def test_Net3DArabidopsisOvules_forward(cache_path):
spec_path = (
Path(__file__).parent /
"../../bioimage-io/UNet3DArabidopsisOvules.model/UNet3DArabidopsisOvules.model.yaml"
).resolve()
assert spec_path.exists(), spec_path
pybio_model = load_model(str(spec_path), cache_path=cache_path)
assert pybio_model.spec.outputs[0].shape.reference_input == "raw"
assert pybio_model.spec.outputs[0].shape.scale == (1, 1, 1, 1, 1)
assert pybio_model.spec.outputs[0].shape.offset == (0, 0, 0, 0, 0)
assert isinstance(pybio_model.spec.prediction.weights.source, BytesIO)
assert pybio_model.spec.test_input is not None
assert pybio_model.spec.test_input.suffix == ".npy", pybio_model.spec.test_input.suffix
assert pybio_model.spec.test_output is not None
assert pybio_model.spec.test_output.suffix == ".npy", pybio_model.spec.test_output.suffix
model: torch.nn.Module = get_instance(pybio_model)
assert isinstance(model, UNet3D)
assert hasattr(model, "forward")
model_weights = torch.load(pybio_model.spec.prediction.weights.source,
map_location=torch.device("cpu"))
model.load_state_dict(model_weights)
pre_transformations = [
get_instance(trf) for trf in pybio_model.spec.prediction.preprocess
]
post_transformations = [
get_instance(trf) for trf in pybio_model.spec.prediction.postprocess
]
ipt = numpy.load(str(pybio_model.spec.test_input))
assert len(ipt.shape) == 5
assert ipt.shape == pybio_model.spec.inputs[0].shape
expected = numpy.load(str(pybio_model.spec.test_output))
assert pybio_model.spec.outputs[
0].shape.reference_input == pybio_model.spec.inputs[0].name
assert all([s == 1 for s in pybio_model.spec.outputs[0].shape.scale])
assert all([off == 0 for off in pybio_model.spec.outputs[0].shape.offset])
assert expected.shape == pybio_model.spec.inputs[0].shape
test_roi = (slice(0, 1), slice(0, 1), slice(0, 32), slice(0, 32),
slice(0, 32)) # to lower test mem consumption
ipt = ipt[test_roi]
expected = expected[test_roi]
ipt = apply_transformations(pre_transformations, ipt)
assert isinstance(ipt, list)
assert len(ipt) == 1
ipt = ipt[0]
out = model.forward(ipt)
out = apply_transformations(post_transformations, out)
assert isinstance(out, list)
assert len(out) == 1
out = out[0]
# assert out.shape == pybio_model.spec.inputs[0].shape # test_roi makes test invalid
numpy.save("out.npy", out)
assert str(
out.dtype).split(".")[-1] == pybio_model.spec.outputs[0].data_type
assert numpy.allclose(expected, out,
atol=0.1) # test_roi requires bigger atol
def test_forward(cache_path):
spec_path = (
Path(__file__).parent /
"../../segmentation/cells/UNet3DPlatyCellProbs.model/UNet3DPlatyCellProbs.model.yaml"
)
assert spec_path.exists(), spec_path.absolute()
pybio_model = load_model(str(spec_path), cache_path=cache_path)
assert pybio_model.spec.outputs[0].shape.reference_input == "raw"
assert pybio_model.spec.outputs[0].shape.scale == (1, 1, 1, 1, 1)
assert pybio_model.spec.outputs[0].shape.offset == (0, 0, 0, 0, 0)
model: torch.nn.Module = get_instance(pybio_model)
assert hasattr(model, "forward")
assert isinstance(model, UNetAnisotropic)
model_weights = torch.load(pybio_model.spec.prediction.weights.source,
map_location=torch.device("cpu"))
model.load_state_dict(model_weights)
pre_transformations = [
get_instance(trf) for trf in pybio_model.spec.prediction.preprocess
]
post_transformations = [
get_instance(trf) for trf in pybio_model.spec.prediction.postprocess
]
ipt_npz = numpy.load(str(pybio_model.spec.test_input))
# npz to ndarray
ipt = [ipt_npz[ipt_npz.files[0]]]
ipt_npz.close()
assert len(ipt) == len(pybio_model.spec.inputs)
assert isinstance(ipt, list)
assert len(ipt) == 1
ipt = ipt[0]
