modify format, and modify the layer grad test, op test

paddle/function/ConvOpTest.cpp

@@ -25,40 +25,32 @@ enum TestType {
   kBackwardFilterTest = 2,
 };
 
-enum LayerType {
-  convolutionType = 0,
-  depthwiseConvolutionType = 1,
-};
-
 template <DeviceType DType1, DeviceType DType2>
 class ConvolutionTest {
 public:
   ConvolutionTest(const std::string& conv1,
                   const std::string& conv2,
-                  LayerType layerType,
                   TestType type,
+                  bool useGroups = true,
                   std::string algo = "auto") {
     for (size_t batchSize : {1, 32}) {
       for (size_t inputSize : {7, 14, 54}) {
         for (size_t filterSize : {1, 3, 5}) {
           for (size_t inputChannels : {3, 64}) {
             for (size_t outputChannels : {3, 64, 128}) {
+              for (size_t groups : {1, 3, 64}) {
                 if (inputChannels > outputChannels) break;
-              if (layerType == depthwiseConvolutionType &&
-                  outputChannels % inputChannels != 0)
-                break;
-
-              size_t groups = 1;
-
-              if (layerType == depthwiseConvolutionType) {
-                groups = inputChannels;
-              }
+                if (groups != 1 &&
+                    (inputChannels != groups || outputChannels % groups != 0))
+                  continue;
+                if (!useGroups) groups = 1;
 
                 for (size_t stride : {1, 2}) {
                   for (size_t padding : {0, 1}) {
                     if (padding >= filterSize) break;
                     size_t outputSize =
-                      (inputSize - filterSize + 2 * padding + stride) / stride;
+                        (inputSize - filterSize + 2 * padding + stride) /
+                        stride;
                     VLOG(3) << " batchSize=" << batchSize
                             << " inputChannels=" << inputChannels
                             << " inputHeight=" << inputSize
@@ -85,10 +77,10 @@ public:
                         batchSize, inputChannels, inputSize, inputSize};
 
                     TensorShape filter;
-                  if (layerType == depthwiseConvolutionType)
+                    if (groups > 1)
                       filter = TensorShape({groups,
                                             outputChannels / groups,
-                                          (size_t)1,
+                                            inputChannels / groups,
                                             filterSize,
                                             filterSize});
                     else
@@ -107,7 +99,8 @@ public:
                     } else if (type == kBackwardInputTest) {
                       test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
                       test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
+                      test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input),
+                                      ADD_TO);
                       test.run();
                     } else if (type == kBackwardFilterTest) {
                       test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
@@ -123,6 +116,7 @@ public:
         }
       }
     }
+  }
 };
 
 // Mainly used to test cases where the height and width (input, filter)
@@ -132,8 +126,8 @@ class ConvolutionTest2 {
 public:
   ConvolutionTest2(const std::string& conv1,
                    const std::string& conv2,
-                   LayerType layerType,
                    TestType type,
+                   bool useGroups = true,
                    std::string algo = "auto") {
     for (size_t batchSize : {16}) {
       for (size_t inputHeight : {7, 31}) {
@@ -142,15 +136,13 @@ public:
             for (size_t filterWidth : {3, 7}) {
               for (size_t inputChannels : {7}) {
                 for (size_t outputChannels : {7, 32}) {
-                  if (layerType == depthwiseConvolutionType &&
-                      outputChannels % inputChannels != 0)
-                    break;
+                  for (size_t groups : {1, 7}) {
+                    if (!useGroups && groups != 1 &&
+                        (inputChannels != groups ||
+                         outputChannels % groups != 0))
+                      continue;
+                    if (!useGroups) groups = 1;
 
-                  size_t groups = 1;
-
-                  if (layerType == depthwiseConvolutionType) {
-                    groups = inputChannels;
-                  }
                     size_t stride = 1;
                     size_t padding = 0;
                     size_t outputHeight =
@@ -185,10 +177,10 @@ public:
                         batchSize, inputChannels, inputHeight, inputWidth};
 
                     TensorShape filter;
-                  if (layerType == depthwiseConvolutionType)
+                    if (groups > 1)
                       filter = TensorShape({groups,
                                             outputChannels / groups,
-                                          (size_t)1,
+                                            inputChannels / groups,
                                             filterHeight,
                                             filterWidth});
                     else
@@ -207,7 +199,8 @@ public:
                     } else if (type == kBackwardInputTest) {
                       test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
                       test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
+                      test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input),
+                                      ADD_TO);
                       test.run();
                     } else if (type == kBackwardFilterTest) {
                       test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
@@ -223,109 +216,37 @@ public:
         }
       }
     }
+  }
 };
 
-// ======Start Convolution TEST======
 TEST(Forward, GEMM) {
   ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
-      "NaiveConv-CPU", "GemmConv-CPU", convolutionType, kForwardTest);
+      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
   ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
-      "NaiveConv-CPU", "GemmConv-CPU", convolutionType, kForwardTest);
+      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
 }
 
