/workflows/gar-publish-dev.yaml +++ b/.github/workflows/gar-publish-dev.yaml @@ -36,5 +36,5 @@ jobs: - name: 'Build Inventory Image' working-directory: . run: | - docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} . --tag $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer:dev + docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} --build-arg BRANCH=dev . --tag $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer:dev docker push $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer --all-tags From b3a23ea4d1872677525a42b66ec5faa648fd37fc Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Tue, 20 Aug 2024 19:18:52 +0200 Subject: [PATCH 10/56] Fix/bbox labels in LuxonisDatasetConverter (#59) * fix: bbox labels in LuxonisDatasetConverter * fix: convert split_ratios to tuple for luxonis-ml * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions --- datadreamer/utils/luxonis_dataset_converter.py | 5 ++--- media/coverage_badge.svg | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 657c115..72dc06d 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -56,7 +56,7 @@ def dataset_generator(): if "boxes" in data[image_path]: boxes = data[image_path]["boxes"] - for box in boxes: + for box, label in zip(boxes, labels): x, y, w, h = box[0], box[1], box[2] - box[0], box[3] - box[1] x = max(0, x) y = max(0, y) @@ -103,5 +103,4 @@ def dataset_generator(): dataset = LuxonisDataset(dataset_name) dataset.add(dataset_generator()) - - dataset.make_splits(split_ratios) + dataset.make_splits(tuple(split_ratios)) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 9d027c7..b4a82e6 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -15,7 +15,7 @@ coverage coverage - 49% - 49% + 56% + 56% From 9653ec37e952b9839c96869946dbf4d51a1a6ae0 Mon Sep 17 00:00:00 2001 From: conorsim <60359299+conorsim@users.noreply.github.com> Date: Wed, 18 Sep 2024 15:29:08 -0600 Subject: [PATCH 11/56] fix: save images as PNG with full quality (#60) --- datadreamer/pipelines/generate_dataset_from_scratch.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 8812a5d..677feb3 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -501,9 +501,9 @@ def read_image_batch(image_batch, batch_num, batch_size): image = Image.fromarray(image) unique_id = uuid.uuid4().hex image_path = os.path.join( - save_dir, f"image_{batch_num * batch_size + i}_{unique_id}.jpg" + save_dir, f"image_{batch_num * batch_size + i}_{unique_id}.png" ) - image.save(image_path) + image.save(image_path, quality=100) images.append(image) batch_image_paths.append(image_path) From e563c97cca1f7d3bf5458e07b439290b0cb0def0 Mon Sep 17 00:00:00 2001 From: conorsim <60359299+conorsim@users.noreply.github.com> Date: Fri, 20 Sep 2024 15:26:00 -0600 Subject: [PATCH 12/56] Fix: loader plugin arg to load image paths (#61) * fix: use plugin arg to load image paths * fix: pre-commit formatting * fix: pass sync_target_directory to loader --- datadreamer/pipelines/generate_dataset_from_scratch.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 677feb3..b1aef52 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -464,7 +464,10 @@ def split_image_paths(image_paths, batch_size): if args.loader_plugin: if "DATASET_ID" in os.environ: image_batches = LOADERS_REGISTRY.get(args.loader_plugin)( - view="all", dataset_id=os.getenv("DATASET_ID") + view="all", + dataset_id=os.getenv("DATASET_ID"), + sync_target_directory=save_dir, + load_image_paths=True, ) else: raise ValueError( @@ -501,9 +504,9 @@ def read_image_batch(image_batch, batch_num, batch_size): image = Image.fromarray(image) unique_id = uuid.uuid4().hex image_path = os.path.join( - save_dir, f"image_{batch_num * batch_size + i}_{unique_id}.png" + save_dir, f"image_{batch_num * batch_size + i}_{unique_id}.jpg" ) - image.save(image_path, quality=100) + image.save(image_path) images.append(image) batch_image_paths.append(image_path) From f800b491bb61845c0a36e980ef5f00181bfe75ca Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Fri, 27 Sep 2024 15:24:05 +0200 Subject: [PATCH 13/56] Add logger, tests and refactor (#62) * fix: unet from config warning * feat: add logger * test: add utils tests * style: utils tests formatting * fix: args extenstion in merge dataset function * docs: docstrings and return types * [Automated] Updated coverage badge * fix: remove axes in bbox visualization * tests: improve image generation tests * [Automated] Updated coverage badge * docs: fix docstrings formatting * fix: type hints * tests: replace default ubuntu runner with buildjet runner * fix: type hint * test: modify memory computation * test: round up ram computation * test: disable output capturing * test: decrease required ram for demanding tests * test: 8vpcu buildjet runner * test: fix buildjet 8cpu runner * test: fix 8vcpu buildjet * test: divide tests into core and heavy * style: tests formatting * test: rename core tests * test: run core tests on pull to dev * test: fix config paths * [Automated] Updated coverage badge * test: update tests * [Automated] Updated coverage badge * test: run core tests on pr to main * test: rename heavy test scripts --------- Co-authored-by: GitHub Actions --- .github/workflows/tests.yaml | 34 +- .github/workflows/unit-tests.yaml | 116 ++++++ .../dataset_annotation/clip_annotator.py | 14 +- .../dataset_annotation/image_annotator.py | 9 +- .../dataset_annotation/owlv2_annotator.py | 23 +- datadreamer/dataset_annotation/utils.py | 4 +- .../image_generation/clip_image_tester.py | 18 +- .../image_generation/image_generator.py | 22 +- .../image_generation/sdxl_image_generator.py | 15 +- .../sdxl_lightning_image_generator.py | 18 +- .../sdxl_turbo_image_generator.py | 8 +- .../generate_dataset_from_scratch.py | 5 + .../prompt_generation/lm_prompt_generator.py | 16 +- .../prompt_generation/lm_synonym_generator.py | 13 +- .../prompt_generation/prompt_generator.py | 2 +- .../prompt_generation/synonym_generator.py | 10 +- .../tinyllama_lm_prompt_generator.py | 11 +- datadreamer/utils/base_converter.py | 32 +- datadreamer/utils/coco_converter.py | 37 +- datadreamer/utils/convert_dataset.py | 17 +- datadreamer/utils/dataset_utils.py | 17 +- .../utils/luxonis_dataset_converter.py | 46 ++- datadreamer/utils/merge_raw_datasets.py | 27 +- datadreamer/utils/nms.py | 8 +- .../utils/single_label_cls_converter.py | 45 ++- datadreamer/utils/yolo_converter.py | 54 ++- media/coverage_badge.svg | 6 +- .../integration/sample_config.yaml | 0 tests/core_tests/integration/test_pipeline.py | 172 +++++++++ .../unittests/test_annotators.py | 24 +- .../unittests/test_converters.py | 0 .../unittests/test_image_generation.py | 47 +-- .../unittests/test_pipeline_arguments.py | 213 +++++++++++ .../unittests/test_prompt_generation.py | 42 +-- tests/core_tests/unittests/test_utils.py | 186 ++++++++++ .../integration/test_pipeline_heavy.py} | 333 +----------------- .../unittests/test_image_generation_heavy.py | 68 ++++ .../unittests/test_prompt_generation_heavy.py | 91 +++++ 38 files changed, 1220 insertions(+), 583 deletions(-) create mode 100644 .github/workflows/unit-tests.yaml rename tests/{ => core_tests}/integration/sample_config.yaml (100%) create mode 100644 tests/core_tests/integration/test_pipeline.py rename tests/{ => core_tests}/unittests/test_annotators.py (82%) rename tests/{ => core_tests}/unittests/test_converters.py (100%) rename tests/{ => core_tests}/unittests/test_image_generation.py (76%) create mode 100644 tests/core_tests/unittests/test_pipeline_arguments.py rename tests/{ => core_tests}/unittests/test_prompt_generation.py (81%) create mode 100644 tests/core_tests/unittests/test_utils.py rename tests/{integration/test_pipeline.py => heavy_tests/integration/test_pipeline_heavy.py} (74%) create mode 100644 tests/heavy_tests/unittests/test_image_generation_heavy.py create mode 100644 tests/heavy_tests/unittests/test_prompt_generation_heavy.py diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 0b1aaf7..6f964ac 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -2,19 +2,23 @@ name: Tests on: pull_request: - branches: [ dev, main ] + branches: [ main ] paths: - 'datadreamer/**/**.py' - - 'tests/**/**.py' + - 'tests/core_tests/**/**.py' - .github/workflows/tests.yaml + workflow_dispatch: jobs: run_tests: strategy: fail-fast: false matrix: - os: [ubuntu-latest, windows-latest, macOS-latest] + os: [buildjet-8vcpu-ubuntu-2204, windows-latest, macOS-latest] version: ['3.10', '3.11'] + exclude: + - os: buildjet-8vcpu-ubuntu-2204 + version: '3.11' runs-on: ${{ matrix.os }} @@ -31,46 +35,43 @@ jobs: cache: pip - name: Install dependencies [Ubuntu] - if: matrix.os == 'ubuntu-latest' + if: matrix.os == 'buildjet-8vcpu-ubuntu-2204' run: | sudo apt update sudo apt install -y pandoc pip install -e .[dev] pip install coverage-badge>=1.1.0 pytest-cov>=4.1.0 - - name: Install dependencies [Windows] if: matrix.os == 'windows-latest' run: | pip install -e .[dev] pip install coverage-badge>=1.1.0 pytest-cov>=4.1.0 - - name: Install dependencies [macOS] if: matrix.os == 'macOS-latest' run: | pip install -e .[dev] pip install coverage-badge>=1.1.0 pytest-cov>=4.1.0 - - name: Run tests with coverage [Ubuntu] - if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' - run: pytest tests --cov=datadreamer --cov-report xml --junit-xml pytest.xml + if: matrix.os == 'buildjet-8vcpu-ubuntu-2204' && matrix.version == '3.10' + run: pytest tests/core_tests --cov=datadreamer --cov-report xml --junit-xml pytest.xml - name: Run tests [Windows, macOS] - if: matrix.os != 'ubuntu-latest' || matrix.version != '3.10' - run: pytest tests --junit-xml pytest.xml + if: matrix.os != 'buildjet-8vcpu-ubuntu-2204' + run: pytest tests/core_tests --junit-xml pytest.xml - name: Generate coverage badge [Ubuntu] - if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + if: matrix.os == 'buildjet-8vcpu-ubuntu-2204' && matrix.version == '3.10' run: coverage-badge -o media/coverage_badge.svg -f - name: Generate coverage report [Ubuntu] - if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + if: matrix.os == 'buildjet-8vcpu-ubuntu-2204' && matrix.version == '3.10' uses: orgoro/coverage@v3.1 with: coverageFile: coverage.xml token: ${{ secrets.GITHUB_TOKEN }} - name: Commit coverage badge [Ubuntu] - if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + if: matrix.os == 'buildjet-8vcpu-ubuntu-2204' && matrix.version == '3.10' run: | git config --global user.name 'GitHub Actions' git config --global user.email 'actions@github.com' @@ -78,9 +79,8 @@ jobs: git add media/coverage_badge.svg git commit -m "[Automated] Updated coverage badge" } - - name: Push changes [Ubuntu] - if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + if: matrix.os == 'buildjet-8vcpu-ubuntu-2204' && matrix.version == '3.10' uses: ad-m/github-push-action@master with: branch: ${{ github.head_ref }} @@ -117,4 +117,4 @@ jobs: - name: Publish Test Results uses: EnricoMi/publish-unit-test-result-action@v2 with: - files: "artifacts/**/*.xml" \ No newline at end of file + files: "artifacts/**/*.xml" diff --git a/.github/workflows/unit-tests.yaml b/.github/workflows/unit-tests.yaml new file mode 100644 index 0000000..59de92a --- /dev/null +++ b/.github/workflows/unit-tests.yaml @@ -0,0 +1,116 @@ +name: Unit tests + +on: + pull_request: + branches: [ dev ] + paths: + - 'datadreamer/**/**.py' + - 'tests/core_tests/unittests/**.py' + - .github/workflows/unit-tests.yaml + +jobs: + run_tests: + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-latest, macOS-latest] + version: ['3.10', '3.11'] + + runs-on: ${{ matrix.os }} + + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + ref: ${{ github.head_ref }} + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.version }} + cache: pip + + - name: Install dependencies [Ubuntu] + if: matrix.os == 'ubuntu-latest' + run: | + sudo apt update + sudo apt install -y pandoc + pip install -e .[dev] + pip install coverage-badge>=1.1.0 pytest-cov>=4.1.0 + - name: Install dependencies [Windows] + if: matrix.os == 'windows-latest' + run: | + pip install -e .[dev] + pip install coverage-badge>=1.1.0 pytest-cov>=4.1.0 + - name: Install dependencies [macOS] + if: matrix.os == 'macOS-latest' + run: | + pip install -e .[dev] + pip install coverage-badge>=1.1.0 pytest-cov>=4.1.0 + - name: Run tests with coverage [Ubuntu] + if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + run: pytest tests/core_tests/unittests --cov=datadreamer --cov-report xml --junit-xml pytest.xml + + - name: Run tests [Windows, macOS] + if: matrix.os != 'ubuntu-latest' || matrix.version != '3.10' + run: pytest tests/core_tests/unittests --junit-xml pytest.xml + + - name: Generate coverage badge [Ubuntu] + if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + run: coverage-badge -o media/coverage_badge.svg -f + + - name: Generate coverage report [Ubuntu] + if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + uses: orgoro/coverage@v3.1 + with: + coverageFile: coverage.xml + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Commit coverage badge [Ubuntu] + if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + run: | + git config --global user.name 'GitHub Actions' + git config --global user.email 'actions@github.com' + git diff --quiet media/coverage_badge.svg || { + git add media/coverage_badge.svg + git commit -m "[Automated] Updated coverage badge" + } + - name: Push changes [Ubuntu] + if: matrix.os == 'ubuntu-latest' && matrix.version == '3.10' + uses: ad-m/github-push-action@master + with: + branch: ${{ github.head_ref }} + + - name: Upload Test Results + if: always() + uses: actions/upload-artifact@v4 + with: + name: Test Results [${{ matrix.os }}] (Python ${{ matrix.version }}) + path: pytest.xml + retention-days: 10 + if-no-files-found: error + + publish-test-results: + name: "Publish Tests Results" + needs: run_tests + runs-on: ubuntu-latest + permissions: + checks: write + pull-requests: write + if: always() + + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + ref: ${{ github.head_ref }} + + - name: Download Artifacts + uses: actions/download-artifact@v4 + with: + path: artifacts + + - name: Publish Test Results + uses: EnricoMi/publish-unit-test-result-action@v2 + with: + files: "artifacts/**/*.xml" diff --git a/datadreamer/dataset_annotation/clip_annotator.py b/datadreamer/dataset_annotation/clip_annotator.py index ff7b9aa..a39d1c6 100644 --- a/datadreamer/dataset_annotation/clip_annotator.py +++ b/datadreamer/dataset_annotation/clip_annotator.py @@ -1,6 +1,7 @@ from __future__ import annotations -from typing import List +import logging +from typing import Dict, List import numpy as np import PIL @@ -10,6 +11,8 @@ from datadreamer.dataset_annotation.image_annotator import BaseAnnotator, TaskList +logger = logging.getLogger(__name__) + class CLIPAnnotator(BaseAnnotator): """A class for image annotation using the CLIP model, specializing in image @@ -47,7 +50,7 @@ def __init__( self.device = device self.model.to(self.device) - def _init_processor(self): + def _init_processor(self) -> CLIPProcessor: """Initializes the CLIP processor. Returns: @@ -57,12 +60,13 @@ def _init_processor(self): return CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") return CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") - def _init_model(self): + def _init_model(self) -> CLIPModel: """Initializes the CLIP model. Returns: CLIPModel: The initialized CLIP model. """ + logger.info(f"Initializing CLIP {self.size} model...") if self.size == "large": return CLIPModel.from_pretrained("openai/clip-vit-large-patch14") return CLIPModel.from_pretrained("openai/clip-vit-base-patch32") @@ -72,7 +76,7 @@ def annotate_batch( images: List[PIL.Image.Image], objects: List[str], conf_threshold: float = 0.1, - synonym_dict: dict[str, List[str]] | None = None, + synonym_dict: Dict[str, List[str]] | None = None, ) -> List[np.ndarray]: """Annotates images using the OWLv2 model. @@ -83,7 +87,7 @@ def annotate_batch( synonym_dict (dict, optional): Dictionary for handling synonyms in labels. Defaults to None. Returns: - List[List[int]]: A list of lists of labels for each image. + List[np.ndarray]: A list of the annotations for each image. """ if synonym_dict is not None: objs_syn = set() diff --git a/datadreamer/dataset_annotation/image_annotator.py b/datadreamer/dataset_annotation/image_annotator.py index 4479ffe..757baab 100644 --- a/datadreamer/dataset_annotation/image_annotator.py +++ b/datadreamer/dataset_annotation/image_annotator.py @@ -4,15 +4,12 @@ from abc import ABC, abstractmethod -# Enum for different labeling tasks class TaskList(enum.Enum): CLASSIFICATION = "classification" OBJECT_DETECTION = "object_detection" SEGMENTATION = "segmentation" - # Add more tasks as needed -# Abstract base class for data labeling class BaseAnnotator(ABC): """Abstract base class for creating annotators. @@ -24,6 +21,8 @@ class BaseAnnotator(ABC): Methods: annotate_batch(): Abstract method to be implemented by subclasses. It should contain the logic for performing annotation based on the task definition. + release(): Abstract method to be implemented by subclasses. It should contain + the logic for releasing the resources used by the annotator. """ def __init__( @@ -35,3 +34,7 @@ def __init__( @abstractmethod def annotate_batch(self): pass + + @abstractmethod + def release(self, empty_cuda_cache=False) -> None: + pass diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index 25f247f..89d4023 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -1,6 +1,7 @@ from __future__ import annotations -from typing import List, Tuple +import logging +from typing import Dict, List, Tuple import numpy as np import PIL @@ -11,6 +12,8 @@ from datadreamer.dataset_annotation.utils import apply_tta from datadreamer.utils.nms import non_max_suppression +logger = logging.getLogger(__name__) + class OWLv2Annotator(BaseAnnotator): """A class for image annotation using the OWLv2 model, specializing in object @@ -48,12 +51,13 @@ def __init__( self.device = device self.model.to(self.device) - def _init_model(self): + def _init_model(self) -> Owlv2ForObjectDetection: """Initializes the OWLv2 model for object detection. Returns: Owlv2ForObjectDetection: The initialized OWLv2 model. """ + logger.info(f"Initializing OWLv2 {self.size} model...") if self.size == "large": return Owlv2ForObjectDetection.from_pretrained( "google/owlv2-large-patch14-ensemble" @@ -62,7 +66,7 @@ def _init_model(self): "google/owlv2-base-patch16-ensemble" ) - def _init_processor(self): + def _init_processor(self) -> Owlv2Processor: """Initializes the processor for the OWLv2 model. Returns: @@ -81,7 +85,7 @@ def _generate_annotations( images: List[PIL.Image.Image], prompts: List[str], conf_threshold: float = 0.1, - ) -> List[dict[str, torch.Tensor]]: + ) -> List[Dict[str, torch.Tensor]]: """Generates annotations for the given images and prompts. Args: @@ -90,7 +94,7 @@ def _generate_annotations( conf_threshold (float, optional): Confidence threshold for the annotations. Defaults to 0.1. Returns: - dict: A dictionary containing the annotations for the images. + List[Dict[str, torch.Tensor]]: The annotations for the given images and prompts. """ n = len(images) batched_prompts = [prompts] * n @@ -107,7 +111,6 @@ def _generate_annotations( ).to(self.device) with torch.no_grad(): outputs = self.model(**inputs) - # print(outputs) preds = self.processor.post_process_object_detection( outputs=outputs, target_sizes=target_sizes, threshold=conf_threshold ) @@ -116,11 +119,11 @@ def _generate_annotations( def _get_annotations( self, - pred: dict[str, torch.Tensor], + pred: Dict[str, torch.Tensor], use_tta: bool, img_dim: int, - synonym_dict: dict[str, List[str]] | None, - synonym_dict_rev: dict[int, int] | None, + synonym_dict: Dict[str, List[str]] | None, + synonym_dict_rev: Dict[int, int] | None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Extracts the annotations from the predictions. @@ -158,7 +161,7 @@ def annotate_batch( conf_threshold: float = 0.1, iou_threshold: float = 0.2, use_tta: bool = False, - synonym_dict: dict[str, List[str]] | None = None, + synonym_dict: Dict[str, List[str]] | None = None, ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]: """Annotates images using the OWLv2 model. diff --git a/datadreamer/dataset_annotation/utils.py b/datadreamer/dataset_annotation/utils.py index 942d1a4..bfb13b7 100644 --- a/datadreamer/dataset_annotation/utils.py +++ b/datadreamer/dataset_annotation/utils.py @@ -1,9 +1,11 @@ from __future__ import annotations +from typing import List + from torchvision import transforms -def apply_tta(image): +def apply_tta(image) -> List[transforms.Compose]: """Apply test-time augmentation (TTA) to the given image. Args: diff --git a/datadreamer/image_generation/clip_image_tester.py b/datadreamer/image_generation/clip_image_tester.py index 2c67965..8147533 100644 --- a/datadreamer/image_generation/clip_image_tester.py +++ b/datadreamer/image_generation/clip_image_tester.py @@ -1,11 +1,14 @@ from __future__ import annotations -from typing import List +import logging +from typing import List, Tuple import torch from PIL import Image from transformers import CLIPModel, CLIPProcessor +logger = logging.getLogger(__name__) + class ClipImageTester: """A class for testing images against a set of textual objects using the CLIP model. @@ -22,6 +25,7 @@ class ClipImageTester: def __init__(self, device: str = "cuda") -> None: """Initializes the ClipImageTester with the CLIP model and processor.""" + logger.info("Initializing CLIP image tester...") self.clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") self.clip_processor = CLIPProcessor.from_pretrained( "openai/clip-vit-base-patch32" @@ -29,7 +33,9 @@ def __init__(self, device: str = "cuda") -> None: self.device = device self.clip.to(self.device) - def test_image(self, image: Image.Image, objects: List[str], conf_threshold=0.05): + def test_image( + self, image: Image.Image, objects: List[str], conf_threshold: float = 0.05 + ) -> Tuple[bool, torch.Tensor, int]: """Tests the generated image against a set of objects using the CLIP model. Args: @@ -60,8 +66,8 @@ def test_images_batch( self, images: List[Image.Image], objects: List[List[str]], - conf_threshold=0.05, - ) -> List[tuple]: + conf_threshold: float = 0.05, + ) -> Tuple[List[bool], List[torch.Tensor], List[int]]: """Tests the generated images against a set of objects using the CLIP model. Args: @@ -70,8 +76,8 @@ def test_images_batch( conf_threshold (float, optional): Confidence threshold for considering an object as present. Defaults to 0.05. Returns: - List[tuple]: A list of tuples containing a boolean indicating if the image passes the test, - the probabilities of the objects, and the number of objects that passed the test. + Tuple[List[bool], List[torch.Tensor], List[int]]: A tuple containing a list of booleans indicating if the images pass the test, + a list of probabilities of the objects, and a list of the number of objects that passed the test. """ # Transform the inputs for the CLIP model objects_array = [] diff --git a/datadreamer/image_generation/image_generator.py b/datadreamer/image_generation/image_generator.py index 4b01f81..bfbc53d 100644 --- a/datadreamer/image_generation/image_generator.py +++ b/datadreamer/image_generation/image_generator.py @@ -30,7 +30,7 @@ class ImageGenerator: set_seed(seed): Sets the seed for random number generators. generate_images(prompts, prompt_objects): Generates images based on provided prompts and optional object prompts. release(empty_cuda_cache): Releases resources and optionally empties the CUDA cache. (Abstract method) - generate_image(prompt, negative_prompt, prompt_objects): Generates a single image based on the provided prompt. (Abstract method) + generate_images_batch(prompts, negative_prompt, prompt_objects): Generates a batch of images based on the provided prompts. Abstract method) Note: The actual model for image generation needs to be defined in the subclass. @@ -64,7 +64,7 @@ def __init__( self.set_seed(seed) @staticmethod - def set_seed(seed: int): + def set_seed(seed: int) -> None: """Sets the seed for random number generators in Python and PyTorch. Args: @@ -78,7 +78,7 @@ def generate_images( self, prompts: Union[str, List[str]], prompt_objects: Optional[List[List[str]]] = None, - ): + ) -> List[Image.Image]: """Generates images based on the provided prompts and optional object prompts. Args: @@ -151,20 +151,20 @@ def release(self, empty_cuda_cache=False) -> None: pass @abstractmethod - def generate_image( + def generate_images_batch( self, - prompt: str, + prompts: List[str], negative_prompt: str, - prompt_objects: Optional[List[str]] = None, - ) -> Image.Image: - """Generates a single image based on the provided prompt. + prompt_objects: Optional[List[List[str]]] = None, + ) -> List[Image.Image]: + """Generates a batch of images based on the provided prompts. Args: - prompt (str): The positive prompt to guide image generation. + prompts (List[str]): A list of positive prompts to guide image generation. negative_prompt (str): The negative prompt to avoid certain features in the image. - prompt_objects (Optional[List[str]]): Optional list of objects to be used in CLIP model testing. + prompt_objects (Optional[List[List[str]]]): Optional list of objects to be used in CLIP model testing. Returns: - Image.Image: The generated image. + List[Image.Image]: A list of generated images. """ pass diff --git a/datadreamer/image_generation/sdxl_image_generator.py b/datadreamer/image_generation/sdxl_image_generator.py index 1882f4a..3c090de 100644 --- a/datadreamer/image_generation/sdxl_image_generator.py +++ b/datadreamer/image_generation/sdxl_image_generator.py @@ -1,13 +1,17 @@ from __future__ import annotations -from typing import List, Optional +import logging +from typing import List, Optional, Tuple import torch from compel import Compel, ReturnedEmbeddingsType from diffusers import DiffusionPipeline +from PIL import Image from datadreamer.image_generation.image_generator import ImageGenerator +logger = logging.getLogger(__name__) + class StableDiffusionImageGenerator(ImageGenerator): """A subclass of ImageGenerator that uses the Stable Diffusion model for image @@ -32,14 +36,14 @@ def __init__(self, *args, **kwargs): self.base, self.refiner = self._init_gen_model() self.base_processor, self.refiner_processor = self._init_processor() - def _init_gen_model(self): + def _init_gen_model(self) -> Tuple[DiffusionPipeline, DiffusionPipeline]: """Initializes the base and refiner models of Stable Diffusion. Returns: tuple: The base and refiner models. """ + logger.info(f"Initializing SDXL on {self.device}...") if self.device == "cpu": - print("Loading SDXL on CPU...") base = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", # variant="fp16", @@ -57,7 +61,6 @@ def _init_gen_model(self): ) refiner.to("cpu") else: - print("Loading SDXL on GPU...") base = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, @@ -77,7 +80,7 @@ def _init_gen_model(self): return base, refiner - def _init_processor(self): + def _init_processor(self) -> Tuple[Compel, Compel]: """Initializes the processors for the base and refiner models. Returns: @@ -102,7 +105,7 @@ def generate_images_batch( prompts: List[str], negative_prompt: str, prompt_objects: Optional[List[List[str]]] = None, - ): + ) -> List[Image.Image]: """Generates a batch of images based on the provided prompts. Args: diff --git a/datadreamer/image_generation/sdxl_lightning_image_generator.py b/datadreamer/image_generation/sdxl_lightning_image_generator.py index 33c5141..f4520e4 100644 --- a/datadreamer/image_generation/sdxl_lightning_image_generator.py +++ b/datadreamer/image_generation/sdxl_lightning_image_generator.py @@ -1,5 +1,6 @@ from __future__ import annotations +import logging from typing import List, Optional import torch @@ -15,6 +16,8 @@ from datadreamer.image_generation.image_generator import ImageGenerator +logger = logging.getLogger(__name__) + class StableDiffusionLightningImageGenerator(ImageGenerator): """A subclass of ImageGenerator specifically designed to use the Stable Diffusion @@ -37,7 +40,7 @@ def __init__(self, *args, **kwargs): self.pipe = self._init_gen_model() self.compel = self._init_compel() - def _init_gen_model(self): + def _init_gen_model(self) -> StableDiffusionXLPipeline: """Initializes the Stable Diffusion Lightning model for image generation. Returns: @@ -46,16 +49,15 @@ def _init_gen_model(self): base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting! + config = UNet2DConditionModel.load_config(base, subfolder="unet") - # Load model. + logger.info(f"Initializing SDXL Lightning on {self.device}...") if self.device == "cpu": - print("Loading SDXL Lightning on CPU...") - unet = UNet2DConditionModel.from_config(base, subfolder="unet") + unet = UNet2DConditionModel.from_config(config) unet.load_state_dict(load_file(hf_hub_download(repo, ckpt))) pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet) else: - print("Loading SDXL Lightning on GPU...") - unet = UNet2DConditionModel.from_config(base, subfolder="unet").to( + unet = UNet2DConditionModel.from_config(config).to( self.device, torch.float16 ) unet.load_state_dict( @@ -73,7 +75,7 @@ def _init_gen_model(self): return pipe - def _init_compel(self): + def _init_compel(self) -> Compel: """Initializes the Compel model for text prompt weighting. Returns: @@ -92,7 +94,6 @@ def generate_images_batch( prompts: List[str], negative_prompt: str, prompt_objects: Optional[List[List[str]]] = None, - batch_size: int = 1, ) -> List[Image.Image]: """Generates a batch of images using the Stable Diffusion Lightning model based on the provided prompts. @@ -101,7 +102,6 @@ def generate_images_batch( prompts (List[str]): A list of positive prompts to guide image generation. negative_prompt (str): The negative prompt to avoid certain features in the image. prompt_objects (Optional[List[List[str]]]): Optional list of objects for each prompt for CLIP model testing. - batch_size (int): The number of images to generate in each batch. Returns: List[Image.Image]: A list of generated images. diff --git a/datadreamer/image_generation/sdxl_turbo_image_generator.py b/datadreamer/image_generation/sdxl_turbo_image_generator.py index e78fa17..abd20a0 100644 --- a/datadreamer/image_generation/sdxl_turbo_image_generator.py +++ b/datadreamer/image_generation/sdxl_turbo_image_generator.py @@ -1,5 +1,6 @@ from __future__ import annotations +import logging from typing import List, Optional import torch @@ -8,6 +9,8 @@ from datadreamer.image_generation.image_generator import ImageGenerator +logger = logging.getLogger(__name__) + class StableDiffusionTurboImageGenerator(ImageGenerator): """A subclass of ImageGenerator specifically designed to use the Stable Diffusion @@ -28,14 +31,14 @@ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.base = self._init_gen_model() - def _init_gen_model(self): + def _init_gen_model(self) -> AutoPipelineForText2Image: """Initializes the Stable Diffusion Turbo model for image generation. Returns: AutoPipelineForText2Image: The initialized Stable Diffusion Turbo model. """ + logger.info(f"Initializing SDXL Turbo on {self.device}...") if self.device == "cpu": - print("Loading SDXL Turbo on CPU...") base = AutoPipelineForText2Image.from_pretrained( "stabilityai/sdxl-turbo", # variant="fp16", @@ -44,7 +47,6 @@ def _init_gen_model(self): ) base.to("cpu") else: - print("Loading SDXL Turbo on GPU...") base = AutoPipelineForText2Image.from_pretrained( "stabilityai/sdxl-turbo", torch_dtype=torch.float16, diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index b1aef52..d3ee3bf 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -11,6 +11,7 @@ import torch from box import Box from luxonis_ml.data import DATASETS_REGISTRY, LOADERS_REGISTRY +from luxonis_ml.utils import setup_logging from PIL import Image from tqdm import tqdm @@ -50,6 +51,8 @@ det_annotators = {"owlv2": OWLv2Annotator} clf_annotators = {"clip": CLIPAnnotator} +setup_logging(use_rich=True) + def parse_args(): # Argument parsing @@ -620,11 +623,13 @@ def read_image_batch(image_batch, batch_num, batch_size): labels_list.append(np.array(labels)) + plt.axis("off") plt.savefig( os.path.join( bbox_dir, f"bbox_{i * args.batch_size_annotation + j}.jpg" ) ) + plt.close() # Save annotations as JSON files diff --git a/datadreamer/prompt_generation/lm_prompt_generator.py b/datadreamer/prompt_generation/lm_prompt_generator.py index 10ca96e..8a3e6e1 100644 --- a/datadreamer/prompt_generation/lm_prompt_generator.py +++ b/datadreamer/prompt_generation/lm_prompt_generator.py @@ -1,8 +1,9 @@ from __future__ import annotations +import logging import random import re -from typing import List, Literal, Optional +from typing import List, Literal, Optional, Tuple import torch from tqdm import tqdm @@ -16,6 +17,8 @@ from datadreamer.prompt_generation.prompt_generator import PromptGenerator +logger = logging.getLogger(__name__) + class LMPromptGenerator(PromptGenerator): """A language model-based prompt generator class, extending PromptGenerator. @@ -62,15 +65,15 @@ def __init__( ) self.model, self.tokenizer, self.pipeline = self._init_lang_model() - def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: + def _init_lang_model(self) -> Tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: """Initializes the language model, tokenizer and pipeline for prompt generation. Returns: tuple: The initialized language model, tokenizer and pipeline. """ selected_dtype = "auto" + logger.info(f"Initializing Mistral-7B language model on {self.device}...") if self.device == "cpu": - print("Loading language model on CPU...") model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.1", torch_dtype="auto", @@ -79,7 +82,7 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin ) else: if self.quantization == "none": - print("Loading FP16 language model on GPU...") + logger.info("Loading FP16 language model...") selected_dtype = torch.float16 model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.1", @@ -88,7 +91,7 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin device_map=self.device, ) else: - print("Loading INT4 language model on GPU...") + logger.info("Loading INT4 language model...") # Create the BitsAndBytesConfig object with the dynamically constructed arguments bnb_config = BitsAndBytesConfig( load_in_4bit=True, @@ -115,7 +118,6 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin device_map=self.device, batch_size=self.batch_size, ) - print("Done!") return model, tokenizer, pipe def _remove_incomplete_sentence(self, text: str) -> str: @@ -219,7 +221,7 @@ def generate_prompts(self) -> List[str]: """ prompts = [] progress_bar = tqdm( - desc="Generating prompts...", position=0, total=self.prompts_number + desc="Generating prompts", position=0, total=self.prompts_number ) while len(prompts) < self.prompts_number: selected_objects_batch = [ diff --git a/datadreamer/prompt_generation/lm_synonym_generator.py b/datadreamer/prompt_generation/lm_synonym_generator.py index fc86db8..850ccfb 100644 --- a/datadreamer/prompt_generation/lm_synonym_generator.py +++ b/datadreamer/prompt_generation/lm_synonym_generator.py @@ -1,7 +1,8 @@ from __future__ import annotations +import logging import re -from typing import List, Optional +from typing import List, Optional, Tuple import torch from transformers import ( @@ -13,6 +14,8 @@ from datadreamer.prompt_generation.synonym_generator import SynonymGenerator +logger = logging.getLogger(__name__) + class LMSynonymGenerator(SynonymGenerator): """Synonym generator that generates synonyms for a list of words using a language @@ -42,14 +45,14 @@ def __init__( super().__init__(synonyms_number, seed, device) self.model, self.tokenizer, self.pipeline = self._init_lang_model() - def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: + def _init_lang_model(self) -> Tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: """Initializes the language model, tokenizer and pipeline for prompt generation. Returns: tuple: The initialized language model, tokenizer and pipeline. """ + logger.info(f"Initializing Mistral-7B language model on {self.device}...") if self.device == "cpu": - print("Loading language model on CPU...") model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.1", torch_dtype="auto", @@ -57,7 +60,7 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin low_cpu_mem_usage=True, ) else: - print("Loading FP16 language model on GPU...") + logger.info("Loading FP16 language model...") model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.1", torch_dtype=torch.float16, @@ -73,7 +76,7 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin torch_dtype=torch.float16 if self.device == "cuda" else "auto", device_map=self.device, ) - print("Done!") + logger.info("Done!") return model, tokenizer, pipe def _generate_synonyms(self, prompt_text: str) -> List[str]: diff --git a/datadreamer/prompt_generation/prompt_generator.py b/datadreamer/prompt_generation/prompt_generator.py index 825243c..50662ac 100644 --- a/datadreamer/prompt_generation/prompt_generator.py +++ b/datadreamer/prompt_generation/prompt_generator.py @@ -49,7 +49,7 @@ def __init__( self.quantization = quantization if quantization is not None else "none" @staticmethod - def set_seed(seed: int): + def set_seed(seed: int) -> None: """Sets the random seed for consistent prompt generation. Args: diff --git a/datadreamer/prompt_generation/synonym_generator.py b/datadreamer/prompt_generation/synonym_generator.py index ec3f306..b5d338f 100644 --- a/datadreamer/prompt_generation/synonym_generator.py +++ b/datadreamer/prompt_generation/synonym_generator.py @@ -1,11 +1,14 @@ from __future__ import annotations import json +import logging from abc import ABC, abstractmethod -from typing import List, Optional +from typing import Dict, List, Optional from tqdm import tqdm +logger = logging.getLogger(__name__) + # Abstract base class for synonym generation class SynonymGenerator(ABC): @@ -38,7 +41,7 @@ def __init__( self.seed = seed self.device = device - def generate_synonyms_for_list(self, words: List[str]) -> dict: + def generate_synonyms_for_list(self, words: List[str]) -> Dict: """Generates synonyms for a list of words and returns them in a dictionary. Args: @@ -51,10 +54,9 @@ def generate_synonyms_for_list(self, words: List[str]) -> dict: for word in tqdm(words, desc="Generating synonyms"): synonyms = self.generate_synonyms(word) synonyms_dict[word] = synonyms - print("Synonyms generated") return synonyms_dict - def save_synonyms(self, synonyms, save_path: str) -> None: + def save_synonyms(self, synonyms: Dict, save_path: str) -> None: """Saves the generated synonyms to a JSON file. Args: diff --git a/datadreamer/prompt_generation/tinyllama_lm_prompt_generator.py b/datadreamer/prompt_generation/tinyllama_lm_prompt_generator.py index 78238e7..9e939a7 100644 --- a/datadreamer/prompt_generation/tinyllama_lm_prompt_generator.py +++ b/datadreamer/prompt_generation/tinyllama_lm_prompt_generator.py @@ -1,13 +1,16 @@ from __future__ import annotations +import logging import re -from typing import List, Literal, Optional +from typing import List, Literal, Optional, Tuple import torch from transformers import AutoModelForCausalLM, AutoTokenizer, Pipeline, pipeline from datadreamer.prompt_generation.lm_prompt_generator import LMPromptGenerator +logger = logging.getLogger(__name__) + class TinyLlamaLMPromptGenerator(LMPromptGenerator): """A language model-based prompt generator class, extending PromptGenerator. @@ -47,14 +50,14 @@ def __init__( quantization, ) - def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: + def _init_lang_model(self) -> Tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: """Initializes the language model, tokenizer and pipeline for prompt generation. Returns: tuple: The initialized language model, tokenizer and pipeline. """ + logger.info(f"Initializing TinyLlama-1.1B language model on {self.device}...") if self.device == "cpu": - print("Loading language model on CPU...") model = AutoModelForCausalLM.from_pretrained( "TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype="auto", @@ -62,7 +65,6 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin low_cpu_mem_usage=True, ) else: - print("Loading language model on GPU...") model = AutoModelForCausalLM.from_pretrained( "TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16, @@ -82,7 +84,6 @@ def _init_lang_model(self) -> tuple[AutoModelForCausalLM, AutoTokenizer, Pipelin device_map=self.device, batch_size=self.batch_size, ) - print("Done!") return model, tokenizer, pipe def _remove_caption_sentences(self, text: str) -> str: diff --git a/datadreamer/utils/base_converter.py b/datadreamer/utils/base_converter.py index 3d97199..5c8243e 100644 --- a/datadreamer/utils/base_converter.py +++ b/datadreamer/utils/base_converter.py @@ -2,6 +2,7 @@ import json from abc import ABC, abstractmethod +from typing import Dict, List, Tuple import numpy as np @@ -13,47 +14,46 @@ def __init__(self, seed=42): np.random.seed(seed) @abstractmethod - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): + def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> None: """Converts a dataset into another format. Args: - - dataset_dir (str): The directory where the source dataset is located. - - output_dir (str): The directory where the processed dataset should be saved. - - split_ratios (list of float): The ratios to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. - + dataset_dir (str): The directory where the source dataset is located. + output_dir (str): The directory where the processed dataset should be saved. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ pass @staticmethod - def read_annotations(annotation_path): + def read_annotations(annotation_path) -> Dict: """Reads annotations from a JSON file located at the specified path. Args: - - annotation_path (str): The path to the JSON file containing annotations. + annotation_path (str): The path to the JSON file containing annotations. Returns: - - dict: A dictionary containing the data loaded from the JSON file. + dict: A dictionary containing the data loaded from the JSON file. """ with open(annotation_path) as f: data = json.load(f) return data @staticmethod - def make_splits(images, split_ratios, shuffle=True): + def make_splits(images, split_ratios, shuffle=True) -> Tuple[List, List, List]: """Splits the list of images into training, validation, and test sets. Args: - - images (list of str): A list of image paths. - - split_ratios (list of float): The ratios to split the data into training, validation, and test sets. - - shuffle (bool, optional): Whether to shuffle the list of images. Defaults to True. + images (list of str): A list of image paths. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + shuffle (bool, optional): Whether to shuffle the list of images. Defaults to True. Returns: - - list of str: A list of image paths for the training set. - - list of str: A list of image paths for the validation set. - - list of str: A list of image paths for the test set. + list of str: A list of image paths for the training set. + list of str: A list of image paths for the validation set. + list of str: A list of image paths for the test set. """ if shuffle: np.random.shuffle(images) diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index ba02d97..bcd3546 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -31,14 +31,14 @@ class COCOConverter(BaseConverter): def __init__(self, seed=42): super().__init__(seed) - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): + def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> None: """Converts a dataset into a COCO format. Args: - - dataset_dir (str): The directory where the source dataset is located. - - output_dir (str): The directory where the processed dataset should be saved. - - split_ratios (list of float): The ratios to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. + dataset_dir (str): The directory where the source dataset is located. + output_dir (str): The directory where the processed dataset should be saved. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ @@ -46,17 +46,18 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): data = BaseConverter.read_annotations(annotation_path) self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) - def process_data(self, data, image_dir, output_dir, split_ratios, copy_files=True): + def process_data( + self, data, image_dir, output_dir, split_ratios, copy_files=True + ) -> None: """Processes the data by dividing it into training and validation sets, and saves the images and labels in COCO format. Args: - - data (dict): The dictionary containing image annotations. - - image_dir (str): The directory where the source images are located. - - output_dir (str): The base directory where the processed data will be saved. - - split_ratios (float): The ratio to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. - + data (dict): The dictionary containing image annotations. + image_dir (str): The directory where the source images are located. + output_dir (str): The base directory where the processed data will be saved. + split_ratios (float): The ratio to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ @@ -126,14 +127,16 @@ def process_data(self, data, image_dir, output_dir, split_ratios, copy_files=Tru dataset_output_dir, images_info, annotations, data["class_names"] ) - def save_labels(self, dataset_output_dir, images_info, annotations, class_names): + def save_labels( + self, dataset_output_dir, images_info, annotations, class_names + ) -> None: """Saves the labels to a JSON file. Args: - - dataset_output_dir (str): The directory where the labels should be saved. - - images_info (list of dict): A list of dictionaries containing image information. - - annotations (list of dict): A list of dictionaries containing annotation information. - - class_names (list of str): A list of class names. + dataset_output_dir (str): The directory where the labels should be saved. + images_info (list of dict): A list of dictionaries containing image information. + annotations (list of dict): A list of dictionaries containing annotation information. + class_names (list of str): A list of class names. No return value. """ diff --git a/datadreamer/utils/convert_dataset.py b/datadreamer/utils/convert_dataset.py index 21f1159..874878b 100644 --- a/datadreamer/utils/convert_dataset.py +++ b/datadreamer/utils/convert_dataset.py @@ -19,7 +19,22 @@ def convert_dataset( dataset_name=None, copy_files=True, seed=42, -): +) -> None: + """Converts a dataset from one format to another. + + Args: + input_dir (str): Directory containing the images and annotations. + output_dir (str): Directory where the processed dataset will be saved. + dataset_format (str): Format of the dataset. Can be 'yolo', 'coco', 'luxonis-dataset', or 'cls-single'. + split_ratios (list): List of ratios for train, val, and test splits. + dataset_plugin (str, optional): Plugin for Luxonis dataset. Defaults to None. + dataset_name (str, optional): Name of the Luxonis dataset. Defaults to None. + copy_files (bool, optional): Whether to copy the files to the output directory. Defaults to True. + seed (int, optional): Random seed. Defaults to 42. + + No return value. + """ + if dataset_format == "yolo": converter = YOLOConverter(seed=seed) elif dataset_format == "coco": diff --git a/datadreamer/utils/dataset_utils.py b/datadreamer/utils/dataset_utils.py index a396ae0..33fe003 100644 --- a/datadreamer/utils/dataset_utils.py +++ b/datadreamer/utils/dataset_utils.py @@ -9,7 +9,22 @@ def save_annotations_to_json( class_names=None, save_dir=None, file_name="annotations.json", -): +) -> None: + """Saves annotations to a JSON file. + + Args: + image_paths (list): List of image paths. + labels_list (list): List of labels. + boxes_list (list, optional): List of bounding boxes. Defaults to None. + class_names (list, optional): List of class names. Defaults to None. + save_dir (str, optional): Directory to save the JSON file. Defaults to None. + file_name (str, optional): Name of the JSON file. Defaults to 'annotations.json'. + + No return value. + """ + if save_dir is None: + save_dir = os.getcwd() + annotations = {} for i in range(len(image_paths)): # for image_path, bboxes, labels in zip(image_paths, boxes_list, labels_list): diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 72dc06d..9a2e6f9 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -1,6 +1,8 @@ from __future__ import annotations +import logging import os +from typing import Dict, List from luxonis_ml.data import DATASETS_REGISTRY, LuxonisDataset from luxonis_ml.data.utils.enums import BucketStorage @@ -8,23 +10,33 @@ from datadreamer.utils import BaseConverter +logger = logging.getLogger(__name__) + class LuxonisDatasetConverter(BaseConverter): """Class for converting a dataset to LuxonisDataset format.""" - def __init__(self, dataset_plugin=None, dataset_name=None, seed=42): + def __init__( + self, dataset_plugin: str = None, dataset_name: str = None, seed: int = 42 + ): super().__init__(seed) self.dataset_plugin = dataset_plugin self.dataset_name = dataset_name - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): + def convert( + self, + dataset_dir: str, + output_dir: str, + split_ratios: List[float], + copy_files: bool = True, + ) -> None: """Converts a dataset into a LuxonisDataset format. Args: - - dataset_dir (str): The directory where the source dataset is located. - - output_dir (str): The directory where the processed dataset should be saved. - - split_ratios (list of float): The ratios to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. + dataset_dir (str): The directory where the source dataset is located. + output_dir (str): The directory where the processed dataset should be saved. