def interpolate(shape, size, scale): """Check input and calculate shape""" if size is None and scale is None: raise ValueError("size and scale both none") if size is not None and scale is not None: raise ValueError("size and scale both not none") if size is not None: Validator.check_int(len(size), 2, Rel.EQ, "size", "interpolate") return size Validator.check_int(scale, 1, Rel.GE, "scale factor", "interpolate") ret = (scale * shape[2], scale * shape[3]) return ret
def __init__(self, max_val=1.0, power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03): super(MSSSIM, self).__init__() validator.check_value_type('max_val', max_val, [int, float], self.cls_name) validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name) self.max_val = max_val validator.check_value_type('power_factors', power_factors, [tuple, list], self.cls_name) self.filter_size = validator.check_int(filter_size, 1, Rel.GE, 'filter_size', self.cls_name) self.filter_sigma = validator.check_positive_float( filter_sigma, 'filter_sigma', self.cls_name) self.k1 = validator.check_value_type('k1', k1, [float], self.cls_name) self.k2 = validator.check_value_type('k2', k2, [float], self.cls_name) window = _create_window(filter_size, filter_sigma) self.level = len(power_factors) self.conv = [] for i in range(self.level): self.conv.append(_conv2d(1, 1, filter_size, Tensor(window))) self.conv[i].weight.requires_grad = False self.multi_convs_list = CellList(self.conv) self.weight_tensor = Tensor(power_factors, mstype.float32) self.avg_pool = AvgPool2d(kernel_size=2, stride=2, pad_mode='valid') self.relu = ReLU() self.reduce_mean = P.ReduceMean() self.prod = P.ReduceProd() self.pow = P.Pow() self.pack = P.Pack(axis=-1) self.concat = P.Concat(axis=1)
def __init__(self, kernel_size=1, stride=1, pad_mode="valid"): super(MaxPool1d, self).__init__(kernel_size, stride, pad_mode) validator.check_value_type('kernel_size', kernel_size, [int], self.cls_name) validator.check_value_type('stride', stride, [int], self.cls_name) self.pad_mode = validator.check_string(pad_mode.upper(), ['VALID', 'SAME'], 'pad_mode', self.cls_name) validator.check_int(kernel_size, 1, Rel.GE, "kernel_size", self.cls_name) validator.check_int(stride, 1, Rel.GE, "stride", self.cls_name) self.kernel_size = (1, kernel_size) self.stride = (1, stride) self.max_pool = P.MaxPool(ksize=self.kernel_size, strides=self.stride, padding=self.pad_mode) self.shape = F.shape self.reduce_mean = P.ReduceMean(keep_dims=True) self.expand = P.ExpandDims() self.squeeze = P.Squeeze(2)
def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03): super(SSIM, self).__init__() validator.check_value_type('max_val', max_val, [int, float], self.cls_name) validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name) self.max_val = max_val self.filter_size = validator.check_int(filter_size, 1, Rel.GE, 'filter_size', self.cls_name) self.filter_sigma = validator.check_positive_float(filter_sigma, 'filter_sigma', self.cls_name) self.k1 = validator.check_value_type('k1', k1, [float], self.cls_name) self.k2 = validator.check_value_type('k2', k2, [float], self.cls_name) window = _create_window(filter_size, filter_sigma) self.conv = _conv2d(1, 1, filter_size, Tensor(window)) self.conv.weight.requires_grad = False self.reduce_mean = P.ReduceMean() self.concat = P.Concat(axis=1)
def __init__(self, in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_init='normal', bias_init='zeros'): Validator.check_value_type("kernel_size", kernel_size, [int], self.cls_name) Validator.check_value_type("stride", stride, [int], self.cls_name) Validator.check_value_type("padding", padding, [int], self.cls_name) Validator.check_value_type("dilation", dilation, [int], self.cls_name) Validator.check_int(kernel_size, 1, Rel.GE, 'kernel_size', self.cls_name) Validator.check_int(stride, 1, Rel.GE, 'stride', self.cls_name) Validator.check_non_negative_int(padding, 'padding', self.cls_name) Validator.check_int(dilation, 1, Rel.GE, 'dilation', self.cls_name) kernel_size = (1, kernel_size) stride = (1, stride) dilation = (1, dilation) get_shape = P.Shape() get_dtype = P.DType() if isinstance(weight_init, Tensor): weight_init_shape = get_shape(weight_init) Validator.check_equal_int(len(weight_init_shape), 3, 'weight_init_shape', self.cls_name) weight_init_dtype = get_dtype(weight_init) weight_init_value = weight_init.asnumpy() weight_init_value = np.expand_dims(weight_init_value, 2) weight_init = Tensor(weight_init_value, weight_init_dtype) super(Conv1d, self).