def test_min_step_input_shape(self):
data = {"min": [1, 2, 3], "step": [0, 1, 3]}
expected = raw_nodes.ParametrizedInputShape(**data)
actual = fields.Union([
fields.ExplicitShape(),
fields.Nested(schema.ParametrizedInputShape())
],
required=True).deserialize(data)
assert actual == expected
class InputTensor(_TensorBase):
shape = fields.Union(
[
fields.ExplicitShape(
bioimageio_description=
"Exact shape with same length as `axes`, e.g. `shape: [1, 512, 512, 1]`"
),
fields.Nested(
ParametrizedInputShape(),
bioimageio_description=
"A sequence of valid shapes given by `shape = min + k * step for k in {0, 1, ...}`.",
),
],
required=True,
bioimageio_description="Specification of input tensor shape.",
)
preprocessing = fields.List(
fields.Nested(Preprocessing()),
bioimageio_description=
"Description of how this input should be preprocessed.")
processing_name = "preprocessing"
@validates_schema
def zero_batch_step_and_one_batch_size(self, data, **kwargs):
axes = data.get("axes")
shape = data.get("shape")
if axes is None or shape is None:
raise ValidationError(
"Failed to validate batch_step=0 and batch_size=1 due to other validation errors"
)
axes = data["axes"]
shape = data["shape"]
bidx = axes.find("b")
if bidx == -1:
return
if isinstance(shape, raw_nodes.ParametrizedInputShape):
step = shape.step
shape = shape.min
elif isinstance(shape, list):
step = [0] * len(shape)
else:
raise ValidationError(f"Unknown shape type {type(shape)}")
if step[bidx] != 0:
raise ValidationError(
"Input shape step has to be zero in the batch dimension (the batch dimension can always be "
"increased, but `step` should specify how to increase the minimal shape to find the largest "
"single batch shape)")
if shape[bidx] != 1:
raise ValidationError(
"Input shape has to be 1 in the batch dimension b.")
def test_output_shape(self):
data = {
"reference_tensor": "in1",
"scale": [1, 2, 3],
"offset": [0, 1, 3]
}
expected = raw_nodes.ImplicitOutputShape(**data)
actual = fields.Union([
fields.ExplicitShape(),
fields.Nested(schema.ImplicitOutputShape())
],
required=True).deserialize(data)
assert actual == expected
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))
def test_explicit_input_shape(self):
data = [1, 2, 3]
expected = data
actual = fields.ExplicitShape().deserialize(data)
assert expected == actual