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ @@ -32,7 +44,21 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): data = BaseConverter.read_annotations(annotation_path) self.process_data(data, dataset_dir, output_dir, split_ratios) - def process_data(self, data, dataset_dir, output_dir, split_ratios): + def process_data( + self, data: Dict, dataset_dir: str, output_dir: str, split_ratios: List[float] + ) -> None: + """Processes the data into LuxonisDataset format. + + Args: + data (dict): The data to process. + dataset_dir (str): The directory where the source dataset is located. + output_dir (str): The directory where the processed dataset should be saved. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + + No return value. + """ + if not os.path.exists(output_dir): + os.makedirs(output_dir) class_names = data["class_names"] image_paths = list(data.keys()) image_paths.remove("class_names") @@ -84,7 +110,7 @@ def dataset_generator(): # if dataset_plugin is set, use that if self.dataset_plugin: if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ: - print(f"Using {self.dataset_plugin} dataset") + logger.info(f"Using {self.dataset_plugin} dataset") dataset_constructor = DATASETS_REGISTRY.get(self.dataset_plugin) dataset = dataset_constructor(dataset_name) else: @@ -96,10 +122,10 @@ def dataset_generator(): "LUXONISML_BUCKET" in os.environ and "GOOGLE_APPLICATION_CREDENTIALS" in os.environ ): - print("Using GCS bucket") + logger.info("Using GCS bucket") dataset = LuxonisDataset(dataset_name, bucket_storage=BucketStorage.GCS) else: - print("Using local dataset") + logger.info("Using local dataset") dataset = LuxonisDataset(dataset_name) dataset.add(dataset_generator()) diff --git a/datadreamer/utils/merge_raw_datasets.py b/datadreamer/utils/merge_raw_datasets.py index 47c1dc0..c6eb64e 100644 --- a/datadreamer/utils/merge_raw_datasets.py +++ b/datadreamer/utils/merge_raw_datasets.py @@ -2,16 +2,33 @@ import argparse import json +import logging import os import shutil +from typing import List +logger = logging.getLogger(__name__) -def merge_datasets(input_dirs, output_dir, copy_files=True): + +def merge_datasets( + input_dirs: List[str], output_dir: str, copy_files: bool = True +) -> None: + """Merges multiple raw datasets into a single dataset. + + Args: + input_dirs (List[str]): A list of input directories containing raw datasets. + output_dir (str): The output directory where the merged dataset will be saved. + copy_files (bool, optional): Whether to copy the files from the input directories + to the output directory. Defaults to True. + + No return value. + """ + # Check if all input directories exist config_tasks = [] config_classes = [] random_seeds = [] for input_dir in input_dirs: - with open(os.path.join(input_dir, "generation_args.json")) as f: + with open(os.path.join(input_dir, "generation_args.yaml")) as f: generation_args = json.load(f) config_tasks.append(generation_args["task"]) config_classes.append(generation_args["class_names"]) @@ -29,7 +46,7 @@ def merge_datasets(input_dirs, output_dir, copy_files=True): raise ValueError("All datasets must have different random seeds") # Create output directory - print(f"Output directory: {output_dir}") + logger.info(f"Output directory: {output_dir}") if os.path.exists(output_dir): shutil.rmtree(output_dir) os.makedirs(output_dir) @@ -45,12 +62,12 @@ def merge_datasets(input_dirs, output_dir, copy_files=True): if copy_files: shutil.copy( os.path.join(input_dir, "generation_args.yaml"), - os.path.join(output_dir, f"generation_args_{i}.json"), + os.path.join(output_dir, f"generation_args_{i}.yaml"), ) else: shutil.move( os.path.join(input_dir, "generation_args.yaml"), - os.path.join(output_dir, f"generation_args_{i}.json"), + os.path.join(output_dir, f"generation_args_{i}.yaml"), ) # Copy or move images diff --git a/datadreamer/utils/nms.py b/datadreamer/utils/nms.py index 530707c..f0f29e4 100644 --- a/datadreamer/utils/nms.py +++ b/datadreamer/utils/nms.py @@ -4,8 +4,10 @@ # https://github.com/ultralytics/yolov5/blob/master/utils/general.py from __future__ import annotations +import logging import os import time +from typing import List import cv2 import numpy as np @@ -22,6 +24,8 @@ ) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ["NUMEXPR_MAX_THREADS"] = str(min(os.cpu_count(), 8)) # NumExpr max threads +logger = logging.getLogger(__name__) + def xywh2xyxy(x): """Convert boxes with shape [n, 4] from [x, y, w, h] to [x1, y1, x2, y2] where x1y1 @@ -42,7 +46,7 @@ def non_max_suppression( agnostic=False, multi_label=False, max_det=300, -): +) -> List[np.ndarray]: """Runs Non-Maximum Suppression (NMS) on inference results. This code is borrowed from: https://github.com/ultralytics/yolov5/blob/47233e1698b89fc437a4fb9463c815e9171be955/utils/general.py#L775 Args: @@ -131,7 +135,7 @@ def non_max_suppression( output[img_idx] = x[keep_box_idx] if (time.time() - tik) > time_limit: - print(f"WARNING: NMS cost time exceed the limited {time_limit}s.") + logger.warning(f"WARNING: NMS cost time exceed the limited {time_limit}s.") break # time limit exceeded return output diff --git a/datadreamer/utils/single_label_cls_converter.py b/datadreamer/utils/single_label_cls_converter.py index e5515d5..c24bec7 100644 --- a/datadreamer/utils/single_label_cls_converter.py +++ b/datadreamer/utils/single_label_cls_converter.py @@ -1,10 +1,14 @@ from __future__ import annotations +import logging import os import shutil +from typing import Dict, List from datadreamer.utils import BaseConverter +logger = logging.getLogger(__name__) + class SingleLabelClsConverter(BaseConverter): """Class for converting a dataset for single-label classification task. @@ -29,17 +33,23 @@ class SingleLabelClsConverter(BaseConverter): │ ├── class_2 """ - def __init__(self, seed=42): + def __init__(self, seed: int = 42): super().__init__(seed) - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): + def convert( + self, + dataset_dir: str, + output_dir: str, + split_ratios: List[float], + copy_files: bool = True, + ) -> None: """Converts a dataset into a format suitable for single-label classification. Args: - - dataset_dir (str): The directory where the source dataset is located. - - output_dir (str): The directory where the processed dataset should be saved. - - split_ratios (list of float): The ratios to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. + dataset_dir (str): The directory where the source dataset is located. + output_dir (str): The directory where the processed dataset should be saved. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ @@ -47,16 +57,23 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): data = BaseConverter.read_annotations(annotation_path) self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) - def process_data(self, data, image_dir, output_dir, split_ratios, copy_files=True): + def process_data( + self, + data: Dict, + image_dir: str, + output_dir: str, + split_ratios: List[float], + copy_files: bool = True, + ) -> None: """Processes the data by removing images with multiple labels, then dividing it into training and validation sets, and saves the images with single labels. Args: - - data (dict): The dictionary containing image annotations. - - image_dir (str): The directory where the source images are located. - - output_dir (str): The base directory where the processed data will be saved. - - split_ratios (float): The ratio to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. + data (dict): The dictionary containing image annotations. + image_dir (str): The directory where the source images are located. + output_dir (str): The base directory where the processed data will be saved. + split_ratios (float): The ratio to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ @@ -64,12 +81,12 @@ def process_data(self, data, image_dir, output_dir, split_ratios, copy_files=Tru class_names = data["class_names"] images.remove("class_names") - print(f"Number of images: {len(images)}") + logger.info(f"Number of images: {len(images)}") # Remove images with multiple labels single_label_images = [img for img in images if len(data[img]["labels"]) == 1] - print(f"Number of images with single label: {len(single_label_images)}") + logger.info(f"Number of images with single label: {len(single_label_images)}") # Split the data into training, validation, and test sets train_images, val_images, test_images = BaseConverter.make_splits( diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index 36452da..715e429 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -2,6 +2,7 @@ import os import shutil +from typing import Dict, List from PIL import Image @@ -32,15 +33,21 @@ class YOLOConverter(BaseConverter): def __init__(self, seed=42): super().__init__(seed) - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): + def convert( + self, + dataset_dir: str, + output_dir: str, + split_ratios: List[float], + copy_files: bool = True, + ): """Converts a dataset into a format suitable for training with YOLO, including creating training and validation splits. Args: - - dataset_dir (str): The directory where the source dataset is located. - - output_dir (str): The directory where the processed dataset should be saved. - - split_ratios (list of float): The ratios to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. + dataset_dir (str): The directory where the source dataset is located. + output_dir (str): The directory where the processed dataset should be saved. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ @@ -48,16 +55,18 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True): data = BaseConverter.read_annotations(annotation_path) self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) - def convert_to_yolo_format(self, box, image_width, image_height): + def convert_to_yolo_format( + self, box: List[float], image_width: int, image_height: int + ) -> List[float]: """Converts bounding box coordinates to YOLO format. Args: - - box (list of float): A list containing the bounding box coordinates [x_min, y_min, x_max, y_max]. - - image_width (int): The width of the image. - - image_height (int): The height of the image. + box (list of float): A list containing the bounding box coordinates [x_min, y_min, x_max, y_max]. + image_width (int): The width of the image. + image_height (int): The height of the image. Returns: - - list of float: A list containing the bounding box in YOLO format [x_center, y_center, width, height]. + list of float: A list containing the bounding box in YOLO format [x_center, y_center, width, height]. """ x_center = (box[0] + box[2]) / 2 / image_width y_center = (box[1] + box[3]) / 2 / image_height @@ -65,16 +74,23 @@ def convert_to_yolo_format(self, box, image_width, image_height): height = (box[3] - box[1]) / image_height return [x_center, y_center, width, height] - def process_data(self, data, image_dir, output_dir, split_ratios, copy_files=True): + def process_data( + self, + data: Dict, + image_dir: str, + output_dir: str, + split_ratios: List[float], + copy_files: bool = True, + ) -> None: """Processes the data by dividing it into training and validation sets, and saves the images and labels in YOLO format. Args: - - data (dict): The dictionary containing image annotations. - - image_dir (str): The directory where the source images are located. - - output_dir (str): The base directory where the processed data will be saved. - - split_ratios (float): The ratio to split the data into training, validation, and test sets. - - copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. + data (dict): The dictionary containing image annotations. + image_dir (str): The directory where the source images are located. + output_dir (str): The base directory where the processed data will be saved. + split_ratios (float): The ratio to split the data into training, validation, and test sets. + copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. @@ -131,13 +147,13 @@ def process_data(self, data, image_dir, output_dir, split_ratios, copy_files=Tru self.create_data_yaml(output_dir, data["class_names"]) - def create_data_yaml(self, root_dir, class_names): + def create_data_yaml(self, root_dir: str, class_names: List[str]) -> None: """Creates a YAML file for dataset configuration, specifying paths and class names. Args: - - root_dir (str): The root directory where the dataset is located. - - class_names (list of str): A list of class names. + root_dir (str): The root directory where the dataset is located. + class_names (list of str): A list of class names. No return value. """ diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index b4a82e6..2fad913 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 56% - 56% + 62% + 62% diff --git a/tests/integration/sample_config.yaml b/tests/core_tests/integration/sample_config.yaml similarity index 100% rename from tests/integration/sample_config.yaml rename to tests/core_tests/integration/sample_config.yaml diff --git a/tests/core_tests/integration/test_pipeline.py b/tests/core_tests/integration/test_pipeline.py new file mode 100644 index 0000000..a6eba19 --- /dev/null +++ b/tests/core_tests/integration/test_pipeline.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import os +import subprocess + +import psutil +import pytest +import torch + +# Get the total memory in GB +total_memory = psutil.virtual_memory().total / (1024**3) +# Get the total disk space in GB +total_disk_space = psutil.disk_usage("/").total / (1024**3) + + +def _check_detection_pipeline(cmd: str, target_folder: str): + # Run the command + result = subprocess.run(cmd, shell=True) + assert result.returncode == 0, "Command failed to run" + # Check that the target folder is a folder + assert os.path.isdir(target_folder), "Directory not created" + files = [ + "annotations.json", + "generation_args.yaml", + "prompts.json", + ] + # Check that all the files were created + for file in files: + assert os.path.isfile(os.path.join(target_folder, file)), f"{file} not created" + # Check that an image with an unique was created + assert ( + len( + list( + filter( + lambda x: "image_" in x and ".jpg" in x, os.listdir(target_folder) + ) + ) + ) + > 0 + ), "Images not created" + # Check that the "bboxes_visualization" folder was created + assert os.path.isdir( + os.path.join(target_folder, "bboxes_visualization") + ), "bboxes_visualization directory not created" + + +# ========================================================= +# DETECTION - SIMPLE LM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_turbo_detection_pipeline(): + # Define target folder + target_folder = "data/data-det-cpu-simple-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--synonym_generator wordnet " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_turbo_detection_pipeline(): + # Define target folder + target_folder = "data/data-det-cuda-simple-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--synonym_generator wordnet " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# TEST WITH CONFIG FILE +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_turbo_config_detection_pipeline(): + # Define target folder + target_folder = "data/data-det-cpu-simple-sdxl-turbo-config/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --save_dir {target_folder} " + f"--num_objects_range 1 2 " + f"--config ./tests/core_tests/integration/sample_config.yaml " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_turbo_config_detection_pipeline(): + # Define target folder + target_folder = "data/data-det-cuda-simple-sdxl-turbo-config/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --save_dir {target_folder} " + f"--num_objects_range 1 2 " + f"--config ./tests/core_tests/integration/sample_config.yaml " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_turbo_config_classification_pipeline(): + # Define target folder + target_folder = "data/data-cls-cpu-simple-sdxl-turbo-config/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task classification " + f"--save_dir {target_folder} " + f"--num_objects_range 1 2 " + f"--image_annotator clip " + f"--config ./tests/core_tests/integration/sample_config.yaml " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_turbo_config_classification_pipeline(): + # Define target folder + target_folder = "data/data-cls-cuda-simple-sdxl-turbo-config/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task classification " + f"--save_dir {target_folder} " + f"--num_objects_range 1 2 " + f"--image_annotator clip " + f"--config ./tests/core_tests/integration/sample_config.yaml " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) diff --git a/tests/unittests/test_annotators.py b/tests/core_tests/unittests/test_annotators.py similarity index 82% rename from tests/unittests/test_annotators.py rename to tests/core_tests/unittests/test_annotators.py index 698ed3d..794b898 100644 --- a/tests/unittests/test_annotators.py +++ b/tests/core_tests/unittests/test_annotators.py @@ -40,16 +40,16 @@ def _check_owlv2_annotator(device: str, size: str = "base"): @pytest.mark.skipif( - not torch.cuda.is_available() or total_disk_space < 15, - reason="Test requires GPU and 15GB of HDD", + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", ) def test_cuda_owlv2_annotator(): _check_owlv2_annotator("cuda") @pytest.mark.skipif( - total_disk_space < 15, - reason="Test requires at least 15GB of HDD", + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", ) def test_cpu_owlv2_annotator(): _check_owlv2_annotator("cpu") @@ -67,32 +67,32 @@ def _check_clip_annotator(device: str, size: str = "base"): @pytest.mark.skipif( - not torch.cuda.is_available() or total_disk_space < 15, - reason="Test requires GPU and 15GB of HDD", + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", ) def test_cuda_clip_base_annotator(): _check_clip_annotator("cuda") @pytest.mark.skipif( - total_disk_space < 15, - reason="Test requires at least 15GB of HDD", + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", ) def test_cpu_clip_base_annotator(): _check_clip_annotator("cpu") @pytest.mark.skipif( - not torch.cuda.is_available() or total_disk_space < 15, - reason="Test requires GPU and 15GB of HDD", + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", ) def test_cuda_clip_large_annotator(): _check_clip_annotator("cuda") @pytest.mark.skipif( - total_disk_space < 15, - reason="Test requires at least 15GB of HDD", + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", ) def test_cpu_clip_large_annotator(): _check_clip_annotator("cpu") diff --git a/tests/unittests/test_converters.py b/tests/core_tests/unittests/test_converters.py similarity index 100% rename from tests/unittests/test_converters.py rename to tests/core_tests/unittests/test_converters.py diff --git a/tests/unittests/test_image_generation.py b/tests/core_tests/unittests/test_image_generation.py similarity index 76% rename from tests/unittests/test_image_generation.py rename to tests/core_tests/unittests/test_image_generation.py index f91fcc1..2436f75 100644 --- a/tests/unittests/test_image_generation.py +++ b/tests/core_tests/unittests/test_image_generation.py @@ -25,6 +25,8 @@ def _check_clip_image_tester(device: str): url = "https://ultralytics.com/images/bus.jpg" im = Image.open(requests.get(url, stream=True).raw) tester = ClipImageTester(device=device) + # Check that the tester is not None + assert tester is not None passed, probs, num_passed = tester.test_image(im, ["bus"]) # Check that the image passed the test assert passed is True @@ -34,21 +36,29 @@ def _check_clip_image_tester(device: str): assert probs.shape == (1, 1) # Check that the probability is not zero assert probs[0, 0] > 0 - # Release the tester + passed_list, probs_list, num_passed_list = tester.test_images_batch([im], [["bus"]]) + # Check that the image passed the test + assert passed_list[0] is True + # Check that the number of objects passed is correct + assert num_passed_list[0] == 1 + # Check that the probability has correct shape + assert len(probs_list) == 1 + # Check that the probability is not zero + assert probs_list[0][0] > 0 tester.release(empty_cuda_cache=True if device != "cpu" else False) @pytest.mark.skipif( - not torch.cuda.is_available() or total_disk_space < 15, - reason="Test requires GPU and 15GB of HDD", + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", ) def test_cuda_clip_image_tester(): _check_clip_image_tester("cuda") @pytest.mark.skipif( - total_disk_space < 15, - reason="Test requires at least 15GB of HDD", + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", ) def test_cpu_clip_image_tester(): _check_clip_image_tester("cpu") @@ -65,6 +75,8 @@ def _check_image_generator( device: str, ): image_generator = image_generator_class(device=device) + # Check that the image generator is not None + assert image_generator is not None # Generate images and check each of them for generated_images_batch in image_generator.generate_images( ["A photo of a cat, dog"], [["cat", "dog"]] @@ -72,24 +84,17 @@ def _check_image_generator( generated_image = generated_images_batch[0] assert generated_image is not None assert isinstance(generated_image, Image.Image) - # Release the generator - image_generator.release(empty_cuda_cache=True if device != "cpu" else False) - - -@pytest.mark.skipif( - not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25, - reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD", -) -def test_cuda_sdxl_image_generator(): - _check_image_generator(StableDiffusionImageGenerator, "cuda") + images = image_generator.generate_images_batch( + ["A photo of a cat, dog"], + "blurry, bad quality", + ) + assert len(images) == 1 + assert images[0] is not None + assert isinstance(images[0], Image.Image) -@pytest.mark.skipif( - total_memory < 16 or total_disk_space < 25, - reason="Test requires at least 16GB of RAM and 25GB of HDD", -) -def test_cpu_sdxl_image_generator(): - _check_image_generator(StableDiffusionImageGenerator, "cpu") + # Release the generator + image_generator.release(empty_cuda_cache=True if device != "cpu" else False) @pytest.mark.skipif( diff --git a/tests/core_tests/unittests/test_pipeline_arguments.py b/tests/core_tests/unittests/test_pipeline_arguments.py new file mode 100644 index 0000000..f435da7 --- /dev/null +++ b/tests/core_tests/unittests/test_pipeline_arguments.py @@ -0,0 +1,213 @@ +from __future__ import annotations + +import subprocess + +import pytest + + +def _check_wrong_argument_choice(cmd: str): + with pytest.raises(subprocess.CalledProcessError): + subprocess.check_call(cmd, shell=True) + + +def _check_wrong_value(cmd: str): + with pytest.raises(ValueError): + try: + subprocess.check_output(cmd, shell=True, stderr=subprocess.STDOUT) + except subprocess.CalledProcessError as e: + raise ValueError(e.output.decode()) from e + + +# ========================================================= +# ARGUMENTS CHECKS +# ========================================================= +def test_invalid_task_value(): + # Define the cmd + cmd = "datadreamer --task invalid_task" + _check_wrong_argument_choice(cmd) + + +def test_invalid_prompts_number_type(): + # Define the cmd + cmd = "datadreamer --prompts_number value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_num_objects_range_type(): + # Define the cmd + cmd = "datadreamer --num_objects_range value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_conf_threshold_range_type(): + # Define the cmd + cmd = "datadreamer --conf_threshold value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_image_tester_patience_type(): + # Define the cmd + cmd = "datadreamer --image_tester_patience value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_seed_type(): + # Define the cmd + cmd = "datadreamer --seed value --device cpu" + _check_wrong_argument_choice(cmd) + + +def test_invalid_prompt_generator(): + # Define the cmd + cmd = "datadreamer --prompt_generator invalide_value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_image_generator(): + # Define the cmd + cmd = "datadreamer --image_generator invalide_value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_image_annotator(): + # Define the cmd + cmd = "datadreamer --image_annotator invalide_value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_det_image_annotator(): + # Define the cmd + cmd = "datadreamer --image_annotator clip" + _check_wrong_argument_choice(cmd) + + +def test_invalid_clf_image_annotator(): + # Define the cmd + cmd = "datadreamer --image_annotator owlv2 --task classification" + _check_wrong_argument_choice(cmd) + + +def test_invalid_device(): + # Define the cmd + cmd = "datadreamer --device invalide_value" + _check_wrong_argument_choice(cmd) + + +def test_invalid_annotator_size(): + # Define the cmd + cmd = "datadreamer --annotator_size invalide_value" + _check_wrong_argument_choice(cmd) + + +def test_empty_class_names(): + # Define the cmd + cmd = "datadreamer --class_names []" + _check_wrong_value(cmd) + + +def test_invalid_class_names(): + # Define the cmd + cmd = "datadreamer --class_names [2, -1]" + _check_wrong_value(cmd) + + +def test_invalid_prompts_number(): + # Define the cmd + cmd = "datadreamer --prompts_number -1" + _check_wrong_value(cmd) + + +def test_negative_conf_threshold(): + # Define the cmd + cmd = "datadreamer --conf_threshold -1" + _check_wrong_value(cmd) + + +def test_big_conf_threshold(): + # Define the cmd + cmd = "datadreamer --conf_threshold 10" + _check_wrong_value(cmd) + + +def test_negative_annotation_iou_threshold(): + # Define the cmd + cmd = "datadreamer --annotation_iou_threshold -1" + _check_wrong_value(cmd) + + +def test_big_annotation_iou_threshold(): + # Define the cmd + cmd = "datadreamer --annotation_iou_threshold 10" + _check_wrong_value(cmd) + + +def test_invalid_image_tester_patience(): + # Define the cmd + cmd = "datadreamer --image_tester_patience -1" + _check_wrong_value(cmd) + + +def test_invalid_seed(): + # Define the cmd + cmd = "datadreamer --seed -1 --device cpu" + _check_wrong_value(cmd) + + +def test_invalid_synonym_generator(): + # Define the cmd + cmd = "datadreamer --device cpu --synonym_generator invalid" + _check_wrong_value(cmd) + + +def test_invalid_lm_quantization(): + # Define the cmd + cmd = "datadreamer --device cude --lm_quantization invalid" + _check_wrong_value(cmd) + + +def test_invalid_device_lm_quantization(): + # Define the cmd + cmd = "datadreamer --device cpu --lm_quantization 4bit" + _check_wrong_value(cmd) + + +def test_invalid_batch_size_prompt(): + # Define the cmd + cmd = "datadreamer --batch_size_prompt -1" + _check_wrong_value(cmd) + + +def test_invalid_batch_size_annotation(): + # Define the cmd + cmd = "datadreamer --batch_size_annotation -1" + _check_wrong_value(cmd) + + +def test_invalid_batch_size_image(): + # Define the cmd + cmd = "datadreamer --batch_size_image -1" + _check_wrong_value(cmd) + + +def test_invalid_num_objects_range(): + # Define the cmd + cmd = "datadreamer --num_objects_range 1" + _check_wrong_value(cmd) + + +def test_many_num_objects_range(): + # Define the cmd + cmd = "datadreamer --num_objects_range 1 2 3" + _check_wrong_value(cmd) + + +def test_desc_num_objects_range(): + # Define the cmd + cmd = "datadreamer --num_objects_range 3 1" + _check_wrong_value(cmd) + + +def test_negative_num_objects_range(): + # Define the cmd + cmd = "datadreamer --num_objects_range -3 1" + _check_wrong_value(cmd) diff --git a/tests/unittests/test_prompt_generation.py b/tests/core_tests/unittests/test_prompt_generation.py similarity index 81% rename from tests/unittests/test_prompt_generation.py rename to tests/core_tests/unittests/test_prompt_generation.py index e77472d..f2dcd9f 100644 --- a/tests/unittests/test_prompt_generation.py +++ b/tests/core_tests/unittests/test_prompt_generation.py @@ -68,14 +68,6 @@ def _check_lm_prompt_generator( prompt_generator.release(empty_cuda_cache=True if device != "cpu" else False) -@pytest.mark.skipif( - total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 35, - reason="Test requires at least 16GB of RAM, 35GB of HDD and CUDA support", -) -def test_cuda_lm_prompt_generator(): - _check_lm_prompt_generator("cuda") - - @pytest.mark.skipif( total_memory < 12 or not torch.cuda.is_available() or total_disk_space < 25, reason="Test requires at least 12GB of RAM, 25GB of HDD and CUDA support", @@ -85,11 +77,11 @@ def test_cuda_4bit_lm_prompt_generator(): @pytest.mark.skipif( - total_memory < 32 or total_disk_space < 35, - reason="Test requires at least 28GB of RAM and 35GB of HDD for running on CPU", + total_memory < 12 or total_disk_space < 12, + reason="Test requires at least 12GB of RAM and 12GB of HDD for running on CPU", ) -def test_cpu_lm_prompt_generator(): - _check_lm_prompt_generator("cpu") +def test_cpu_tinyllama_lm_prompt_generator(): + _check_lm_prompt_generator("cpu", TinyLlamaLMPromptGenerator) @pytest.mark.skipif( @@ -100,14 +92,6 @@ def test_cuda_tinyllama_lm_prompt_generator(): _check_lm_prompt_generator("cuda", TinyLlamaLMPromptGenerator) -@pytest.mark.skipif( - total_memory < 12 or total_disk_space < 12, - reason="Test requires at least 12GB of RAM and 12GB of HDD for running on CPU", -) -def test_cpu_tinyllama_lm_prompt_generator(): - _check_lm_prompt_generator("cpu", TinyLlamaLMPromptGenerator) - - def _check_synonym_generator(device: str, synonym_generator_class=LMSynonymGenerator): synonyms_num = 3 generator = synonym_generator_class(synonyms_number=synonyms_num, device=device) @@ -126,28 +110,12 @@ def _check_synonym_generator(device: str, synonym_generator_class=LMSynonymGener generator.release(empty_cuda_cache=True if device != "cpu" else False) -@pytest.mark.skipif( - total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 35, - reason="Test requires at least 16GB of RAM, 35GB of HDD and CUDA support", -) -def test_cuda_synonym_generator(): - _check_synonym_generator("cuda") - - -@pytest.mark.skipif( - total_memory < 32 or total_disk_space < 35, - reason="Test requires at least 28GB of RAM and 35GB of HDD for running on CPU", -) -def test_cpu_synonym_generator(): - _check_synonym_generator("cpu") - - def test_cpu_wordnet_synonym_generator(): _check_synonym_generator("cpu", WordNetSynonymGenerator) @pytest.mark.skipif( - torch.cuda.is_available(), + not torch.cuda.is_available(), reason="Test requires CUDA support", ) def test_cuda_wordnet_synonym_generator(): diff --git a/tests/core_tests/unittests/test_utils.py b/tests/core_tests/unittests/test_utils.py new file mode 100644 index 0000000..bac169b --- /dev/null +++ b/tests/core_tests/unittests/test_utils.py @@ -0,0 +1,186 @@ +import json +import os +import shutil +import unittest + +import numpy as np +from PIL import Image + +from datadreamer.utils import ( + dataset_utils, + merge_raw_datasets, +) + + +def create_sample_image( + image_name, image_size=(100, 100), color=(255, 0, 0), save_dir="test_images" +): + """Create and save a simple image with a solid color. + + Args: + image_name (str): The name of the image file. + image_size (tuple): The size of the image (width, height). + color (tuple): The RGB color of the image. + save_dir (str): The directory to save the images. + """ + # Create the directory if it doesn't exist + os.makedirs(save_dir, exist_ok=True) + + # Create a blank image with the given color + img = Image.new("RGB", image_size, color) + + # Save the image to the specified directory + img.save(os.path.join(save_dir, image_name)) + + +class TestSaveAnnotationsToJson(unittest.TestCase): + def setUp(self): + # Create a temporary directory for saving images and JSON file + self.test_dir = "test_dir" + self.image_dir = "test_images" + os.makedirs(self.test_dir, exist_ok=True) + os.makedirs(self.image_dir, exist_ok=True) + + # Create sample images + create_sample_image("image1.jpg", save_dir=self.image_dir) + create_sample_image("image2.jpg", save_dir=self.image_dir) + + self.file_name = "annotations.json" + self.image_paths = [ + os.path.join(self.image_dir, "image1.jpg"), + os.path.join(self.image_dir, "image2.jpg"), + ] + self.labels_list = [ + [0], # Labels for image1 + [1], # Labels for image2 + ] + self.labels_list = np.array(self.labels_list) + self.boxes_list = [ + [[10, 10, 50, 50]], # Bounding boxes for image1 + [[20, 20, 40, 40]], # Bounding boxes for image2 + ] + self.boxes_list = np.array(self.boxes_list) + self.class_names = ["class_1", "class_2"] + + def tearDown(self): + # Clean up the test directory after each test + for file in os.listdir(self.test_dir): + os.remove(os.path.join(self.test_dir, file)) + for file in os.listdir(self.image_dir): + os.remove(os.path.join(self.image_dir, file)) + os.rmdir(self.test_dir) + os.rmdir(self.image_dir) + + def test_save_annotations_to_json(self): + # Test saving annotations to JSON + dataset_utils.save_annotations_to_json( + self.image_paths, + self.labels_list, + boxes_list=self.boxes_list, + class_names=self.class_names, + save_dir=self.test_dir, + file_name=self.file_name, + ) + + # Load the saved JSON file and check contents + with open(os.path.join(self.test_dir, self.file_name), "r") as f: + annotations = json.load(f) + + # Check if annotations are correct + self.assertEqual(len(annotations), 3) # 2 images + class_names + self.assertIn("image1.jpg", annotations) + self.assertIn("image2.jpg", annotations) + self.assertEqual(annotations["image1.jpg"]["labels"], [0]) + self.assertEqual(annotations["image2.jpg"]["labels"], [1]) + self.assertEqual(annotations["class_names"], self.class_names) + + +class TestMergeDatasets(unittest.TestCase): + def setUp(self): + # Create temporary directories for test datasets + self.input_dir_1 = "input_dir_1" + self.input_dir_2 = "input_dir_2" + self.input_dir_3 = "input_dir_3" + self.output_dir = "output_dir" + os.makedirs(self.input_dir_1, exist_ok=True) + os.makedirs(self.input_dir_2, exist_ok=True) + os.makedirs(self.input_dir_3, exist_ok=True) + + # Create generation_args.json files + self.generation_args_1 = { + "task": "object_detection", + "class_names": ["class_1", "class_2"], + "seed": 1, + } + self.generation_args_2 = { + "task": "object_detection", + "class_names": ["class_1", "class_2"], + "seed": 2, + } + with open(os.path.join(self.input_dir_1, "generation_args.yaml"), "w") as f: + json.dump(self.generation_args_1, f) + with open(os.path.join(self.input_dir_2, "generation_args.yaml"), "w") as f: + json.dump(self.generation_args_2, f) + + # Create annotations.json files + self.annotations_1 = { + "image1.jpg": {"labels": [0]}, + "image2.jpg": {"labels": [1]}, + "class_names": ["class_1", "class_2"], + } + self.annotations_2 = { + "image3.jpg": {"labels": [0]}, + "image4.jpg": {"labels": [1]}, + "class_names": ["class_1", "class_2"], + } + with open(os.path.join(self.input_dir_1, "annotations.json"), "w") as f: + json.dump(self.annotations_1, f) + with open(os.path.join(self.input_dir_2, "annotations.json"), "w") as f: + json.dump(self.annotations_2, f) + + # Create image files + with open(os.path.join(self.input_dir_1, "image1.jpg"), "wb") as f: + f.write(os.urandom(1024)) # Dummy image content + with open(os.path.join(self.input_dir_1, "image2.jpg"), "wb") as f: + f.write(os.urandom(1024)) # Dummy image content + with open(os.path.join(self.input_dir_2, "image3.jpg"), "wb") as f: + f.write(os.urandom(1024)) # Dummy image content + with open(os.path.join(self.input_dir_2, "image4.jpg"), "wb") as f: + f.write(os.urandom(1024)) # Dummy image content + + def tearDown(self): + # Clean up the test directories after each test + shutil.rmtree(self.input_dir_1) + shutil.rmtree(self.input_dir_2) + if os.path.exists(self.output_dir): + shutil.rmtree(self.output_dir) + + def test_merge_datasets(self): + # Test merging datasets + merge_raw_datasets.merge_datasets( + [self.input_dir_1, self.input_dir_2], self.output_dir, copy_files=True + ) + + # Check if output directory is created + self.assertTrue(os.path.exists(self.output_dir)) + + # Check if annotations.json is merged correctly + with open(os.path.join(self.output_dir, "annotations.json"), "r") as f: + merged_annotations = json.load(f) + + print(merged_annotations) + + self.assertEqual(len(merged_annotations), 5) # 4 images in total + class_names + self.assertIn("image1.jpg", merged_annotations) + self.assertIn("image2.jpg", merged_annotations) + self.assertIn("image3.jpg", merged_annotations) + self.assertIn("image4.jpg", merged_annotations) + self.assertEqual(merged_annotations["class_names"], ["class_1", "class_2"]) + + # Check if images are copied correctly + for image_name in ["image1.jpg", "image2.jpg", "image3.jpg", "image4.jpg"]: + self.assertTrue(os.path.exists(os.path.join(self.output_dir, image_name))) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/integration/test_pipeline.py b/tests/heavy_tests/integration/test_pipeline_heavy.py similarity index 74% rename from tests/integration/test_pipeline.py rename to tests/heavy_tests/integration/test_pipeline_heavy.py index 293d3a7..03750ea 100644 --- a/tests/integration/test_pipeline.py +++ b/tests/heavy_tests/integration/test_pipeline_heavy.py @@ -8,7 +8,7 @@ import torch # Get the total memory in GB -total_memory = psutil.virtual_memory().total / (1024**3) +total_memory = psutil.virtual_memory().total / (1024 * 3) # Get the total disk space in GB total_disk_space = psutil.disk_usage("/").total / (1024**3) @@ -44,214 +44,6 @@ def _check_detection_pipeline(cmd: str, target_folder: str): ), "bboxes_visualization directory not created" -def _check_wrong_argument_choice(cmd: str): - with pytest.raises(subprocess.CalledProcessError): - subprocess.check_call(cmd, shell=True) - - -def _check_wrong_value(cmd: str): - with pytest.raises(ValueError): - try: - subprocess.check_output(cmd, shell=True, stderr=subprocess.STDOUT) - except subprocess.CalledProcessError as e: - raise ValueError(e.output.decode()) from e - - -# ========================================================= -# ARGUMENTS CHECKS -# ========================================================= -def test_invalid_task_value(): - # Define the cmd - cmd = "datadreamer --task invalid_task" - _check_wrong_argument_choice(cmd) - - -def test_invalid_prompts_number_type(): - # Define the cmd - cmd = "datadreamer --prompts_number value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_num_objects_range_type(): - # Define the cmd - cmd = "datadreamer --num_objects_range value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_conf_threshold_range_type(): - # Define the cmd - cmd = "datadreamer --conf_threshold value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_image_tester_patience_type(): - # Define the cmd - cmd = "datadreamer --image_tester_patience value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_seed_type(): - # Define the cmd - cmd = "datadreamer --seed value --device cpu" - _check_wrong_argument_choice(cmd) - - -def test_invalid_prompt_generator(): - # Define the cmd - cmd = "datadreamer --prompt_generator invalide_value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_image_generator(): - # Define the cmd - cmd = "datadreamer --image_generator invalide_value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_image_annotator(): - # Define the cmd - cmd = "datadreamer --image_annotator invalide_value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_det_image_annotator(): - # Define the cmd - cmd = "datadreamer --image_annotator clip" - _check_wrong_argument_choice(cmd) - - -def test_invalid_clf_image_annotator(): - # Define the cmd - cmd = "datadreamer --image_annotator owlv2 --task classification" - _check_wrong_argument_choice(cmd) - - -def test_invalid_device(): - # Define the cmd - cmd = "datadreamer --device invalide_value" - _check_wrong_argument_choice(cmd) - - -def test_invalid_annotator_size(): - # Define the cmd - cmd = "datadreamer --annotator_size invalide_value" - _check_wrong_argument_choice(cmd) - - -def test_empty_class_names(): - # Define the cmd - cmd = "datadreamer --class_names []" - _check_wrong_value(cmd) - - -def test_invalid_class_names(): - # Define the cmd - cmd = "datadreamer --class_names [2, -1]" - _check_wrong_value(cmd) - - -def test_invalid_prompts_number(): - # Define the cmd - cmd = "datadreamer --prompts_number -1" - _check_wrong_value(cmd) - - -def test_negative_conf_threshold(): - # Define the cmd - cmd = "datadreamer --conf_threshold -1" - _check_wrong_value(cmd) - - -def test_big_conf_threshold(): - # Define the cmd - cmd = "datadreamer --conf_threshold 10" - _check_wrong_value(cmd) - - -def test_negative_annotation_iou_threshold(): - # Define the cmd - cmd = "datadreamer --annotation_iou_threshold -1" - _check_wrong_value(cmd) - - -def test_big_annotation_iou_threshold(): - # Define the cmd - cmd = "datadreamer --annotation_iou_threshold 10" - _check_wrong_value(cmd) - - -def test_invalid_image_tester_patience(): - # Define the cmd - cmd = "datadreamer --image_tester_patience -1" - _check_wrong_value(cmd) - - -def test_invalid_seed(): - # Define the cmd - cmd = "datadreamer --seed -1 --device cpu" - _check_wrong_value(cmd) - - -def test_invalid_synonym_generator(): - # Define the cmd - cmd = "datadreamer --device cpu --synonym_generator invalid" - _check_wrong_value(cmd) - - -def test_invalid_lm_quantization(): - # Define the cmd - cmd = "datadreamer --device cude --lm_quantization invalid" - _check_wrong_value(cmd) - - -def test_invalid_device_lm_quantization(): - # Define the cmd - cmd = "datadreamer --device cpu --lm_quantization 4bit" - _check_wrong_value(cmd) - - -def test_invalid_batch_size_prompt(): - # Define the cmd - cmd = "datadreamer --batch_size_prompt -1" - _check_wrong_value(cmd) - - -def test_invalid_batch_size_annotation(): - # Define the cmd - cmd = "datadreamer --batch_size_annotation -1" - _check_wrong_value(cmd) - - -def test_invalid_batch_size_image(): - # Define the cmd - cmd = "datadreamer --batch_size_image -1" - _check_wrong_value(cmd) - - -def test_invalid_num_objects_range(): - # Define the cmd - cmd = "datadreamer --num_objects_range 1" - _check_wrong_value(cmd) - - -def test_many_num_objects_range(): - # Define the cmd - cmd = "datadreamer --num_objects_range 1 2 3" - _check_wrong_value(cmd) - - -def test_desc_num_objects_range(): - # Define the cmd - cmd = "datadreamer --num_objects_range 3 1" - _check_wrong_value(cmd) - - -def test_negative_num_objects_range(): - # Define the cmd - cmd = "datadreamer --num_objects_range -3 1" - _check_wrong_value(cmd) - - # ========================================================= # DETECTION - SIMPLE LM # ========================================================= @@ -389,50 +181,6 @@ def test_cuda_simple_sdxl_detection_pipeline(): _check_detection_pipeline(cmd, target_folder) -@pytest.mark.skipif( - total_memory < 16 or total_disk_space < 35, - reason="Test requires at least 16GB of RAM and 35GB of HDD", -) -def test_cpu_simple_sdxl_lightning_detection_pipeline(): - # Define target folder - target_folder = "data/data-det-cpu-simple-sdxl-lightning/" - # Define the command to run the datadreamer - cmd = ( - f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " - f"--prompts_number 1 " - f"--prompt_generator simple " - f"--num_objects_range 1 2 " - f"--image_generator sdxl-lightning " - f"--use_image_tester " - f"--device cpu" - ) - # Check the run of the pipeline - _check_detection_pipeline(cmd, target_folder) - - -@pytest.mark.skipif( - not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, - reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", -) -def test_cuda_simple_sdxl_lightning_detection_pipeline(): - # Define target folder - target_folder = "data/data-det-cuda-simple-sdxl-lightning/" - # Define the command to run the datadreamer - cmd = ( - f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " - f"--prompts_number 1 " - f"--prompt_generator simple " - f"--num_objects_range 1 2 " - f"--image_generator sdxl-lightning " - f"--use_image_tester " - f"--device cuda" - ) - # Check the run of the pipeline - _check_detection_pipeline(cmd, target_folder) - - # ========================================================= # DETECTION - LLM # ========================================================= @@ -1056,82 +804,3 @@ def test_cuda_tiny_sdxl_classification_pipeline(): ) # Check the run of the pipeline _check_detection_pipeline(cmd, target_folder) - - -# ========================================================= -# TEST WITH CONFIG FILE -# ========================================================= -@pytest.mark.skipif( - total_memory < 16 or total_disk_space < 35, - reason="Test requires at least 16GB of RAM and 35GB of HDD", -) -def test_cpu_simple_sdxl_turbo_config_detection_pipeline(): - # Define target folder - target_folder = "data/data-det-cpu-simple-sdxl-turbo-config/" - # Define the command to run the datadreamer - cmd = ( - f"datadreamer --save_dir {target_folder} " - f"--num_objects_range 1 2 " - f"--config ./sample_config.yaml " - f"--device cpu" - ) - # Check the run of the pipeline - _check_detection_pipeline(cmd, target_folder) - - -@pytest.mark.skipif( - not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, - reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", -) -def test_cuda_simple_sdxl_turbo_config_detection_pipeline(): - # Define target folder - target_folder = "data/data-det-cuda-simple-sdxl-turbo-config/" - # Define the command to run the datadreamer - cmd = ( - f"datadreamer --save_dir {target_folder} " - f"--num_objects_range 1 2 " - f"--config ./sample_config.yaml " - f"--device cuda" - ) - # Check the run of the pipeline - _check_detection_pipeline(cmd, target_folder) - - -@pytest.mark.skipif( - total_memory < 16 or total_disk_space < 35, - reason="Test requires at least 16GB of RAM and 35GB of HDD", -) -def test_cpu_simple_sdxl_turbo_config_classification_pipeline(): - # Define target folder - target_folder = "data/data-cls-cpu-simple-sdxl-turbo-config/" - # Define the command to run the datadreamer - cmd = ( - f"datadreamer --task classification " - f"--save_dir {target_folder} " - f"--num_objects_range 1 2 " - f"--image_annotator clip " - f"--config ./sample_config.yaml " - f"--device cpu" - ) - # Check the run of the pipeline - _check_detection_pipeline(cmd, target_folder) - - -@pytest.mark.skipif( - not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, - reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", -) -def test_cuda_simple_sdxl_turbo_config_classification_pipeline(): - # Define target folder - target_folder = "data/data-cls-cuda-simple-sdxl-turbo-config/" - # Define the command to run the datadreamer - cmd = ( - f"datadreamer --task classification " - f"--save_dir {target_folder} " - f"--num_objects_range 1 2 " - f"--image_annotator clip " - f"--config ./sample_config.yaml " - f"--device cuda" - ) - # Check the run of the pipeline - _check_detection_pipeline(cmd, target_folder) diff --git a/tests/heavy_tests/unittests/test_image_generation_heavy.py b/tests/heavy_tests/unittests/test_image_generation_heavy.py new file mode 100644 index 0000000..30141cc --- /dev/null +++ b/tests/heavy_tests/unittests/test_image_generation_heavy.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +from typing import Type, Union + +import psutil +import pytest +import torch +from PIL import Image + +from datadreamer.image_generation import ( + StableDiffusionImageGenerator, + StableDiffusionLightningImageGenerator, + StableDiffusionTurboImageGenerator, +) + +# Get the total memory in GB +total_memory = psutil.virtual_memory().total / (1024**3) +# Get the total disk space in GB +total_disk_space = psutil.disk_usage("/").total / (1024**3) + + +def _check_image_generator( + image_generator_class: Type[ + Union[ + StableDiffusionImageGenerator, + StableDiffusionTurboImageGenerator, + StableDiffusionLightningImageGenerator, + ] + ], + device: str, +): + image_generator = image_generator_class(device=device) + # Check that the image generator is not None + assert image_generator is not None + # Generate images and check each of them + for generated_images_batch in image_generator.generate_images( + ["A photo of a cat, dog"], [["cat", "dog"]] + ): + generated_image = generated_images_batch[0] + assert generated_image is not None + assert isinstance(generated_image, Image.Image) + + images = image_generator.generate_images_batch( + ["A photo of a cat, dog"], + "blurry, bad quality", + ) + assert len(images) == 1 + assert images[0] is not None + assert isinstance(images[0], Image.Image) + + # Release the generator + image_generator.release(empty_cuda_cache=True if device != "cpu" else False) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25, + reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD", +) +def test_cuda_sdxl_image_generator(): + _check_image_generator(StableDiffusionImageGenerator, "cuda") + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 25, + reason="Test requires at least 16GB of RAM and 25GB of HDD", +) +def test_cpu_sdxl_image_generator(): + _check_image_generator(StableDiffusionImageGenerator, "cpu") diff --git a/tests/heavy_tests/unittests/test_prompt_generation_heavy.py b/tests/heavy_tests/unittests/test_prompt_generation_heavy.py new file mode 100644 index 0000000..a943f5c --- /dev/null +++ b/tests/heavy_tests/unittests/test_prompt_generation_heavy.