assert ipt.shape == pybio_model.spec.inputs[0].shape
# Don't test with the real test io, but a smaller test_roi.
# Because the results differ due to edge effects, load the small test output instead
# (this one is not linked to in the model.yaml)
assert len(ipt.shape) == 5, ipt.shape
test_roi = (slice(0, 1), slice(0, 1), slice(0, 32), slice(0, 32),
slice(0, 32)) # to lower test mem consumption
ipt = ipt[test_roi]
expected_npz = numpy.load(
str(pybio_model.spec.test_output).replace("test_output.npz",
"test_output_small.npz"))
# npz to npy
expected = expected_npz[expected_npz.files[0]]
expected_npz.close()
ipt = apply_transformations(pre_transformations, ipt)
assert isinstance(ipt, list)
assert len(ipt) == 1
out = model.forward(*ipt)
out = apply_transformations(post_transformations, out)
assert isinstance(out, list)
assert len(out) == 1
out = out[0]
# assert out.shape == pybio_model.spec.inputs[0].shape # test_roi makes this test invalid
assert str(
out.dtype).split(".")[-1] == pybio_model.spec.outputs[0].data_type
assert numpy.allclose(out, expected)
def test_UNet2dNucleiBroads_load_weights(cache_path):
spec_path = (
Path(__file__).parent /
"../../../specs/models/unet2d/nuclei_broad/UNet2DNucleiBroad.model.yaml"
).resolve()
assert spec_path.exists(), spec_path
pybio_model = load_model(str(spec_path), cache_path=cache_path)
assert isinstance(pybio_model.spec.prediction.weights.source, BytesIO)
def test_dummy_input(cache_path, dummy_input):
spec_path = (
Path(__file__).parent / "../../bioimage-io/UNet3DArabidopsisOvules.model/UNet3DArabidopsisOvules.model.yaml"
)
assert spec_path.exists()
pybio_model = load_model(str(spec_path), cache_path=cache_path)
for dummy, spec in zip(dummy_input, pybio_model.spec.inputs):
assert str(dummy.dtype) == spec.data_type
assert dummy.shape == spec.shape
def test_RandomForestClassifierBroadNucleusDataBinarized(cache_path):
spec_path = (
Path(__file__).parent /
"../../../specs/models/sklearnbased/RandomForestClassifierBroadNucleusDataBinarized.model.yaml"
)
pybio_model = load_model(str(spec_path),
kwargs={"c_indices": [None]},
cache_path=cache_path)
model = utils.train(pybio_model)
ipt = [numpy.arange(24).reshape((2, 3, 4))]
out = model(ipt)
assert len(out) == 1
assert out[0].shape == ipt[0].shape
def test_UNet2dNucleiBroads(cache_path):
spec_path = (
Path(__file__).parent /
"../../../specs/models/unet2d/nuclei_broad/UNet2DNucleiBroad.model.yaml"
).resolve()
assert spec_path.exists(), spec_path
pybio_model = load_model(str(spec_path), cache_path=cache_path)
with BytesIO() as f:
model = utils.train(pybio_model, n_iterations=1, out_file=f)
f.seek(0)
loaded = torch.load(f)
state = model.state_dict()
for t in state:
assert t in loaded
assert torch.equal(state[t], loaded[t])
def test_2sUNetDA(cache_path):
spec_path = (Path(__file__).parent / "../2sUNetDA.model.yaml").resolve()
assert spec_path.exists(), spec_path
pybio_model = load_model(str(spec_path), cache_path=cache_path)
assert isinstance(pybio_model.spec.prediction.weights.source, BytesIO)
assert pybio_model.spec.test_input is not None
assert pybio_model.spec.test_output is not None
model: torch.nn.Module = get_instance(pybio_model)
if torch.cuda.is_available():
test_device = torch.device("cuda")
model = model.to(device=test_device)
else:
test_device = torch.device("cpu")
model.eval()
model_weights = torch.load(pybio_model.spec.prediction.weights.source, map_location=test_device)
model.load_state_dict(model_weights)
pre_transformations = [get_instance(trf) for trf in pybio_model.spec.prediction.preprocess]
post_transformations = [get_instance(trf) for trf in pybio_model.spec.prediction.postprocess]
test_ipt = numpy.load(str(pybio_model.spec.test_input))
test_out = numpy.load(str(pybio_model.spec.test_output))
assert hasattr(model, "forward")
preprocessed_inputs = apply_transformations(pre_transformations, test_ipt)
assert isinstance(preprocessed_inputs, list)
assert len(preprocessed_inputs) == 1
out = model.forward(*[t.to(test_device) for t in preprocessed_inputs])
postprocessed_outputs = apply_transformations(post_transformations, out)
assert isinstance(postprocessed_outputs, list)
assert len(postprocessed_outputs) == 1
out = postprocessed_outputs[0]
assert numpy.allclose(test_out, out)