def test_model_schema_accepts_run_mode(model_dict):
from bioimageio.spec.model.schema import Model
model_schema = Model()
model_dict.update({"run_mode": {"name": "special_run_mode", "kwargs": dict(marathon=True)}})
validated_data = model_schema.load(model_dict)
assert validated_data
def test_model_schema_accepts_valid_weight_formats(model_dict, format):
from bioimageio.spec.model.schema import Model
model_schema = Model()
model_dict.update({"weights": {format: {"source": "local_weights"}}})
if format == "pytorch_state_dict":
model_dict["weights"][format]["architecture"] = "file.py:Model"
model_dict["weights"][format]["architecture_sha256"] = "0" * 64 # dummy sha256
validated_data = model_schema.load(model_dict)
assert validated_data
def test_model_has_parent_with_id(model_dict):
from bioimageio.spec.model.schema import Model
model_dict["parent"] = dict(id="10.5281/zenodo.5764892")
valid_data = Model().load(model_dict)
assert valid_data
def test_model_has_parent_with_uri(model_dict):
from bioimageio.spec.model.schema import Model
model_dict["parent"] = dict(uri="https://doi.org/10.5281/zenodo.5744489")
valid_data = Model().load(model_dict)
assert valid_data
def test_model_does_not_accept_unknown_fields(unet2d_nuclei_broad_latest):
from bioimageio.spec.model.schema import Model
unet2d_nuclei_broad_latest["unknown_additional_field"] = "shouldn't be here"
with pytest.raises(ValidationError):
Model().load(unet2d_nuclei_broad_latest)
def test_model_0_4_raises_on_duplicate_tensor_names(invalid_rdf_v0_4_0_duplicate_tensor_names):
from bioimageio.spec.model.schema import Model
from bioimageio.spec.model.v0_3.schema import Model as Model_v03
model_schema = Model()
with pytest.raises(ValidationError):
model_schema.load(invalid_rdf_v0_4_0_duplicate_tensor_names)
# as 0.3 the model should still be valid with some small changes
model_schema = Model_v03()
data = dict(invalid_rdf_v0_4_0_duplicate_tensor_names)
data["format_version"] = "0.3.3"
data["language"] = "python"
data["framework"] = "pytorch"
data["source"] = data["weights"]["pytorch_state_dict"].pop("architecture")
data["kwargs"] = data["weights"]["pytorch_state_dict"].pop("kwargs")
data["sha256"] = data["weights"]["pytorch_state_dict"].pop("architecture_sha256")
valid_data = model_schema.load(data)
assert valid_data
def test_output_ref_shape_too_small(model_dict):
from bioimageio.spec.model.schema import Model
model_dict["outputs"] = [
{
"name": "output_1",
"description": "Output 1",
"data_type": "float32",
"axes": "xyc",
"shape": {"reference_tensor": "input_1", "scale": [1, 2, 3], "offset": [0, 0, 0]},
"halo": [256, 128, 0],
}
]
with pytest.raises(ValidationError) as e:
Model().load(model_dict)
assert e.value.messages == {
"_schema": ["Minimal shape [128. 256. 9.] of output output_1 is too small for halo [256, 128, 0]."]
}
def test_output_fixed_shape_too_small(model_dict):
from bioimageio.spec.model.schema import Model
model_dict["outputs"] = [
{
"name": "output_1",
"description": "Output 1",
"data_type": "float32",
"axes": "xyc",
"shape": [128, 128, 3],
"halo": [32, 128, 0],
}
]
with pytest.raises(ValidationError) as e:
Model().load(model_dict)
assert e.value.messages == {
"_schema": ["Minimal shape [128 128 3] of output output_1 is too small for halo [32, 128, 0]."]
}
def test_output_ref_shape_mismatch(model_dict):
from bioimageio.spec.model.schema import Model
model_dict["outputs"] = [
{
"name": "output_1",
"description": "Output 1",
"data_type": "float32",
"axes": "xyc",
"shape": {"reference_tensor": "input_1", "scale": [1, 2, 3, 4], "offset": [0, 0, 0, 0]},
}
]
with pytest.raises(ValidationError) as e:
Model().load(model_dict)
assert e.value.messages == {
"_schema": [
"Referenced tensor input_1 with 3 dimensions does not match output tensor output_1 with 4 dimensions."
]
}