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +import psutil +import pytest +import torch + +from datadreamer.prompt_generation.lm_prompt_generator import LMPromptGenerator +from datadreamer.prompt_generation.lm_synonym_generator import LMSynonymGenerator + +# Get the total memory in GB +total_memory = psutil.virtual_memory().total / (1024**3) +# Get the total disk space in GB +total_disk_space = psutil.disk_usage("/").total / (1024**3) + + +def _check_lm_prompt_generator( + device: str, prompt_generator_class=LMPromptGenerator, quantization: str = "none" +): + object_names = ["aeroplane", "bicycle", "bird", "boat"] + prompt_generator = prompt_generator_class( + class_names=object_names, + prompts_number=2, + device=device, + quantization=quantization, + ) + prompts = prompt_generator.generate_prompts() + # Check that the some prompts were generated + assert len(prompts) > 0 + # Iterate through the prompts + for selected_objects, prompt_text in prompts: + # Selected objects aren't empty + assert len(selected_objects) > 0 + # The slected objects are in the range + assert ( + prompt_generator.num_objects_range[0] + <= len(selected_objects) + <= prompt_generator.num_objects_range[1] + ) + # Check the generated text + assert len(prompt_text) > 0 and prompt_text.lower().startswith("a photo of") + prompt_generator.release(empty_cuda_cache=True if device != "cpu" else False) + + +@pytest.mark.skipif( + total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 35, + reason="Test requires at least 16GB of RAM, 35GB of HDD and CUDA support", +) +def test_cuda_lm_prompt_generator(): + _check_lm_prompt_generator("cuda") + + +@pytest.mark.skipif( + total_memory < 32 or total_disk_space < 35, + reason="Test requires at least 28GB of RAM and 35GB of HDD for running on CPU", +) +def test_cpu_lm_prompt_generator(): + _check_lm_prompt_generator("cpu") + + +def _check_synonym_generator(device: str, synonym_generator_class=LMSynonymGenerator): + synonyms_num = 3 + generator = synonym_generator_class(synonyms_number=synonyms_num, device=device) + synonyms = generator.generate_synonyms_for_list(["astronaut", "cat", "dog"]) + # Check that the some synonyms were generated + assert len(synonyms) > 0 + # Iterate through the synonyms + for word, synonym_list in synonyms.items(): + # Check that the word is not empty + assert len(word) > 0 + # Check that the synonym list is not empty + assert len(synonym_list) > 0 + # Check that the synonyms are not empty + for synonym in synonym_list: + assert len(synonym) > 0 + generator.release(empty_cuda_cache=True if device != "cpu" else False) + + +@pytest.mark.skipif( + total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 35, + reason="Test requires at least 16GB of RAM, 35GB of HDD and CUDA support", +) +def test_cuda_synonym_generator(): + _check_synonym_generator("cuda") + + +@pytest.mark.skipif( + total_memory < 32 or total_disk_space < 35, + reason="Test requires at least 28GB of RAM and 35GB of HDD for running on CPU", +) +def test_cpu_synonym_generator(): + _check_synonym_generator("cpu") From 196e4ca6653709906890a5884d80d2389a34e6a9 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Wed, 2 Oct 2024 13:48:31 +0200 Subject: [PATCH 14/56] feat: add prompt weighting for sdxl-turbo (#65) --- .../sdxl_turbo_image_generator.py | 29 +++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/datadreamer/image_generation/sdxl_turbo_image_generator.py b/datadreamer/image_generation/sdxl_turbo_image_generator.py index abd20a0..72cb2be 100644 --- a/datadreamer/image_generation/sdxl_turbo_image_generator.py +++ b/datadreamer/image_generation/sdxl_turbo_image_generator.py @@ -4,6 +4,7 @@ from typing import List, Optional import torch +from compel import Compel, ReturnedEmbeddingsType from diffusers import AutoPipelineForText2Image from PIL import Image @@ -30,6 +31,7 @@ def __init__(self, *args, **kwargs): arguments.""" super().__init__(*args, **kwargs) self.base = self._init_gen_model() + self.compel = self._init_compel() def _init_gen_model(self) -> AutoPipelineForText2Image: """Initializes the Stable Diffusion Turbo model for image generation. @@ -57,6 +59,20 @@ def _init_gen_model(self) -> AutoPipelineForText2Image: return base + def _init_compel(self) -> Compel: + """Initializes the Compel model for text prompt weighting. + + Returns: + Compel: The initialized Compel model. + """ + compel = Compel( + tokenizer=[self.base.tokenizer, self.base.tokenizer_2], + text_encoder=[self.base.text_encoder, self.base.text_encoder_2], + returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, + requires_pooled=[False, True], + ) + return compel + def generate_images_batch( self, prompts: List[str], @@ -76,9 +92,18 @@ def generate_images_batch( Returns: List[Image.Image]: A list of generated images. """ + if prompt_objects is not None: + for i in range(len(prompt_objects)): + for obj in prompt_objects[i]: + prompts[i] = prompts[i].replace(obj, f"({obj})1.5", 1) + + conditioning, pooled = self.compel(prompts) + conditioning_neg, pooled_neg = self.compel([negative_prompt] * len(prompts)) images = self.base( - prompt=prompts, - negative_prompt=negative_prompt, + prompt_embeds=conditioning, + pooled_prompt_embeds=pooled, + negative_prompt_embeds=conditioning_neg, + negative_pooled_prompt_embeds=pooled_neg, guidance_scale=0.0, num_inference_steps=4, ).images From a5afb36142e0fd7a2a647f727fbbdf0234234f85 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Wed, 2 Oct 2024 13:48:43 +0200 Subject: [PATCH 15/56] Rework GHCR publish actions (#64) * chore: add manual GHCR publish trigger from any branch * chore: remove GAR publish on release * chore: rename GHCR on release publish action * chore: modify commit hash extraction --- .github/workflows/gar-publish.yaml | 2 -- .github/workflows/ghcr-publish-manual.yaml | 41 ++++++++++++++++++++++ .github/workflows/ghcr-publish.yaml | 2 +- 3 files changed, 42 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/ghcr-publish-manual.yaml diff --git a/.github/workflows/gar-publish.yaml b/.github/workflows/gar-publish.yaml index 3f228c1..eb21edc 100644 --- a/.github/workflows/gar-publish.yaml +++ b/.github/workflows/gar-publish.yaml @@ -4,8 +4,6 @@ name: Deploy single image to GAR (Google Artifact Registry) on: workflow_dispatch: - release: - types: [published] env: PROJECT_ID: easyml-394818 GAR_LOCATION: us-central1 diff --git a/.github/workflows/ghcr-publish-manual.yaml b/.github/workflows/ghcr-publish-manual.yaml new file mode 100644 index 0000000..8419920 --- /dev/null +++ b/.github/workflows/ghcr-publish-manual.yaml @@ -0,0 +1,41 @@ +name: Manually deploy image to GHCR + +on: + workflow_dispatch: + inputs: + branch: + description: 'Branch to deploy' + required: true + default: 'dev' + +env: + GHCR_REGISTRY: ghcr.io + IMAGE_NAME: datadreamer + +jobs: + push-store: + name: Push the image to GHCR + runs-on: ubuntu-latest + + steps: + - name: 'Checkout GitHub Action' + uses: actions/checkout@v2 + with: + ref: ${{ inputs.branch }} # Checkout the selected branch + + - name: 'Extract short commit hash' + id: commit_hash + run: echo "short_hash=$(git rev-parse --short HEAD)" >> $GITHUB_ENV + + - name: Docker login to GHCR + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: luxonis-ml + password: ${{ secrets.GHCR_PAT }} + + - name: 'Build and Push Image to GHCR' + run: | + docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} --build-arg BRANCH=${{ inputs.branch }} . \ + --tag ghcr.io/luxonis/datadreamer:${{ steps.commit_hash.outputs.short_hash }} + docker push ghcr.io/luxonis/datadreamer --all-tags diff --git a/.github/workflows/ghcr-publish.yaml b/.github/workflows/ghcr-publish.yaml index f0d2539..7786c7c 100644 --- a/.github/workflows/ghcr-publish.yaml +++ b/.github/workflows/ghcr-publish.yaml @@ -1,4 +1,4 @@ -name: Docker Build and Publish +name: Deploy latest image to GHCR on release on: workflow_dispatch: From 154c50e067345360455c7722991fdbf05c31f1c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20=C4=8Cuhel?= <79118988+HonzaCuhel@users.noreply.github.com> Date: Mon, 7 Oct 2024 17:35:28 +0200 Subject: [PATCH 16/56] Add profanity filter for input class names (#63) * Add safety features * Fix lm prompt testing * Correct lm prompt testing * [Automated] Updated coverage badge * Rework the safety features * Add truncation * Remove truncation * Update Profanity Filter * Profanity Filter refactor --------- Co-authored-by: GitHub Actions --- README.md | 4 +- .../generate_dataset_from_scratch.py | 16 + datadreamer/prompt_generation/__init__.py | 2 + .../prompt_generation/lm_prompt_generator.py | 4 +- .../prompt_generation/profanity_filter.py | 192 ++++ datadreamer/utils/bad_words.py | 961 ++++++++++++++++++ datadreamer/utils/config.py | 2 + media/coverage_badge.svg | 4 +- requirements.txt | 4 +- .../unittests/test_prompt_generation.py | 25 + 10 files changed, 1206 insertions(+), 8 deletions(-) create mode 100644 datadreamer/prompt_generation/profanity_filter.py create mode 100644 datadreamer/utils/bad_words.py diff --git a/README.md b/README.md index f15831f..8d44539 100644 --- a/README.md +++ b/README.md @@ -175,6 +175,7 @@ datadreamer --config - `--image_tester_patience`: Patience level for image tester. Default is `1`. - `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`. - `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`. +- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`. - `--batch_size_prompt`: Batch size for prompt generation. Default is 64. - `--batch_size_annotation`: Batch size for annotation. Default is `1`. - `--batch_size_image`: Batch size for image generation. Default is `1`. @@ -191,6 +192,7 @@ datadreamer --config | Prompt Generation | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Semantically rich prompts | | | [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) | Tiny LM | | | Simple random generator | Joins randomly chosen object names | +| Profanity Filter | [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) | Fast and accurate LM profanity filter | | Image Generation | [SDXL-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | Slow and accurate (1024x1024 images) | | | [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo) | Fast and less accurate (512x512 images) | | | [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning) | Fast and accurate (1024x1024 images) | @@ -292,7 +294,7 @@ The above license does not cover the models. Please see the license of each mode ## 🙏 Acknowledgements -This library was made possible by the use of several open-source projects, including Transformers, Diffusers, and others listed in the requirements.txt. +This library was made possible by the use of several open-source projects, including Transformers, Diffusers, and others listed in the requirements.txt. Furthermore, we utilized a bad words list from [`@coffeeandfun/google-profanity-words`](https://github.com/coffee-and-fun/google-profanity-words) Node.js module created by Robert James Gabriel from Coffee & Fun LLC. [SD-XL 1.0 License](https://github.com/Stability-AI/generative-models/blob/main/model_licenses/LICENSE-SDXL1.0) [SDXL-Turbo License](https://github.com/Stability-AI/generative-models/blob/main/model_licenses/LICENSE-SDXL-Turbo) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index d3ee3bf..345450f 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -24,6 +24,7 @@ from datadreamer.prompt_generation import ( LMPromptGenerator, LMSynonymGenerator, + ProfanityFilter, SimplePromptGenerator, TinyLlamaLMPromptGenerator, WordNetSynonymGenerator, @@ -200,6 +201,13 @@ def parse_args(): help="Size of the annotator model to use", ) + parser.add_argument( + "--disable_lm_filter", + default=None, + action="store_true", + help="Whether to use only bad words in profanity filter", + ) + parser.add_argument( "--batch_size_prompt", type=int, @@ -388,6 +396,14 @@ def main(): # Check arguments check_args(args) + profanity_filter = ProfanityFilter( + seed=args.seed, device=args.device, use_lm=not args.disable_lm_filter + ) + # Check class names for bad words + if not profanity_filter.is_safe(args.class_names): + raise ValueError(f"Class names: '{args.class_names}' contain bad words!") + profanity_filter.release(empty_cuda_cache=True) + # Directories for saving images and bboxes save_dir = args.save_dir if not args.annotate_only: diff --git a/datadreamer/prompt_generation/__init__.py b/datadreamer/prompt_generation/__init__.py index 1e7f853..b274179 100644 --- a/datadreamer/prompt_generation/__init__.py +++ b/datadreamer/prompt_generation/__init__.py @@ -2,6 +2,7 @@ from .lm_prompt_generator import LMPromptGenerator from .lm_synonym_generator import LMSynonymGenerator +from .profanity_filter import ProfanityFilter from .simple_prompt_generator import SimplePromptGenerator from .tinyllama_lm_prompt_generator import TinyLlamaLMPromptGenerator from .wordnet_synonym_generator import WordNetSynonymGenerator @@ -10,6 +11,7 @@ "SimplePromptGenerator", "LMPromptGenerator", "LMSynonymGenerator", + "ProfanityFilter", "TinyLlamaLMPromptGenerator", "WordNetSynonymGenerator", ] diff --git a/datadreamer/prompt_generation/lm_prompt_generator.py b/datadreamer/prompt_generation/lm_prompt_generator.py index 8a3e6e1..ba1dfd5 100644 --- a/datadreamer/prompt_generation/lm_prompt_generator.py +++ b/datadreamer/prompt_generation/lm_prompt_generator.py @@ -188,9 +188,7 @@ def _test_prompt(self, prompt: str, selected_objects: List[str]) -> bool: Returns: bool: True if the prompt is valid, False otherwise. """ - return prompt.lower().startswith( - "a photo of" - ) # and all(obj.lower() in prompt.lower() for obj in selected_objects) + return prompt.lower().startswith("a photo of") def generate_prompts_batch(self, prompt_texts_batch: List[str]) -> List[str]: """Generates a list of prompts using the language model. diff --git a/datadreamer/prompt_generation/profanity_filter.py b/datadreamer/prompt_generation/profanity_filter.py new file mode 100644 index 0000000..7a9da63 --- /dev/null +++ b/datadreamer/prompt_generation/profanity_filter.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import logging +import random +from typing import List, Optional, Tuple + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from datadreamer.utils.bad_words import BAD_WORDS_LIST + +logger = logging.getLogger(__name__) + + +class ProfanityFilter: + """Class for filtering bad words from texts and checking if texts are safe. + + Attributes: + device (str): Device to run the language model on ('cuda' for GPU, 'cpu' for CPU). + use_lm (bool): Whether to use a language model for checking text safety. + seed (Optional[float]): Seed for randomization. + model (AutoModelForCausalLM): The pre-trained causal language model for checking text safety. + tokenizer (AutoTokenizer): The tokenizer for the pre-trained language model. + + Methods: + set_seed(seed): Sets the random seed for consistent prompt generation. + _init_lang_model(): Initializes the language model and tokenizer. + _contains_bad_words(texts): Checks if a list of texts contain bad words. + _check_lm_safety(text): Checks if a text is safe using a language model. + is_safe(classes): Checks if a list of classes is safe. + release(empty_cuda_cache): Releases the model and optionally empties the CUDA cache. + """ + + LLM_PROMPT = """You are Qwen, created by Alibaba Cloud. You are a helpful assistant who classifies the classes as appropriate or inappropriate. Inappropriate classes are those that directly relate to drugs, hate, racism, harassment, nudity, sexual or offensive words. Here are inappropriate examples: +- 'ass', +- 'a**', +- 'bitch', +- 'pussy', +- and 'f**k'. + +Otherwise, the classes are considered appropriate. They can talk about people, characters, animals, nature, history, human conflicts, and so on. Some acceptable examples are: +- 'cat', +- 'angry barking dog', +- 'alien', +- 'dracula', +- 'war', +- 'soldier', +- 'pluto', +- 'sun', +- and 'mercury.' + +Respond 'inappropriate' if the classes are unacceptable, otherwise respond with 'appropriate'.""" + + def __init__( + self, + device: str = "cuda", + use_lm: bool = False, + seed: Optional[float] = 42, + ) -> None: + """Initializes the ProfanityFilter with parameters.""" + self.seed = seed + if seed is not None: + self.set_seed(seed) + self.device = device + self.use_lm = use_lm + if self.use_lm: + self.model, self.tokenizer = self._init_lang_model() + + @staticmethod + def set_seed(seed: int) -> None: + """Sets the random seed for consistent prompt generation. + + Args: + seed (int): The random seed. + """ + random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + def _init_lang_model(self) -> Tuple[AutoModelForCausalLM, AutoTokenizer]: + """Initializes the language model and tokenizer for prompt generation. + + Returns: + tuple: The initialized language model and tokenizer. + """ + logger.info( + f"Initializing Qwen2.5-1.5B-Instruct language model on {self.device}..." + ) + model_name = "Qwen/Qwen2.5-1.5B-Instruct" + if self.device == "cpu": + model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="cpu", + low_cpu_mem_usage=True, + ) + else: + model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.float16, + device_map=self.device, + ) + tokenizer = AutoTokenizer.from_pretrained(model_name) + return model, tokenizer + + def _contains_bad_words(self, texts: List[str]) -> bool: + """Checks if a list of texts contain bad words. + + Args: + texts (List[str]): List of texts to checks against bad words list. + + Returns: + bool: True if any of the texts contain bad words, False otherwise. + """ + return any(text.lower() in BAD_WORDS_LIST for text in texts) + + def _check_lm_safety(self, text: str) -> bool: + """Checks if a text is safe using a language model. + + Args: + text (str): Text to check for bad words. + + Returns: + bool: True if the text is safe, False otherwise. + """ + if self.use_lm: + messages = [ + { + "role": "system", + "content": self.LLM_PROMPT, + }, + {"role": "user", "content": text}, + ] + processed_text = self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + model_inputs = self.tokenizer([processed_text], return_tensors="pt").to( + self.model.device + ) + with torch.no_grad(): + generated_ids = self.model.generate( + **model_inputs, + max_new_tokens=10, + do_sample=False, + top_k=None, + top_p=None, + temperature=None, + ) + generated_ids = [ + output_ids[len(input_ids) :] + for input_ids, output_ids in zip( + model_inputs.input_ids, generated_ids + ) + ] + response = self.tokenizer.batch_decode( + generated_ids, skip_special_tokens=True + )[0] + return "inappropriate" not in response.lower().strip() + return True + + def is_safe(self, classes: List[str]) -> bool: + """Checks if a list of classes is safe. + + Args: + classes (List[str]): List of classes to check for safety. + + Returns: + bool: True if the classes are safe, False otherwise. + """ + logger.info(f"Profanity filter is checking classes: {classes}") + return not self._contains_bad_words(classes) and self._check_lm_safety( + ",".join(classes) + ) + + def release(self, empty_cuda_cache=False) -> None: + """Releases the model and optionally empties the CUDA cache.""" + if self.use_lm: + if self.device == "cuda": + self.model = self.model.to("cpu") + if empty_cuda_cache: + with torch.no_grad(): + torch.cuda.empty_cache() + + +if __name__ == "__main__": + # Example usage of the class + profanity_filter = ProfanityFilter(use_lm=True, device="cpu") + classes_1 = ["cat", "fish", "dog", "ass", "person", "soldier", "war"] + print(f"Are classes#1 {classes_1} safe: {profanity_filter.is_safe(classes_1)}") + classes_2 = ["cat", "fish", "dog", "person", "soldier", "war"] + print(f"Are classes#2 {classes_2} safe: {profanity_filter.is_safe(classes_2)}") + profanity_filter.release() diff --git a/datadreamer/utils/bad_words.py b/datadreamer/utils/bad_words.py new file mode 100644 index 0000000..532b19f --- /dev/null +++ b/datadreamer/utils/bad_words.py @@ -0,0 +1,961 @@ +# Source of this list: https://github.com/coffee-and-fun/google-profanity-words/blob/main/data/en.txt +BAD_WORDS_LIST = [ + "2 girls 1 cup", + "2g1c", + "4r5e", + "5h1t", + "5hit", + "a55", + "a_s_s", + "acrotomophilia", + "alabama hot pocket", + "alaskan pipeline", + "anal", + "anilingus", + "anus", + "apeshit", + "ar5e", + "arrse", + "arse", + "arsehole", + "ass", + "ass-fucker", + "ass-hat", + "ass-pirate", + "assbag", + "assbandit", + "assbanger", + "assbite", + "assclown", + "asscock", + "asscracker", + "asses", + "assface", + "assfucker", + "assfukka", + "assgoblin", + "asshat", + "asshead", + "asshole", + "assholes", + "asshopper", + "assjacker", + "asslick", + "asslicker", + "assmonkey", + "assmunch", + "assmuncher", + "asspirate", + "assshole", + "asssucker", + "asswad", + "asswhole", + "asswipe", + "auto erotic", + "autoerotic", + "b!tch", + "b00bs", + "b17ch", + "b1tch", + "babeland", + "baby batter", + "baby juice", + "ball gag", + "ball gravy", + "ball kicking", + "ball licking", + "ball sack", + "ball sucking", + "ballbag", + "balls", + "ballsack", + "bampot", + "bangbros", + "bareback", + "barely legal", + "barenaked", + "bastard", + "bastardo", + "bastinado", + "bbw", + "bdsm", + "beaner", + "beaners", + "beastial", + "beastiality", + "beastility", + "beaver cleaver", + "beaver lips", + "bellend", + "bestial", + "bestiality", + "bi+ch", + "biatch", + "big black", + "big breasts", + "big knockers", + "big tits", + "bimbos", + "birdlock", + "bitch", + "bitcher", + "bitchers", + "bitches", + "bitchin", + "bitching", + "black cock", + "blonde action", + "blonde on blonde action", + "bloody", + "blow job", + "blow your load", + "blowjob", + "blowjobs", + "blue waffle", + "blumpkin", + "boiolas", + "bollock", + "bollocks", + "bollok", + "bollox", + "bondage", + "boner", + "boob", + "boobie", + "boobs", + "booobs", + "boooobs", + "booooobs", + "booooooobs", + "booty call", + "breasts", + "brown showers", + "brunette action", + "buceta", + "bugger", + "bukkake", + "bulldyke", + "bullet vibe", + "bullshit", + "bum", + "bung hole", + "bunghole", + "bunny fucker", + "busty", + "butt", + "butt-pirate", + "buttcheeks", + "butthole", + "buttmunch", + "buttplug", + "c0ck", + "c0cksucker", + "camel toe", + "camgirl", + "camslut", + "camwhore", + "carpet muncher", + "carpetmuncher", + "cawk", + "chinc", + "chink", + "choad", + "chocolate rosebuds", + "chode", + "cipa", + "circlejerk", + "cl1t", + "cleveland steamer", + "clit", + "clitface", + "clitoris", + "clits", + "clover clamps", + "clusterfuck", + "cnut", + "cock", + "cock-sucker", + "cockbite", + "cockburger", + "cockface", + "cockhead", + "cockjockey", + "cockknoker", + "cockmaster", + "cockmongler", + "cockmongruel", + "cockmonkey", + "cockmunch", + "cockmuncher", + "cocknose", + "cocknugget", + "cocks", + "cockshit", + "cocksmith", + "cocksmoker", + "cocksuck", + "cocksuck", + "cocksucked", + "cocksucked", + "cocksucker", + "cocksucking", + "cocksucks", + "cocksuka", + "cocksukka", + "cok", + "cokmuncher", + "coksucka", + "coochie", + "coochy", + "coon", + "coons", + "cooter", + "coprolagnia", + "coprophilia", + "cornhole", + "cox", + "crap", + "creampie", + "cum", + "cumbubble", + "cumdumpster", + "cumguzzler", + "cumjockey", + "cummer", + "cumming", + "cums", + "cumshot", + "cumslut", + "cumtart", + "cunilingus", + "cunillingus", + "cunnie", + "cunnilingus", + "cunt", + "cuntface", + "cunthole", + "cuntlick", + "cuntlick", + "cuntlicker", + "cuntlicker", + "cuntlicking", + "cuntlicking", + "cuntrag", + "cunts", + "cyalis", + "cyberfuc", + "cyberfuck", + "cyberfucked", + "cyberfucker", + "cyberfuckers", + "cyberfucking", + "d1ck", + "dammit", + "damn", + "darkie", + "date rape", + "daterape", + "deep throat", + "deepthroat", + "dendrophilia", + "dick", + "dickbag", + "dickbeater", + "dickface", + "dickhead", + "dickhole", + "dickjuice", + "dickmilk", + "dickmonger", + "dickslap", + "dicksucker", + "dickwad", + "dickweasel", + "dickweed", + "dickwod", + "dike", + "dildo", + "dildos", + "dingleberries", + "dingleberry", + "dink", + "dinks", + "dipshit", + "dirsa", + "dirty pillows", + "dirty sanchez", + "dlck", + "dog style", + "dog-fucker", + "doggie style", + "doggiestyle", + "doggin", + "dogging", + "doggy style", + "doggystyle", + "dolcett", + "domination", + "dominatrix", + "dommes", + "donkey punch", + "donkeyribber", + "doochbag", + "dookie", + "doosh", + "double dong", + "double penetration", + "douche", + "douchebag", + "dp action", + "dry hump", + "duche", + "dumbshit", + "dumshit", + "dvda", + "dyke", + "eat my ass", + "ecchi", + "ejaculate", + "ejaculated", + "ejaculates", + "ejaculating", + "ejaculatings", + "ejaculation", + "ejakulate", + "erotic", + "erotism", + "escort", + "eunuch", + "f u c k", + "f u c k e r", + "f4nny", + "f_u_c_k", + "fag", + "fagbag", + "fagg", + "fagging", + "faggit", + "faggitt", + "faggot", + "faggs", + "fagot", + "fagots", + "fags", + "fagtard", + "fanny", + "fannyflaps", + "fannyfucker", + "fanyy", + "fart", + "farted", + "farting", + "farty", + "fatass", + "fcuk", + "fcuker", + "fcuking", + "fecal", + "feck", + "fecker", + "felatio", + "felch", + "felching", + "fellate", + "fellatio", + "feltch", + "female squirting", + "femdom", + "figging", + "fingerbang", + "fingerfuck", + "fingerfucked", + "fingerfucker", + "fingerfuckers", + "fingerfucking", + "fingerfucks", + "fingering", + "fistfuck", + "fistfucked", + "fistfucker", + "fistfuckers", + "fistfucking", + "fistfuckings", + "fistfucks", + "fisting", + "flamer", + "flange", + "fook", + "fooker", + "foot fetish", + "footjob", + "frotting", + "fuck", + "fuck buttons", + "fucka", + "fucked", + "fucker", + "fuckers", + "fuckhead", + "fuckheads", + "fuckin", + "fucking", + "fuckings", + "fuckingshitmotherfucker", + "fuckme", + "fucks", + "fucktards", + "fuckwhit", + "fuckwit", + "fudge packer", + "fudgepacker", + "fuk", + "fuker", + "fukker", + "fukkin", + "fuks", + "fukwhit", + "fukwit", + "futanari", + "fux", + "fux0r", + "g-spot", + "gang bang", + "gangbang", + "gangbanged", + "gangbanged", + "gangbangs", + "gay sex", + "gayass", + "gaybob", + "gaydo", + "gaylord", + "gaysex", + "gaytard", + "gaywad", + "genitals", + "giant cock", + "girl on", + "girl on top", + "girls gone wild", + "goatcx", + "goatse", + "god damn", + "god-dam", + "god-damned", + "goddamn", + "goddamned", + "gokkun", + "golden shower", + "goo girl", + "gooch", + "goodpoop", + "gook", + "goregasm", + "gringo", + "grope", + "group sex", + "guido", + "guro", + "hand job", + "handjob", + "hard core", + "hardcore", + "hardcoresex", + "heeb", + "hell", + "hentai", + "heshe", + "ho", + "hoar", + "hoare", + "hoe", + "hoer", + "homo", + "homoerotic", + "honkey", + "honky", + "hooker", + "hore", + "horniest", + "horny", + "hot carl", + "hot chick", + "hotsex", + "how to kill", + "how to murder", + "huge fat", + "humping", + "incest", + "intercourse", + "jack off", + "jack-off", + "jackass", + "jackoff", + "jail bait", + "jailbait", + "jap", + "jelly donut", + "jerk off", + "jerk-off", + "jigaboo", + "jiggaboo", + "jiggerboo", + "jism", + "jiz", + "jiz", + "jizm", + "jizm", + "jizz", + "juggs", + "kawk", + "kike", + "kinbaku", + "kinkster", + "kinky", + "kiunt", + "knob", + "knobbing", + "knobead", + "knobed", + "knobend", + "knobhead", + "knobjocky", + "knobjokey", + "kock", + "kondum", + "kondums", + "kooch", + "kootch", + "kum", + "kumer", + "kummer", + "kumming", + "kums", + "kunilingus", + "kunt", + "kyke", + "l3i+ch", + "l3itch", + "labia", + "leather restraint", + "leather straight jacket", + "lemon party", + "lesbo", + "lezzie", + "lmfao", + "lolita", + "lovemaking", + "lust", + "lusting", + "m0f0", + "m0fo", + "m45terbate", + "ma5terb8", + "ma5terbate", + "make me come", + "male squirting", + "masochist", + "master-bate", + "masterb8", + "masterbat*", + "masterbat3", + "masterbate", + "masterbation", + "masterbations", + "masturbate", + "menage a trois", + "milf", + "minge", + "missionary position", + "mo-fo", + "mof0", + "mofo", + "mothafuck", + "mothafucka", + "mothafuckas", + "mothafuckaz", + "mothafucked", + "mothafucker", + "mothafuckers", + "mothafuckin", + "mothafucking", + "mothafuckings", + "mothafucks", + "mother fucker", + "motherfuck", + "motherfucked", + "motherfucker", + "motherfuckers", + "motherfuckin", + "motherfucking", + "motherfuckings", + "motherfuckka", + "motherfucks", + "mound of venus", + "mr hands", + "muff", + "muff diver", + "muffdiver", + "muffdiving", + "mutha", + "muthafecker", + "muthafuckker", + "muther", + "mutherfucker", + "n1gga", + "n1gger", + "nambla", + "nawashi", + "nazi", + "negro", + "neonazi", + "nig nog", + "nigg3r", + "nigg4h", + "nigga", + "niggah", + "niggas", + "niggaz", + "nigger", + "niggers", + "niglet", + "nimphomania", + "nipple", + "nipples", + "nob", + "nob jokey", + "nobhead", + "nobjocky", + "nobjokey", + "nsfw images", + "nude", + "nudity", + "numbnuts", + "nutsack", + "nympho", + "nymphomania", + "octopussy", + "omorashi", + "one cup two girls", + "one guy one jar", + "orgasim", + "orgasim", + "orgasims", + "orgasm", + "orgasms", + "orgy", + "p0rn", + "paedophile", + "paki", + "panooch", + "panties", + "panty", + "pawn", + "pecker", + "peckerhead", + "pedobear", + "pedophile", + "pegging", + "penis", + "penisfucker", + "phone sex", + "phonesex", + "phuck", + "phuk", + "phuked", + "phuking", + "phukked", + "phukking", + "phuks", + "phuq", + "piece of shit", + "pigfucker", + "pimpis", + "pis", + "pises", + "pisin", + "pising", + "pisof", + "piss", + "piss pig", + "pissed", + "pisser", + "pissers", + "pisses", + "pissflap", + "pissflaps", + "pissin", + "pissin", + "pissing", + "pissoff", + "pissoff", + "pisspig", + "playboy", + "pleasure chest", + "pole smoker", + "polesmoker", + "pollock", + "ponyplay", + "poo", + "poof", + "poon", + "poonani", + "poonany", + "poontang", + "poop", + "poop chute", + "poopchute", + "porn", + "porno", + "pornography", + "pornos", + "prick", + "pricks", + "prince albert piercing", + "pron", + "pthc", + "pube", + "pubes", + "punanny", + "punany", + "punta", + "pusies", + "pusse", + "pussi", + "pussies", + "pussy", + "pussylicking", + "pussys", + "pusy", + "puto", + "queaf", + "queef", + "queerbait", + "queerhole", + "quim", + "raghead", + "raging boner", + "rape", + "raping", + "rapist", + "rectum", + "renob", + "retard", + "reverse cowgirl", + "rimjaw", + "rimjob", + "rimming", + "rosy palm", + "rosy palm and her 5 sisters", + "ruski", + "rusty trombone", + "s hit", + "s&m", + "s.o.b.", + "s_h_i_t", + "sadism", + "sadist", + "santorum", + "scat", + "schlong", + "scissoring", + "screwing", + "scroat", + "scrote", + "scrotum", + "semen", + "sex", + "sexo", + "sexy", + "sh!+", + "sh!t", + "sh1t", + "shag", + "shagger", + "shaggin", + "shagging", + "shaved beaver", + "shaved pussy", + "shemale", + "shi+", + "shibari", + "shit", + "shit-ass", + "shit-bag", + "shit-bagger", + "shit-brain", + "shit-breath", + "shit-cunt", + "shit-dick", + "shit-eating", + "shit-face", + "shit-faced", + "shit-fit", + "shit-head", + "shit-heel", + "shit-hole", + "shit-house", + "shit-load", + "shit-pot", + "shit-spitter", + "shit-stain", + "shitass", + "shitbag", + "shitbagger", + "shitblimp", + "shitbrain", + "shitbreath", + "shitcunt", + "shitdick", + "shite", + "shiteating", + "shited", + "shitey", + "shitface", + "shitfaced", + "shitfit", + "shitfuck", + "shitfull", + "shithead", + "shitheel", + "shithole", + "shithouse", + "shiting", + "shitings", + "shitload", + "shitpot", + "shits", + "shitspitter", + "shitstain", + "shitted", + "shitter", + "shitters", + "shittiest", + "shitting", + "shittings", + "shitty", + "shitty", + "shity", + "shiz", + "shiznit", + "shota", + "shrimping", + "skank", + "skeet", + "slanteye", + "slut", + "slutbag", + "sluts", + "smeg", + "smegma", + "smut", + "snatch", + "snowballing", + "sodomize", + "sodomy", + "son-of-a-bitch", + "spac", + "spic", + "spick", + "splooge", + "splooge moose", + "spooge", + "spread legs", + "spunk", + "strap on", + "strapon", + "strappado", + "strip club", + "style doggy", + "suck", + "sucks", + "suicide girls", + "sultry women", + "swastika", + "swinger", + "t1tt1e5", + "t1tties", + "tainted love", + "tard", + "taste my", + "tea bagging", + "teets", + "teez", + "testical", + "testicle", + "threesome", + "throating", + "thundercunt", + "tied up", + "tight white", + "tit", + "titfuck", + "tits", + "titt", + "tittie5", + "tittiefucker", + "titties", + "titty", + "tittyfuck", + "tittywank", + "titwank", + "tongue in a", + "topless", + "tosser", + "towelhead", + "tranny", + "tribadism", + "tub girl", + "tubgirl", + "turd", + "tushy", + "tw4t", + "twat", + "twathead", + "twatlips", + "twatty", + "twink", + "twinkie", + "two girls one cup", + "twunt", + "twunter", + "undressing", + "upskirt", + "urethra play", + "urophilia", + "v14gra", + "v1gra", + "va-j-j", + "vag", + "vagina", + "venus mound", + "viagra", + "vibrator", + "violet wand", + "vjayjay", + "vorarephilia", + "voyeur", + "vulva", + "w00se", + "wang", + "wank", + "wanker", + "wanky", + "wet dream", + "wetback", + "white power", + "whoar", + "whore", + "willies", + "willy", + "wrapping men", + "wrinkled starfish", + "xrated", + "xx", + "xxx", + "yaoi", + "yellow showers", + "yiffy", + "zoophilia", + "🖕", +] diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index bf0c5cb..b5761b4 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -36,6 +36,8 @@ class Config(LuxonisConfig): batch_size_image: int = 1 use_image_tester: bool = False image_tester_patience: int = 1 + # Profanity filter arguments + disable_lm_filter: bool = False # Annotation arguments image_annotator: Literal["owlv2", "clip"] = "owlv2" conf_threshold: float = 0.15 diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 2fad913..179c6a1 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -15,7 +15,7 @@ coverage coverage - 62% - 62% + 63% + 63% diff --git a/requirements.txt b/requirements.txt index efbf9b5..0b92960 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ torch>=2.0.0 torchvision>=0.16.0 -transformers>=4.0.0 +transformers>=4.37.0 diffusers>=0.24.0 compel>=2.0.0 tqdm>=4.0.0 @@ -12,6 +12,6 @@ accelerate>=0.25.0 scipy>=1.10.0 bitsandbytes>=0.42.0 nltk>=3.8.1 -luxonis-ml[all]>=0.1.0 +luxonis-ml[all]>=0.3.0 python-box>=7.1.1 gcsfs>=2023.1.0 diff --git a/tests/core_tests/unittests/test_prompt_generation.py b/tests/core_tests/unittests/test_prompt_generation.py index f2dcd9f..58671a0 100644 --- a/tests/core_tests/unittests/test_prompt_generation.py +++ b/tests/core_tests/unittests/test_prompt_generation.py @@ -6,6 +6,7 @@ from datadreamer.prompt_generation.lm_prompt_generator import LMPromptGenerator from datadreamer.prompt_generation.lm_synonym_generator import LMSynonymGenerator +from datadreamer.prompt_generation.profanity_filter import ProfanityFilter from datadreamer.prompt_generation.simple_prompt_generator import SimplePromptGenerator from datadreamer.prompt_generation.tinyllama_lm_prompt_generator import ( TinyLlamaLMPromptGenerator, @@ -120,3 +121,27 @@ def test_cpu_wordnet_synonym_generator(): ) def test_cuda_wordnet_synonym_generator(): _check_synonym_generator("cuda", WordNetSynonymGenerator) + + +def _check_profanity_filter(device: str) -> None: + """Check the profanity filter. + + Args: + device (str): The device to run the language model on ('cuda' for GPU, 'cpu' for CPU). + """ + profanity_filter = ProfanityFilter(device=device, use_lm=True) + assert profanity_filter.is_safe(["cat", "dog", "plane", "person"]) + assert not profanity_filter.is_safe(["cat", "dog", "ass", "person"]) + profanity_filter.release(empty_cuda_cache=True if device != "cpu" else False) + + +def test_cpu_lm_profanity_filter(): + _check_profanity_filter("cpu") + + +@pytest.mark.skipif( + not torch.cuda.is_available(), + reason="Test requires CUDA support", +) +def test_cuda_lm_profanity_filter(): + _check_profanity_filter("cuda") From 725d353f9c7837a24661ec003672c348c1560b55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20=C4=8Cuhel?= <79118988+HonzaCuhel@users.noreply.github.com> Date: Tue, 8 Oct 2024 16:28:45 +0200 Subject: [PATCH 17/56] Add Qwen2.5 LM as prompt generator (#66) * Add Qwen2.5 LM as prompt generator * Format code * fix: prompt text visualization * fix: padding_side="left" in Qwen2.5 to remove warning * docs: update args description --------- Co-authored-by: Nikita Sokovnin --- README.md | 3 +- .../generate_dataset_from_scratch.py | 15 +- datadreamer/prompt_generation/__init__.py | 2 + .../qwen2_lm_prompt_generator.py | 187 ++++ datadreamer/utils/config.py | 2 +- .../generate_dataset_and_train_yolo.ipynb | 841 +++++++++--------- .../unittests/test_prompt_generation.py | 27 + .../integration/test_pipeline_heavy.py | 190 ++++ 8 files changed, 846 insertions(+), 421 deletions(-) create mode 100644 datadreamer/prompt_generation/qwen2_lm_prompt_generator.py diff --git a/README.md b/README.md index 8d44539..37f9af4 100644 --- a/README.md +++ b/README.md @@ -161,7 +161,7 @@ datadreamer --config - `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`. - `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`. - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3. -- `--prompt_generator`: Choose between `simple`, `lm` (language model) and `tiny` (tiny LM). Default is `simple`. +- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`. - `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`. - `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`. - `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`. @@ -191,6 +191,7 @@ datadreamer --config | ----------------- | ------------------------------------------------------------------------------------- | --------------------------------------- | | Prompt Generation | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Semantically rich prompts | | | [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) | Tiny LM | +| | [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) | Qwen2.5 LM | | | Simple random generator | Joins randomly chosen object names | | Profanity Filter | [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) | Fast and accurate LM profanity filter | | Image Generation | [SDXL-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | Slow and accurate (1024x1024 images) | diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 345450f..33811bf 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -3,6 +3,7 @@ import argparse import os import shutil +import textwrap import uuid import matplotlib.patches as patches @@ -25,6 +26,7 @@ LMPromptGenerator, LMSynonymGenerator, ProfanityFilter, + Qwen2LMPromptGenerator, SimplePromptGenerator, TinyLlamaLMPromptGenerator, WordNetSynonymGenerator, @@ -36,6 +38,7 @@ "simple": SimplePromptGenerator, "lm": LMPromptGenerator, "tiny": TinyLlamaLMPromptGenerator, + "qwen2": Qwen2LMPromptGenerator, } synonym_generators = { @@ -101,8 +104,8 @@ def parse_args(): parser.add_argument( "--prompt_generator", type=str, - choices=["simple", "lm", "tiny"], - help="Prompt generator to use: simple or language model", + choices=["simple", "lm", "tiny", "qwen2"], + help="Prompt generator to use: simple, lm, tiny, or qwen2 (default).", ) parser.add_argument( "--image_generator", @@ -321,10 +324,10 @@ def check_args(args): if args.lm_quantization != "none" and ( args.device == "cpu" or not torch.cuda.is_available() - or args.prompt_generator != "lm" + or args.prompt_generator not in ["lm", "qwen2"] ): raise ValueError( - "LM Quantization is only available for CUDA devices and Mistral LM" + "LM Quantization is only available for CUDA devices and Mistral/Qwen2.5 prompt generators" ) # Check batch_size_prompt @@ -633,7 +636,9 @@ def read_image_batch(image_batch, batch_num, batch_size): ) # Add prompt text as title if generated_prompts: - plt.title(generated_prompts[i * args.batch_size_annotation + j][1]) + title = generated_prompts[i * args.batch_size_annotation + j][1] + wrapped_title = "\n".join(textwrap.wrap(title, width=50)) + plt.title(wrapped_title) else: plt.title("Annotated image") diff --git a/datadreamer/prompt_generation/__init__.py b/datadreamer/prompt_generation/__init__.py index b274179..20a10ef 100644 --- a/datadreamer/prompt_generation/__init__.py +++ b/datadreamer/prompt_generation/__init__.py @@ -3,6 +3,7 @@ from .lm_prompt_generator import LMPromptGenerator from .lm_synonym_generator import LMSynonymGenerator from .profanity_filter import ProfanityFilter +from .qwen2_lm_prompt_generator import Qwen2LMPromptGenerator from .simple_prompt_generator import SimplePromptGenerator from .tinyllama_lm_prompt_generator import TinyLlamaLMPromptGenerator from .wordnet_synonym_generator import WordNetSynonymGenerator @@ -13,5 +14,6 @@ "LMSynonymGenerator", "ProfanityFilter", "TinyLlamaLMPromptGenerator", + "Qwen2LMPromptGenerator", "WordNetSynonymGenerator", ] diff --git a/datadreamer/prompt_generation/qwen2_lm_prompt_generator.py b/datadreamer/prompt_generation/qwen2_lm_prompt_generator.py new file mode 100644 index 0000000..48a6f5b --- /dev/null +++ b/datadreamer/prompt_generation/qwen2_lm_prompt_generator.py @@ -0,0 +1,187 @@ +from __future__ import annotations + +import logging +import re +from typing import List, Literal, Optional, Tuple + +import torch +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + Pipeline, + pipeline, +) + +from datadreamer.prompt_generation.lm_prompt_generator import LMPromptGenerator + +logger = logging.getLogger(__name__) + + +class Qwen2LMPromptGenerator(LMPromptGenerator): + """A language model-based prompt generator class, extending PromptGenerator. + + Attributes: + device (str): Device to run the language model on ('cuda' for GPU, 'cpu' for CPU). + model (AutoModelForCausalLM): The pre-trained causal language model for generating prompts. + tokenizer (AutoTokenizer): The tokenizer for the pre-trained language model. + pipeline (pipeline): The HuggingFace pipeline for generating text. + + Methods: + _init_lang_model(): Initializes the language model and tokenizer. + _remove_caption_sentences(text): Removes caption sentences from the generated prompt. + _create_lm_prompt_text(selected_objects): Creates a text prompt for the language model. + _postprocess_prompt(prompt): Post-processes the generated prompt. + generate_prompts_batch(prompt_texts_batch): Generates a batch of prompts using the language model. + """ + + def __init__( + self, + class_names: List[str], + prompts_number: int = 10, + num_objects_range: Optional[List[int]] = None, + batch_size: int = 1, + seed: Optional[float] = 42, + device: str = "cuda", + quantization: Optional[Literal["none", "4bit"]] = "none", + ) -> None: + """Initializes the LMPromptGenerator with class names and other settings.""" + super().__init__( + class_names, + prompts_number, + num_objects_range, + batch_size, + seed, + device, + quantization, + ) + + def _init_lang_model(self) -> Tuple[AutoModelForCausalLM, AutoTokenizer, Pipeline]: + """Initializes the language model, tokenizer and pipeline for prompt generation. + + Returns: + tuple: The initialized language model, tokenizer and pipeline. + """ + selected_dtype = "auto" + logger.info( + f"Initializing Qwen2.5-1.5B-Instruct language model on {self.device}..." + ) + if self.device == "cpu": + model = AutoModelForCausalLM.from_pretrained( + "Qwen/Qwen2.5-1.5B-Instruct", + torch_dtype="auto", + device_map="cpu", + low_cpu_mem_usage=True, + ) + else: + if self.quantization == "none": + logger.info("Loading FP16 language model...") + selected_dtype = torch.float16 + model = AutoModelForCausalLM.from_pretrained( + "Qwen/Qwen2.5-1.5B-Instruct", + torch_dtype=selected_dtype, + trust_remote_code=True, + device_map=self.device, + ) + else: + logger.info("Loading INT4 language model...") + # Create the BitsAndBytesConfig object with the dynamically constructed arguments + bnb_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + ) + selected_dtype = torch.bfloat16 + + model = AutoModelForCausalLM.from_pretrained( + "Qwen/Qwen2.5-1.5B-Instruct", + quantization_config=bnb_config, + torch_dtype=selected_dtype, + device_map=self.device, + trust_remote_code=True, + ) + + tokenizer = AutoTokenizer.from_pretrained( + "Qwen/Qwen2.5-1.5B-Instruct", padding_side="left" + ) + pipe = pipeline( + "text-generation", + model=model, + tokenizer=tokenizer, + torch_dtype=selected_dtype, + device_map=self.device, + batch_size=self.batch_size, + ) + return model, tokenizer, pipe + + def _remove_caption_sentences(self, text: str) -> str: + """Removes caption sentences from the generated prompt. + + Args: + text (str): The generated prompt text. + + Returns: + str: The cleaned prompt text. + """ + pattern = re.compile(r"\s*Caption reads: [^\.!?]*[\.\!?]", re.IGNORECASE) + # Replace the matched sentences with an empty string + cleaned_text = re.sub(pattern, "", text) + return cleaned_text + + def _create_lm_prompt_text(self, selected_objects: List[str]) -> str: + """Creates a language model text prompt based on selected objects. + + Args: + selected_objects (List[str]): Objects to include in the prompt. + + Returns: + str: A text prompt for the language model. + """ + return f"<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a chatbot who describes content of images!<|im_end|>\n<|im_start|>user\nGenerate a short and concise caption for an image. The caption must begin with this template: 'A photo of {', '.join(selected_objects)}'. The objects within the scene interact in a meaningful way. Complete the caption with a short scene description.<|im_end|>\n<|im_start|>assistant\n" + + def _postprocess_prompt(self, prompt: str) -> str: + """Post-processes the generated prompt. + + Args: + prompt (str): The generated prompt. + + Returns: + str: The post-processed prompt. + """ + instructional_pattern = r"<\|im_start\|>system\n.*?<\|im_end\|>\n<\|im_start\|>user\n.*?<\|im_end\|>\n<\|im_start\|>assistant\n" + # Remove the instructional text to isolate the caption + prompt = ( + re.sub(instructional_pattern, "", prompt).replace('"', "").replace("'", "") + ) + prompt = self._remove_caption_sentences( + self._remove_incomplete_sentence(prompt) + ) + return prompt + + def generate_prompts_batch(self, prompt_texts_batch: List[str]) -> List[str]: + """Generates a list of prompts using the language model. + + Args: + prompt_texts_batch (List[str]): List of text prompts for the language model. + + Returns: + List[str]: List of generated prompts. + """ + sequences = self.pipeline(prompt_texts_batch, max_new_tokens=70) + decoded_prompts = [ + self._postprocess_prompt(sequence[0]["generated_text"]) + for sequence in sequences + ] + + return decoded_prompts + + +if __name__ == "__main__": + # Example usage of the class + object_names = ["aeroplane", "bicycle", "bird", "boat", "city"] + prompt_generator = Qwen2LMPromptGenerator( + class_names=object_names, prompts_number=5, device="cpu" + ) + generated_prompts = prompt_generator.generate_prompts() + for prompt in generated_prompts: + print(prompt) diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index b5761b4..c114321 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -21,7 +21,7 @@ class Config(LuxonisConfig): List[float], Field(default=[0.8, 0.1, 0.1], min_length=3, max_length=3) ] = [0.8, 0.1, 0.1] # Prompt generation arguments - prompt_generator: Literal["simple", "lm", "tiny"] = "simple" + prompt_generator: Literal["simple", "lm", "tiny", "qwen2"] = "qwen2" synonym_generator: Literal["none", "llm", "wordnet"] = "none" num_objects_range: Annotated[ List[int], Field(default=[1, 3], min_length=2, max_length=2) diff --git a/examples/generate_dataset_and_train_yolo.ipynb b/examples/generate_dataset_and_train_yolo.ipynb index 77344cf..4f5cc17 100644 --- a/examples/generate_dataset_and_train_yolo.ipynb +++ b/examples/generate_dataset_and_train_yolo.ipynb @@ -1,436 +1,449 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# DataDreamer Tutorial: Generating a dataset for object detection, training a model, and deploying it to the OAK (optional)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5_2ivH03etO", - "metadata": { - "id": "b5_2ivH03etO" - }, - "outputs": [], - "source": [ - "!pip install datadreamer" - ] - }, - { - "cell_type": "markdown", - "id": "c3704c07", - "metadata": { - "id": "c3704c07" - }, - "source": [ - "## Generate a dataset with your own classes (might take some time to download all models)" - ] - }, - { - "cell_type": "markdown", - "id": "M4v-QieP4tXL", - "metadata": { - "id": "M4v-QieP4tXL" - }, - "source": [ - "Make sure you are using the GPU runtime type (in Google Colab).\n", - "\n", - "~8 min to generate 100 images\n", - "\n", - "~2 min to annotate them" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ab1e2f9", - "metadata": { - "id": "6ab1e2f9", - "scrolled": true - }, - "outputs": [], - "source": [ - "!datadreamer --save_dir generated_dataset \\\n", - " --class_names robot tractor horse car person bear \\\n", - " --prompts_number 100 \\\n", - " --prompt_generator simple \\\n", - " --num_objects_range 2 3 \\\n", - " --image_generator sdxl-turbo \\\n", - " --use_tta \\\n", - " --image_annotator owlv2 \\\n", - " --conf_threshold 0.15 \\\n", - " --seed 42" - ] - }, - { - "cell_type": "markdown", - "id": "7a10755e", - "metadata": {}, - "source": [ - "### Parameters\n", - "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", - "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", - "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", - "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", - "- `--task`: Choose between detection and classification. Default is `detection`.\n", - "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", - "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", - "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", - "- `--prompt_generator`: Choose between `simple`, `lm` (language model) and `tiny` (tiny LM). Default is `simple`.\n", - "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`.