Remove the useless code.

paddle/function/CMakeLists.txt

@@ -41,7 +41,6 @@ if(WITH_GPU)
     add_simple_unittest(DepthwiseConvOpTest)
 endif()
 
-add_simple_unittest(ConvOpTest)
 add_simple_unittest(Im2ColTest)
 add_simple_unittest(GemmConvOpTest)
 endif()

paddle/function/ConvOpTest.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 <gtest/gtest.h>
-#include <memory>
-#include "Function.h"
-#include "FunctionTest.h"
-
-namespace paddle {
-
-enum TestType {
-  kForwardTest = 0,
-  kBackwardInputTest = 1,
-  kBackwardFilterTest = 2,
-};
-
-template <DeviceType DType1, DeviceType DType2>
-class ConvolutionTest {
-public:
-  ConvolutionTest(const std::string& conv1,
-                  const std::string& conv2,
-                  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}) {
-              if (inputChannels > outputChannels) break;
-              size_t groups;
-              if (!useGroups) {
-                groups = 1;
-              } else {
-                if (outputChannels % inputChannels != 0) continue;
-                groups = inputChannels;
-              }
-
-              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;
-                  VLOG(3) << " batchSize=" << batchSize
-                          << " inputChannels=" << inputChannels
-                          << " inputHeight=" << inputSize
-                          << " inputWidth=" << inputSize
-                          << " outputChannels=" << outputChannels
-                          << " filterHeight=" << filterSize
-                          << " filterWidth=" << filterSize
-                          << " outputHeight=" << outputSize
-                          << " outputWidth=" << outputSize
-                          << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DType1, DType2> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", groups)
-                          .set("algo", algo));
-
-                  TensorShape input{
-                      batchSize, inputChannels, inputSize, inputSize};
-
-                  TensorShape filter;
-                  if (groups > 1)
-                    filter = TensorShape({groups,
-                                          outputChannels / groups,
-                                          inputChannels / groups,
-                                          filterSize,
-                                          filterSize});
-                  else
-                    filter = TensorShape({outputChannels,
-                                          inputChannels,
-                                          filterSize,
-                                          filterSize});
-                  TensorShape output{
-                      batchSize, outputChannels, outputSize, outputSize};
-
-                  if (type == kForwardTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.run();
-                  } 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.run();
-                  } else if (type == kBackwardFilterTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
-                                    ADD_TO);
-                    test.run();
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-// Mainly used to test cases where the height and width (input, filter)
-// are not equal.
-template <DeviceType DType1, DeviceType DType2>
-class ConvolutionTest2 {
-public:
-  ConvolutionTest2(const std::string& conv1,
-                   const std::string& conv2,
-                   TestType type,
-                   bool useGroups = true,
-                   std::string algo = "auto") {
-    for (size_t batchSize : {16}) {
-      for (size_t inputHeight : {7, 31}) {
-        for (size_t inputWidth : {10, 54}) {
-          for (size_t filterHeight : {1, 5}) {
-            for (size_t filterWidth : {3, 7}) {
-              for (size_t inputChannels : {7}) {
-                for (size_t outputChannels : {7}) {
-                  size_t groups;
-                  if (!useGroups) {
-                    groups = 1;
-                  } else {
-                    if (outputChannels % inputChannels != 0) continue;
-                    groups = inputChannels;
-                  }
-
-                  size_t stride = 1;
-                  size_t padding = 0;
-                  size_t outputHeight =
-                      (inputHeight - filterHeight + 2 * padding + stride) /
-                      stride;
-                  size_t outputWidth =
-                      (inputWidth - filterWidth + 2 * padding + stride) /
-                      stride;
-                  VLOG(3) << " batchSize=" << batchSize
-                          << " inputChannels=" << inputChannels
-                          << " inputHeight=" << inputHeight
-                          << " inputWidth=" << inputWidth
-                          << " outputChannels=" << outputChannels
-                          << " filterHeight=" << filterHeight
-                          << " filterWidth=" << filterWidth
-                          << " outputHeight=" << outputHeight
-                          << " outputWidth=" << outputWidth
-                          << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DType1, DType2> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", groups)
-                          .set("algo", algo));
-
-                  TensorShape input{
-                      batchSize, inputChannels, inputHeight, inputWidth};
-
-                  TensorShape filter;
-                  if (groups > 1)
-                    filter = TensorShape({groups,
-                                          outputChannels / groups,
-                                          inputChannels / groups,
-                                          filterHeight,
-                                          filterWidth});
-                  else
-                    filter = TensorShape({outputChannels,
-                                          inputChannels,
-                                          filterHeight,
-                                          filterWidth});
-                  TensorShape output{
-                      batchSize, outputChannels, outputHeight, outputWidth};
-
-                  if (type == kForwardTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.run();
-                  } 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.run();
-                  } else if (type == kBackwardFilterTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
-                                    ADD_TO);
-                    test.run();
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-// ======Start Convolution TEST======
-
-TEST(Forward, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
-      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
-      "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", kForwardTest, false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
-}
-
-TEST(BackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      kBackwardInputTest,
-      false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      kBackwardInputTest,
-      false);
-}
-
-TEST(BackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      kBackwardFilterTest,
-      false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      kBackwardFilterTest,
-      false);
-}
-#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, GEMM2) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
-}
-
-TEST(DepthwiseConvBackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "DepthwiseConvGradInput-GPU",
-      kBackwardInputTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "DepthwiseConvGradInput-GPU",
-      kBackwardInputTest);
-}
-
-TEST(DepthwiseConvBackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "DepthwiseConvGradFilter-GPU",
-      kBackwardFilterTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "DepthwiseConvGradFilter-GPU",
-      kBackwardFilterTest);
-}
-
-#endif
-// ======End DepthwiseConvolution TEST======
-
-}  // namespace paddle