modules/dnn/src/caffe/caffe_io.cpp - 4.x - Alx89/OpenCV

modules/dnn/src/caffe/caffe_io.cpp
1 190 строк45 KB
Vincent Rabaud
Use the one argument version of SetTotalBytesLimit.
19 авг 2021, 15:31
19 авг 2021, 15:319cfa843
/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's 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.
//
//   * The name of the copyright holders may not 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 Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/

/*M///////////////////////////////////////////////////////////////////////////////////////
//COPYRIGHT
//
//All contributions by the University of California:
//Copyright (c) 2014, The Regents of the University of California (Regents)
//All rights reserved.
//
//All other contributions:
//Copyright (c) 2014, the respective contributors
//All rights reserved.
//
//Caffe uses a shared copyright model: each contributor holds copyright over
//their contributions to Caffe. The project versioning records all such
//contribution and copyright details. If a contributor wants to further mark
//their specific copyright on a particular contribution, they should indicate
//their copyright solely in the commit message of the change when it is
//committed.
//
//LICENSE
//
//Redistribution and use in source and binary forms, with or without
//modification, are permitted provided that the following conditions are met:
//
//1. Redistributions of source code must retain the above copyright notice, this
//   list of conditions and the following disclaimer.
//2. 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.
//
//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 SERVICES;
//LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
//ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
//(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
//SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
//CONTRIBUTION AGREEMENT
//
//By contributing to the BVLC/caffe repository through pull-request, comment,
//or otherwise, the contributor releases their content to the
//license and copyright terms herein.
//
//M*/

#include "../precomp.hpp"

#ifdef HAVE_PROTOBUF
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/stubs/common.h>
#include <google/protobuf/text_format.h>

#include <opencv2/core.hpp>

#include <map>
#include <string>
#include <fstream>
#include <vector>

#include "caffe_io.hpp"
#include "glog_emulator.hpp"

namespace cv {
namespace dnn {

using std::string;
using std::map;
using namespace caffe;
using namespace ::google::protobuf;
using namespace ::google::protobuf::io;

// Return true iff the net is not the current version.
bool NetNeedsUpgrade(const NetParameter& net_param);

// Return true iff any layer contains parameters specified using
// deprecated V0LayerParameter.
bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);

// Perform all necessary transformations to upgrade a V0NetParameter into a
// NetParameter (including upgrading padding layers and LayerParameters).
bool UpgradeV0Net(const NetParameter& v0_net_param, NetParameter* net_param);

// Upgrade NetParameter with padding layers to pad-aware conv layers.
// For any padding layer, remove it and put its pad parameter in any layers
// taking its top blob as input.
// Error if any of these above layers are not-conv layers.
void UpgradeV0PaddingLayers(const NetParameter& param,
                            NetParameter* param_upgraded_pad);

// Upgrade a single V0LayerConnection to the V1LayerParameter format.
bool UpgradeV0LayerParameter(V1LayerParameter* v0_layer_connection,
                             V1LayerParameter* layer_param);

V1LayerParameter_LayerType UpgradeV0LayerType(const string& type);

// Return true iff any layer contains deprecated data transformation parameters.
bool NetNeedsDataUpgrade(const NetParameter& net_param);

// Perform all necessary transformations to upgrade old transformation fields
// into a TransformationParameter.
void UpgradeNetDataTransformation(NetParameter* net_param);

// Return true iff the Net contains any layers specified as V1LayerParameters.
bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param);

// Perform all necessary transformations to upgrade a NetParameter with
// deprecated V1LayerParameters.
bool UpgradeV1Net(NetParameter* net_param);

bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param,
                             LayerParameter* layer_param);

const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);

bool NetNeedsBatchNormUpgrade(const NetParameter& net_param);

void UpgradeNetBatchNorm(NetParameter* net_param);