\n", - "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", - "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", - "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", - "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", - "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", - "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", - "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", - "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", - "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", - "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", - "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", - "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", - "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", - "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", - "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", - "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", - "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7add74d9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 497 - }, - "id": "7add74d9", - "outputId": "a5389937-2a4d-448b-e2f2-6be98018d9be" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "import os\n", - "\n", - "Image(filename = os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_70.jpg\"))" - ] - }, - { - "cell_type": "markdown", - "id": "64fe2dc9", - "metadata": { - "id": "64fe2dc9" - }, - "source": [ - "## Convert the dataset to YOLO format" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3dd01a6a", - "metadata": { - "id": "3dd01a6a" - }, - "outputs": [], - "source": [ - "from datadreamer.utils.convert_dataset import convert_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9b9bb74d", - "metadata": { - "id": "9b9bb74d" - }, - "outputs": [], - "source": [ - "convert_dataset(input_dir=\"generated_dataset\", output_dir=\"generated_dataset_yolo\", dataset_format=\"yolo\", split_ratios=[0.8, 0.1, 0.1], copy_files=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a167a842", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "a167a842", - "outputId": "6f272b02-5b41-4f4c-cd41-2ed37e461e58" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data.yaml train val\n" - ] - } - ], - "source": [ - "!ls generated_dataset_yolo" - ] - }, - { - "cell_type": "markdown", - "id": "d2d660b0", - "metadata": { - "id": "d2d660b0" - }, - "source": [ - "# Train your model (YOLOv8 as an example)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "982e475e", - "metadata": { - "id": "982e475e", - "scrolled": true - }, - "outputs": [], - "source": [ - "!pip install ultralytics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "184cf0fa", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "184cf0fa", - "outputId": "6d5837d1-cbc1-4460-f9ec-93ec290c7fc5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6.23M/6.23M [00:00<00:00, 327MB/s]\n" - ] - } - ], - "source": [ - "from ultralytics import YOLO\n", - "model = YOLO(\"yolov8n.pt\") # load a pretrained model" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "8ce1517f-7258-406d-9139-9adadb1a1570", + "metadata": {}, + "source": [ + "\n", + "\n", + "# DataDreamer Tutorial: Generating a dataset for object detection, training a model, and deploying it to the OAK (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5_2ivH03etO", + "metadata": { + "id": "b5_2ivH03etO" + }, + "outputs": [], + "source": [ + "!pip install datadreamer" + ] + }, + { + "cell_type": "markdown", + "id": "c3704c07", + "metadata": { + "id": "c3704c07" + }, + "source": [ + "## Generate a dataset with your own classes (might take some time to download all models)" + ] + }, + { + "cell_type": "markdown", + "id": "M4v-QieP4tXL", + "metadata": { + "id": "M4v-QieP4tXL" + }, + "source": [ + "Make sure you are using the GPU runtime type (in Google Colab).\n", + "\n", + "~8 min to generate 100 images\n", + "\n", + "~2 min to annotate them" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ab1e2f9", + "metadata": { + "id": "6ab1e2f9", + "scrolled": true + }, + "outputs": [], + "source": [ + "!datadreamer --save_dir generated_dataset \\\n", + " --class_names robot tractor horse car person bear \\\n", + " --prompts_number 100 \\\n", + " --prompt_generator simple \\\n", + " --num_objects_range 2 3 \\\n", + " --image_generator sdxl-turbo \\\n", + " --use_tta \\\n", + " --image_annotator owlv2 \\\n", + " --conf_threshold 0.15 \\\n", + " --seed 42" + ] + }, + { + "cell_type": "markdown", + "id": "7a10755e", + "metadata": {}, + "source": [ + "### Parameters\n", + "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", + "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", + "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", + "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", + "- `--task`: Choose between detection and classification. Default is `detection`.\n", + "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", + "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", + "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", + "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", + "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`.\n", + "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", + "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", + "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", + "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", + "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", + "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", + "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", + "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", + "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", + "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", + "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", + "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", + "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", + "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", + "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", + "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7add74d9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 }, + "id": "7add74d9", + "outputId": "a5389937-2a4d-448b-e2f2-6be98018d9be" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "bb4e6754", - "metadata": { - "id": "bb4e6754", - "scrolled": true - }, - "outputs": [], - "source": [ - "results = model.train(data = \"generated_dataset_yolo/data.yaml\", epochs=50)" + "data": { + "image/jpeg": 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K5zTb14vC+qW4gdhIVzIBwtdb4y8OvpWiRzG9lmh4Xy3OcE9xXNafqTW/hnUNOWNcTHJY9RXUnoeC9y7f6LpA8F297aT5usDeN2cnvxXHQ27zPtXr710TabbR2VsLeR5GkG5gexqK4ghgQuqEsvUZqeezsVy31MSSxmjY7wAB3zVcrg4qzcXDSPnJqvnnJreN7ambt0EyQKs2xkl+TcQg64qHJKgAVYijkWP0zUy2BFtJVQgsflXtUUtyZ2JAJA9BTTApwFYse9aN7YxW1tAsPnbpFDyMwwq1zuUU0jRJtGTKfNiyG6dqgR2izt6mtTVoLSBLeO2KsQn7xw2cmsogdRW9NqUbkTVnYaVLc008U8HHWh9p6VqSMooooAKWminDk4zigBaVXKNkVOloHJHmDj0prWmHAVwQe9RzJ6Dsz6U+DRz8LtLPrLc/+lEld5XCfBxdnwv0xc5xLcjP/bxJXd1YgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKp6nqlpo9kbu9kdIQQuUjaQknoAqgk/gKALlFZB8T6Mvh+DXTegadcKjQy+W2ZN3ChUxuJPYYz7UQ+KNFn0W41db9FsrZis7yo0bRMMZVkYBlbkcEZ5HrQBr1zvj7/knfiX/ALBdz/6KatDSNe03XUmbT52doGCyxyRPFJGSMjcjgMMjkZHNZ/j7/knfiX/sF3P/AKKagDCRgEX6U1nGM1DECUX6VneJLqSx0dpYmAfcACawtY0uaryRxqGc4FThFKgjvXO6tNt8MW9w0mXIUkr3q9a6jG1xDbeZ85iDbai7uM5PwRqBSSSxwNrOz59+B/Su0kuIkniicgPIcL7149p+qSaVqqXK8qp+ZfUV20+u297qWkyRhgTICQR6jFZ0U/ZxPSzl/wC31fU6TUZHt4ldOSD0JxmuAN5KnivzktTHIf4GPX3rpPGmp26xR20bstwpD5HYVw2rX5n1mG7LskZUYbGOnWtDzGeqS7r7SLozxfvBGdqpz2rymz8U6np0MsAlYlvlw4+7XoHhXxDFeWlwVRiIxje3Q8VxV3YWd19svp5BHI8jFIwelNNJalWb2Mq51OKSxjiC7pN29mPrWhonhq68RyeZGRHEv3pGHFZWlaTNqurRWcQ+8eSew9a9pisYNH02CytlKjozDqfesa9T2a03NKVNzepk6XpVtoCbLbMkrHBcryTVHx9qaWtilkVLNKP3lb9qwn1CSZvkitk4B7muL1APqniBnYB85KnPCr61wxbcuaR2tWVkeZXCqs7CPOzPGaEt5ZWACHn1rtx4cg82e7mZW8vkKD1PrS2aQXKkLCrOw3L0BArv+s2Whx/V9dSv4Y8Nqsq3dxEH2YIDHofXFdyJhaOCXJ3Hjgtgn6VjQajawT7IVkSZV+bzFKj8OxFY91ql2kjKWhVGJYMrFsfnXHPmqSuzrhywjZHZskt05KSiOTrsZfvfnV6ye7s7kTpDIylQJY1O7afUD0ribTVI7RX3Ss8/DCVn3cnt7Vu6F4pVrwqZFJfjHc1k4SWpfOnod5HJb3sZkhIWQDowx+Yrzjx/f6hpNwkoUm1kOC4HRvQ13UmrWuAZsK3RWzisHWvs2vWFzps3ltuGUIPPsa0g4u1zNqSvY4/wnr1xqNzcWynAkgcHI6fKa4uQBnbzeTmuy8M6Nc6NoeuX8kbJJFEY43HXPqK40RPOxOe/Oa7qair8uxyVZN2vuUniZJcoCRnjFb0EJQQlpjliGLD+Hn/61QpGioFCg+uafbKXO1j1alXldL1R25UrTqf4J/8ApLNrxVrDT6ZDbx30k6jHDAY/OsDTpI20u/aVcyBQE4o1acvthVQoWrGiRRvpGrCRwpEQKg9zW0fhPMe5Ttr/AMuJQZPunG2o7y986QshIyKWOBHiDKoXH3veoLi32coeKFy3HrYrlMjINRNkUZYHGDUjgCDlTuPetURYjDnj2qVZyzL5hJXuKgCMRkAke1aOkW0U1wwuHEQC5Ut61NSUVG7BblkSxyRgIhVR61q/2nLqCfZ7qTbAsYQKqjLY6DNJf2sVrFD9pbfM65Yx9B6VluZreVQiOso5G5ea40o1NjXWJNrVvbIsYtbNoQCVdmPJNYrROrbSpB9K6iC1abTQ95LuyxcAY3BvSsVry3lvXldSoUbUX/GtaFR25ewpx6macY4pKCKOldZiJR2paKYDaQnmnGkA5zQBLHJhdvQetOQZzhunSoQCe1SzQyWsgR8ZIB4PrUtDPpf4N/8AJLtLz/z1uf8A0okrvK4L4MnPwt0o/wDTS5/9KJK72qEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVTUdTsdHsnvdSvIbS1QgNNM4RQScDJPvVuigDx7QNX06TwH4EvEu4Z7XRriI6kI23/Zt0EqK0gH3QGZc56de1Wr9xqR13xDZK9xoo1nTrhpIkLLPHB5fmyKP4lHHI6+WfSvV6KAON8P3ttrfj3VtY0qVZ9NGn21qbmPmOWZXlYhT0barqCe27FM+I+l3k/g/wAQ3ceu6hbwJpk5NnGkBifEbZBLRl+e+GHtiu1rnfH3/JO/Ev8A2C7n/wBFNQByaXYiuIIH6yqSv4VieO70R6KI0ZS/mDcKxJNf83xfpq+aGtYu4/2hzmqHjC4iudVmaGTchx344rn3Luaeq380vgm2VsKzY6VhWery2+ppfTszYi8sbTzVFr2eW0jtmkJiToKrSZ247VXKriuRygF+a6mw1GDydPZlG6BgGPcYrmCu5xx2pdzK42k89qxoq9KJ6ecf8jCr6nSeJNSivtSlliIKhdoNcve3jXKxRSYxEMDFSybgp7VQYfvM1pFdTzm7noXhzXbSx8F3Mb2Uh25BcLkMT71yVlpl7q1wkdvG8jMePRfrXW+Bjd3llcadcWw+wSRkeYRgA12On2VlodrHBbJlzhRxlmPqayq1VD1NIU3P0Knh7wvFobQfKr3Tj98/oPQVoa3OI5EBYAnOKuXN7FZA72zJt5zwfwrgNe8QSXEnlIdpJyO4P41wTbmzthHlOigBGgFFK7rmYguT0HT8a5yWW303TbuSF2eZn8sSdcY4rWN2lx4Ft3DAOp+mGB5rhNW1EjzYt6ARuCir34yM0UoOTsOcklcu67dNDArpEjHaEZ5HDMw/3e1ZgvPtdmr3Esqoh/doiBcn8KzZLh7u0LSyDe7knjp9apRhow0sTdDgY612xpe7ZnNKpqbt7e7I4mllnFwRxGknUe+Kzf7Wka4BwyocZLOXqpJdDy9q9/vEDkn61Wzk47elaQpLqZyqPoWZ9TnuWbJA54wMVLo9xLFqMbiQqCeTVNVLMAO/FdHo+nC7wioMkcsKqpywg0yafNKSZ2um2lxrSEq29QRg78Mp9vWuZ13VtQ0PXoY48hgMEMmMjPevRfCUaWEX2dlIlz3HNaPifwnba5ab2T98nzI3cGvOo2TudtVvY4fw3qcerWV7p91OwkdWKAHBz6CuJYLCQDxzWtotvcaZ4v8As8sbK8UhGOhIrKvZIptUnZl4MjYA6da6o2Tt0Oao7pMpzs3mjnI9KuRqoiBVznGTx0pl5ZtvSSJGz1wRircMuJkbyRkDBQ9DSrP3Vbud2VL95Vv/ACT/APSSGa1jliJQZlPTJqS2s2tdNu0uVMbuoK7u4q09oZQXshl25MP8Q+nrVeWWZ3miuNzMseApHIq4OXTY85pFK0ANuOOOjGq9wphkaGQ8fwMeMircVtdfYhIsZAz/ABcZqO+Xfp4M/wB9D+79xmrjuSyjdokSIVYlj1qO5BCopOeAadblnWQcEIhbBqvJIZG3HitkmZvUdHK0alRjFPhneGdZTg7T0IpLSRIrmOSRN6KwJX1Fb+p2lpfQG+tmSJSPunA59B61FSai0mtGVGLauiKxvYLyYRTo7uzAIuf1zWxrGlzXc0l5FLvKFVVRjIHQ1zsGj6hEYLlQo3MCMMCV+ordE8umOyToXJAKug/M1yVElJOmzSG1pFTVrUwW6K9ywiMZYL/EW965cqVPIIPWuwW5GqSNNPGxVBtyCNv+eaxJLASu1uh+dCWklYYGAK2oT5dJE1I31RWgt4WsbieRyCmAgH94nv8Ahmqcu0zNtOQTwa0/KW7c2ts4S3RwSGOC7VXuo0t5ctjzP7gXaFreMtSGtCrIhjA3Yye1R5pXJdtx5NJitUQFFFA60wFzgVYlAntI3UYaIbHyfyNV6sWaCab7P080bQfftSY0fSXwY/5JXpX/AF0uf/SiSu9rg/g2jJ8LtLRxhlluQQex+0SV3lMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzvj7/knfiX/ALBdz/6KauirnfH3/JO/Ev8A2C7n/wBFNQB81K7K6upww6GpX3uNzMWPXmoR1AqfO1OayYxEwFxSEcGkRs9acDl8Dml1EgQ4YnHao25cY61Ih+Yj2pHAU5rGh/DiernP/Iwq+ojklSCean0PQ7jXNTW3gXjq744UUy2tJb+6jggGXkOBXr2h6PF4a0hIj5Zu3G6QgcmirUUEcFOHMy1bwQadb29lAgCIAMnuauiERXE1xMFAizjPGTiqmlx/bdR85h8kfIz0zWN4p1eRrm4txwg+XA5J/AVxLXVnbbXlRh+IvFEdwSy/KEbDZ61xtxqEcquXZtxHyFuKi1STExeHCY/h6nPvWHcXO/cGZmY966qdBbmVSs1odZoGrh/D+oaWJcuMyrvPX1xXOXErsi5OTj0rPt5mglDoefap5LjfGCOG6VsqPLLQwdTmVmKs7LvVjwy4wKFkdYX+bAJ79qqsSCDVmOVGBVhwRWriZpjWRdw2nIwM0qAF9pIAPenpGu4kkAenrSoiYO4jdmpbKSuWrW0V7iNSwZSeucCu7s9NjsYo50Yg5GFUcVzeiPb2mZzGJfKIySOB711K6qL50htlh2vwSO1ediJSk/I7qMYx9TqdJmjjgl1BwPlHBzmuA174lawLqSKB9kfQKOP1rb1HUm021+xu6GBR8wxjdXnOuXVve3QNpGixAfiT+Na4OKfQzxL89Ta0bxbdX2qxtewrNLghZiPmXg9T3qstsGZ5W/1gOQtW/h9pEN/qUjXDhIlQgH/aPArb1/RX0SVpHj81X+7Io+WrrSSm0jKMW4psyWuVmtVW4lEL5G0d6qXEgimDBs7V6jvViG3gmIkeIF+prPvAPnCnjiud2dl5o9LLfiqf4J/+ksgmvJZJAA+1uox1FaNhrU0cbPPCJVBx5hA3r+OKxoYm+1AKMkmte5RYbB4yu0kda65WVonkq+5UvYZWZpkuTNATnk8r9R2ptnco0gs7hVeOT5d2fu57iqlncFIGUMdwPGDWhZpa6ncRxeS0dwDw0YyCfcf1qn7u4XuUre2hZ50t7pTuQqFk+U5/l+tVLnTby0z51u6gDO4DK/mOKv3OhXqzyCBUm+YnEbAsPw61Gl1qOlARlpogfvRyKdp/A8VpF9Yu5LXcraXbQ3NwftMwjiTlvU/Sun0JowJrcKrQhiYzIvXPSseK3t9YIS0jEF8ekIPyy/7vofan2N/Pb3YtJ1EY3hW8wkbSD1P0rCvGU1oXC0WbGrWUUEYNv5p3MTJxkD6Cq8E8kto6SvyflQOvJ9vpVq1ubdzdLJKdxOC8b53emKuzLbRwpCGV2A3bn+8K5FLlXK0a+aMOwOxjCZdjZyQMbQfWn3trFNazyRMcOoYkHg+pqfWJYYo0mjQNuXa2R/EP/rVgR31xFMB5jKFXaPUCt4xlJ8yIbS0KKndcLsXC7hhc1HMpWZ1PUMR+tbVve6fJPG91a4KncZIjgk+69Kz72wngYShTJDL8ySoDtauyMtdTGS0KVKafHBLNIEjjZnPYDNRsjI7IwIZTggjkVpcgMUUgNBNAEnlggYb5j2rSsIbe11i1jlcOw+eTsFOM1mRLtkjmY/KGAx61YnBbW2wN245x68VLu9ClofS3wkcS/DmxkXo1xdsPxuZK7euD+DZz8LtLOMfvbnj/ALeJK7yqRIUUUUwCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK53x9/yTvxL/2C7n/0U1dFXO+Pv+Sd+Jf+wXc/+imoA+Z0Pzg+1TOf3fNQxjODmpJCdoArNgLGAFz60+PC5Yd6jI2IBTyMRjFIBAcSfhTnwzADvVuz0W/1C3W6h8gRsSAHcg8HHpW1ofg/U59RR3jtZIozuZTKQD7fdrz6eMoQglKSuj1c1/eY6rKOzZ0vgnw6mn6f/aN9GPNlGYww+6KnubqeS6nuCRt+6vFXtSOtPGYRZ2aKBxiduB/3zWXb6fq6xQlobRleTODO3IH/AAGuKeMpSd+ZCpqMY2Ojt5F0rRw8sgV3G7nivNNZ8SWxknUtuYkszLzn2roPEbatqknltHaRRoMBRO3/AMTXIT+HNRMTJGlmueuGb/CtqeJw/WSM5SdtDnLu9EsYZUwjHO0cVlyMGJGBz6dq6BvBuqfxTW303n/Cm/8ACG6nz+8tv++j/hXbHGYZfbRxy5pHO0tamp6BeaTbpNcPCyM+wbCSc4+ntWYOTXXTqwqR5oO6IaaEZiRQDyDS7fmAoZQDxV6CJA565zUkM+zO4fhVX5s4p2GHValpFJs1tM1j7FKyuN8Uo2yKR2q1PNc6bOl1p85a3PKkdvY1z3A+tSxXUsP3JCAeo7GsnSV7o0VW6sza1HXzq8IWWPbKByQeGrHESjJclFHUmhrpm6KgPcgVFI7SfeOauFNRVo6Eynzbl7TdRntpwbWd4+eFHT616p4f10avY/2ZqYEglGORyD6ivH7fCSAlq6vR7tHuoo/NIUsBnptrnrwvqjejPSzNLVopNH1N7Z1AGATjtWJeZYM4BAIyOO1dp42sUmltrlLnl4gGPZiPeuTgdophvxNhWUDqOQa5JOyTXdHpZcvfqL+5P/0kLHToiEuzNgFfukdT7VavdsmYd6b2wCcZ2imB/wB1HmFkUE5Hv9KqG9eOWVlXLbcB2X7n096tKUpc1zzVypEY0q10pmlv7rJP3YYx8zfXPSq13q80doosVW2iJxhB8x+p700oLgiWaUFicnJyTTLqXyYQEC5zxwOK61vqZX7FZkmkjW6Mh8yRscE5J9avw32pw5huJw1oOouRvX8AefyrO+0zxRHy5WB/iYd6ryTSzHMsjufVjmtOVslux0djqWmWlyJ4NNDyDPMjcD3A7Vu2L6d4svN1xEtveIP3e0AiX61xWmyiK5Uum9RyR7Vdtb2CHUftERaIhwyMO1ZzVmVFmq0jaPeS2xtBEiP+8Vh8xPbHtTTINTuJZRMsLAEZY10mr6avi/To7uzZV1KBcPGcAyr/AI1xqsLJwvlnzkbDrIuMdsVi4Jq63Lu9maNvCt5p0tobmKR0YOu08kd6w7krbTyETEZOQo+8B9av6IZJdXhhBG12K4/CsSaznN1IgQ5DsCT7VtTWurIkyy0rzRvO8CDy4wQAvJyQBk9+9WR4iunto4HKtBHnZEVGB+WMVXjuHh0udF4LkRlj1I7j6Vl5wa1ikyG7GrHrl3bTmSApHnjaFGKc+vNO5e7srWdyeWKbW/MVkMSTSDJaqcULmZvRzaDPAyy2ctuzMCWV9xH0z0pV0zQZJEePVpIxn7ssOf144rDOcdKaDl1U9M0cvZjuup0F1oVs5CxazZbV6Ak81Omn6VDereXOsRkoBmOOMtuOMda5+5wrrt9KgL72GcCp5X3C67H098INn/CtNP8AKJMfn3W0kYyPtEmK7muC+DP/ACS3S/8Arpc/+lEld7WqICiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACud8ff8k78S/wDYLuf/AEU1dFXO+Pv+Sd+Jf+wXc/8AopqAPmiM4UU12ywFJG3yg0gbMorMCZj0FSSdBzUBI8wA8gmpphyMUmM63QZ47bw0s8zBIo/Md2PQAMSTXW+FtWM9lNONLv44whmV3jX96AOigMTn2IFcdpNkmpeEWsnYqs6Sxlh1GSRmt5l8RSeGrjSbi3htWe3aNLqB33M5/iIKjaPYE/WvjKvs25JvW/4X/E9vE4epLESklpd9jat/E1vcT3qXFleWk8EBn8qdV3PGOpUqxB57Z7il0fXrfXrRZLWyuoIfKVoZJ1VfMDDJwAxP5gdeMiuJ07RLmz1S4uhY6daLPp8lp5NsW5dipDFioJ6H6e9bmjpPptrZW7ENJDDHEyrnDFVAOPyrKrGjFe49dCI0Kz6fijC1TVNRR9TvoYoDY6fMY5VcN5kgXG8g5wMZOBg5x2rRlKRxNK8gEaqWZieAOuc1FqukanKup2EUcMWn6lOZZGkLCWLcAJFUYw2cEgkjGe9SXemtfi5tLpE+wuqbAjMr5BJbJHQcLjHvW0p0rK39LT8d/wDhjJ4Stfb8V/mZGm6neX9/eQyQpFEkUctuCCH2uXA3/XaDjtmorTU7ltXFjJPZXXyuZDaggwFccP8AMevI7HjpVu18Omx1i6vI7mVw8CJEs1zI53Ddndk8jkY6456UiaXe3Gr293eGyt/IDACB2dpNwxgkhcDvjnnFaupRu7bW/G337/8ABI+qVtrfiv8AMyvGvGkwf9dx/I1xUeM5Nd/4xtIzp1srzAAzjJ49DXMQ2elLjzZJ3H+xKq/+ymvYyytBYez7voy45ZiamsUv/Aor82Y0pwB6mowGkkVR1JwK6yNfDC432N1IfU3qj+SVMreFQQV0m4yDnP27/wCxr0frELf8Bj/sfF32X/gUf/kjCSweJkEiHcRxmorpBHlcfNXe3lncalp8E1j4e1BIwAyT4Misv1Cj+dcnc2hEzicFHXhlZcFfrWSxCvr+TLeT4q2iX/gUf/kjD8k7dxz7U5rZkt/OYYBOADWssEeAGkBHbFJfeXOqRm4jiC84Per+sxb/AOAxf2LirbL/AMCj/wDJGKKKufYoP+f2P9P8aX7FD/z+x/p/jWn1in/Sf+RH9jYzsv8AwKH/AMkVkPGQOlX7FSzL85wT0B/lUa2sSjBvIz/n61JFHEn3byM/TH+NRKvBq36MqOUYtO9l/wCBR/8Akj0HU40vPC9u+wlYW5OSSAa5W2kME+VPllMhWPfj/IrQ0nXZo7SSw2i7Ei7QoPI98VQ1K0ufMMdzA9vI4DBXQg4z15+lcE5Rul5nrYLLsRGU5NLWEl8Ud2vUVL2WWc7yxccYAwKYZpWnkOwKo529adBbzsipHEzsOMqOtPksr6BGaa1mQdSzRkCtfaQWx539k4t9F/4FH/MxT/rHyAMckCoOVVpCoPYZq3LbrLKzeeoPfjp+tMe3jZFJuV2DjPY/rW6rw/pMzeT4vsv/AAKP/wAkVUBa3lO3KjBPtVathUs1t3jjnI8zAYlwf6VVayt84F4oPvj/ABq1iIef3Ml5NjOy/wDAo/5jLPCrNK3KovT1zVqztEuXecIwgjUMxz0ogs0KtGs4dZBghRyf1q5K8Tab9kgxFErAlickn/aNRLEQ/pMpZPi+y/8AAo/5lrS9bnsopJ7dJWmDhgY04x6E11wfQ/GcQS7xYaqox5g/i+vrXI6fqH9nwBBsZMHvgH3rOZ/OkDG6QtuJBHX+dYxrLmfb5mjynF22X/gUf/kjsT4H1DQJF1U3Vvc+Q4ZYogSZB7e9cr4gvFg1u5EFmsBBJ/eqdwzzn0rS0fxFd6bdq8t0bpFG3ynfp/8AXqbxRBLr8w1JtMubULHhnwSpA7k4FaKvC+v5Mh5Pi7aJf+BR/wDkjjZDss40YESFjIST1B//AFVWNX5LWKRyWvI8/hx+tJ9ihI4vI/y/+vW6xFP+k/8AIz/sfF9l/wCBQ/8Akij2xSxuEPNXPsMH/P5H+n+NJ9hg/wCfyP8AT/Gj6xT/AKT/AMg/sfGdl/4FD/5IrFwGJGSKjzukzV/7FBt/4+4/r/k00WMAP/H7H+n+NH1in/Sf+Qf2PjOy/wDAof8AyRDOhynuKjli24561oTWsbbc3SLgd/8A9dRfYI5X2i8RmPQAf/XpPEU+/wCDGsmxknZRX/gUf/kj6O+DH/JK9K/66XP/AKUSV3tcJ8HE8v4X6Ymc7ZbkZ/7eJK7uuhO6ueXKLi3F7oKKKKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArN1bXtP0QwreyTB593lpDbSTM23GTtjVjgZHPvWlXM+MvGNn4TtbZJJrZb++Yx2i3UoiiyMbndz0RcgnucgDk0AX5PFGix6HFrJv0awlYLHIisxdicBVUAsWyCNoGcg8cUReKNFl0SfWBfotjAxSaSRWRo2BAKsjAMGyQNpGeRxzXFFdN0vS/CmqQ6pDqel2mqz3GoX8JDR+bLHMDKduQqiWTH+yCPTNU7xxfHWPEdorzaINfsLoyRoSssUKxrJKoH3lDAHI6+WT2oA9H0jXdO12OZ9PnZzC+yWOSJ4pIyRkBkcBhkHIyOazvH3/ACTvxL/2C7n/ANFNVDw5eW+s+Odb1jS5Vm0xrK1tvtEfMc0ytKzbT0barqCR647VH8R9LvJ/B/iG7j13ULeBNMnJs40gMT4jbIJaMvz3ww9sUAfOmSoAp4UnDjpSpGZ3VB1NWDCIsx571m2CK4IMi5Hep5Ww49Kjt4JLi42J1BqxeWM8YyVz6kVDkr7lKLtc3tE1hLDRlElpcOsUpQum3GWJI7+ldHrPjRcfvLC7RcnBwvb8a5mGIDwRJL3Ooov5If8AGrXiG4jnORjywBtAHAHavn/qdGpO8lufV5th40ldP7Uv0Ks/je028wXYK/7A/wAaji8f2KzR7oLnhhn5R/jXKXxDPIqocY6isoqWPHWvQhlWHa1v958zKpJM9fvvGdrPAJ47O68vJG75ccfjWVJ41tY1Lm1ugCMZIX/GsVdJ1XTfB7z3qLFBJxErthsHvisK/uwZthHHljGB3xSjlWH2s/vCVRnbHxpZBTI1rdbQ4UnC/wCNMufGGneeA0FypI9F/wAa8/Fwfs0kTE/Mc/jT7W3udRuVWKPzH44PAq/7Jw67/eR7Rnqsw8N6uLKKd2SFZN8rSTpjG09ApJ6kVoR2HgC3A2xpKfaN3/pXP6DoOsS237qXT7RFO0hbfe4/E/41e1eyurC1igbU55ZHbcSAEAA57etdVClGhHkhsJu5ref4Li+7pIcev2X/ABqeC38C6sPLFjAjey7SPyNQWel5tU+0T3DSMMn96QB+FQ3ugRTruikYSDkFjyPoeo/UVpcR0MWl6r4bsifD0qXunck2Vy27Geuxu30rmNVtBqV39pbSxDI6jzEZQ2D9e9a/hTXrjTrv+z78k4HU/wAS+v4f41L8QFv9OMN5ZTMkDna4X17Gjcd7HKHQWYcWSY/3BVdvDCMcmxTP+6Kq/wBuaixybyX/AL6oOtagf+XyX/vqtIxI5kTP4XRhxZL/AN8iqx8I7m/48Pypf7WviebqX/vo1ImpXZOTdS/99GnyvuF0M/4QsHrp7fgf/r0J4Ity2Gt2Q+7kVcXUbo9biT/vo1IuoTDlpGb6mjlYrlRPBclnKs9o5V0OQDJ1qTULO61XXrS2vosSPBgAMDwNxB7elWLi9mmh2xS7Gz1rBvbm8t75ZmupPNRPlkzyo5/+vXLXjs/NHrZVL3qq/uT/APSWal74DvuHtZYmwPuEbaq28GvaPIBLbTmHG10dS6OvcU6PXNSSBJP7TlIc4VgAfpkYqceJNftYPOknhePvuUEj6itXG61R5qk1sc5qunxCTzbQP9nI3Oh+9GfQ+3vWLJI0o25+UdAO1dhc+MZpXUS2Fpcq3UquP1zWc954b1RyskM+nzn+NCGXPuKcVZBJ3OZHAI9KdMhDqT0YA1b1LTZdPmG474X/ANXKo+Vqsf2VJLpVtcAhWLMCHIUBeMHmqcrCSb2KNtN5d5E/JCsOldGosFsGha5JR3LN3K89h3rHsvsthdpLNIs5BwUUZU59TV+1YzxTLEsSbckbVJ3Cues77GkVbcvaZaxmZ7Zis8Q+ZG24wKrNoZhnlLYQAEqFO4/WrlhGsT+aUkVCMMc5ANVp5CZJbaAhM8BiTk1zKUnJ2ZpZWOcnISXdG5cnqSMc16l4c1OLxH4Rmsb9STEPLYjqRjg1w1tpYWydrlMMXwfUj2rT0G+j0fU45LckR3B2OjZ+764HpXRKonotyIxa1ZyN/aPYX01s/WNiM+o7VCGJXHSu68daYJnS5s1M0i583YM4XHU1wWRiumnLmVzOasxy96OAcU1TikJOasgeRzgHilRcuM+tPRBxmlRczqOvNAyS8GGVe1JZDF/H+P8AKp9Vj2svGKgsf+P6L8f5Gsqv8KXozuy1f7bR/wAUfzR9LfCD/kmmn/8AXe6/9KJK7muG+EH/ACTTT/8Arvdf+lEldzWkPhRzYj+NP1f5hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO+Pv+Sd+Jf+wXc/8Aopq6Kud8ff8AJO/Ev/YLuf8A0U1AHz7p2lzWswnlAZAuapXDfabwmPglsYrq40VrLY7hTjrWYNNt7eUHLMSc5FeZHEaty3Op0rJJC6ZYeT5jzLhj0NX0iVkYH5s1KjfuQmMioZGkhkU7PlrnlOU5GqiorQhUj/hXxHf+1B/6LrN1eVZn2h+EPQdBirKuT4PdM9L8H/xw1kan95jjOB8wz39KMMryPpM/dor/ABT/APbTDvJCJGyxbcPpXQ+B9Bgvp5dU1AgWNp8xz/E3YVy05LS8jn0rd0qe6a0/swTFbV3DOvv3P5V66Xunx7ep30WlL4tEl/qRkFqQUtYlbG1f731rz3XtAfTb14ROLiQnKhOSF9W9K1L3xhP9o8q1j8uKAbImVypGP0Nc6dSup55FMu3zmzI+etJJoHZlFYXaVYwpLscAetdPa+GvEdhdRJaZSZuQEkxj6+grFmmifUWkQlY04Ur1471tad4z1axJTel0DwC4+b8x1qmyT1fSI7200hP7WlhM6Al3jGABWJqEsep6xEsTh4iyopHcdT/KuXvfEGuSWp+1NhJI/mjCBQM9B61lWepT211zuEajDYP3cnms7XGz10r0xxSEdjXn8ur6jIirYXRJwSVByAKt2Xii8t4FW8VGbuS1Kwzf1e32pHdRD95Ad31XuK6ohPEfg1o2wZAnlk+jL0P8jXl+qeMIpPIKKVVHDOPX2rvfA2owefcWAcNFcKJoD6jGCPyx+VFmg0Z5XOjW87xOMMrEEe4OKZurqfH2kHTtcaVVxHP84OOM965Q8VtF6GViRWqZGqslSBqpAXUY1OrmqUb1YDUAWNiP1yD6is+8s1nvVgY7w8Z68etXFeoHb/iawn/YP9awr7R9V+Z6mVfHV/69z/8ASWc6llc2cht5HdEB+UlcgHtmrtvLJMZ7We7WO4PCAgFSK6XbHJjeitj1FZWoaDbSEzwARyA5GO9atHmJnM3WkahppLND5kf99Dkf/WrMCyJcKzBlO4EV6roaw3dn9kmmzMgxkjlh71ia74OkgXzrJ1aM9fY/Sl6juNsdYeW3+x6laNNaMOQ6/qDVfVNANxbC502cz26Dox+eIf3W9vQ1g3Ty2cLWd1HKjr90xuQD+FaWjazLp7xSlzG/RSe/sfUVDLRhxkxXG2VM7WwymtgwOdVC6ehiUgHl8fWt7UNFs/EMJvtNUR3SDMkC9/dR3Ht1rm3hNnq8T/eXIOW4BqKhUdjppVfegRlWOTCswOc1HJstQ++MgYysjckGn3kzrDbORG8f3mOcL7VhapPINwEymN8MqZ5HrXBTi56G0pWHG/e7DwwbxMAcAck1XinmguI5WR8xruBYd6qQwNua5aRoo8EhxwSfQVHc3ksw2GRjGBgKT2rujTSehi5dz0nTtXku/DwaC1D3BbbIIjyc9SPwrzrWtMexvZWTJhL8EjBU+hHrWx4Q1FrLWIkllYRXC7MZ4B7V0PjPw3a2mmfarGNzIDiYg53D1oj7krDl70bnmy9ORSkU/AwcU3H5V0mJJEGfgVLaqPt8a5/ipkcmzIWi24u1PvSYy/rhAlXaciqton+mxN068fhVnVto2AcmorUhrmI9OvH4VjU/hP0Z35f/AL9S/wAUfzR9H/CD/kmmn/8AXe6/9KJK7muG+EH/ACTTT/8Arvdf+lEldzW0PhRy4j+NP1f5hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO+Pv+Sd+Jf+wXc/8Aopq6Kud8ff8AJO/Ev/YLuf8A0U1AHjaJGAM5IIpzlEdRjjHFUkE52YQ4OOatzRsJFDDtXgy3PTuWZGhCqVPOKz7u5kYfd4HSniMbMFvmzwKfJZq8S7jzmhNJ3JdzIVv+KYkGel4px/wE1zV9KwLFmO4ktjPeui3AeH3XPJugcf8AATWdpvhm/wDEt9JDYmP5OXeRsAf1Nb4LVs93iN2Uf8U//bTCtIWnlMj/AHF5YmtA3S2do21v3sgxx2HWvTYfhpB/Yq27yEXSjll6H1qlc/DnSZmLGXUIG7lo8j9Aa9FyPk7Hk8jF2zTtu2P3Negz/DGIZNtrULf7Eq7TVCT4Za/sLxpbuAeiyc4q+dMLM4qtbQdWGk3wndFZcYPyBj+GelW7vwR4htM+ZpszAd0G7+VZUuk6hCcSWcyf7yEU7oRtxa2J55JrstJANzRxhQCW7Zx2qnpqXGpaqlvAoM07BckZAJPJrKWKfOwq/AzjFdR4VksNPWS5vzJBOrDyZACMcc1DshrU6+1+FDk+de6yY27iCPn9TT734U2irus9buBMAcGWMFT+VVpfF94oxZX/AJqj+8N1JD4/vIztmSKQ98oVNJMehwWu+HNR0G+EN5tcNkrIhyrfnzXaeEpF0OHS75XYlZsTZPQN2H4GofFGrN4gSyHkBArfNg54Peufs9fVFu7W6B8qVt0RQfcb/DgUO7BJI9o8e6aNU8PtPGN0kA8xcdx3/SvGh8y5/OvbPCmoJrPha2L/ADER+WwPtxz+GPzryjxLpEmh65PbEEQyNviPtTi+hMkZQOKeGpqtbgEyzFPQBc5qNrq2B+VmYfStEyLFpW5qUSVmnUIV6K7fQVGdVKyHEJ2Y455p3CxtK9Rs+b+I/wCyf61ljWgB81s/ttOacupKf9JMMgCfKV7n/OawrvSPqvzPUypPnq/9e5/+ks6FJKm3krgVzya9ATzDOP8AgNaltdJMiurqAexYA1toeWX7WLy7kXR+Vh97B61YOu2kLFJ1KH+LI4NUvtscQG51GfeqOoNb3Sb0uIw45xuHNJsLEGrva6lC8UaqzKcoy9q5RnwTBchlwa0LiRfP8yIPG2OQBgGop1S6s2KsrSr2PBqWky07Edjq9xYTK8MrHYflPQiu2ja28RwJOERdQXnbnCzHHf0b+debvtQgFWU981sW2opbrHLbOQ4wHj7N70pRuhp2Oo1FbdbNtOdkt5CuTuyMN3zXOR/YIWAlZ7hhx8owAfx6iuvtb/R9atVvNTtnm8gfNs+/jvnByRWHruiQ2TJfWFxbz2czZj8pySoPTIPNY06PJoaSncx3Zb2RFlvBFhsBHXCqPbFQ3thLbzNn/V7sI/Zh6ioG3zXhVeWLVNcyTW7qp445VuQfwrWzTsib33HRwKbot9rSMQgFW65PpXqkOr22o6Ck0jrsMflzKB39a8oVLe7HysIJAMndyp/qK6XRZZNLsf8ASZAbcNkqhDBkPU/hWdRdS4djndYsW0+/ljUExbiEbHBqpsxHur0DWNKk8RadGtg0e1HLpnjjHJNcDMjxRBXGCCQRW0HzIykrMEICZIqSxCveLux+NRqV+zgZ5p1tEz3CBRk5zTewLc0dajh3q0J7YNU7Qj7VF6jP8ql1LK7B370yBNt5CexB/lWVX+FL0Z3Zd/v1L/FH80fR/wAIP+Saaf8A9d7r/wBKJK7muG+EH/JNNP8A+u91/wClEldzW0PhRy4j+NP1f5hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO+Pv+Sd+Jf+wXc/+imroq53x9/yTvxL/wBgu5/9FNQB5IHEe1MjpSPIFkG9c56VG/lvOqqeSMg0ydGTBLA4r51xPTEdI5Wzuxj0oPA+ZjjtVu2Q3SoihAoGSapzjZJJFuB/nTS6C6XOfyf7NwQdvndfwre0ixtoTLeWpdXckAk8gdqyNQ0+O1tlkQuSzY5PFZ0SlkyTznHFb4ZyV+VH0ObPB4mlCpKo4puTXu3/AJb9VsegprOq27ALdOR2BAP860rLxDqc7+WCshAySY8AD3Nebi2Tfje/HvTpLRHgPk3CpKO0p4NdPPU7fieJ9XwH/P8Af/gH/wBsel3esXDploIpowcOyJuCn0OakszBdxEw4jbupBH8jXl2mm2lLrd210NuAPLlAHv1FbMVn4ffHmNqcX4q38hRzVOsfxF9XwH/AD/f/gH/ANsegveS2kYMm1l6Z3H+uaamoWMzAusqt6K24fln+lcFdad4fEYMF7dE55EnH/stRR6Pp8ihknlZCequD/SmpVP5fxD6vgP+f7/8A/8Atj0MWmgzzGSa3tmlIxueLacemcVjap8OLDV1ZrK+aIk5VFYSKp+mc1zB0vSftHlh77Gcc4H45x0q+vhjTGGRPc4/3x/hT9pUX2fxH9WwH/P9/wDgH/2xk3nwr122djAYJlHTBZT/ACrNbwV4phc7bbBHYyrz+ZrqT4XsAeJ7rH/XQf4U9fDGl7Czz3YA6nzB/hT9vU/l/H/gC+q4D/n+/wDwD/7Y4i4tvEemRs91p06xKPmcxkgD6jismOS0uJ082Pyh3ZD/AErpbzTWmvHj0+b91nCicZJ/Ef4Va0zw5cNcKNQgdoepa2YEkew5P6Vaq1Evh/H/AIBP1bAX/jv/AMA/+2Oi+FWqSObuykDKv31JU49D/Suw8U6Da69pzLLKkcsQLJJ6cVy9r4Y8K3AxLd6jbv8A3ZZFH81q5cfDzR3sZpbG8u5pQhKL5ikE9ugrPnqXuo/iV9WwH/P9/wDgH/2xkQfDH7XZQXBvoy0kavgoeMjPrSH4XTIPlnt2H1Yf0p8Hw9umsopSJS7KCyLIAVPcYIqtN4TS2OJxeJ9T/wDWq/a1f5fxJ+rZf/z/AH/4B/8AbEg+HF0vQwfgxpjeAzGf3k1up/3j/hRFoGl5/e/bWHfZMB/7KavxeFvDM3ButRiP+06kfotHtav8v4h9VwH/AD/f/gH/ANsZU3hC3jTP9oW6n3yay30i3TWILM3sZjdCzShOFPPGPw/Wu1T4f6FOuYNSuCf+uqH9MVGPh0ttMJre7dyucCQf4VEpVJ2TXVdToofUsMpyjVcm4ySXLbdW3uznv+Ec03P/ACGIAfdMf1qa38F29yT5GoWch9MAk1p3XhWZcmW3z/tKCaqJpNvbHJ35HqcYrfmPFsiGXwFIgLZgx7J/9eoIfASzk/6bZRk9mmYH8q1UmniwIpZAB0+cnFNeaWQjzGLAetO6JsUz8PpQP3LRzMOnlyByfpWPceGWgkeOeEo4OCHjwRXRJIY33xPscd1baaSW7mkOZZZJP95yaVyrHGXfhkXCgGfG3phBVJfB7K2RdjPY7K7p3hck+Rz/ALxqAQ55oU2gsmctYaHqOm3Pmw3MMgP3lfIDD8qb/ZN8ty8aRYtnJZVVwdhrrFgU55xikZAGIHIpqoFjzm6s72xvGZ7aaP5uC0ZxStbTXaTTu4URrnnqT6V6QlzPFwHJX+6wyP1prixnP7+wiBJyXi+U0udXHY83hthHtluchT92PoW/wFbKwK2nm4N3HE+Nojxwq+mK6aTwtpWoXBliuDHKe0veqM3g6/sbe48uBbpJF+Vk5I+lROTexUVYm8HXiQu9pPLuHSNwRjB+tYPi+w+y6i7RgGJzkFeme/SqkKXWm3ANxHIh6MrIRkV0DRQ3+lMoUblOdwA5PrTT5ZXG9VY5WDTbi6lSCFMyFd2CetbsejvaWVvciIJKuRLvbB9uD/Sp9P0sz2xuRO6SK2FxgYrW1FQtlI88MLAthFz0965quJaklcuFNWucTq0YEi+uOaZAuLq3OeMH+Va+raPcyW8TxJG7gHcqHkj1rOjUrLbZXacHIPXpW7qRlSduzOjL42x1L/FH80fRHwg/5Jpp/wD13uv/AEokrua4b4Qf8k00/wD673X/AKUSV3NdUPhRx4j+NP1f5hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVn6rrVloyRteG4/ekhFgtZZ2OOvEasaANCisKXxjoMWm2t/wDb/MgunZIBDC8kkjLncBGql8rg5GOMc4qaXxRosOiwaw1+jWNwwSGSNGdpWOQFVFBYtwflAzweODQBr1zvj7/knfiX/sF3P/opq1dL1ax1qyF5p84mhLFCdpVlYHBVlIBUg9QQDWV4+/5J34l/7Bdz/wCimoA8WubpYzGqKDjADCmzSnySXfFZswkUR5bDCrImSVFRsEnua8Hlsd/OPhv2iQEMQvQ1BJqERuFKn5u5ptwUSB0XB4rntk8al1bg9q1p01IlzfQ39SnE8CMp43dKf4Y0ttX1EQf8s1OXPrWQlwXt1ib7wOa6SwmutE06OWydUuJX3MxGcAVrQjyykn5Hbi3fA0PWf/tp29/4FtHQC2JjkCdfU15xqVjNp968Eww6n866m3+ImoRBlvLaOTIwHjOMfhWb4mvodT2XMWMlQcit2jyjBR8DDGpRJgcNVESGkEtNEs0lnfHHSiaTzYhGVZucja20g1RSYiniUkmnYLlj/hI9Utmayc+YsgCgsckD2PatKG+uEUbLhjjs6hv8DWR5gYDOOOlOWXFGnRBdm9/bM2MPGje8bbT+R/xpZNTiltTGRKhb7xZc/wAs1h+bmnK+D1pILl+1RHlJjljY9AA3P5V0WmzNA78biAFPtn/IrkGkXHzKG+ozUS2E01stzaSsspOcB9vftVb7gmetwQiaIBtpBHcVDcaAOXt12P6xPtP6V5lFrniPSgAbm4CD/nqu9fz5rd0f4hX87ukiRSGPG75fX3B/pSsO50THX9PPyXU5UfwzJ5g/PrTR4v1C3+W80+O4XuYWwf8Avk1YtfHNswxc27J6lTmtNNV8PammJDCc9pFxRoBnw6j4e1QZlha2k77kKEflUr+G4bhN9jeqw6jPP6ippvDOlXi5tLloT22PuH5HNZc/hXWrRi9ldpMOwB2Ef5+tOwxJtE1K2yfIMgH8UZz+lQreXlqceZLEfRs/yNSf214i0nAu7WVlB5Lrkfn/APXq/b+MbG6QLeW6DPrj+TU9QKS+JrmJ9sipKB+Bq6msaffJi5twM/3l3frSGPwxq82yFQsp4DRkqCf5VBL4SCHdaX7rjnEi5/UU7Eksmj6RdDMEhjY/3H/oaqt4ckifdFMky/3XGKRNPuY3KzSQNj+JCc/lUoWWH7kzD6Giwit9ht45l+12bqnQhCfzFaDeH9NmXMRdD23NmmC/uFGCUcdwwp63kT/eiMbeqNScQuRTeH4cDy4EYj+7IRn881n3GjJ5DmLKzKMmNnwa2xeKP4iarzzrMRuRCB60uUdzjSxDEZOehzQM9hXTypbsMGKP8hVZbO3ZuAg/KizC5g9aQgVsS2UIPBXFQG1j/vLTsFzPBwc09b24t3DQzOmPQ8flVpraPuwqJ7aMD71HKFy1HrEF1H5Oo2kcyHguFGfy/wAKik0HTpInbT5fLV+duSVB9u4qi8YX7p4+lNSV4G3Jkeo9am3kNSZYHhoQWcWwb7iNid6McHnvVO4sZ7RyZ/LkDuCiN255qSTVbmAGRCWABOz1+lU7vxGuoW6RNAvB3o5PKmsJ4e+sWaqr0EmZbdmSWUQqxOGRcAfj61i3cMRm82ObzCGOc8k1av5xeqAhCyLj5duQff261cvYbe3sHMcW2R1UMS3U8HgfhXLK9NWe7O7LnzYyl/ij+aPYPhD/AMk10/8A673X/pRJXcVw/wAIf+SbWH/Xe6/9KJK7ivah8KPOxH8afq/zCiiiqMQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArD8TatYadZJBeeIF0SS4z5V0fLB+UjIHmKV7gYIzzxW5RQB5D4bvINE1XS9Y1V/L0orqVvHqc6sizSPPHIJ33fcMgV8HgHHGAQKmsXGnnRPEV4rwaKdc1C5EkiFVhjmEgikYfwqSTyenmD1r1iigDkvBUi3l54k1W2z/Z19qXmWrYwsoWGJGkX1BdG574zVf4jwa63g/xDJa6jp8enjTJ/MgksneVh5bbsSCUAZ7fKce9drXO+Pv+Sd+Jf+wXc/8AopqAPB51aU72GAf0rNnn8lgVOcVskrKiK54IGQKydStViRvLX5j3rxqbV7M7JK6uLbTeevOOtJd2wWRlQdRniq2l284MgatV4iYkkfqnDVTajLRkR2KiwiPSSWQbxKBu74waxtW8QXVzMUgYxRLwoXqa6G4iRdGMqsfnmHH4GuQtbcTTlmRnAICoB95uwrfDWcpN+X6noYz/AHGh6z/9tLNjqV1G6/aNzxNxkj+tdBHJhCp5B5FTyJKtg0FzZrG8CDeuQSAehx2FUIHBjwp6dPpW7S3R5KIZJdmVFQK5LZzUjxzSyN5UTsP9lSabtZTh1I+oxS2HYcHxTlk96DtxTSQBRcklWQiniTPeq6sM0E0XAuB+etOEmKpbiDUgl45oAtmTg/Sowsgj/czyRPjqjY/SoGlG3rU0b/KO/FMC1a3mqW6HddrKcYXegq9YWhlkcqFMsrbnKrjJrOSTbyRXT6Rq+kaZaq08bzXLckKOE/GpbGjRsfDE90OSAB1JrRbwvPboWVdyjuKNP8c6QihH82M9yVyP0zXVWWrWN9Ev2a5ikz2Vh/KosVc40W01s3AZT7cVZh1e/t/uzuQOzciuxltoLhSropz3xXHazGunSsH6dvenqguX38bxWNjJLqFsGVFJyh+97YrzSHV/Des311PqU17Z3E8m5Htmwqj0x3rVkujNcZfBAHCnoK5e/wBGtJTcRwr5c4bzIyOdwxyuP1H41cGnoxXOrsvDE1/mTw74liupI/n8meIq4/GopvEniqyZ4rvSo5imQzpuUHH4EVh+DRJpmu294upRxxq2wkDO4ccEEg4/CvWPE0SJpN7fWoDrNAzoV55xz/n3rRCPPLfx8+cS6U/PeK4Vv04q2fG9gOZ7e+h9d0JIH4iuAZpX3bSePeoBcXER3DeF7spxV8qIuejr448PscG+2H0aNh/Spk8XaC5G3U4OfUkfzrgoGkbDSNksMhjyfzrUht0urOaGVpDMBvifrux1Uj9RQ1ZAtTtF1rT5FBS8hYHoQ4pr6pbY+WVGP+8K53wpqtoc6XeQwOHz5ErxKWQ+mSPy965vWbvWtJ1SW1ku+AcowiUBl7HpSVx7HeHUlM4Zp0EYUjaCOTUc18Gx5d2sRB5xg5HpzXnP9v6iAAbuQt3OFx/Khtd1Ig/6Y+PXA/woswuejSapbEcypj/eFUl1GGMIrXgcrnJJGW+tcD/beqPyLt19OBTTrGokf8fcvPvT5Qud3Lqtt5isLoghs4D8H2+lV5tWtmnWT7Ryv8IbiuGOrakzEC9mwO4c0n9o327m5uM56mQ0coXO3fWLZn3l8nHAznFVn1e3yx3Stk5wEb0x6Vx51G8PBu5iP+uhoNzO/WaX8WNHKFzqZNdhGf3U5/7Zms6XVY/J2w2kpKlmDFccViF3PWRz+Nbfg4CTxNbwSf6uZXib3BU0cqQXKtpqdw9/G+1QpO0rjOQa6bUZJZE27QVXBYjselYdhYZ1FAsZKrNsLH1Brp/FVsmmamIhGQLuJJQSehxyPzBrixcFJcy6Ho5U7Yukv70fzR658If+Sa6f/wBd7r/0okrua4b4Qf8AJNdP/wCu91/6USV3Nd0PhRx4j+NP1f5hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO+Pv8AknfiX/sF3P8A6KauirnfH3/JO/Ev/YLuf/RTUAeC3DlHDDnA5qv5nnyBZMgdTXpD6Lp9xFhoVUsOTWc3g+FGLwSnJ7MMivHlTa2O/c5WKFImeRshCMj3qKW4dLeTbgqegNdBqXh+7ADJHuK9AD2rmr6zvYn3m1lCnj7tRGLvqgaS2JLk/wDFOxHGCZs4/A1zunKYUeVgytGjNG3T5vX8q37hmOgRblKkTEYI56GqcVk721vaPLGJlQTRYP3lYfdPvXVhtHJen6nXjdcFQ9Z/+2lzSdVgkS/hu1LzvbhY278f/WrOdPscrwklvLOOe4zVHbNb6ohClWQfMB6DrV+6lE4EpyWdeSe/ofyrqcTyL3Oj0nxzf6TGsKQ2z2/9wxAfqK3bfxl4f1FimqaYse7+NUDj+Wa82iYSQDkZU4NXFiUIG61D0KR6K/hjwjrK7rC+ijY84STBH/ATWDd/De+AMllcRzx5O3nBxXL52n7ua09O17UNPRRbXkyAfw7sr+R4pAUrzw/qmnsRPauMd8VnMGQ4YFT716Da/EC6A8u/tIbhO5Xg/keKsnUPCGs/LcwfZZG7ldv6jii4WPNg/rzRuGeRivQrj4e2F4hl0vUlIPIDYI/MVz9/4G1qxBb7N58Y/iiOf0ouKxzbYwSPSp43pstu8TFXRkYdmGDTrdd0iA9D1p30JLyxeZb5DfOT93Haqs89tbna9wA3oo3Gs3VL6SG5khhcqp6+tZOWZuMkn9auNO+o7nSQX1uz7ROhJ/vfLWkkskbqY2eNxyCDgj3FcilhdyLlYTj3IFTxy3tiRG4dVborcj8KTproK56roXjq4tGW31JjNCeBL/Ev19a2tUvo7+HznAK4/dj+teV2lws6A9x1Fb2naoZIhbl9w6JWcosdyC5kMVywB4qhdmTesyEhgcgg8gir9/EyfvGFZtycxoacdwKUlpJczefaLl2OZIVOCD6qO4Pp2rs9B8YrpVr/AGfqiym2Y42OhzF7jPUe1cgnDZrXg1W8iQRidnjHRJAHUfga057CNyfRfDF55zWevWlusxyUfoM/Ugim23gyzZRGmuaTPEwwy78HH5mqsGoCX/WWOnufVrVf6Yq2jWT8vo2nn/dQrTVQLEcnw7ugNtrfWUka8KPO5x+VX9M8E3yXCLcIQM/65JVZR+GQahMWksMnRIv+AyEUkdvo0jbf7NeNsZ2rOQcetPnFZFXxP4GuNP1YX9ukhsXbdK8MZ/dH1wO38qyfFkcep6OlykqS3NpwzDq6Hv8A1+ua6gQWUPMK38eOy3bCuK1t5bbWnu1Ez2jjy5BK+5vfNCeu4M5EK7KGAA29STzihSACVww5Bq9f2Zs7jCNutpBvjfOAV/xqn5W8Hyzkg/StBEYI4YHOeCDTWySgHUmnhOnbvzTnjEQG8fMR8o7/AFoAaqhPvZLg/rSF+ArDOD9KQg89SaUqcDuaYEf3X2gcVKMBAT+VIIJGORG5+gqUWtweDFJ7fKaTaAhDFsgevrWv4ZcweI7KUDdsk3EdOgJqpHpl25+W3lJ/3TWjYW02kNNe3ShCsRWIEjJY8dKTasB1kvivw3psciaZp1wbmU72klbhGPPABHSubutVm1S4hknMjMMgNI2TWf8AYpbiGG4RTtI2u3oamVDHcxjsc/yrCpFKnL0Z6GWu+No/4o/mj6F+EH/JNNP/AOu91/6USV3NcN8IP+Saaf8A9d7r/wBKJK7mt4fCjlxH8afq/wAwoooqjEKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArnfH3/JO/Ev/YLuf/RTV0Vc74+/5J34l/7Bdz/6KagDjE4Cgk5xU6SlRgmolACL3GO9JznOa85s7krllnLLwRimbVP3gD9ahCtj73401i4wQcmgLHL+M7PydPSUYw0wGAMY4Ncc9tc3UdtPBLbNIsagKJQrDAxznvXceM5C2jRg9PPX+TV5vexPH5aleZFDLjnAxToL35fL9Tsxv+40PWf/ALaaojksGluJoTJNMgCRgBj79O1Y91PM8asVMb91Pb2rsvDEHmWE0M9tmC2Xckzd371yGobi0xYjiVunfmuqO55D2K1tMNzDoG5rWtsGM4fNc07BJARnFadoYZh+7cxyegb+holC4kzXIPamIpC1EstxB/rEEi+o4NOhuom+XfhvRhWdmi7k65HNKRnmkUg8k/lThhjxx7UgFhmnt3DQyyRt6oxFb1j4y1mxwPtHnKO0o3frWCcdKQhj06UgOym8Y6TqEB/tjRw5OBvixn9f8a5LUBYrdP8A2a03kYBHmgBge44qrN/qiPcUsw2wM467KaA5xo3u71wvJLck9hWtClpYwbgwaYjIyM5/wqtZ+XCpDsMnlj6+1MR7T7YrzGR4mOXCcGui/RGZYF/cux8lQAT0q8s0c6RpcQlJR0z3P9Kh1I6ZFNHLZW48gqMBmJOe/NS3XkLbQTJI7RTZ2B+XiH17ipvfoUU2d7G/8zGYs9R/Wuy0DxFDY3ShijW7H50ZRx7iuPkZQhjcrwPwNNs2EkTLnkDbn27UpLQR7wsWjarADGtrMvsAaoTeGNJudwNoi4OOOK8k095FJiWV0bsQcc1YGt6xaSkJqFwpXjG8kVmkNnoM3gXS2OU81Po1U38CRD/V3jj/AHlBrmIPG2uw/euVmHpIgNaEXxCvwAJbSB/oSKdgNMeELqE/u7mNh/tAinDQNRj6CJvo1VE+ITfx2H/fMn/1qnT4gW5+/Zyj6MKOVCJxpt/GMNb7vowpPscyP5rWrhsY3bckf5zSp4805uGgnH5f41KPG2lt/BOP+Aj/ABp2C41kMahpEdQe5U1T1HSReQFkCsxXp2YVor4x0s9pv++aH8WaQwxiX/vikFzzSS01LTC0X2P7TZls+VIu7af6VA0sRHOiIPYlq7y913TZSWiSUH1IGDWNPfwuTjdiq5xWOZ812OI9Jt1+qsf6092vQNw0+2ye/kEn+dbf26IdSaikvosHk/iafO+w7GKh1HfuWyhz/wBcF/rUjNrbD5Y44x/soi1cOowq3JpG1SA+v5U+Z9gsUwNYGMyqD/vD+lJ5OpuxMl+VHoGNSvqEDHuaj+3wDs36UXfYBrWNw33r9j9WNR/2Ymfnus/8BJqyt4r8JEzH2pk87g7TGyN1w3pTuxWNm3m0628Om1W5lNx5m4qU+UjtzWR5qPcAA554qqZC3en26Fp1YD7vJrKr/Dl6M7st/wB9o/4o/mj6J+EH/JNNP/673X/pRJXc1w3wg/5Jpp//AF3uv/SiSu5reHwo5sR/Gn6v8woooqjEKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArnfH3/JO/Ev8A2C7n/wBFNXRVzvj7/knfiX/sF3P/AKKagDhhIdi/MPTNTCdQdrA59azIZg6oCwyatsVIXkdO1eYz0EWA4GT8xHfFAcHK4AqoWZiNjYpDuUZ3U0BleMv+QPEP+m6/yauIawkOkpcsxZ9xxn+BRxXaeLWD6PEe4nUH8mrFhMY1O70q+wsSligHHGc5p0X78vkdeMX+w0PWf/tpjWmsz2Gp28YYm3WMo8ZPDFh1/WsrU50nuZGjAAPX0zXUCz0xdKlurvAkMkjIM4LLjCgfjiuKkchBk8nk12Rs9TxmUpOTgVoWej3144EcDgerDFUQSJAQcEHNdvovj0xGO31O1Eq/dEsIw4/DpVSbWwlYopomt2iZVWdR/Cy5FVZZdrbb2xaMjgsoz+nWvSIiuojfp+oQzg8+TJ+7bH8jVS8t4gPKv4GiJ6GVcA+wPT8qzUirHCIu9f8AQ7lf9wnH6GpFa5i/10Q/3lrdufDNrM2Ytgbr7/mKoPpGp2jfunDr/ckGf1/wqtGGpWFxE/RsN6Gp4I/MBJYA/WqlweovLSSP/aQbl/x/WokgRubS8Un+6xwf1qeRDuXrhCkZyOlVr66CWnydWXb+OcUyaS8jhaOeMkdNwqrd3EUsUKISWDZYY9KXIFxLW0tZ7hIZW2srDcM/eHcD3p3iGOGPVGNuuyN1BUenbH6VLcaOstkl7YzeZIFzNETyD6j1H61VSYXsXkXJxIPuuavqSyvkyWcag8hyMVYkuFjuB5kfmCMBUUtgcdaphXhmEb8Ddk/40soaa5WFOS7/ACj3NUIt7hdwswRFIbhR29vp1qOykEdwOoUnBB7Vs3kNroehpBJEZLqdgzSr91SDyAfasaYLHPkdDSWugzR3GOXcKkuSJGWQdCMGqxkDQhqlR8x7ffNZ26gCoScYqkuqAPIHWJVVtoyCSa1YfvimWumeHJy5u9Xltptx3LsBGc/SiNm9Rmemph32/Z0+u/Aqd7gxpG7W5xIPl2SA/oea0/7C8OsAsfihQBzh4xSL4X0pnyviWycdtyD/ABq+VC1Mw3Oxd7W1wqeuB/jUR1aALlUkP1wK3/8AhEYJlVY9e09lUYUFRUMvgC4ZcxanYvj0Yj+lCSCzMm21E3U6QwwszscAZFaAS483yzEd3oDn+lN0rQ72yujcL5Um0sg+b8Mjit1kvGHzRxgAf3//AK1RK19AMKQuuQ6lT71CWJp08/nSsewOBUZ6UkMuaHpkGr6y1vcFwgj3fIcV07eENHiwHEnJA5fqaxfB3/IxP/1xrs7mN2nikTG6JtwB6Hgj+tXcRiDw5oMciho1JY4ALnr6VNHo2hqCRZxYDbcspOTVr+z33u/mrlny3pjcW496mj05VnWYSMHDE8dCDnjH407gVvsWjRhttrbkrkECMHsT/SlMGnxsNlpGMoXyIwMAd6sS2kJd2kmwG6AsAF4I4/OmyHT2GGu4h8nln94OlJgS24jdAUQKPpXAeKDu8Q3I7AKP/Ha7j+1tItQQb6BTnkB8/wAq4DX7qC51q5nhlV42K7WHfgCpSdxmYVOcL1PTNWrG4dFmgZtzSFd23oNtUzPGpzvH51cjmkRo4owqxSks2ByT1pVf4cvRnZlv++0f8UfzR9CfCD/kmmn/APXe6/8ASiSu5rhvhB/yTTT/APrvdf8ApRJXc1vD4Uc2I/jT9X+YUUUVRiFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFIzBVLMQABkk9qWue8YWOr6lpkNnpkFtPDJMPtsM1y0HmwgHKBwjY3HAPHK5HGc0APi8aeHp9FbWItRD2Kz/ZxIIny8mcbVXbucnPG0HPap4vFGiy6JPrAv0WxgYpNJIrI0bAgFWRgGDZIG0jPI45rzHSri9tLmHU9Wsbe00vT/FF09w0M5lWIvFLHub5FwiOyDd/tZwuKu3ji+OseI7RXm0Qa/YXRkjQlZYoVjWSVQPvKGAOR18sntQB6PpGu6drscz6fOzmF9ksckTxSRkjIDI4DDIORkc1nePv+Sd+Jf+wXc/8AopqoeHLy31nxzresaXKs2mNZWtt9oj5jmmVpWbaejbVdQSPXHao/iPBrreD/ABDJa6jp8enjTJ/MgksneVh5bbsSCUAZ7fKce9AHl9lqJv7ORbV1iuR8q7hkBvX6Vo6Q96YnXUHiZ1cgFDjI9687a9ME4miPzA5BAwDXRWPicmVjJGPKwMY4JNec4s7VJHTveQ2svzzBlchRg8gmpZZHAdgeg4BHFcrd3NpbzSvFiVpl/d/7BJ5FdLaiXyUedwzsoOB0FK1i07mZ4ikd9BtzIAHMqlseuDVbX76fSdQTVbNFZXTy5lK56dKd4idVsY4ecmYOM9+uf6VuTWiXETRyqrI3VTU03ab+R34qPNgaK85/+2nmOr6vLqdx9on2A4wFQYArFdic56V3F/4IjklzbXLRjP3WGQPxrKm8Eaon3Hhk9BnBNdsakDxJU5djmMDGauac1tHdq9y2FXkD1NXj4U1cBj9nHH+1UDeG9VXrbH8Ktyi9LiUZJ3sbMuu6cLdYokPy9DnBH0NOtfHmoWY8veLi3PBin+YY+tc1NpdxbkicLG3oTz+VQGMKeX/SphTithynJ7no2neJPDl22XjfTbhiMlOYyfp2/SuhEbSQh7cxXsR6eWwyfwNeL1PaahdWMm+2nkib/ZbFU4EqR6lLHBK5WSFoXxypH8vzrGu/D0crPIiLIjHgKMEVm2XxBv41Ed/BFeR+rDDf4VtQeK/D1999rmwkPfG5f1yP5VPK0VdMxJbS4sYyY55VVRzG/PHpzVER2kuqTNPMIY2B24Xoegz6Cu2ka0urcmLV7K4iBBCv1/Hk1wOsx7NQuAChyf4DkfhTVxOxJPbXFmAY33x/wyxciq631vPxdw/P08yPj8xUTSypaw+S7jqCFNIuk3rqJGi8tT3kO3P5015iLBsvtSMbWX7QUG7b/EBUYKwFQ0a+aBuLdwPStbw7bzWk84F9bwO8fHzBs45wfSqFxHc3sjyE+bM524XuaV7sfS5Xa8Nyn2d+IT29D61HcZQLG8eGQbc+vvVmDQdUmkAFnKgz96QbQPxNO1n5bsIcZCjoOtNWvZC6ahpsK3aGI3UUJB48zIB/ECtZNEvEGY2gmX1jlU1z1kwEpU5wa104YVE9xxLSQTQygSxlfrXKXHNzLnn5z/Ounjb96v5VzNxxdzDvvP8AOnT3YSIgBSgdqDj059jTgQO1akBtAGa0tD0ttSvMvkW8Zy5B6+1U4beW8mSGFCzucACu60yyFlapAg6ck+p9aipKyGjUijREVEUKijAArF8QaqlqDaq4DsuT7CtGW7W3ieR2AVASxNeb3969/fzXLk/O2QPQdhWFOPM9Sr2NJbuBV4JPsBTTqUA/hc/hWSGxSE8dK6OVE3Nqy8QzabfG5tI13Fdp8wZqzL431mV2Pmxp7Kg4rnOc04D5sGnyoLmy/ivWJCP9McZ9MCqcmualL9+8nP8AwM1S24HApB0PpTshXHyTySNl2Zj6sxNNBK8g4PtTSfmNIu7vmgLjjJIR9403LH7xJp31pMH60ABHGa3rIiS0jY9Ufj8R/wDWrDYjbjvV/TJ3LrCANvUn88VlXV6b9Gd2Wf77R/xR/NH0j8IP+Saaf/13uv8A0okrua4b4Qf8k00//rvdf+lEldzWkPhRz4j+NP1f5hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO+Pv+Sd+Jf8AsF3P/opq6Kud8ff8k78S/wDYLuf/AEU1AHy/KgEYGDnbkjB4p0UjIEduQv6U6KdpGAbJVhyM9alljMhEaRKu48c8iuJytozotfVB9pbAKvnDZ5Fa+m63c26sPO8x3kyUkXOB3xWKxNuqo0bq6k5Yd6kt4y7sw3FTy2OopOzRSumdT4ikWWKEqycMCBn5gD7fhXQLtJ3q/I565rz6SQmTJO7POT1HtXTWuqyyKofhuScDtXKvjfyPWrO+Bo+s/wD206LaWAOceuec1FLG4fcgQr3qiNRE0xigkRmXhh1I75q0hkGAuCnetDguhXUoVzkgnp1pojj+fAIK8EVS1C+/4mFkkLow3fMueeeOlaEoz+843rw386oW5k6hoFrfxu0vDHOG9PeuSv8Awo1shkinaRR2Kc16Qi7IVVh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+ "text/plain": [ + "" ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_70.jpg\"))" + ] + }, + { + "cell_type": "markdown", + "id": "64fe2dc9", + "metadata": { + "id": "64fe2dc9" + }, + "source": [ + "## Convert the dataset to YOLO format" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3dd01a6a", + "metadata": { + "id": "3dd01a6a" + }, + "outputs": [], + "source": [ + "from datadreamer.utils.convert_dataset import convert_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9b9bb74d", + "metadata": { + "id": "9b9bb74d" + }, + "outputs": [], + "source": [ + "convert_dataset(\n", + " input_dir=\"generated_dataset\",\n", + " output_dir=\"generated_dataset_yolo\",\n", + " dataset_format=\"yolo\",\n", + " split_ratios=[0.8, 0.1, 0.1],\n", + " copy_files=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a167a842", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "a167a842", + "outputId": "6f272b02-5b41-4f4c-cd41-2ed37e461e58" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "d8b05e33", - "metadata": { - "id": "d8b05e33" - }, - "source": [ - "## Show the predictions" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "data.yaml train val\n" + ] + } + ], + "source": [ + "!ls generated_dataset_yolo" + ] + }, + { + "cell_type": "markdown", + "id": "d2d660b0", + "metadata": { + "id": "d2d660b0" + }, + "source": [ + "# Train your model (YOLOv8 as an example)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "982e475e", + "metadata": { + "id": "982e475e", + "scrolled": true + }, + "outputs": [], + "source": [ + "!pip install ultralytics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "184cf0fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "184cf0fa", + "outputId": "6d5837d1-cbc1-4460-f9ec-93ec290c7fc5" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "b559b1f9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "b559b1f9", - "outputId": "37ab5dd6-ecf6-4fb5-86b0-dae0b092c14c" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(filename = os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "id": "dec0cb11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "dec0cb11", - "outputId": "677a9ba3-0386-4b77-dd53-53d9407119e5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ultralytics YOLOv8.0.225 🚀 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", - "Model summary (fused): 168 layers, 3006818 parameters, 0 gradients, 8.1 GFLOPs\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 21 images, 0 backgrounds, 0 corrupt: 100%|██████████| 21/21 [00:00" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename=os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dec0cb11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "dec0cb11", + "outputId": "677a9ba3-0386-4b77-dd53-53d9407119e5" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating the .blob file using [tools.luxonis.com](http://tools.luxonis.com)\n", - "After the training and validation, you can convert the fine-tuned PyTorch model to a `.blob` format.\n", - "\n", - "Please follow these steps to do so:\n", - "1. Download the fine-tuned weights from `runs/detect/train/weights/best.pt` to your device (as shown in screenshot below)\n", - "2. Go to the page [tools.luxonis.com](http://tools.luxonis.com)\n", - "3. On the page set Yolo Version to `YoloV8 (detection only)` ( (as shown in the screeenshot below)\n", - "4. On the page set File to the downloaded `best` weights (as shown in the screeenshot below)\n", - "5. On the page set Input shape to `640` (as shown in the screeenshot below)\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics YOLOv8.0.225 🚀 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", + "Model summary (fused): 168 layers, 3006818 parameters, 0 gradients, 8.1 GFLOPs\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - 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)" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 21 images, 0 backgrounds, 0 corrupt: 100%|██████████| 21/21 [00:00 Date: Thu, 17 Oct 2024 11:14:05 +0900 Subject: [PATCH 18/56] Add FastSAM --- datadreamer/dataset_annotation/__init__.py | 9 +- .../dataset_annotation/fastsam_annotator.py | 103 ++++++++++++++++++ .../generate_dataset_from_scratch.py | 68 ++++++++++-- datadreamer/utils/coco_converter.py | 18 ++- datadreamer/utils/config.py | 4 +- datadreamer/utils/convert_dataset.py | 10 +- datadreamer/utils/dataset_utils.py | 6 + .../utils/luxonis_dataset_converter.py | 7 +- datadreamer/utils/yolo_converter.py | 44 +++++++- requirements.txt | 1 + 10 files changed, 247 insertions(+), 23 deletions(-) create mode 100644 datadreamer/dataset_annotation/fastsam_annotator.py diff --git a/datadreamer/dataset_annotation/__init__.py b/datadreamer/dataset_annotation/__init__.py index f4da035..7dbd45b 100644 --- a/datadreamer/dataset_annotation/__init__.py +++ b/datadreamer/dataset_annotation/__init__.py @@ -1,7 +1,14 @@ from __future__ import annotations from .clip_annotator import CLIPAnnotator +from .fastsam_annotator import FastSAMAnnotator from .image_annotator import BaseAnnotator, TaskList from .owlv2_annotator import OWLv2Annotator -__all__ = ["BaseAnnotator", "TaskList", "OWLv2Annotator", "CLIPAnnotator"] +__all__ = [ + "BaseAnnotator", + "TaskList", + "OWLv2Annotator", + "CLIPAnnotator", + "FastSAMAnnotator", +] diff --git a/datadreamer/dataset_annotation/fastsam_annotator.py b/datadreamer/dataset_annotation/fastsam_annotator.py new file mode 100644 index 0000000..cc9826f --- /dev/null +++ b/datadreamer/dataset_annotation/fastsam_annotator.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import logging +from typing import List, Literal, Tuple + +import numpy as np +import PIL +from ultralytics import FastSAM + +logger = logging.getLogger(__name__) + + +class FastSAMAnnotator: + """A class for image annotation using the FastSAM model, specializing in instance + segmentation. + + Attributes: + model (FastSAM): The FastSAM model. + + + Methods: + annotate_batch(image, prompts, conf_threshold, use_tta, synonym_dict): Annotates the given image with bounding boxes and labels. + """ + + def __init__( + self, + device: str = "cuda", + size: Literal["base", "large"] = "large", + ) -> None: + """Initializes the FastSAMAnnotator object. + + Args: + size (str): The size of the FastSAM model to use ('s' or 'x'). + """ + self.size = size + self.device = device + self.model = self._init_model() + + def _init_model(self) -> FastSAM: + """Initializes the FastSAM model for instance segmentation. + + Returns: + FastSAM: The initialized FastSAM model. + """ + model_size = "s" if self.size == "base" else "x" + logger.info(f"Initializing FastSAM {model_size} model...") + return FastSAM(f"FastSAM-{model_size}.pt") + + def annotate_batch( + self, + images: List[PIL.Image.Image], + prompts: List[str], + boxes_batch: List[np.ndarray], + scores_batch: List[np.ndarray], + labels_batch: List[np.ndarray], + conf_threshold: float = 0.5, + iou_threshold: float = 0.2, + ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray], List[np.ndarray]]: + """Annotates images using the OWLv2 model. + + Args: + images: The images to be annotated. + prompts: Prompts to guide the annotation. + conf_threshold (float, optional): Confidence threshold for the annotations. Defaults to 0.1. + iou_threshold (float, optional): Intersection over union threshold for non-maximum suppression. Defaults to 0.2. + use_tta (bool, optional): Flag to apply test-time augmentation. Defaults to False. + synonym_dict (dict, optional): Dictionary for handling synonyms in labels. Defaults to None. + + Returns: + tuple: A tuple containing the final bounding boxes, scores, and labels for the annotations. + """ + final_segments = [] + + n = len(images) + + for i in range(n): + batch_segments = [] + for box, label in zip(boxes_batch[i], labels_batch[i]): + result = self.model( + images[i], + device=self.device, + bboxes=box, + texts=prompts[label], + labels=[1], + conf=conf_threshold, + iou=iou_threshold, + verbose=False, + ) + mask_segment = result[0].masks.xy[0] + print("mask", mask_segment.shape) + batch_segments.append(mask_segment) + final_segments.append(batch_segments) + + return boxes_batch, scores_batch, labels_batch, final_segments + + +if __name__ == "__main__": + import requests + from PIL import Image + + url = "https://ultralytics.com/images/bus.jpg" + im = Image.open(requests.get(url, stream=True).raw) + annotator = FastSAMAnnotator(device="cpu", size="base") diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 33811bf..8b0e3e7 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -16,7 +16,11 @@ from PIL import Image from tqdm import tqdm -from datadreamer.dataset_annotation import CLIPAnnotator, OWLv2Annotator +from datadreamer.dataset_annotation import ( + CLIPAnnotator, + FastSAMAnnotator, + OWLv2Annotator, +) from datadreamer.image_generation import ( StableDiffusionImageGenerator, StableDiffusionLightningImageGenerator, @@ -54,6 +58,8 @@ det_annotators = {"owlv2": OWLv2Annotator} clf_annotators = {"clip": CLIPAnnotator} +inst_seg_annotators = {"owlv2_fastsam": FastSAMAnnotator} +inst_seg_to_det = {"owlv2_fastsam": OWLv2Annotator} setup_logging(use_rich=True) @@ -70,7 +76,7 @@ def parse_args(): parser.add_argument( "--task", type=str, - choices=["detection", "classification"], + choices=["detection", "classification", "instance-segmentation"], help="Task to generate data for", ) @@ -116,7 +122,7 @@ def parse_args(): parser.add_argument( "--image_annotator", type=str, - choices=["owlv2", "clip"], + choices=["owlv2", "clip", "owlv2_fastsam"], help="Image annotator to use", ) @@ -357,6 +363,14 @@ def check_args(args): "--image_annotator must be one of the available annotators for classification task" ) + if ( + args.task == "instance-segmentation" + and args.image_annotator not in inst_seg_annotators + ): + raise ValueError( + "--image_annotator must be one of the available annotators for instance segmentation task" + ) + # Check coorect task and dataset_format if args.task == "classification" and args.dataset_format in ["coco", "yolo"]: raise ValueError( @@ -368,6 +382,11 @@ def check_args(args): "--dataset_format must be one of the available dataset formats for detection task: raw, coco, yolo, luxonis-dataset" ) + if args.task == "instance-segmentation" and args.dataset_format in ["cls-single"]: + raise ValueError( + "--dataset_format must be one of the available dataset formats for instance segmentation task: raw, coco, yolo, luxonis-dataset" + ) + # Check split_ratios if ( len(args.split_ratios) != 3 @@ -540,6 +559,7 @@ def read_image_batch(image_batch, batch_num, batch_size): boxes_list = [] scores_list = [] labels_list = [] + segment_list = [] image_paths = [] if args.task == "classification": @@ -583,7 +603,12 @@ def read_image_batch(image_batch, batch_num, batch_size): ) else: # Detection annotation - annotator_class = det_annotators[args.image_annotator] + if args.task == "detection": + annotator_class = det_annotators[args.image_annotator] + else: + annotator_class = inst_seg_to_det[args.image_annotator] + inst_seg_annotator_class = inst_seg_annotators[args.image_annotator] + inst_seg_annotator = inst_seg_annotator_class(device=args.device) annotator = annotator_class(device=args.device, size=args.annotator_size) for i, image_batch in tqdm( @@ -608,14 +633,42 @@ def read_image_batch(image_batch, batch_num, batch_size): boxes_list.extend(boxes_batch) scores_list.extend(scores_batch) + if args.task == "instance-segmentation": + ( + boxes_batch, + scores_batch, + local_labels_batch, + masks_batch, + ) = inst_seg_annotator.annotate_batch( + images=images, + prompts=args.class_names, + boxes_batch=boxes_batch, + scores_batch=scores_batch, + labels_batch=local_labels_batch, + conf_threshold=args.conf_threshold, + iou_threshold=args.annotation_iou_threshold, + ) + print( + "mask_batch", + len(masks_batch), + len(masks_batch[0]), + len(scores_batch), + scores_batch[0].shape, + ) + segment_list.extend(masks_batch) + for j, image in enumerate(images): labels = [] # Save bbox visualizations fig, ax = plt.subplots(1) ax.imshow(image) - for box, score, label in zip( - boxes_batch[j], scores_batch[j], local_labels_batch[j] - ): + for k in range(len(boxes_batch[j])): + box = boxes_batch[j][k] + score = scores_batch[j][k] + label = local_labels_batch[j][k] + if args.task == "instance-segmentation": + mask = masks_batch[j][k] + print("mask", type(mask)) labels.append(label) x1, y1, x2, y2 = box rect = patches.Rectangle( @@ -658,6 +711,7 @@ def read_image_batch(image_batch, batch_num, batch_size): image_paths=image_paths, labels_list=labels_list, boxes_list=boxes_list, + masks_list=segment_list if len(segment_list) > 0 else None, class_names=args.class_names, save_dir=save_dir, ) diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index bcd3546..c234d6a 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -4,6 +4,7 @@ import os import shutil +import numpy as np from PIL import Image from datadreamer.utils.base_converter import BaseConverter @@ -28,8 +29,9 @@ class COCOConverter(BaseConverter): │ ├── labels.json """ - def __init__(self, seed=42): + def __init__(self, seed=42, is_instance_segmentation: bool = False): super().