From 7bef6c83bd63d04e1324ab35e4b3a78d5692c135 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 17 Apr 2024 17:36:23 -0400 Subject: [PATCH 01/47] Update environment.yml with new dependencies --- environment.yml | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/environment.yml b/environment.yml index 157cf02c..ac2f70fa 100644 --- a/environment.yml +++ b/environment.yml @@ -1,6 +1,7 @@ -name: geo_deep_env +name: geo_ml_env channels: - pytorch + - nvidia - conda-forge dependencies: - coverage>=6.3.1 @@ -11,14 +12,16 @@ dependencies: - pynvml>=11.0 - pystac>=0.3.0 - pytest>=7.1 - - python>=3.10 - - pytorch==1.12 + - python>=3.11 + - pytorch>=1.12 + - pytorch-cuda>=11.6 - rich>=11.1 - ruamel_yaml>=0.15 - scikit-image>=0.18 - torchgeo>=0.3 - - torchvision>=0.12 + - torchvision>=0.13 - pip: + - geo-inference - hydra-colorlog>=1.1.0 - hydra-optuna-sweeper>=1.1.0 - git+https://github.com/CosmiQ/solaris.git@0.5.0 From cae1bf7739746524eb9a10cf91da71152e6ece09 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 17 Apr 2024 17:38:24 -0400 Subject: [PATCH 02/47] Refactor code to support geo-inference package --- inference_segmentation.py | 499 ++++++-------------------------------- 1 file changed, 75 insertions(+), 424 deletions(-) diff --git a/inference_segmentation.py b/inference_segmentation.py index 9e75fd71..aba11eb1 100644 --- a/inference_segmentation.py +++ b/inference_segmentation.py @@ -1,245 +1,52 @@ import csv -import itertools from math import sqrt +from tqdm import tqdm +from pathlib import Path from numbers import Number from tempfile import mkstemp -from typing import List, Union, Sequence +from omegaconf import DictConfig +from typing import Dict, Sequence, Union +from dataset.stacitem import SingleBandItemEO -import torch -import torch.nn.functional as F -# import torch should be first. Unclear issue, mentionned here: https://github.com/pytorch/pytorch/issues/2083 -import numpy as np -import time -import fiona # keep this import. it sets GDAL_DATA to right value -import rasterio -import ttach as tta -from scipy.special import softmax -from collections import OrderedDict -from fiona.crs import to_string -from ruamel_yaml.comments import CommentedSeq -from tqdm import tqdm -from rasterio import features -from rasterio.windows import Window -from rasterio.plot import reshape_as_image, reshape_as_raster -from pathlib import Path -from omegaconf import OmegaConf, DictConfig, open_dict -from omegaconf.listconfig import ListConfig -from dataset.stacitem import SingleBandItemEO from utils.aoiutils import aois_from_csv -from utils.geoutils import create_new_raster_from_base -from utils.inference import stretch_heatmap, class_from_heatmap from utils.logger import get_logger, set_tracker -from models.model_choice import define_model, read_checkpoint -from utils import augmentation -from utils.inference import generate_patch_list +from geo_inference.geo_inference import GeoInference from utils.utils import get_device_ids, get_key_def, \ add_metadata_from_raster_to_sample, set_device # Set the logging file logging = get_logger(__name__) -def _pad(arr, chunk_size): - """ Pads img_arr """ - w_diff = chunk_size - arr.shape[0] - h_diff = chunk_size - arr.shape[1] - if len(arr.shape) > 2: - padded_arr = np.pad(arr, ((0, w_diff), (0, h_diff), (0, 0)), mode="reflect") - else: - padded_arr = np.pad(arr, ((0, w_diff), (0, h_diff)), mode="reflect") - - return padded_arr - - -def ras2vec(raster_file, output_path): - # Create a generic polygon schema for the output vector file - i = 0 - feat_schema = {'geometry': 'Polygon', - 'properties': OrderedDict([('value', 'int')]) - } - class_value_domain = set() - out_features = [] - - print(" - Processing raster file: {}".format(raster_file)) - with rasterio.open(raster_file, 'r') as src: - raster = src.read(1) - mask = raster != 0 - # Vectorize the polygons - polygons = features.shapes(raster, mask, transform=src.transform) - - # Create shapely polygon features - for polygon in polygons: - feature = {'geometry': { - 'type': 'Polygon', - 'coordinates': None}, - 'properties': OrderedDict([('value', 0)])} - - feature['geometry']['coordinates'] = polygon[0]['coordinates'] - value = int(polygon[1]) # Pixel value of the class (layer) - class_value_domain.add(value) - feature['properties']['value'] = value - i += 1 - out_features.append(feature) - - print(" - Writing output vector file: {}".format(output_path)) - num_layers = list(class_value_domain) # Number of unique pixel value - for num_layer in num_layers: - polygons = [feature for feature in out_features if feature['properties']['value'] == num_layer] - layer_name = 'vector_' + str(num_layer).rjust(3, '0') - print(" - Writing layer: {}".format(layer_name)) - - with fiona.open(output_path, 'w', - crs=to_string(src.crs), - layer=layer_name, - schema=feat_schema, - driver='GPKG') as dest: - for polygon in polygons: - dest.write(polygon) - print("") - print("Number of features written: {}".format(i)) - - -def gen_img_samples(src, patch_list, chunk_size, *band_order): - """ - TODO - Args: - src: input image (rasterio object) - patch_list: list of patches index - chunk_size: image tile size - *band_order: ignore - - Returns: generator object - - """ - for patch in patch_list: - patch_x, patch_y, patch_width, patch_height, hann_window = patch - window = Window.from_slices(slice(patch_y, patch_y + patch_height), - slice(patch_x, patch_x + patch_width)) - if band_order: - patch_array = reshape_as_image(src.read(band_order[0], window=window)) - else: - patch_array = reshape_as_image(src.read(window=window)) - patch_array = _pad(patch_array, chunk_size) - - yield patch_array, (patch_y, patch_height), (patch_x, patch_width), hann_window - -def sigmoid(x): - return 1/(1+np.exp(-x)) +def stac_input_to_temp_csv(input_stac_item: Union[str, Path]) -> Path: + """Saves a stac item path or url to a temporary csv""" + _, stac_temp_csv = mkstemp(suffix=".csv") + with open(stac_temp_csv, "w", newline="") as fh: + csv.writer(fh).writerow([str(input_stac_item), None, "inference", Path(input_stac_item).stem]) + return Path(stac_temp_csv) -@torch.no_grad() -def segmentation(param, - input_image, - num_classes: int, - model, - chunk_size: int, - use_hanning: bool, - device, - scale: List, - tp_mem, - heatmap_dtype=np.uint16, - debug=False, - ): +def calc_eval_batchsize(gpu_devices_dict: dict, batch_size: int, sample_size: int, max_pix_per_mb_gpu: int = 280): """ - - Args: - param: parameter dict - input_image: opened image (rasterio object) - num_classes: number of classes - model: model weights - chunk_size: image tile size - device: cuda/cpu device - scale: scale range - tp_mem: memory temp file for saving numpy array to disk - debug: True/False - heatmap_dtype: - Output data type for heatmap. Ex.: Uint16 captures more information, but takes more space in memory or disk. - - Returns: - + Calculate maximum batch size that could fit on GPU during evaluation based on thumb rule with harcoded + "pixels per MB of GPU RAM" as threshold. The batch size often needs to be smaller if crop is applied during training + @param gpu_devices_dict: dictionary containing info on GPU devices as returned by lst_device_ids (utils.py) + @param batch_size: batch size for training + @param sample_size: size of patches + @return: returns a downgraded evaluation batch size if the original batch size is considered too high compared to + the GPU's memory """ - sample = {"image": None, "mask": None, 'metadata': None} - print_log = True if logging.level == 20 else False # 20 is INFO - model.eval() # switch to evaluate mode - - # initialize test time augmentation - # transforms = tta.aliases.d4_transform() - transforms = tta.Compose([]) - tf_len = len(transforms) - h_padded, w_padded = input_image.height + chunk_size, input_image.width + chunk_size - patch_list = generate_patch_list(w_padded, h_padded, chunk_size, use_hanning) - - fp = np.memmap(tp_mem, dtype='float16', mode='w+', shape=(tf_len, h_padded, w_padded, num_classes)) - img_gen = gen_img_samples(src=input_image, patch_list=patch_list, chunk_size=chunk_size) - single_class_mode = False if num_classes > 1 else True - start_time = time.time() - for sub_image, h_idxs, w_idxs, hann_win in tqdm( - img_gen, position=0, leave=True, desc='Inferring on patches', - total=len(patch_list) - ): - hann_win = np.expand_dims(hann_win, -1) - image_metadata = add_metadata_from_raster_to_sample(sat_img_arr=sub_image, - raster_handle=input_image, - raster_info={}) - - sample['metadata'] = image_metadata - totensor_transform = augmentation.compose_transforms(param, - dataset="tst", - scale=scale, - aug_type='totensor', - print_log=print_log) - sample["image"] = sub_image - sample = totensor_transform(sample) - inputs = sample["image"].unsqueeze_(0) - inputs = inputs.to(device) - if inputs.shape[1] == 4 and any("module.modelNIR" in s for s in model.state_dict().keys()): - # Init NIR TODO: make a proper way to read the NIR channel - # and put an option to be able to give the index of the NIR channel - inputs_NIR = inputs[:, -1, ...] # Extract the NIR channel -> [batch size, H, W] since it's only one channel - inputs_NIR.unsqueeze_(1) # add a channel to get the good size -> [:, 1, :, :] - inputs = inputs[:, :-1, ...] # take out the NIR channel and take only the RGB for the inputs - inputs = [inputs, inputs_NIR] - output_lst = [] - for transformer in transforms: - # augment inputs - augmented_input = transformer.augment_image(inputs) - with torch.cuda.amp.autocast(): - augmented_output = model(augmented_input) - if isinstance(augmented_output, OrderedDict) and 'out' in augmented_output.keys(): - augmented_output = augmented_output['out'] - logging.debug(f'Shape of augmented output: {augmented_output.shape}') - # reverse augmentation for outputs - deaugmented_output = transformer.deaugment_mask(augmented_output).squeeze(dim=0) - output_lst.append(deaugmented_output) - outputs = torch.stack(output_lst) - outputs = outputs.permute(0, 2, 3, 1).squeeze(dim=0) - outputs = outputs.cpu().numpy() * hann_win - fp[:, h_idxs[0]:h_idxs[0] + h_idxs[1], w_idxs[0]:w_idxs[0] + w_idxs[1], :] += outputs - fp.flush() - del fp - - fp = np.memmap(tp_mem, dtype='float16', mode='r', shape=(tf_len, h_padded, w_padded, num_classes)) - pred_heatmap = np.zeros((h_padded, w_padded, num_classes), dtype=heatmap_dtype) - for row, col in tqdm(itertools.product(range(0, h_padded, chunk_size), - range(0, w_padded, chunk_size)), - leave=False, position=0, desc="Writing to array"): - arr1 = (fp[:, row:row + chunk_size, col:col + chunk_size, :]).mean(axis=0) - if single_class_mode: - arr1 = sigmoid(arr1) - else: - arr1 = softmax(arr1, axis=-1) - heatmap_max = np.iinfo(heatmap_dtype).max - arr1 = stretch_heatmap(heatmap_arr=arr1, out_max=heatmap_max) - - pred_heatmap[row:row + chunk_size, col:col + chunk_size, :] = arr1.astype(heatmap_dtype) - end_time = time.time() - start_time - # logging.info('Segmentation operation completed in {:.0f}m {:.0f}s'.format(end_seg // 60, end_seg % 60)) - logging.info('Segmentation Completed in {:.0f}m {:.0f}s'.format(end_time // 60, end_time % 60)) - - if debug: - logging.debug(f'Bin count of final output: {np.unique(pred_heatmap, return_counts=True)}') - input_image.close() - return pred_heatmap[:input_image.height, :input_image.width] - + eval_batch_size_rd = batch_size + # get max ram for smallest gpu + smallest_gpu_ram = min(gpu_info['max_ram'] for _, gpu_info in gpu_devices_dict.items()) + # rule of thumb to determine eval batch size based on approximate max pixels a gpu can handle during evaluation + pix_per_mb_gpu = (batch_size / len(gpu_devices_dict.keys()) * sample_size ** 2) / smallest_gpu_ram + if pix_per_mb_gpu >= max_pix_per_mb_gpu: + eval_batch_size = smallest_gpu_ram * max_pix_per_mb_gpu / sample_size ** 2 + eval_batch_size_rd = int(eval_batch_size - eval_batch_size % len(gpu_devices_dict.keys())) + eval_batch_size_rd = 1 if eval_batch_size_rd < 1 else eval_batch_size_rd + logging.warning(f'Validation and test batch size downgraded from {batch_size} to {eval_batch_size} ' + f'based on max ram of smallest GPU available') + return eval_batch_size_rd def calc_inference_chunk_size(gpu_devices_dict: dict, max_pix_per_mb_gpu: int = 200, default: int = 512) -> int: """ @@ -256,117 +63,21 @@ def calc_inference_chunk_size(gpu_devices_dict: dict, max_pix_per_mb_gpu: int = # rule of thumb to determine max chunk size based on approximate max pixels a gpu can handle during inference max_chunk_size = sqrt(max_pix_per_mb_gpu * smallest_gpu_ram) max_chunk_size_rd = int(max_chunk_size - (max_chunk_size % 256)) # round to the closest multiple of 256 - logging.info(f'Data will be split into chunks of {max_chunk_size_rd}') + logging.info(f'Data will be split into chunks of {max_chunk_size_rd} if chunk_size is not specified.') return max_chunk_size_rd -def override_model_params_from_checkpoint( - params: DictConfig, - checkpoint_params) -> DictConfig: - """ - Overrides model-architecture related parameters from provided checkpoint parameters - @param params: Original parameters as inputted through hydra - @param checkpoint_params: Checkpoint parameters as saved during checkpoint creation when training - @return: - """ - bands = get_key_def('bands', params['dataset'], expected_type=Sequence) - classes = get_key_def('classes_dict', params['dataset'], expected_type=(dict, DictConfig)) - clip_limit = get_key_def('clahe_clip_limit', params['tiling'], expected_type=int) - normalization = get_key_def('normalization', params['augmentation'], expected_type=DictConfig) - scale_data = get_key_def('scale_data', params['augmentation'], expected_type=ListConfig) - - bands_ckpt = get_key_def('bands', checkpoint_params['dataset'], expected_type=Sequence) - classes_ckpt = get_key_def('classes_dict', checkpoint_params['dataset'], expected_type=(dict, DictConfig)) - model_ckpt = get_key_def('model', checkpoint_params, expected_type=(dict, DictConfig)) - clip_limit_ckpt = get_key_def('clahe_clip_limit', checkpoint_params['tiling'], expected_type=int) - normalization_ckpt = get_key_def('normalization', checkpoint_params['augmentation'], expected_type=(dict, DictConfig)) - # Workaround for "omegaconf.errors.UnsupportedValueType: Value 'CommentedSeq' is not a supported primitive type" - if normalization_ckpt is not None and isinstance(list(normalization_ckpt.values())[0], CommentedSeq): - normalization_ckpt = {k: [float(val) for val in v] for k, v in normalization_ckpt.items()} - scale_data_ckpt = get_key_def('scale_data', checkpoint_params['augmentation'], expected_type=(List, ListConfig)) - scale_data_ckpt = list(scale_data_ckpt) - - if model_ckpt != params.model or classes_ckpt != classes or bands_ckpt != bands \ - or clip_limit != clip_limit_ckpt: - logging.info("\nParameters from checkpoint will override inputted parameters." - f"\n\t\t\t Inputted | Overriden" - f"\nModel:\t\t {params.model} | {model_ckpt}" - f"\nInput bands:\t\t{bands} | {bands_ckpt}" - f"\nOutput classes:\t\t{classes} | {classes_ckpt}" - f"\nNormalization means and stds:\t\t{normalization} | {normalization_ckpt}" - f"\nScale data range:\t\t{scale_data} | {scale_data_ckpt}" - f"\nRaster enhance clip limit:\t\t{clip_limit} | {clip_limit_ckpt}") - with open_dict(params): - params['model'] = model_ckpt - params['dataset']['bands'] = bands_ckpt - params['dataset']['classes_dict'] = classes_ckpt - params['augmentation']['normalization'] = normalization_ckpt - params['augmentation']['scale_data'] = scale_data_ckpt - params['tiling']['clahe_clip_limit'] = clip_limit_ckpt - return params - - -def stac_input_to_temp_csv(input_stac_item: Union[str, Path]) -> Path: - """Saves a stac item path or url to a temporary csv""" - _, stac_temp_csv = mkstemp(suffix=".csv") - with open(stac_temp_csv, "w", newline="") as fh: - csv.writer(fh).writerow([str(input_stac_item), None, "inference", Path(input_stac_item).stem]) - return Path(stac_temp_csv) - - -def main(params: Union[DictConfig, dict]) -> None: - """ - Function to manage details about the inference on segmentation task. - 1. Read the parameters from the config given. - 2. Read and load the state dict from the previous training or the given one. - 3. Make the inference on the data specified in the config. - ------- - :param params: (dict) Parameters inputted during execution. - """ - # Main params +def main(params:Union[DictConfig, Dict]): + working_folder = get_key_def('root_dir', params['inference'], default="inference", to_path=True) working_folder.mkdir(exist_ok=True) - - state_dict = get_key_def('state_dict_path', params['inference'], to_path=True, + model_path = get_key_def('model_path', + params['inference'], + to_path=True, validate_path_exists=True, - wildcard='*pth.tar') - inference_image = get_key_def(key='output_path', config=params['inference'], to_path=True, expected_type=str) - if inference_image: - inference_image.parent.mkdir(exist_ok=True) - - models_dir = get_key_def('checkpoint_dir', params['inference'], default=working_folder / 'checkpoints', to_path=True) - models_dir.mkdir(exist_ok=True) - data_dir = get_key_def('raw_data_dir', params['dataset'], default="data", to_path=True, validate_path_exists=True) - download_data = get_key_def('download_data', params['inference'], default=False, expected_type=bool) - override = get_key_def('override_model_params', params['inference'], default=False, expected_type=bool) - - # Override params from checkpoint - checkpoint = read_checkpoint(state_dict, out_dir=models_dir, update=True) - if override: - params = override_model_params_from_checkpoint(params=params,checkpoint_params=checkpoint['params']) - - # Dataset params - bands_requested = get_key_def('bands', params['dataset'], default=[1, 2, 3], expected_type=Sequence) - classes_dict = get_key_def('classes_dict', params['dataset'], expected_type=DictConfig) - num_classes = len(classes_dict) - num_classes = num_classes + 1 if num_classes > 1 else num_classes # multiclass account for background - num_bands = len(bands_requested) - - # Default input directory based on default output directory - raw_data_csv = get_key_def('raw_data_csv', params['inference'], expected_type=str, to_path=True, - validate_path_exists=True) - input_stac_item = get_key_def('input_stac_item', params['inference'], expected_type=str, to_path=True, - validate_path_exists=True) - prep_data_only = get_key_def('prep_data_only', params['inference'], default=False, expected_type=bool) - - # LOGGING PARAMETERS - exper_name = get_key_def('project_name', params['general'], default='gdl-training') - run_name = get_key_def(['tracker', 'run_name'], params, default='gdl') - tracker_uri = get_key_def(['tracker', 'uri'], params, default=None, expected_type=str, to_path=False) - set_tracker(mode='inference', type='mlflow', task='segmentation', experiment_name=exper_name, run_name=run_name, - tracker_uri=tracker_uri, params=params, keys2log=['general', 'dataset', 'model', 'inference']) - - # OPTIONAL PARAMETERS + wildcard='*pt') + + # Set the device num_devices = get_key_def('gpu', params['inference'], default=0, expected_type=(int, bool)) if num_devices > 1: logging.warning(f"Inference is not yet implemented for multi-gpu use. Will request only 1 GPU.") @@ -375,27 +86,30 @@ def main(params: Union[DictConfig, dict]) -> None: if not (0 <= max_used_ram <= 100): raise ValueError(f'\nMax used ram parameter should be a percentage. Got {max_used_ram}.') max_used_perc = get_key_def('max_used_perc', params['inference'], default=25, expected_type=int) - scale = get_key_def('scale_data', params['augmentation'], default=[0, 1], expected_type=ListConfig) - raster_to_vec = get_key_def('ras2vec', params['inference'], default=False) - debug = get_key_def('debug', params, default=False, expected_type=bool) - if debug: - logging.warning(f'\nDebug mode activated. Some debug features may mobilize extra disk space and ' - f'cause delays in execution.') - - # list of GPU devices that are available and unused. If no GPUs, returns empty dict gpu_devices_dict = get_device_ids(num_devices, max_used_ram_perc=max_used_ram, max_used_perc=max_used_perc) max_pix_per_mb_gpu = get_key_def('max_pix_per_mb_gpu', params['inference'], default=25, expected_type=int) auto_chunk_size = calc_inference_chunk_size(gpu_devices_dict=gpu_devices_dict, max_pix_per_mb_gpu=max_pix_per_mb_gpu, default=512) + + chunk_size = get_key_def('chunk_size', params['inference'], default=auto_chunk_size, expected_type=int) + batch_size = calc_eval_batchsize(gpu_devices_dict=gpu_devices_dict, + batch_size=8, + sample_size=chunk_size, + max_pix_per_mb_gpu=1000) device = set_device(gpu_devices_dict=gpu_devices_dict) - + + + # Dataset params + bands_requested = get_key_def('bands', params['dataset'], default=[1, 2, 3], expected_type=Sequence) + download_data = get_key_def('download_data', params['inference'], default=False, expected_type=bool) + data_dir = get_key_def('raw_data_dir', params['dataset'], default="data", to_path=True, validate_path_exists=True) clahe_clip_limit = get_key_def('clahe_clip_limit', params['tiling'], expected_type=Number, default=0) - heatmap_dtype = get_key_def('heatmap_dtype', params['inference'], default=np.uint16) - save_heatmap = get_key_def('save_heatmap', params['inference'], default=True, expected_type=bool) - use_hanning = get_key_def('use_hanning', params['inference'], default=True, expected_type=bool) - heatmap_threshold = get_key_def('heatmap_threshold', params['inference'], default=0.5, expected_type=float) - + raw_data_csv = get_key_def('raw_data_csv', params['inference'], expected_type=str, to_path=True, + validate_path_exists=True) + input_stac_item = get_key_def('input_stac_item', params['inference'], expected_type=str, to_path=True, + validate_path_exists=True) + if raw_data_csv and input_stac_item: raise ValueError(f"Input imagery should be either a csv of stac item. Got inputs from both \"raw_data_csv\" " f"and \"input stac item\"") @@ -405,16 +119,7 @@ def main(params: Union[DictConfig, dict]) -> None: logging.warning(f"Requested bands are not valid stac item common names. Got: {bands_requested}") bands_requested = [SingleBandItemEO.band_to_cname(band) for band in bands_requested] logging.warning(f"Will request: {bands_requested}") - - model = define_model( - net_params=params.model, - in_channels=num_bands, - out_classes=num_classes, - main_device=device, - devices=[list(gpu_devices_dict.keys())], - checkpoint_dict=checkpoint, - ) - + # GET LIST OF INPUT IMAGES FOR INFERENCE list_aois = aois_from_csv( csv_path=raw_data_csv, @@ -423,76 +128,22 @@ def main(params: Union[DictConfig, dict]) -> None: data_dir=data_dir, equalize_clahe_clip_limit=clahe_clip_limit, ) - - if len(list_aois) > 1 and inference_image: - raise ValueError(f"\n\"inference.output_path\" should be set for a single inference only. \n" - f"Got {len(list_aois)} AOIs for inference.