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54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from pathlib import Path
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import pytest
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import torch
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from transformers import CLIPVisionModelWithProjection # type: ignore
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from refiners.fluxion.utils import load_from_safetensors, no_grad
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from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
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@pytest.fixture(scope="module")
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def our_encoder(
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clip_image_encoder_huge_weights_path: Path,
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test_device: torch.device,
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test_dtype_fp32_bf16_fp16: torch.dtype,
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) -> CLIPImageEncoderH:
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encoder = CLIPImageEncoderH(device=test_device, dtype=test_dtype_fp32_bf16_fp16)
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tensors = load_from_safetensors(clip_image_encoder_huge_weights_path)
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encoder.load_state_dict(tensors)
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return encoder
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@pytest.fixture(scope="module")
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def ref_encoder(
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unclip21_transformers_stabilityai_path: str,
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test_device: torch.device,
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test_dtype_fp32_bf16_fp16: torch.dtype,
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use_local_weights: bool,
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) -> CLIPVisionModelWithProjection:
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return CLIPVisionModelWithProjection.from_pretrained( # type: ignore
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unclip21_transformers_stabilityai_path,
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local_files_only=use_local_weights,
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subfolder="image_encoder",
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).to(device=test_device, dtype=test_dtype_fp32_bf16_fp16) # type: ignore
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@no_grad()
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@pytest.mark.flaky(reruns=3)
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def test_encoder(
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ref_encoder: CLIPVisionModelWithProjection,
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our_encoder: CLIPImageEncoderH,
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):
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assert ref_encoder.dtype == our_encoder.dtype
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assert ref_encoder.device == our_encoder.device
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x = torch.randn((1, 3, 224, 224), dtype=ref_encoder.dtype, device=ref_encoder.device)
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ref_embeddings = ref_encoder(x).image_embeds
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our_embeddings = our_encoder(x)
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assert ref_embeddings.shape == (1, 1024)
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assert our_embeddings.shape == (1, 1024)
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assert torch.allclose(our_embeddings, ref_embeddings, atol=0.05)
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