def new_method(self, *args, **kwargs): [threshold], _ = parse_user_args(method, *args, **kwargs) type_check(threshold, (tuple, ), "threshold") type_check_list(threshold, (int, ), "threshold") if len(threshold) != 2: raise ValueError("threshold must be a sequence of two numbers.") for element in threshold: check_value(element, (0, UINT8_MAX)) if threshold[1] < threshold[0]: raise ValueError("threshold must be in min max format numbers.") return method(self, *args, **kwargs)
def check_crop_size(size): """Wrapper method to check the parameters of crop size.""" type_check(size, (int, list, tuple), "size") if isinstance(size, int): check_value(size, (1, FLOAT_MAX_INTEGER)) elif isinstance(size, (tuple, list)) and len(size) == 2: for index, value in enumerate(size): type_check(value, (int, ), "size[{}]".format(index)) check_value(value, (1, FLOAT_MAX_INTEGER)) else: raise TypeError( "Size should be a single integer or a list/tuple (h, w) of length 2." )
def check_random_color_adjust_param(value, input_name, center=1, bound=(0, FLOAT_MAX_INTEGER), non_negative=True): """Check the parameters in random color adjust operation.""" type_check(value, (numbers.Number, list, tuple), input_name) if isinstance(value, numbers.Number): if value < 0: raise ValueError("The input value of {} cannot be negative.".format(input_name)) elif isinstance(value, (list, tuple)): if len(value) != 2: raise TypeError("If {0} is a sequence, the length must be 2.".format(input_name)) if value[0] > value[1]: raise ValueError("{0} value should be in (min,max) format. Got ({1}, {2}).".format(input_name, value[0], value[1])) check_range(value, bound)
def new_method(self, *args, **kwargs): [transformation_matrix, mean_vector], _ = parse_user_args(method, *args, **kwargs) type_check(transformation_matrix, (np.ndarray,), "transformation_matrix") type_check(mean_vector, (np.ndarray,), "mean_vector") if transformation_matrix.shape[0] != transformation_matrix.shape[1]: raise ValueError("transformation_matrix should be a square matrix. " "Got shape {} instead".format(transformation_matrix.shape)) if mean_vector.shape[0] != transformation_matrix.shape[0]: raise ValueError("mean_vector length {0} should match either one dimension of the square" "transformation_matrix {1}.".format(mean_vector.shape[0], transformation_matrix.shape)) return method(self, *args, **kwargs)
def check_degrees(degrees): """Check if the degrees is legal.""" type_check(degrees, (numbers.Number, list, tuple), "degrees") if isinstance(degrees, numbers.Number): check_value(degrees, (0, float("inf")), "degrees") elif isinstance(degrees, (list, tuple)): if len(degrees) == 2: type_check_list(degrees, (numbers.Number, ), "degrees") if degrees[0] > degrees[1]: raise ValueError( "degrees should be in (min,max) format. Got (max,min).") else: raise TypeError("If degrees is a sequence, the length must be 2.")
def new_method(self, *args, **kwargs): [size, padding, pad_if_needed, fill_value, padding_mode], _ = parse_user_args(method, *args, **kwargs) check_crop_size(size) type_check(pad_if_needed, (bool, ), "pad_if_needed") if padding is not None: check_padding(padding) if fill_value is not None: check_fill_value(fill_value) if padding_mode is not None: type_check(padding_mode, (Border, ), "padding_mode") return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [kernel_size, sigma], _ = parse_user_args(method, *args, **kwargs) type_check(kernel_size, (int, list, tuple), "kernel_size") if isinstance(kernel_size, int): check_value(kernel_size, (1, FLOAT_MAX_INTEGER), "kernel_size") check_odd(kernel_size, "kernel_size") elif isinstance(kernel_size, (list, tuple)) and len(kernel_size) == 2: for index, value in enumerate(kernel_size): type_check(value, (int, ), "kernel_size[{}]".format(index)) check_value(value, (1, FLOAT_MAX_INTEGER), "kernel_size") check_odd(value, "kernel_size[{}]".format(index)) else: raise TypeError( "Kernel size should be a single integer or a list/tuple (kernel_width, kernel_height) of length 2." ) if sigma is not None: type_check(sigma, (numbers.Number, list, tuple), "sigma") if isinstance(sigma, numbers.Number): check_value(sigma, (0, FLOAT_MAX_INTEGER), "sigma") elif isinstance(sigma, (list, tuple)) and len(sigma) == 2: for index, value in enumerate(sigma): type_check(value, (numbers.Number, ), "size[{}]".format(index)) check_value(value, (0, FLOAT_MAX_INTEGER), "sigma") else: raise TypeError( "Sigma should be a single number or a list/tuple of length 2 for width and height." ) return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [bits], _ = parse_user_args(method, *args, **kwargs) if bits is not None: type_check(bits, (list, tuple, int), "bits") if isinstance(bits, int): check_value(bits, [1, 8]) if isinstance(bits, (list, tuple)): if len(bits) != 2: raise TypeError("Size of bits should be a single integer or a list/tuple (min, max) of length 2.") for item in bits: check_uint8(item, "bits") # also checks if min <= max check_range(bits, [1, 8]) return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [degrees, resample, expand, center, fill_value], _ = parse_user_args(method, *args, **kwargs) check_degrees(degrees) if resample is not None: type_check(resample, (Inter,), "resample") if expand is not None: type_check(expand, (bool,), "expand") if center is not None: check_2tuple(center, "center") if fill_value is not None: check_fill_value(fill_value) return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [policy], _ = parse_user_args(method, *args, **kwargs) type_check(policy, (list,), "policy") if not policy: raise ValueError("policy can not be empty.") for sub_ind, sub in enumerate(policy): type_check(sub, (list,), "policy[{0}]".format([sub_ind])) if not sub: raise ValueError("policy[{0}] can not be empty.".format(sub_ind)) for op_ind, tp in enumerate(sub): check_2tuple(tp, "policy[{0}][{1}]".format(sub_ind, op_ind)) check_tensor_op(tp[0], "op of (op, prob) in policy[{0}][{1}]".format(sub_ind, op_ind)) check_value(tp[1], (0, 1), "prob of (op, prob) policy[{0}][{1}]".format(sub_ind, op_ind)) return method(self, *args, **kwargs)
def check_random_color_adjust_param(value, input_name, center=1, bound=(0, FLOAT_MAX_INTEGER), non_negative=True): """Check the parameters in random color adjust operation.""" type_check(value, (numbers.Number, list, tuple), input_name) if isinstance(value, numbers.Number): if value < 0: raise ValueError( "The input value of {} cannot be negative.".format(input_name)) elif isinstance(value, (list, tuple)) and len(value) == 2: check_range(value, bound) if value[0] > value[1]: raise ValueError( "value should be in (min,max) format. Got (max,min).")
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(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): [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): [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)
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,), "scale") type_check_list(scale, (float, int), "scale") check_range(scale, [0, FLOAT_MAX_INTEGER]) if scale[0] > scale[1]: raise ValueError("scale should be in (min,max) format. Got (max,min).") if ratio is not None: type_check(ratio, (tuple,), "ratio") type_check_list(ratio, (float, int), "ratio") check_range(ratio, [0, FLOAT_MAX_INTEGER]) if ratio[0] > ratio[1]: raise ValueError("ratio should be in (min,max) format. Got (max,min).") if max_attempts is not None: check_value(max_attempts, (1, FLOAT_MAX_INTEGER))
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 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): [batch_size, alpha, is_single], _ = parse_user_args(method, *args, **kwargs) type_check(is_single, (bool,), "is_single") type_check(batch_size, (int,), "batch_size") type_check(alpha, (int, float), "alpha") check_value(batch_size, (1, FLOAT_MAX_INTEGER)) check_positive(alpha, "alpha") return method(self, *args, **kwargs)
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") type_check(alpha, (int, float), "alpha") type_check(prob, (int, float), "prob") 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): [distortion_scale, prob, interpolation], _ = parse_user_args(method, *args, **kwargs) type_check(distortion_scale, (float, ), "distortion_scale") type_check(prob, (float, ), "prob") check_value(distortion_scale, [0., 1.], "distortion_scale") check_value(prob, [0., 1.], "prob") type_check(interpolation, (Inter, ), "interpolation") return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [cutoff, ignore], _ = parse_user_args(method, *args, **kwargs) type_check(cutoff, (int, float), "cutoff") check_value(cutoff, [0, 100], "cutoff") if ignore is not None: type_check(ignore, (list, tuple, int), "ignore") if isinstance(ignore, int): check_value(ignore, [0, 255], "ignore") if isinstance(ignore, (list, tuple)): for item in ignore: type_check(item, (int,), "item") check_value(item, [0, 255], "ignore") return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [is_hwc], _ = parse_user_args(method, *args, **kwargs) type_check(is_hwc, (bool, ), "is_hwc") return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [transform, ratio], _ = parse_user_args(method, *args, **kwargs) type_check(ratio, (float, int), "ratio") check_value(ratio, [0., 1.], "ratio") type_check(transform, (TensorOp,), "transform") return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [rescale, shift], _ = parse_user_args(method, *args, **kwargs) check_pos_float32(rescale) type_check(shift, (numbers.Number,), "shift") return method(self, *args, **kwargs)
def new_method(self, *args, **kwargs): [prob], _ = parse_user_args(method, *args, **kwargs) type_check(prob, (float, int,), "prob") check_value(prob, [0., 1.], "prob") return method(self, *args, **kwargs)