New metric: CLIP IQA

CHANGELOG.md

@@ -35,6 +35,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added argument `extended_summary` to `MeanAveragePrecision` such that precision, recall, iou can be easily returned ([#1983](https://github.com/Lightning-AI/torchmetrics/pull/1983))
 
 
+- Added `CLIPImageQualityAssessment` to image package ([#1931](https://github.com/Lightning-AI/torchmetrics/pull/1931))
+
+
 ### Changed
 
 -

docs/source/image/clip_iqa.rst

@@ -0,0 +1,24 @@
+.. customcarditem::
+   :header: CLIP IQA
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
+   :tags: Image
+
+.. include:: ../links.rst
+
+########################################
+CLIP Image Quality Assessment (CLIP-IQA)
+########################################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.image.CLIPImageQualityAssessment
+    :noindex:
+    :exclude-members: update, compute
+
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.image.clip_image_quality_assessment
+    :noindex:

docs/source/links.rst

@@ -144,6 +144,8 @@
 .. _Seamless Scene Segmentation paper: https://arxiv.org/abs/1905.01220
 .. _Fleiss kappa: https://en.wikipedia.org/wiki/Fleiss%27_kappa
 .. _VIF: https://ieeexplore.ieee.org/abstract/document/1576816
+.. _CLIP-IQA: https://arxiv.org/abs/2207.12396
+.. _CLIP: https://arxiv.org/abs/2103.00020
 .. _PPL : https://arxiv.org/pdf/1812.04948
 .. _CIOU: https://arxiv.org/abs/2005.03572
 .. _DIOU: https://arxiv.org/abs/1911.08287v1

requirements/image.txt

@@ -5,3 +5,4 @@ scipy >1.0.0, <1.11.0
 torchvision >=0.8, <=0.15.2
 torch-fidelity <=0.4.0  # bumping to alow install version from master, now used in testing
 lpips <=0.1.4
+piq <=0.8.0

src/torchmetrics/functional/image/__init__.py

@@ -11,6 +11,7 @@
 # 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.functional.image.clip_iqa import clip_image_quality_assessment
 from torchmetrics.functional.image.d_lambda import spectral_distortion_index
 from torchmetrics.functional.image.ergas import error_relative_global_dimensionless_synthesis
 from torchmetrics.functional.image.gradients import image_gradients
@@ -30,6 +31,7 @@
 from torchmetrics.functional.image.vif import visual_information_fidelity
 
 __all__ = [
+    "clip_image_quality_assessment",
     "spectral_distortion_index",
     "error_relative_global_dimensionless_synthesis",
     "image_gradients",

