diff --git a/pyproject.toml b/pyproject.toml index 5eaf33941..5450ce38d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,6 +60,8 @@ gptqmodel = [ { index = "pruna_internal", marker = "sys_platform != 'darwin' or platform_machine != 'arm64'"}, { index = "pypi", marker = "sys_platform == 'darwin' and platform_machine == 'arm64'"}, ] +image-reward = { git = "https://github.com/PrunaAI/ImageReward" } +hpsv2 = { git = "https://github.com/PrunaAI/HPSv2" } [project] name = "pruna" @@ -70,7 +72,7 @@ authors = [ ] license = {file = "LICENSE"} readme = "README.md" -requires-python = ">=3.9,<3.13" +requires-python = ">=3.10,<3.13" keywords = ["AI", "machine learning", "model optimization", "pruning"] classifiers = [ "Development Status :: 4 - Beta", @@ -106,7 +108,6 @@ dependencies = [ "codecarbon", "pynvml", "thop", - "timm", "bitsandbytes; sys_platform != 'darwin' or platform_machine != 'arm64'", "optimum-quanto>=0.2.5", "ctranslate2==4.6.0", @@ -115,11 +116,14 @@ dependencies = [ "torchao", "llmcompressor", "gliner; python_version >= '3.10'", - "piq", "opencv-python", "kernels", - "aenum" - + "aenum", + "image-reward", + "piq", + "clip", + "hpsv2", + "t2v-metrics", ] [project.optional-dependencies] diff --git a/src/pruna/evaluation/metrics/__init__.py b/src/pruna/evaluation/metrics/__init__.py index 2559d501b..d8cccddf3 100644 --- a/src/pruna/evaluation/metrics/__init__.py +++ b/src/pruna/evaluation/metrics/__init__.py @@ -20,6 +20,8 @@ from pruna.evaluation.metrics.metric_memory import DiskMemoryMetric, InferenceMemoryMetric, TrainingMemoryMetric from pruna.evaluation.metrics.metric_model_architecture import TotalMACsMetric, TotalParamsMetric from pruna.evaluation.metrics.metric_pairwise_clip import PairwiseClipScore +from pruna.evaluation.metrics.metric_reward import HPSMetric, HPSv2Metric, ImageRewardMetric +from pruna.evaluation.metrics.metric_sharpness import SharpnessMetric from pruna.evaluation.metrics.metric_torch import TorchMetricWrapper __all__ = [ @@ -37,4 +39,8 @@ "TotalMACsMetric", "PairwiseClipScore", "CMMD", + "ImageRewardMetric", + "HPSMetric", + "HPSv2Metric", + "SharpnessMetric", ] diff --git a/src/pruna/evaluation/metrics/metric_reward.py b/src/pruna/evaluation/metrics/metric_reward.py new file mode 100644 index 000000000..150385bdd --- /dev/null +++ b/src/pruna/evaluation/metrics/metric_reward.py @@ -0,0 +1,444 @@ +# Copyright 2025 - Pruna AI GmbH. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import Any, List + +import PIL +import torch +from torch import Tensor +from torchvision.transforms import ToPILImage + +from pruna.engine.utils import set_to_best_available_device +from pruna.evaluation.metrics.metric_stateful import StatefulMetric +from pruna.evaluation.metrics.registry import MetricRegistry +from pruna.evaluation.metrics.result import MetricResult +from pruna.evaluation.metrics.utils import ( + SINGLE, + get_call_type_for_single_metric, + metric_data_processor, +) +from pruna.logging.logger import pruna_logger + +IMAGE_REWARD = "image_reward" +HPS_REWARD = "hps" +HPSv2_REWARD = "hpsv2" +VQA_REWARD = "vqa" + + +class BaseModelRewardMetric(StatefulMetric): + """Base class for model reward metrics.""" + + higher_is_better: bool = True + default_call_type: str = "y" + metric_units: str = "score" + + # Type annotations for dynamically added attributes + scores: List[float] + prompts: List[str] + + def __init__( + self, + device: str | torch.device | None = None, + call_type: str = SINGLE, + model_load_kwargs: dict = {}, + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.device = set_to_best_available_device(device) + self.call_type = get_call_type_for_single_metric(call_type, self.default_call_type) + self.to_pil = ToPILImage() + + # Initialize state for accumulating scores + self.add_state("scores", []) + self.add_state("prompts", []) + self._load(**model_load_kwargs) + + def _load(self, **kwargs: Any) -> None: + raise NotImplementedError("Subclasses must implement this method") + + def _score_image(self, prompt: str, image: PIL.Image.Image) -> float: + raise NotImplementedError("Subclasses must implement this method") + + def update(self, x: List[str] | Tensor, gt: Tensor, outputs: Tensor) -> None: + """ + Update the metric with new batch data. + + Parameters + ---------- + x : List[str] | Tensor + The input prompts for text-to-image generation. + gt : Tensor + The ground truth images (not used for ImageReward). + outputs : Tensor + The generated images to evaluate. + """ + # Prepare inputs + metric_inputs = metric_data_processor(x, gt, outputs, self.call_type, device=self.device) + prompts = self._extract_prompts(x) + images = metric_inputs[1] if len(metric_inputs) > 1 else outputs + + # Format images as PIL Images + formatted_images = [self._format_image(image) for image in images] + + # Score images with prompts + for prompt, image in zip(prompts, formatted_images): + score = self._score_image(prompt=prompt, image=image) + self.scores.append(score) + self.prompts.append(prompt) + + def compute(self) -> MetricResult: + """ + Compute the final ImageReward metric. + + Returns + ------- + MetricResult + The computed ImageReward score. + """ + if not self.scores: + pruna_logger.warning("No scores available for ImageReward computation") + return MetricResult(self.metric_name, self.__dict__.copy(), 0.0) + + # Calculate mean score + mean_score = torch.mean(torch.tensor(self.scores)).item() + + return MetricResult(self.metric_name, self.__dict__.copy(), mean_score) + + def _extract_prompts(self, x: List[str] | Tensor) -> List[str]: + """ + Extract prompts from input data. + + Parameters + ---------- + x : List[str] | Tensor + The input data containing prompts. + + Returns + ------- + List[str] + The extracted prompts. + """ + if isinstance(x, list): + return x + elif isinstance(x, Tensor): + # If x is a tensor, we need to handle it differently + # This might be the case for some data formats + pruna_logger.warning("Input x is a tensor, assuming it contains encoded prompts") + return [f"prompt_{i}" for i in range(x.shape[0])] + else: + pruna_logger.error(f"Unexpected input type for prompts: {type(x)}") + return [] + + def _format_image(self, image: Tensor) -> PIL.Image.Image: + """ + Format a single image with its prompt using ImageReward. + + Parameters + ---------- + image : Tensor + The image to score. + + Returns + ------- + PIL.Image.Image + The formatted PIL image. + """ + # Convert tensor to PIL Image + if image.dim() == 4: + # Batch dimension, take first image + image = image[0] + + # Handle different tensor formats + if image.dim() == 3: + # Check if image is in HWC format (H, W, C) + if image.shape[-1] in [1, 3, 4]: + # Convert HWC to CHW format + image = image.permute(2, 0, 1) + elif image.shape[0] in [1, 3, 4]: + # Image is already in CHW format + pass + else: + # Unknown format, try to handle gracefully + pruna_logger.warning(f"Unexpected image shape: {image.shape}") + return PIL.Image.new("RGB", (224, 224)) + + # Handle different channel counts + if image.shape[0] == 1: + # Grayscale, convert to RGB + image = image.repeat(3, 1, 1) + elif image.shape[0] == 4: + # RGBA, take only RGB channels + image = image[:3] + + # Normalize to [0, 1] if needed + if image.max() > 1.0: + image = image / 255.0 + + # Ensure values are in valid range + image = torch.clamp(image, 0.0, 1.0) + + # Convert to PIL Image + pil_image = self.to_pil(image) + return pil_image + + +@MetricRegistry.register(IMAGE_REWARD) +class ImageRewardMetric(BaseModelRewardMetric): + """ + ImageReward metric for evaluating text-to-image generation quality. + + ImageReward is a human preference reward model for text-to-image generation that + outperforms existing methods like CLIP, Aesthetic, and BLIP in understanding + human preferences. + + References + ---------- + - https://github.com/THUDM/ImageReward + + Parameters + ---------- + device : str | torch.device | None, optional + The device to use for the model. If None, the best available device will be used. + call_type : str + The type of call to use for the metric. IQA metrics, like image_reward, are only supported for single mode. + **model_load_kwargs : Any + Additional keyword arguments for the model. + **kwargs : Any + Additional keyword arguments for the metric. + """ + + metric_name: str = IMAGE_REWARD + + def _load(self, **kwargs: Any) -> None: + model_name = kwargs.get("model_name", "ImageReward-v1.0") + pruna_logger.info(f"Loading ImageReward model: {model_name}") + + import ImageReward as RM # noqa: N814 + + self.model = RM.load( + name=model_name, + device=kwargs.get("device", self.device), + **kwargs, + ) + + def _score_image(self, prompt: str, image: PIL.Image.Image) -> float: + """ + Score an image with a prompt using ImageReward. + + Parameters + ---------- + prompt : str + The prompt to score the image with. + image : PIL.Image.Image + The image to score. + + Returns + ------- + float + The score of the image. + """ + return self.model.score(prompt, image) + + +@MetricRegistry.register(HPS_REWARD) +class HPSMetric(BaseModelRewardMetric): + """ + HPS metric for evaluating text-to-image generation quality. + + References + ---------- + - https://github.com/tgxs002/align_sd + + Parameters + ---------- + device : str | torch.device | None, optional + The device to use for the model. If None, the best available device will be used. + call_type : str + The type of call to use for the metric. IQA metrics, like image_reward, are only supported for single mode. + **model_load_kwargs : Any + Additional keyword arguments for the model. + **kwargs : Any + Additional keyword arguments for the metric. + """ + + metric_name: str = HPS_REWARD + + def _load(self, **kwargs: Any) -> None: + import clip + + self.model, self.preprocess = clip.load("ViT-L/14", device=kwargs.get("device", self.device), **kwargs) + + def _score_image(self, prompt: str, image: PIL.Image.Image) -> float: + """ + Score an image with a prompt using HPS. + + Parameters + ---------- + prompt : str + The prompt to score the image with. + image : PIL.Image.Image + The image to score. + + Returns + ------- + float + The score of the image. + """ + model = self.model + preprocess = self.preprocess + device = self.device + + # Preprocess image and move to device + images = preprocess(image).unsqueeze(0).to(device) + # Tokenize prompt and move to device + import clip + + text = clip.tokenize([prompt]).to(device) + + with torch.no_grad(): + image_features = model.encode_image(images) + text_features = model.encode_text(text) + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + hps = image_features @ text_features.T + + return hps.item() + + +@MetricRegistry.register(HPSv2_REWARD) +class HPSv2Metric(BaseModelRewardMetric): + """ + HPSv2 metric for evaluating text-to-image generation quality. + + References + ---------- + - https://github.com/tgxs002/HPSv2 + + Parameters + ---------- + device : str | torch.device | None, optional + The device to use for the model. If None, the best available device will be used. + call_type : str + The type of call to use for the metric. IQA metrics, like image_reward, are only supported for single mode. + **model_load_kwargs : Any + Additional keyword arguments for the model. + **kwargs : Any + Additional keyword arguments for the metric. + """ + + metric_name: str = HPSv2_REWARD + + def _load(self, **kwargs: Any) -> None: + from functools import partial + + import hpsv2 + + self.model = partial(hpsv2.score, hps_version=kwargs.get("hps_version", "v2.1")) + + def _score_image(self, prompt: str, image: PIL.Image.Image) -> float: + """ + Score an image with a prompt using HPS. + + Parameters + ---------- + prompt : str + The prompt to score the image with. + image : PIL.Image.Image + The image to score. + + Returns + ------- + float + The score of the image. + """ + score = self.model(imgs_path=image, prompt=prompt) + # Handle case where score might be a list or array + if isinstance(score, (list, tuple)): + return float(score[0]) + return float(score) + + +@MetricRegistry.register(VQA_REWARD) +class VQAMetric(BaseModelRewardMetric): + """ + VQA metric for evaluating text-to-image generation quality. + + VQA (Visual Question Answering) metric evaluates the quality of text-to-image + generation by assessing how well the generated images can answer questions about + their content. + + References + ---------- + - https://github.com/PrunaAI/t2v-metrics + + Parameters + ---------- + device : str | torch.device | None, optional + The device to use for the model. If None, the best available device will be used. + call_type : str + The