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
This commit is contained in:
Cédric Deltheil 2023-12-16 16:16:54 +01:00 committed by Cédric Deltheil
parent 832f012fe4
commit 68cc346905
3 changed files with 95 additions and 0 deletions

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@ -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__(

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@ -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,

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@ -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