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# 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, Tuple
import torch
import torch.nn.functional as F
from PIL import Image
from torch import Tensor
from torch.nn import Module as _DINOModel
from torchmetrics import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TRANSFORMERS_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
from torchvision import transforms
from torchvision.transforms import Compose as _DINOProcessor
from typing_extensions import Literal
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["DINOScore.plot"]
_DEFAULT_MODEL: str = "dino_vits16"
class DINOScore(Metric):
r"""Calculates `DINO Score`_ which is a image-to-image similarity metric.
.. note:: Metric is not scriptable
Args:
model_name_or_path: string indicating the version of the DINO model to use. Available models are:
- `"dino_vits16"`
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
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = True
plot_lower_bound: float = 0.0
score: Tensor
n_samples: Tensor
plot_upper_bound = 100.0
def __init__(
self,
model_name_or_path: Literal[
"dino_vits16",
] = _DEFAULT_MODEL, # type: ignore[assignment]
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.model, self.processor = self._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")
@staticmethod
def _get_model_and_processor(
model_name_or_path: Literal[
"dino_vits16",
] = "dino_vits16",
) -> Tuple[_DINOModel, _DINOProcessor]:
if _TRANSFORMERS_AVAILABLE:
model = torch.hub.load('facebookresearch/dino:main', model_name_or_path)
processor = transforms.Compose([
transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
return model, processor
raise ModuleNotFoundError(
"`dino_score` metric requires `transformers` package be installed."
" Either install with `pip install transformers>=4.0` or `pip install torchmetrics[multimodal]`."
)
@staticmethod
def _dino_score_update(
images1: Union[Image.Image, List[Image.Image]],
images2: Union[Image.Image, List[Image.Image]],
model: _DINOModel,
processor: _DINOProcessor,
) -> Tuple[Tensor, int]:
if len(images1) != len(images2):
raise ValueError(
f"Expected the number of images to be the same but got {len(images1)} and {len(images2)}"
)
device = next(model.parameters()).device
img1_processed_input = [processor(i) for i in images1]
img2_processed_input = [processor(i) for i in images2]
img1_processed_input = torch.stack(img1_processed_input).to(device)
img2_processed_input = torch.stack(img2_processed_input).to(device)
img1_features = model(img1_processed_input)
img2_features = model(img2_processed_input)
# cosine similarity between feature vectors
score = 100 * F.cosine_similarity(img1_features, img2_features, dim=-1)
return score, len(images1)
def update(self, images1: Union[Image.Image, List[Image.Image]],
images2: Union[Image.Image, List[Image.Image]]) -> None:
"""Update DINO score on a batch of images and text.
Args:
images1: Either a single [N, C, H, W] tensor or a list of [C, H, W] tensors
images2: Either a single [N, C, H, W] tensor or a list of [C, H, W] tensors
Raises:
ValueError:
If not all images have format [C, H, W]
ValueError:
If the number of images do not match
"""
score, n_samples = self._dino_score_update(images1, images2, self.model, self.processor)
self.score += score.sum(0)
self.n_samples += n_samples
def compute(self) -> Tensor:
"""Compute accumulated dino 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
"""
return self._plot(val, ax)
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