// Check for deprecations and upgrade the NetParameter as needed.
bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);


bool NetNeedsUpgrade(const NetParameter& net_param) {
  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param) ||
          NetNeedsBatchNormUpgrade(net_param);
}

bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
  for (int i = 0; i < net_param.layers_size(); ++i) {
    if (net_param.layers(i).has_layer()) {
      return true;
    }
  }
  return false;
}

bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param) {
  return net_param.layers_size() > 0;
}

bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
                  NetParameter* net_param) {
  // First upgrade padding layers to padded conv layers.
  NetParameter v0_net_param;
  UpgradeV0PaddingLayers(v0_net_param_padding_layers, &v0_net_param);
  // Now upgrade layer parameters.
  bool is_fully_compatible = true;
  net_param->Clear();
  if (v0_net_param.has_name()) {
    net_param->set_name(v0_net_param.name());
  }
  for (int i = 0; i < v0_net_param.layers_size(); ++i) {
    is_fully_compatible &= UpgradeV0LayerParameter(v0_net_param.mutable_layers(i),
                                                   net_param->add_layers());
  }
  for (int i = 0; i < v0_net_param.input_size(); ++i) {
    net_param->add_input(v0_net_param.input(i));
  }
  for (int i = 0; i < v0_net_param.input_dim_size(); ++i) {
    net_param->add_input_dim(v0_net_param.input_dim(i));
  }
  if (v0_net_param.has_force_backward()) {
    net_param->set_force_backward(v0_net_param.force_backward());
  }
  return is_fully_compatible;
}

void UpgradeV0PaddingLayers(const NetParameter& param,
                            NetParameter* param_upgraded_pad) {
  // Copy everything other than the layers from the original param.
  param_upgraded_pad->Clear();
  param_upgraded_pad->CopyFrom(param);
  param_upgraded_pad->clear_layers();
  // Figure out which layer each bottom blob comes from.
  map<string, int> blob_name_to_last_top_idx;
  for (int i = 0; i < param.input_size(); ++i) {
    const string& blob_name = param.input(i);
    blob_name_to_last_top_idx[blob_name] = -1;
  }
  for (int i = 0; i < param.layers_size(); ++i) {
    const V1LayerParameter& layer_connection = param.layers(i);
    const V0LayerParameter& layer_param = layer_connection.layer();
    // Add the layer to the new net, unless it's a padding layer.
    if (layer_param.type() != "padding") {
      param_upgraded_pad->add_layers()->CopyFrom(layer_connection);
    }
    for (int j = 0; j < layer_connection.bottom_size(); ++j) {
      const string& blob_name = layer_connection.bottom(j);
      if (blob_name_to_last_top_idx.find(blob_name) ==
          blob_name_to_last_top_idx.end()) {
        LOG(FATAL) << "Unknown blob input " << blob_name << " to layer " << j;
      }
      const int top_idx = blob_name_to_last_top_idx[blob_name];
      if (top_idx == -1) {
        continue;
      }
      const V1LayerParameter& source_layer = param.layers(top_idx);
      if (source_layer.layer().type() == "padding") {
        // This layer has a padding layer as input -- check that it is a conv
        // layer or a pooling layer and takes only one input.  Also check that
        // the padding layer input has only one input and one output.  Other
        // cases have undefined behavior in Caffe.
        CHECK((layer_param.type() == "conv") || (layer_param.type() == "pool"))
            << "Padding layer input to "
            "non-convolutional / non-pooling layer type "
            << layer_param.type();
        CHECK_EQ(layer_connection.bottom_size(), 1)
            << "Conv Layer takes a single blob as input.";
        CHECK_EQ(source_layer.bottom_size(), 1)
            << "Padding Layer takes a single blob as input.";
        CHECK_EQ(source_layer.top_size(), 1)
            << "Padding Layer produces a single blob as output.";
        int layer_index = param_upgraded_pad->layers_size() - 1;
        param_upgraded_pad->mutable_layers(layer_index)->mutable_layer()
            ->set_pad(source_layer.layer().pad());
        param_upgraded_pad->mutable_layers(layer_index)
            ->set_bottom(j, source_layer.bottom(0));
      }
    }
    for (int j = 0; j < layer_connection.top_size(); ++j) {
      const string& blob_name = layer_connection.top(j);
      blob_name_to_last_top_idx[blob_name] = i;
    }
  }
}

