Clean Code

test=develop

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -28,6 +28,46 @@ using mkldnn::stream;
 using platform::to_void_cast;
 using platform::GetMKLDNNFormat;
 
+inline void GetWeightsTz(std::vector<int>& weights_tz, int groups,  // NOLINT
+                         bool is_conv3d) {
+  if (groups > 1) {
+    if (is_conv3d) {
+      int output = weights_tz[0];
+      int input = weights_tz[1];
+      int dimension = weights_tz[2];
+      int height = weights_tz[3];
+      int width = weights_tz[4];
+      weights_tz.resize(6);
+      weights_tz[0] = groups;
+      weights_tz[1] = output / groups;
+      weights_tz[2] = input;
+      weights_tz[3] = dimension;
+      weights_tz[4] = height;
+      weights_tz[5] = width;
+    } else {
+      int output = weights_tz[0];
+      int input = weights_tz[1];
+      int height = weights_tz[2];
+      int width = weights_tz[3];
+      weights_tz.resize(5);
+      weights_tz[0] = groups;
+      weights_tz[1] = output / groups;
+      weights_tz[2] = input;
+      weights_tz[3] = height;
+      weights_tz[4] = width;
+    }
+  }
+}
+
+inline mkldnn::memory::format GetWeightsFormat(mkldnn::memory::format format,
+                                               int groups, bool is_conv3d) {
+  if (is_conv3d) {
+    return (groups == 1) ? format : mkldnn::memory::format::goidhw;
+  } else {
+    return (groups == 1) ? format : mkldnn::memory::format::goihw;
+  }
+}
+
 template <typename T>
 class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
@@ -53,7 +93,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
                        filter->format() != memory::format::format_undef,
                    "Wrong layout/format set for Filter tensor");
     PADDLE_ENFORCE(input->dims().size() == 4 || input->dims().size() == 5,
-                   "Input must be with 4 or 5dimensions, i.e. NCHW or NCDHW");
+                   "Input must be with 4 or 5 dimensions, i.e. NCHW or NCDHW");
     PADDLE_ENFORCE(filter->dims().size() == 4 || filter->dims().size() == 5,
                    "Filter must be with 4 or 5 dimensions, i.e. OIHW or OIDHW");
     if (bias) {
@@ -87,33 +127,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> weights_tz =
         paddle::framework::vectorize2int(filter->dims());
     int g = std::max(groups, 1);
-    if (g > 1) {
-      if (is_conv3d) {
-        int o = weights_tz[0];
-        int i = weights_tz[1];
-        int d = weights_tz[2];
-        int h = weights_tz[3];
-        int w = weights_tz[4];
-        weights_tz.resize(6);
-        weights_tz[0] = g;
-        weights_tz[1] = o / g;
-        weights_tz[2] = i;
-        weights_tz[3] = d;
-        weights_tz[4] = h;
-        weights_tz[5] = w;
-      } else {
-        int o = weights_tz[0];
-        int i = weights_tz[1];
-        int h = weights_tz[2];
-        int w = weights_tz[3];
-        weights_tz.resize(5);
-        weights_tz[0] = g;
-        weights_tz[1] = o / g;
-        weights_tz[2] = i;
-        weights_tz[3] = h;
-        weights_tz[4] = w;
-      }
-    }
+    GetWeightsTz(weights_tz, g, is_conv3d);
     std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
 
     // Get unique name for storing MKLDNN primitives
@@ -126,12 +140,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 
     auto src_format = input->format();
     mkldnn::memory::format weights_format =
-        (g == 1) ? filter->format() : mkldnn::memory::format::goihw;
-
-    if (is_conv3d) {
-      weights_format =
-          (g == 1) ? filter->format() : mkldnn::memory::format::goidhw;
-    }
+        GetWeightsFormat(filter->format(), g, is_conv3d);
 
     auto user_src_md = platform::MKLDNNMemDesc(
         {src_tz}, platform::MKLDNNGetDataType<T>(), src_format);
@@ -146,15 +155,11 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto chosen_memory_format =
         platform::data_format_to_memory_format(data_format);
 
