/* Copyright (c) 2016 Baidu, Inc. 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 <vector>
#include <string>
#include "paddle/gserver/layers/DataLayer.h"
#include "ModelConfig.pb.h"
#include "paddle/trainer/Trainer.h"
#include "paddle/utils/GlobalConstants.h"
#include "paddle/gserver/layers/ExpandConvTransLayer.h"

#include "TestUtil.h"
#include "LayerGradUtil.h"

using namespace paddle;  // NOLINT
using namespace std;     // NOLINT

P_DECLARE_bool(use_gpu);
P_DECLARE_int32(gpu_id);
P_DECLARE_double(checkgrad_eps);
P_DECLARE_bool(thread_local_rand_use_global_seed);
P_DECLARE_bool(prev_batch_state);

// 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(
        (2 * conv->padding() + conv->img_size() - conv->filter_size()) /
            ((float)conv->stride()) +
        1.5);

    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(
        (2 * conv->padding() + conv->img_size() - conv->filter_size()) /
            ((float)conv->stride()) +
        1.5);
    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());
}


// Do one forward pass of convTrans layer and check to see if its output
// matches the given result
void doOneConvtTest(size_t imgSize, size_t output_x, size_t stride,
                    size_t padding, size_t filter_size, MatrixPtr& result) {
    TestConfig configt;
    configt.biasSize = 1;
    configt.layerConfig.set_type("exconvt");
    configt.layerConfig.set_num_filters(1);
    configt.layerConfig.set_partial_sum(1);
    configt.layerConfig.set_shared_biases(true);

    configt.inputDefs.push_back({INPUT_DATA, "layer_0", output_x * output_x,
                                 filter_size * filter_size});
    LayerInputConfig* input = configt.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(1);
    conv->set_padding(padding);
    conv->set_padding_y(padding);
    conv->set_stride(stride);
    conv->set_stride_y(stride);
    conv->set_groups(1);
    conv->set_filter_channels(1);
    conv->set_img_size(imgSize);
    conv->set_output_x(output_x);

    configt.layerConfig.set_size(conv->img_size() * conv->img_size() *
                                configt.layerConfig.num_filters());
    configt.layerConfig.set_name("convTrans");

    std::vector<DataLayerPtr> dataLayers;
    LayerMap layerMap;
    vector<Argument> datas;
    initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans",
                  1, false, false);
    dataLayers[0]->getOutputValue()->zeroMem();
    dataLayers[0]->getOutputValue()->add(1.0);

    // test layer initialize
    std::vector<ParameterPtr> parameters;
    LayerPtr convtLayer;
    initTestLayer(configt, &layerMap, &parameters, &convtLayer);
    convtLayer->getBiasParameter()->zeroMem();
    convtLayer->getParameters()[0]->zeroMem();
    convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->add(1.0);
    convtLayer->forward(PASS_GC);

    checkMatrixEqual(convtLayer->getOutputValue(), result);
}

TEST(Layer, convTransLayerFwd2) {
    size_t imgSize, output_x, stride, padding, filter_size;
    MatrixPtr result;

    imgSize = 5;
    output_x = 1;
    stride = 1;
    padding = 0;
    filter_size = 5;
    result = Matrix::create(1, imgSize * imgSize, false, false);
    result->zeroMem();
    result->add(1.0);
    doOneConvtTest(imgSize, output_x, stride, padding, filter_size, result);

    imgSize = 5;
    output_x = 2;
    stride = 1;
    padding = 0;
    filter_size = 4;
    float resultData[] = {1, 2, 2, 2, 1,
                          2, 4, 4, 4, 2,
                          2, 4, 4, 4, 2,
                          2, 4, 4, 4, 2,
                          1, 2, 2, 2, 1};
    result = Matrix::create(resultData, 1, imgSize * imgSize, false, false);
    doOneConvtTest(imgSize, output_x, stride, padding, filter_size, result);

    imgSize = 5;
    output_x = 2;
    stride = 2;
    padding = 1;
    filter_size = 5;
    float resultData2[] = {1, 2, 2, 2, 1,
                           2, 4, 4, 4, 2,
                           2, 4, 4, 4, 2,
                           2, 4, 4, 4, 2,
                           1, 2, 2, 2, 1};
    result = Matrix::create(resultData2, 1, imgSize * imgSize, false, false);
    doOneConvtTest(imgSize, output_x, stride, padding, filter_size, result);

    imgSize = 5;
    output_x = 2;
    stride = 2;
    padding = 0;
    filter_size = 3;
    float resultData3[] = {1, 1, 2, 1, 1,
                           1, 1, 2, 1, 1,
                           2, 2, 4, 2, 2,
                           1, 1, 2, 1, 1,
                           1, 1, 2, 1, 1};
    result = Matrix::create(resultData3, 1, imgSize * imgSize, false, false);
    doOneConvtTest(imgSize, output_x, stride, padding, filter_size, result);
}

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();
}