fuse interface of depthwise to expandconv

paddle/gserver/layers/ConvBaseLayer.cpp

@@ -21,8 +21,7 @@ bool ConvBaseLayer::init(const LayerMap& layerMap,
                          const ParameterMap& parameterMap) {
   /* Initialize the basic parent class */
   Layer::init(layerMap, parameterMap);
-  isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv" ||
-               config_.type() == "depthwise_conv")
+  isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv")
                   ? false
                   : true;
 

paddle/gserver/layers/DepthwiseConvLayer.cpp

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "DepthwiseConvLayer.h"
-#include "paddle/utils/Logging.h"
-#include "paddle/utils/Stat.h"
-
-namespace paddle {
-
-REGISTER_LAYER(depthwise_conv, DepthwiseConvLayer);
-
-bool DepthwiseConvLayer::init(const LayerMap &layerMap,
-                              const ParameterMap &parameterMap) {
-  /* Initialize the basic convolutional parent class */
-  ExpandConvBaseLayer::init(layerMap, parameterMap);
-
-  size_t numInputs = config_.inputs_size();
-  inputShape_.resize(numInputs);
-  filterShape_.resize(numInputs);
-  outputShape_.resize(numInputs);
-
-  for (int i = 0; i < config_.inputs_size(); i++) {
-    std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
-    std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
-    createFunction(forward_,
-                   "DepthwiseConv",
-                   FuncConfig()
-                       .set("paddings", paddings)
-                       .set("strides", strides)
-                       .set("groups", (size_t)groups_[i]));
-
-    createFunction(backward_,
-                   "DepthwiseConvGradInput",
-                   FuncConfig()
-                       .set("paddings", paddings)
-                       .set("strides", strides)
-                       .set("groups", (size_t)groups_[i]));
-
-    createFunction(backward_,
-                   "DepthwiseConvGradFilter",
-                   FuncConfig()
-                       .set("paddings", paddings)
-                       .set("strides", strides)
-                       .set("groups", (size_t)groups_[i]));
-  }
-  return true;
-}
-
-}  // namespace paddle

paddle/gserver/layers/DepthwiseConvLayer.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <vector>
-#include "ExpandConvLayer.h"
-#include "paddle/math/Matrix.h"
-
-namespace paddle {
-
-/**
- * @brief A subclass of convolution layer.
- * This layer does the depthwise convolution calculation of mobilenet.
- * The config file api is img_depthwise_conv_layer.
- */
-
-class DepthwiseConvLayer : public ExpandConvLayer {
-public:
-  explicit DepthwiseConvLayer(const LayerConfig& config)
-      : ExpandConvLayer(config) {}
-
-  ~DepthwiseConvLayer() {}
-
-  bool init(const LayerMap& layerMap,
-            const ParameterMap& parameterMap) override;
-};
-
-}  // namespace paddle

paddle/gserver/layers/ExpandConvLayer.cpp

@@ -38,10 +38,24 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
   inputShape_.resize(numInputs);
   filterShape_.resize(numInputs);
   outputShape_.resize(numInputs);
+
+  string convType;
+  string convGradInputType;
+  string convGradFilterType;
+
   for (int i = 0; i < config_.inputs_size(); i++) {
     std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
     std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
 
+    if (useGpu_ && (size_t)groups_[i] == (size_t)channels_[i] && !isDeconv_) {
+      convType = "DepthwiseConv" convGradInputType =
+          "DepthwiseConvGradInput" convGradFilterType =
+              "DepthwiseConvGradFilter"
+    } else {
+      convType = "GemmConv" convGradInputType =
+          "GemmConvGradInput" convGradFilterType = "GemmConvGradFilter"
+    }
+
     if (FLAGS_use_nnpack) {
       CHECK_EQ(isDeconv_, false);
       createFunction(forward_,
@@ -53,21 +67,21 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
                          .set("algo", std::string("auto")));
     } else {
       createFunction(forward_,
-                     !isDeconv_ ? "GemmConv" : "GemmConvGradInput",
+                     !isDeconv_ ? convType : convGradInputType,
                      FuncConfig()
                          .set("paddings", paddings)
                          .set("strides", strides)
                          .set("groups", (size_t)groups_[i]));
 
       createFunction(backward_,
-                     !isDeconv_ ? "GemmConvGradInput" : "GemmConv",
+                     !isDeconv_ ? convGradInputType : convType,
                      FuncConfig()
                          .set("paddings", paddings)
                          .set("strides", strides)
                          .set("groups", (size_t)groups_[i]));
 
       createFunction(backward_,
-                     "GemmConvGradFilter",
+                     convGradFilterType,
                      FuncConfig()
                          .set("paddings", paddings)
                          .set("strides", strides)