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62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
from pathlib import Path
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import pytest
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import torch
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from torch.utils.data import Dataset
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from torchvision.datasets import CIFAR10 # type: ignore
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from refiners.foundationals import dinov2
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from refiners.training_utils.metrics import dinov2_frechet_distance
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class CifarDataset(Dataset[torch.Tensor]):
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def __init__(self, ds: Dataset[list[torch.Tensor]], max_len: int = 512) -> None:
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self.ds = ds
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ds_length = len(self.ds) # type: ignore
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self.length = min(ds_length, max_len)
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def __len__(self) -> int:
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return self.length
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def __getitem__(self, i: int) -> torch.Tensor:
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return self.ds[i][0]
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@pytest.fixture(scope="module")
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def dinov2_l(
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dinov2_large_weights_path: Path,
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test_device: torch.device,
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) -> dinov2.DINOv2_large:
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model = dinov2.DINOv2_large(device=test_device)
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model.load_from_safetensors(dinov2_large_weights_path)
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return model
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def test_dinov2_frechet_distance(test_datasets_path: Path, dinov2_l: dinov2.DINOv2_large) -> None:
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path = str(test_datasets_path / "CIFAR10")
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ds_train = CifarDataset(
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CIFAR10(
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root=path,
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train=True,
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download=True,
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transform=dinov2.preprocess,
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)
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)
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ds_test = CifarDataset(
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CIFAR10(
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root=path,
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train=False,
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download=True,
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transform=dinov2.preprocess,
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)
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)
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# Computed using dgm-eval (https://github.com/layer6ai-labs/dgm-eval)
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# with interpolate_offset=0 and random_sample=False.
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expected_d = 837.978
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d = dinov2_frechet_distance(ds_train, ds_test, dinov2_l)
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assert expected_d - 1e-2 < d < expected_d + 1e-2
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