\n") - - if prep_data_only: - logging.info(f"[prep_data_only mode] Data preparation for inference is complete. Exiting...") - exit() - + + # Create the inference object + device_str = "gpu" if device.type == 'cuda' else "cpu" + gpu_index = device.index if device.type == 'cuda' else 0 + + geo_inference = GeoInference(model=str(model_path), + work_dir=str(working_folder), + batch_size=batch_size, + mask_to_vec=False, + device=device_str, + gpu_id=gpu_index, + ) + # LOOP THROUGH LIST OF INPUT IMAGES for aoi in tqdm(list_aois, desc='Inferring from images', position=0, leave=True): - output_path = working_folder / f"{aoi.aoi_id}_pred.tif" if not inference_image else inference_image - inference_heatmap = output_path.parent / f"{output_path.stem}_heatmap.tif" - temp_file = output_path.parent / f"{output_path.stem}_heatmap.dat" logging.info(f'\nReading image: {aoi.aoi_id}') - inf_meta = aoi.raster.meta - - pred_heatmap = segmentation( - param=params, - input_image=aoi.raster, - num_classes=num_classes, - model=model, - chunk_size=chunk_size, - use_hanning=use_hanning, - device=device, - scale=scale, - tp_mem=temp_file, - heatmap_dtype=heatmap_dtype, - debug=debug - ) - - inf_meta.update({"driver": "GTiff", - "height": pred_heatmap.shape[1], - "width": pred_heatmap.shape[2], - "count": pred_heatmap.shape[0], - "dtype": 'uint8', - "compress": 'lzw'}) - logging.info(f'\nSuccessfully inferred on {aoi.aoi_id}\nWriting to file: {output_path}') - - pred_img = class_from_heatmap(heatmap_arr=pred_heatmap, heatmap_threshold=heatmap_threshold) - - if save_heatmap: - logging.info(f"\nSaving heatmap...") - pred_heatmap = reshape_as_raster(pred_heatmap) - create_new_raster_from_base( - input_raster=aoi.raster, - output_raster=inference_heatmap, - write_array=pred_heatmap, - dtype=heatmap_dtype, - checkpoint_path=state_dict, - classes_dict=classes_dict, - ) - logging.info(f'\nSaved heatmap to {inference_heatmap}') - - create_new_raster_from_base( - input_raster=aoi.raster, - output_raster=output_path, - write_array=pred_img, - checkpoint_path=state_dict, - classes_dict=classes_dict, - ) - del pred_heatmap - - try: - temp_file.unlink() - except OSError as e: - logging.warning(f'File Error: {temp_file, e.strerror}') - if raster_to_vec: - start_vec = time.time() - inference_vec = working_folder.joinpath(f"{aoi.aoi_id}_pred.gpkg") - ras2vec(output_path, inference_vec) - end_vec = time.time() - start_vec - logging.info('Vectorization completed in {:.0f}m {:.0f}s'.format(end_vec // 60, end_vec % 60)) + input_path = aoi.raster.name + geo_inference(input_path, patch_size=chunk_size) + \ No newline at end of file From 22ecdaa20270eaa520239b98c0631d3da7495070 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 17 Apr 2024 17:39:05 -0400 Subject: [PATCH 03/47] Update model_path in inference config files --- config/inference/default_binary.yaml | 3 +-- config/inference/default_multiclass.yaml | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/config/inference/default_binary.yaml b/config/inference/default_binary.yaml index e5825a8a..2039cce1 100644 --- a/config/inference/default_binary.yaml +++ b/config/inference/default_binary.yaml @@ -3,13 +3,12 @@ inference: raw_data_csv: tests/inference/inference_segmentation_binary.csv root_dir: inferences input_stac_item: # alternatively, use a path or url to stac item directly - state_dict_path: ${general.save_weights_dir}/ + model_path: ${general.save_weights_dir}/ output_path: checkpoint_dir: # (string, optional): directory in which to save the object if url chunk_size: # if empty, will be calculated automatically from max_pix_per_mb_gpu # Maximum number of pixels each Mb of GPU Ram to allow. E.g. if GPU has 1000 Mb of Ram and this parameter is set to # 10, chunk_size will be set to sqrt(1000 * 10) = 100. - use_hanning: True # enables smoothening with Hann windows; creates overlapping tiles. max_pix_per_mb_gpu: 25 prep_data_only: False override_model_params: False diff --git a/config/inference/default_multiclass.yaml b/config/inference/default_multiclass.yaml index 1917eaef..ac8b0954 100644 --- a/config/inference/default_multiclass.yaml +++ b/config/inference/default_multiclass.yaml @@ -3,7 +3,7 @@ inference: raw_data_csv: tests/inference/inference_segmentation_multiclass.csv root_dir: inferences input_stac_item: # alternatively, use a path or url to stac item directly - state_dict_path: ${general.save_weights_dir}/ + model_path: ${general.save_weights_dir}/ output_path: checkpoint_dir: # (string, optional): directory in which to save the object if url chunk_size: # if empty, will be calculated automatically from max_pix_per_mb_gpu From 24dd60b79deed6692f3ceea1e3872a9114597803 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 1 May 2024 15:12:36 -0400 Subject: [PATCH 04/47] Refactor batch size calculation and add stride size in geo_inference --- inference_segmentation.