text: /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
  
  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 <string>
  #include <vector>
  #include "ModelConfig.pb.h"
  #include "paddle/legacy/gserver/layers/DataLayer.h"
  #include "paddle/legacy/math/MathUtils.h"
  #include "paddle/legacy/utils/GlobalConstants.h"
  
  #include "LayerGradUtil.h"
  #include "paddle/testing/TestUtil.h"
  
  using namespace paddle;  // NOLINT
  using namespace std;     // NOLINT
  
  DECLARE_bool(use_gpu);
  DECLARE_int32(gpu_id);
  DECLARE_double(checkgrad_eps);
  DECLARE_bool(thread_local_rand_use_global_seed);
  DECLARE_bool(prev_batch_state);
  
  // Do one forward pass of ConvLayer using either exconv or cudnn_conv
  MatrixPtr doOneConvTest(size_t imgSize,
                          size_t output_x,
                          size_t stride,
                          size_t padding,
                          size_t filter_size,
                          size_t channel,
                          size_t numfilters,
                          size_t groups,
                          MatrixPtr& inputData,
                          real* param,
                          bool useGpu,
                          bool isDeconv = false) {
    TestConfig config;
    config.biasSize = numfilters;
    string layerType;
    if (useGpu) {
      layerType = (isDeconv) ? "cudnn_convt" : "cudnn_conv";
    } else {
      layerType = (isDeconv) ? "exconvt" : "exconv";
    }
    config.layerConfig.set_type(layerType);
    config.layerConfig.set_num_filters(numfilters);
    config.layerConfig.set_partial_sum(1);
    config.layerConfig.set_shared_biases(true);
  
    size_t weightSize = channel * filter_size * filter_size *
                        config.layerConfig.num_filters() / groups;
    if (isDeconv) {
      config.inputDefs.push_back(
          {INPUT_DATA, "layer_0", output_x * output_x * channel, weightSize});
      config.layerConfig.set_size(imgSize * imgSize *
                                  config.layerConfig.num_filters());
    } else {
      config.inputDefs.push_back(
          {INPUT_DATA, "layer_0", imgSize * imgSize * channel, weightSize});
      config.layerConfig.set_size(output_x * output_x *
                                  config.layerConfig.num_filters());
    }
  
    LayerInputConfig* input = config.layerConfig.add_inputs();
    ConvConfig* conv = input->mutable_conv_conf();
    conv->set_filter_size(filter_size);
    conv->set_filter_size_y(filter_size);
    conv->set_channels(channel);
    conv->set_padding(padding);
    conv->set_padding_y(padding);
    conv->set_stride(stride);
    conv->set_stride_y(stride);
    conv->set_groups(groups);
    conv->set_img_size(imgSize);
    conv->set_output_x(output_x);
  
    if (isDeconv) {
      conv->set_filter_channels(numfilters / groups);
    } else {
      conv->set_filter_channels(channel / groups);
    }
  
    config.layerConfig.set_name("conv");
  
    std::vector<DataLayerPtr> dataLayers;
    LayerMap layerMap;
    vector<Argument> datas;
    initDataLayer(
        config, &dataLayers, &datas, &layerMap, "conv", 1, false, useGpu);
    dataLayers[0]->getOutputValue()->zeroMem();
    dataLayers[0]->getOutputValue()->copyFrom(*inputData);
  
    // test layer initialize
    std::vector<ParameterPtr> parameters;
    LayerPtr convLayer;
    initTestLayer(config, &layerMap, &parameters, &convLayer);
    convLayer->getBiasParameter()->zeroMem();
    convLayer->getParameters()[0]->zeroMem();
    convLayer->getParameters()[0]
        ->getBuf(PARAMETER_VALUE)
        ->copyFrom(param, weightSize);
    convLayer->forward(PASS_GC);
  
    return convLayer->getOutputValue();
  }
  
  TEST(Layer, convParaUnified) {
  #ifdef PADDLE_WITH_CUDA
    MatrixPtr input, resultCpu, resultGpu;
  
    /// TEST1 for conv ///
    input = Matrix::create(1, 4 * 4, false, false);
    real inputData[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
    real param[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1};
  
    input->setData(inputData);
  
    resultCpu = doOneConvTest(/* imgSize */ 4,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 3,
                              /*channel*/ 1,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param,
                              /*useGpu*/ false);
  
    resultGpu = doOneConvTest(/* imgSize */ 4,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 3,
                              /*channel*/ 1,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param,
                              /*useGpu*/ true);
    checkMatrixEqual(resultCpu, resultGpu);
  
    /// TEST1 for deconv ///
    input = Matrix::create(1, 2 * 2, false, false);
    real inputDataT[] = {1, 2, 3, 4};
    input->setData(inputDataT);
  
    resultCpu = doOneConvTest(/* imgSize */ 4,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 3,
                              /*channel*/ 1,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param,
                              /*useGpu*/ false,
                              /*isDeconv*/ true);
  
    resultGpu = doOneConvTest(/* imgSize */ 4,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 3,
                              /*channel*/ 1,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param,
                              /*useGpu*/ true,
                              /*isDeconv*/ true);
    checkMatrixEqual(resultCpu, resultGpu);
  
    /// TEST2 for conv ///
    input = Matrix::create(1, 3 * 3 * 2, false, false);
    real inputData2[] = {
        1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
    real param2[] = {1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1};
  
    input->setData(inputData2);
  
    resultCpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param2,
                              /*useGpu*/ false);
  
    resultGpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param2,
                              /*useGpu*/ true);
    checkMatrixEqual(resultCpu, resultGpu);
  
    /// TEST3 for conv ///
    real param3[] = {1, 2, 3, 4, 4, 3, 2, 1};
  
    resultCpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 2,
                              input,
                              param3,
                              /*useGpu*/ false);
  
    resultGpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 2,
                              input,
                              param3,
                              /*useGpu*/ true);
    checkMatrixEqual(resultCpu, resultGpu);
  
    /// TEST2 for deconv ///
    input = Matrix::create(1, 2 * 2 * 2, false, false);
    real inputData2T[] = {1, 2, 3, 4, 5, 6, 7, 8};
    input->setData(inputData2T);
  
    resultCpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param2,
                              /*useGpu*/ false,
                              /*isDeconv*/ true);
  
    resultGpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 1,
                              input,
                              param2,
                              /*useGpu*/ true,
                              /*isDeconv*/ true);
    checkMatrixEqual(resultCpu, resultGpu);
  
    /// TEST3 for deconv ///
    resultCpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 2,
                              input,
                              param3,
                              /*useGpu*/ false,
                              /*isDeconv*/ true);
  
    resultGpu = doOneConvTest(/* imgSize */ 3,
                              /* output_x */ 2,
                              /* stride */ 1,
                              /* padding */ 0,
                              /* filter_size */ 2,
                              /*channel*/ 2,
                              /*numfilters*/ 2,
                              /*groups*/ 2,
                              input,
                              param3,
                              /*useGpu*/ true,
                              /*isDeconv*/ true);
    checkMatrixEqual(resultCpu, resultGpu);
  #endif
  }
  
  int main(int argc, char** argv) {
    testing::InitGoogleTest(&argc, argv);
    initMain(argc, argv);
    FLAGS_thread_local_rand_use_global_seed = true;
    srand(1);
    return RUN_ALL_TESTS();
  }
  