class ParametrizedInputShape(SharedBioImageIOSchema):
min = fields.List(
fields.Integer(), required=True, bioimageio_description="The minimum input shape with same length as `axes`"
)
step = fields.List(
fields.Integer(), required=True, bioimageio_description="The minimum shape change with same length as `axes`"
)
@validates_schema
def matching_lengths(self, data, **kwargs):
min_ = data["min"]
step = data["step"]
if min_ is None or step is None:
return
if len(min_) != len(step):
raise ValidationError(f"'min' and 'step' have to have the same length! (min: {min_}, step: {step})")
class OutputTensor(_TensorBase):
shape = fields.Union(
[
fields.ExplicitShape(),
fields.Nested(
ImplicitOutputShape(),
bioimageio_description=
"In reference to the shape of an input tensor, the shape of the output "
"tensor is `shape = shape(input_tensor) * scale + 2 * offset`.",
),
],
required=True,
bioimageio_description="Specification of output tensor shape.",
)
halo = fields.List(
fields.Integer(),
bioimageio_description=
"The halo to crop from the output tensor (for example to crop away boundary effects or "
"for tiling). The halo should be cropped from both sides, i.e. `shape_after_crop = shape - 2 * halo`. The "
"`halo` is not cropped by the bioimage.io model, but is left to be cropped by the consumer software. Use "
"`shape:offset` if the model output itself is cropped and input and output shapes not fixed.",
)
postprocessing = fields.List(
fields.Nested(Postprocessing()),
bioimageio_description=
"Description of how this output should be postprocessed.",
)
processing_name = "postprocessing"
@validates_schema
def matching_halo_length(self, data, **kwargs):
shape = data.get("shape")
halo = data.get("halo")
if halo is None:
return
elif isinstance(shape, list) or isinstance(
shape, raw_nodes.ImplicitOutputShape):
if shape is None or len(halo) != len(shape):
raise ValidationError(
f"halo {halo} has to have same length as shape {shape}!")
else:
raise NotImplementedError(type(shape))
class OutputTensor(_TensorBase):
shape = fields.Union(
[
fields.ExplicitShape(),
fields.Nested(
ImplicitOutputShape(),
bioimageio_description="In reference to the shape of an input tensor, the shape of the output "
"tensor is `shape = shape(input_tensor) * scale + 2 * offset`.",
),
],
required=True,
bioimageio_description="Specification of output tensor shape.",
)
halo = fields.List(
fields.Integer(),
bioimageio_description=lambda: "Hint to describe the potentially corrupted edge region of the output tensor, due to "
"boundary effects. "
"The `halo` is not cropped by the bioimage.io model, but is left to be cropped by the consumer software. "
f"An example implementation of prediction with tiling, accounting for the halo can be found [here]("
f"{get_ref_url('function', '_predict_with_tiling_impl', 'https://github.com/bioimage-io/core-bioimage-io-python/blob/main/bioimageio/core/prediction.py')}). "
"Use `shape:offset` if the model output itself is cropped and input and output shapes not fixed. ",
)
postprocessing = fields.List(
fields.Nested(Postprocessing()),
bioimageio_description="Description of how this output should be postprocessed.",
)
processing_name = "postprocessing"
@validates_schema
def matching_halo_length(self, data, **kwargs):
shape = data["shape"]
halo = data.get("halo")
if halo is None:
return
elif isinstance(shape, list) or isinstance(shape, raw_nodes.ImplicitOutputShape):
if len(halo) != len(shape):
raise ValidationError(f"halo {halo} has to have same length as shape {shape}!")
else:
raise NotImplementedError(type(shape))
class OnnxWeightsEntry(_WeightsEntryBase):
bioimageio_description = "ONNX weights format"
weights_format = fields.String(validate=field_validators.Equal("onnx"), required=True, load_only=True)
opset_version = fields.Integer()
def test_wrong_dtype(self):
data = [[1, 2], [3, 4.5]]
with raises(ValidationError):
fields.Array(fields.Integer(strict=True)).deserialize(data)
def test_2d(self):
data = [[1, 2], [3, 4]]
expected = numpy.array(data, dtype=int)
actual = fields.Array(fields.Integer(strict=True)).deserialize(data)
assert_equal(actual, expected)
def test_invalid_scalar(self):
data = "invalid"
with raises(ValidationError):
fields.Array(fields.Integer(strict=True)).deserialize(data)
def test_scalar(self):
data = 1
expected = data
actual = fields.Array(fields.Integer(strict=True)).deserialize(data)
assert_equal(actual, expected)
def test_uneuqal_sublen(self):
with raises(ValidationError):
fields.Array(fields.Integer(strict=True)).deserialize([[1, 2],
[3]])
def test_unequal_nesting_depth(self):
with raises(ValidationError):
fields.Array(fields.Integer(strict=True)).deserialize([[1, 2], 3])
