def new_method(self, *args, **kwargs):
[image_batch_format, alpha, prob], _ = parse_user_args(method, *args, **kwargs)
type_check(image_batch_format, (ImageBatchFormat,), "image_batch_format")
check_pos_float32(alpha)
check_positive(alpha, "alpha")
check_value(prob, [0, 1], "prob")
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[alpha], _ = parse_user_args(method, *args, **kwargs)
type_check(alpha, (int, float), "alpha")
check_positive(alpha, "alpha")
check_pos_float32(alpha)
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[prob, scale, ratio, value, inplace, max_attempts], _ = parse_user_args(method, *args, **kwargs)
type_check(prob, (float, int,), "prob")
type_check_list(scale, (float, int,), "scale")
if len(scale) != 2:
raise TypeError("scale should be a list or tuple of length 2.")
type_check_list(ratio, (float, int,), "ratio")
if len(ratio) != 2:
raise TypeError("ratio should be a list or tuple of length 2.")
type_check(value, (int, list, tuple, str), "value")
type_check(inplace, (bool,), "inplace")
type_check(max_attempts, (int,), "max_attempts")
check_erasing_value(value)
check_value(prob, [0., 1.], "prob")
if scale[0] > scale[1]:
raise ValueError("scale should be in (min,max) format. Got (max,min).")
check_range(scale, [0, FLOAT_MAX_INTEGER])
check_positive(scale[1], "scale[1]")
if ratio[0] > ratio[1]:
raise ValueError("ratio should be in (min,max) format. Got (max,min).")
check_value_ratio(ratio[0], [0, FLOAT_MAX_INTEGER])
check_value_ratio(ratio[1], [0, FLOAT_MAX_INTEGER])
if isinstance(value, int):
check_value(value, (0, 255))
if isinstance(value, (list, tuple)):
for item in value:
type_check(item, (int,), "value")
check_value(item, [0, 255], "value")
check_value(max_attempts, (1, FLOAT_MAX_INTEGER))
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[batch_size, alpha, is_single], _ = parse_user_args(method, *args, **kwargs)
check_value(batch_size, (1, FLOAT_MAX_INTEGER))
check_positive(alpha, "alpha")
type_check(is_single, (bool,), "is_single")
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[transforms, num_ops], _ = parse_user_args(method, *args, **kwargs)
type_check(num_ops, (int,), "num_ops")
check_positive(num_ops, "num_ops")
if num_ops > len(transforms):
raise ValueError("num_ops is greater than transforms list size.")
type_check_list(transforms, (TensorOp,), "tensor_ops")
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[degrees, translate, scale, shear, resample,
fill_value], _ = parse_user_args(method, *args, **kwargs)
check_degrees(degrees)
if translate is not None:
type_check(translate, (list, tuple), "translate")
type_check_list(translate, (int, float), "translate")
if len(translate) != 2 and len(translate) != 4:
raise TypeError(
"translate should be a list or tuple of length 2 or 4.")
for i, t in enumerate(translate):
check_value(t, [-1.0, 1.0], "translate at {0}".format(i))
if scale is not None:
type_check(scale, (tuple, list), "scale")
type_check_list(scale, (int, float), "scale")
if len(scale) == 2:
if scale[0] > scale[1]:
raise ValueError(
"Input scale[1] must be equal to or greater than scale[0]."
)
check_range(scale, [0, FLOAT_MAX_INTEGER])
check_positive(scale[1], "scale[1]")
else:
raise TypeError("scale should be a list or tuple of length 2.")
if shear is not None:
type_check(shear, (numbers.Number, tuple, list), "shear")
if isinstance(shear, numbers.Number):
check_positive(shear, "shear")
else:
type_check_list(shear, (int, float), "shear")
if len(shear) not in (2, 4):
raise TypeError("shear must be of length 2 or 4.")
if len(shear) == 2 and shear[0] > shear[1]:
raise ValueError(
"Input shear[1] must be equal to or greater than shear[0]"
)
if len(shear) == 4 and (shear[0] > shear[1]
or shear[2] > shear[3]):
raise ValueError(
"Input shear[1] must be equal to or greater than shear[0] and "
"shear[3] must be equal to or greater than shear[2].")