__init__(seed) + self.is_instance_segmentation = is_instance_segmentation def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> None: """Converts a dataset into a COCO format. @@ -99,15 +101,23 @@ def process_data( "height": image_height, } ) - - for box, label in zip(annotation["boxes"], annotation["labels"]): + masks = ( + annotation["masks"] + if "masks" in annotation and self.is_instance_segmentation + else [None for i in range(len(annotation["boxes"]))] + ) + for box, label, mask in zip( + annotation["boxes"], annotation["labels"], masks + ): annotations.append( { "id": annotation_id, "image_id": len(images_info), "category_id": label, "bbox": [box[0], box[1], box[2] - box[0], box[3] - box[1]], - "segmentation": None, # [[box[0], box[1], box[2], box[1], box[2], box[3], box[0], box[3]]], # bbox mask + "segmentation": np.array(mask).reshape(-1) + if mask is not None + else None, # [[box[0], box[1], box[2], box[1], box[2], box[3], box[0], box[3]]], # bbox mask "area": (box[2] - box[0]) * (box[3] - box[1]), "iscrowd": 0, } diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index c114321..b85141a 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -10,7 +10,7 @@ class Config(LuxonisConfig): save_dir: str = "generated_dataset" class_names: List[str] = ["bear", "bicycle", "bird", "person"] prompts_number: int = 10 - task: Literal["detection", "classification"] = "detection" + task: Literal["detection", "classification", "instance-segmentation"] = "detection" seed: int = 42 device: Literal["cuda", "cpu"] = "cuda" annotate_only: bool = False @@ -39,7 +39,7 @@ class Config(LuxonisConfig): # Profanity filter arguments disable_lm_filter: bool = False # Annotation arguments - image_annotator: Literal["owlv2", "clip"] = "owlv2" + image_annotator: Literal["owlv2", "clip", "owlv2_fastsam"] = "owlv2" conf_threshold: float = 0.15 annotation_iou_threshold: float = 0.2 use_tta: bool = False diff --git a/datadreamer/utils/convert_dataset.py b/datadreamer/utils/convert_dataset.py index 874878b..2e063ed 100644 --- a/datadreamer/utils/convert_dataset.py +++ b/datadreamer/utils/convert_dataset.py @@ -17,6 +17,7 @@ def convert_dataset( split_ratios, dataset_plugin=None, dataset_name=None, + is_instance_segmentation=False, copy_files=True, seed=42, ) -> None: @@ -36,14 +37,19 @@ def convert_dataset( """ if dataset_format == "yolo": - converter = YOLOConverter(seed=seed) + converter = YOLOConverter( + seed=seed, is_instance_segmentation=is_instance_segmentation + ) elif dataset_format == "coco": - converter = COCOConverter(seed=seed) + converter = COCOConverter( + seed=seed, is_instance_segmentation=is_instance_segmentation + ) elif dataset_format == "luxonis-dataset": converter = LuxonisDatasetConverter( dataset_plugin=dataset_plugin, dataset_name=dataset_name, seed=seed, + is_instance_segmentation=is_instance_segmentation, ) elif dataset_format == "cls-single": converter = SingleLabelClsConverter(seed=seed) diff --git a/datadreamer/utils/dataset_utils.py b/datadreamer/utils/dataset_utils.py index 33fe003..a1c5971 100644 --- a/datadreamer/utils/dataset_utils.py +++ b/datadreamer/utils/dataset_utils.py @@ -6,6 +6,7 @@ def save_annotations_to_json( image_paths, labels_list, boxes_list=None, + masks_list=None, class_names=None, save_dir=None, file_name="annotations.json", @@ -16,6 +17,7 @@ def save_annotations_to_json( image_paths (list): List of image paths. labels_list (list): List of labels. boxes_list (list, optional): List of bounding boxes. Defaults to None. + masks_list (list, optional): List of instance segmentation masks. Defaults to None. class_names (list, optional): List of class names. Defaults to None. save_dir (str, optional): Directory to save the JSON file. Defaults to None. file_name (str, optional): Name of the JSON file. Defaults to 'annotations.json'. @@ -38,6 +40,10 @@ def save_annotations_to_json( bboxes = boxes_list[i] annotations[image_name]["boxes"] = bboxes.tolist() + if masks_list is not None: + masks = masks_list[i] + annotations[image_name]["masks"] = masks + annotations["class_names"] = class_names # Save to JSON file diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 9a2e6f9..c10f161 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -17,9 +17,14 @@ class LuxonisDatasetConverter(BaseConverter): """Class for converting a dataset to LuxonisDataset format.""" def __init__( - self, dataset_plugin: str = None, dataset_name: str = None, seed: int = 42 + self, + dataset_plugin: str = None, + dataset_name: str = None, + seed: int = 42, + is_instance_segmentation: bool = False, ): super().__init__(seed) + self.is_instance_segmentation = is_instance_segmentation self.dataset_plugin = dataset_plugin self.dataset_name = dataset_name diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index 715e429..abcafa5 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -30,8 +30,9 @@ class YOLOConverter(BaseConverter): │ ├── labels """ - def __init__(self, seed=42): + def __init__(self, seed=42, is_instance_segmentation: bool = False): super().__init__(seed) + self.is_instance_segmentation = is_instance_segmentation def convert( self, @@ -74,6 +75,26 @@ def convert_to_yolo_format( height = (box[3] - box[1]) / image_height return [x_center, y_center, width, height] + def convert_masks_to_yolo_format( + self, masks: List[List[float]], image_width: int, image_height: int + ) -> List[float]: + """Converts masks to YOLO format. + + Args: + masks (list of list of float): A list containing the masks. + image_width (int): The width of the image. + image_height (int): The height of the image. + + Returns: + list of float: A list containing the masks in YOLO format. + """ + yolo_masks = [] + for mask in masks: + x, y = mask[0], mask[1] + yolo_masks.append(x / image_width) + yolo_masks.append(y / image_height) + return yolo_masks + def process_data( self, data: Dict, @@ -130,11 +151,22 @@ def process_data( label_output_dir, os.path.splitext(image_name)[0] + ".txt" ) with open(label_file, "w") as f: - for box, label in zip(annotation["boxes"], annotation["labels"]): - yolo_box = self.convert_to_yolo_format( - box, image_width, image_height - ) - f.write(f"{label} {' '.join(map(str, yolo_box))}\n") + if self.is_instance_segmentation: + for box, label in zip( + annotation["boxes"], annotation["labels"] + ): + yolo_box = self.convert_to_yolo_format( + box, image_width, image_height + ) + f.write(f"{label} {' '.join(map(str, yolo_box))}\n") + else: + for masks, label in zip( + annotation["masks"], annotation["labels"] + ): + yolo_box = self.convert_masks_to_yolo_format( + masks, image_width, image_height + ) + f.write(f"{label} {' '.join(map(str, yolo_box))}\n") if copy_files: shutil.copy( diff --git a/requirements.txt b/requirements.txt index 0b92960..3ca8298 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,3 +15,4 @@ nltk>=3.8.1 luxonis-ml[all]>=0.3.0 python-box>=7.1.1 gcsfs>=2023.1.0 +ultralytics>=8.3.13 \ No newline at end of file From f2dbf3378cbafbd9b35428846617b11d37af208d Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Sun, 20 Oct 2024 03:22:44 +0900 Subject: [PATCH 19/56] Update --- README.md | 5 +- .../dataset_annotation/fastsam_annotator.py | 62 +-- .../generate_dataset_from_scratch.py | 38 +- datadreamer/utils/coco_converter.py | 40 +- datadreamer/utils/config.py | 2 +- .../utils/luxonis_dataset_converter.py | 14 + datadreamer/utils/yolo_converter.py | 16 +- .../generate_dataset_and_train_yolo.ipynb | 5 +- ..._segmentation_dataset_and_train_yolo.ipynb | 387 ++++++++++++++ tests/core_tests/integration/test_pipeline.py | 40 ++ tests/core_tests/unittests/test_annotators.py | 57 +- .../integration/test_pipeline_heavy.py | 502 +++++++++++++++++- 12 files changed, 1088 insertions(+), 80 deletions(-) create mode 100644 examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb diff --git a/README.md b/README.md index 37f9af4..3bb5c65 100644 --- a/README.md +++ b/README.md @@ -157,13 +157,13 @@ datadreamer --config ### 🔧 Additional Parameters -- `--task`: Choose between detection and classification. Default is `detection`. +- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`. - `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`. - `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`. - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3. - `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`. - `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`. -- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`. +- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`. - `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`. - `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`. - `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `""`. @@ -199,6 +199,7 @@ datadreamer --config | | [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning) | Fast and accurate (1024x1024 images) | | Image Annotation | [OWLv2](https://huggingface.co/google/owlv2-base-patch16-ensemble) | Open-Vocabulary object detector | | | [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) | Zero-shot-image-classification | +| | [FastSAM](https://docs.ultralytics.com/models/fast-sam) | Zero-shot-instance-segmentation | diff --git a/datadreamer/dataset_annotation/fastsam_annotator.py b/datadreamer/dataset_annotation/fastsam_annotator.py index cc9826f..7a6e41d 100644 --- a/datadreamer/dataset_annotation/fastsam_annotator.py +++ b/datadreamer/dataset_annotation/fastsam_annotator.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import List, Literal, Tuple +from typing import List, Literal import numpy as np import PIL @@ -16,10 +16,12 @@ class FastSAMAnnotator: Attributes: model (FastSAM): The FastSAM model. - + device (str): The device on which the model will run ('cuda' for GPU, 'cpu' for CPU). + size (str): The size of the FastSAM model to use ('s' or 'x'). Methods: - annotate_batch(image, prompts, conf_threshold, use_tta, synonym_dict): Annotates the given image with bounding boxes and labels. + _init_model(): Initializes the FastSAM model. + annotate_batch(images, boxes_batch, conf_threshold, iou_threshold): Annotates the given image with given bounding boxes. """ def __init__( @@ -49,49 +51,41 @@ def _init_model(self) -> FastSAM: def annotate_batch( self, images: List[PIL.Image.Image], - prompts: List[str], boxes_batch: List[np.ndarray], - scores_batch: List[np.ndarray], - labels_batch: List[np.ndarray], - conf_threshold: float = 0.5, + conf_threshold: float = 0.15, iou_threshold: float = 0.2, - ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray], List[np.ndarray]]: - """Annotates images using the OWLv2 model. + ) -> List[List[List[float]]]: + """Annotates images for the task of instance segmentation using the FastSAM + model. Args: images: The images to be annotated. - prompts: Prompts to guide the annotation. - conf_threshold (float, optional): Confidence threshold for the annotations. Defaults to 0.1. + boxes_batch: The bounding boxes of found objects. + conf_threshold (float, optional): Confidence threshold for the annotations. Defaults to 0.15. iou_threshold (float, optional): Intersection over union threshold for non-maximum suppression. Defaults to 0.2. - use_tta (bool, optional): Flag to apply test-time augmentation. Defaults to False. - synonym_dict (dict, optional): Dictionary for handling synonyms in labels. Defaults to None. Returns: - tuple: A tuple containing the final bounding boxes, scores, and labels for the annotations. + List: A list containing the final segment masks represented as a polygon. """ final_segments = [] n = len(images) for i in range(n): - batch_segments = [] - for box, label in zip(boxes_batch[i], labels_batch[i]): - result = self.model( - images[i], - device=self.device, - bboxes=box, - texts=prompts[label], - labels=[1], - conf=conf_threshold, - iou=iou_threshold, - verbose=False, - ) - mask_segment = result[0].masks.xy[0] - print("mask", mask_segment.shape) - batch_segments.append(mask_segment) - final_segments.append(batch_segments) - - return boxes_batch, scores_batch, labels_batch, final_segments + result = self.model( + images[i], + device=self.device, + bboxes=boxes_batch[i], + labels=1, + conf=conf_threshold, + iou=iou_threshold, + verbose=False, + ) + + mask_segments = result[0].masks.xy + final_segments.append(list(map(lambda x: x.tolist(), mask_segments))) + + return final_segments if __name__ == "__main__": @@ -100,4 +94,6 @@ def annotate_batch( url = "https://ultralytics.com/images/bus.jpg" im = Image.open(requests.get(url, stream=True).raw) - annotator = FastSAMAnnotator(device="cpu", size="base") + annotator = FastSAMAnnotator(device="cpu", size="large") + final_segments = annotator.annotate_batch([im], [np.array([[3, 229, 559, 650]])]) + print(len(final_segments), len(final_segments[0]), len(final_segments[0][0])) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 8b0e3e7..92d5035 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -58,8 +58,8 @@ det_annotators = {"owlv2": OWLv2Annotator} clf_annotators = {"clip": CLIPAnnotator} -inst_seg_annotators = {"owlv2_fastsam": FastSAMAnnotator} -inst_seg_to_det = {"owlv2_fastsam": OWLv2Annotator} +inst_seg_annotators = {"owlv2-fastsam": FastSAMAnnotator} +inst_seg_to_det = {"owlv2-fastsam": OWLv2Annotator} setup_logging(use_rich=True) @@ -122,7 +122,7 @@ def parse_args(): parser.add_argument( "--image_annotator", type=str, - choices=["owlv2", "clip", "owlv2_fastsam"], + choices=["owlv2", "clip", "owlv2-fastsam"], help="Image annotator to use", ) @@ -634,27 +634,12 @@ def read_image_batch(image_batch, batch_num, batch_size): scores_list.extend(scores_batch) if args.task == "instance-segmentation": - ( - boxes_batch, - scores_batch, - local_labels_batch, - masks_batch, - ) = inst_seg_annotator.annotate_batch( + masks_batch = inst_seg_annotator.annotate_batch( images=images, - prompts=args.class_names, boxes_batch=boxes_batch, - scores_batch=scores_batch, - labels_batch=local_labels_batch, conf_threshold=args.conf_threshold, iou_threshold=args.annotation_iou_threshold, ) - print( - "mask_batch", - len(masks_batch), - len(masks_batch[0]), - len(scores_batch), - scores_batch[0].shape, - ) segment_list.extend(masks_batch) for j, image in enumerate(images): @@ -667,8 +652,16 @@ def read_image_batch(image_batch, batch_num, batch_size): score = scores_batch[j][k] label = local_labels_batch[j][k] if args.task == "instance-segmentation": - mask = masks_batch[j][k] - print("mask", type(mask)) + if k < len(masks_batch[j]): + mask = masks_batch[j][k] + # Unzip the list of points into separate x and y lists + x_points, y_points = zip(*mask) + + # Fill the polygon defined by the points to create the mask + ax.fill( + x_points, y_points, "blue", alpha=0.5 + ) # 'blue' for mask color and alpha for transparency + labels.append(label) x1, y1, x2, y2 = box rect = patches.Rectangle( @@ -724,6 +717,7 @@ def read_image_batch(image_batch, batch_num, batch_size): "yolo", args.split_ratios, copy_files=False, + is_instance_segmentation=args.task == "instance-segmentation", seed=args.seed, ) # Convert annotations to COCO format @@ -733,6 +727,7 @@ def read_image_batch(image_batch, batch_num, batch_size): args.save_dir, "coco", args.split_ratios, + is_instance_segmentation=args.task == "instance-segmentation", copy_files=False, seed=args.seed, ) @@ -746,6 +741,7 @@ def read_image_batch(image_batch, batch_num, batch_size): args.split_ratios, dataset_plugin=args.dataset_plugin, dataset_name=args.dataset_name, + is_instance_segmentation=args.task == "instance-segmentation", copy_files=False, seed=args.seed, ) diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index c234d6a..760dccf 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -48,6 +48,21 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> Non data = BaseConverter.read_annotations(annotation_path) self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) + def convert_masks_to_coco_format(self, masks): + """Converts masks to COCO format. + + Args: + masks (list of np.ndarray): A list of masks. + + Returns: + list of list of floats: A list of lists of floats representing the segmentation mask polygon. + """ + segmentations = [] + for mask in masks: + segmentation = np.array(mask).reshape(-1).tolist() + segmentations.append(segmentation) + return segmentations + def process_data( self, data, image_dir, output_dir, split_ratios, copy_files=True ) -> None: @@ -102,26 +117,35 @@ def process_data( } ) masks = ( - annotation["masks"] - if "masks" in annotation and self.is_instance_segmentation - else [None for i in range(len(annotation["boxes"]))] + annotation.get("masks") + if self.is_instance_segmentation + else [None] * len(annotation["boxes"]) ) + + # Loop through boxes, labels, and masks, appending to annotations for box, label, mask in zip( annotation["boxes"], annotation["labels"], masks ): + bbox = [box[0], box[1], box[2] - box[0], box[3] - box[1]] + segmentation = ( + np.array(mask).reshape(-1).tolist() + if mask is not None + else None + ) + area = (box[2] - box[0]) * (box[3] - box[1]) + annotations.append( { "id": annotation_id, "image_id": len(images_info), "category_id": label, - "bbox": [box[0], box[1], box[2] - box[0], box[3] - box[1]], - "segmentation": np.array(mask).reshape(-1) - if mask is not None - else None, # [[box[0], box[1], box[2], box[1], box[2], box[3], box[0], box[3]]], # bbox mask - "area": (box[2] - box[0]) * (box[3] - box[1]), + "bbox": bbox, + "segmentation": segmentation, + "area": area, "iscrowd": 0, } ) + annotation_id += 1 if copy_files: diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index b85141a..234f145 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -39,7 +39,7 @@ class Config(LuxonisConfig): # Profanity filter arguments disable_lm_filter: bool = False # Annotation arguments - image_annotator: Literal["owlv2", "clip", "owlv2_fastsam"] = "owlv2" + image_annotator: Literal["owlv2", "clip", "owlv2-fastsam"] = "owlv2" conf_threshold: float = 0.15 annotation_iou_threshold: float = 0.2 use_tta: bool = False diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index c10f161..943e549 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -85,6 +85,20 @@ def dataset_generator(): }, } + if "masks" in data[image_path]: # polyline format + poly = [] + masks = data[image_path]["masks"] + for m in masks: + poly = [[point[0] / width, point[1] / height] for point in m] + yield { + "file": image_full_path, + "annotation": { + "type": "polyline", + "class": class_names[label], + "points": poly, + }, + } + if "boxes" in data[image_path]: boxes = data[image_path]["boxes"] for box, label in zip(boxes, labels): diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index abcafa5..0925009 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -152,14 +152,6 @@ def process_data( ) with open(label_file, "w") as f: if self.is_instance_segmentation: - for box, label in zip( - annotation["boxes"], annotation["labels"] - ): - yolo_box = self.convert_to_yolo_format( - box, image_width, image_height - ) - f.write(f"{label} {' '.join(map(str, yolo_box))}\n") - else: for masks, label in zip( annotation["masks"], annotation["labels"] ): @@ -167,6 +159,14 @@ def process_data( masks, image_width, image_height ) f.write(f"{label} {' '.join(map(str, yolo_box))}\n") + else: + for box, label in zip( + annotation["boxes"], annotation["labels"] + ): + yolo_box = self.convert_to_yolo_format( + box, image_width, image_height + ) + f.write(f"{label} {' '.join(map(str, yolo_box))}\n") if copy_files: shutil.copy( diff --git a/examples/generate_dataset_and_train_yolo.ipynb b/examples/generate_dataset_and_train_yolo.ipynb index 4f5cc17..dbbf376 100644 --- a/examples/generate_dataset_and_train_yolo.ipynb +++ b/examples/generate_dataset_and_train_yolo.ipynb @@ -78,13 +78,13 @@ "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", - "- `--task`: Choose between detection and classification. Default is `detection`.\n", + "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n", "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", @@ -96,6 +96,7 @@ "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb new file mode 100644 index 0000000..6099c1a --- /dev/null +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8ce1517f-7258-406d-9139-9adadb1a1570", + "metadata": {}, + "source": [ + "\n", + "\n", + "# DataDreamer Tutorial: Generating a dataset for instance segmentation, training a model, and deploying it to the OAK (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5_2ivH03etO", + "metadata": { + "id": "b5_2ivH03etO" + }, + "outputs": [], + "source": [ + "!pip install datadreamer@git+https://github.com/luxonis/datadreamer@feat/add-instance-segmentation" + ] + }, + { + "cell_type": "markdown", + "id": "c3704c07", + "metadata": { + "id": "c3704c07" + }, + "source": [ + "## Generate a dataset with your own classes (might take some time to download all models)" + ] + }, + { + "cell_type": "markdown", + "id": "M4v-QieP4tXL", + "metadata": { + "id": "M4v-QieP4tXL" + }, + "source": [ + "Make sure you are using the GPU runtime type (in Google Colab).\n", + "\n", + "~8 min to generate 100 images\n", + "\n", + "~2 min to annotate them" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ab1e2f9", + "metadata": { + "id": "6ab1e2f9", + "scrolled": true + }, + "outputs": [], + "source": [ + "!datadreamer --save_dir generated_dataset \\\n", + " --class_names cat dog \\\n", + " --prompts_number 10 \\\n", + " --prompt_generator simple \\\n", + " --num_objects_range 1 2 \\\n", + " --image_generator sdxl-turbo \\\n", + " --use_tta \\\n", + " --image_annotator owlv2-fastsam \\\n", + " --conf_threshold 0.2 \\\n", + " --seed 42" + ] + }, + { + "cell_type": "markdown", + "id": "7a10755e", + "metadata": {}, + "source": [ + "### Parameters\n", + "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", + "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", + "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", + "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", + "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", + "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", + "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", + "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", + "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", + "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n", + "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", + "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", + "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", + "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", + "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", + "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", + "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", + "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", + "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", + "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", + "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", + "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", + "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", + "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", + "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", + "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", + "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7add74d9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "7add74d9", + "outputId": "a5389937-2a4d-448b-e2f2-6be98018d9be" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_5.jpg\"))" + ] + }, + { + "cell_type": "markdown", + "id": "64fe2dc9", + "metadata": { + "id": "64fe2dc9" + }, + "source": [ + "## Convert the dataset to YOLO format" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3dd01a6a", + "metadata": { + "id": "3dd01a6a" + }, + "outputs": [], + "source": [ + "from datadreamer.utils.convert_dataset import convert_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9b9bb74d", + "metadata": { + "id": "9b9bb74d" + }, + "outputs": [], + "source": [ + "convert_dataset(\n", + " input_dir=\"generated_dataset\",\n", + " output_dir=\"generated_dataset_yolo\",\n", + " dataset_format=\"yolo\",\n", + " split_ratios=[0.8, 0.1, 0.1],\n", + " copy_files=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a167a842", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a167a842", + "outputId": "6f272b02-5b41-4f4c-cd41-2ed37e461e58" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data.yaml train val\n" + ] + } + ], + "source": [ + "!ls generated_dataset_yolo" + ] + }, + { + "cell_type": "markdown", + "id": "d2d660b0", + "metadata": { + "id": "d2d660b0" + }, + "source": [ + "# Train your model (YOLOv8 as an example)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "982e475e", + "metadata": { + "id": "982e475e", + "scrolled": true + }, + "outputs": [], + "source": [ + "!pip install ultralytics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "184cf0fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "184cf0fa", + "outputId": "6d5837d1-cbc1-4460-f9ec-93ec290c7fc5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6.23M/6.23M [00:00<00:00, 327MB/s]\n" + ] + } + ], + "source": [ + "from ultralytics import YOLO\n", + "\n", + "model = YOLO(\"yolov8n-seg.pt\") # load a pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb4e6754", + "metadata": { + "id": "bb4e6754", + "scrolled": true + }, + "outputs": [], + "source": [ + "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=50)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b05e33", + "metadata": { + "id": "d8b05e33" + }, + "source": [ + "## Show the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b559b1f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "b559b1f9", + "outputId": "37ab5dd6-ecf6-4fb5-86b0-dae0b092c14c" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename=os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dec0cb11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dec0cb11", + "outputId": "677a9ba3-0386-4b77-dd53-53d9407119e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics YOLOv8.0.225 🚀 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", + "Model summary (fused): 168 layers, 3006818 parameters, 0 gradients, 8.1 GFLOPs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 21 images, 0 backgrounds, 0 corrupt: 100%|██████████| 21/21 [00:00= 3 + for point in masks[0][0]: + # Check that it is a 2D point + assert len(point) == 2 + assert 0 <= point[0] <= w and 0 <= point[1] <= h + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", +) +def test_cuda_fastsam_base_annotator(): + _check_fastsam_annotator("cuda") + + +@pytest.mark.skipif( + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", +) +def test_cpu_fastsam_base_annotator(): + _check_fastsam_annotator("cpu") + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", +) +def test_cuda_fastsam_large_annotator(): + _check_fastsam_annotator("cuda", size="large") + + +@pytest.mark.skipif( + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", +) +def test_cpu_fastsam_large_annotator(): + _check_fastsam_annotator("cpu", size="large") diff --git a/tests/heavy_tests/integration/test_pipeline_heavy.py b/tests/heavy_tests/integration/test_pipeline_heavy.py index ad9ec8a..6918065 100644 --- a/tests/heavy_tests/integration/test_pipeline_heavy.py +++ b/tests/heavy_tests/integration/test_pipeline_heavy.py @@ -501,7 +501,7 @@ def test_cuda_qwen2_sdxl_detection_pipeline(): # ========================================================= -# CLASSIFICATION - SIMPLE LM +# INSTANCE SEGMENTATION - SIMPLE LM # ========================================================= @pytest.mark.skipif( total_memory < 16 or total_disk_space < 35, @@ -650,7 +650,7 @@ def test_cuda_simple_sdxl_classification_pipeline(): # ========================================================= -# CLASSIFICATION - LLM +# INSTANCE SEGMENTATION - LLM # ========================================================= @pytest.mark.skipif( total_memory < 32 or total_disk_space < 55, @@ -799,7 +799,7 @@ def test_cuda_4bit_lm_sdxl_classification_pipeline(): # ========================================================= -# CLASSIFICATION - TinyLlama LLM +# INSTANCE SEGMENTATION - TinyLlama LLM # ========================================================= @pytest.mark.skipif( total_memory < 16 or total_disk_space < 35, @@ -994,3 +994,499 @@ def test_cuda_qwen2_sdxl_classification_pipeline(): ) # Check the run of the pipeline _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - SIMPLE LM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-simple-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 55, + reason="Test requires GPU, at least 16GB of RAM and 55GB of HDD", +) +def test_cuda_simple_llm_synonym_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-llm-synonym-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--image_annotator owlv2-fastsam " + f"--use_image_tester " + f"--synonym_generator llm " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_wordnet_synonym_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-wordnet-synonym-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--synonym_generator wordnet " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-simple-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--image_annotator owlv2-fastsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--image_annotator owlv2-fastsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - LLM +# ========================================================= +@pytest.mark.skipif( + total_memory < 32 or total_disk_space < 55, + reason="Test requires at least 32GB of RAM and 55GB of HDD for running on CPU", +) +def test_cpu_lm_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-lm-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 55, + reason="Test requires at least 16GB of RAM, 55GB of HDD and CUDA support", +) +def test_cuda_lm_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-lm-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 14 or not torch.cuda.is_available() or total_disk_space < 45, + reason="Test requires at least 14GB of RAM, 45GB of HDD and CUDA support", +) +def test_cuda_4bit_lm_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-4bit-lm-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--lm_quantization 4bit " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 32 or total_disk_space < 55, + reason="Test requires at least 32GB of RAM and 55GB of HDD for running on CPU", +) +def test_cpu_lm_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-lm-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--image_annotator owlv2-fastsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 55, + reason="Test requires at least 16GB of RAM, CUDA support and 55GB of HDD", +) +def test_cuda_lm_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-lm-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--image_annotator owlv2-fastsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 14 or not torch.cuda.is_available() or total_disk_space < 45, + reason="Test requires at least 14GB of RAM, CUDA support and 45GB of HDD", +) +def test_cuda_4bit_lm_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-4bit-lm-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--lm_quantization 4bit " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - TinyLlama LLM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_tiny_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-tiny-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--image_annotator owlv2-fastsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_tiny_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-tiny-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_tiny_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-tiny-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_tiny_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-tiny-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - Qwen2.5 LLM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_qwen2_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-qwen2-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--image_annotator owlv2-fastsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_qwen2_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-qwen2-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_qwen2_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-qwen2-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_qwen2_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-qwen2-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien mars cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-fastsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) From 535d09ae78129e1197b3b59755486cab714607bb Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Sun, 20 Oct 2024 03:37:19 +0900 Subject: [PATCH 20/56] Update Colab notebook --- .../generate_instance_segmentation_dataset_and_train_yolo.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 6099c1a..79fc286 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -62,6 +62,8 @@ " --prompt_generator simple \\\n", " --num_objects_range 1 2 \\\n", " --image_generator sdxl-turbo \\\n", + " --task instance-segmentation \\\n", + " --annotator_size large \\\n", " --use_tta \\\n", " --image_annotator owlv2-fastsam \\\n", " --conf_threshold 0.2 \\\n", From 454d749a1496ac782cda2473a9c226387ad36ba2 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Sun, 20 Oct 2024 22:02:20 +0900 Subject: [PATCH 21/56] Add vizualization --- .../generate_dataset_from_scratch.py | 7 ++----- datadreamer/utils/coco_converter.py | 1 + .../utils/luxonis_dataset_converter.py | 11 ++++++---- datadreamer/utils/yolo_converter.py | 21 +++++++++++-------- ..._segmentation_dataset_and_train_yolo.ipynb | 3 +++ examples/visualize_detection_dataset.py | 21 ++++++++++++++++--- 6 files changed, 43 insertions(+), 21 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 92d5035..a53310d 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -651,16 +651,13 @@ def read_image_batch(image_batch, batch_num, batch_size): box = boxes_batch[j][k] score = scores_batch[j][k] label = local_labels_batch[j][k] + if args.task == "instance-segmentation": if k < len(masks_batch[j]): mask = masks_batch[j][k] - # Unzip the list of points into separate x and y lists x_points, y_points = zip(*mask) - # Fill the polygon defined by the points to create the mask - ax.fill( - x_points, y_points, "blue", alpha=0.5 - ) # 'blue' for mask color and alpha for transparency + ax.fill(x_points, y_points, "blue", alpha=0.5) labels.append(label) x1, y1, x2, y2 = box diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index 760dccf..a023dcf 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -128,6 +128,7 @@ def process_data( ): bbox = [box[0], box[1], box[2] - box[0], box[3] - box[1]] segmentation = ( + # (np.array(mask)*np.array([image_width, image_height])).reshape(-1).tolist() np.array(mask).reshape(-1).tolist() if mask is not None else None diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 943e549..02acb3c 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -86,16 +86,19 @@ def dataset_generator(): } if "masks" in data[image_path]: # polyline format - poly = [] masks = data[image_path]["masks"] - for m in masks: - poly = [[point[0] / width, point[1] / height] for point in m] + for mask, label in zip(masks, labels): + poly = [] + for m in mask: + poly += [ + (point[0] / width, point[1] / height) for point in m + ] yield { "file": image_full_path, "annotation": { "type": "polyline", "class": class_names[label], - "points": poly, + "points": poly, # masks, }, } diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index 0925009..8df0811 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -4,6 +4,7 @@ import shutil from typing import Dict, List +import numpy as np from PIL import Image from datadreamer.utils import BaseConverter @@ -76,24 +77,26 @@ def convert_to_yolo_format( return [x_center, y_center, width, height] def convert_masks_to_yolo_format( - self, masks: List[List[float]], image_width: int, image_height: int + self, masks: List[List[float]], w: int, h: int ) -> List[float]: """Converts masks to YOLO format. Args: masks (list of list of float): A list containing the masks. - image_width (int): The width of the image. - image_height (int): The height of the image. + w (int): The width of the image. + h (int): The height of the image. Returns: list of float: A list containing the masks in YOLO format. """ - yolo_masks = [] - for mask in masks: - x, y = mask[0], mask[1] - yolo_masks.append(x / image_width) - yolo_masks.append(y / image_height) - return yolo_masks + # yolo_masks = [] + # for mask in masks: + # x, y = mask[0], mask[1] + # yolo_masks.append(x / image_width) + # yolo_masks.append(y / image_height) + # return yolo_masks + return (np.array(masks) / np.array([w, h])).reshape(-1).tolist() + # return np.array(masks).reshape(-1).tolist() def process_data( self, diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 79fc286..49f5e76 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -270,6 +270,9 @@ }, "outputs": [], "source": [ + "import os\n", + "os.environ['WANDB_DISABLED'] = 'true'\n", + "\n", "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=50)" ] }, diff --git a/examples/visualize_detection_dataset.py b/examples/visualize_detection_dataset.py index 686d46e..a9235cb 100644 --- a/examples/visualize_detection_dataset.py +++ b/examples/visualize_detection_dataset.py @@ -3,9 +3,10 @@ import os import cv2 +import numpy as np -def draw_rounded_rectangle(img, pt1, pt2, color, thickness, r, d): +def draw_rounded_rectangle(img, pt1, pt2, color, thickness, r, masks=None): x1, y1 = pt1 x2, y2 = pt2 @@ -24,6 +25,13 @@ def draw_rounded_rectangle(img, pt1, pt2, color, thickness, r, d): cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness) +def draw_mask(image, mask, color, alpha=0.5): + overlay = image.copy() + mask = np.array([[int(p[0]), int(p[1])] for p in mask]) + cv2.fillPoly(overlay, [mask], color) + cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image) + + def draw_bboxes_and_labels(image, annotations, class_names): font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 1.5 # Increased font size @@ -32,7 +40,10 @@ def draw_bboxes_and_labels(image, annotations, class_names): text_color = (255, 255, 255) # White text rectangle_radius = 8 - for bbox, label in zip(annotations["boxes"], annotations["labels"]): + for i in range(len(annotations["boxes"])): + bbox = annotations["boxes"][i] + label = annotations["labels"][i] + x_min, y_min, x_max, y_max = map(int, bbox) label_text = class_names[label] @@ -54,6 +65,10 @@ def draw_bboxes_and_labels(image, annotations, class_names): 1, ) + if "masks" in annotations: + masks = annotations["masks"][i] + draw_mask(image, masks, (0, 255, 0), 0.5) + # Draw text background draw_rounded_rectangle( image, @@ -89,7 +104,7 @@ def visualize_dataset(dataset_dir, save_images): for image_name, annotations in all_annotations.items(): image_path = image_name - image = cv2.imread(image_path) + image = cv2.imread(os.path.join(dataset_dir, image_path)) image = draw_bboxes_and_labels(image, annotations, class_names) if save_images: From 7bb93e98877ddd9af57b5d03967eb3ae7dbb8b21 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Mon, 21 Oct 2024 09:52:06 +0900 Subject: [PATCH 22/56] Update README.md and tests --- README.md | 17 + ..._segmentation_dataset_and_train_yolo.ipynb | 7159 ++++++++++++++++- .../core_tests/integration/sample_config.yaml | 4 +- .../integration/test_pipeline_heavy.py | 120 +- 4 files changed, 6868 insertions(+), 432 deletions(-) diff --git a/README.md b/README.md index 3bb5c65..7337d34 100644 --- a/README.md +++ b/README.md @@ -272,6 +272,23 @@ save_dir/ } ``` +3. Instance Segmentation Annotations (instance_segmentation_annotations.json): + +- Each entry corresponds to an image and contains bounding boxes, masks and labels for objects in the image. +- Format: + +```bash +{ + "image_path": { + "boxes": [[x_min, y_min, x_max, y_max], ...], + "masks": [[x0, y0], [x1, y1], ...] + "labels": [label_index, ...] + }, + ... + "class_names": ["class1", "class2", ...] +} +``` + ## ⚠️ Limitations diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 49f5e76..2ad6aab 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -1,392 +1,6811 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "8ce1517f-7258-406d-9139-9adadb1a1570", - "metadata": {}, - "source": [ - "\n", - "\n", - "# DataDreamer Tutorial: Generating a dataset for instance segmentation, training a model, and deploying it to the OAK (optional)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5_2ivH03etO", - "metadata": { - "id": "b5_2ivH03etO" - }, - "outputs": [], - "source": [ - "!pip install datadreamer@git+https://github.com/luxonis/datadreamer@feat/add-instance-segmentation" - ] - }, - { - "cell_type": "markdown", - "id": "c3704c07", - "metadata": { - "id": "c3704c07" - }, - "source": [ - "## Generate a dataset with your own classes (might take some time to download all models)" - ] - }, - { - "cell_type": "markdown", - "id": "M4v-QieP4tXL", - "metadata": { - "id": "M4v-QieP4tXL" - }, - "source": [ - "Make sure you are using the GPU runtime type (in Google Colab).\n", - "\n", - "~8 min to generate 100 images\n", - "\n", - "~2 min to annotate them" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ab1e2f9", - "metadata": { - "id": "6ab1e2f9", - "scrolled": true - }, - "outputs": [], - "source": [ - "!datadreamer --save_dir generated_dataset \\\n", - " --class_names cat dog \\\n", - " --prompts_number 10 \\\n", - " --prompt_generator simple \\\n", - " --num_objects_range 1 2 \\\n", - " --image_generator sdxl-turbo \\\n", - " --task instance-segmentation \\\n", - " --annotator_size large \\\n", - " --use_tta \\\n", - " --image_annotator owlv2-fastsam \\\n", - " --conf_threshold 0.2 \\\n", - " --seed 42" - ] - }, - { - "cell_type": "markdown", - "id": "7a10755e", - "metadata": {}, - "source": [ - "### Parameters\n", - "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", - "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", - "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", - "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", - "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", - "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", - "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", - "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", - "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", - "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n", - "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", - "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", - "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", - "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", - "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", - "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", - "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", - "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", - "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", - "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", - "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", - "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", - "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", - "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", - "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", - "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", - "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", - "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7add74d9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 497 + "cells": [ + { + "cell_type": "markdown", + "id": "8ce1517f-7258-406d-9139-9adadb1a1570", + "metadata": { + "id": "8ce1517f-7258-406d-9139-9adadb1a1570" + }, + "source": [ + "\n", + "\n", + "# DataDreamer Tutorial: Generating a dataset for instance segmentation, training a model, and deploying it to the OAK (optional)" + ] }, - "id": "7add74d9", - "outputId": "a5389937-2a4d-448b-e2f2-6be98018d9be" - }, - "outputs": [ { - "data": { - "image/jpeg": 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- "text/plain": [ - "" + "cell_type": "code", + "execution_count": 1, + "id": "b5_2ivH03etO", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b5_2ivH03etO", + "outputId": "d4c24006-c285-49b8-ad83-be674983238a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ], + "source": [ + "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@feat/add-instance-segmentation" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "from IPython.display import Image\n", - "\n", - "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_5.jpg\"))" - ] - }, - { - "cell_type": "markdown", - "id": "64fe2dc9", - "metadata": { - "id": "64fe2dc9" - }, - "source": [ - "## Convert the dataset to YOLO format" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3dd01a6a", - "metadata": { - "id": "3dd01a6a" - }, - "outputs": [], - "source": [ - "from datadreamer.utils.convert_dataset import convert_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9b9bb74d", - "metadata": { - "id": "9b9bb74d" - }, - "outputs": [], - "source": [ - "convert_dataset(\n", - " input_dir=\"generated_dataset\",\n", - " output_dir=\"generated_dataset_yolo\",\n", - " dataset_format=\"yolo\",\n", - " split_ratios=[0.8, 0.1, 0.1],\n", - " copy_files=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a167a842", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "a167a842", - "outputId": "6f272b02-5b41-4f4c-cd41-2ed37e461e58" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "data.yaml train val\n" - ] - } - ], - "source": [ - "!ls generated_dataset_yolo" - ] - }, - { - "cell_type": "markdown", - "id": "d2d660b0", - "metadata": { - "id": "d2d660b0" - }, - "source": [ - "# Train your model (YOLOv8 as an example)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "982e475e", - "metadata": { - "id": "982e475e", - "scrolled": true - }, - "outputs": [], - "source": [ - "!pip install ultralytics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "184cf0fa", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "id": "c3704c07", + "metadata": { + "id": "c3704c07" + }, + "source": [ + "## Generate a dataset with your own classes (might take some time to download all models)" + ] }, - "id": "184cf0fa", - "outputId": "6d5837d1-cbc1-4460-f9ec-93ec290c7fc5" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n" - ] + "cell_type": "markdown", + "id": "M4v-QieP4tXL", + "metadata": { + "id": "M4v-QieP4tXL" + }, + "source": [ + "Make sure you are using the GPU runtime type (in Google Colab).\n", + "\n", + "~4 min to generate 28 images\n", + "\n", + "~43 secs to annotate them" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6.23M/6.23M [00:00<00:00, 327MB/s]\n" - ] - } - ], - "source": [ - "from ultralytics import YOLO\n", - "\n", - "model = YOLO(\"yolov8n-seg.pt\") # load a pretrained model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb4e6754", - "metadata": { - "id": "bb4e6754", - "scrolled": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ['WANDB_DISABLED'] = 'true'\n", - "\n", - "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=50)" - ] - }, - { - "cell_type": "markdown", - "id": "d8b05e33", - "metadata": { - "id": "d8b05e33" - }, - "source": [ - "## Show the predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b559b1f9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "cell_type": "code", + "execution_count": 2, + "id": "6ab1e2f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6ab1e2f9", + "outputId": "e055777f-91db-4da6-89e8-08cc5960dedf", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "\u001b[32mINFO \u001b[0m Profanity filter is checking classes: \u001b[1m[\u001b[0m\u001b[32m'tractor'\u001b[0m, \u001b[32m'horse'\u001b[0m, \u001b[32m'bear'\u001b[0m\u001b[1m]\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py\u001b\\\u001b[2mprofanity_filter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py#170\u001b\\\u001b[2m170\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[32mINFO \u001b[0m Initializing SDXL Turbo on cuda\u001b[33m...\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py\u001b\\\u001b[2msdxl_turbo_image_generator.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py#42\u001b\\\u001b[2m42\u001b[0m\u001b]8;;\u001b\\\n", + "Loading pipeline components...: 100% 7/7 [00:05<00:00, 1.19it/s]\n", + "Generating images: 0% 0/28 [00:00" + "cell_type": "markdown", + "id": "7a10755e", + "metadata": { + "id": "7a10755e" + }, + "source": [ + "### Parameters\n", + "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", + "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", + "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", + "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", + "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", + "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", + "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", + "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", + "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", + "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n", + "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", + "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", + "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", + "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", + "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", + "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", + "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", + "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", + "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", + "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", + "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", + "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", + "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", + "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", + "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", + "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", + "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(filename=os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dec0cb11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "dec0cb11", - "outputId": "677a9ba3-0386-4b77-dd53-53d9407119e5" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ultralytics YOLOv8.0.225 🚀 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", - "Model summary (fused): 168 layers, 3006818 parameters, 0 gradients, 8.1 GFLOPs\n" - ] + "cell_type": "code", + "execution_count": 3, + "id": "7add74d9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "7add74d9", + "outputId": "66fdd0fa-a7f0-4bc7-cd0f-d61e4d062718" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_5.jpg\"))" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 21 images, 0 backgrounds, 0 corrupt: 100%|██████████| 21/21 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "W&B syncing is set to `offline` in this directory.
