diff --git a/mindspore/_checkparam.py b/mindspore/_checkparam.py index 1c7668768c0..3765527bcec 100644 --- a/mindspore/_checkparam.py +++ b/mindspore/_checkparam.py @@ -127,6 +127,8 @@ def _check_3d_int_or_tuple(arg_name, arg_value, prim_name, allow_five=False, continue if not greater_zero and item >= 0: continue + _raise_message() + if third_one: if ret_value[-3] != 1: _raise_message(third_one) diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index e25af8e2bb7..b497b4434c4 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -309,9 +309,11 @@ class Conv3DBackpropFilter(PrimitiveWithInfer): Inputs: - **x** (Tensor) - The input of the convolution, then the shape is :math:`(C_{out}, C_{in}, D_{in}, K_1, K_2)`. + Currently dout data type only support float16 and float32. - **dout** (Tensor) - The gradients w.r.t the output of the convolution. The shape conforms to the default - data_format :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. - - **w_size** (Tensor) - A tuple describes the shape of the weight which conforms to the format + data_format :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. Currently dout data type only support float16 + and float32. + - **w_size** (tuple(int)) - A tuple describes the shape of the weight which conforms to the format :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`. Outputs: @@ -420,7 +422,7 @@ class Conv3DBackpropFilter(PrimitiveWithInfer): out = { 'value': None, 'shape': w_size_v, - 'dtype': mstype.float32, + 'dtype': x['dtype'], } return out diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 152434f3112..d8b1a1c7b3b 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -7412,24 +7412,52 @@ class Conv3D(PrimitiveWithInfer): (\text{ks_w} - 1) \times (\text{dilation} - 1) }{\text{stride}}} \right \rfloor` respectively. Args: - out_channel (int): The dimension of the output. - kernel_size (Union[int, tuple[int]]): The kernel size of the 3D convolution. + out_channels (int): The number of output channel :math:`C_{out}`. + kernel_size (Union[int, tuple[int]]): The data type is int or a tuple of 3 integers. Specifies the height + and width of the 3D convolution window. Single int means the value is for the depth, height and the width + of the kernel. A tuple of 3 ints means the first value is for the depth, height and the other is for the + width of the kernel. mode (int): Modes for different convolutions. Not currently used. - pad_mode (str): Modes to fill padding. It could be "valid", "same", or "pad". Default: "valid". + stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents + the depth, height and width of movement are both strides, or a tuple of two int numbers that + represent depth, height and width of movement respectively. Default: 1. + pad_mode (str): Specifies padding mode. The optional values are + "same", "valid", "pad". Default: "same". + + - same: Adopts the way of completion. The depth, height and width of the output will be the same as + the input. The total number of padding will be calculated in depth, horizontal and vertical + directions and evenly distributed to head and tail, top and bottom, left and right if possible. + Otherwise, the last extra padding will be done from the tail, bottom and the right side. + If this mode is set, `padding` must be 0. + + - valid: Adopts the way of discarding. The possible largest depth, height and width of output + will be returned without padding. Extra pixels will be discarded. If this mode is set, `padding` + must be 0. + + - pad: Implicit paddings on both sides of the input. The number of `padding` will be padded to the input + Tensor borders. `padding` must be greater than or equal to 0. + pad (Union(int, tuple[int])): The pad value to be filled. Default: 0. If `pad` is an integer, the paddings of head, tail, top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of six integers, the padding of head, tail, top, bottom, left and right equal to pad[0], pad[1], pad[2], pad[3], pad[4] and pad[5] correspondingly. - stride (Union(int, tuple[int])): The stride to be applied to the convolution filter. Default: 1. - dilation (Union(int, tuple[int])): Specifies the space to use between kernel elements. Default: 1. - group (int): Splits input into groups. Default: 1. + dilation (Union[int, tuple[int]]): The data type is int or a tuple of 3 integers + : math:`(dilation_d, dilation_h, dilation_w)`. + Currently, dilation on depth only supports the case of 1. + Specifies the dilation rate to use for