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