Run `wandb online` or set WANDB_MODE=online to enable cloud syncing." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Freezing layer 'model.22.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/generated_dataset_yolo/train/labels... 22 images, 0 backgrounds, 0 corrupt: 100%|██████████| 22/22 [00:00<00:00, 894.59it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/generated_dataset_yolo/train/labels.cache\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<00:00, 5119.17it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/generated_dataset_yolo/val/labels.cache\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting labels to runs/segment/train4/labels.jpg... \n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001429, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n", + "Image sizes 640 train, 640 val\n", + "Using 2 dataloader workers\n", + "Logging results to \u001b[1mruns/segment/train4\u001b[0m\n", + "Starting training for 200 epochs...\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1/200 2.78G 0.794 3.071 3.092 1.307 18 640: 100%|██████████| 2/2 [00:02<00:00, 1.08s/it]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.57it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.0152 1 0.863 0.688 0.0152 1 0.863 0.595\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2/200 2.74G 0.9745 3.363 3.213 1.413 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.01it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.59it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.016 1 0.83 0.665 0.016 1 0.83 0.615\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 3/200 2.83G 0.9595 3.171 3.163 1.394 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 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metrics/mAP50-95(M)▃▆▂▅▆█▁▁▇▇███▇█▇▇▇▇███████████▇▇▇▇███▇██
metrics/precision(B)▁▁▁▁███████▇▇███████████████████████████
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model/GFLOPs▁
model/parameters▁
model/speed_PyTorch(ms)▁
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val/box_loss▁▆▅▅▅▇▆▅▇▂▆▆▇██▄▃▄▄▄▆▄▃▃▂▂▂▂▃▂▂▂▂▂▂▂▂▂▂▂
val/cls_loss███▆▆▆▇▄▄▃▄▄▃▃▄▄▃▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
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val/seg_loss▇█▃▂▂▂▃▂▁▁▁▂▂▁▂▁▁▁▂▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁

Run summary:


lr/pg02e-05
lr/pg12e-05
lr/pg22e-05
metrics/mAP50(B)0.995
metrics/mAP50(M)0.995
metrics/mAP50-95(B)0.96208
metrics/mAP50-95(M)0.995
metrics/precision(B)0.98676
metrics/precision(M)0.98676
metrics/recall(B)1
metrics/recall(M)1
model/GFLOPs12.111
model/parameters3264201
model/speed_PyTorch(ms)141.034
train/box_loss0.44995
train/cls_loss1.00653
train/dfl_loss1.1189
train/seg_loss0.26298
val/box_loss0.39441
val/cls_loss0.50032
val/dfl_loss1.07174
val/seg_loss0.44151

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "You can sync this run to the cloud by running:
wandb sync /content/wandb/offline-run-20241021_000800-nado7ubu" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/offline-run-20241021_000800-nado7ubu/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "os.environ['WANDB_DISABLED'] = 'true'\n", + "\n", + "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=200)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b05e33", + "metadata": { + "id": "d8b05e33" + }, + "source": [ + "## Show the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b559b1f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "b559b1f9", + "outputId": "b2389b7d-a09c-4099-ba0d-6c722e267a06" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename=os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dec0cb11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dec0cb11", + "outputId": "fca326c9-c7e1-4767-8e07-383c971a89eb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00 Date: Mon, 21 Oct 2024 10:03:06 +0900 Subject: [PATCH 23/56] Update COCO converter --- datadreamer/utils/coco_converter.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index a023dcf..62f4676 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -48,21 +48,6 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> Non data = BaseConverter.read_annotations(annotation_path) self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) - def convert_masks_to_coco_format(self, masks): - """Converts masks to COCO format. - - Args: - masks (list of np.ndarray): A list of masks. - - Returns: - list of list of floats: A list of lists of floats representing the segmentation mask polygon. - """ - segmentations = [] - for mask in masks: - segmentation = np.array(mask).reshape(-1).tolist() - segmentations.append(segmentation) - return segmentations - def process_data( self, data, image_dir, output_dir, split_ratios, copy_files=True ) -> None: @@ -128,7 +113,6 @@ def process_data( ): bbox = [box[0], box[1], box[2] - box[0], box[3] - box[1]] segmentation = ( - # (np.array(mask)*np.array([image_width, image_height])).reshape(-1).tolist() np.array(mask).reshape(-1).tolist() if mask is not None else None From 5a0795dd3b86b07b4e06218bedfed34a857dab5d Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Mon, 21 Oct 2024 10:05:14 +0900 Subject: [PATCH 24/56] Refactor YOLO converter --- datadreamer/utils/yolo_converter.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index 8df0811..5f8fc51 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -89,14 +89,7 @@ def convert_masks_to_yolo_format( Returns: list of float: A list containing the masks in YOLO format. """ - # yolo_masks = [] - # for mask in masks: - # x, y = mask[0], mask[1] - # yolo_masks.append(x / image_width) - # yolo_masks.append(y / image_height) - # return yolo_masks return (np.array(masks) / np.array([w, h])).reshape(-1).tolist() - # return np.array(masks).reshape(-1).tolist() def process_data( self, From c0cf6abb9500afb5c16e350f48f08f6701017a2d Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Mon, 21 Oct 2024 10:07:22 +0900 Subject: [PATCH 25/56] Refactor visualize function --- examples/visualize_detection_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/visualize_detection_dataset.py b/examples/visualize_detection_dataset.py index a9235cb..fead256 100644 --- a/examples/visualize_detection_dataset.py +++ b/examples/visualize_detection_dataset.py @@ -6,7 +6,7 @@ import numpy as np -def draw_rounded_rectangle(img, pt1, pt2, color, thickness, r, masks=None): +def draw_rounded_rectangle(img, pt1, pt2, color, thickness, r): x1, y1 = pt1 x2, y2 = pt2 From a1c6b6a81501e123505f702d434300bd325556ea Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Mon, 21 Oct 2024 01:25:39 +0000 Subject: [PATCH 26/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 179c6a1..2fad913 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -15,7 +15,7 @@ coverage coverage - 63% - 63% + 62% + 62% From 7879220443b1bfe029c1703f8610b3c0a9248528 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Mon, 21 Oct 2024 10:08:52 +0000 Subject: [PATCH 27/56] fix: different color for different classes in the segmenetation visuaization --- datadreamer/pipelines/generate_dataset_from_scratch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index a53310d..744278e 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -657,7 +657,7 @@ def read_image_batch(image_batch, batch_num, batch_size): mask = masks_batch[j][k] x_points, y_points = zip(*mask) - ax.fill(x_points, y_points, "blue", alpha=0.5) + ax.fill(x_points, y_points, label, alpha=0.5) labels.append(label) x1, y1, x2, y2 = box From 4fae718b3938b7854b572143f29f39b50e64273c Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Thu, 24 Oct 2024 22:17:55 +0200 Subject: [PATCH 28/56] Switch to SlimSAM --- README.md | 6 +- datadreamer/dataset_annotation/__init__.py | 4 +- .../dataset_annotation/fastsam_annotator.py | 99 ------------------- datadreamer/dataset_annotation/utils.py | 25 +++++ .../generate_dataset_from_scratch.py | 9 +- datadreamer/utils/coco_converter.py | 2 +- datadreamer/utils/config.py | 2 +- .../utils/luxonis_dataset_converter.py | 8 +- ..._segmentation_dataset_and_train_yolo.ipynb | 2 +- requirements.txt | 7 +- tests/core_tests/integration/test_pipeline.py | 4 +- tests/core_tests/unittests/test_annotators.py | 14 +-- .../integration/test_pipeline_heavy.py | 40 ++++---- 13 files changed, 73 insertions(+), 149 deletions(-) delete mode 100644 datadreamer/dataset_annotation/fastsam_annotator.py diff --git a/README.md b/README.md index 7337d34..f08632d 100644 --- a/README.md +++ b/README.md @@ -157,13 +157,13 @@ datadreamer --config ### 🔧 Additional Parameters -- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`. +- `--task`: Choose between detection, classification, instance segmentation and semantic segmentation. Default is `detection`. - `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`. - `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`. - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3. - `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`. - `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`. -- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`. +- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`. - `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`. - `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`. - `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `""`. @@ -199,7 +199,7 @@ datadreamer --config | | [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning) | Fast and accurate (1024x1024 images) | | Image Annotation | [OWLv2](https://huggingface.co/google/owlv2-base-patch16-ensemble) | Open-Vocabulary object detector | | | [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) | Zero-shot-image-classification | -| | [FastSAM](https://docs.ultralytics.com/models/fast-sam) | Zero-shot-instance-segmentation | +| | [SlimSAM](https://huggingface.co/Zigeng/SlimSAM-uniform-50) | Zero-shot-instance-segmentation | diff --git a/datadreamer/dataset_annotation/__init__.py b/datadreamer/dataset_annotation/__init__.py index 7dbd45b..cfdf51a 100644 --- a/datadreamer/dataset_annotation/__init__.py +++ b/datadreamer/dataset_annotation/__init__.py @@ -1,14 +1,14 @@ from __future__ import annotations from .clip_annotator import CLIPAnnotator -from .fastsam_annotator import FastSAMAnnotator from .image_annotator import BaseAnnotator, TaskList from .owlv2_annotator import OWLv2Annotator +from .slimsam_annotator import SlimSAMAnnotator __all__ = [ "BaseAnnotator", "TaskList", "OWLv2Annotator", "CLIPAnnotator", - "FastSAMAnnotator", + "SlimSAMAnnotator", ] diff --git a/datadreamer/dataset_annotation/fastsam_annotator.py b/datadreamer/dataset_annotation/fastsam_annotator.py deleted file mode 100644 index 7a6e41d..0000000 --- a/datadreamer/dataset_annotation/fastsam_annotator.py +++ /dev/null @@ -1,99 +0,0 @@ -from __future__ import annotations - -import logging -from typing import List, Literal - -import numpy as np -import PIL -from ultralytics import FastSAM - -logger = logging.getLogger(__name__) - - -class FastSAMAnnotator: - """A class for image annotation using the FastSAM model, specializing in instance - segmentation. - - Attributes: - model (FastSAM): The FastSAM model. - device (str): The device on which the model will run ('cuda' for GPU, 'cpu' for CPU). - size (str): The size of the FastSAM model to use ('s' or 'x'). - - Methods: - _init_model(): Initializes the FastSAM model. - annotate_batch(images, boxes_batch, conf_threshold, iou_threshold): Annotates the given image with given bounding boxes. - """ - - def __init__( - self, - device: str = "cuda", - size: Literal["base", "large"] = "large", - ) -> None: - """Initializes the FastSAMAnnotator object. - - Args: - size (str): The size of the FastSAM model to use ('s' or 'x'). - """ - self.size = size - self.device = device - self.model = self._init_model() - - def _init_model(self) -> FastSAM: - """Initializes the FastSAM model for instance segmentation. - - Returns: - FastSAM: The initialized FastSAM model. - """ - model_size = "s" if self.size == "base" else "x" - logger.info(f"Initializing FastSAM {model_size} model...") - return FastSAM(f"FastSAM-{model_size}.pt") - - def annotate_batch( - self, - images: List[PIL.Image.Image], - boxes_batch: List[np.ndarray], - conf_threshold: float = 0.15, - iou_threshold: float = 0.2, - ) -> List[List[List[float]]]: - """Annotates images for the task of instance segmentation using the FastSAM - model. - - Args: - images: The images to be annotated. - boxes_batch: The bounding boxes of found objects. - conf_threshold (float, optional): Confidence threshold for the annotations. Defaults to 0.15. - iou_threshold (float, optional): Intersection over union threshold for non-maximum suppression. Defaults to 0.2. - - Returns: - List: A list containing the final segment masks represented as a polygon. - """ - final_segments = [] - - n = len(images) - - for i in range(n): - result = self.model( - images[i], - device=self.device, - bboxes=boxes_batch[i], - labels=1, - conf=conf_threshold, - iou=iou_threshold, - verbose=False, - ) - - mask_segments = result[0].masks.xy - final_segments.append(list(map(lambda x: x.tolist(), mask_segments))) - - return final_segments - - -if __name__ == "__main__": - import requests - from PIL import Image - - url = "https://ultralytics.com/images/bus.jpg" - im = Image.open(requests.get(url, stream=True).raw) - annotator = FastSAMAnnotator(device="cpu", size="large") - final_segments = annotator.annotate_batch([im], [np.array([[3, 229, 559, 650]])]) - print(len(final_segments), len(final_segments[0]), len(final_segments[0][0])) diff --git a/datadreamer/dataset_annotation/utils.py b/datadreamer/dataset_annotation/utils.py index bfb13b7..499363c 100644 --- a/datadreamer/dataset_annotation/utils.py +++ b/datadreamer/dataset_annotation/utils.py @@ -2,6 +2,8 @@ from typing import List +import cv2 +import numpy as np from torchvision import transforms @@ -32,3 +34,26 @@ def apply_tta(image) -> List[transforms.Compose]: augmented_images = [t(image) for t in tta_transforms] return augmented_images + + +def mask_to_polygon(mask: np.ndarray) -> List[List[int]]: + """Converts a binary mask to a polygon. + + Args: + mask: The binary mask to be converted. + + Returns: + List: A list of vertices of the polygon. + """ + # Find contours in the binary mask + contours, _ = cv2.findContours( + mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + ) + + # Find the contour with the largest area + largest_contour = max(contours, key=cv2.contourArea) + + # Extract the vertices of the contour + polygon = largest_contour.reshape(-1, 2).tolist() + + return polygon diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 744278e..bed6d72 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -18,8 +18,8 @@ from datadreamer.dataset_annotation import ( CLIPAnnotator, - FastSAMAnnotator, OWLv2Annotator, + SlimSAMAnnotator, ) from datadreamer.image_generation import ( StableDiffusionImageGenerator, @@ -58,8 +58,8 @@ det_annotators = {"owlv2": OWLv2Annotator} clf_annotators = {"clip": CLIPAnnotator} -inst_seg_annotators = {"owlv2-fastsam": FastSAMAnnotator} -inst_seg_to_det = {"owlv2-fastsam": OWLv2Annotator} +inst_seg_annotators = {"owlv2-slimsam": SlimSAMAnnotator} +inst_seg_to_det = {"owlv2-slimsam": OWLv2Annotator} setup_logging(use_rich=True) @@ -122,7 +122,7 @@ def parse_args(): parser.add_argument( "--image_annotator", type=str, - choices=["owlv2", "clip", "owlv2-fastsam"], + choices=["owlv2", "clip", "owlv2-slimsam"], help="Image annotator to use", ) @@ -637,7 +637,6 @@ def read_image_batch(image_batch, batch_num, batch_size): masks_batch = inst_seg_annotator.annotate_batch( images=images, boxes_batch=boxes_batch, - conf_threshold=args.conf_threshold, iou_threshold=args.annotation_iou_threshold, ) segment_list.extend(masks_batch) diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index 62f4676..bb69a78 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -113,7 +113,7 @@ def process_data( ): bbox = [box[0], box[1], box[2] - box[0], box[3] - box[1]] segmentation = ( - np.array(mask).reshape(-1).tolist() + np.array(mask).reshape(1, -1).tolist() if mask is not None else None ) diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index 234f145..9d36267 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -39,7 +39,7 @@ class Config(LuxonisConfig): # Profanity filter arguments disable_lm_filter: bool = False # Annotation arguments - image_annotator: Literal["owlv2", "clip", "owlv2-fastsam"] = "owlv2" + image_annotator: Literal["owlv2", "clip", "owlv2-slimsam"] = "owlv2" conf_threshold: float = 0.15 annotation_iou_threshold: float = 0.2 use_tta: bool = False diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 02acb3c..e5783e6 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -89,10 +89,10 @@ def dataset_generator(): masks = data[image_path]["masks"] for mask, label in zip(masks, labels): poly = [] - for m in mask: - poly += [ - (point[0] / width, point[1] / height) for point in m - ] + print(mask) + poly += [ + (point[0] / width, point[1] / height) for point in mask + ] yield { "file": image_full_path, "annotation": { diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 2ad6aab..2ae760e 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -298,7 +298,7 @@ " --disable_lm_filter \\\n", " --annotator_size base \\\n", " --use_tta \\\n", - " --image_annotator owlv2-fastsam \\\n", + " --image_annotator owlv2-slimsam \\\n", " --conf_threshold 0.2 \\\n", " --seed 42" ] diff --git a/requirements.txt b/requirements.txt index 3ca8298..3afd902 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ torch>=2.0.0 torchvision>=0.16.0 -transformers>=4.37.0 +transformers>=4.45.2 diffusers>=0.24.0 compel>=2.0.0 tqdm>=4.0.0 @@ -12,7 +12,6 @@ accelerate>=0.25.0 scipy>=1.10.0 bitsandbytes>=0.42.0 nltk>=3.8.1 -luxonis-ml[all]>=0.3.0 +luxonis-ml[all]>=0.4.0 python-box>=7.1.1 -gcsfs>=2023.1.0 -ultralytics>=8.3.13 \ No newline at end of file +gcsfs>=2023.1.0 \ No newline at end of file diff --git a/tests/core_tests/integration/test_pipeline.py b/tests/core_tests/integration/test_pipeline.py index 221c44b..a49617c 100644 --- a/tests/core_tests/integration/test_pipeline.py +++ b/tests/core_tests/integration/test_pipeline.py @@ -184,7 +184,7 @@ def test_cpu_simple_sdxl_turbo_config_instance_segmentation_pipeline(): f"datadreamer --task instance-segmentation " f"--save_dir {target_folder} " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--config ./tests/core_tests/integration/sample_config.yaml " f"--device cpu" ) @@ -204,7 +204,7 @@ def test_cuda_simple_sdxl_turbo_config_instance_segmentation_pipeline(): f"datadreamer --task instance-segmentation " f"--save_dir {target_folder} " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--config ./tests/core_tests/integration/sample_config.yaml " f"--device cuda" ) diff --git a/tests/core_tests/unittests/test_annotators.py b/tests/core_tests/unittests/test_annotators.py index 1c25959..4f6db02 100644 --- a/tests/core_tests/unittests/test_annotators.py +++ b/tests/core_tests/unittests/test_annotators.py @@ -8,7 +8,7 @@ from PIL import Image from datadreamer.dataset_annotation.clip_annotator import CLIPAnnotator -from datadreamer.dataset_annotation.fastsam_annotator import FastSAMAnnotator +from datadreamer.dataset_annotation.fastsam_annotator import SlimSAMAnnotator from datadreamer.dataset_annotation.owlv2_annotator import OWLv2Annotator # Get the total disk space in GB @@ -99,10 +99,10 @@ def test_cpu_clip_large_annotator(): _check_clip_annotator("cpu", size="large") -def _check_fastsam_annotator(device: str, size: str = "base"): +def _check_slimsam_annotator(device: str, size: str = "base"): url = "https://ultralytics.com/images/bus.jpg" im = Image.open(requests.get(url, stream=True).raw) - annotator = FastSAMAnnotator(device=device, size=size) + annotator = SlimSAMAnnotator(device=device, size=size) masks = annotator.annotate_batch([im], [np.array([[3, 229, 559, 650]])]) w, h = im.width, im.height # Check that the masks are lists @@ -124,7 +124,7 @@ def _check_fastsam_annotator(device: str, size: str = "base"): reason="Test requires GPU and 16GB of HDD", ) def test_cuda_fastsam_base_annotator(): - _check_fastsam_annotator("cuda") + _check_slimsam_annotator("cuda") @pytest.mark.skipif( @@ -132,7 +132,7 @@ def test_cuda_fastsam_base_annotator(): reason="Test requires at least 16GB of HDD", ) def test_cpu_fastsam_base_annotator(): - _check_fastsam_annotator("cpu") + _check_slimsam_annotator("cpu") @pytest.mark.skipif( @@ -140,7 +140,7 @@ def test_cpu_fastsam_base_annotator(): reason="Test requires GPU and 16GB of HDD", ) def test_cuda_fastsam_large_annotator(): - _check_fastsam_annotator("cuda", size="large") + _check_slimsam_annotator("cuda", size="large") @pytest.mark.skipif( @@ -148,4 +148,4 @@ def test_cuda_fastsam_large_annotator(): reason="Test requires at least 16GB of HDD", ) def test_cpu_fastsam_large_annotator(): - _check_fastsam_annotator("cpu", size="large") + _check_slimsam_annotator("cpu", size="large") diff --git a/tests/heavy_tests/integration/test_pipeline_heavy.py b/tests/heavy_tests/integration/test_pipeline_heavy.py index a648af1..6b7dc3f 100644 --- a/tests/heavy_tests/integration/test_pipeline_heavy.py +++ b/tests/heavy_tests/integration/test_pipeline_heavy.py @@ -1014,7 +1014,7 @@ def test_cpu_simple_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--device cpu" @@ -1038,7 +1038,7 @@ def test_cuda_simple_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--device cuda" @@ -1063,7 +1063,7 @@ def test_cuda_simple_llm_synonym_sdxl_turbo_instance_segmentation_pipeline(): f"--prompt_generator simple " f"--num_objects_range 1 2 " f"--image_generator sdxl-turbo " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--use_image_tester " f"--synonym_generator llm " f"--device cuda" @@ -1087,7 +1087,7 @@ def test_cuda_simple_wordnet_synonym_sdxl_turbo_instance_segmentation_pipeline() f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--synonym_generator wordnet " @@ -1111,7 +1111,7 @@ def test_cpu_simple_sdxl_instance_segmentation_pipeline(): f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--num_objects_range 1 2 " f"--image_generator sdxl " f"--use_image_tester " @@ -1135,7 +1135,7 @@ def test_cuda_simple_sdxl_instance_segmentation_pipeline(): f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--num_objects_range 1 2 " f"--image_generator sdxl " f"--use_image_tester " @@ -1163,7 +1163,7 @@ def test_cpu_lm_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--device cpu" @@ -1187,7 +1187,7 @@ def test_cuda_lm_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--device cuda" @@ -1211,7 +1211,7 @@ def test_cuda_4bit_lm_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--lm_quantization 4bit " @@ -1235,7 +1235,7 @@ def test_cpu_lm_sdxl_instance_segmentation_pipeline(): f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--num_objects_range 1 2 " f"--image_generator sdxl " f"--use_image_tester " @@ -1259,7 +1259,7 @@ def test_cuda_lm_sdxl_instance_segmentation_pipeline(): f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--num_objects_range 1 2 " f"--image_generator sdxl " f"--use_image_tester " @@ -1284,7 +1284,7 @@ def test_cuda_4bit_lm_sdxl_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl " f"--use_image_tester " f"--lm_quantization 4bit " @@ -1311,7 +1311,7 @@ def test_cpu_tiny_sdxl_turbo_instance_segmentation_pipeline(): f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--num_objects_range 1 2 " f"--image_generator sdxl-turbo " f"--use_image_tester " @@ -1336,7 +1336,7 @@ def test_cuda_tiny_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--device cuda" @@ -1360,7 +1360,7 @@ def test_cpu_tiny_sdxl_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl " f"--use_image_tester " f"--device cpu" @@ -1384,7 +1384,7 @@ def test_cuda_tiny_sdxl_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl " f"--use_image_tester " f"--device cuda" @@ -1410,7 +1410,7 @@ def test_cpu_qwen2_sdxl_turbo_instance_segmentation_pipeline(): f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--num_objects_range 1 2 " f"--image_generator sdxl-turbo " f"--use_image_tester " @@ -1435,7 +1435,7 @@ def test_cuda_qwen2_sdxl_turbo_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl-turbo " f"--use_image_tester " f"--device cuda" @@ -1459,7 +1459,7 @@ def test_cpu_qwen2_sdxl_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl " f"--use_image_tester " f"--device cpu" @@ -1483,7 +1483,7 @@ def test_cuda_qwen2_sdxl_instance_segmentation_pipeline(): f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " - f"--image_annotator owlv2-fastsam " + f"--image_annotator owlv2-slimsam " f"--image_generator sdxl " f"--use_image_tester " f"--device cuda" From f40e5a0493c4d8b837fda44aca03726c887a2d29 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Thu, 24 Oct 2024 22:22:05 +0200 Subject: [PATCH 29/56] Switch to SlimSAM --- .../dataset_annotation/slimsam_annotator.py | 152 ++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 datadreamer/dataset_annotation/slimsam_annotator.py diff --git a/datadreamer/dataset_annotation/slimsam_annotator.py b/datadreamer/dataset_annotation/slimsam_annotator.py new file mode 100644 index 0000000..b22c807 --- /dev/null +++ b/datadreamer/dataset_annotation/slimsam_annotator.py @@ -0,0 +1,152 @@ +from __future__ import annotations + +import logging +from typing import List + +import numpy as np +import PIL +import torch +from transformers import SamModel, SamProcessor + +from datadreamer.dataset_annotation.image_annotator import BaseAnnotator +from datadreamer.dataset_annotation.utils import mask_to_polygon + +logger = logging.getLogger(__name__) + + +class SlimSAMAnnotator(BaseAnnotator): + """A class for image annotation using the SlimSAM model, specializing in instance + segmentation. + + Attributes: + model (SAM): The SAM model for instance segmentation. + processor (SamProcessor): The processor for the SAM model. + device (str): The device on which the model will run ('cuda' for GPU, 'cpu' for CPU). + size (str): The size of the SAM model to use ('base' or 'large'). + + Methods: + _init_model(): Initializes the SAM model. + _init_processor(): Initializes the processor for the SAM model. + annotate_batch(image, prompts, conf_threshold, use_tta, synonym_dict): Annotates the given image with bounding boxes and labels. + release(empty_cuda_cache): Releases resources and optionally empties the CUDA cache. + """ + + def __init__( + self, + seed: float = 42, + device: str = "cuda", + size: str = "base", + ) -> None: + """Initializes the SAMAnnotator with a specific seed and device. + + Args: + seed (float): Seed for reproducibility. Defaults to 42. + device (str): The device to run the model on. Defaults to 'cuda'. + """ + super().__init__(seed) + self.size = size + self.model = self._init_model() + self.processor = self._init_processor() + self.device = device + self.model.to(self.device) + + def _init_model(self) -> SamModel: + """Initializes the SAM model for object detection. + + Returns: + SamModel: The initialized SAM model. + """ + logger.info(f"Initializing `SlimSAM {self.size} model...") + if self.size == "large": + return SamModel.from_pretrained("Zigeng/SlimSAM-uniform-50") + return SamModel.from_pretrained("Zigeng/SlimSAM-uniform-77") + + def _init_processor(self) -> SamProcessor: + """Initializes the processor for the SAM model. + + Returns: + SamProcessor: The initialized processor. + """ + if self.size == "large": + SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-50") + return SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-77") + + def annotate_batch( + self, + images: List[PIL.Image.Image], + boxes_batch: List[np.ndarray], + iou_threshold: float = 0.2, + ) -> List[List[List[float]]]: + """Annotates images for the task of instance segmentation using the FastSAM + model. + + Args: + images: The images to be annotated. + boxes_batch: The bounding boxes of found objects. + iou_threshold (float, optional): Intersection over union threshold for non-maximum suppression. Defaults to 0.2. + + Returns: + List: A list containing the final segment masks represented as a polygon. + """ + final_segments = [] + + n = len(images) + + for i in range(n): + boxes = boxes_batch[i].tolist() + if len(boxes) == 0: + final_segments.append([]) + continue + + inputs = self.processor( + images[i], input_boxes=[boxes], return_tensors="pt" + ).to(self.device) + + with torch.no_grad(): + outputs = self.model(**inputs, return_dict=True) + + masks = self.processor.image_processor.post_process_masks( + outputs.pred_masks.cpu(), + inputs["original_sizes"].cpu(), + inputs["reshaped_input_sizes"].cpu(), + )[0] + + iou_scores = outputs.iou_scores.cpu() + + image_masks = [] + for j in range(len(boxes)): + keep_idx = iou_scores[0, j] >= iou_threshold + filtered_masks = masks[j, keep_idx].cpu().float() + final_masks = filtered_masks.permute(1, 2, 0) + final_masks = final_masks.mean(axis=-1) + final_masks = (final_masks > 0).int() + final_masks = final_masks.numpy().astype(np.uint8) + polygon = mask_to_polygon(final_masks) + image_masks.append(polygon) + + final_segments.append(image_masks) + + return final_segments + + def release(self, empty_cuda_cache: bool = False) -> None: + """Releases the model and optionally empties the CUDA cache. + + Args: + empty_cuda_cache (bool, optional): Whether to empty the CUDA cache. Defaults to False. + """ + self.model = self.model.to("cpu") + if empty_cuda_cache: + with torch.no_grad(): + torch.cuda.empty_cache() + + +if __name__ == "__main__": + import requests + from PIL import Image + + url = "https://ultralytics.com/images/bus.jpg" + im = Image.open(requests.get(url, stream=True).raw) + annotator = SlimSAMAnnotator(device="cpu", size="large") + final_segments = annotator.annotate_batch([im], [np.array([[3, 229, 559, 650]])]) + print(len(final_segments), len(final_segments[0])) + print(final_segments[0][0][:5]) From 853d5ade312c2a2706aa58f64cba241b5405add3 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Thu, 24 Oct 2024 22:41:51 +0200 Subject: [PATCH 30/56] Update instance segmentation example --- .../utils/luxonis_dataset_converter.py | 1 - ..._segmentation_dataset_and_train_yolo.ipynb | 4090 ++++++++++++----- 2 files changed, 2929 insertions(+), 1162 deletions(-) diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index e5783e6..64f4968 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -89,7 +89,6 @@ def dataset_generator(): masks = data[image_path]["masks"] for mask, label in zip(masks, labels): poly = [] - print(mask) poly += [ (point[0] / width, point[1] / height) for point in mask ] diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 2ae760e..a7a5d30 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -21,7 +21,7 @@ "base_uri": "https://localhost:8080/" }, "id": "b5_2ivH03etO", - "outputId": "d4c24006-c285-49b8-ad83-be674983238a" + "outputId": "c92b1e2e-cd3e-4a7d-8be6-776e0dfad5bc" }, "outputs": [ { @@ -30,12 +30,51 @@ "text": [ " Installing build dependencies ... 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This behaviour is the source of the following dependency conflicts.\n", + "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.3 which is incompatible.\n", + "google-cloud-firestore 2.16.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.3 which is incompatible.\n", + "tensorboard 2.17.0 requires protobuf!=4.24.0,<5.0.0,>=3.19.6, but you have protobuf 5.28.3 which is incompatible.\n", + "tensorflow 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.28.3 which is incompatible.\n", + "tensorflow-metadata 1.16.1 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.28.3 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" ] } ], "source": [ - "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@feat/add-instance-segmentation" + "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@dev" ] }, { @@ -57,7 +96,7 @@ "source": [ "Make sure you are using the GPU runtime type (in Google Colab).\n", "\n", - "~4 min to generate 28 images\n", + "~4 min to generate 30 images\n", "\n", "~43 secs to annotate them" ] @@ -71,7 +110,7 @@ "base_uri": "https://localhost:8080/" }, "id": "6ab1e2f9", - "outputId": "e055777f-91db-4da6-89e8-08cc5960dedf", + "outputId": "6f57eb7a-f261-46bc-e574-3631cade8660", "scrolled": true }, "outputs": [ @@ -79,218 +118,2061 @@ "name": "stdout", "output_type": "stream", "text": [ + "2024-10-24 20:24:16.241793: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-10-24 20:24:16.272474: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-10-24 20:24:16.282212: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-10-24 20:24:16.304239: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-10-24 20:24:17.906040: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n", "\u001b[32mINFO \u001b[0m Profanity filter is checking classes: \u001b[1m[\u001b[0m\u001b[32m'tractor'\u001b[0m, \u001b[32m'horse'\u001b[0m, \u001b[32m'bear'\u001b[0m\u001b[1m]\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py\u001b\\\u001b[2mprofanity_filter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py#170\u001b\\\u001b[2m170\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[32mINFO \u001b[0m Initializing SDXL Turbo on cuda\u001b[33m...\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py\u001b\\\u001b[2msdxl_turbo_image_generator.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py#42\u001b\\\u001b[2m42\u001b[0m\u001b]8;;\u001b\\\n", - "Loading pipeline components...: 100% 7/7 [00:05<00:00, 1.19it/s]\n", - "Generating images: 0% 0/28 [00:00" ] @@ -424,7 +2306,7 @@ "base_uri": "https://localhost:8080/" }, "id": "a167a842", - "outputId": "4e5f7ecb-b795-4f10-828d-b8378ad5d491" + "outputId": "715988c2-ab27-4ce2-b12c-2fa01188c537" }, "outputs": [ { @@ -454,10 +2336,23 @@ "execution_count": 7, "id": "982e475e", "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, "id": "982e475e", + "outputId": "1f4cb9f5-1d01-4882-a730-434e5122546f", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/877.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m877.1/877.1 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], "source": [ "!pip install -q ultralytics" ] @@ -467,9 +2362,31 @@ "execution_count": 8, "id": "184cf0fa", "metadata": { - "id": "184cf0fa" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "184cf0fa", + "outputId": "dcc43a26-bc78-4d3d-ddb3-6932a8584df9" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating new Ultralytics Settings v0.0.6 file ✅ \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", + "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6.74M/6.74M [00:00<00:00, 110MB/s]\n" + ] + } + ], "source": [ "from ultralytics import YOLO\n", "\n", @@ -483,20 +2400,10 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "73419dc7b5a942bbbdbfe9e0a2552c23", - "0e7d0e4096284f8489b30be03692e919", - "0b7fe81c0e7943bda46c9aa63b9ce8ea", - "23b7f9ae5b094585b21bd36f7cde08ca", - "b55d573195ad489cb621b47b1e1c7d52", - "571d1e0c1e704169a5a91b03d0d589e7", - "31693faf3df247a4aface2c6706d9b64", - "f81e7e7eb9f6491786aca2d98dcd9c71" - ] + "height": 1000 }, "id": "bb4e6754", - "outputId": "eb28d115-aed1-4fe6-a595-dfdcb26a7afe", + "outputId": "66b28d5a-6544-46fa-ee73-3074f141e981", "scrolled": true }, "outputs": [ @@ -504,8 +2411,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", - "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8n-seg.pt, data=generated_dataset_yolo/data.yaml, epochs=200, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train4, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train4\n", + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8n-seg.pt, data=generated_dataset_yolo/data.yaml, epochs=200, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train\n", + "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 755k/755k [00:00<00:00, 24.2MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Overriding model.yaml nc=80 with nc=3\n", "\n", " from n params module arguments \n", @@ -535,7 +2456,7 @@ "YOLOv8n-seg summary: 261 layers, 3,264,201 parameters, 3,264,185 gradients, 12.1 GFLOPs\n", "\n", "Transferred 381/417 items from pretrained weights\n", - "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train4', view at http://localhost:6006/\n" + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train', view at http://localhost:6006/\n" ] }, { @@ -548,7 +2469,7 @@ { "data": { "text/html": [ - "Tracking run with wandb version 0.18.3" + "Tracking run with wandb version 0.18.5" ], "text/plain": [ "" @@ -574,7 +2495,21 @@ "output_type": "stream", "text": [ "Freezing layer 'model.22.dfl.conv.weight'\n", - "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5.35M/5.35M [00:00<00:00, 102MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n" ] }, @@ -582,7 +2517,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/generated_dataset_yolo/train/labels... 22 images, 0 backgrounds, 0 corrupt: 100%|██████████| 22/22 [00:00<00:00, 894.59it/s]" + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/generated_dataset_yolo/train/labels... 24 images, 0 backgrounds, 0 corrupt: 100%|██████████| 24/24 [00:00<00:00, 1156.29it/s]" ] }, { @@ -598,7 +2533,7 @@ "output_type": "stream", "text": [ "\n", - "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<00:00, 5119.17it/s]" + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<00:00, 610.38it/s]" ] }, { @@ -619,13 +2554,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Plotting labels to runs/segment/train4/labels.jpg... \n", + "Plotting labels to runs/segment/train/labels.jpg... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001429, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n", "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n", "Image sizes 640 train, 640 val\n", "Using 2 dataloader workers\n", - "Logging results to \u001b[1mruns/segment/train4\u001b[0m\n", + "Logging results to \u001b[1mruns/segment/train\u001b[0m\n", "Starting training for 200 epochs...\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" @@ -635,15 +2570,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 1/200 2.78G 0.794 3.071 3.092 1.307 18 640: 100%|██████████| 2/2 [00:02<00:00, 1.08s/it]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.57it/s]" + " 1/200 2.81G 0.9583 3.042 3.096 1.435 24 640: 100%|██████████| 2/2 [00:04<00:00, 2.23s/it]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.71it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0152 1 0.863 0.688 0.0152 1 0.863 0.595\n" + " all 3 3 0.0154 1 0.995 0.763 0.0154 1 0.995 0.758\n" ] }, { @@ -665,15 +2600,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 2/200 2.74G 0.9745 3.363 3.213 1.413 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.01it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.59it/s]" + " 2/200 2.73G 0.9505 2.288 3.178 1.424 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.016 1 0.83 0.665 0.016 1 0.83 0.615\n" + " all 3 3 0.0156 1 0.83 0.648 0.0156 1 0.83 0.626\n" ] }, { @@ -695,15 +2630,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 3/200 2.83G 0.9595 3.171 3.163 1.394 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.41it/s]" + " 3/200 2.83G 0.7481 2.647 3.072 1.264 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.70it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0149 1 0.995 0.813 0.0149 1 0.995 0.863\n" + " all 3 3 0.0135 1 0.913 0.706 0.0135 1 0.913 0.83\n" ] }, { @@ -725,15 +2660,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 4/200 2.76G 0.8419 2.756 3.12 1.332 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.42it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.01it/s]" + " 4/200 2.76G 0.7107 2.303 2.99 1.222 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.73it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0142 1 0.731 0.559 0.0142 1 0.731 0.658\n" + " all 3 3 0.012 1 0.72 0.593 0.012 1 0.72 0.598\n" ] }, { @@ -755,15 +2690,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 5/200 2.76G 0.7403 1.975 2.961 1.285 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.39it/s]" + " 5/200 2.78G 0.7299 1.833 2.877 1.219 29 640: 100%|██████████| 2/2 [00:00<00:00, 3.91it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.77it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0148 1 0.591 0.335 0.0148 1 0.618 0.482\n" + " all 3 3 0.0138 1 0.863 0.757 0.0138 1 0.863 0.794\n" ] }, { @@ -785,15 +2720,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 6/200 2.75G 0.5791 1.45 2.634 1.19 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.61it/s]" + " 6/200 2.78G 0.5273 1.158 2.513 1.083 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.41it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0168 1 0.641 0.376 0.0168 1 0.641 0.539\n" + " all 3 3 0.0169 1 0.863 0.757 0.0169 1 0.863 0.794\n" ] }, { @@ -815,15 +2750,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 7/200 2.76G 0.6529 1.048 2.372 1.132 19 640: 100%|██████████| 2/2 [00:01<00:00, 1.92it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.95it/s]\n" + " 7/200 2.78G 0.6557 1.336 2.353 1.261 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.54it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.0175 1 0.913 0.797 0.0175 1 0.913 0.814\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.019 1 0.624 0.402 0.019 1 0.624 0.546\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -832,15 +2780,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 8/200 2.73G 0.8078 1.601 2.268 1.313 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.37it/s]" + " 8/200 2.76G 0.6405 1.144 2.119 1.26 23 640: 100%|██████████| 2/2 [00:01<00:00, 1.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.32it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0208 1 0.83 0.593 0.0208 1 0.83 0.724\n" + " all 3 3 0.0199 1 0.995 0.895 0.0199 1 0.995 0.962\n" ] }, { @@ -862,15 +2810,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 9/200 2.76G 0.8734 1.444 2.121 1.336 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.73it/s]" + " 9/200 2.78G 0.7267 0.9904 1.839 1.238 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.35it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.47it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0224 1 0.863 0.638 0.0224 1 0.863 0.737\n" + " all 3 3 0.0194 1 0.995 0.841 0.0194 1 0.995 0.962\n" ] }, { @@ -892,15 +2840,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 10/200 2.78G 0.794 1.74 1.915 1.337 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.89it/s]" + " 10/200 2.78G 0.6091 0.7611 1.561 1.187 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0238 1 0.995 0.664 0.0238 1 0.995 0.786\n" + " all 3 3 0.0199 1 0.995 0.841 0.0199 1 0.995 0.962\n" ] }, { @@ -922,15 +2870,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 11/200 2.79G 0.8365 1.086 1.723 1.354 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.44it/s]" + " 11/200 2.78G 0.5849 0.9609 1.406 1.14 28 640: 100%|██████████| 2/2 [00:00<00:00, 3.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0242 1 0.995 0.751 0.0242 1 0.995 0.864\n" + " all 3 3 0.0192 1 0.995 0.858 0.0192 1 0.995 0.962\n" ] }, { @@ -952,15 +2900,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 12/200 2.76G 0.6774 1.02 1.762 1.155 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.23it/s]" + " 12/200 2.74G 0.5477 1.397 1.503 1.126 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.34it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0221 1 0.995 0.787 0.0221 1 0.995 0.94\n" + " all 3 3 0.0184 1 0.995 0.841 0.0184 1 0.995 0.962\n" ] }, { @@ -982,92 +2930,88 @@ "name": "stderr", "output_type": "stream", "text": [ - " 13/200 2.74G 0.6792 1.08 1.827 1.243 16 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.95it/s]\n" + " 13/200 2.76G 0.6779 0.7902 1.345 1.218 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.07it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0183 1 0.995 0.753 0.0183 1 0.995 0.94\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.0175 1 0.995 0.813 0.0175 1 0.995 0.962\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 14/200 2.75G 0.7153 0.8324 1.535 1.184 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", - 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" all 3 3 0.0141 1 0.995 0.763 0.0141 1 0.995 0.951\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 15/200 2.74G 0.7023 0.8258 1.409 1.316 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.0145 1 0.995 0.84 0.0145 1 0.995 0.962\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 16/200 2.74G 0.7734 1.09 1.697 1.259 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.10it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.33it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0124 1 0.995 0.73 0.0124 1 0.995 0.863\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1076,32 +3020,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 17/200 2.78G 0.8269 0.8279 1.658 1.273 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.51it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.80it/s]\n" + " 16/200 2.76G 0.697 0.6692 1.41 1.284 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0562 1 0.995 0.752 0.0562 1 0.995 0.69\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.013 1 0.995 0.88 0.013 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 18/200 2.75G 0.7636 0.7614 1.539 1.301 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.43it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.333 1 0.995 0.802 0.333 1 0.995 0.587\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1110,15 +3050,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 19/200 2.78G 0.7204 0.9516 1.419 1.197 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.20it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.87it/s]" + " 17/200 2.79G 0.6502 1.052 1.293 1.191 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.65it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00974 1 0.995 0.785 0.00974 1 0.995 0.433\n" + " all 3 3 0.0121 1 0.995 0.686 0.0121 1 0.995 0.995\n" ] }, { @@ -1140,15 +3080,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 20/200 2.78G 0.6958 1.272 1.479 1.189 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.15it/s]" + " 18/200 2.76G 0.6758 0.6604 1.322 1.227 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00888 1 0.913 0.672 0.00888 1 0.747 0.408\n" + " all 3 3 0.0109 1 0.995 0.785 0.0109 1 0.995 0.995\n" ] }, { @@ -1170,15 +3110,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 21/200 2.78G 0.6758 0.7882 1.464 1.163 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.47it/s]" + " 19/200 2.78G 0.629 0.7494 1.193 1.182 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.84it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.68it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00826 1 0.753 0.563 0.00551 0.667 0.556 0.345\n" + " all 3 3 0.873 1 0.995 0.819 0.873 1 0.995 0.951\n" ] }, { @@ -1200,15 +3140,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 22/200 2.81G 0.931 0.8528 1.548 1.456 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.24it/s]" + " 20/200 2.8G 0.5509 0.8281 1.175 1.091 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.34it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00794 1 0.995 0.741 0.00794 1 0.995 0.731\n" + " all 3 3 0.969 1 0.995 0.763 0.969 1 0.995 0.896\n" ] }, { @@ -1230,28 +3170,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 23/200 2.76G 0.883 0.8969 1.455 1.36 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.53it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.43it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " all 3 3 0.00781 1 0.995 0.796 0.00781 1 0.995 0.895\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + " 21/200 2.8G 0.5286 0.746 1.088 1.079 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.96it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.95 1 0.995 0.741 0.95 1 0.995 0.904\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1260,15 +3187,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 24/200 2.74G 0.7606 0.711 1.423 1.274 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.12it/s]" + " 22/200 2.76G 0.7352 0.9631 1.335 1.246 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00744 1 0.995 0.569 0.00744 1 0.995 0.895\n" + " all 3 3 0.939 1 0.995 0.764 0.939 1 0.995 0.895\n" ] }, { @@ -1290,15 +3217,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 25/200 2.76G 0.7699 0.7434 1.451 1.269 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.14it/s]" + " 23/200 2.78G 0.6152 0.738 1.216 1.092 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.197 1 0.995 0.569 0.197 1 0.995 0.887\n" + " all 3 3 0.968 1 0.995 0.863 0.968 1 0.995 0.83\n" ] }, { @@ -1320,15 +3247,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 26/200 2.78G 0.6797 0.7577 1.407 1.112 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.29it/s]" + " 24/200 2.76G 0.6584 0.7053 1.219 1.18 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.41it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.967 1 0.995 0.752 0.967 1 0.995 0.885\n" + " all 3 3 0.975 1 0.995 0.863 0.975 1 0.995 0.684\n" ] }, { @@ -1350,15 +3277,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 27/200 2.78G 0.5848 0.7211 1.409 1.127 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.46it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.81it/s]" + " 25/200 2.78G 0.6005 0.6082 1.197 1.148 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.39it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.976 1 0.995 0.84 0.976 1 0.995 0.929\n" + " all 3 3 0.975 1 0.995 0.847 0.975 1 0.995 0.676\n" ] }, { @@ -1380,15 +3307,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 28/200 2.77G 0.874 0.7393 1.373 1.308 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.93it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.31it/s]" + " 26/200 2.8G 0.6141 0.8144 1.342 1.114 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.38it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.82it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.976 1 0.995 0.84 0.976 1 0.995 0.929\n" + " all 3 3 0.964 1 0.995 0.83 0.964 1 0.995 0.808\n" ] }, { @@ -1410,15 +3337,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 29/200 2.76G 0.763 0.7376 1.367 1.298 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.03it/s]" + " 27/200 2.79G 0.677 0.5949 1.186 1.244 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.23it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.709 0.995 0.807 1 0.709 0.995 0.929\n" + " all 3 3 0.963 1 0.995 0.895 0.963 1 0.995 0.863\n" ] }, { @@ -1440,15 +3367,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 30/200 2.76G 0.7695 0.6632 1.434 1.225 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.29it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.34it/s]\n" + " 28/200 2.77G 0.5461 0.6246 1.188 1.064 30 640: 100%|██████████| 2/2 [00:00<00:00, 4.23it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.04it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.963 1 0.995 0.895 0.963 1 0.995 0.863\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.984 0.995 0.719 1 0.984 0.995 0.895\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1457,15 +3397,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 31/200 2.75G 0.7186 0.6687 1.374 1.205 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.71it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.17it/s]" + " 29/200 2.74G 0.5103 0.6436 1.228 1.104 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.81it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.984 0.995 0.719 1 0.984 0.995 0.895\n" + " all 3 3 0.941 1 0.995 0.895 0.941 1 0.995 0.912\n" ] }, { @@ -1487,15 +3427,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 32/200 2.78G 0.708 0.7252 1.366 1.183 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.87it/s]" + " 30/200 2.77G 0.6871 0.5663 1.134 1.259 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.12it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.708 0.979 1 0.995 0.912\n" + " all 3 3 0.979 1 0.995 0.885 0.979 1 0.995 0.962\n" ] }, { @@ -1517,58 +3457,62 @@ "name": "stderr", "output_type": "stream", "text": [ - " 33/200 2.78G 0.698 0.9198 1.265 1.175 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.97it/s]" + " 31/200 2.75G 0.6316 1.025 1.241 1.196 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.47it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.08it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.97 1 0.995 0.763 0.97 1 0.995 0.907\n" + " all 3 3 0.979 1 0.995 0.885 0.979 1 0.995 0.962\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 32/200 2.8G 0.5306 0.7407 1.11 1.06 31 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 1 1 0.995 0.852 1 1 0.995 0.912\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 34/200 2.73G 0.5212 0.6669 1.352 1.122 14 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.05it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.97 1 0.995 0.763 0.97 1 0.995 0.907\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 33/200 2.79G 0.6779 0.8553 1.202 1.222 30 640: 100%|██████████| 2/2 [00:00<00:00, 2.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.61it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 1 0.994 0.995 0.84 1 0.994 0.995 0.929\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1577,15 +3521,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 35/200 2.76G 0.6894 0.8063 1.307 1.179 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.35it/s]" + " 34/200 2.81G 0.6393 0.6588 1.134 1.207 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.975 1 0.995 0.895 0.975 1 0.995 0.895\n" + " all 3 3 1 0.994 0.995 0.84 1 0.994 0.995 0.929\n" ] }, { @@ -1607,15 +3551,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 36/200 2.77G 0.6342 0.6469 1.179 1.086 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.50it/s]" + " 35/200 2.78G 0.5601 0.7836 1.093 1.099 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.34it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.53it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.969 1 0.995 0.857 0.969 1 0.995 0.797\n" + " all 3 3 0.983 1 0.995 0.895 0.983 1 0.995 0.912\n" ] }, { @@ -1637,15 +3581,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 37/200 2.77G 0.8963 1.439 1.324 1.326 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.54it/s]" + " 36/200 2.77G 0.6071 0.6343 1.098 1.144 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.42it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.969 1 0.995 0.857 0.969 1 0.995 0.797\n" + " all 3 3 0.798 1 0.995 0.786 0.798 1 0.995 0.863\n" ] }, { @@ -1667,15 +3611,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 38/200 2.77G 0.6453 0.5816 1.252 1.081 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.66it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.21it/s]" + " 37/200 2.77G 0.5417 0.501 1.046 1.054 34 640: 100%|██████████| 2/2 [00:00<00:00, 4.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.97 1 0.995 0.83 0.97 1 0.995 0.653\n" + " all 3 3 0.798 1 0.995 0.786 0.798 1 0.995 0.863\n" ] }, { @@ -1697,15 +3641,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 39/200 2.76G 0.7483 0.8435 1.437 1.208 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.90it/s]" + " 38/200 2.78G 0.6499 0.7335 1.172 1.12 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.665 0.913 0.772 1 0.665 0.913 0.666\n" + " all 3 3 0.981 1 0.995 0.863 0.981 1 0.995 0.912\n" ] }, { @@ -1727,15 +3671,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 40/200 2.77G 0.764 0.6151 1.333 1.262 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.64it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.00it/s]" + " 39/200 2.79G 0.5988 0.7146 1.003 1.156 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.665 0.913 0.772 1 0.665 0.913 0.666\n" + " all 3 3 0.985 1 0.995 0.852 0.985 1 0.995 0.907\n" ] }, { @@ -1757,15 +3701,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 41/200 2.78G 0.7048 1.037 1.218 1.114 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.53it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.53it/s]" + " 40/200 2.77G 0.6527 0.5838 1.083 1.122 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.46it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.559 1 0.83 0.698 0.559 1 0.83 0.693\n" + " all 3 3 0.985 1 0.995 0.852 0.985 1 0.995 0.907\n" ] }, { @@ -1787,15 +3731,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 42/200 2.8G 0.6763 0.695 1.257 1.141 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.07it/s]" + " 41/200 2.8G 0.5489 0.5629 0.9834 1.037 34 640: 100%|██████████| 2/2 [00:00<00:00, 4.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.85it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.486 1 0.753 0.636 0.486 1 0.753 0.658\n" + " all 3 3 0.987 1 0.995 0.852 0.987 1 0.995 0.907\n" ] }, { @@ -1817,15 +3761,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 43/200 2.76G 0.7237 0.7255 1.244 1.193 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.97it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.96it/s]" + " 42/200 2.79G 0.5075 0.5155 0.9245 1.062 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.41it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.05it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.486 1 0.753 0.636 0.486 1 0.753 0.658\n" + " all 3 3 0.987 1 0.995 0.752 0.987 1 0.995 0.907\n" ] }, { @@ -1847,15 +3791,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 44/200 2.75G 0.8198 1.073 1.391 1.228 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.89it/s]" + " 43/200 2.77G 0.7016 0.7532 1.123 1.243 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.727 1 0.913 0.722 0.727 1 0.913 0.821\n" + " all 3 3 0.987 1 0.995 0.752 0.987 1 0.995 0.907\n" ] }, { @@ -1877,15 +3821,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 45/200 2.77G 0.8204 1.331 1.545 1.275 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.54it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.05it/s]" + " 44/200 2.77G 0.703 0.6425 1.191 1.184 31 640: 100%|██████████| 2/2 [00:00<00:00, 2.35it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.75it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.727 1 0.913 0.722 0.727 1 0.913 0.821\n" + " all 3 3 0.986 1 0.995 0.676 0.986 1 0.995 0.907\n" ] }, { @@ -1907,15 +3851,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 46/200 2.79G 0.6875 0.8194 1.49 1.173 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.80it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.27it/s]" + " 45/200 2.77G 0.6144 0.7645 1.084 1.127 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.92it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.44it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.979 0.995 0.686 1 0.979 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.676 0.986 1 0.995 0.907\n" ] }, { @@ -1937,15 +3881,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 47/200 2.75G 0.8966 1.198 1.514 1.326 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.97it/s]" + " 46/200 2.75G 0.595 0.5509 0.9787 1.164 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.36it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.11it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.979 0.995 0.686 1 0.979 0.995 0.962\n" + " all 3 3 0.994 1 0.995 0.75 0.994 1 0.995 0.871\n" ] }, { @@ -1967,15 +3911,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 48/200 2.76G 0.586 0.8328 1.18 1.214 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.69it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.52it/s]" + " 47/200 2.77G 0.7183 0.6334 1.258 1.256 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.17it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.57it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.653 0.979 1 0.995 0.995\n" + " all 3 3 0.994 1 0.995 0.75 0.994 1 0.995 0.871\n" ] }, { @@ -1997,15 +3941,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 49/200 2.77G 0.6485 0.5949 1.183 1.118 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.03it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.61it/s]\n" + " 48/200 2.79G 0.6762 0.5441 1.106 1.161 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.764 0.987 1 0.995 0.847\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.653 0.979 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2014,15 +3971,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 50/200 2.77G 0.7331 1.04 1.334 1.242 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.38it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.98it/s]" + " 49/200 2.79G 0.6396 0.7419 1.03 1.112 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.40it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.896 0.985 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.764 0.987 1 0.995 0.847\n" ] }, { @@ -2044,15 +4001,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 51/200 2.74G 0.7259 0.8039 1.293 1.195 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.86it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.63it/s]" + " 50/200 2.77G 0.6097 0.8468 1.127 1.135 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.66it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.896 0.985 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.797 0.986 1 0.995 0.895\n" ] }, { @@ -2074,15 +4031,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 52/200 2.79G 0.7508 0.9683 1.539 1.202 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.30it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.80it/s]" + " 51/200 2.75G 0.7059 0.5626 1.164 1.185 30 640: 100%|██████████| 2/2 [00:00<00:00, 4.11it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.982 1 0.995 0.912 0.982 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.797 0.986 1 0.995 0.895\n" ] }, { @@ -2104,15 +4061,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 53/200 2.75G 0.7076 0.6069 1.266 1.159 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.47it/s]" + " 52/200 2.79G 0.5749 0.6226 1.039 1.095 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.12it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.13it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.982 1 0.995 0.912 0.982 1 0.995 0.962\n" + " all 3 3 0.981 1 0.995 0.642 0.981 1 0.995 0.593\n" ] }, { @@ -2134,15 +4091,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 54/200 2.75G 0.7378 0.7723 1.246 1.216 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.95it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.44it/s]" + " 53/200 2.79G 0.5441 0.641 1.005 1.096 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.41it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 0.981 1 0.995 0.642 0.981 1 0.995 0.593\n" ] }, { @@ -2164,15 +4121,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 55/200 2.75G 0.6865 0.8221 1.171 1.182 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.42it/s]" + " 54/200 2.77G 0.6492 0.615 1.143 1.237 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.99it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.24it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 1 0.983 0.995 0.645 1 0.983 0.995 0.355\n" ] }, { @@ -2194,15 +4151,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 56/200 2.75G 0.5388 0.8311 1.103 1.099 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.52it/s]" + " 55/200 2.77G 0.6034 0.7347 1.062 1.081 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.78it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + " all 3 3 1 0.983 0.995 0.645 1 0.983 0.995 0.355\n" ] }, { @@ -2224,15 +4181,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 57/200 2.79G 0.8337 0.8106 1.37 1.229 18 640: 100%|██████████| 2/2 [00:00<00:00, 4.