-    weights_format =
-        (g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw;
-
     if (is_conv3d) {
       chosen_memory_format =
           platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
-      weights_format =
-          (g == 1) ? chosen_memory_format : mkldnn::memory::format::goidhw;
     }
+    weights_format = GetWeightsFormat(chosen_memory_format, g, is_conv3d);
 
     auto src_md = platform::MKLDNNMemDesc(
         src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
@@ -397,43 +402,12 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> weights_tz =
         paddle::framework::vectorize2int(filter->dims());
     int g = std::max(groups, 1);
-    if (g > 1) {
-      if (is_conv3d) {
-        int o = weights_tz[0];
-        int i = weights_tz[1];
-        int d = weights_tz[2];
-        int h = weights_tz[3];
-        int w = weights_tz[4];
-        weights_tz.resize(6);
-        weights_tz[0] = g;
-        weights_tz[1] = o / g;
-        weights_tz[2] = i;
-        weights_tz[3] = d;
-        weights_tz[4] = h;
-        weights_tz[5] = w;
-      } else {
-        int o = weights_tz[0];
-        int i = weights_tz[1];
-        int h = weights_tz[2];
-        int w = weights_tz[3];
-        weights_tz.resize(5);
-        weights_tz[0] = g;
-        weights_tz[1] = o / g;
-        weights_tz[2] = i;
-        weights_tz[3] = h;
-        weights_tz[4] = w;
-      }
-    }
+    GetWeightsTz(weights_tz, g, is_conv3d);
     std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
 
     auto src_format = input->format();
     mkldnn::memory::format weights_format =
-        (g == 1) ? filter->format() : mkldnn::memory::format::goihw;
-
-    if (is_conv3d) {
-      weights_format =
-          (g == 1) ? filter->format() : mkldnn::memory::format::goidhw;
-    }
+        GetWeightsFormat(filter->format(), g, is_conv3d);
 
     // Get an unique name from "argument" name of "Output" variable
     // as well as attributes of primitive to be created
@@ -461,15 +435,11 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     auto chosen_memory_format =
         platform::data_format_to_memory_format(data_format);
 
-    weights_format =
-        (g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw;
-
     if (is_conv3d) {
       chosen_memory_format =
           platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
-      weights_format =
-          (g == 1) ? chosen_memory_format : mkldnn::memory::format::goidhw;
     }
+    weights_format = GetWeightsFormat(chosen_memory_format, g, is_conv3d);
 
     auto src_md = platform::MKLDNNMemDesc(
         src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);

paddle/fluid/operators/conv_op.cc

@@ -134,14 +134,14 @@ void Conv2DOpMaker::Make() {
            "The format of output tensor is X (one-dimensional) of size equal"
            "to the number of output channels. Only used with MKL-DNN.")
       .AsDispensable();
-  AddOutput("Output",
-            "(Tensor) The output tensor of convolution operator. "
-            "The format of output tensor is also NCHW.");
   AddInput("ResidualData",
            "(Tensor) Tensor with residual data "
            "to which convolution output will be added."
            "Used with fuse_residual_connection fusion.")
       .AsDispensable();
+  AddOutput("Output",
+            "(Tensor) The output tensor of convolution operator. "
+            "The format of output tensor is also NCHW.");
   AddAttr<std::vector<int>>("strides",
                             "(vector<int> default:{1, 1}), the "
                             "strides(h_stride, w_stride) of "
@@ -251,14 +251,14 @@ void Conv3DOpMaker::Make() {
            "is the width of the filter."
            "If the groups attribute is greater than 1, C equals the number of "
            "input image channels divided by the groups.");
-  AddOutput("Output",
-            "(Tensor) The output tensor of convolution operator."
-            "The format of output tensor is also NCDHW.");
   AddInput("ResidualData",
            "(Tensor) Tensor with residual data "
            "to which convolution output will be added."
            "Used with fuse_residual_connection fusion.")
       .AsDispensable();
+  AddOutput("Output",
+            "(Tensor) The output tensor of convolution operator."
+            "The format of output tensor is also NCDHW.");
   AddAttr<std::vector<int>>("strides",
                             "(vector<int>, default:{1, 1, 1}), the "
                             "strides(d_stride, h_stride, w_stride) of "