add DINOv2-FD metric

.gitignore

@@ -12,6 +12,7 @@ venv/
 # tests' model weights
 tests/weights/
 tests/repos/
+tests/datasets/
 
 # ruff
 .ruff_cache

src/refiners/foundationals/dinov2/__init__.py

@@ -5,6 +5,7 @@ from .dinov2 import (
     DINOv2_large_reg,
     DINOv2_small,
     DINOv2_small_reg,
+    preprocess,
 )
 from .vit import ViT
 
@@ -16,4 +17,5 @@ __all__ = [
     "DINOv2_small",
     "DINOv2_small_reg",
     "ViT",
+    "preprocess",
 ]

src/refiners/foundationals/dinov2/dinov2.py

@@ -1,9 +1,25 @@
 import torch
+from PIL import Image
 
+from refiners.fluxion.utils import image_to_tensor, normalize
 from refiners.foundationals.dinov2.vit import ViT
 
-# TODO: add preprocessing logic like
+
-# https://github.com/facebookresearch/dinov2/blob/2302b6b/dinov2/data/transforms.py#L77
+def preprocess(img: Image.Image, dim: int = 224) -> torch.Tensor:
+    """
+    Preprocess an image for use with DINOv2. Uses ImageNet mean and standard deviation.
+    Note that this only resizes and normalizes the image, there is no center crop.
+
+    Args:
+        img: The image.
+        dim: The square dimension to resize the image. Typically 224 or 518.
+
+    Returns:
+        A float32 tensor with shape (3, dim, dim).
+    """
+    img = img.convert("RGB").resize((dim, dim))  # type: ignore
+    t = image_to_tensor(img).squeeze()
+    return normalize(t, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
 
 class DINOv2_small(ViT):

src/refiners/training_utils/metrics.py

@@ -0,0 +1,117 @@
+from pathlib import Path
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+
+from refiners.foundationals import dinov2
+
+
+def get_dinov2_representations(
+    model: dinov2.ViT,
+    dataloader: DataLoader[torch.Tensor],
+    dtype: torch.dtype = torch.float64,
+) -> torch.Tensor:
+    """
+    Get DINOV2 representations required to compute DINOv2-FD.
+
+    Args:
+        model: The DINOv2 model to use.
+        dataloader: A dataloader that returns batches of preprocessed images.
+        dtype: The dtype to use for the representations. Use float64 for good precision.
+
+    Returns:
+        A tensor with shape (batch, embedding_dim).
+    """
+    r: list[torch.Tensor] = []
+    for batch in dataloader:
+        assert isinstance(batch, torch.Tensor)
+        batch_size = batch.shape[0]
+        assert batch.shape == (batch_size, 3, 224, 224)
+        batch = batch.to(model.device)
+
+        with torch.no_grad():
+            pred = model(batch)[:, 0]  # only keep class embeddings
+
+        assert isinstance(pred, torch.Tensor)
+        assert pred.shape == (batch_size, model.embedding_dim)
+
+        r.append(pred.to(dtype))
+
+    return torch.cat(r)
+
+
+def frechet_distance(reps_a: torch.Tensor, reps_b: torch.Tensor) -> float:
+    """
+    Compute the Fréchet distance between two sets of representations.
+
+    Args:
+        reps_a: First set of representations (typically the reference). Shape (batch, N).
+        reps_a: Second set of representations (typically the test set). Shape (batch, N).
+    """
+    assert reps_a.dim() == 2 and reps_b.dim() == 2, "representations must have shape (batch, N)"
+    assert reps_a.shape[1] == reps_b.shape[1], "representations must have the same dimension"
+
+    mean_a = torch.mean(reps_a, dim=0)
+    cov_a = torch.cov(reps_a.t())
+    mean_b = torch.mean(reps_b, dim=0)
+    cov_b = torch.cov(reps_b.t())
+
+    # The trace of the square root of a matrix is the sum of the square roots of its eigenvalues.
+    trace = (torch.linalg.eigvals(cov_a.mm(cov_b)) ** 0.5).real.sum()  # type: ignore
+    assert isinstance(trace, torch.Tensor)
+
+    score = ((mean_a - mean_b) ** 2).sum() + cov_a.trace() + cov_b.trace() - 2 * trace
+    return score.item()
+
+
+class DinoDataset(Dataset[torch.Tensor]):
+    def __init__(self, path: str | Path) -> None:
+        if isinstance(path, str):
+            path = Path(path)
+        self.image_paths = sorted(path.glob("*.png"))
+
+    def __len__(self) -> int:
+        return len(self.image_paths)
+
+    def __getitem__(self, i: int) -> torch.Tensor:
+        path = self.image_paths[i]
+        img = Image.open(path)  # type: ignore
+        return dinov2.preprocess(img)
+
+
+def dinov2_frechet_distance(
+    dataset_a: Dataset[torch.Tensor] | str | Path,
+    dataset_b: Dataset[torch.Tensor] | str | Path,
+    model: dinov2.ViT,
+    batch_size: int = 64,
+    dtype: torch.dtype = torch.float64,
+) -> float:
+    """
+    Compute DINOv2-based Fréchet Distance between two datasets.
+
+    There may be small discrepancies with other implementations due to the fact that DINOv2 in Refiners
+    uses the new style interpolation whereas DINOv2-FD historically uses the legacy implementation
+    (see https://github.com/facebookresearch/dinov2/pull/378)
+
+    Args:
+        dataset_a: First dataset (typically the reference). Can also be a path to a directory of PNG images.
+            If a dataset is passed, it must preprocess the data using `dinov2.preprocess`.
+        dataset_b: Second dataset (typically the test set). See `dataset_a` for details. Size can be different.
+        model: The DINOv2 model to use.
+        batch_size: The batch size to use.
+        dtype: The dtype to use for the representations. Use float64 for good precision.
+    """
+
+    if not isinstance(dataset_a, Dataset):
+        dataset_a = DinoDataset(dataset_a)
+    if not isinstance(dataset_b, Dataset):
+        dataset_b = DinoDataset(dataset_b)
+
+    dataloader_a = DataLoader(dataset_a, batch_size=batch_size, shuffle=False)
+    dataloader_b = DataLoader(dataset_b, batch_size=batch_size, shuffle=False)
+
+    reps_a = get_dinov2_representations(model, dataloader_a, dtype)
+    reps_b = get_dinov2_representations(model, dataloader_b, dtype)
+
+    return frechet_distance(reps_a, reps_b)