 #ifndef PADDLE_ONLY_CPU
 TEST(Forward, GEMM2) {
   ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-CPU", "GemmConv-GPU", convolutionType, kForwardTest);
+      "GemmConv-CPU", "GemmConv-GPU", kForwardTest);
   ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "GemmConv-GPU", convolutionType, kForwardTest);
+      "GemmConv-CPU", "GemmConv-GPU", kForwardTest);
 }
 
 TEST(BackwardInput, GEMM) {
   ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      convolutionType,
-      kBackwardInputTest);
+      "GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
   ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      convolutionType,
-      kBackwardInputTest);
+      "GemmConvGradInput-CPU", "GemmConvGradInput-GPU", kBackwardInputTest);
 }
 
 TEST(BackwardFilter, GEMM) {
   ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      convolutionType,
-      kBackwardFilterTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      convolutionType,
-      kBackwardFilterTest);
-}
-#endif
-// ======End Convolution TEST======
-
-// ======Start DepthwiseConvolution TEST======
-// TODO(zhaolong) The depthwise convolution cpu test will be added when the cpu
-// version of depthwiseConv is implemented.
-
-#ifndef PADDLE_ONLY_CPU
-TEST(DepthwiseConvForward, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_GPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-GPU",
-      "DepthwiseConv-GPU",
-      depthwiseConvolutionType,
-      kForwardTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-GPU",
-      "DepthwiseConv-GPU",
-      depthwiseConvolutionType,
-      kForwardTest);
-}
-
-TEST(DepthwiseConvForward, GEMM2) {
-  ConvolutionTest<DEVICE_TYPE_GPU, DEVICE_TYPE_GPU> test(
-      "DepthwiseConv-GPU",
-      "DepthwiseConv-GPU",
-      depthwiseConvolutionType,
-      kForwardTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "DepthwiseConv-GPU",
-      "DepthwiseConv-GPU",
-      depthwiseConvolutionType,
-      kForwardTest);
-}
-
-TEST(DepthwiseConvBackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "DepthwiseConvGradInput-GPU",
-      "DepthwiseConvGradInput-GPU",
-      depthwiseConvolutionType,
-      kBackwardInputTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "DepthwiseConvGradInput-GPU",
-      "DepthwiseConvGradInput-GPU",
-      depthwiseConvolutionType,
-      kBackwardInputTest);
-}
-
-TEST(DepthwiseConvBackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "DepthwiseConvGradFilter-GPU",
-      "DepthwiseConvGradFilter-GPU",
-      depthwiseConvolutionType,
-      kBackwardFilterTest);
+      "GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
   ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "DepthwiseConvGradFilter-GPU",
-      "DepthwiseConvGradFilter-GPU",
-      depthwiseConvolutionType,
-      kBackwardFilterTest);
+      "GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", kBackwardFilterTest);
 }
 #endif
-// ======End DepthwiseConvolution TEST======
 
 }  // namespace paddle

paddle/gserver/layers/ExpandConvLayer.cpp

@@ -39,21 +39,22 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
   filterShape_.resize(numInputs);
   outputShape_.resize(numInputs);
 
-  string convType;
-  string convGradInputType;
-  string convGradFilterType;
+  std::string convType;
+  std::string convGradInputType;
+  std::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"
+      convType = "DepthwiseConv";
+      convGradInputType = "DepthwiseConvGradInput";
+      convGradFilterType = "DepthwiseConvGradFilter";
     } else {
-      convType = "GemmConv" convGradInputType =
-          "GemmConvGradInput" convGradFilterType = "GemmConvGradFilter"
+      convType = "GemmConv";
+      convGradInputType = "GemmConvGradInput";
+      convGradFilterType = "GemmConvGradFilter";
     }
 
     if (FLAGS_use_nnpack) {

paddle/gserver/tests/test_LayerGrad.cpp

@@ -349,13 +349,13 @@ TEST(Layer, CosSimVecMatLayer) {
 
 void testDepthwiseConvLayer(const string& type, bool useGpu) {
   TestConfig config;
-  config.biasSize = 16;
+  config.biasSize = 32;
   config.layerConfig.set_type(type);
-  config.layerConfig.set_num_filters(16);
+  config.layerConfig.set_num_filters(32);
   config.layerConfig.set_partial_sum(1);
   config.layerConfig.set_shared_biases(true);
 
-  config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192 / 2});
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
   LayerInputConfig* input = config.layerConfig.add_inputs();
   ConvConfig* conv = input->mutable_conv_conf();
   conv->set_filter_size(2);
@@ -388,8 +388,11 @@ void testDepthwiseConvLayer(const string& type, bool useGpu) {
 }
 
 TEST(Layer, depthwiseConvLayer) {
+  //  'depthwise_conv' is a sepecial case of 'exconv' whose
+  //  groups size equals to the input channels size.
+  testDepthwiseConvLayer("exconv", /* useGpu= */ false);
 #ifndef PADDLE_ONLY_CPU
-  testDepthwiseConvLayer("depthwise_conv", /* useGpu= */ true);
+  testDepthwiseConvLayer("exconv", /* useGpu= */ true);
 #endif
 }