follow comments

paddle/operators/conv3d_op.cc

@@ -87,11 +87,11 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
             "The format of output tensor is also NCDHW.");
   AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
       .SetDefault({1, 1, 1});
-  AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
+  AddAttr<std::vector<int>>("paddings", "The paddings of convolution operator.")
       .SetDefault({0, 0, 0});
   AddAttr<int>(
       "groups",
-      "group size of convolution operator. "
+      "The group size of convolution operator. "
       "Refer to grouped convolution in Alex Krizhevsky's paper: "
       "when group=2, the first half of the filters are only connected to the "
       "first half of the input channels, and the second half only connected "

paddle/operators/conv3d_op.h

@@ -93,10 +93,13 @@ class GemmConv3DKernel : public framework::OpKernel<T> {
     Tensor col_matrix = col;
     col_matrix.Resize(col_matrix_shape);
 
-    framework::DDim input_shape = {input->dims()[1], input->dims()[2],
-                                   input->dims()[3], input->dims()[4]};
-    framework::DDim filter_matrix_shape = {filter.dims()[0],
-                                           filter.numel() / filter.dims()[0]};
+    framework::DDim input_shape = {
+        input->dims()[1], input->dims()[2], input->dims()[3],
+        input->dims()[4]};  // channel, depth, height, width
+    framework::DDim filter_matrix_shape = {
+        filter.dims()[0],
+        filter.numel() / filter.dims()[0]};  // filter_out_channel,
+    // filter_in_channel*filter_depth*filter_height*filter_width
     filter.Resize(filter_matrix_shape);
 
     framework::DDim output_matrix_shape = {
@@ -177,15 +180,18 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
     Tensor col_matrix = col;
     col_matrix.Resize(col_matrix_shape);
 
-    framework::DDim input_shape = {input->dims()[1], input->dims()[2],
-                                   input->dims()[3], input->dims()[4]};
+    framework::DDim input_shape = {
+        input->dims()[1], input->dims()[2], input->dims()[3],
+        input->dims()[4]};  // channel, depth, height, width
     framework::DDim output_matrix_shape = {output_grad->dims()[1],
                                            output_grad->dims()[2] *
                                                output_grad->dims()[3] *
                                                output_grad->dims()[4]};
 
-    framework::DDim filter_matrix_shape = {filter.dims()[0],
-                                           filter.numel() / filter.dims()[0]};
+    framework::DDim filter_matrix_shape = {
+        filter.dims()[0],
+        filter.numel() / filter.dims()[0]};  // filter_out_channel,
+    // filter_in_channel*filter_depth*filter_height*filter_width
     filter.Resize(filter_matrix_shape);
 
     // convolution backward input operator:  gemm + col2vol

python/paddle/v2/framework/tests/test_conv3d_op.py

@@ -34,7 +34,7 @@ def conv3d_forward_naive(input, filter, group, conv_param):
                     for k in range(sub_out_c):
                         out[:, g * sub_out_c + k, d, i, j] = \
                             np.sum(input_pad_masked * f_sub[k, :, :, :, :],
-                                   axis=(1, 2, 3,4))
+                                   axis=(1, 2, 3, 4))
 
     return out
 
@@ -65,7 +65,6 @@ class TestConv3dOp(OpTest):
         self.check_grad(
             set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
 
-    def test_check_grad_no_filter(self):
         self.check_grad(
             ['Input'],
             'Output',
@@ -80,8 +79,6 @@ class TestConv3dOp(OpTest):
             no_grad_set=set(['Input']))
 
     def init_test_case(self):
-        # self.groups = 1
-        # self.op_type = "conv3d"
         self.pad = [0, 0, 0]
         self.stride = [1, 1, 1]
         self.input_size = [2, 3, 5, 5, 5]  # NCDHW
@@ -98,8 +95,6 @@ class TestConv3dOp(OpTest):
 
 class TestCase1(TestConv3dOp):
     def init_test_case(self):
-        # self.groups = 1
-        # self.op_type = "conv3d"
         self.pad = [1, 1, 1]
         self.stride = [1, 1, 1]
         self.input_size = [2, 3, 5, 5, 5]  # NCDHW
@@ -114,7 +109,6 @@ class TestCase1(TestConv3dOp):
         self.op_type = "conv3d"
 
 
-'''
 class TestWithGroup1(TestConv3dOp):
     def init_group(self):
         self.groups = 3
@@ -129,7 +123,7 @@ class TestWithGroup2(TestCase1):
 
     def init_op_type(self):
         self.op_type = "conv3d"
-'''
+
 
 if __name__ == '__main__':
     unittest.main()