dilated convolution. + If set to be :math:`k > 1`, there will be :math:`k - 1` pixels skipped + for each sampling location. Its value must be greater or equal to 1 and + bounded by the height and width of the input. Default: 1. + group (int): Splits filter into groups, `in_ channels` and `out_channels` must be + divisible by the number of groups. Default: 1. data_format (str): The optional value for data format. Currently only support "NCDHW". Inputs: - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`. + Currently input data type only support float16 and float32. - **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is - :math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`. - - **bias** (Tensor) - Tensor of shape :math:`C_{in}`. Currently, only support none or zero. + :math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`. Currently weight data type only support float16 and float32. + - **bias** (Tensor) - Tensor of shape :math:`C_{in}`. Currently, only support none. Outputs: Tensor, the value that applied 3D convolution. The shape is :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. @@ -7437,6 +7465,16 @@ class Conv3D(PrimitiveWithInfer): Supported Platforms: ``Ascend`` + Raises: + TypeError: If `out_channel` or `group` is not an int. + TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple. + ValueError: If `out_channel`, `kernel_size`, `stride` or `dilation` is less than 1. + ValueError: If `pad` is less than 0. + ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'. + ValueError: If `pad` is a tuple whose length is not equal to 6. + ValueError: If `pad_mode` is not equal to 'pad' and `pad` is not equal to (0, 0, 0, 0, 0, 0). + ValueError: If `data_format` is not 'NCDHW'. + Examples: >>> input = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float16) >>> weight = Tensor(np.ones([32, 3, 4, 3, 3]), mindspore.float16) @@ -7506,7 +7544,7 @@ class Conv3D(PrimitiveWithInfer): validator.check_equal_int(len(x_shape), 5, "x rank", self.name) if b_shape is not None: raise ValueError("Bias currently only support None.") - validator.check(f"x_shape[1] / group", x_shape[1] // self.group, "w_shape[1]", w_shape[1], Rel.EQ, self.name) + validator.check(f"x_shape[1] // group", x_shape[1] // self.group, "w_shape[1]", w_shape[1], Rel.EQ, self.name) validator.check('out_channel', self.out_channel, 'w_shape[0]', w_shape[0], Rel.EQ, self.name) validator.check('kernel_size', self.kernel_size, 'w_shape[1:4]', tuple(w_shape[2:]), Rel.EQ, self.name) @@ -7591,10 +7629,11 @@ class Conv3DBackpropInput(PrimitiveWithInfer): Inputs: - **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is - :math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`. + :math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`. Currently weight data type only support float16 and float32. - **dout** (Tensor) - the gradients w.r.t the output of the convolution. The shape conforms to the default - data_format :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. - - **input_size** (Tensor) - A tuple describes the shape of the input which conforms to the format + data_format :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. Currently dout data type only support float16 + and float32. + - **input_size** (tuple(int)) - A tuple describes the shape of the input which conforms to the format :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`. Outputs: @@ -7603,6 +7642,16 @@ class Conv3DBackpropInput(PrimitiveWithInfer): Supported Platforms: ``Ascend`` + Raises: + TypeError: If `out_channel` or `group` is not an int. + TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple. + ValueError: If `out_channel`, `kernel_size`, `stride` or `dilation` is less than 1. + ValueError: If `pad` is less than 0. + ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'. + ValueError: If `pad` is a tuple whose length is not equal to 6. + ValueError: If `pad_mode` is not equal to 'pad' and `pad` is not equal to (0, 0, 0, 0, 0, 0). + ValueError: If `data_format` is not 'NCDHW'. + Examples: >>> dout = Tensor(np.ones([16, 32, 10, 32, 32]), mindspore.float16) >>> weight = Tensor(np.ones([32, 32, 4, 6, 2]), mindspore.float16) @@ -7658,13 +7707,13 @@ class Conv3DBackpropInput(PrimitiveWithInfer): def __infer__(self, w, doutput, x_size): validator.check_equal_int(len(w['shape']), 5, 'The dimension of weight ', self.name) validator.check_equal_int(len(doutput['shape']), 5, 'The dimension of dout', self.name) - validator.check_equal_int(len(x_size['shape']), 