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/inference_segmentation.py b/inference_segmentation.py index aba11eb1..c1d1e96f 100644 --- a/inference_segmentation.py +++ b/inference_segmentation.py @@ -93,10 +93,7 @@ def main(params:Union[DictConfig, Dict]): chunk_size = get_key_def('chunk_size', params['inference'], default=auto_chunk_size, expected_type=int) - batch_size = calc_eval_batchsize(gpu_devices_dict=gpu_devices_dict, - batch_size=8, - sample_size=chunk_size, - max_pix_per_mb_gpu=1000) + batch_size = get_key_def('batch_size', params['inference'], default=8, expected_type=int) device = set_device(gpu_devices_dict=gpu_devices_dict) @@ -145,5 +142,5 @@ def main(params:Union[DictConfig, Dict]): for aoi in tqdm(list_aois, desc='Inferring from images', position=0, leave=True): logging.info(f'\nReading image: {aoi.aoi_id}') input_path = aoi.raster.name - geo_inference(input_path, patch_size=chunk_size) + geo_inference(input_path, patch_size=chunk_size, stride_size=chunk_size//2) \ No newline at end of file From 71b60c27b0c2e7da3b7f4a938a309ad6e7838763 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 1 May 2024 15:12:55 -0400 Subject: [PATCH 05/47] Add batch_size parameter to inference configuration files --- config/inference/default_binary.yaml | 1 + config/inference/default_multiclass.yaml | 1 + 2 files changed, 2 insertions(+) diff --git a/config/inference/default_binary.yaml b/config/inference/default_binary.yaml index 2039cce1..718cf6fa 100644 --- a/config/inference/default_binary.yaml +++ b/config/inference/default_binary.yaml @@ -6,6 +6,7 @@ inference: model_path: ${general.save_weights_dir}/ output_path: checkpoint_dir: # (string, optional): directory in which to save the object if url + batch_size: 8 chunk_size: # if empty, will be calculated automatically from max_pix_per_mb_gpu # Maximum number of pixels each Mb of GPU Ram to allow. E.g. if GPU has 1000 Mb of Ram and this parameter is set to # 10, chunk_size will be set to sqrt(1000 * 10) = 100. diff --git a/config/inference/default_multiclass.yaml b/config/inference/default_multiclass.yaml index ac8b0954..76bfd63a 100644 --- a/config/inference/default_multiclass.yaml +++ b/config/inference/default_multiclass.yaml @@ -6,6 +6,7 @@ inference: model_path: ${general.save_weights_dir}/ output_path: checkpoint_dir: # (string, optional): directory in which to save the object if url + batch_size: 8 chunk_size: # if empty, will be calculated automatically from max_pix_per_mb_gpu # Maximum number of pixels each Mb of GPU Ram to allow. E.g. if GPU has 1000 Mb of Ram and this parameter is set to # 10, chunk_size will be set to sqrt(1000 * 10) = 100. From 138f76e41a7834d89e8ba28b6ca2d43726272fa9 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 15 May 2024 09:31:20 -0400 Subject: [PATCH 06/47] Update geo_inference function call in inference_segmentation.py --- inference_segmentation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inference_segmentation.py b/inference_segmentation.py index c1d1e96f..567ad040 100644 --- a/inference_segmentation.py +++ b/inference_segmentation.py @@ -142,5 +142,5 @@ def main(params:Union[DictConfig, Dict]): for aoi in tqdm(list_aois, desc='Inferring from images', position=0, leave=True): logging.info(f'\nReading image: {aoi.aoi_id}') input_path = aoi.raster.name - geo_inference(input_path, patch_size=chunk_size, stride_size=chunk_size//2) + geo_inference(input_path, patch_size=chunk_size) \ No newline at end of file From fd5dfe8be9e688be8983c653547fb7b62a3090d6 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 15 May 2024 09:54:11 -0400 Subject: [PATCH 07/47] Remove unused function and add logging parameters --- inference_segmentation.py | 33 ++++++++------------------------- 1 file changed, 8 insertions(+), 25 deletions(-) diff --git a/inference_segmentation.py b/inference_segmentation.py index 567ad040..cbef54b3 100644 --- a/inference_segmentation.py +++ b/inference_segmentation.py @@ -12,8 +12,7 @@ from utils.aoiutils import aois_from_csv from utils.logger import get_logger, set_tracker from geo_inference.geo_inference import GeoInference -from utils.utils import get_device_ids, get_key_def, \ -add_metadata_from_raster_to_sample, set_device +from utils.utils import get_device_ids, get_key_def, set_device # Set the logging file logging = get_logger(__name__) @@ -25,29 +24,6 @@ def stac_input_to_temp_csv(input_stac_item: Union[str, Path]) -> Path: csv.writer(fh).writerow([str(input_stac_item), None, "inference", Path(input_stac_item).stem]) return Path(stac_temp_csv) -def calc_eval_batchsize(gpu_devices_dict: dict, batch_size: int, sample_size: int, max_pix_per_mb_gpu: int = 280): - """ - Calculate maximum batch size that could fit on GPU during evaluation based on thumb rule with harcoded - "pixels per MB of GPU RAM" as threshold. The batch size often needs to be smaller if crop is applied during training - @param gpu_devices_dict: dictionary containing info on GPU devices as returned by lst_device_ids (utils.py) - @param batch_size: batch size for training - @param sample_size: size of patches - @return: returns a downgraded evaluation batch size if the original batch size is considered too high compared to - the GPU's memory - """ - eval_batch_size_rd = batch_size - # get max ram for smallest gpu - smallest_gpu_ram = min(gpu_info['max_ram'] for _, gpu_info in gpu_devices_dict.items()) - # rule of thumb to determine eval batch size based on approximate max pixels a gpu can handle during evaluation - pix_per_mb_gpu = (batch_size / len(gpu_devices_dict.keys()) * sample_size ** 2) / smallest_gpu_ram - if pix_per_mb_gpu >= max_pix_per_mb_gpu: - eval_batch_size = smallest_gpu_ram * max_pix_per_mb_gpu / sample_size ** 2 - eval_batch_size_rd = int(eval_batch_size - eval_batch_size % len(gpu_devices_dict.keys())) - eval_batch_size_rd = 1 if eval_batch_size_rd < 1 else eval_batch_size_rd - logging.warning(f'Validation and test batch size downgraded from {batch_size} to {eval_batch_size} ' - f'based on max ram of smallest GPU available') - return eval_batch_size_rd - def calc_inference_chunk_size(gpu_devices_dict: dict, max_pix_per_mb_gpu: int = 200, default: int = 512) -> int: """ Calculate maximum chunk_size that could fit on GPU during inference based on thumb rule with hardcoded @@ -116,6 +92,13 @@ def main(params:Union[DictConfig, Dict]): logging.warning(f"Requested bands are not valid stac item common names. Got: {bands_requested}") bands_requested = [SingleBandItemEO.band_to_cname(band) for band in bands_requested] logging.warning(f"Will request: {bands_requested}") + + # LOGGING PARAMETERS + exper_name = get_key_def('project_name', params['general'], default='gdl-training') + run_name = get_key_def(['tracker', 'run_name'], params, default='gdl') + tracker_uri = get_key_def(['tracker', 'uri'], params, default=None, expected_type=str, to_path=False) + set_tracker(mode='inference', type='mlflow', task='segmentation', experiment_name=exper_name, run_name=run_name, + tracker_uri=tracker_uri, params=params, keys2log=['general', 'dataset', 'model', 'inference']) # GET LIST OF INPUT IMAGES FOR INFERENCE list_aois = aois_from_csv( From e7d610eadd5935207587131149afd3fa120a5d44 Mon Sep 17 00:00:00 2001 From: valhassan Date: Wed, 15 May 2024 13:22:20 -0400 Subject: [PATCH 08/47] Add inference segmentation test files --- tests/data/inference/test.csv | 1 + tests/data/inference/test.tif | Bin 0 -> 4560564 bytes tests/data/inference/test_model.pt | Bin 0 -> 15254910 bytes tests/test_inference_segmentation.py | 27 +++++++++++++++++++++++++++ 4 files changed, 28 insertions(+) create mode 100644 tests/data/inference/test.csv create mode 100644 tests/data/inference/test.tif create mode 100644 tests/data/inference/test_model.pt create mode 100644 tests/test_inference_segmentation.py diff --git a/tests/data/inference/test.csv b/tests/data/inference/test.csv new file mode 100644 index 00000000..75eec158 --- /dev/null +++ b/tests/data/inference/test.csv @@ -0,0 +1 @@ +tests/data/inference/test.tif diff --git a/tests/data/inference/test.tif b/tests/data/inference/test.tif new file mode 100644 index 0000000000000000000000000000000000000000..5911c7fb0bd3bc3b6792156f737ddc25dd019df7 GIT binary patch literal 4560564 zcmeFYX_y<;cK5ps#uzYQz<>c`_uOjDvs9(hJWD01B$eiQX!Sf#>Up%?w%c~wZrg3U zJz^W%*v5bX13u@FbGQjf$PYB;*N6?vl-W@B8I`xZe-{vwN#l 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