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[WWB]: Added validation for Inpainting pipeline (#1451)
Co-authored-by: Ilya Lavrenov <[email protected]>
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tools/who_what_benchmark/whowhatbench/inpaint_evaluator.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,133 @@ | ||
| import os | ||
| from typing import Any, Union | ||
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| import datasets | ||
| import pandas as pd | ||
| from tqdm import tqdm | ||
| from transformers import set_seed | ||
| import torch | ||
| import openvino_genai | ||
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| from .registry import register_evaluator | ||
| from .text2image_evaluator import Text2ImageEvaluator | ||
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| from .whowhat_metrics import ImageSimilarity | ||
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| def preprocess_fn(example): | ||
| return { | ||
| "prompts": example["inpaint_caption"], | ||
| "images": example["coco_image"], | ||
| "masks": example["mask"], | ||
| } | ||
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| def prepare_default_data(num_samples=None): | ||
| DATASET_NAME = "phiyodr/InpaintCOCO" | ||
| NUM_SAMPLES = 10 if num_samples is None else num_samples | ||
| set_seed(42) | ||
| default_dataset = datasets.load_dataset( | ||
| DATASET_NAME, split="test", streaming=True | ||
| ).filter(lambda example: example["inpaint_caption"] != "").take(NUM_SAMPLES) | ||
| return default_dataset.map( | ||
| lambda x: preprocess_fn(x), remove_columns=default_dataset.column_names | ||
| ) | ||
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| @register_evaluator("image-inpainting") | ||
| class InpaintingEvaluator(Text2ImageEvaluator): | ||
| def __init__( | ||
| self, | ||
| base_model: Any = None, | ||
| gt_data: str = None, | ||
| test_data: Union[str, list] = None, | ||
| metrics="similarity", | ||
| similarity_model_id: str = "openai/clip-vit-large-patch14", | ||
| num_inference_steps=4, | ||
| crop_prompts=True, | ||
| num_samples=None, | ||
| gen_image_fn=None, | ||
| seed=42, | ||
| is_genai=False, | ||
| ) -> None: | ||
| assert ( | ||
| base_model is not None or gt_data is not None | ||
| ), "Text generation pipeline for evaluation or ground trush data must be defined" | ||
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| self.test_data = test_data | ||
| self.metrics = metrics | ||
| self.crop_prompt = crop_prompts | ||
| self.num_samples = num_samples | ||
| self.num_inference_steps = num_inference_steps | ||
| self.seed = seed | ||
| self.similarity = None | ||
| self.similarity = ImageSimilarity(similarity_model_id) | ||
| self.last_cmp = None | ||
| self.gt_dir = os.path.dirname(gt_data) | ||
| self.generation_fn = gen_image_fn | ||
| self.is_genai = is_genai | ||
| self.resolution = None | ||
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| if base_model: | ||
| self.gt_data = self._generate_data( | ||
| base_model, gen_image_fn, os.path.join(self.gt_dir, "reference") | ||
| ) | ||
| else: | ||
| self.gt_data = pd.read_csv(gt_data, keep_default_na=False) | ||
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| def _generate_data(self, model, gen_image_fn=None, image_dir="reference"): | ||
| def default_gen_image_fn(model, prompt, image, mask, num_inference_steps, generator=None): | ||
| with torch.no_grad(): | ||
| output = model( | ||
| prompt, | ||
| image=image, | ||
| mask_image=mask, | ||
| num_inference_steps=num_inference_steps, | ||
| output_type="pil", | ||
| generator=generator, | ||
| ) | ||
| return output.images[0] | ||
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| generation_fn = gen_image_fn or default_gen_image_fn | ||
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| if self.test_data: | ||
| if isinstance(self.test_data, str): | ||
| data = pd.read_csv(self.test_data) | ||
| else: | ||
| if isinstance(self.test_data, dict): | ||
| assert "prompts" in self.test_data | ||
| assert "images" in self.test_data | ||
| assert "masks" in self.test_data | ||
| data = dict(self.test_data) | ||
| data = pd.DataFrame.from_dict(data) | ||
| else: | ||
| data = pd.DataFrame.from_dict(prepare_default_data(self.num_samples)) | ||
|
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| prompts = data["prompts"] | ||
| images = data["images"] | ||
| masks = data["masks"] | ||
| output_images = [] | ||
| rng = torch.Generator(device="cpu") | ||
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| if not os.path.exists(image_dir): | ||
| os.makedirs(image_dir) | ||
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| for i, (prompt, image, mask) in tqdm(enumerate(zip(prompts, images, masks)), desc="Evaluate pipeline"): | ||
| set_seed(self.seed) | ||
| rng = rng.manual_seed(self.seed) | ||
| output = generation_fn( | ||
| model, | ||
| prompt, | ||
| image=image, | ||
| mask=mask, | ||
| num_inference_steps=self.num_inference_steps, | ||
| generator=openvino_genai.TorchGenerator(self.seed) if self.is_genai else rng | ||
| ) | ||
| image_path = os.path.join(image_dir, f"{i}.png") | ||
| output.save(image_path) | ||
| output_images.append(image_path) | ||
|
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| res_data = {"prompts": list(prompts), "images": output_images} | ||
| df = pd.DataFrame(res_data) | ||
|
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| return df |
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