add minimal unit tests for DINOv2

To be completed with tests using image preprocessing, e.g. test cosine
similarity on a relevant pair of images

src/refiners/foundationals/dinov2/dinov2.py

@@ -2,6 +2,9 @@ import torch
 
 from refiners.foundationals.dinov2.vit import ViT
 
+# TODO: add preprocessing logic like
+# https://github.com/facebookresearch/dinov2/blob/2302b6b/dinov2/data/transforms.py#L77
+
 
 class DINOv2_small(ViT):
     def __init__(

src/refiners/foundationals/dinov2/vit.py

@@ -269,6 +269,7 @@ class ViT(fl.Chain):
                 ),
                 dim=1,
             ),
+            # TODO: support https://github.com/facebookresearch/dinov2/blob/2302b6b/dinov2/models/vision_transformer.py#L179
             PositionalEncoder(
                 sequence_length=num_patches**2 + 1,
                 embedding_dim=embedding_dim,

tests/foundationals/dinov2/test_dinov2.py

@@ -0,0 +1,91 @@
+from pathlib import Path
+from warnings import warn
+
+import pytest
+import torch
+from transformers import AutoModel  # type: ignore
+from transformers.models.dinov2.modeling_dinov2 import Dinov2Model  # type: ignore
+
+from refiners.fluxion.utils import load_from_safetensors, manual_seed
+from refiners.foundationals.dinov2 import DINOv2_base, DINOv2_large, DINOv2_small
+from refiners.foundationals.dinov2.vit import ViT
+
+# TODO: add DINOv2 with registers ("dinov2_vits14_reg", etc). At the time of writing, those are not yet supported in
+# transformers (https://github.com/huggingface/transformers/issues/27379). Alternatively, it is also possible to use
+# facebookresearch/dinov2 directly (https://github.com/finegrain-ai/refiners/pull/132).
+FLAVORS = [
+    "dinov2_vits14",
+    "dinov2_vitb14",
+    "dinov2_vitl14",
+]
+
+
+@pytest.fixture(scope="module", params=FLAVORS)
+def flavor(request: pytest.FixtureRequest) -> str:
+    return request.param
+
+
+@pytest.fixture(scope="module")
+def our_backbone(test_weights_path: Path, flavor: str, test_device: torch.device) -> ViT:
+    weights = test_weights_path / f"{flavor}_pretrain.safetensors"
+    if not weights.is_file():
+        warn(f"could not find weights at {weights}, skipping")
+        pytest.skip(allow_module_level=True)
+    match flavor:
+        case "dinov2_vits14":
+            backbone = DINOv2_small(device=test_device)
+        case "dinov2_vitb14":
+            backbone = DINOv2_base(device=test_device)
+        case "dinov2_vitl14":
+            backbone = DINOv2_large(device=test_device)
+        case _:
+            raise ValueError(f"Unexpected DINOv2 flavor: {flavor}")
+    tensors = load_from_safetensors(weights)
+    backbone.load_state_dict(tensors)
+    return backbone
+
+
+@pytest.fixture(scope="module")
+def dinov2_weights_path(test_weights_path: Path, flavor: str):
+    match flavor:
+        case "dinov2_vits14":
+            name = "dinov2-small"
+        case "dinov2_vitb14":
+            name = "dinov2-base"
+        case "dinov2_vitl14":
+            name = "dinov2-large"
+        case _:
+            raise ValueError(f"Unexpected DINOv2 flavor: {flavor}")
+    r = test_weights_path / "facebook" / name
+    if not r.is_dir():
+        warn(f"could not find DINOv2 weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture(scope="module")
+def ref_backbone(dinov2_weights_path: Path, test_device: torch.device) -> Dinov2Model:
+    backbone = AutoModel.from_pretrained(dinov2_weights_path)  # type: ignore
+    assert isinstance(backbone, Dinov2Model)
+    return backbone.to(test_device)  # type: ignore
+
+
+def test_encoder(
+    ref_backbone: Dinov2Model,
+    our_backbone: ViT,
+    test_device: torch.device,
+):
+    manual_seed(42)
+
+    # Position encoding interpolation [1] at runtime is not supported yet. So stick to the default image resolution
+    # e.g. using (224, 224) pixels as input would give a runtime error (sequence size mismatch)
+    # [1]: https://github.com/facebookresearch/dinov2/blob/2302b6b/dinov2/models/vision_transformer.py#L179
+    assert our_backbone.image_size == 518
+
+    x = torch.randn(1, 3, 518, 518).to(test_device)
+
+    with torch.no_grad():
+        ref_features = ref_backbone(x).last_hidden_state
+        our_features = our_backbone(x)
+
+    assert (our_features - ref_features).abs().max() < 1e-3