bool UpgradeV0LayerParameter(V1LayerParameter* v0_layer_connection_,
                             V1LayerParameter* layer_param) {
  CV_Assert(v0_layer_connection_ != NULL);
  const V1LayerParameter& v0_layer_connection = *v0_layer_connection_;
  bool is_fully_compatible = true;
  layer_param->Clear();
  for (int i = 0; i < v0_layer_connection.bottom_size(); ++i) {
    layer_param->add_bottom(v0_layer_connection.bottom(i));
  }
  for (int i = 0; i < v0_layer_connection.top_size(); ++i) {
    layer_param->add_top(v0_layer_connection.top(i));
  }
  if (v0_layer_connection.has_layer()) {
    const V0LayerParameter& v0_layer_param = v0_layer_connection.layer();
    if (v0_layer_param.has_name()) {
      layer_param->set_name(v0_layer_param.name());
    }
    const string& type = v0_layer_param.type();
    if (v0_layer_param.has_type()) {
      layer_param->set_type(UpgradeV0LayerType(type));
    }
    layer_param->mutable_blobs()->Swap(v0_layer_connection_->mutable_blobs());
    for (int i = 0; i < v0_layer_param.blobs_lr_size(); ++i) {
      layer_param->add_blobs_lr(v0_layer_param.blobs_lr(i));
    }
    for (int i = 0; i < v0_layer_param.weight_decay_size(); ++i) {
      layer_param->add_weight_decay(v0_layer_param.weight_decay(i));
    }
    if (v0_layer_param.has_num_output()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->set_num_output(
            v0_layer_param.num_output());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->set_num_output(
            v0_layer_param.num_output());
      } else {
        LOG(ERROR) << "Unknown parameter num_output for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_biasterm()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->set_bias_term(
            v0_layer_param.biasterm());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->set_bias_term(
            v0_layer_param.biasterm());
      } else {
        LOG(ERROR) << "Unknown parameter biasterm for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_weight_filler()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->
            mutable_weight_filler()->CopyFrom(v0_layer_param.weight_filler());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->
            mutable_weight_filler()->CopyFrom(v0_layer_param.weight_filler());
      } else {
        LOG(ERROR) << "Unknown parameter weight_filler for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_bias_filler()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->
            mutable_bias_filler()->CopyFrom(v0_layer_param.bias_filler());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->
            mutable_bias_filler()->CopyFrom(v0_layer_param.bias_filler());
      } else {
        LOG(ERROR) << "Unknown parameter bias_filler for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_pad()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad());
      } else if (type == "pool") {
        layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
      } else {
        LOG(ERROR) << "Unknown parameter pad for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_kernelsize()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->add_kernel_size(
            v0_layer_param.kernelsize());
      } else if (type == "pool") {
        layer_param->mutable_pooling_param()->set_kernel_size(
            v0_layer_param.kernelsize());
      } else {
        LOG(ERROR) << "Unknown parameter kernelsize for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_group()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->set_group(
            v0_layer_param.group());
      } else {
        LOG(ERROR) << "Unknown parameter group for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_stride()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->add_stride(
            v0_layer_param.stride());
      } else if (type == "pool") {
        layer_param->mutable_pooling_param()->set_stride(
            v0_layer_param.stride());
      } else {
        LOG(ERROR) << "Unknown parameter stride for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_pool()) {
      if (type == "pool") {
        V0LayerParameter_PoolMethod pool = v0_layer_param.pool();
        switch (pool) {
        case V0LayerParameter_PoolMethod_MAX:
          layer_param->mutable_pooling_param()->set_pool(
              PoolingParameter_PoolMethod_MAX);
          break;
        case V0LayerParameter_PoolMethod_AVE:
          layer_param->mutable_pooling_param()->set_pool(
              PoolingParameter_PoolMethod_AVE);
          break;
        case V0LayerParameter_PoolMethod_STOCHASTIC:
          layer_param->mutable_pooling_param()->set_pool(
              PoolingParameter_PoolMethod_STOCHASTIC);
          break;
        default:
          LOG(ERROR) << "Unknown pool method " << (int)pool;
          is_fully_compatible = false;
        }
      } else {
        LOG(ERROR) << "Unknown parameter pool for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_dropout_ratio()) {
      if (type == "dropout") {
        layer_param->mutable_dropout_param()->set_dropout_ratio(
            v0_layer_param.dropout_ratio());
      } else {
        LOG(ERROR) << "Unknown parameter dropout_ratio for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_local_size()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_local_size(
            v0_layer_param.local_size());
      } else {
        LOG(ERROR) << "Unknown parameter local_size for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_alpha()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_alpha(v0_layer_param.alpha());
