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73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
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 PIL import Image
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from tests.utils import ensure_similar_images
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from refiners.fluxion.utils import image_to_tensor, no_grad, normalize, tensor_to_image
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from refiners.foundationals.swin.mvanet import MVANet
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def _img_open(path: Path) -> Image.Image:
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return Image.open(path) # type: ignore
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@pytest.fixture(scope="module")
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def ref_path(test_e2e_path: Path) -> Path:
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return test_e2e_path / "test_mvanet_ref"
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@pytest.fixture(scope="module")
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def ref_cactus(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "cactus.png").convert("RGB")
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@pytest.fixture
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def expected_cactus_mask(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_cactus_mask.png")
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@pytest.fixture
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def mvanet_model(mvanet_weights_path: Path, test_device: torch.device) -> MVANet:
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model = MVANet(device=test_device).eval() # .eval() is important!
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model.load_from_safetensors(mvanet_weights_path)
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return model
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@no_grad()
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def test_mvanet(
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mvanet_model: MVANet,
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ref_cactus: Image.Image,
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expected_cactus_mask: Image.Image,
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test_device: torch.device,
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):
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in_t = image_to_tensor(ref_cactus.resize((1024, 1024), Image.Resampling.BILINEAR)).squeeze()
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in_t = normalize(in_t, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]).unsqueeze(0)
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prediction: torch.Tensor = mvanet_model(in_t.to(test_device)).sigmoid()
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cactus_mask = tensor_to_image(prediction).resize(ref_cactus.size, Image.Resampling.BILINEAR)
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ensure_similar_images(cactus_mask.convert("RGB"), expected_cactus_mask.convert("RGB"))
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@no_grad()
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def test_mvanet_to(
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mvanet_weights_path: Path,
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ref_cactus: Image.Image,
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expected_cactus_mask: Image.Image,
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test_device: torch.device,
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):
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if test_device.type == "cpu":
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warn("not running on CPU, skipping")
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pytest.skip()
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model = MVANet(device=torch.device("cpu")).eval()
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model.load_from_safetensors(mvanet_weights_path)
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model.to(test_device)
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in_t = image_to_tensor(ref_cactus.resize((1024, 1024), Image.Resampling.BILINEAR)).squeeze()
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in_t = normalize(in_t, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]).unsqueeze(0)
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prediction: torch.Tensor = model(in_t.to(test_device)).sigmoid()
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cactus_mask = tensor_to_image(prediction).resize(ref_cactus.size, Image.Resampling.BILINEAR)
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ensure_similar_images(cactus_mask.convert("RGB"), expected_cactus_mask.convert("RGB"))
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