C++ LayerPtr::getOutput方法代码示例

TEST(Layer, WarpCTCLayer) {
  for (auto layerSize : {10, 64}) {
    for (auto batchSize : {1, 10, 32}) {
      for (auto normByTimes : {false, true}) {
        for (auto useGpu : {false, true}) {
#ifndef PADDLE_WITH_CUDA
          if (useGpu) continue;
#endif
          LOG(INFO) << "layerSize=" << layerSize << " batchSize=" << batchSize
                    << " normByTimes = " << normByTimes << " useGpu=" << useGpu;

          FLAGS_use_gpu = useGpu;

          Argument data0;
          initArgument(batchSize, layerSize, useGpu, data0);

          Argument data1;
          data1.resizeAndCopyFrom(data0);

          LayerPtr dataLayer0 =
              createDataLayer("data", batchSize, layerSize, useGpu, data0);
          LayerPtr dataLayer1 =
              createDataLayer("data", batchSize, layerSize, useGpu, data1);

          LayerPtr labelLayer =
              createLabelLayer("label", batchSize, layerSize, useGpu);

          LayerPtr warpctcLayer = createWarpCTCLayer(
              "cost", layerSize, useGpu, normByTimes, dataLayer0, labelLayer);
          LayerPtr ctcLayer = createCTCLayer(
              "cost", layerSize, useGpu, normByTimes, dataLayer1, labelLayer);

          /// Check cost
          LOG(INFO) << "Check cost: "
                    << checkError(*(warpctcLayer->getOutput().value),
                                  *(ctcLayer->getOutput().value))
                    << " different elements.";

          /// Check gradients
          LOG(INFO) << "Check gradients: "
                    << checkError(*(dataLayer0->getOutput().grad),
                                  *(dataLayer1->getOutput().grad))
                    << " different elements";
        }
      }
    }
  }
}

LayerPtr createCTCLayer(string name,
                        size_t numClasses,
                        bool useGpu,
                        bool normByTimes,
                        LayerPtr dataLayer,
                        LayerPtr labelLayer) {
  LayerMap layerMap;
  layerMap[dataLayer->getName()] = dataLayer;
  layerMap[labelLayer->getName()] = labelLayer;

  ParameterMap parameterMap;

  LayerConfig layerConfig;
  layerConfig.set_name(name);
  layerConfig.set_type("ctc");
  layerConfig.set_size(numClasses);
  layerConfig.set_norm_by_times(normByTimes);

  layerConfig.add_inputs();
  LayerInputConfig& input0 = *(layerConfig.mutable_inputs(0));
  input0.set_input_layer_name(dataLayer->getName());

  layerConfig.add_inputs();
  LayerInputConfig& input1 = *(layerConfig.mutable_inputs(1));
  input1.set_input_layer_name(labelLayer->getName());

  LayerPtr layer = LayerPtr(new CTCLayer(layerConfig));
  layerMap[layer->getName()] = layer;
  layer->init(layerMap, parameterMap);

  ActivationFunction* softmaxActivation = ActivationFunction::create("softmax");

  softmaxActivation->forward(dataLayer->getOutput()).check();
  layer->forward(PASS_GC);

  layer->backward();
  softmaxActivation->backward(dataLayer->getOutput()).check();

  return layer;
}

// Test that the convTrans forward is the same as conv backward
TEST(Layer, convTransLayerFwd) {
    // Setting up conv-trans layer
    TestConfig configt;
    configt.biasSize = 3;
    configt.layerConfig.set_type("exconvt");
    configt.layerConfig.set_num_filters(3);
    configt.layerConfig.set_partial_sum(1);
    configt.layerConfig.set_shared_biases(true);

    configt.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384});
    LayerInputConfig* input = configt.layerConfig.add_inputs();
    ConvConfig* conv = input->mutable_conv_conf();
    conv->set_filter_size(2);
    conv->set_filter_size_y(4);
    conv->set_channels(16);
    conv->set_padding(0);
    conv->set_padding_y(1);
    conv->set_stride(2);
    conv->set_stride_y(2);
    conv->set_groups(1);
    conv->set_filter_channels(3 / conv->groups());
    conv->set_img_size(16);
    conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
                                  conv->padding(), conv->stride(),
                                  /* caffeMode */ true));
    configt.layerConfig.set_size(conv->img_size() * conv->img_size() *
                                configt.layerConfig.num_filters());
    configt.layerConfig.set_name("convTrans");

    // data layer initialize
    std::vector<DataLayerPtr> dataLayers;
    LayerMap layerMap;
    vector<Argument> datas;
    initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans",
                  100, false, false);
    // test layer initialize
    std::vector<ParameterPtr> parameters;
    LayerPtr convtLayer;
    initTestLayer(configt, &layerMap, &parameters, &convtLayer);
    convtLayer->getBiasParameter()->zeroMem();
    convtLayer->forward(PASS_GC);

    // Setting up conv-layer config
    TestConfig config;
    config.biasSize = 16;
    config.layerConfig.set_type("exconv");
    config.layerConfig.set_num_filters(16);
    config.layerConfig.set_partial_sum(1);
    config.layerConfig.set_shared_biases(true);

    config.inputDefs.push_back({INPUT_DATA, "layer_1", 768, 384});
    input = config.layerConfig.add_inputs();
    conv = input->mutable_conv_conf();
    conv->set_filter_size(2);
    conv->set_filter_size_y(4);
    conv->set_channels(3);
    conv->set_padding(0);
    conv->set_padding_y(1);
    conv->set_stride(2);
    conv->set_stride_y(2);
    conv->set_groups(1);
    conv->set_filter_channels(conv->channels() / conv->groups());
    conv->set_img_size(16);
    conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
                                  conv->padding(), conv->stride(),
                                  /* caffeMode */ true));
    config.layerConfig.set_size(conv->output_x() * conv->output_x() *
                                config.layerConfig.num_filters());
    config.layerConfig.set_name("conv");

    // data layer initialize
    std::vector<DataLayerPtr> dataLayers2;
    LayerMap layerMap2;
    vector<Argument> datas2;
    initDataLayer(config, &dataLayers2, &datas2, &layerMap2, "conv",
                  100, false, false);
    // test layer initialize
    std::vector<ParameterPtr> parameters2;
    LayerPtr convLayer;
    initTestLayer(config, &layerMap2, &parameters2, &convLayer);

    // Sync convLayer and convtLayer parameter
    convLayer->getBiasParameter()->zeroMem();
    convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(
            *(convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)));

    // Set convLayer outputGrad as convTransLayer input value
    convLayer->forward(PASS_GC);
    convLayer->getOutput().grad->copyFrom(*(dataLayers[0]->getOutputValue()));

    vector<int> callbackFlags(parameters2.size(), 0);
    auto callback = [&](Parameter* para) { ++callbackFlags[para->getID()]; };
    convLayer->backward(callback);

    // Check that the convLayer backward is the same as convTransLayer forward
    checkMatrixEqual(convtLayer->getOutputValue(),
                     dataLayers2[0]->getOutputGrad());
}