From 9a4be0dd313c598b942611d84dd82ee923925d6b Mon Sep 17 00:00:00 2001 From: liuxiao93 Date: Tue, 30 Mar 2021 19:07:01 +0800 Subject: [PATCH] Modified pad validator of conv3d. --- mindspore/ops/operations/nn_ops.py | 51 +++++++++++++----------------- 1 file changed, 22 insertions(+), 29 deletions(-) diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index a02beecace..385cb6ab4c 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -7759,8 +7759,8 @@ class Conv3D(PrimitiveWithInfer): 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)`. Currently weight data type only support float16 and float32. + - **weight** (Tensor) - Set size of kernel is :math:`(k_d, K_h, K_w)`, then the shape is + :math:`(C_{out}, C_{in}//groups, k_d, 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: @@ -7815,18 +7815,7 @@ class Conv3D(PrimitiveWithInfer): f"six positive int numbers, but got `{len(pad)}`.") self.add_prim_attr("pad", pad) self.padding = pad - validator.check_int_range(self.padding[0], 0, self.kernel_size[0], Rel.INC_LEFT, - 'pad_d belonging [0, kernel_size_d)', self.name) - validator.check_int_range(self.padding[1], 0, self.kernel_size[0], Rel.INC_LEFT, - 'pad_d belonging [0, kernel_size_d)', self.name) - validator.check_int_range(self.padding[2], 0, self.kernel_size[1], Rel.INC_LEFT, - 'pad_h belonging [0, kernel_size_h)', self.name) - validator.check_int_range(self.padding[3], 0, self.kernel_size[1], Rel.INC_LEFT, - 'pad_h belonging [0, kernel_size_h)', self.name) - validator.check_int_range(self.padding[4], 0, self.kernel_size[2], Rel.INC_LEFT, - 'pad_w belonging [0, kernel_size_w)', self.name) - validator.check_int_range(self.padding[5], 0, self.kernel_size[2], Rel.INC_LEFT, - 'pad_w belonging [0, kernel_size_w)', self.name) + validator.check_value_type('pad_mode', pad_mode, [str], self.name) self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name) self.add_prim_attr('pad_mode', self.pad_mode) @@ -7902,6 +7891,21 @@ class Conv3D(PrimitiveWithInfer): w_out = math.floor(w_out) self.pad_list = [pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right] + filter_d = (self.kernel_size[0] - 1) * dilation_d + 1 + filter_h = (self.kernel_size[1] - 1) * dilation_h + 1 + filter_w = (self.kernel_size[2] - 1) * dilation_w + 1 + validator.check_int_range(self.pad_list[0], 0, filter_d, Rel.INC_LEFT, + 'pad_d belonging [0, filter_d)', self.name) + validator.check_int_range(self.pad_list[1], 0, filter_d, Rel.INC_LEFT, + 'pad_d belonging [0, filter_d)', self.name) + validator.check_int_range(self.pad_list[2], 0, filter_h, Rel.INC_LEFT, + 'pad_h belonging [0, filter_h)', self.name) + validator.check_int_range(self.pad_list[3], 0, filter_h, Rel.INC_LEFT, + 'pad_h belonging [0, filter_h)', self.name) + validator.check_int_range(self.pad_list[4], 0, filter_w, Rel.INC_LEFT, + 'pad_w belonging [0, filter_w)', self.name) + validator.check_int_range(self.pad_list[5], 0, filter_w, Rel.INC_LEFT, + 'pad_w belonging [0, filter_w)', self.name) self.add_prim_attr('pad_list', (pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right)) out_channel = self.out_channel out_shape = [x_shape[0], out_channel, d_out, h_out, w_out] @@ -8124,8 +8128,8 @@ class Conv3DTranspose(PrimitiveWithInfer): - **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})`. 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)`. Currently weight data type only support float16 + - **weight** (Tensor) - Set size of kernel is :math:`(k_d, K_h, K_w)`, then the shape is + :math:`(C_{in}, C_{out}//groups, k_d, 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. @@ -8189,6 +8193,7 @@ class Conv3DTranspose(PrimitiveWithInfer): raise ValueError(f"For `conv3d` attr 'pad' should be an positive int number or a tuple of " f"six positive int numbers, but got `{len(pad)}`.") self.pad_list = pad + validator.check_value_type('pad_mode', pad_mode, [str], self.name) self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name) self.add_prim_attr('pad_mode', self.pad_mode) @@ -8198,21 +8203,9 @@ class Conv3DTranspose(PrimitiveWithInfer): if self.pad_mode == 'pad': for item in self.pad_list: validator.check_non_negative_int(item, 'pad item', self.name) - validator.check_int_range(self.pad_list[0], 0, self.kernel_size[0], Rel.INC_LEFT, - 'pad_d belonging [0, kernel_size_d)', self.name) - validator.check_int_range(self.pad_list[1], 0, self.kernel_size[0], Rel.INC_LEFT, - 'pad_d belonging [0, kernel_size_d)', self.name) - validator.check_int_range(self.pad_list[2], 0, self.kernel_size[1], Rel.INC_LEFT, - 'pad_h belonging [0, kernel_size_h)', self.name) - validator.check_int_range(self.pad_list[3], 0, self.kernel_size[1], Rel.INC_LEFT, - 'pad_h belonging [0, kernel_size_h)', self.name) - validator.check_int_range(self.pad_list[4], 0, self.kernel_size[2], Rel.INC_LEFT, - 'pad_w belonging [0, kernel_size_w)', self.name) - validator.check_int_range(self.pad_list[5], 0, self.kernel_size[2], Rel.INC_LEFT, - 'pad_w belonging [0, kernel_size_w)', self.name) self.mode = validator.check_equal_int(mode, 1, 'mode', self.name) self.add_prim_attr('mode', self.mode) - self.mode = validator.check_equal_int(group, 1, 'group', self.name) + self.group = validator.check_equal_int(group, 1, 'group', self.name) self.add_prim_attr('groups', self.group) self.format = validator.check_string(data_format, ['NCDHW'], 'format', self.name) self.add_prim_attr('data_format', self.format)