!14402 For r1.2, Modified pad validator of conv3d.

From: @liu_xiao_93
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

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)