5, 'The dimension of input_size', self.name) x_size_v = x_size['value'] + validator.check_equal_int(len(x_size_v), 5, 'The dimension of input_size', self.name) validator.check_value_type('x_size', x_size_v, [tuple], self.name) for i, dim_len in enumerate(x_size_v): validator.check_value_type("x_size[%d]" % i, dim_len, [int], self.name) args = {'doutput': doutput['dtype'], 'w': w['dtype']} - valid_dtypes = [mstype.float16] + valid_dtypes = [mstype.float16, mstype.float32] validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name) validator.check("filter's batch", w['shape'][0], "dout's channel", doutput['shape'][1], Rel.EQ, self.name) validator.check("filter's channel", w['shape'][1], "input_size's channel", x_size_v[1], Rel.EQ, self.name) @@ -7745,10 +7794,12 @@ class Conv3DTranspose(PrimitiveWithInfer): Inputs: - **dout** (Tensor) - the gradients w.r.t the output of the convolution. The shape conforms to the default - data_format :math:`(N, C_{in}, D_{out}, H_{out}, W_{out})`. + data_format :math:`(N, C_{in}, D_{out}, H_{out}, W_{out})`. Currently dout data type only support float16 + and float32. - **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is - :math:`(C_{in}//groups, C_{out}, D_{in}, K_h, K_w)`. - - **bias** (Tensor) - Tensor of shape :math:`C_{out}`. Currently, only support none or zero. + :math:`(C_{in}//groups, C_{out}, D_{in}, K_h, K_w)`. Currently weight data type only support float16 + and float32. + - **bias** (Tensor) - Tensor of shape :math:`C_{out}`. Currently, only support none. Outputs: Tensor, the gradients w.r.t the input of convolution 3D. It has the same shape as the input. @@ -7756,6 +7807,18 @@ class Conv3DTranspose(PrimitiveWithInfer): Supported Platforms: ``Ascend`` + Raise: + TypeError: If `in_channel`, `out_channel` or `group` is not an int. + TypeError: If `kernel_size`, `stride`, `pad` or `dilation` is neither an int not a tuple. + ValueError: If `in_channel`, `out_channel`, `kernel_size`, `stride` or `dilation` is less than 1. + ValueError: If `pad` is less than 0. + ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'. + ValueError: If `pad` is a tuple whose length is not equal to 6. + ValueError: If `pad_mode` is not equal to 'pad' and `pad` is not equal to (0, 0, 0, 0, 0, 0). + ValueError: If `data_format` is not 'NCDHW'. + TypeError: If dout and weight data type not float16. + ValueError: If bias not none. The rank of dout and weight is not 5. + Examples: >>> input_x = Tensor(np.ones([32, 16, 10, 32, 32]), mindspore.float16) >>> weight = Tensor(np.ones([16, 3, 4, 6, 2]), mindspore.float16) @@ -7818,6 +7881,12 @@ class Conv3DTranspose(PrimitiveWithInfer): self.output_padding = _check_3d_int_or_tuple('output_padding', output_padding, self.name, allow_five=True, ret_five=True, greater_zero=False) + validator.check_int_range(self.kernel_size[0]*self.kernel_size[1]*self.kernel_size[2], 1, 343, Rel.INC_BOTH, + 'The product of height, width and depth of kernel_size belonging [1, 343]', self.name) + validator.check_int_range(self.stride[0]*self.stride[1]*self.stride[2], 1, 343, Rel.INC_BOTH, + 'The product of height, width and depth of stride belonging [1, 343]', self.name) + validator.check_int_range(self.stride[1]*self.stride[2], 1, 256, Rel.INC_BOTH, + 'The product of height, width and depth of stride belonging [1, 256]', self.name) validator.check_int_range(self.output_padding[2], 0, max(self.dilation[2], self.stride[2]), Rel.INC_LEFT, 'output_padding_d belonging [0, max(stride_d, dilation_d))', self.name) validator.check_int_range(self.output_padding[3], 0, max(self.dilation[3], self.stride[3]), Rel.INC_LEFT, @@ -7832,11 +7901,15 @@ class Conv3DTranspose(PrimitiveWithInfer): raise ValueError("Bias currently only support None.") valid_dtypes = [mstype.float16, mstype.float32] validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name) - validator.check("filter's batch", w['shape'][0], "input x's channel", - x['shape'][1], Rel.EQ, self.name) + # infer shape x_shape = x['shape'] w_shape = w['shape'] + validator.check_equal_int(len(w_shape), 5, "weight rank", self.name) + validator.check_equal_int(len(x_shape), 5, "x rank", self.name) + validator.check("filter's batch", w_shape[0], "input x's channel", + x_shape[1], Rel.EQ, self.name) + self.add_prim_attr('pad_list', self.pad_list) pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right = self.pad_list d_out = (x_shape[2] - 1) * self.stride[2] - (pad_head + pad_tail) + self.dilation[2] * \