      } else {
        LOG(ERROR) << "Unknown parameter alpha for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_beta()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_beta(v0_layer_param.beta());
      } else {
        LOG(ERROR) << "Unknown parameter beta for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_k()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_k(v0_layer_param.k());
      } else {
        LOG(ERROR) << "Unknown parameter k for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_source()) {
      if (type == "data") {
        layer_param->mutable_data_param()->set_source(v0_layer_param.source());
      } else if (type == "hdf5_data") {
        layer_param->mutable_hdf5_data_param()->set_source(
            v0_layer_param.source());
      } else if (type == "images") {
        layer_param->mutable_image_data_param()->set_source(
            v0_layer_param.source());
      } else if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_source(
            v0_layer_param.source());
      } else if (type == "infogain_loss") {
        layer_param->mutable_infogain_loss_param()->set_source(
            v0_layer_param.source());
      } else {
        LOG(ERROR) << "Unknown parameter source for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_scale()) {
      layer_param->mutable_transform_param()->
          set_scale(v0_layer_param.scale());
    }
    if (v0_layer_param.has_meanfile()) {
      layer_param->mutable_transform_param()->
          set_mean_file(v0_layer_param.meanfile());
    }
    if (v0_layer_param.has_batchsize()) {
      if (type == "data") {
        layer_param->mutable_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else if (type == "hdf5_data") {
        layer_param->mutable_hdf5_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else if (type == "images") {
        layer_param->mutable_image_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else {
        LOG(ERROR) << "Unknown parameter batchsize for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_cropsize()) {
      layer_param->mutable_transform_param()->
          set_crop_size(v0_layer_param.cropsize());
    }
    if (v0_layer_param.has_mirror()) {
      layer_param->mutable_transform_param()->
          set_mirror(v0_layer_param.mirror());
    }
    if (v0_layer_param.has_rand_skip()) {
      if (type == "data") {
        layer_param->mutable_data_param()->set_rand_skip(
            v0_layer_param.rand_skip());
      } else if (type == "images") {
        layer_param->mutable_image_data_param()->set_rand_skip(
            v0_layer_param.rand_skip());
      } else {
        LOG(ERROR) << "Unknown parameter rand_skip for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_shuffle_images()) {
      if (type == "images") {
        layer_param->mutable_image_data_param()->set_shuffle(
            v0_layer_param.shuffle_images());
      } else {
        LOG(ERROR) << "Unknown parameter shuffle for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_new_height()) {
      if (type == "images") {
        layer_param->mutable_image_data_param()->set_new_height(
            v0_layer_param.new_height());
      } else {
        LOG(ERROR) << "Unknown parameter new_height for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_new_width()) {
      if (type == "images") {
        layer_param->mutable_image_data_param()->set_new_width(
            v0_layer_param.new_width());
      } else {
        LOG(ERROR) << "Unknown parameter new_width for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_concat_dim()) {
      if (type == "concat") {
        layer_param->mutable_concat_param()->set_concat_dim(
            v0_layer_param.concat_dim());
      } else {
        LOG(ERROR) << "Unknown parameter concat_dim for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_fg_threshold()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_fg_threshold(
            v0_layer_param.det_fg_threshold());
      } else {
        LOG(ERROR) << "Unknown parameter det_fg_threshold for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_bg_threshold()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_bg_threshold(
            v0_layer_param.det_bg_threshold());
      } else {
        LOG(ERROR) << "Unknown parameter det_bg_threshold for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_fg_fraction()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_fg_fraction(
            v0_layer_param.det_fg_fraction());
      } else {
        LOG(ERROR) << "Unknown parameter det_fg_fraction for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_context_pad()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_context_pad(
            v0_layer_param.det_context_pad());
      } else {
        LOG(ERROR) << "Unknown parameter det_context_pad for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_crop_mode()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_crop_mode(
            v0_layer_param.det_crop_mode());
      } else {
        LOG(ERROR) << "Unknown parameter det_crop_mode for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_hdf5_output_param()) {
      if (type == "hdf5_output") {
        layer_param->mutable_hdf5_output_param()->CopyFrom(
            v0_layer_param.hdf5_output_param());
      } else {
        LOG(ERROR) << "Unknown parameter hdf5_output_param for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
  }
  return is_fully_compatible;
}

