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dinov2: add some coverage for registers

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Those are not supported yet in HF: so just compared with a precomputed
norm. Note: in the initial PR [1] the Refiners' implementation has been
tested against the official code using Torch Hub.

[1]:
https://github.com/finegrain-ai/refiners/pull/132#issuecomment-1852021656
This commit is contained in:
Cédric Deltheil 2023-12-18 10:10:39 +01:00 committed by Cédric Deltheil
parent f0ea1a2509
commit e7892254eb
1 changed files with 60 additions and 4 deletions
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64
tests/foundationals/dinov2/test_dinov2.py
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@ -1,3 +1,4 @@
from math import isclose
from pathlib import Path
from warnings import warn
@ -7,16 +8,23 @@ 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 import (
DINOv2_base,
DINOv2_base_reg,
DINOv2_large,
DINOv2_large_reg,
DINOv2_small,
DINOv2_small_reg,
)
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",
"dinov2_vits14_reg4",
"dinov2_vitb14_reg4",
"dinov2_vitl14_reg4",
]
@ -25,6 +33,26 @@ def flavor(request: pytest.FixtureRequest) -> str:
return request.param
# Temporary: see comments in `test_encoder_only`
@pytest.fixture(scope="module")
def seed_expected_norm(flavor: str) -> tuple[int, float]:
match flavor:
case "dinov2_vits14":
return (42, 1977.9213867)
case "dinov2_vitb14":
return (42, 1902.6384277)
case "dinov2_vitl14":
return (42, 1763.9187011)
case "dinov2_vits14_reg4":
return (42, 989.2380981)
case "dinov2_vitb14_reg4":
return (42, 974.4362182)
case "dinov2_vitl14_reg4":
return (42, 924.8797607)
case _:
raise ValueError(f"Unexpected DINOv2 flavor: {flavor}")
@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"
@ -38,6 +66,12 @@ def our_backbone(test_weights_path: Path, flavor: str, test_device: torch.device
backbone = DINOv2_base(device=test_device)
case "dinov2_vitl14":
backbone = DINOv2_large(device=test_device)
case "dinov2_vits14_reg4":
backbone = DINOv2_small_reg(device=test_device)
case "dinov2_vitb14_reg4":
backbone = DINOv2_base_reg(device=test_device)
case "dinov2_vitl14_reg4":
backbone = DINOv2_large_reg(device=test_device)
case _:
raise ValueError(f"Unexpected DINOv2 flavor: {flavor}")
tensors = load_from_safetensors(weights)
@ -47,6 +81,12 @@ def our_backbone(test_weights_path: Path, flavor: str, test_device: torch.device
@pytest.fixture(scope="module")
def dinov2_weights_path(test_weights_path: Path, flavor: str):
# TODO: 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).
if flavor.endswith("_reg4"):
warn(f"DINOv2 with registers are not yet supported in Hugging Face, skipping")
pytest.skip(allow_module_level=True)
match flavor:
case "dinov2_vits14":
name = "dinov2-small"
@ -89,3 +129,19 @@ def test_encoder(
our_features = our_backbone(x)
assert (our_features - ref_features).abs().max() < 1e-3
# Mainly for DINOv2 + registers coverage (this test can be removed once `test_encoder` supports all flavors)
def test_encoder_only(
our_backbone: ViT,
seed_expected_norm: tuple[int, float],
test_device: torch.device,
):
seed, expected_norm = seed_expected_norm
manual_seed(seed)
x = torch.randn(1, 3, 518, 518).to(test_device)
our_features = our_backbone(x)
assert isclose(our_features.norm().item(), expected_norm, rel_tol=1e-04)