src/torchmetrics/functional/image/clip_iqa.py

@@ -0,0 +1,326 @@
+# 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 Dict, List, Literal, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from torchmetrics.functional.multimodal.clip_score import _get_clip_model_and_processor
+from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
+from torchmetrics.utilities.imports import _PIQ_GREATER_EQUAL_0_8, _TRANSFORMERS_GREATER_EQUAL_4_10
+
+if _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-base-patch16")
+        _CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
+
+    if _SKIP_SLOW_DOCTEST and not _try_proceed_with_timeout(_download_clip):
+        __doctest_skip__ = ["clip_score"]
+
+else:
+    __doctest_skip__ = ["clip_image_quality_assessment"]
+    _CLIPModel = None
+    _CLIPProcessor = None
+
+if not _PIQ_GREATER_EQUAL_0_8:
+    __doctest_skip__ = ["clip_image_quality_assessment"]
+
+_PROMPTS: Dict[str, Tuple[str, str]] = {
+    "quality": ("Good photo.", "Bad photo."),
+    "brightness": ("Bright photo.", "Dark photo."),
+    "noisiness": ("Clean photo.", "Noisy photo."),
+    "colorfullness": ("Colorful photo.", "Dull photo."),
+    "sharpness": ("Sharp photo.", "Blurry photo."),
+    "contrast": ("High contrast photo.", "Low contrast photo."),
+    "complexity": ("Complex photo.", "Simple photo."),
+    "natural": ("Natural photo.", "Synthetic photo."),
+    "happy": ("Happy photo.", "Sad photo."),
+    "scary": ("Scary photo.", "Peaceful photo."),
+    "new": ("New photo.", "Old photo."),
+    "warm": ("Warm photo.", "Cold photo."),
+    "real": ("Real photo.", "Abstract photo."),
+    "beutiful": ("Beautiful photo.", "Ugly photo."),
+    "lonely": ("Lonely photo.", "Sociable photo."),
+    "relaxing": ("Relaxing photo.", "Stressful photo."),
+}
+
+
+def _get_clip_iqa_model_and_processor(
+    model_name_or_path: Literal[
+        "clip_iqa",
+        "openai/clip-vit-base-patch16",
+        "openai/clip-vit-base-patch32",
+        "openai/clip-vit-large-patch14-336",
+        "openai/clip-vit-large-patch14",
+    ]
+) -> Tuple[_CLIPModel, _CLIPProcessor]:
+    """Extract the CLIP model and processor from the model name or path."""
+    if model_name_or_path == "clip_iqa":
+        if not _PIQ_GREATER_EQUAL_0_8:
+            raise ValueError(
+                "For metric `clip_iqa` to work with argument `model_name_or_path` set to default value `'clip_iqa'`"
+                ", package `piq` version v0.8.0 or later must be installed. Either install with `pip install piq` or"
+                "`pip install torchmetrics[image]`"
+            )
+
+        import piq
+
+        model = piq.clip_iqa.clip.load().eval()
+        # any model checkpoint can be used here because the tokenizer is the same for all
+        processor = _CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
+        return model, processor
+    return _get_clip_model_and_processor(model_name_or_path)
+
+
+def _clip_iqa_format_prompts(prompts: Tuple[Union[str, Tuple[str, str]]] = ("quality",)) -> Tuple[List[str], List[str]]:
+    """Converts the provided keywords into a list of prompts for the model to calculate the achor vectors.
+
+    Args:
+        prompts: A string, list of strings or tuple of strings. If a string is provided, it must be one of the
+            availble prompts. If a list of strings is provided, all strings must be one of the availble prompts.
+            If a tuple of strings is provided, it must be of length 2 and the first string must be a positive prompt
+            and the second string must be a negative prompt.
+
+    Returns:
+        Tuple containing a list of prompts and a list of the names of the prompts. The first list is double the length
+        of the second list.
+
+    Examples::
+
+        >>> # single prompt
+        >>> _clip_iqa_format_prompts(("quality",))
+        (['Good photo.', 'Bad photo.'], ['quality'])
+        >>> # multiple prompts
+        >>> _clip_iqa_format_prompts(("quality", "brightness"))
+        (['Good photo.', 'Bad photo.', 'Bright photo.', 'Dark photo.'], ['quality', 'brightness'])
+        >>> # Custom prompts
+        >>> _clip_iqa_format_prompts(("quality", ("Super good photo.", "Super bad photo.")))
+        (['Good photo.', 'Bad photo.', 'Super good photo.', 'Super bad photo.'], ['quality', 'user_defined_0'])
+
+    """
+    if not isinstance(prompts, tuple):
+        raise ValueError("Argument `prompts` must be a tuple containing strings or tuples of strings")
+
+    prompts_names: List[str] = []
+    prompts_list: List[str] = []
+    count = 0
+    for p in prompts:
+        if not isinstance(p, (str, tuple)):
+            raise ValueError("Argument `prompts` must be a tuple containing strings or tuples of strings")
+        if isinstance(p, str):
