Add CLIP score

.azure/gpu-pipeline.yml

@@ -37,7 +37,7 @@ jobs:
 
     container:
       image: "$(docker-image)"
-      options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all --name ci-container -v /usr/bin/docker:/tmp/docker:ro"
+      options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all --shm-size=8g --name ci-container -v /usr/bin/docker:/tmp/docker:ro"
 
     workspace:
       clean: all

.github/workflows/docs-check.yml

@@ -8,7 +8,7 @@ jobs:
 
   test-docs:
     runs-on: ubuntu-20.04
-    timeout-minutes: 15
+    timeout-minutes: 20
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4

CHANGELOG.md

@@ -32,6 +32,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `TschuprowsT` ([#1334](https://github.com/Lightning-AI/metrics/pull/1334))
 
 
+- Added `CLIPScore` to new multimodal package ([#1314](https://github.com/Lightning-AI/metrics/pull/1314))
+
+
 ### Changed
 
 - Changed `MeanAveragePrecision` to vectorize `_find_best_gt_match` operation ([#1259](https://github.com/Lightning-AI/metrics/pull/1259))

docs/source/index.rst

@@ -182,6 +182,14 @@ Or directly from conda
 
    nominal/*
 
+.. toctree::
+   :maxdepth: 2
+   :name: multimodal
+   :caption: Multimodal
+   :glob:
+
+   multimodal/*
+
 .. toctree::
    :maxdepth: 2
    :name: pairwise

docs/source/links.rst

@@ -99,3 +99,5 @@
 .. _LogCosh Error: https://arxiv.org/pdf/2101.10427.pdf
 .. _Tschuprow's T: https://en.wikipedia.org/wiki/Tschuprow%27s_T
 .. _Pearson's Contingency Coefficient: https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/pearcont.htm
+.. _CLIP score: https://arxiv.org/pdf/2104.08718.pdf
+.. _Huggingface OpenAI: https://huggingface.co/openai

docs/source/multimodal/clip_score.rst

@@ -0,0 +1,20 @@
+.. customcarditem::
+   :header: CLIP Score
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
+   :tags: Image
+
+############################################
+CLIP Score
+############################################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.multimodal.clip_score.CLIPScore
+    :noindex:
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.multimodal.clip_score.clip_score
+    :noindex:

requirements/devel.txt

@@ -9,6 +9,7 @@
 -r text.txt
 # -r detection.txt  # version collision with min versio of PyTorch
 -r audio.txt
+-r multimodal.txt
 
 # add extra testing
 -r image_test.txt

requirements/multimodal.txt

@@ -0,0 +1 @@
+transformers>=4.10.0

requirements/text_test.txt

@@ -1,6 +1,6 @@
 jiwer>=2.3.0
 rouge-score>=0.0.4
 bert_score==0.3.10
-transformers>=4.4.0
+transformers>=4.10.0
 huggingface-hub<0.7  # hotfix, failing SDR for latest PT 1.11
 sacrebleu>=2.0.0

src/torchmetrics/functional/multimodal/__init__.py

@@ -0,0 +1,17 @@
+# Copyright The PyTorch 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 torchmetrics.utilities.imports import _TRANSFORMERS_AVAILABLE
+
+if _TRANSFORMERS_AVAILABLE:
+    from torchmetrics.functional.multimodal.clip_score import clip_score  # noqa: F401