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.24it/s]\n" + " 56/200 2.79G 0.6689 0.7051 1.118 1.134 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.78it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 1 0.951 0.995 0.863 1 0.951 0.995 0.598\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2241,15 +4211,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 58/200 2.75G 0.8342 0.7238 1.28 1.227 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.52it/s]" + " 57/200 2.81G 0.5432 0.6506 0.8974 1.055 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.59it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.973 1 0.995 0.83 0.973 1 0.995 0.912\n" + " all 3 3 1 0.951 0.995 0.863 1 0.951 0.995 0.598\n" ] }, { @@ -2271,15 +4241,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 59/200 2.75G 0.6904 0.7557 1.099 1.099 26 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.35it/s]" + " 58/200 2.77G 0.4963 0.5577 0.9065 1.031 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.47it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.973 1 0.995 0.83 0.973 1 0.995 0.912\n" + " all 3 3 1 0.977 0.995 0.863 1 0.977 0.995 0.266\n" ] }, { @@ -2301,32 +4271,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 60/200 2.75G 0.8214 0.8577 1.239 1.26 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.75it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.45it/s]\n" + " 59/200 2.77G 0.612 0.7789 1.068 1.168 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.79it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.994 1 0.995 0.735 0.994 1 0.995 0.885\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 1 0.977 0.995 0.863 1 0.977 0.995 0.266\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 61/200 2.75G 0.7685 0.5507 1.197 1.217 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.91it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.14it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.994 1 0.995 0.735 0.994 1 0.995 0.885\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2335,15 +4301,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 62/200 2.76G 0.867 0.7007 1.298 1.374 14 640: 100%|██████████| 2/2 [00:00<00:00, 2.17it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.37it/s]" + " 60/200 2.77G 0.5792 0.6189 0.9685 1.121 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.07it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.22it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.992 1 0.995 0.84 0.992 1 0.995 0.912\n" + " all 3 3 1 0.991 0.995 0.895 0.663 0.667 0.556 0.0556\n" ] }, { @@ -2365,15 +4331,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 63/200 2.75G 0.7329 0.6407 1.103 1.182 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.39it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.86it/s]" + " 61/200 2.79G 0.6452 0.752 1.03 1.142 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.19it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.992 1 0.995 0.84 0.992 1 0.995 0.912\n" + " all 3 3 1 0.991 0.995 0.895 0.663 0.667 0.556 0.0556\n" ] }, { @@ -2395,15 +4361,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 64/200 2.77G 0.7402 0.809 1.136 1.125 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.90it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.33it/s]" + " 62/200 2.79G 0.6621 0.5832 1.21 1.145 19 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.96it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.983 0.995 0.807 1 0.983 0.995 0.929\n" + " all 3 3 0.993 1 0.995 0.929 0.993 1 0.995 0.444\n" ] }, { @@ -2425,15 +4391,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 65/200 2.76G 0.5545 0.8058 1.04 1.115 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.99it/s]" + " 63/200 2.79G 0.6145 0.846 1.018 1.134 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.13it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.63it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.983 0.995 0.807 1 0.983 0.995 0.929\n" + " all 3 3 0.993 1 0.995 0.929 0.993 1 0.995 0.444\n" ] }, { @@ -2455,15 +4421,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 66/200 2.75G 0.888 0.7873 1.457 1.297 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.62it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.26it/s]" + " 64/200 2.79G 0.6153 0.4738 1.005 1.195 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.99 1 0.995 0.818 0.99 1 0.995 0.995\n" + " all 3 3 0.994 1 0.995 0.929 0.994 1 0.995 0.687\n" ] }, { @@ -2485,15 +4451,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 67/200 2.77G 0.7276 0.6194 1.098 1.107 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.64it/s]" + " 65/200 2.77G 0.5882 0.5018 0.8927 1.172 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.33it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.04it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.99 1 0.995 0.818 0.99 1 0.995 0.995\n" + " all 3 3 0.994 1 0.995 0.929 0.994 1 0.995 0.687\n" ] }, { @@ -2515,15 +4481,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 68/200 2.75G 0.5356 0.8722 1.035 1.008 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.21it/s]" + " 66/200 2.77G 0.6998 0.7637 1.222 1.209 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.92it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.12it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.713 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.907 0.983 1 0.995 0.863\n" ] }, { @@ -2545,15 +4511,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 69/200 2.75G 0.7544 0.7592 1.393 1.254 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.92it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.93it/s]" + " 67/200 2.79G 0.6309 0.8498 0.9978 1.145 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.01it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.27it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.713 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.907 0.983 1 0.995 0.863\n" ] }, { @@ -2575,15 +4541,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 70/200 2.75G 0.6781 0.7027 1.047 1.137 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.67it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.43it/s]" + " 68/200 2.77G 0.6761 0.588 1.082 1.221 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.63it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.983 1 0.995 0.697 0.983 1 0.995 0.863\n" + " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.907\n" ] }, { @@ -2605,15 +4571,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 71/200 2.76G 0.5566 0.5015 1.118 1.096 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.36it/s]" + " 69/200 2.77G 0.6545 0.6614 1.048 1.126 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.44it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.983 1 0.995 0.697 0.983 1 0.995 0.863\n" + " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.907\n" ] }, { @@ -2635,15 +4601,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 72/200 2.81G 0.7059 0.5541 1.327 1.252 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.29it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.12it/s]" + " 70/200 2.77G 0.5807 0.634 0.9454 1.079 31 640: 100%|██████████| 2/2 [00:00<00:00, 3.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.97it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.924 1 0.995 0.682 0.924 1 0.995 0.832\n" + " all 3 3 0.985 1 0.995 0.863 0.985 1 0.995 0.962\n" ] }, { @@ -2665,15 +4631,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 73/200 2.76G 0.6503 0.4222 1.137 1.099 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.99it/s]" + " 71/200 2.77G 0.6684 0.4863 0.9834 1.231 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.12it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.924 1 0.995 0.682 0.924 1 0.995 0.832\n" + " all 3 3 0.985 1 0.995 0.863 0.985 1 0.995 0.962\n" ] }, { @@ -2695,15 +4661,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 74/200 2.77G 0.613 0.597 1.154 1.098 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.23it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.63it/s]" + " 72/200 2.77G 0.5383 0.4674 0.9989 1.108 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.90it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.799 1 0.995 0.697 0.799 1 0.995 0.83\n" + " all 3 3 0.97 1 0.995 0.929 0.97 1 0.995 0.94\n" ] }, { @@ -2725,15 +4691,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 75/200 2.77G 0.7407 0.739 1.281 1.161 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.24it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.19it/s]" + " 73/200 2.79G 0.751 0.6377 1.158 1.17 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.25it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.799 1 0.995 0.697 0.799 1 0.995 0.83\n" + " all 3 3 0.97 1 0.995 0.929 0.97 1 0.995 0.94\n" ] }, { @@ -2755,15 +4721,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 76/200 2.75G 0.7392 0.7957 1.107 1.196 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.68it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.56it/s]" + " 74/200 2.79G 0.6371 0.5049 0.8836 1.152 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.32it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.974 1 0.995 0.73 0.974 1 0.995 0.863\n" + " all 3 3 0.975 1 0.995 0.895 0.975 1 0.995 0.94\n" ] }, { @@ -2785,15 +4751,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 77/200 2.77G 0.5959 0.6653 0.9648 1.081 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.29it/s]" + " 75/200 2.75G 0.7281 0.7069 1.018 1.197 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.68it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.974 1 0.995 0.73 0.974 1 0.995 0.863\n" + " all 3 3 0.975 1 0.995 0.895 0.975 1 0.995 0.94\n" ] }, { @@ -2815,15 +4781,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 78/200 2.77G 0.6506 0.5285 1.045 1.119 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.30it/s]" + " 76/200 2.77G 0.7603 0.9357 1.084 1.261 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.00it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.895 0.983 1 0.995 0.94\n" ] }, { @@ -2845,15 +4811,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 79/200 2.81G 0.6239 0.588 1.036 1.048 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.20it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.74it/s]" + " 77/200 2.79G 0.5921 0.5092 0.8859 1.061 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.20it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.51it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.895 0.983 1 0.995 0.94\n" ] }, { @@ -2875,15 +4841,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 80/200 2.75G 0.6568 0.7361 1.037 1.155 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.16it/s]" + " 78/200 2.79G 0.577 0.4941 0.9087 1.093 28 640: 100%|██████████| 2/2 [00:00<00:00, 3.43it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.11it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.83 0.986 1 0.995 0.962\n" + " all 3 3 0.984 1 0.995 0.895 0.984 1 0.995 0.995\n" ] }, { @@ -2905,15 +4871,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 81/200 2.75G 0.6159 0.588 1.059 1.104 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.04it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.66it/s]" + " 79/200 2.77G 0.7406 0.5359 1.127 1.164 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.15it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.83 0.986 1 0.995 0.962\n" + " all 3 3 0.984 1 0.995 0.895 0.984 1 0.995 0.995\n" ] }, { @@ -2935,28 +4901,32 @@ "name": "stderr", "output_type": "stream", "text": [ - " 82/200 2.73G 0.7803 0.5306 1.307 1.284 13 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.67it/s]" + " 80/200 2.79G 0.7346 0.6198 1.153 1.306 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.07it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.714 0.987 1 0.995 0.962\n" + " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 81/200 2.75G 0.6564 0.598 1.097 1.137 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.13it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.71it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2965,15 +4935,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 83/200 2.75G 0.7378 0.7282 1.307 1.223 11 640: 100%|██████████| 2/2 [00:00<00:00, 2.65it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.05it/s]" + " 82/200 2.78G 0.6806 0.747 1.023 1.159 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.92it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.30it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.714 0.987 1 0.995 0.962\n" + " all 3 3 0.985 1 0.995 0.83 0.985 1 0.995 0.995\n" ] }, { @@ -2995,15 +4965,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 84/200 2.79G 0.7356 0.5783 1.145 1.187 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.41it/s]" + " 83/200 2.77G 0.6687 0.6102 0.9566 1.172 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.38it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.87it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.747 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.83 0.985 1 0.995 0.995\n" ] }, { @@ -3025,15 +4995,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 85/200 2.75G 0.7554 0.5599 1.178 1.238 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.48it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.14it/s]" + " 84/200 2.79G 0.7126 0.6014 0.968 1.171 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.04it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.747 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.847 0.985 1 0.995 0.995\n" ] }, { @@ -3055,15 +5025,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 86/200 2.8G 0.689 0.674 1.013 1.138 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.94it/s]\n" + " 85/200 2.77G 0.5952 0.6163 0.9467 1.089 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.07it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.985 1 0.995 0.847 0.985 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -3072,15 +5055,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 87/200 2.77G 0.7633 0.7658 1.06 1.244 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.03it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.43it/s]" + " 86/200 2.77G 0.4459 0.4924 0.8475 1.055 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n" + " all 3 3 0.937 1 0.995 0.879 0.937 1 0.995 0.995\n" ] }, { @@ -3102,15 +5085,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 88/200 2.77G 0.6823 0.4675 1.017 1.217 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.36it/s]" + " 87/200 2.79G 0.5956 0.6826 1.022 1.156 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.995\n" + " all 3 3 0.937 1 0.995 0.879 0.937 1 0.995 0.995\n" ] }, { @@ -3132,15 +5115,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 89/200 2.79G 0.5818 0.5296 0.9357 1.035 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.15it/s]" + " 88/200 2.79G 0.5529 0.6333 0.8344 1.034 32 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.995\n" + " all 3 3 0.929 1 0.995 0.863 0.929 1 0.995 0.995\n" ] }, { @@ -3162,15 +5145,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 90/200 2.8G 0.6256 0.4748 1.006 1.125 13 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.03it/s]" + " 89/200 2.81G 0.5817 0.6426 0.8719 1.108 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.52it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.06it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.94\n" + " all 3 3 0.929 1 0.995 0.863 0.929 1 0.995 0.995\n" ] }, { @@ -3192,15 +5175,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 91/200 2.75G 0.6188 0.5238 1.121 1.126 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.52it/s]" + " 90/200 2.77G 0.5982 0.5639 0.8665 1.144 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.69it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.94\n" + " all 3 3 0.873 1 0.995 0.83 0.873 1 0.995 0.995\n" ] }, { @@ -3222,15 +5205,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 92/200 2.75G 0.6475 0.5957 1.019 1.118 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.77it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.53it/s]" + " 91/200 2.75G 0.6225 0.5776 1.015 1.157 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.80it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.84 0.986 1 0.995 0.895\n" + " all 3 3 0.873 1 0.995 0.83 0.873 1 0.995 0.995\n" ] }, { @@ -3252,15 +5235,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 93/200 2.79G 0.5474 1.04 0.8371 1.03 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.58it/s]" + " 92/200 2.77G 0.4875 0.3284 0.7068 1.109 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.28it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.58it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.84 0.986 1 0.995 0.895\n" + " all 3 3 0.975 1 0.995 0.731 0.975 1 0.995 0.885\n" ] }, { @@ -3282,15 +5265,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 94/200 2.75G 0.6746 0.6793 1.066 1.171 13 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.88it/s]" + " 93/200 2.79G 0.5405 0.5058 0.8203 1.12 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.76it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.73it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.895\n" + " all 3 3 0.975 1 0.995 0.731 0.975 1 0.995 0.885\n" ] }, { @@ -3312,15 +5295,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 95/200 2.75G 0.66 0.7117 0.9496 1.131 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.45it/s]" + " 94/200 2.79G 0.5286 0.5116 0.804 1.055 31 640: 100%|██████████| 2/2 [00:00<00:00, 2.40it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.895\n" + " all 3 3 0.967 1 0.995 0.814 0.967 1 0.995 0.962\n" ] }, { @@ -3342,15 +5325,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 96/200 2.77G 0.669 0.4866 1.044 1.193 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.30it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.30it/s]" + " 95/200 2.79G 0.4865 0.4779 0.7034 1.03 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.10it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.78it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.852\n" + " all 3 3 0.967 1 0.995 0.814 0.967 1 0.995 0.962\n" ] }, { @@ -3372,15 +5355,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 97/200 2.8G 0.5996 0.4582 0.9039 1.154 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.25it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.25it/s]" + " 96/200 2.81G 0.4951 0.5934 0.816 0.9905 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.07it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.852\n" + " all 3 3 0.98 1 0.995 0.896 0.98 1 0.995 0.962\n" ] }, { @@ -3402,15 +5385,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 98/200 2.77G 0.6977 1.052 1.111 1.195 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.36it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.64it/s]" + " 97/200 2.79G 0.5855 0.5365 0.8842 1.054 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.971 1 0.995 0.852 0.971 1 0.995 0.94\n" + " all 3 3 0.98 1 0.995 0.896 0.98 1 0.995 0.962\n" ] }, { @@ -3432,15 +5415,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 99/200 2.77G 0.5432 0.5559 0.8922 1.064 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.37it/s]" + " 98/200 2.79G 0.5104 0.4402 0.7637 1.067 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.13it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.59it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.971 1 0.995 0.852 0.971 1 0.995 0.94\n" + " all 3 3 0.983 1 0.995 0.896 0.983 1 0.995 0.962\n" ] }, { @@ -3462,15 +5445,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 100/200 2.82G 0.6063 0.5036 1.015 1.061 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.65it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.18it/s]" + " 99/200 2.76G 0.6647 0.5047 0.9825 1.166 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.51it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.68it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.895 0.979 1 0.995 0.94\n" + " all 3 3 0.983 1 0.995 0.896 0.983 1 0.995 0.962\n" ] }, { @@ -3492,15 +5475,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 101/200 2.75G 0.7022 0.5224 1.057 1.146 16 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.99it/s]" + " 100/200 2.77G 0.4676 0.4558 0.7198 0.983 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.55it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.895 0.979 1 0.995 0.94\n" + " all 3 3 0.987 1 0.995 0.879 0.987 1 0.995 0.951\n" ] }, { @@ -3522,15 +5505,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 102/200 2.77G 0.6843 0.51 0.9352 1.141 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.26it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.38it/s]" + " 101/200 2.79G 0.6673 0.6273 0.9181 1.15 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.863 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.879 0.987 1 0.995 0.951\n" ] }, { @@ -3552,15 +5535,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 103/200 2.75G 0.6863 0.5164 1.049 1.209 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.32it/s]" + " 102/200 2.79G 0.4351 0.4311 0.7462 1.008 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.863 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -3582,15 +5565,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 104/200 2.79G 0.566 0.4932 0.8738 1.07 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.85it/s]" + " 103/200 2.77G 0.4994 0.6399 0.7985 1.061 30 640: 100%|██████████| 2/2 [00:00<00:00, 2.62it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.756 0.986 1 0.995 0.929\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -3612,15 +5595,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 105/200 2.75G 0.6416 0.8384 1.06 1.14 14 640: 100%|██████████| 2/2 [00:00<00:00, 4.67it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.55it/s]" + " 104/200 2.77G 0.5192 0.6322 0.7951 1.058 29 640: 100%|██████████| 2/2 [00:00<00:00, 2.52it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.35it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.756 0.986 1 0.995 0.929\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3642,28 +5625,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 106/200 2.77G 0.6143 0.5127 0.9753 1.151 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.66it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " all 3 3 0.987 1 0.995 0.758 0.987 1 0.995 0.995\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + " 105/200 2.77G 0.6023 0.6443 0.8863 1.125 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.72it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.49it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -3672,15 +5642,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 107/200 2.78G 0.7773 1.029 1.161 1.252 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.78it/s]" + " 106/200 2.79G 0.5844 0.6149 0.8273 1.087 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.19it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.758 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3702,15 +5672,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 108/200 2.75G 0.533 0.4626 0.9247 1.048 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.19it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.66it/s]" + " 107/200 2.81G 0.5319 0.5602 0.829 1.03 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.84it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3732,15 +5702,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 109/200 2.77G 0.6707 0.4935 0.9969 1.179 12 640: 100%|██████████| 2/2 [00:00<00:00, 2.39it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.43it/s]" + " 108/200 2.77G 0.5152 0.4653 0.9078 1.096 18 640: 100%|██████████| 2/2 [00:00<00:00, 4.19it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.35it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3762,15 +5732,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 110/200 2.77G 0.6729 0.4916 1.083 1.114 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.37it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.35it/s]" + " 109/200 2.79G 0.456 0.5731 0.7045 1.022 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.48it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3792,15 +5762,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 111/200 2.78G 0.6963 0.5948 1.017 1.136 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.47it/s]" + " 110/200 2.79G 0.4688 0.4529 0.7285 0.9873 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -3822,15 +5792,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 112/200 2.75G 0.6316 0.4456 0.9924 1.079 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.89it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.60it/s]" + " 111/200 2.81G 0.5863 0.627 0.87 1.082 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.62it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.97it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.852 0.987 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -3852,15 +5822,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 113/200 2.77G 0.5224 0.4967 0.8122 1.025 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.23it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.08it/s]" + " 112/200 2.77G 0.474 0.5144 0.7142 1.079 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.77it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.26it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.852 0.987 1 0.995 0.962\n" + " all 3 3 0.985 1 0.995 0.962 0.985 1 0.995 0.995\n" ] }, { @@ -3882,15 +5852,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 114/200 2.73G 0.5403 0.4416 0.9397 1.09 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.60it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.60it/s]" + " 113/200 2.79G 0.5502 0.6755 0.8016 1.046 29 640: 100%|██████████| 2/2 [00:00<00:00, 2.75it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.83it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.962 0.985 1 0.995 0.995\n" ] }, { @@ -3912,15 +5882,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 115/200 2.75G 0.5727 0.502 0.8876 1.146 13 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.51it/s]" + " 114/200 2.75G 0.6288 0.4798 0.7952 1.149 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n" ] }, { @@ -3942,58 +5912,62 @@ "name": "stderr", "output_type": "stream", "text": [ - " 116/200 2.76G 0.5485 0.5809 0.9535 1.065 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.18it/s]" + " 115/200 2.77G 0.5762 0.5305 0.8851 1.117 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.47it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.99it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.885 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 116/200 2.81G 0.5338 0.4947 0.7355 1.041 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.25it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 117/200 2.75G 0.4955 0.4099 0.8349 1.024 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.90it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.885 0.987 1 0.995 0.995\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 117/200 2.77G 0.4463 0.6745 0.7318 1.009 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.84it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4002,15 +5976,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 118/200 2.75G 0.5923 0.6652 0.9217 1.051 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.51it/s]" + " 118/200 2.75G 0.6975 0.5969 0.9205 1.25 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { @@ -4032,62 +6006,58 @@ "name": "stderr", "output_type": "stream", "text": [ - " 119/200 2.77G 0.609 0.4595 0.8211 1.128 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.58it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.09it/s]\n" + " 119/200 2.77G 0.443 0.4898 0.7086 1.021 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.92it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 120/200 2.75G 0.6231 0.5781 1.006 1.122 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.99it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.79it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 120/200 2.77G 0.5391 0.5614 0.8701 1.04 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.57it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 121/200 2.77G 0.5762 0.9087 0.905 1.08 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.69it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.65it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4096,15 +6066,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 122/200 2.75G 0.5714 0.4716 0.8216 1.058 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.68it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.20it/s]" + " 121/200 2.79G 0.4734 0.5794 0.7894 1.062 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { @@ -4126,15 +6096,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 123/200 2.75G 0.5502 0.5089 0.852 1.079 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.95it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.41it/s]" + " 122/200 2.77G 0.5546 0.4944 0.8185 1.072 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.84it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4156,15 +6126,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 124/200 2.75G 0.6137 0.4057 0.7874 1.09 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.77it/s]\n", - 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" all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4216,15 +6186,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 126/200 2.77G 0.6097 0.7312 0.8403 1.094 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.64it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.20it/s]" + " 125/200 2.75G 0.4458 0.5522 0.6966 0.9878 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.24it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4246,15 +6216,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 127/200 2.79G 0.492 0.4076 0.7375 0.9588 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", - 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" all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 129/200 2.77G 0.5816 0.5468 0.9033 1.091 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.32it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 128/200 2.77G 0.56 0.5056 0.7226 1.053 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.28it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.37it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 130/200 2.77G 0.6392 0.4588 0.7785 1.094 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.91it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.962\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4340,15 +6306,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 131/200 2.79G 0.5477 0.4129 0.8522 1.083 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.08it/s]" + " 129/200 2.79G 0.4384 0.4542 0.6742 1.021 30 640: 100%|██████████| 2/2 [00:00<00:00, 3.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4370,15 +6336,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 132/200 2.75G 0.6358 0.5506 1.036 1.174 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.58it/s]" + " 130/200 2.77G 0.7 0.7683 0.8775 1.19 27 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4400,15 +6366,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 133/200 2.75G 0.5177 0.4507 0.809 1.027 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.00it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.87it/s]" + " 131/200 2.79G 0.4507 0.3894 0.7 1.014 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.20it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.10it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4430,15 +6396,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 134/200 2.75G 0.5144 0.4483 0.8161 1.095 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.31it/s]" + " 132/200 2.77G 0.5543 0.6216 0.8271 1.096 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4460,15 +6426,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 135/200 2.71G 0.5643 0.4754 0.9486 1.186 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", - 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" all 3 3 0.985 1 0.995 0.895 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4520,15 +6486,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 137/200 2.77G 0.5833 0.4331 0.7557 1.046 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.54it/s]" + " 135/200 2.75G 0.6022 0.4884 0.8812 1.173 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.37it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.895 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4550,15 +6516,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 138/200 2.76G 0.4614 0.3644 0.7709 0.9599 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.88it/s]\n", - 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" all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4610,15 +6576,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 140/200 2.77G 0.6003 0.7076 0.959 1.12 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.38it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.36it/s]" + " 138/200 2.81G 0.4485 0.3906 0.6376 0.9531 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.68it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4640,75 +6606,75 @@ "name": "stderr", "output_type": "stream", "text": [ - " 141/200 2.77G 0.6165 0.6826 0.918 1.054 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.96it/s]" + " 139/200 2.77G 0.4332 0.4712 0.6937 1.004 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.88it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.30it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 140/200 2.79G 0.4546 0.3461 0.7288 1.014 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.51it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.14it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 142/200 2.8G 0.6499 0.604 0.8206 1.139 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.64it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.47it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 141/200 2.77G 0.4839 0.5361 0.7684 1.086 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.43it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 143/200 2.75G 0.4709 0.3645 0.8066 1.069 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.35it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.68it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4717,15 +6683,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 144/200 2.74G 0.5932 0.4871 0.817 1.147 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.26it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.49it/s]" + " 142/200 2.77G 0.5427 0.6068 0.7644 1.011 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.94 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4747,15 +6713,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 145/200 2.75G 0.5792 0.4397 0.795 1.094 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.56it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.65it/s]" + " 143/200 2.77G 0.533 1.119 0.7007 1.11 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.20it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.94 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4777,15 +6743,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 146/200 2.76G 0.5253 0.5255 0.8925 1.031 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.55it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.37it/s]" + " 144/200 2.77G 0.494 0.4664 0.6727 1.047 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4807,15 +6773,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 147/200 2.78G 0.555 0.6465 0.7055 1.026 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.41it/s]" + " 145/200 2.77G 0.482 0.4516 0.7589 1.085 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4837,15 +6803,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 148/200 2.77G 0.5269 0.4604 0.8034 1.031 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.07it/s]" + " 146/200 2.79G 0.4651 0.5891 0.6903 0.9894 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4867,15 +6833,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 149/200 2.75G 0.4203 0.3653 0.6666 1.038 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.02it/s]\n", - 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" all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -5047,15 +7017,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 155/200 2.73G 0.6319 0.4829 0.9325 1.183 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.42it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.13it/s]" + " 154/200 2.79G 0.4127 0.5978 0.6627 0.9756 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.45it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -5077,15 +7047,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 156/200 2.74G 0.4604 0.4138 0.7124 0.9908 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", - 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" all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5124,15 +7107,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 158/200 2.77G 0.4502 0.3701 0.6804 0.9987 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.32it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.44it/s]" + " 157/200 2.79G 0.4172 0.4201 0.6559 0.9819 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5154,15 +7137,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 159/200 2.77G 0.4283 0.3652 0.6534 1.01 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - 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" all 3 3 0.986 1 0.995 0.885 0.986 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5214,15 +7197,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 161/200 2.82G 0.466 0.5268 0.7672 0.9704 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.88it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]" + " 160/200 2.77G 0.365 0.3852 0.5683 1.012 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.02it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.40it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.885 0.986 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5244,15 +7227,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 162/200 2.77G 0.6334 0.6523 0.8408 1.081 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.55it/s]\n", - 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" all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.951\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -5291,15 +7287,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 164/200 2.77G 0.573 0.915 0.8181 1.085 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.12it/s]" + " 163/200 2.77G 0.3872 0.4061 0.5936 1.015 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.82it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.50it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.962\n" ] }, { @@ -5321,15 +7317,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 165/200 2.75G 0.5735 0.3785 0.8579 1.089 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.83it/s]\n", - 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" all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.962\n" ] }, { @@ -5381,15 +7377,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 167/200 2.77G 0.4522 0.4865 0.6884 0.9968 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.25it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.86it/s]" + " 166/200 2.79G 0.3455 0.4332 0.6185 0.9756 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.55it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5411,15 +7407,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 168/200 2.77G 0.5797 0.5913 0.7426 1.044 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.66it/s]\n", - 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" all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5471,15 +7467,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 170/200 2.75G 0.5183 0.3331 0.6984 1.099 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.51it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.62it/s]" + " 169/200 2.77G 0.5542 0.5127 0.7711 1.14 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.26it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.22it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5501,15 +7497,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 171/200 2.75G 0.4504 0.4429 0.6613 1.007 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.84it/s]\n", - 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" all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5561,15 +7557,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 173/200 2.77G 0.6181 0.4741 0.9122 1.09 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.67it/s]" + " 172/200 2.77G 0.4577 0.5669 0.7086 1.033 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5591,15 +7587,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 174/200 2.75G 0.4773 0.5172 0.7039 1.005 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.69it/s]\n", - 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" all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5651,15 +7647,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 176/200 2.75G 0.5011 0.4729 0.6648 1.049 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.37it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.79it/s]\n" + " 175/200 2.77G 0.4155 0.428 0.6205 1.011 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.59it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -5668,15 +7677,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 177/200 2.79G 0.4915 0.4244 0.7123 1.048 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.88it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.67it/s]" + " 176/200 2.77G 0.4463 0.3981 0.6633 1.043 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.60it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5698,15 +7707,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 178/200 2.77G 0.5064 0.4514 0.6745 1.077 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.15it/s]" + " 177/200 2.81G 0.4065 0.3935 0.6349 0.99 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5728,15 +7737,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 179/200 2.75G 0.4005 0.3353 0.5836 1.008 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.12it/s]" + " 178/200 2.82G 0.5287 0.4907 0.6616 1.091 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.02it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5758,15 +7767,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 180/200 2.75G 0.6025 0.4374 0.7449 1.085 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - 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" all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5818,15 +7827,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 182/200 2.75G 0.5816 0.572 0.8252 1.094 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.56it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.32it/s]" + " 181/200 2.77G 0.4164 0.4149 0.619 1.018 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.35it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5848,15 +7857,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 183/200 2.74G 0.4526 0.5938 0.6959 1.008 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.77it/s]\n", - 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" all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5908,15 +7917,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 185/200 2.75G 0.4407 0.4098 0.7066 1.002 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.76it/s]" + " 184/200 2.77G 0.4282 0.6303 0.7407 1.015 30 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.49it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5938,15 +7947,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 186/200 2.73G 0.6075 0.6999 0.8587 1.085 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.54it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.49it/s]" + " 185/200 2.77G 0.4778 0.4118 0.7255 1.081 29 640: 100%|██████████| 2/2 [00:00<00:00, 3.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5968,15 +7977,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 187/200 2.75G 0.5467 0.4813 0.8547 1.099 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.77it/s]" + " 186/200 2.77G 0.4588 0.5811 0.6453 1.073 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5998,15 +8007,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 188/200 2.78G 0.4071 0.3879 0.6083 0.9642 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.20it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.28it/s]" + " 187/200 2.77G 0.3905 0.4229 0.6209 1 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.91it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.58it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -6028,15 +8037,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 189/200 2.77G 0.4637 0.4227 0.5746 1.018 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.26it/s]\n" + " 188/200 2.79G 0.4073 0.3619 0.5942 0.9655 30 640: 100%|██████████| 2/2 [00:00<00:00, 2.32it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.99it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n", + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6045,15 +8054,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 190/200 2.73G 0.6052 0.622 0.7479 1.1 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.14it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.71it/s]" + " 189/200 2.77G 0.388 0.3389 0.5601 0.9484 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -6067,8 +8076,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Closing dataloader mosaic\n", - "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6077,15 +8084,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 191/200 2.67G 0.2912 0.2252 0.8512 0.9826 7 640: 100%|██████████| 2/2 [00:01<00:00, 1.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 3.58it/s]" + " 190/200 2.75G 0.3754 0.4782 0.5723 1.004 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6099,6 +8106,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "Closing dataloader mosaic\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6107,15 +8116,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 192/200 2.68G 0.3667 0.3339 0.943 1.032 7 640: 100%|██████████| 2/2 [00:00<00:00, 3.18it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.43it/s]" + " 191/200 2.75G 0.3184 0.1912 1.009 1.012 9 640: 100%|██████████| 2/2 [00:00<00:00, 2.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.45it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6137,15 +8146,32 @@ "name": "stderr", "output_type": "stream", "text": [ - " 193/200 2.67G 0.4169 0.2416 1.138 1.108 6 640: 100%|██████████| 2/2 [00:00<00:00, 4.17it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.34it/s]" + " 192/200 2.7G 0.4175 0.2957 1.168 1.075 9 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.88it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 193/200 2.75G 0.2685 0.2553 0.8985 0.9084 10 640: 100%|██████████| 2/2 [00:00<00:00, 4.47it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.65it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6167,15 +8193,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 194/200 2.68G 0.34 0.4287 0.8749 0.9711 7 640: 100%|██████████| 2/2 [00:00<00:00, 3.80it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.07it/s]" + " 194/200 2.75G 0.4466 0.3397 1.222 1.162 10 640: 100%|██████████| 2/2 [00:00<00:00, 4.33it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.78it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6197,15 +8223,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 195/200 2.67G 0.2565 0.2773 0.7892 0.9078 7 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.58it/s]" + " 195/200 2.75G 0.2575 0.2706 0.8466 0.9785 8 640: 100%|██████████| 2/2 [00:00<00:00, 4.58it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.34it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6227,15 +8253,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 196/200 2.68G 0.357 0.248 1.021 1.044 6 640: 100%|██████████| 2/2 [00:00<00:00, 2.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.86it/s]" + " 196/200 2.7G 0.247 0.257 0.7281 0.9132 10 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6257,15 +8283,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 197/200 2.72G 0.3966 0.2981 1.045 1.056 7 640: 100%|██████████| 2/2 [00:00<00:00, 2.97it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.43it/s]\n" + " 197/200 2.78G 0.3157 0.3094 0.9316 0.9796 10 640: 100%|██████████| 2/2 [00:00<00:00, 3.62it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6274,15 +8300,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 198/200 2.68G 0.3017 0.3045 0.8399 0.8772 8 640: 100%|██████████| 2/2 [00:00<00:00, 2.44it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.53it/s]\n" + " 198/200 2.7G 0.3052 0.2678 0.8774 1.013 8 640: 100%|██████████| 2/2 [00:00<00:00, 2.73it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.64it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6291,8 +8330,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " 199/200 2.67G 0.31 0.2672 0.8157 1.029 8 640: 100%|██████████| 2/2 [00:00<00:00, 2.41it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.49it/s]" + " 199/200 2.75G 0.2492 0.2917 0.7622 0.8656 9 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.34it/s]" ] }, { @@ -6321,8 +8360,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " 200/200 2.68G 0.4499 0.263 1.007 1.119 6 640: 100%|██████████| 2/2 [00:00<00:00, 2.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.43it/s]" + " 200/200 2.7G 0.2499 0.3162 0.7796 0.9195 9 640: 100%|██████████| 2/2 [00:00<00:00, 2.68it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.15it/s]" ] }, { @@ -6344,12 +8383,12 @@ "output_type": "stream", "text": [ "\n", - "200 epochs completed in 0.093 hours.\n", - "Optimizer stripped from runs/segment/train4/weights/last.pt, 6.8MB\n", - "Optimizer stripped from runs/segment/train4/weights/best.pt, 6.8MB\n", + "200 epochs completed in 0.086 hours.\n", + "Optimizer stripped from runs/segment/train/weights/last.pt, 6.8MB\n", + "Optimizer stripped from runs/segment/train/weights/best.pt, 6.8MB\n", "\n", - "Validating runs/segment/train4/weights/best.pt...\n", - "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Validating runs/segment/train/weights/best.pt...\n", + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" ] }, @@ -6357,33 +8396,19 @@ "name": "stderr", "output_type": "stream", "text": [ - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.38it/s]\n" + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 21.43it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", - " tractor 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", - "Speed: 0.3ms preprocess, 9.0ms inference, 0.0ms loss, 1.7ms postprocess per image\n", - "Results saved to \u001b[1mruns/segment/train4\u001b[0m\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n", + " tractor 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n", + "Speed: 0.3ms preprocess, 5.2ms inference, 0.0ms loss, 1.5ms postprocess per image\n", + "Results saved to \u001b[1mruns/segment/train\u001b[0m\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "73419dc7b5a942bbbdbfe9e0a2552c23", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.000 MB of 0.000 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -6392,7 +8417,7 @@ " .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n", " .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n", " \n", - "

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Run summary:


lr/pg02e-05
lr/pg12e-05
lr/pg22e-05
metrics/mAP50(B)0.995
metrics/mAP50(M)0.995
metrics/mAP50-95(B)0.96208
metrics/mAP50-95(M)0.995
metrics/precision(B)0.98676
metrics/precision(M)0.98676
metrics/recall(B)1
metrics/recall(M)1
model/GFLOPs12.111
model/parameters3264201
model/speed_PyTorch(ms)141.034
train/box_loss0.44995
train/cls_loss1.00653
train/dfl_loss1.1189
train/seg_loss0.26298
val/box_loss0.39441
val/cls_loss0.50032
val/dfl_loss1.07174
val/seg_loss0.44151

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Run summary:


lr/pg02e-05
lr/pg12e-05
lr/pg22e-05
metrics/mAP50(B)0.995
metrics/mAP50(M)0.995
metrics/mAP50-95(B)0.995
metrics/mAP50-95(M)0.995
metrics/precision(B)0.98703
metrics/precision(M)0.98703
metrics/recall(B)1
metrics/recall(M)1
model/GFLOPs12.111
model/parameters3264201
model/speed_PyTorch(ms)136.898
train/box_loss0.24987
train/cls_loss0.77958
train/dfl_loss0.91948
train/seg_loss0.31617
val/box_loss0.36786
val/cls_loss0.56683
val/dfl_loss1.16621
val/seg_loss0.32534

" ], "text/plain": [ "" @@ -6404,7 +8429,7 @@ { "data": { "text/html": [ - "You can sync this run to the cloud by running:
wandb sync /content/wandb/offline-run-20241021_000800-nado7ubu" + "You can sync this run to the cloud by running:
wandb sync /content/wandb/offline-run-20241024_203139-ozs9fgrx" ], "text/plain": [ "" @@ -6416,7 +8441,7 @@ { "data": { "text/html": [ - "Find logs at: ./wandb/offline-run-20241021_000800-nado7ubu/logs" + "Find logs at: ./wandb/offline-run-20241024_203139-ozs9fgrx/logs" ], "text/plain": [ "" @@ -6453,12 +8478,12 @@ "height": 1000 }, "id": "b559b1f9", - "outputId": "b2389b7d-a09c-4099-ba0d-6c722e267a06" + "outputId": "bcb3fae6-27eb-4384-f532-c573ae45c599" }, "outputs": [ { "data": { - "image/jpeg": 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"text/plain": [ "" ] @@ -6481,14 +8506,14 @@ "base_uri": "https://localhost:8080/" }, "id": "dec0cb11", - "outputId": "fca326c9-c7e1-4767-8e07-383c971a89eb" + "outputId": "72cf4330-fa0f-47aa-82c5-242dc6978dcd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" ] }, @@ -6497,17 +8522,17 @@ "output_type": "stream", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00 Date: Thu, 24 Oct 2024 23:02:47 +0200 Subject: [PATCH 31/56] Update tests --- tests/core_tests/unittests/test_annotators.py | 4 ++++ .../unittests/test_image_generation.py | 16 ---------------- .../unittests/test_image_generation_heavy.py | 16 ++++++++++++++++ 3 files changed, 20 insertions(+), 16 deletions(-) diff --git a/tests/core_tests/unittests/test_annotators.py b/tests/core_tests/unittests/test_annotators.py index 4f6db02..48964fa 100644 --- a/tests/core_tests/unittests/test_annotators.py +++ b/tests/core_tests/unittests/test_annotators.py @@ -66,6 +66,8 @@ def _check_clip_annotator(device: str, size: str = "base"): # Check that the labels are ndarray of integers assert isinstance(labels[0], np.ndarray) and labels[0].dtype == np.int64 + annotator.release(empty_cuda_cache=True if device != "cpu" else False) + @pytest.mark.skipif( not torch.cuda.is_available() or total_disk_space < 16, @@ -118,6 +120,8 @@ def _check_slimsam_annotator(device: str, size: str = "base"): assert len(point) == 2 assert 0 <= point[0] <= w and 0 <= point[1] <= h + annotator.release(empty_cuda_cache=True if device != "cpu" else False) + @pytest.mark.skipif( not torch.cuda.is_available() or total_disk_space < 16, diff --git a/tests/core_tests/unittests/test_image_generation.py b/tests/core_tests/unittests/test_image_generation.py index 2436f75..6ff15ef 100644 --- a/tests/core_tests/unittests/test_image_generation.py +++ b/tests/core_tests/unittests/test_image_generation.py @@ -111,19 +111,3 @@ def test_cuda_sdxl_turbo_image_generator(): ) def test_cpu_sdxl_turbo_image_generator(): _check_image_generator(StableDiffusionTurboImageGenerator, "cpu") - - -@pytest.mark.skipif( - not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25, - reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD", -) -def test_cuda_sdxl_lightning_image_generator(): - _check_image_generator(StableDiffusionLightningImageGenerator, "cuda") - - -@pytest.mark.skipif( - total_memory < 16 or total_disk_space < 25, - reason="Test requires at least 16GB of RAM and 25GB of HDD", -) -def test_cpu_sdxl_lightning_image_generator(): - _check_image_generator(StableDiffusionLightningImageGenerator, "cpu") diff --git a/tests/heavy_tests/unittests/test_image_generation_heavy.py b/tests/heavy_tests/unittests/test_image_generation_heavy.py index 30141cc..49721f7 100644 --- a/tests/heavy_tests/unittests/test_image_generation_heavy.py +++ b/tests/heavy_tests/unittests/test_image_generation_heavy.py @@ -66,3 +66,19 @@ def test_cuda_sdxl_image_generator(): ) def test_cpu_sdxl_image_generator(): _check_image_generator(StableDiffusionImageGenerator, "cpu") + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25, + reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD", +) +def test_cuda_sdxl_lightning_image_generator(): + _check_image_generator(StableDiffusionLightningImageGenerator, "cuda") + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 25, + reason="Test requires at least 16GB of RAM and 25GB of HDD", +) +def test_cpu_sdxl_lightning_image_generator(): + _check_image_generator(StableDiffusionLightningImageGenerator, "cpu") From ff771ad37177c652defd4e602f1503c104351234 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Thu, 24 Oct 2024 23:07:11 +0200 Subject: [PATCH 32/56] Fix: annotator tests --- tests/core_tests/unittests/test_annotators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/core_tests/unittests/test_annotators.py b/tests/core_tests/unittests/test_annotators.py index 48964fa..7bd024b 100644 --- a/tests/core_tests/unittests/test_annotators.py +++ b/tests/core_tests/unittests/test_annotators.py @@ -8,8 +8,8 @@ from PIL import Image from datadreamer.dataset_annotation.clip_annotator import CLIPAnnotator -from datadreamer.dataset_annotation.fastsam_annotator import SlimSAMAnnotator from datadreamer.dataset_annotation.owlv2_annotator import OWLv2Annotator +from datadreamer.dataset_annotation.slimsam_annotator import SlimSAMAnnotator # Get the total disk space in GB total_disk_space = psutil.disk_usage("/").total / (1024**3) From 335cc058c4ec9783e37ffd879420aa29e43b89f5 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Thu, 24 Oct 2024 21:27:23 +0000 Subject: [PATCH 33/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 2fad913..179c6a1 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -15,7 +15,7 @@ coverage coverage - 62% - 62% + 63% + 63% From f887910fba7b1bafb834abcc1aeda50d3fb05441 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 10:43:38 +0200 Subject: [PATCH 34/56] Update docs & luxonis dataset creation --- README.md | 2 +- datadreamer/utils/luxonis_dataset_converter.py | 5 +++++ requirements.txt | 2 +- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f08632d..9fd4745 100644 --- a/README.md +++ b/README.md @@ -281,7 +281,7 @@ save_dir/ { "image_path": { "boxes": [[x_min, y_min, x_max, y_max], ...], - "masks": [[x0, y0], [x1, y1], ...] + "masks": [[[x0, y0],[x1, y1],...], [[x0, y0],[x1, y1],...], ....] "labels": [label_index, ...] }, ... diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 64f4968..8462ea1 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -28,6 +28,11 @@ def __init__( self.dataset_plugin = dataset_plugin self.dataset_name = dataset_name + if self.is_instance_segmentation: + logger.warning( + "Instance segmentation will be treated as semantic segmentation until the support for instance segmentation is added to Luxonis-ml." + ) + def convert( self, dataset_dir: str, diff --git a/requirements.txt b/requirements.txt index 3afd902..2ab6b13 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,6 @@ accelerate>=0.25.0 scipy>=1.10.0 bitsandbytes>=0.42.0 nltk>=3.8.1 -luxonis-ml[all]>=0.4.0 +luxonis-ml[all]>=0.4.1 python-box>=7.1.1 gcsfs>=2023.1.0 \ No newline at end of file From b8151cbee76a4246eb8d9384b960fa836209c5d8 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Fri, 25 Oct 2024 10:26:41 +0000 Subject: [PATCH 35/56] fix: return SliamSAM processor --- datadreamer/dataset_annotation/slimsam_annotator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datadreamer/dataset_annotation/slimsam_annotator.py b/datadreamer/dataset_annotation/slimsam_annotator.py index b22c807..520a2e1 100644 --- a/datadreamer/dataset_annotation/slimsam_annotator.py +++ b/datadreamer/dataset_annotation/slimsam_annotator.py @@ -68,7 +68,7 @@ def _init_processor(self) -> SamProcessor: SamProcessor: The initialized processor. """ if self.size == "large": - SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-50") + return SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-50") return SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-77") def annotate_batch( From af08e4b8dd9a922b4aa5c5114517bc0bbb2c3fd3 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Fri, 25 Oct 2024 11:09:15 +0000 Subject: [PATCH 36/56] fix: handle empty polygon list --- datadreamer/dataset_annotation/slimsam_annotator.py | 3 ++- datadreamer/dataset_annotation/utils.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/datadreamer/dataset_annotation/slimsam_annotator.py b/datadreamer/dataset_annotation/slimsam_annotator.py index 520a2e1..3461f35 100644 --- a/datadreamer/dataset_annotation/slimsam_annotator.py +++ b/datadreamer/dataset_annotation/slimsam_annotator.py @@ -122,7 +122,8 @@ def annotate_batch( final_masks = (final_masks > 0).int() final_masks = final_masks.numpy().astype(np.uint8) polygon = mask_to_polygon(final_masks) - image_masks.append(polygon) + if len(polygon) != 0: + image_masks.append(polygon) final_segments.append(image_masks) diff --git a/datadreamer/dataset_annotation/utils.py b/datadreamer/dataset_annotation/utils.py index 499363c..dd6b643 100644 --- a/datadreamer/dataset_annotation/utils.py +++ b/datadreamer/dataset_annotation/utils.py @@ -49,7 +49,8 @@ def mask_to_polygon(mask: np.ndarray) -> List[List[int]]: contours, _ = cv2.findContours( mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE ) - + if len(contours) == 0: + return [] # Find the contour with the largest area largest_contour = max(contours, key=cv2.contourArea) From c566beab0b33aadb9eeed5e103f59ff0cdb71194 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 13:41:35 +0200 Subject: [PATCH 37/56] Fix: remove long outputs from Jupyter Notebook --- ..._segmentation_dataset_and_train_yolo.ipynb | 8173 +---------------- 1 file changed, 8 insertions(+), 8165 deletions(-) diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index a7a5d30..1dbd2d5 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b5_2ivH03etO", "metadata": { "colab": { @@ -23,56 +23,7 @@ "id": "b5_2ivH03etO", "outputId": "c92b1e2e-cd3e-4a7d-8be6-776e0dfad5bc" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.8/168.8 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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This behaviour is the source of the following dependency conflicts.\n", - "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.3 which is incompatible.\n", - "google-cloud-firestore 2.16.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.3 which is incompatible.\n", - "tensorboard 2.17.0 requires protobuf!=4.24.0,<5.0.0,>=3.19.6, but you have protobuf 5.28.3 which is incompatible.\n", - "tensorflow 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.28.3 which is incompatible.\n", - "tensorflow-metadata 1.16.1 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.28.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], + "outputs": [], "source": [ "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@dev" ] @@ -103,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "6ab1e2f9", "metadata": { "colab": { @@ -113,2062 +64,7 @@ "outputId": "6f57eb7a-f261-46bc-e574-3631cade8660", "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-10-24 20:24:16.241793: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-10-24 20:24:16.272474: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-10-24 20:24:16.282212: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2024-10-24 20:24:16.304239: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-10-24 20:24:17.906040: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "\u001b[32mINFO \u001b[0m Profanity filter is checking classes: \u001b[1m[\u001b[0m\u001b[32m'tractor'\u001b[0m, \u001b[32m'horse'\u001b[0m, \u001b[32m'bear'\u001b[0m\u001b[1m]\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py\u001b\\\u001b[2mprofanity_filter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py#170\u001b\\\u001b[2m170\u001b[0m\u001b]8;;\u001b\\\n", - "\u001b[32mINFO \u001b[0m Initializing SDXL Turbo on cuda\u001b[33m...\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py\u001b\\\u001b[2msdxl_turbo_image_generator.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py#42\u001b\\\u001b[2m42\u001b[0m\u001b]8;;\u001b\\\n", - "model_index.json: 100% 685/685 [00:00<00:00, 4.24MB/s]\n", - "Fetching 18 files: 0% 0/18 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "W&B syncing is set to `offline` in this directory.
