# Copyright The Lightning team. # # 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 typing import Any, List, Optional, Sequence, Union import torch from torch import Tensor from typing_extensions import Literal from torchmetrics import Metric from torchmetrics.functional.multimodal.clip_score import _clip_score_update, _get_clip_model_and_processor from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_10 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["CLIPScore.plot"] if _SKIP_SLOW_DOCTEST and _TRANSFORMERS_GREATER_EQUAL_4_10: from transformers import CLIPModel as _CLIPModel from transformers import CLIPProcessor as _CLIPProcessor def _download_clip() -> None: _CLIPModel.from_pretrained("openai/clip-vit-large-patch14") _CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") if not _try_proceed_with_timeout(_download_clip): __doctest_skip__ = ["CLIPScore", "CLIPScore.plot"] else: __doctest_skip__ = ["CLIPScore", "CLIPScore.plot"] class CLIPScore(Metric): r"""Calculates `CLIP Score`_ which is a text-to-image similarity metric. CLIP Score is a reference free metric that can be used to evaluate the correlation between a generated caption for an image and the actual content of the image. It has been found to be highly correlated with human judgement. The metric is defined as: .. math:: \text{CLIPScore(I, C)} = max(100 * cos(E_I, E_C), 0) which corresponds to the cosine similarity between visual `CLIP`_ embedding :math:`E_i` for an image :math:`i` and textual CLIP embedding :math:`E_C` for an caption :math:`C`. The score is bound between 0 and 100 and the closer to 100 the better. .. note:: Metric is not scriptable As input to ``forward`` and ``update`` the metric accepts the following input - ``images`` (:class:`~torch.Tensor` or list of tensors): tensor with images feed to the feature extractor with. If a single tensor it should have shape ``(N, C, H, W)``. If a list of tensors, each tensor should have shape ``(C, H, W)``. ``C`` is the number of channels, ``H`` and ``W`` are the height and width of the image. - ``text`` (:class:`~str` or :class:`~list` of :class:`~str`): text to compare with the images, one for each image. As output of `forward` and `compute` the metric returns the following output - ``clip_score`` (:class:`~torch.Tensor`): float scalar tensor with mean CLIP score over samples Args: model_name_or_path: string indicating the version of the CLIP model to use. Available models are: - `"openai/clip-vit-base-patch16"` - `"openai/clip-vit-base-patch32"` - `"openai/clip-vit-large-patch14-336"` - `"openai/clip-vit-large-patch14"` kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ModuleNotFoundError: If transformers package is not installed or version is lower than 4.10.0 Example: >>> import torch >>> from torchmetrics.multimodal.clip_score import CLIPScore >>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16") >>> score = metric(torch.randint(255, (3, 224, 224), generator=torch.manual_seed(42)), "a photo of a cat") >>> score.detach() tensor(24.4255) """ is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = True plot_lower_bound: float = 0.0 plot_upper_bound = 100.0 score: Tensor n_samples: Tensor def __init__( self, model_name_or_path: Literal[ "openai/clip-vit-base-patch16", "openai/clip-vit-base-patch32", "openai/clip-vit-large-patch14-336", "openai/clip-vit-large-patch14", ] = "openai/clip-vit-large-patch14", **kwargs: Any, ) -> None: super().__init__(**kwargs) self.model, self.processor = _get_clip_model_and_processor(model_name_or_path) self.add_state("score", torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("n_samples", torch.tensor(0, dtype=torch.long), dist_reduce_fx="sum") def update(self, images: Union[Tensor, List[Tensor]], text: Union[str, List[str]]) -> None: """Update CLIP score on a batch of images and text. Args: images: Either a single [N, C, H, W] tensor or a list of [C, H, W] tensors text: Either a single caption or a list of captions Raises: ValueError: If not all images have format [C, H, W] ValueError: If the number of images and captions do not match """ score, n_samples = _clip_score_update(images, text, self.model, self.processor) self.score += score.sum(0) self.n_samples += n_samples def compute(self) -> Tensor: """Compute accumulated clip score.""" return torch.max(self.score / self.n_samples, torch.zeros_like(self.score)) def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.multimodal.clip_score import CLIPScore >>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16") >>> metric.update(torch.randint(255, (3, 224, 224)), "a photo of a cat") >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.multimodal.clip_score import CLIPScore >>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16") >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(255, (3, 224, 224)), "a photo of a cat")) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)