+            if p not in _PROMPTS:
+                raise ValueError(
+                    f"All elements of `prompts` must be one of {_PROMPTS.keys()} if not custom tuple promts, got {p}."
+                )
+            prompts_names.append(p)
+            prompts_list.extend(_PROMPTS[p])
+        if isinstance(p, tuple) and len(p) != 2:
+            raise ValueError("If a tuple is provided in argument `prompts`, it must be of length 2")
+        if isinstance(p, tuple):
+            prompts_names.append(f"user_defined_{count}")
+            prompts_list.extend(p)
+            count += 1
+
+    return prompts_list, prompts_names
+
+
+def _clip_iqa_get_anchor_vectors(
+    model_name_or_path: str,
+    model: _CLIPModel,
+    processor: _CLIPProcessor,
+    prompts_list: List[str],
+    device: Union[str, torch.device],
+) -> Tensor:
+    """Calculates the anchor vectors for the CLIP IQA metric.
+
+    Args:
+        model_name_or_path: string indicating the version of the CLIP model to use.
+        model: The CLIP model
+        processor: The CLIP processor
+        prompts_list: A list of prompts
+        device: The device to use for the calculation
+
+    """
+    if model_name_or_path == "clip_iqa":
+        text_processed = processor(text=prompts_list)
+        anchors_text = torch.zeros(
+            len(prompts_list), processor.tokenizer.model_max_length, dtype=torch.long, device=device
+        )
+        for i, tp in enumerate(text_processed["input_ids"]):
+            anchors_text[i, : len(tp)] = torch.tensor(tp, dtype=torch.long, device=device)
+
+        anchors = model.encode_text(anchors_text).float()
+    else:
+        text_processed = processor(text=prompts_list, return_tensors="pt", padding=True)
+        anchors = model.get_text_features(
+            text_processed["input_ids"].to(device), text_processed["attention_mask"].to(device)
+        )
+    return anchors / anchors.norm(p=2, dim=-1, keepdim=True)
+
+
+def _clip_iqa_update(
+    model_name_or_path: str,
+    images: Tensor,
+    model: _CLIPModel,
+    processor: _CLIPProcessor,
+    data_range: Union[int, float],
+    device: Union[str, torch.device],
+) -> Tensor:
+    images = images / float(data_range)
+    """Update function for CLIP IQA."""
+    if model_name_or_path == "clip_iqa":
+        default_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=device).view(1, 3, 1, 1)
+        default_std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=device).view(1, 3, 1, 1)
+        images = (images - default_mean) / default_std
+        img_features = model.encode_image(images.float(), pos_embedding=False).float()
+    else:
+        processed_input = processor(images=[i.cpu() for i in images], return_tensors="pt", padding=True)
+        img_features = model.get_image_features(processed_input["pixel_values"].to(device))
+    return img_features / img_features.norm(p=2, dim=-1, keepdim=True)
+
+
+def _clip_iqa_compute(
+    img_features: Tensor,
+    anchors: Tensor,
+    prompts_names: List[str],
+    format_as_dict: bool = True,
+) -> Union[Tensor, Dict[str, Tensor]]:
+    """Final computation of CLIP IQA."""
+    logits_per_image = 100 * img_features @ anchors.t()
+    probs = logits_per_image.reshape(logits_per_image.shape[0], -1, 2).softmax(-1)[:, :, 0]
+    if len(prompts_names) == 1:
+        return probs.squeeze()
+    if format_as_dict:
+        return {p: probs[:, i] for i, p in enumerate(prompts_names)}
+    return probs
+
+
+def clip_image_quality_assessment(
+    images: Tensor,
+    model_name_or_path: Literal[
+        "clip_iqa",
+        "openai/clip-vit-base-patch16",
+        "openai/clip-vit-base-patch32",
+        "openai/clip-vit-large-patch14-336",
+        "openai/clip-vit-large-patch14",
+    ] = "clip_iqa",
+    data_range: Union[int, float] = 1.0,
+    prompts: Tuple[Union[str, Tuple[str, str]]] = ("quality",),
+) -> Union[Tensor, Dict[str, Tensor]]:
+    """Calculates `CLIP-IQA`_, that can be used to measure the visual content of images.
+
+    The metric is based on the `CLIP`_ model, which is a neural network trained on a variety of (image, text) pairs to
+    be able to generate a vector representation of the image and the text that is similar if the image and text are
+    semantically similar.
+
+    The metric works by calculating the cosine similarity between user provided images and pre-defined promts. The
+    promts always comes in pairs of "positive" and "negative" such as "Good photo." and "Bad photo.". By calculating
+    the similartity between image embeddings and both the "positive" and "negative" prompt, the metric can determine
+    which prompt the image is more similar to. The metric then returns the probability that the image is more similar
+    to the first prompt than the second prompt.