src/torchmetrics/functional/multimodal/clip_score.py

@@ -0,0 +1,140 @@
+# Copyright The PyTorch 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 List, Tuple, Union
+
+import torch
+from torch import Tensor
+from typing_extensions import Literal
+
+from torchmetrics.utilities.imports import _TRANSFORMERS_AVAILABLE
+
+if _TRANSFORMERS_AVAILABLE:
+    from transformers import CLIPModel as _CLIPModel
+    from transformers import CLIPProcessor as _CLIPProcessor
+else:
+    __doctest_skip__ = ["clip_score"]
+    _CLIPModel = None  # type:ignore
+    _CLIPProcessor = None  # type:ignore
+
+
+def _clip_score_update(
+    images: Union[Tensor, List[Tensor]],
+    text: Union[str, List[str]],
+    model: _CLIPModel,
+    processor: _CLIPProcessor,
+) -> Tuple[Tensor, int]:
+    if not isinstance(images, list):
+        if images.ndim == 3:
+            images = [images]
+    else:  # unwrap into list
+        images = [i for i in images]
+
+    if not all(i.ndim == 3 for i in images):
+        raise ValueError("Expected all images to be 3d but found image that has either more or less")
+
+    if not isinstance(text, list):
+        text = [text]
+
+    if len(text) != len(images):
+        raise ValueError(
+            f"Expected the number of images and text examples to be the same but got {len(images)} and {len(text)}"
+        )
+    device = images[0].device
+    processed_input = processor(
+        text=text, images=[i.cpu() for i in images], return_tensors="pt", padding=True
+    )  # type:ignore
+
+    img_features = model.get_image_features(processed_input["pixel_values"].to(device))
+    img_features = img_features / img_features.norm(p=2, dim=-1, keepdim=True)
+
+    txt_features = model.get_text_features(
+        processed_input["input_ids"].to(device), processed_input["attention_mask"].to(device)
+    )
+    txt_features = txt_features / txt_features.norm(p=2, dim=-1, keepdim=True)
+
+    # cosine similarity between feature vectors
+    score = 100 * (img_features * txt_features).sum(axis=-1)
+    return score, len(text)
+
+
+def _get_model_and_processor(
+    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",
+) -> Tuple[_CLIPModel, _CLIPProcessor]:
+    if _TRANSFORMERS_AVAILABLE:
+        model = _CLIPModel.from_pretrained(model_name_or_path)
+        processor = _CLIPProcessor.from_pretrained(model_name_or_path)
+        return model, processor
+    else:
+        raise ModuleNotFoundError(
+            "`clip_score` metric requires `transformers` package be installed."
+            " Either install with `pip install transformers>=4.0` or `pip install torchmetrics[multimodal]`."
+        )
+
+
+def clip_score(
+    images: Union[Tensor, List[Tensor]],
+    text: Union[str, List[str]],
+    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",
+) -> Tensor:
+    """`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
+
+    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
+        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"`
+            and `"openai/clip-vit-large-patch14"`,
+
+    Raises:
+        ModuleNotFoundError:
+            If transformers package is not installed
+        ValueError:
+            If not all images have format [C, H, W]
+        ValueError:
+            If the number of images and captions do not match
+
+    Example:
+        >>> import torch
+        >>> _ = torch.manual_seed(42)
+        >>> from torchmetrics.functional.multimodal import clip_score
+        >>> score = clip_score(torch.randint(255, (3, 224, 224)), "a photo of a cat", "openai/clip-vit-base-patch16")
+        >>> print(score.detach())
+        tensor(24.4255)
+    """
+    model, processor = _get_model_and_processor(model_name_or_path)
+    device = images.device if isinstance(images, Tensor) else images[0].device
+    score, _ = _clip_score_update(images, text, model.to(device), processor)
+    score = score.mean(0)
+    return torch.max(score, torch.zeros_like(score))

src/torchmetrics/multimodal/__init__.py

@@ -0,0 +1,17 @@
+# Copyright The PyTorch 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 torchmetrics.utilities.imports import _TRANSFORMERS_AVAILABLE
+
+if _TRANSFORMERS_AVAILABLE:
+    from torchmetrics.multimodal.clip_score import CLIPScore  # noqa: F401

src/torchmetrics/multimodal/clip_score.py

@@ -0,0 +1,105 @@
+# Copyright The PyTorch 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, Union
+
+import torch
+from torch import Tensor
+from typing_extensions import Literal
+
+from torchmetrics.functional.multimodal.clip_score import _clip_score_update, _get_model_and_processor
+from torchmetrics.utilities.imports import _TRANSFORMERS_AVAILABLE
+
+if not _TRANSFORMERS_AVAILABLE:
+    __doctest_skip__ = ["CLIPScore"]
+
+from torchmetrics import Metric
+
+
+class CLIPScore(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
+
+    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"`
+            and `"openai/clip-vit-large-patch14"`,
+
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+
+    Raises:
+        ModuleNotFoundError:
+            If transformers package is not installed
+
+    Example:
+        >>> import torch
+        >>> _ = torch.manual_seed(42)
+        >>> from torchmetrics.multimodal import CLIPScore
+        >>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16")
+        >>> score = metric(torch.randint(255, (3, 224, 224)), "a photo of a cat")
+        >>> print(score.detach())
+        tensor(25.0936)
+    """
+
+    is_differentiable: bool = False
+    higher_is_better: bool = True
+    full_state_update: bool = True
+    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_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:
+        """Updates 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:
+        """Computes accumulated clip score."""
+        return torch.max(self.score / self.n_samples, torch.zeros_like(self.score))