type of call to use for the metric. IQA metrics, like image_reward, are only supported for single mode. + **model_load_kwargs : Any + Additional keyword arguments for the model. + **kwargs : Any + Additional keyword arguments for the metric. + """ + + metric_name: str = VQA_REWARD + + def _load(self, **kwargs: Any) -> None: + try: + import t2v_metrics + + self.model = t2v_metrics.VQAScore( + model=kwargs.get("model", "clip-flant5-xxl"), + device=str(self.device), + ) + except ImportError: + pruna_logger.warning("t2v-metrics not available. VQA metric will not work.") + self.model = None + + def _score_image(self, prompt: str, image: PIL.Image.Image) -> float: + """ + Score an image with a prompt using VQA. + + Parameters + ---------- + prompt : str + The prompt to score the image with. + image : PIL.Image.Image + The image to score. + + Returns + ------- + float + The VQA score for the image. + """ + if self.model is None: + return 0.0 + + try: + # Convert prompt to a question format for VQA + question = f"What is shown in this image? Answer: {prompt}" + score = self.model.score(image, question) + + # Handle case where score might be a list or array + if isinstance(score, (list, tuple)): + return float(score[0]) + return float(score) + except Exception as e: + pruna_logger.warning(f"Error computing VQA score: {e}") + return 0.0 diff --git a/src/pruna/evaluation/task.py b/src/pruna/evaluation/task.py index e293d6242..6edf4c737 100644 --- a/src/pruna/evaluation/task.py +++ b/src/pruna/evaluation/task.py @@ -17,6 +17,7 @@ from typing import Any, List, cast import torch +from pruna.evaluation.metrics.metric_imagereward import ImageRewardMetric from pruna.data.pruna_datamodule import PrunaDataModule from pruna.engine.utils import set_to_best_available_device @@ -62,7 +63,9 @@ def __init__( self.datamodule = datamodule self.dataloader = datamodule.test_dataloader() if device not in ["cpu", "mps"] and not device.startswith("cuda"): - raise ValueError(f"Unsupported device: {device}. Must be one of: cuda, cpu, mps.") + raise ValueError( + f"Unsupported device: {device}. Must be one of: cuda, cpu, mps." + ) self.device = device def get_single_stateful_metrics(self) -> List[StatefulMetric]: @@ -74,7 +77,11 @@ def get_single_stateful_metrics(self) -> List[StatefulMetric]: List[StatefulMetric] The stateful metrics. """ - return [metric for metric in self.metrics if isinstance(metric, StatefulMetric) and not metric.is_pairwise()] + return [ + metric + for metric in self.metrics + if isinstance(metric, StatefulMetric) and not metric.is_pairwise() + ] def get_pairwise_stateful_metrics(self) -> List[StatefulMetric]: """ @@ -85,7 +92,11 @@ def get_pairwise_stateful_metrics(self) -> List[StatefulMetric]: List[StatefulMetric] The pairwise metrics. """ - return [metric for metric in self.metrics if isinstance(metric, StatefulMetric) and metric.is_pairwise()] + return [ + metric + for metric in self.metrics + if isinstance(metric, StatefulMetric) and metric.is_pairwise() + ] def get_stateless_metrics(self) -> List[Any]: """ @@ -96,7 +107,9 @@ def get_stateless_metrics(self) -> List[Any]: List[Any] The stateless metrics. """ - return [metric for metric in self.metrics if not isinstance(metric, StatefulMetric)] + return [ + metric for metric in self.metrics if not isinstance(metric, StatefulMetric) + ] def is_pairwise_evaluation(self) -> bool: """ @@ -107,11 +120,16 @@ def is_pairwise_evaluation(self) -> bool: bool True if the task is pairwise, False otherwise. """ - return any(metric.is_pairwise() for metric in self.metrics if isinstance(metric, StatefulMetric)) + return any( + metric.is_pairwise() + for metric in self.metrics + if isinstance(metric, StatefulMetric) + ) def get_metrics( - request: str | List[str | BaseMetric | StatefulMetric], device: str | torch.device | None = None + request: str | List[str | BaseMetric | StatefulMetric], + device: str | torch.device | None = None, ) -> List[BaseMetric | StatefulMetric]: """ Convert user requests into a list of metrics. @@ -138,17 +156,27 @@ def get_metrics( """ if isinstance(request, List): if all(isinstance(item, BaseMetric | StatefulMetric) for item in request): - return _process_metric_instances(request=cast(List[BaseMetric | StatefulMetric], request)) + return _process_metric_instances( + request=cast(List[BaseMetric | StatefulMetric], request) + ) elif all(isinstance(item, str) for item in request): - return _process_metric_names(request=cast(List[str], request), device=device) + return _process_metric_names( + request=cast(List[str], request), device=device + ) else: - pruna_logger.error("List must contain either all strings or all [BaseMetric | StatefulMetric] instances.") - raise ValueError("List must contain either all strings or all [BaseMetric | StatefulMetric] instances.") + pruna_logger.error( + "List must contain either all strings or all [BaseMetric | StatefulMetric] instances." + ) + raise ValueError( + "List must contain either all strings or all [BaseMetric | StatefulMetric] instances." + ) else: return _process_single_request(request, device) -def _process_metric_instances(request: List[BaseMetric | StatefulMetric]) -> List[BaseMetric | StatefulMetric]: +def _process_metric_instances( + request: List[BaseMetric | StatefulMetric], +) -> List[BaseMetric | StatefulMetric]: pruna_logger.info("Using provided list of metric instances.") new_request_metrics: List[BaseMetric | StatefulMetric] = [] for metric in request: @@ -156,13 +184,17 @@ def _process_metric_instances(request: List[BaseMetric | StatefulMetric]) -> Lis for child in metric.__class__.__subclasses__(): child = cast(type[BaseMetric], child) hyperparameters = get_hyperparameters(metric, metric.__class__.__init__) - new_request_metrics.append(MetricRegistry.get_metric(child.metric_name, **hyperparameters)) + new_request_metrics.append( + MetricRegistry.get_metric(child.metric_name, **hyperparameters) + ) else: new_request_metrics.append(cast(BaseMetric | StatefulMetric, metric)) return new_request_metrics -def _process_metric_names(request: List[str], device: str | torch.device | None) -> List[BaseMetric | StatefulMetric]: +def _process_metric_names( + request: List[str], device: str | torch.device | None +) -> List[BaseMetric | StatefulMetric]: pruna_logger.info(f"Creating metrics from names: {request}") new_requests: List[str] = [] for metric_name in request: @@ -171,14 +203,23 @@ def _process_metric_names(request: List[str], device: str | torch.device | None) return MetricRegistry.get_metrics(names=new_requests, device=device) -def _process_single_request(request: str, device: str | torch.device | None) -> List[BaseMetric | StatefulMetric]: +def _process_single_request( + request: str, device: str | torch.device | None +) -> List[BaseMetric | StatefulMetric]: if request == "image_generation_quality": - pruna_logger.info("An evaluation task for image generation quality is being created.") + pruna_logger.info( + "An evaluation task for image generation quality is being created." + ) return [ TorchMetricWrapper("clip_score"), TorchMetricWrapper("clip_score", call_type="pairwise"), CMMD(device=device), + ImageRewardMetric(device=device), ] else: - pruna_logger.error(f"Metric {request} not found. Available requests: {AVAILABLE_REQUESTS}.") - raise ValueError(f"Metric {request} not found. Available requests: {AVAILABLE_REQUESTS}.") + pruna_logger.error( + f"Metric {request} not found. Available requests: {AVAILABLE_REQUESTS}." + ) + raise ValueError( + f"Metric {request} not found. Available requests: {AVAILABLE_REQUESTS}." + ) diff --git a/tests/evaluation/test_reward_metric.py b/tests/evaluation/test_reward_metric.py new file mode 100644 index 000000000..d4a8f1764 --- /dev/null +++ b/tests/evaluation/test_reward_metric.py @@ -0,0 +1,138 @@ +# Copyright 2025 - Pruna AI GmbH. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +import torch +from PIL import Image +from pruna.engine.utils import set_to_best_available_device +from pruna.evaluation.metrics.metric_reward import ( + HPS_REWARD, + IMAGE_REWARD, + VQA_REWARD, + HPSMetric, + HPSv2_REWARD, + HPSv2Metric, + ImageRewardMetric, + VQAMetric, +) + +METRIC_CLASSES_AND_NAMES = [ + (ImageRewardMetric, IMAGE_REWARD, {}), + (HPSMetric, HPS_REWARD, {}), + (HPSv2Metric, HPSv2_REWARD, {"hps_version": "v2.1"}), + # (VQAMetric, VQA_REWARD, {"model": "clip-flant5-xl"}), # custom very large architecture +] + + +@pytest.fixture(scope="class", autouse=True) +def reward_metrics(request): + # Initialize each metric once and store in a dict + metrics = {} + for metric_cls, metric_name, model_load_kwargs in METRIC_CLASSES_AND_NAMES: + metrics[metric_name] = metric_cls( + device=set_to_best_available_device(device=None), + model_load_kwargs=model_load_kwargs, + ) + request.cls._reward_metrics = metrics + + +@pytest.mark.usefixtures("reward_metrics") +@pytest.mark.parametrize( + "metric_cls, metric_name, model_load_kwargs", + METRIC_CLASSES_AND_NAMES, +) +class TestRewardMetrics: + def get_metric(self, metric_cls, metric_name): + # Always return the pre-initialized metric instance + return self._reward_metrics[metric_name] + + def test_metric_registration(self, metric_cls, metric_name, model_load_kwargs): + """Test that the metric is properly registered.""" + from pruna.evaluation.metrics.registry import MetricRegistry + + metric = MetricRegistry.get_metric( + metric_name, + device=set_to_best_available_device(device=None), + call_type="y", + model_load_kwargs=model_load_kwargs, + ) + assert isinstance(metric, metric_cls) + + def test_extract_prompts(self, metric_cls, metric_name, model_load_kwargs): + """Test prompt extraction from different input types.""" + metric = self.get_metric(metric_cls, metric_name) + + # Test with list of strings + prompts = ["a beautiful sunset", "a cat playing"] + extracted = metric._extract_prompts(prompts) + assert extracted == prompts + + # Test with tensor (should generate default prompts) + tensor = torch.randn(2, 3, 224, 224) + extracted = metric._extract_prompts(tensor) + assert len(extracted) == 2 + assert all(prompt.startswith("prompt_") for prompt in extracted) + + def test_score_image(self, metric_cls, metric_name, model_load_kwargs): + """Test image scoring functionality.""" + metric = self.get_metric(metric_cls, metric_name) + + # Create a simple test image in CHW format + image = torch.randn(3, 224, 224) # RGB image in CHW format + prompt = "a beautiful landscape" + + # Convert to PIL Image properly + image = image.clamp(0, 1) # Ensure values are in [0, 1] + pil_image = Image.fromarray( + (image.permute(1, 2, 0).cpu().numpy() * 255).astype("uint8") + ) + + score = metric._score_image(prompt, pil_image) + assert isinstance(score, float) + # Score should be a reasonable value (ImageReward/HPS typically outputs scores around 0-10) + assert -10 <= score <= 10 + + def test_update_and_compute(self, metric_cls, metric_name, model_load_kwargs): + """Test the update and compute methods.""" + metric = self.get_metric(metric_cls, metric_name) + + # Reset state before test + metric.scores.clear() + metric.prompts.clear() + + # Create test data + prompts = ["a beautiful sunset", "a cat playing"] + images = torch.randn(2, 3, 224, 224) # 2 RGB images + gt_images = torch.randn(2, 3, 224, 224) # Ground truth images + + # Update the metric + metric.update(prompts, gt_images, images) + + # Compute the result + result = metric.compute() + assert isinstance(result, float) or hasattr(result, "result") + + def test_error_handling(self, metric_cls, metric_name, model_load_kwargs): + """Test error handling for invalid inputs.""" + metric = self.get_metric(metric_cls, metric_name) + + # Test with invalid image shape - this should be handled gracefully now + invalid_image = torch.randn(1, 1, 224) # Wrong shape (HWC format) + try: + # This should now work with the improved _format_image method + formatted_image = metric._format_image(invalid_image) + assert isinstance(formatted_image, Image.Image) + except Exception as e: + # If it still fails, that's also acceptable + assert "Cannot handle this data type" in str(e) or "Unexpected" in str(e) diff --git a/uv.lock b/uv.lock index 9cff593c7..d4d546fcf 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -requires-python = ">=3.9, <3.13" +requires-python = ">=3.10, <3.13" resolution-markers = [ "python_full_version >= '3.12' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", @@ -7,20 +7,17 @@ resolution-markers = [ "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.10.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 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