+
+    Build in promts are:
+        * quality: "Good photo." vs "Bad photo."
+        * brightness: "Bright photo." vs "Dark photo."
+        * noisiness: "Clean photo." vs "Noisy photo."
+        * colorfullness: "Colorful photo." vs "Dull photo."
+        * sharpness: "Sharp photo." vs "Blurry photo."
+        * contrast: "High contrast photo." vs "Low contrast photo."
+        * complexity: "Complex photo." vs "Simple photo."
+        * natural: "Natural photo." vs "Synthetic photo."
+        * happy: "Happy photo." vs "Sad photo."
+        * scary: "Scary photo." vs "Peaceful photo."
+        * new: "New photo." vs "Old photo."
+        * warm: "Warm photo." vs "Cold photo."
+        * real: "Real photo." vs "Abstract photo."
+        * beutiful: "Beautiful photo." vs "Ugly photo."
+        * lonely: "Lonely photo." vs "Sociable photo."
+        * relaxing: "Relaxing photo." vs "Stressful photo."
+
+    Args:
+        images: Either a single [N, C, H, W] tensor or a list of [C, H, W] tensors
+        model_name_or_path: string indicating the version of the CLIP model to use. By default this argument is set to
+            ``clip_iqa`` which corresponds to the model used in the original paper. Other availble models are
+            `"openai/clip-vit-base-patch16"`, `"openai/clip-vit-base-patch32"`, `"openai/clip-vit-large-patch14-336"`
+            and `"openai/clip-vit-large-patch14"`
+        data_range: The maximum value of the input tensor. For example, if the input images are in range [0, 255],
+            data_range should be 255. The images are normalized by this value.
+        prompts: A string, tuple of strings or nested tuple of strings. If a string is provided, it must be one of the
+            availble prompts. If a tuple of strings is provided, all strings must be one of the availble prompts.
+            If a nested tuple of strings is provided, it must be of length 2 and the first string must be a positive
+            prompt and the second string must be a negative prompt.
+
+    .. note:: If using the default `clip_iqa` model, the package `piq` must be installed. Either install with
+        `pip install piq` or `pip install torchmetrics[image]`.
+
+    Returns:
+        A tensor of shape ``(N,)`` if a single promts is provided. If a list of promts is provided, a dictionary of
+        with the promts as keys and tensors of shape ``(N,)`` as values.
+
+    Raises:
+        ModuleNotFoundError:
+            If transformers package is not installed or version is lower than 4.10.0
+        ValueError:
+            If not all images have format [C, H, W]
+        ValueError:
+            If promts is a tuple and it is not of length 2
+        ValueError:
+            If promts is a string and it is not one of the available promts
+        ValueError:
+            If promts is a list of strings and not all strings are one of the available promts
+
+    Example::
+        Single promt:
+
+        >>> from torchmetrics.functional.image import clip_image_quality_assessment
+        >>> import torch
+        >>> _ = torch.manual_seed(42)
+        >>> imgs = torch.randint(255, (2, 3, 224, 224)).float()
+        >>> clip_image_quality_assessment(imgs, prompts=("quality",))
+        tensor([0.8894, 0.8902])
+
+    Example::
+        Multiple promts:
+
+        >>> from torchmetrics.functional.image import clip_image_quality_assessment
+        >>> import torch
+        >>> _ = torch.manual_seed(42)
+        >>> imgs = torch.randint(255, (2, 3, 224, 224)).float()
+        >>> clip_image_quality_assessment(imgs, prompts=("quality", "brightness"))
+        {'quality': tensor([0.8894, 0.8902]), 'brightness': tensor([0.5507, 0.5208])}
+
+    Example::
+        Custom promts. Must always be a tuple of length 2, with a positive and negative prompt.
+
+        >>> from torchmetrics.functional.image import clip_image_quality_assessment
+        >>> import torch
+        >>> _ = torch.manual_seed(42)
+        >>> imgs = torch.randint(255, (2, 3, 224, 224)).float()
+        >>> clip_image_quality_assessment(imgs, prompts=(("Super good photo.", "Super bad photo."), "brightness"))
+        {'user_defined_0': tensor([0.9652, 0.9629]), 'brightness': tensor([0.5507, 0.5208])}
+
+    """
+    prompts_list, prompts_names = _clip_iqa_format_prompts(prompts)
+
+    model, processor = _get_clip_iqa_model_and_processor(model_name_or_path)
+    device = images.device
+    model = model.to(device)
+
+    with torch.inference_mode():
+        anchors = _clip_iqa_get_anchor_vectors(model_name_or_path, model, processor, prompts_list, device)
+        img_features = _clip_iqa_update(model_name_or_path, images, model, processor, data_range, device)
+        return _clip_iqa_compute(img_features, anchors, prompts_names)