/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

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Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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*/

#include <string>
#include <vector>

#include "hdf5.h"

#include "gtest/gtest.h"

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layers/hdf5_data_layer.hpp"
#include "caffe/proto/caffe.pb.h"

#include "caffe/test/test_caffe_main.hpp"

namespace caffe {

template <typename TypeParam>
class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
  typedef typename TypeParam::Dtype Dtype;

 protected:
  HDF5DataLayerTest()
      : filename(NULL),
        blob_top_data_(new Blob<Dtype>()),
        blob_top_label_(new Blob<Dtype>()),
        blob_top_label2_(new Blob<Dtype>()) {}
  virtual void SetUp() {
    blob_top_vec_.push_back(blob_top_data_);
    blob_top_vec_.push_back(blob_top_label_);
    blob_top_vec_.push_back(blob_top_label2_);

    // Check out generate_sample_data.py in the same directory.
    filename = new string(
    CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data_list.txt" CMAKE_EXT);
    LOG(INFO)<< "Using sample HDF5 data file " << filename;
  }

  virtual ~HDF5DataLayerTest() {
    delete blob_top_data_;
    delete blob_top_label_;
    delete blob_top_label2_;
    delete filename;
  }

  string* filename;
  Blob<Dtype>* const blob_top_data_;
  Blob<Dtype>* const blob_top_label_;
  Blob<Dtype>* const blob_top_label2_;
  vector<Blob<Dtype>*> blob_bottom_vec_;
  vector<Blob<Dtype>*> blob_top_vec_;
};

TYPED_TEST_CASE(HDF5DataLayerTest, TestDtypesAndDevices);

TYPED_TEST(HDF5DataLayerTest, TestRead) {
  typedef typename TypeParam::Dtype Dtype;
  // Create LayerParameter with the known parameters.
  // The data file we are reading has 10 rows and 8 columns,
  // with values from 0 to 10*8 reshaped in row-major order.
  LayerParameter param;
  param.add_top("data");
  param.add_top("label");
  param.add_top("label2");

  HDF5DataParameter* hdf5_data_param = param.mutable_hdf5_data_param();
  int batch_size = 5;
  hdf5_data_param->set_batch_size(batch_size);
  hdf5_data_param->set_source(*(this->filename));
  int num_cols = 8;
  int height = 6;
  int width = 5;

  // Test that the layer setup got the correct parameters.
  HDF5DataLayer<Dtype> layer(param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_data_->num(), batch_size);
  EXPECT_EQ(this->blob_top_data_->channels(), num_cols);
  EXPECT_EQ(this->blob_top_data_->height(), height);
  EXPECT_EQ(this->blob_top_data_->width(), width);

  EXPECT_EQ(this->blob_top_label_->num_axes(), 2);
  EXPECT_EQ(this->blob_top_label_->shape(0), batch_size);
  EXPECT_EQ(this->blob_top_label_->shape(1), 1);

  EXPECT_EQ(this->blob_top_label2_->num_axes(), 2);
  EXPECT_EQ(this->blob_top_label2_->shape(0), batch_size);
  EXPECT_EQ(this->blob_top_label2_->shape(1), 1);

  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);

  // Go through the data 10 times (5 batches).
  const int data_size = num_cols * height * width;
  for (int iter = 0; iter < 10; ++iter) {
    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);

    // On even iterations, we're reading the first half of the data.
    // On odd iterations, we're reading the second half of the data.
    // NB: label is 1-indexed
    int label_offset = 1 + ((iter % 2 == 0) ? 0 : batch_size);
    int label2_offset = 1 + label_offset;
    int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;

    // Every two iterations we are reading the second file,
    // which has the same labels, but data is offset by total data size,
    // which is 2400 (see generate_sample_data).
    int file_offset = (iter % 4 < 2) ? 0 : 2400;

    for (int i = 0; i < batch_size; ++i) {
      EXPECT_EQ(
        label_offset + i,
        this->blob_top_label_->cpu_data()[i]);
      EXPECT_EQ(
        label2_offset + i,
        this->blob_top_label2_->cpu_data()[i]);
    }
    for (int i = 0; i < batch_size; ++i) {
      for (int j = 0; j < num_cols; ++j) {
        for (int h = 0; h < height; ++h) {
          for (int w = 0; w < width; ++w) {
            int idx = (
              i * num_cols * height * width +
              j * height * width +
              h * width + w);
            EXPECT_EQ(
              file_offset + data_offset + idx,
              this->blob_top_data_->cpu_data()[idx])
              << "debug: i " << i << " j " << j
              << " iter " << iter;
          }
        }
      }
    }
  }
}

}  // namespace caffe