type_check(resample, (Inter, ), "resample")
if fill_value is not None:
check_fill_value(fill_value)
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[transforms, num_ops], _ = parse_user_args(method, *args, **kwargs)
type_check(transforms, (list,), "transforms")
if not transforms:
raise ValueError("transforms list is empty.")
for transform in transforms:
if isinstance(transform, TensorOp):
raise ValueError("transform list only accepts Python operations.")
type_check(num_ops, (int,), "num_ops")
check_positive(num_ops, "num_ops")
if num_ops > len(transforms):
raise ValueError("num_ops cannot be greater than the length of transforms list.")
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[transforms, num_ops], _ = parse_user_args(method, *args, **kwargs)
type_check(num_ops, (int, ), "num_ops")
check_positive(num_ops, "num_ops")
if num_ops > len(transforms):
raise ValueError("num_ops is greater than transforms list size.")
parsed_transforms = []
for op in transforms:
if op and getattr(op, 'parse', None):
parsed_transforms.append(op.parse())
else:
parsed_transforms.append(op)
type_check_list(parsed_transforms, (TensorOp, TensorOperation),
"transforms")
return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs):
[transforms, num_ops], _ = parse_user_args(method, *args, **kwargs)
type_check(num_ops, (int,), "num_ops")
check_positive(num_ops, "num_ops")
if num_ops > len(transforms):
raise ValueError("num_ops is greater than transforms list size.")
parsed_transforms = []
for op in transforms:
if op and getattr(op, 'parse', None):
parsed_transforms.append(op.parse())
else:
parsed_transforms.append(op)
type_check(parsed_transforms, (list, tuple,), "transforms")
for index, arg in enumerate(parsed_transforms):
if not isinstance(arg, (TensorOp, TensorOperation)):
raise TypeError("Type of Transforms[{0}] must be c_transform, but got {1}".format(index, type(arg)))
return method(self, *args, **kwargs)
def check_size_scale_ration_max_attempts_paras(size, scale, ratio,
max_attempts):
"""Wrapper method to check the parameters of RandomCropDecodeResize and SoftDvppDecodeRandomCropResizeJpeg."""
check_crop_size(size)
if scale is not None:
type_check(scale, (tuple, list), "scale")
if len(scale) != 2:
raise TypeError("scale should be a list/tuple of length 2.")
type_check_list(scale, (float, int), "scale")
if scale[0] > scale[1]:
raise ValueError(
"scale should be in (min,max) format. Got (max,min).")
check_range(scale, [0, FLOAT_MAX_INTEGER])
check_positive(scale[1], "scale[1]")
if ratio is not None:
type_check(ratio, (tuple, list), "ratio")
if len(ratio) != 2:
raise TypeError("ratio should be a list/tuple of length 2.")
type_check_list(ratio, (float, int), "ratio")
if ratio[0] > ratio[1]:
raise ValueError(
"ratio should be in (min,max) format. Got (max,min).")
check_range(ratio, [0, FLOAT_MAX_INTEGER])
check_positive(ratio[0], "ratio[0]")
check_positive(ratio[1], "ratio[1]")
if max_attempts is not None:
check_value(max_attempts, (1, FLOAT_MAX_INTEGER))
def new_method(self, *args, **kwargs):
[prob, scale, ratio, value, inplace, max_attempts], _ = parse_user_args(method, *args, **kwargs)
check_value(prob, [0., 1.], "prob")
if scale[0] > scale[1]:
raise ValueError("scale should be in (min,max) format. Got (max,min).")
check_range(scale, [0, FLOAT_MAX_INTEGER])
check_positive(scale[1], "scale[1]")
if ratio[0] > ratio[1]:
raise ValueError("ratio should be in (min,max) format. Got (max,min).")
check_range(ratio, [0, FLOAT_MAX_INTEGER])
check_positive(ratio[0], "ratio[0]")
check_positive(ratio[1], "ratio[1]")
check_erasing_value(value)
type_check(inplace, (bool,), "inplace")
check_value(max_attempts, (1, FLOAT_MAX_INTEGER))
return method(self, *args, **kwargs)
