class scale_linear(SharedProcessingSchema):
bioimageio_description = "Fixed linear scaling."
axes = fields.Axes(
required=True,
valid_axes="czyx",
bioimageio_description="The subset of axes to scale jointly. "
"For example xy to scale the two image axes for 2d data jointly. "
"The batch axis (b) is not valid here.",
)
gain = fields.Array(
fields.Float(), missing=fields.Float(missing=1.0), bioimageio_description="multiplicative factor"
) # todo: check if gain match input axes
offset = fields.Array(
fields.Float(), missing=fields.Float(missing=0.0), bioimageio_description="additive term"
) # todo: check if offset match input axes
@validates_schema
def either_gain_or_offset(self, data, **kwargs):
gain = data["gain"]
if isinstance(gain, (float, int)):
gain = [gain]
offset = data["offset"]
if isinstance(offset, (float, int)):
offset = [offset]
if all(g == 1.0 for g in gain) and all(off == 0 for off in offset):
raise ValidationError("Specify gain!=1.0 or offset!=0.0")
class zero_mean_unit_variance(SharedProcessingSchema):
bioimageio_description = "Subtract mean and divide by variance."
mode = fields.ProcMode(required=True)
axes = fields.Axes(
required=True,
valid_axes="czyx",
bioimageio_description="The subset of axes to normalize jointly. For example xy to normalize the two image "
"axes for 2d data jointly. The batch axis (b) is not valid here.",
)
mean = fields.Array(
fields.Float(),
bioimageio_description="The mean value(s) to use for `mode == fixed`. For example `[1.1, 2.2, 3.3]` in the "
"case of a 3 channel image where the channels are not normalized jointly.",
) # todo: check if means match input axes (for mode 'fixed')
std = fields.Array(
fields.Float(),
bioimageio_description="The standard deviation values to use for `mode == fixed`. Analogous to mean.",
)
eps = fields.Float(
missing=1e-6,
bioimageio_description="epsilon for numeric stability: `out = (tensor - mean) / (std + eps)`. "
"Default value: 10^-6.",
)
@validates_schema
def mean_and_std_match_mode(self, data, **kwargs):
if data["mode"] == "fixed" and ("mean" not in data or "std" not in data):
raise ValidationError(
"`kwargs` for 'zero_mean_unit_variance' preprocessing with `mode` 'fixed' require additional "
"`kwargs`: `mean` and `std`."
)
elif data["mode"] != "fixed" and ("mean" in data or "std" in data):
raise ValidationError(
"`kwargs`: `mean` and `std` for 'zero_mean_unit_variance' preprocessing are only valid for `mode` 'fixed'."
)
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])