tests/conftest.py

@@ -6,6 +6,9 @@ from pytest import fixture
 
 PARENT_PATH = Path(__file__).parent
 
+collect_ignore = ["weights", "repos", "datasets"]
+collect_ignore_glob = ["*_ref"]
+
 
 @fixture(scope="session")
 def test_device() -> torch.device:
@@ -21,6 +24,12 @@ def test_weights_path() -> Path:
     return Path(from_env) if from_env else PARENT_PATH / "weights"
 
 
+@fixture(scope="session")
+def test_datasets_path() -> Path:
+    from_env = os.getenv("REFINERS_TEST_DATASETS_DIR")
+    return Path(from_env) if from_env else PARENT_PATH / "datasets"
+
+
 @fixture(scope="session")
 def test_repos_path() -> Path:
     from_env = os.getenv("REFINERS_TEST_REPOS_DIR")

tests/training_utils/test_metrics.py

@@ -0,0 +1,67 @@
+from pathlib import Path
+from warnings import warn
+
+import pytest
+import torch
+from torch.utils.data import Dataset
+from torchvision.datasets import CIFAR10  # type: ignore
+
+from refiners.foundationals import dinov2
+from refiners.training_utils.metrics import dinov2_frechet_distance
+
+
+class CifarDataset(Dataset[torch.Tensor]):
+    def __init__(self, ds: Dataset[list[torch.Tensor]], max_len: int = 512) -> None:
+        self.ds = ds
+        ds_length = len(self.ds)  # type: ignore
+        self.length = min(ds_length, max_len)
+
+    def __len__(self) -> int:
+        return self.length
+
+    def __getitem__(self, i: int) -> torch.Tensor:
+        return self.ds[i][0]
+
+
+@pytest.fixture(scope="module")
+def dinov2_l(
+    test_weights_path: Path,
+    test_device: torch.device,
+) -> dinov2.DINOv2_large:
+    weights = test_weights_path / f"dinov2_vitl14_pretrain.safetensors"
+    if not weights.is_file():
+        warn(f"could not find weights at {weights}, skipping")
+        pytest.skip(allow_module_level=True)
+
+    model = dinov2.DINOv2_large(device=test_device)
+    model.load_from_safetensors(weights)
+    return model
+
+
+def test_dinov2_frechet_distance(test_datasets_path: Path, dinov2_l: dinov2.DINOv2_large) -> None:
+    path = str(test_datasets_path / "CIFAR10")
+
+    ds_train = CifarDataset(
+        CIFAR10(
+            root=path,
+            train=True,
+            download=True,
+            transform=dinov2.preprocess,
+        )
+    )
+
+    ds_test = CifarDataset(
+        CIFAR10(
+            root=path,
+            train=False,
+            download=True,
+            transform=dinov2.preprocess,
+        )
+    )
+
+    # Computed using dgm-eval (https://github.com/layer6ai-labs/dgm-eval)
+    # with interpolate_offset=0 and random_sample=False.
+    expected_d = 837.978
+
+    d = dinov2_frechet_distance(ds_train, ds_test, dinov2_l)
+    assert expected_d - 1e-2 < d < expected_d + 1e-2