__init__(in_channels, out_channels, kernel_size, stride, pad_mode, padding, dilation, group, has_bias, weight_init, bias_init) self.padding = (0, 0, padding, padding) self.conv2d = P.Conv2D(out_channel=self.out_channels, kernel_size=self.kernel_size, mode=1, pad_mode=self.pad_mode, pad=self.padding, stride=self.stride, dilation=self.dilation, group=self.group) self.bias_add = P.BiasAdd() if pad_mode not in ('valid', 'same', 'pad'): raise ValueError( 'Attr \'pad_mode\' of \'Conv1d\' Op passed ' + str(pad_mode) + ', should be one of values in \'valid\', \'same\', \'pad\'.') self.expand_dims = P.ExpandDims() self.squeeze = P.Squeeze(2) self.shape = P.Shape()
def bilinear(shape, size, scale, align_corners): """Check input and calculate shape""" if not isinstance(align_corners, bool): raise TypeError("align_corners should be type boolean") if size is None and scale is None: raise ValueError("size and scale both none") if size is not None and scale is not None: raise ValueError("size and scale both not none") if size is not None: if not isinstance(size, (tuple, list)): raise ValueError("size must be tuple or list") Validator.check_int(len(size), 2, Rel.EQ, "size", "bilinear") Validator.check_int(size[0], 1, Rel.GE, "size[0]", "bilinear") Validator.check_int(size[1], 1, Rel.GE, "size[1]", "bilinear") return size Validator.check_int(scale, 1, Rel.GE, "scale factor", "bilinear") ret = (scale * shape[2], scale * shape[3]) return ret
def _get_matrix_diag_part_assist(x_shape, x_dtype): Validator.check_int(len(x_shape), 2, Rel.GE, "x rank", "_get_matrix_diag_part_assist") base_eye = np.eye(x_shape[-2], x_shape[-1]).reshape(-1) assist = np.tile(base_eye, x_shape[:-2]).reshape(x_shape) return Tensor(assist, x_dtype)
def __init__(self, in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_init='normal', bias_init='zeros'): Validator.check_value_type("kernel_size", kernel_size, [int], self.cls_name) Validator.check_value_type("stride", stride, [int], self.cls_name) Validator.check_value_type("padding", padding, [int], self.cls_name) Validator.check_value_type("dilation", dilation, [int], self.cls_name) Validator.check_int(kernel_size, 1, Rel.GE, 'kernel_size', self.cls_name) Validator.check_int(stride, 1, Rel.GE, 'stride', self.cls_name) Validator.check_non_negative_int(padding, 'padding', self.cls_name) Validator.check_int(dilation, 1, Rel.GE, 'dilation', self.cls_name) kernel_size = (1, kernel_size) stride = (1, stride) dilation = (1, dilation) get_shape = P.Shape() get_dtype = P.DType() if isinstance(weight_init, Tensor): weight_init_shape = get_shape(weight_init) Validator.check_equal_int(len(weight_init_shape), 3, 'weight_init_shape', self.cls_name) weight_init_dtype = get_dtype(weight_init) weight_init_value = weight_init.asnumpy() weight_init_value = np.expand_dims(weight_init_value, 2) weight_init = Tensor(weight_init_value, weight_init_dtype) # out_channels and in_channels swap. # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel, # then Conv1dTranspose's out_channel refers to Conv2DBackpropInput's in_channel. super(Conv1dTranspose, self).__init__(in_channels, out_channels, kernel_size, stride, pad_mode, padding, dilation, group, has_bias, weight_init, bias_init, transposed=True) self.padding = (0, 0, padding, padding) self.in_channels = in_channels self.out_channels = out_channels self.shape = P.Shape() if pad_mode not in ('valid', 'same', 'pad'): raise ValueError( 'Attr \'pad_mode\' of \'Conv1dTranspose\' Op passed ' + str(pad_mode) + ', should be one of values in \'valid\', \'same\', \'pad\'.') self.is_valid = self.pad_mode == 'valid' self.is_same = self.pad_mode == 'same' self.is_pad = self.pad_mode == 'pad' if Validator.check_bool(has_bias): self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias') # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel. self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels, kernel_size=kernel_size, mode=1, pad_mode=pad_mode, pad=self.padding, stride=stride, dilation=dilation, group=group) self.bias_add = P.BiasAdd() self.expand_dims = P.ExpandDims() self.squeeze = P.Squeeze(2)
def _check_ndim(predict_nidm, target_ndim): validator.check_int(predict_nidm, target_ndim, Rel.EQ, 'predict_nidm', 'target_ndim')
def triu(x_shape, x_dtype, k): Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "triu") Validator.check_is_int(k, "k value", "triu") mask = np.triu(np.ones(x_shape), k) return Tensor(mask, x_dtype)