Run `wandb online` or set WANDB_MODE=online to enable cloud syncing." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Freezing layer 'model.22.dfl.conv.weight'\n", - "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", - "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5.35M/5.35M [00:00<00:00, 102MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/generated_dataset_yolo/train/labels... 24 images, 0 backgrounds, 0 corrupt: 100%|██████████| 24/24 [00:00<00:00, 1156.29it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/generated_dataset_yolo/train/labels.cache\n", - "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<00:00, 610.38it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/generated_dataset_yolo/val/labels.cache\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting labels to runs/segment/train/labels.jpg... \n", - "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", - "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001429, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n", - "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n", - "Image sizes 640 train, 640 val\n", - "Using 2 dataloader workers\n", - "Logging results to \u001b[1mruns/segment/train\u001b[0m\n", - "Starting training for 200 epochs...\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 1/200 2.81G 0.9583 3.042 3.096 1.435 24 640: 100%|██████████| 2/2 [00:04<00:00, 2.23s/it]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.71it/s]" - ] - }, - { - "name": "stdout", - 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Run summary:


lr/pg02e-05
lr/pg12e-05
lr/pg22e-05
metrics/mAP50(B)0.995
metrics/mAP50(M)0.995
metrics/mAP50-95(B)0.995
metrics/mAP50-95(M)0.995
metrics/precision(B)0.98703
metrics/precision(M)0.98703
metrics/recall(B)1
metrics/recall(M)1
model/GFLOPs12.111
model/parameters3264201
model/speed_PyTorch(ms)136.898
train/box_loss0.24987
train/cls_loss0.77958
train/dfl_loss0.91948
train/seg_loss0.31617
val/box_loss0.36786
val/cls_loss0.56683
val/dfl_loss1.16621
val/seg_loss0.32534

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "You can sync this run to the cloud by running:
wandb sync /content/wandb/offline-run-20241024_203139-ozs9fgrx" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: ./wandb/offline-run-20241024_203139-ozs9fgrx/logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import os\n", "os.environ['WANDB_DISABLED'] = 'true'\n", From 07a58f0026589c5951c86cd7cb5be57badbe188f Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 13:48:09 +0200 Subject: [PATCH 38/56] Fix: README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9fd4745..c3b9464 100644 --- a/README.md +++ b/README.md @@ -157,7 +157,7 @@ datadreamer --config ### 🔧 Additional Parameters -- `--task`: Choose between detection, classification, instance segmentation and semantic segmentation. Default is `detection`. +- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`. - `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`. - `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`. - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3. From 057a9b47fc0c3f6b006655044677d107f8f146a8 Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 13:51:33 +0200 Subject: [PATCH 39/56] Add OWLv2 non-square pixel fix --- .../dataset_annotation/owlv2_annotator.py | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index 89d4023..6a2068a 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -236,6 +236,22 @@ def annotate_batch( torch.cat(all_labels), num_classes=len(prompts) ) + # Fix the bounding boxes + width_ratio = 1 + height_ratio = 1 + width = images[i].width + height = images[i].height + if width > height: + height_ratio = height / width + elif height > width: + width_ratio = width / height + + all_boxes = [ + box + / torch.tensor([width_ratio, height_ratio, width_ratio, height_ratio]) + for box in all_boxes + ] + # Apply NMS # transform predictions to shape [N, 5 + num_classes], N is the number of bboxes for nms function all_boxes_cat = torch.cat( @@ -294,8 +310,8 @@ def release(self, empty_cuda_cache: bool = False) -> None: url = "https://ultralytics.com/images/bus.jpg" im = Image.open(requests.get(url, stream=True).raw) - annotator = OWLv2Annotator(device="cpu", size="large") + annotator = OWLv2Annotator(device="cpu", size="base") final_boxes, final_scores, final_labels = annotator.annotate_batch( - [im], ["robot", "horse"] + [im], ["bus", "person"] ) annotator.release() From 437d067f3ece97e093e81fcf9c142e5a1a7f39af Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 13:58:09 +0200 Subject: [PATCH 40/56] Rename vars --- datadreamer/pipelines/generate_dataset_from_scratch.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index bed6d72..a8fabe0 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -59,7 +59,7 @@ det_annotators = {"owlv2": OWLv2Annotator} clf_annotators = {"clip": CLIPAnnotator} inst_seg_annotators = {"owlv2-slimsam": SlimSAMAnnotator} -inst_seg_to_det = {"owlv2-slimsam": OWLv2Annotator} +inst_seg_detectors = {"owlv2-slimsam": OWLv2Annotator} setup_logging(use_rich=True) @@ -606,7 +606,7 @@ def read_image_batch(image_batch, batch_num, batch_size): if args.task == "detection": annotator_class = det_annotators[args.image_annotator] else: - annotator_class = inst_seg_to_det[args.image_annotator] + annotator_class = inst_seg_detectors[args.image_annotator] inst_seg_annotator_class = inst_seg_annotators[args.image_annotator] inst_seg_annotator = inst_seg_annotator_class(device=args.device) annotator = annotator_class(device=args.device, size=args.annotator_size) From cd819c419e2b9da1fbaf963f83a784cbcb3b01ea Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 14:44:49 +0200 Subject: [PATCH 41/56] Fix: correct all SlimSAM mentions --- datadreamer/dataset_annotation/slimsam_annotator.py | 2 +- examples/generate_dataset_and_train_yolo.ipynb | 2 +- ...ate_instance_segmentation_dataset_and_train_yolo.ipynb | 2 +- tests/core_tests/unittests/test_annotators.py | 8 ++++---- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/datadreamer/dataset_annotation/slimsam_annotator.py b/datadreamer/dataset_annotation/slimsam_annotator.py index 3461f35..7e4e7cd 100644 --- a/datadreamer/dataset_annotation/slimsam_annotator.py +++ b/datadreamer/dataset_annotation/slimsam_annotator.py @@ -77,7 +77,7 @@ def annotate_batch( boxes_batch: List[np.ndarray], iou_threshold: float = 0.2, ) -> List[List[List[float]]]: - """Annotates images for the task of instance segmentation using the FastSAM + """Annotates images for the task of instance segmentation using the SlimSAM model. Args: diff --git a/examples/generate_dataset_and_train_yolo.ipynb b/examples/generate_dataset_and_train_yolo.ipynb index dbbf376..9ad1359 100644 --- a/examples/generate_dataset_and_train_yolo.ipynb +++ b/examples/generate_dataset_and_train_yolo.ipynb @@ -84,7 +84,7 @@ "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n", "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 1dbd2d5..11f9812 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -99,7 +99,7 @@ "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n", "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", diff --git a/tests/core_tests/unittests/test_annotators.py b/tests/core_tests/unittests/test_annotators.py index 7bd024b..4e78df2 100644 --- a/tests/core_tests/unittests/test_annotators.py +++ b/tests/core_tests/unittests/test_annotators.py @@ -127,7 +127,7 @@ def _check_slimsam_annotator(device: str, size: str = "base"): not torch.cuda.is_available() or total_disk_space < 16, reason="Test requires GPU and 16GB of HDD", ) -def test_cuda_fastsam_base_annotator(): +def test_cuda_slimsam_base_annotator(): _check_slimsam_annotator("cuda") @@ -135,7 +135,7 @@ def test_cuda_fastsam_base_annotator(): total_disk_space < 16, reason="Test requires at least 16GB of HDD", ) -def test_cpu_fastsam_base_annotator(): +def test_cpu_slimsam_base_annotator(): _check_slimsam_annotator("cpu") @@ -143,7 +143,7 @@ def test_cpu_fastsam_base_annotator(): not torch.cuda.is_available() or total_disk_space < 16, reason="Test requires GPU and 16GB of HDD", ) -def test_cuda_fastsam_large_annotator(): +def test_cuda_slimsam_large_annotator(): _check_slimsam_annotator("cuda", size="large") @@ -151,5 +151,5 @@ def test_cuda_fastsam_large_annotator(): total_disk_space < 16, reason="Test requires at least 16GB of HDD", ) -def test_cpu_fastsam_large_annotator(): +def test_cpu_slimsam_large_annotator(): _check_slimsam_annotator("cpu", size="large") From 5e4534794b80b3786a406ed008b797896bbbba30 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Fri, 25 Oct 2024 12:57:39 +0000 Subject: [PATCH 42/56] fix: different image sizes for owlv2 postprocessing --- datadreamer/dataset_annotation/owlv2_annotator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index 6a2068a..8d9eeed 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -98,7 +98,7 @@ def _generate_annotations( """ n = len(images) batched_prompts = [prompts] * n - target_sizes = torch.Tensor(images[0].size[::-1]).repeat((n, 1)).to(self.device) + target_sizes = torch.Tensor([img.size[::-1] for img in images]).to(self.device) # resize the images to the model's input size img_size = (1008, 1008) if self.size == "large" else (960, 960) From 3b915baf7ea3047a9d765aadbaa165490732178c Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Fri, 25 Oct 2024 16:16:52 +0200 Subject: [PATCH 43/56] Update OWLv2 bbox correction --- .../dataset_annotation/owlv2_annotator.py | 49 +++++++++++++------ 1 file changed, 33 insertions(+), 16 deletions(-) diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index 8d9eeed..d375e80 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -154,6 +154,34 @@ def _get_annotations( return boxes, scores, labels + def _correct_bboxes_misalignment( + self, input_boxes: List[torch.Tensor], width: int, height: int + ) -> List[torch.Tensor]: + """This function corrects the bounding boxes misalignment appearing when using + the `transformers==4.45.2`. + + Problem description: With a non-square aspect ratio, the predictions are shifted in the smaller dimension. + Solution: https://discuss.huggingface.co/t/owl-v2-bounding-box-misalignment-problem/66181 + + Args: + input_boxes (List[torch.Tensor]): The bounding boxes to be corrected. + width (int): The width of the image. + height (int): The height of the image. + + Returns: + List[torch.Tensor]: The corrected bounding boxes. + """ + width_ratio = 1 + height_ratio = 1 + if width > height: + height_ratio = height / width + elif height > width: + width_ratio = width / height + return [ + box * torch.tensor([width_ratio, height_ratio, width_ratio, height_ratio]) + for box in input_boxes + ] + def annotate_batch( self, images: List[PIL.Image.Image], @@ -218,6 +246,11 @@ def annotate_batch( all_scores = [scores.to("cpu")] all_labels = [labels.to("cpu")] + # Fix the bounding boxes misalignment + all_boxes = self._correct_bboxes_misalignment( + all_boxes, images[i].width, images[i].height + ) + # Flip boxes back if using TTA if use_tta: aug_boxes, aug_scores, aug_labels = self._get_annotations( @@ -236,22 +269,6 @@ def annotate_batch( torch.cat(all_labels), num_classes=len(prompts) ) - # Fix the bounding boxes - width_ratio = 1 - height_ratio = 1 - width = images[i].width - height = images[i].height - if width > height: - height_ratio = height / width - elif height > width: - width_ratio = width / height - - all_boxes = [ - box - / torch.tensor([width_ratio, height_ratio, width_ratio, height_ratio]) - for box in all_boxes - ] - # Apply NMS # transform predictions to shape [N, 5 + num_classes], N is the number of bboxes for nms function all_boxes_cat = torch.cat( From 68487e41351da3e077492ace3bcb9e2e2f94280b Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Fri, 25 Oct 2024 15:53:21 +0000 Subject: [PATCH 44/56] fix: pass segmentation annotator size --- datadreamer/pipelines/generate_dataset_from_scratch.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index a8fabe0..81908a5 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -608,7 +608,9 @@ def read_image_batch(image_batch, batch_num, batch_size): else: annotator_class = inst_seg_detectors[args.image_annotator] inst_seg_annotator_class = inst_seg_annotators[args.image_annotator] - inst_seg_annotator = inst_seg_annotator_class(device=args.device) + inst_seg_annotator = inst_seg_annotator_class( + device=args.device, size=args.annotator_size + ) annotator = annotator_class(device=args.device, size=args.annotator_size) for i, image_batch in tqdm( From 5401431483148521cdd05d4b02f2b6d51be0d328 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin Date: Fri, 25 Oct 2024 15:57:15 +0000 Subject: [PATCH 45/56] fix: shifted annotations when tta is used --- .../dataset_annotation/owlv2_annotator.py | 59 +++++++++---------- 1 file changed, 29 insertions(+), 30 deletions(-) diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index d375e80..f08e222 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -121,7 +121,8 @@ def _get_annotations( self, pred: Dict[str, torch.Tensor], use_tta: bool, - img_dim: int, + img_width: int, + img_height: int, synonym_dict: Dict[str, List[str]] | None, synonym_dict_rev: Dict[int, int] | None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: @@ -130,7 +131,8 @@ def _get_annotations( Args: pred: The predictions from the model. use_tta (bool): Flag to whether the test-time augmentation was applied. - img_dim (int): The dimension of the image. + img_width (int): The width of the image. + img_height (int): The height of the image. synonym_dict (dict): Dictionary for handling synonyms in labels. synonym_dict_rev (dict): Dictionary for handling synonyms in labels. @@ -143,19 +145,22 @@ def _get_annotations( pred["scores"], pred["labels"], ) - # Flip boxes back if using TTA - if use_tta: - boxes[:, [0, 2]] = img_dim - boxes[:, [2, 0]] if synonym_dict is not None: labels = torch.tensor( [synonym_dict_rev[label.item()] for label in labels], dtype=torch.int64 ) + boxes = self._correct_bboxes_misalignment(boxes, img_width, img_height) + + # Flip boxes back if using TTA + if use_tta: + boxes[:, [0, 2]] = img_width - boxes[:, [2, 0]] + return boxes, scores, labels def _correct_bboxes_misalignment( - self, input_boxes: List[torch.Tensor], width: int, height: int + self, input_boxes: torch.Tensor, width: int, height: int ) -> List[torch.Tensor]: """This function corrects the bounding boxes misalignment appearing when using the `transformers==4.45.2`. @@ -164,23 +169,19 @@ def _correct_bboxes_misalignment( Solution: https://discuss.huggingface.co/t/owl-v2-bounding-box-misalignment-problem/66181 Args: - input_boxes (List[torch.Tensor]): The bounding boxes to be corrected. + input_boxes (torch.Tensor): The bounding boxes to be corrected. width (int): The width of the image. height (int): The height of the image. Returns: List[torch.Tensor]: The corrected bounding boxes. """ - width_ratio = 1 - height_ratio = 1 - if width > height: - height_ratio = height / width - elif height > width: - width_ratio = width / height - return [ - box * torch.tensor([width_ratio, height_ratio, width_ratio, height_ratio]) - for box in input_boxes - ] + width_ratio = width / height if width < height else 1 + height_ratio = height / width if height < width else 1 + ratios = torch.tensor( + [width_ratio, height_ratio] * 2, device=input_boxes.device + ) + return input_boxes * ratios def annotate_batch( self, @@ -234,36 +235,34 @@ def annotate_batch( final_labels = [] for i, (pred, aug_pred) in enumerate(zip(preds, augmented_preds)): + img_width, img_height = images[i].size boxes, scores, labels = self._get_annotations( pred, False, - images[i].size[0], + img_width, + img_height, synonym_dict, synonym_dict_rev if synonym_dict is not None else None, ) - all_boxes = [boxes.to("cpu")] - all_scores = [scores.to("cpu")] - all_labels = [labels.to("cpu")] - - # Fix the bounding boxes misalignment - all_boxes = self._correct_bboxes_misalignment( - all_boxes, images[i].width, images[i].height - ) + all_boxes = [boxes] + all_scores = [scores] + all_labels = [labels] # Flip boxes back if using TTA if use_tta: aug_boxes, aug_scores, aug_labels = self._get_annotations( aug_pred, True, - images[i].size[0], + img_width, + img_height, synonym_dict, synonym_dict_rev if synonym_dict is not None else None, ) - all_boxes.append(aug_boxes.to("cpu")) - all_scores.append(aug_scores.to("cpu")) - all_labels.append(aug_labels.to("cpu")) + all_boxes.append(aug_boxes) + all_scores.append(aug_scores) + all_labels.append(aug_labels) one_hot_labels = torch.nn.functional.one_hot( torch.cat(all_labels), num_classes=len(prompts) From d47253a336a71c04f354a4d3c2d2f4075ce3b03c Mon Sep 17 00:00:00 2001 From: HonzaCuhel Date: Mon, 28 Oct 2024 10:04:57 +0100 Subject: [PATCH 46/56] Fix OWLv2 device --- datadreamer/dataset_annotation/owlv2_annotator.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index f08e222..9da41b4 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -245,9 +245,9 @@ def annotate_batch( synonym_dict_rev if synonym_dict is not None else None, ) - all_boxes = [boxes] - all_scores = [scores] - all_labels = [labels] + all_boxes = [boxes.cpu()] + all_scores = [scores.cpu()] + all_labels = [labels.cpu()] # Flip boxes back if using TTA if use_tta: @@ -260,9 +260,9 @@ def annotate_batch( synonym_dict_rev if synonym_dict is not None else None, ) - all_boxes.append(aug_boxes) - all_scores.append(aug_scores) - all_labels.append(aug_labels) + all_boxes.append(aug_boxes.cpu()) + all_scores.append(aug_scores.cpu()) + all_labels.append(aug_labels.cpu()) one_hot_labels = torch.nn.functional.one_hot( torch.cat(all_labels), num_classes=len(prompts) From 3aeab4d9b843f31695a119c64ee312867dc66ee1 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 29 Oct 2024 10:59:23 +0000 Subject: [PATCH 47/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 179c6a1..6c15cac 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 63% - 63% + 75% + 75% From 1914f7ddc5e4a866cdbace559f8b269755caf93a Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Tue, 29 Oct 2024 12:57:40 +0100 Subject: [PATCH 48/56] chore: update version to 0.2.0 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d7aae02..2468bdf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "datadreamer" -version = "0.1.5" +version = "0.2.0" description = "A library for dataset generation and knowledge extraction from foundation computer vision models." readme = "README.md" requires-python = ">=3.8" From 72f2aa486e80625e8935f1f65c56e1c7a4d1e8a6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20=C4=8Cuhel?= <79118988+HonzaCuhel@users.noreply.github.com> Date: Wed, 30 Oct 2024 13:01:22 +0100 Subject: [PATCH 49/56] Fix: convert images to RGB (#69) * Fix: convert images to RGB * Change the source branch to install * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions --- datadreamer/pipelines/generate_dataset_from_scratch.py | 4 +++- ...erate_instance_segmentation_dataset_and_train_yolo.ipynb | 2 +- media/coverage_badge.svg | 6 +++--- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 81908a5..9708f4f 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -552,7 +552,9 @@ def read_image_batch(image_batch, batch_num, batch_size): batch_image_paths.append(image_path) else: - images = [Image.open(image_path) for image_path in image_batch] + images = [ + Image.open(image_path).convert("RGB") for image_path in image_batch + ] batch_image_paths = image_batch return images, batch_image_paths diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 11f9812..1107e00 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -25,7 +25,7 @@ }, "outputs": [], "source": [ - "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@dev" + "!pip install -q datadreamer" ] }, { diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 6c15cac..179c6a1 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 75% - 75% + 63% + 63% From 749696d9865149ea1a9e54fa05f5029370db100f Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 30 Oct 2024 12:19:54 +0000 Subject: [PATCH 50/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 179c6a1..6c15cac 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 63% - 63% + 75% + 75% From 77be96effc9d6598f690ece3beb2887042e27ad5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20=C4=8Cuhel?= <79118988+HonzaCuhel@users.noreply.github.com> Date: Sun, 3 Nov 2024 21:48:09 +0100 Subject: [PATCH 51/56] Fix: LuxonisDataset Converter - bbox computation (#70) Fix: computation of x, y, w, h --- datadreamer/utils/luxonis_dataset_converter.py | 14 +++++++------- media/coverage_badge.svg | 6 +++--- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 8462ea1..5cde762 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -109,18 +109,18 @@ def dataset_generator(): if "boxes" in data[image_path]: boxes = data[image_path]["boxes"] for box, label in zip(boxes, labels): - x, y, w, h = box[0], box[1], box[2] - box[0], box[3] - box[1] - x = max(0, x) - y = max(0, y) + x, y = max(0, box[0] / width), max(0, box[1] / height) + w = min(box[2] / width - x, 1 - x) + h = min(box[3] / height - y, 1 - y) yield { "file": image_full_path, "annotation": { "class": class_names[label], "type": "boundingbox", - "x": x / width, - "y": y / height, - "w": w / width, - "h": h / height, + "x": x, + "y": y, + "w": w, + "h": h, }, } diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 6c15cac..179c6a1 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 75% - 75% + 63% + 63% From ec4df44c9d24e44e15047719637662436047db23 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Sun, 3 Nov 2024 21:04:58 +0000 Subject: [PATCH 52/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 179c6a1..6c15cac 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 63% - 63% + 75% + 75% From 6a62e9429574d4ea27398466a981de00003a0482 Mon Sep 17 00:00:00 2001 From: Nikita Sokovnin <49622375+sokovninn@users.noreply.github.com> Date: Fri, 8 Nov 2024 21:15:44 +0100 Subject: [PATCH 53/56] Add images without annotations to LuxonisDataset (#71) * fix: add images with no annotations to LuxonisDataset * fix(workflow): use branch we run workflow from * test: confirm current branch in workflow * fix(workflows): correctly pass branch arg * fix(workflows): tags * [Automated] Updated coverage badge * chore: change min required luxonis-ml version to 0.5.0 --------- Co-authored-by: conorsim Co-authored-by: GitHub Actions --- .github/workflows/gar-publish-dev.yaml | 6 ++---- datadreamer/utils/luxonis_dataset_converter.py | 6 ++++++ media/coverage_badge.svg | 6 +++--- requirements.txt | 2 +- 4 files changed, 12 insertions(+), 8 deletions(-) diff --git a/.github/workflows/gar-publish-dev.yaml b/.github/workflows/gar-publish-dev.yaml index b5a7408..9b0bb31 100644 --- a/.github/workflows/gar-publish-dev.yaml +++ b/.github/workflows/gar-publish-dev.yaml @@ -15,9 +15,7 @@ jobs: steps: - name: 'Checkout GitHub Action' - uses: actions/checkout@main - with: - ref: dev # Checkout the dev branch + uses: actions/checkout@v4 - id: 'auth' name: 'Authenticate to Google Cloud' @@ -36,5 +34,5 @@ jobs: - name: 'Build Inventory Image' working-directory: . run: | - docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} --build-arg BRANCH=dev . --tag $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer:dev + docker build --build-arg GITHUB_TOKEN=${{secrets.GHCR_PAT}} --build-arg BRANCH=${{ github.ref_name }} . --tag $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer:dev docker push $GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/internal/datadreamer --all-tags \ No newline at end of file diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 5cde762..1173994 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -80,6 +80,12 @@ def dataset_generator(): image_full_path = os.path.join(dataset_dir, image_path) width, height = Image.open(image_full_path).size labels = data[image_path]["labels"] + + if len(labels) == 0: + yield { + "file": image_full_path, + } + for label in labels: yield { "file": image_full_path, diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 6c15cac..179c6a1 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 75% - 75% + 63% + 63% diff --git a/requirements.txt b/requirements.txt index 2ab6b13..df5cc3e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,6 @@ accelerate>=0.25.0 scipy>=1.10.0 bitsandbytes>=0.42.0 nltk>=3.8.1 -luxonis-ml[all]>=0.4.1 +luxonis-ml[all]>=0.5.0 python-box>=7.1.1 gcsfs>=2023.1.0 \ No newline at end of file From e9bde26b0fb58b979e3f7479a32c1ec6371394dd Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Fri, 8 Nov 2024 20:33:04 +0000 Subject: [PATCH 54/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 179c6a1..6c15cac 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 63% - 63% + 75% + 75% From c89ca341f5bdb90624d76b267278528c2a10bb4f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20=C4=8Cuhel?= <79118988+HonzaCuhel@users.noreply.github.com> Date: Tue, 12 Nov 2024 11:39:38 +0100 Subject: [PATCH 55/56] Add an option to keep images with no annotation (#72) * Add option to keep images with no annotation, by default removing & refactor * [Automated] Updated coverage badge * Rename 'keep_empty_images' to 'keep_unlabeled_images' --------- Co-authored-by: GitHub Actions --- README.md | 1 + .../dataset_annotation/slimsam_annotator.py | 2 +- .../generate_dataset_from_scratch.py | 10 + datadreamer/utils/base_converter.py | 16 +- datadreamer/utils/coco_converter.py | 52 +- datadreamer/utils/config.py | 2 + datadreamer/utils/convert_dataset.py | 58 +- .../utils/luxonis_dataset_converter.py | 27 +- .../utils/single_label_cls_converter.py | 2 + datadreamer/utils/yolo_converter.py | 29 +- .../generate_dataset_and_train_yolo.ipynb | 1 + ..._segmentation_dataset_and_train_yolo.ipynb | 793 +++++++++--------- media/coverage_badge.svg | 6 +- requirements.txt | 2 +- 14 files changed, 568 insertions(+), 433 deletions(-) diff --git a/README.md b/README.md index c3b9464..17921df 100644 --- a/README.md +++ b/README.md @@ -176,6 +176,7 @@ datadreamer --config - `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`. - `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`. - `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`. +- `--keep_unlabeled_images`: Whether to keep images without any annotations. Default if `False`. - `--batch_size_prompt`: Batch size for prompt generation. Default is 64. - `--batch_size_annotation`: Batch size for annotation. Default is `1`. - `--batch_size_image`: Batch size for image generation. Default is `1`. diff --git a/datadreamer/dataset_annotation/slimsam_annotator.py b/datadreamer/dataset_annotation/slimsam_annotator.py index 7e4e7cd..7f6de7b 100644 --- a/datadreamer/dataset_annotation/slimsam_annotator.py +++ b/datadreamer/dataset_annotation/slimsam_annotator.py @@ -56,7 +56,7 @@ def _init_model(self) -> SamModel: Returns: SamModel: The initialized SAM model. """ - logger.info(f"Initializing `SlimSAM {self.size} model...") + logger.info(f"Initializing SlimSAM {self.size} model...") if self.size == "large": return SamModel.from_pretrained("Zigeng/SlimSAM-uniform-50") return SamModel.from_pretrained("Zigeng/SlimSAM-uniform-77") diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 9708f4f..4d52acb 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -217,6 +217,13 @@ def parse_args(): help="Whether to use only bad words in profanity filter", ) + parser.add_argument( + "--keep_unlabeled_images", + default=None, + action="store_true", + help="Whether to keep images without any annotations", + ) + parser.add_argument( "--batch_size_prompt", type=int, @@ -718,6 +725,7 @@ def read_image_batch(image_batch, batch_num, batch_size): args.split_ratios, copy_files=False, is_instance_segmentation=args.task == "instance-segmentation", + keep_unlabeled_images=args.keep_unlabeled_images, seed=args.seed, ) # Convert annotations to COCO format @@ -728,6 +736,7 @@ def read_image_batch(image_batch, batch_num, batch_size): "coco", args.split_ratios, is_instance_segmentation=args.task == "instance-segmentation", + keep_unlabeled_images=args.keep_unlabeled_images, copy_files=False, seed=args.seed, ) @@ -742,6 +751,7 @@ def read_image_batch(image_batch, batch_num, batch_size): dataset_plugin=args.dataset_plugin, dataset_name=args.dataset_name, is_instance_segmentation=args.task == "instance-segmentation", + keep_unlabeled_images=args.keep_unlabeled_images, copy_files=False, seed=args.seed, ) diff --git a/datadreamer/utils/base_converter.py b/datadreamer/utils/base_converter.py index 5c8243e..40003ed 100644 --- a/datadreamer/utils/base_converter.py +++ b/datadreamer/utils/base_converter.py @@ -14,13 +14,21 @@ def __init__(self, seed=42): np.random.seed(seed) @abstractmethod - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> None: + def convert( + self, + dataset_dir: str, + output_dir: str, + split_ratios: List[float], + keep_unlabeled_images: bool = False, + copy_files: bool = True, + ) -> None: """Converts a dataset into another format. Args: dataset_dir (str): The directory where the source dataset is located. output_dir (str): The directory where the processed dataset should be saved. split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. @@ -28,7 +36,7 @@ def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> Non pass @staticmethod - def read_annotations(annotation_path) -> Dict: + def read_annotations(annotation_path: str) -> Dict: """Reads annotations from a JSON file located at the specified path. Args: @@ -42,7 +50,9 @@ def read_annotations(annotation_path) -> Dict: return data @staticmethod - def make_splits(images, split_ratios, shuffle=True) -> Tuple[List, List, List]: + def make_splits( + images: List[str], split_ratios: List[float], shuffle: bool = True + ) -> Tuple[List, List, List]: """Splits the list of images into training, validation, and test sets. Args: diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index bb69a78..40d599a 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -1,14 +1,18 @@ from __future__ import annotations import json +import logging import os import shutil +from typing import Dict, List import numpy as np from PIL import Image from datadreamer.utils.base_converter import BaseConverter +logger = logging.getLogger(__name__) + class COCOConverter(BaseConverter): """Class for converting a dataset to COCO format. @@ -33,23 +37,44 @@ def __init__(self, seed=42, is_instance_segmentation: bool = False): super().__init__(seed) self.is_instance_segmentation = is_instance_segmentation - def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> None: + def convert( + self, + dataset_dir: str, + output_dir: str, + split_ratios: List[float], + keep_unlabeled_images: bool = False, + copy_files: bool = True, + ) -> None: """Converts a dataset into a COCO format. Args: dataset_dir (str): The directory where the source dataset is located. output_dir (str): The directory where the processed dataset should be saved. split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ annotation_path = os.path.join(dataset_dir, "annotations.json") data = BaseConverter.read_annotations(annotation_path) - self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) + self.process_data( + data, + dataset_dir, + output_dir, + split_ratios, + keep_unlabeled_images, + copy_files, + ) def process_data( - self, data, image_dir, output_dir, split_ratios, copy_files=True + self, + data: Dict, + image_dir: str, + output_dir: str, + split_ratios: List[float], + keep_unlabeled_images: bool = False, + copy_files: bool = True, ) -> None: """Processes the data by dividing it into training and validation sets, and saves the images and labels in COCO format. @@ -58,7 +83,8 @@ def process_data( data (dict): The dictionary containing image annotations. image_dir (str): The directory where the source images are located. output_dir (str): The base directory where the processed data will be saved. - split_ratios (float): The ratio to split the data into training, validation, and test sets. + split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. @@ -66,6 +92,18 @@ def process_data( images = list(data.keys()) images.remove("class_names") + empty_images = list(filter(lambda x: len(data[x]["labels"]) == 0, images)) + if keep_unlabeled_images and len(empty_images) > 0: + logger.warning( + f"{len(empty_images)} images with no annotations will be included in the dataset." + ) + elif not keep_unlabeled_images and len(empty_images) > 0: + logger.info( + f"{len(empty_images)} images with no annotations will be excluded from the dataset." + ) + for image in empty_images: + images.remove(image) + train_images, val_images, test_images = BaseConverter.make_splits( images, split_ratios ) @@ -147,7 +185,11 @@ def process_data( ) def save_labels( - self, dataset_output_dir, images_info, annotations, class_names + self, + dataset_output_dir: str, + images_info: List[Dict], + annotations: List[Dict], + class_names: List[str], ) -> None: """Saves the labels to a JSON file. diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index 9d36267..6227b61 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -49,3 +49,5 @@ class Config(LuxonisConfig): loader_plugin: str = "" dataset_name: str = "" dataset_id: str = "" + # Dataset arguments + keep_unlabeled_images: bool = False diff --git a/datadreamer/utils/convert_dataset.py b/datadreamer/utils/convert_dataset.py index 2e063ed..1bcea34 100644 --- a/datadreamer/utils/convert_dataset.py +++ b/datadreamer/utils/convert_dataset.py @@ -1,6 +1,7 @@ from __future__ import annotations import argparse +from typing import List, Optional from datadreamer.utils import ( COCOConverter, @@ -11,15 +12,16 @@ def convert_dataset( - input_dir, - output_dir, - dataset_format, - split_ratios, - dataset_plugin=None, - dataset_name=None, - is_instance_segmentation=False, - copy_files=True, - seed=42, + input_dir: str, + output_dir: str, + dataset_format: str, + split_ratios: List[float], + dataset_plugin: Optional[str] = None, + dataset_name: Optional[str] = None, + is_instance_segmentation: bool = False, + keep_unlabeled_images: bool = False, + copy_files: bool = True, + seed: int = 42, ) -> None: """Converts a dataset from one format to another. @@ -27,9 +29,11 @@ def convert_dataset( input_dir (str): Directory containing the images and annotations. output_dir (str): Directory where the processed dataset will be saved. dataset_format (str): Format of the dataset. Can be 'yolo', 'coco', 'luxonis-dataset', or 'cls-single'. - split_ratios (list): List of ratios for train, val, and test splits. + split_ratios (lis of float): List of ratios for train, val, and test splits. dataset_plugin (str, optional): Plugin for Luxonis dataset. Defaults to None. dataset_name (str, optional): Name of the Luxonis dataset. Defaults to None. + is_instance_segmentation (bool, optional): Whether the dataset is for instance segmentation. Defaults to False. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the files to the output directory. Defaults to True. seed (int, optional): Random seed. Defaults to 42. @@ -56,7 +60,9 @@ def convert_dataset( else: raise ValueError(f"Invalid dataset format: {dataset_format}") - converter.convert(input_dir, output_dir, split_ratios, copy_files) + converter.convert( + input_dir, output_dir, split_ratios, keep_unlabeled_images, copy_files + ) def main(): @@ -95,6 +101,18 @@ def main(): type=str, help="Name of the dataset to create if dataset_plugin is used", ) + parser.add_argument( + "--is_instance_segmentation", + default=None, + action="store_true", + help="Whether the dataset is for instance segmentation.", + ) + parser.add_argument( + "--keep_unlabeled_images", + default=None, + action="store_true", + help="Whether to keep images without any annotations", + ) parser.add_argument( "--copy_files", type=bool, @@ -111,14 +129,16 @@ def main(): args = parser.parse_args() convert_dataset( - args.input_dir, - args.output_dir, - args.dataset_format, - args.split_ratios, - args.dataset_plugin, - args.dataset_name, - args.copy_files, - args.seed, + input_dir=args.input_dir, + output_dir=args.output_dir, + dataset_format=args.dataset_format, + split_ratios=args.split_ratios, + dataset_plugin=args.dataset_plugin, + dataset_name=args.dataset_name, + is_instance_segmentation=args.is_instance_segmentation, + keep_unlabeled_images=args.keep_unlabeled_images, + copy_files=args.copy_files, + seed=args.seed, ) diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 1173994..fec3d36 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -38,6 +38,7 @@ def convert( dataset_dir: str, output_dir: str, split_ratios: List[float], + keep_unlabeled_images: bool = False, copy_files: bool = True, ) -> None: """Converts a dataset into a LuxonisDataset format. @@ -46,16 +47,24 @@ def convert( dataset_dir (str): The directory where the source dataset is located. output_dir (str): The directory where the processed dataset should be saved. split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ annotation_path = os.path.join(dataset_dir, "annotations.json") data = BaseConverter.read_annotations(annotation_path) - self.process_data(data, dataset_dir, output_dir, split_ratios) + self.process_data( + data, dataset_dir, output_dir, split_ratios, keep_unlabeled_images + ) def process_data( - self, data: Dict, dataset_dir: str, output_dir: str, split_ratios: List[float] + self, + data: Dict, + dataset_dir: str, + output_dir: str, + split_ratios: List[float], + keep_unlabeled_images: bool = False, ) -> None: """Processes the data into LuxonisDataset format. @@ -81,7 +90,10 @@ def dataset_generator(): width, height = Image.open(image_full_path).size labels = data[image_path]["labels"] - if len(labels) == 0: + if len(labels) == 0 and keep_unlabeled_images: + logger.warning( + f"Image {image_path} has no annotations. Training on empty images with `luxonis-train` will result in an error." + ) yield { "file": image_full_path, } @@ -161,4 +173,13 @@ def dataset_generator(): dataset = LuxonisDataset(dataset_name) dataset.add(dataset_generator()) + + if not keep_unlabeled_images: + n_empty_images = len( + list(filter(lambda x: len(data[x]["labels"]) == 0, image_paths)) + ) + if n_empty_images > 0: + logger.info( + f"Removed {n_empty_images} empty images with no annotations from the dataset." + ) dataset.make_splits(tuple(split_ratios)) diff --git a/datadreamer/utils/single_label_cls_converter.py b/datadreamer/utils/single_label_cls_converter.py index c24bec7..daa3bd8 100644 --- a/datadreamer/utils/single_label_cls_converter.py +++ b/datadreamer/utils/single_label_cls_converter.py @@ -41,6 +41,7 @@ def convert( dataset_dir: str, output_dir: str, split_ratios: List[float], + keep_unlabeled_images: bool = False, copy_files: bool = True, ) -> None: """Converts a dataset into a format suitable for single-label classification. @@ -49,6 +50,7 @@ def convert( dataset_dir (str): The directory where the source dataset is located. output_dir (str): The directory where the processed dataset should be saved. split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index 5f8fc51..e4ad15a 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -1,5 +1,6 @@ from __future__ import annotations +import logging import os import shutil from typing import Dict, List @@ -9,6 +10,8 @@ from datadreamer.utils import BaseConverter +logger = logging.getLogger(__name__) + class YOLOConverter(BaseConverter): """Class for converting a dataset to YOLO format. @@ -40,6 +43,7 @@ def convert( dataset_dir: str, output_dir: str, split_ratios: List[float], + keep_unlabeled_images: bool = False, copy_files: bool = True, ): """Converts a dataset into a format suitable for training with YOLO, including @@ -49,13 +53,21 @@ def convert( dataset_dir (str): The directory where the source dataset is located. output_dir (str): The directory where the processed dataset should be saved. split_ratios (list of float): The ratios to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. No return value. """ annotation_path = os.path.join(dataset_dir, "annotations.json") data = BaseConverter.read_annotations(annotation_path) - self.process_data(data, dataset_dir, output_dir, split_ratios, copy_files) + self.process_data( + data, + dataset_dir, + output_dir, + split_ratios, + keep_unlabeled_images, + copy_files, + ) def convert_to_yolo_format( self, box: List[float], image_width: int, image_height: int @@ -97,6 +109,7 @@ def process_data( image_dir: str, output_dir: str, split_ratios: List[float], + keep_unlabeled_images: bool = False, copy_files: bool = True, ) -> None: """Processes the data by dividing it into training and validation sets, and @@ -107,14 +120,26 @@ def process_data( image_dir (str): The directory where the source images are located. output_dir (str): The base directory where the processed data will be saved. split_ratios (float): The ratio to split the data into training, validation, and test sets. + keep_unlabeled_images (bool, optional): Whether to keep images with no annotations. Defaults to False. copy_files (bool, optional): Whether to copy the source files to the output directory, otherwise move them. Defaults to True. - No return value. """ images = list(data.keys()) images.remove("class_names") + empty_images = list(filter(lambda x: len(data[x]["labels"]) == 0, images)) + if keep_unlabeled_images and len(empty_images) > 0: + logger.warning( + f"{len(empty_images)} images with no annotations will be included in the dataset." + ) + elif not keep_unlabeled_images and len(empty_images) > 0: + logger.info( + f"{len(empty_images)} images with no annotations will be excluded from the dataset." + ) + for image in empty_images: + images.remove(image) + train_images, val_images, test_images = BaseConverter.make_splits( images, split_ratios ) diff --git a/examples/generate_dataset_and_train_yolo.ipynb b/examples/generate_dataset_and_train_yolo.ipynb index 9ad1359..2a08030 100644 --- a/examples/generate_dataset_and_train_yolo.ipynb +++ b/examples/generate_dataset_and_train_yolo.ipynb @@ -97,6 +97,7 @@ "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", + "- `--keep_unlabeled_images`: Whether to keep images without any annotations. Default if `False`.\n", "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 1107e00..1588001 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -1,422 +1,423 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "8ce1517f-7258-406d-9139-9adadb1a1570", - "metadata": { - "id": "8ce1517f-7258-406d-9139-9adadb1a1570" - }, - "source": [ - "\n", - "\n", - "# DataDreamer Tutorial: Generating a dataset for instance segmentation, training a model, and deploying it to the OAK (optional)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5_2ivH03etO", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b5_2ivH03etO", - "outputId": "c92b1e2e-cd3e-4a7d-8be6-776e0dfad5bc" - }, - "outputs": [], - "source": [ - "!pip install -q datadreamer" - ] - }, - { - "cell_type": "markdown", - "id": "c3704c07", - "metadata": { - "id": "c3704c07" - }, - "source": [ - "## Generate a dataset with your own classes (might take some time to download all models)" - ] - }, - { - "cell_type": "markdown", - "id": "M4v-QieP4tXL", - "metadata": { - "id": "M4v-QieP4tXL" - }, - "source": [ - "Make sure you are using the GPU runtime type (in Google Colab).\n", - "\n", - "~4 min to generate 30 images\n", - "\n", - "~43 secs to annotate them" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ab1e2f9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6ab1e2f9", - "outputId": "6f57eb7a-f261-46bc-e574-3631cade8660", - "scrolled": true - }, - "outputs": [], - "source": [ - "!datadreamer --save_dir generated_dataset \\\n", - " --class_names tractor horse bear \\\n", - " --prompts_number 30 \\\n", - " --prompt_generator simple \\\n", - " --num_objects_range 1 1 \\\n", - " --image_generator sdxl-turbo \\\n", - " --task instance-segmentation \\\n", - " --disable_lm_filter \\\n", - " --annotator_size base \\\n", - " --use_tta \\\n", - " --image_annotator owlv2-slimsam \\\n", - " --conf_threshold 0.2 \\\n", - " --seed 42" - ] - }, - { - "cell_type": "markdown", - "id": "7a10755e", - "metadata": { - "id": "7a10755e" - }, - "source": [ - "### Parameters\n", - "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", - "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", - "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", - "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", - "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", - "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", - "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", - "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", - "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", - "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n", - "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", - "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", - "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", - "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", - "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", - "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", - "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", - "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", - "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", - "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", - "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", - "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", - "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", - "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", - "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", - "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", - "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", - "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7add74d9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 497 - }, - "id": "7add74d9", - "outputId": "cafd066a-b524-4006-e2d0-cd949d65c567" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "from IPython.display import Image\n", - "\n", - "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_5.jpg\"))" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "8ce1517f-7258-406d-9139-9adadb1a1570", + "metadata": { + "id": "8ce1517f-7258-406d-9139-9adadb1a1570" + }, + "source": [ + "\n", + "\n", + "# DataDreamer Tutorial: Generating a dataset for instance segmentation, training a model, and deploying it to the OAK (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5_2ivH03etO", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "markdown", - "id": "64fe2dc9", - "metadata": { - "id": "64fe2dc9" - }, - "source": [ - "## Convert the dataset to YOLO format" - ] + "id": "b5_2ivH03etO", + "outputId": "c92b1e2e-cd3e-4a7d-8be6-776e0dfad5bc" + }, + "outputs": [], + "source": [ + "!pip install -q datadreamer" + ] + }, + { + "cell_type": "markdown", + "id": "c3704c07", + "metadata": { + "id": "c3704c07" + }, + "source": [ + "## Generate a dataset with your own classes (might take some time to download all models)" + ] + }, + { + "cell_type": "markdown", + "id": "M4v-QieP4tXL", + "metadata": { + "id": "M4v-QieP4tXL" + }, + "source": [ + "Make sure you are using the GPU runtime type (in Google Colab).\n", + "\n", + "~4 min to generate 30 images\n", + "\n", + "~43 secs to annotate them" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ab1e2f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3dd01a6a", - "metadata": { - "id": "3dd01a6a" - }, - "outputs": [], - "source": [ - "from datadreamer.utils.convert_dataset import convert_dataset" - ] + "id": "6ab1e2f9", + "outputId": "6f57eb7a-f261-46bc-e574-3631cade8660", + "scrolled": true + }, + "outputs": [], + "source": [ + "!datadreamer --save_dir generated_dataset \\\n", + " --class_names tractor horse bear \\\n", + " --prompts_number 30 \\\n", + " --prompt_generator simple \\\n", + " --num_objects_range 1 1 \\\n", + " --image_generator sdxl-turbo \\\n", + " --task instance-segmentation \\\n", + " --disable_lm_filter \\\n", + " --annotator_size base \\\n", + " --use_tta \\\n", + " --image_annotator owlv2-slimsam \\\n", + " --conf_threshold 0.2 \\\n", + " --seed 42" + ] + }, + { + "cell_type": "markdown", + "id": "7a10755e", + "metadata": { + "id": "7a10755e" + }, + "source": [ + "### Parameters\n", + "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", + "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", + "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", + "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", + "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", + "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", + "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", + "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", + "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", + "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n", + "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", + "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", + "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", + "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", + "- `--negative_prompt`: Negative prompts to guide the generation away from certain features. Default is `\"cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy\"`.\n", + "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", + "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", + "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", + "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", + "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", + "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", + "- `--keep_unlabeled_images`: Whether to keep images without any annotations. Default if `False`.\n", + "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", + "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", + "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", + "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", + "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", + "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7add74d9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 }, + "id": "7add74d9", + "outputId": "cafd066a-b524-4006-e2d0-cd949d65c567" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "id": "9b9bb74d", - "metadata": { - "id": "9b9bb74d" - }, - "outputs": [], - "source": [ - "convert_dataset(\n", - " input_dir=\"generated_dataset\",\n", - " output_dir=\"generated_dataset_yolo\",\n", - " dataset_format=\"yolo\",\n", - " split_ratios=[0.8, 0.1, 0.1],\n", - " copy_files=True,\n", - " is_instance_segmentation=True,\n", - ")" + "data": { + "image/jpeg": 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+ "text/plain": [ + "" ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_5.jpg\"))" + ] + }, + { + "cell_type": "markdown", + "id": "64fe2dc9", + "metadata": { + "id": "64fe2dc9" + }, + "source": [ + "## Convert the dataset to YOLO format" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3dd01a6a", + "metadata": { + "id": "3dd01a6a" + }, + "outputs": [], + "source": [ + "from datadreamer.utils.convert_dataset import convert_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9b9bb74d", + "metadata": { + "id": "9b9bb74d" + }, + "outputs": [], + "source": [ + "convert_dataset(\n", + " input_dir=\"generated_dataset\",\n", + " output_dir=\"generated_dataset_yolo\",\n", + " dataset_format=\"yolo\",\n", + " split_ratios=[0.8, 0.1, 0.1],\n", + " copy_files=True,\n", + " is_instance_segmentation=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a167a842", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "a167a842", + "outputId": "715988c2-ab27-4ce2-b12c-2fa01188c537" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "id": "a167a842", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "a167a842", - "outputId": "715988c2-ab27-4ce2-b12c-2fa01188c537" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data.yaml test train\tval\n" - ] - } - ], - "source": [ - "!ls generated_dataset_yolo" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "data.yaml test train\tval\n" + ] + } + ], + "source": [ + "!ls generated_dataset_yolo" + ] + }, + { + "cell_type": "markdown", + "id": "d2d660b0", + "metadata": { + "id": "d2d660b0" + }, + "source": [ + "# Train your model (YOLOv8 as an example)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "982e475e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "markdown", - "id": "d2d660b0", - "metadata": { - "id": "d2d660b0" - }, - "source": [ - "# Train your model (YOLOv8 as an example)" - ] + "id": "982e475e", + "outputId": "1f4cb9f5-1d01-4882-a730-434e5122546f", + "scrolled": true + }, + "outputs": [], + "source": [ + "!pip install -q ultralytics" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "184cf0fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "184cf0fa", + "outputId": "dcc43a26-bc78-4d3d-ddb3-6932a8584df9" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "982e475e", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "982e475e", - "outputId": "1f4cb9f5-1d01-4882-a730-434e5122546f", - "scrolled": true - }, - "outputs": [], - "source": [ - "!pip install -q ultralytics" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating new Ultralytics Settings v0.0.6 file ✅ \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", + "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n" + ] }, { - "cell_type": "code", - "execution_count": 8, - "id": "184cf0fa", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "184cf0fa", - "outputId": "dcc43a26-bc78-4d3d-ddb3-6932a8584df9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating new Ultralytics Settings v0.0.6 file ✅ \n", - "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", - "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", - "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6.74M/6.74M [00:00<00:00, 110MB/s]\n" - ] - } - ], - "source": [ - "from ultralytics import YOLO\n", - "\n", - "model = YOLO(\"yolov8n-seg.pt\") # load a pretrained model" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6.74M/6.74M [00:00<00:00, 110MB/s]\n" + ] + } + ], + "source": [ + "from ultralytics import YOLO\n", + "\n", + "model = YOLO(\"yolov8n-seg.pt\") # load a pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb4e6754", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb4e6754", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "bb4e6754", - "outputId": "66b28d5a-6544-46fa-ee73-3074f141e981", - "scrolled": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ['WANDB_DISABLED'] = 'true'\n", - "\n", - "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=200)" - ] + "id": "bb4e6754", + "outputId": "66b28d5a-6544-46fa-ee73-3074f141e981", + "scrolled": true + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['WANDB_DISABLED'] = 'true'\n", + "\n", + "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=200)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b05e33", + "metadata": { + "id": "d8b05e33" + }, + "source": [ + "## Show the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b559b1f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "b559b1f9", + "outputId": "bcb3fae6-27eb-4384-f532-c573ae45c599" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "d8b05e33", - "metadata": { - "id": "d8b05e33" - }, - "source": [ - "## Show the predictions" + "data": { + "image/jpeg": "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- "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(filename=os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "dec0cb11", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "dec0cb11", - "outputId": "72cf4330-fa0f-47aa-82c5-242dc6978dcd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", - "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00 - + coverage coverage - 75% - 75% + 63% + 63% diff --git a/requirements.txt b/requirements.txt index df5cc3e..e0f23a6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ torch>=2.0.0 torchvision>=0.16.0 transformers>=4.45.2 -diffusers>=0.24.0 +diffusers>=0.31.0 compel>=2.0.0 tqdm>=4.0.0 Pillow>=9.0.0 From 0ebcd68f10f7cfa3000851990aa058bab0bc5c39 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 12 Nov 2024 11:14:56 +0000 Subject: [PATCH 56/56] [Automated] Updated coverage badge --- media/coverage_badge.svg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/media/coverage_badge.svg b/media/coverage_badge.svg index 179c6a1..6c15cac 100644 --- a/media/coverage_badge.svg +++ b/media/coverage_badge.svg @@ -9,13 +9,13 @@ - + coverage coverage - 63% - 63% + 75% + 75%