V1LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
  if (type == "accuracy") {
    return V1LayerParameter_LayerType_ACCURACY;
  } else if (type == "bnll") {
    return V1LayerParameter_LayerType_BNLL;
  } else if (type == "concat") {
    return V1LayerParameter_LayerType_CONCAT;
  } else if (type == "conv") {
    return V1LayerParameter_LayerType_CONVOLUTION;
  } else if (type == "data") {
    return V1LayerParameter_LayerType_DATA;
  } else if (type == "dropout") {
    return V1LayerParameter_LayerType_DROPOUT;
  } else if (type == "euclidean_loss") {
    return V1LayerParameter_LayerType_EUCLIDEAN_LOSS;
  } else if (type == "flatten") {
    return V1LayerParameter_LayerType_FLATTEN;
  } else if (type == "hdf5_data") {
    return V1LayerParameter_LayerType_HDF5_DATA;
  } else if (type == "hdf5_output") {
    return V1LayerParameter_LayerType_HDF5_OUTPUT;
  } else if (type == "im2col") {
    return V1LayerParameter_LayerType_IM2COL;
  } else if (type == "images") {
    return V1LayerParameter_LayerType_IMAGE_DATA;
  } else if (type == "infogain_loss") {
    return V1LayerParameter_LayerType_INFOGAIN_LOSS;
  } else if (type == "innerproduct") {
    return V1LayerParameter_LayerType_INNER_PRODUCT;
  } else if (type == "lrn") {
    return V1LayerParameter_LayerType_LRN;
  } else if (type == "multinomial_logistic_loss") {
    return V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
  } else if (type == "pool") {
    return V1LayerParameter_LayerType_POOLING;
  } else if (type == "relu") {
    return V1LayerParameter_LayerType_RELU;
  } else if (type == "sigmoid") {
    return V1LayerParameter_LayerType_SIGMOID;
  } else if (type == "softmax") {
    return V1LayerParameter_LayerType_SOFTMAX;
  } else if (type == "softmax_loss") {
    return V1LayerParameter_LayerType_SOFTMAX_LOSS;
  } else if (type == "split") {
    return V1LayerParameter_LayerType_SPLIT;
  } else if (type == "tanh") {
    return V1LayerParameter_LayerType_TANH;
  } else if (type == "window_data") {
    return V1LayerParameter_LayerType_WINDOW_DATA;
  } else {
    LOG(FATAL) << "Unknown layer name: " << type;
    return V1LayerParameter_LayerType_NONE;
  }
}

bool NetNeedsDataUpgrade(const NetParameter& net_param) {
  for (int i = 0; i < net_param.layers_size(); ++i) {
    if (net_param.layers(i).type() == V1LayerParameter_LayerType_DATA) {
      DataParameter layer_param = net_param.layers(i).data_param();
      if (layer_param.has_scale()) { return true; }
      if (layer_param.has_mean_file()) { return true; }
      if (layer_param.has_crop_size()) { return true; }
      if (layer_param.has_mirror()) { return true; }
    }
    if (net_param.layers(i).type() == V1LayerParameter_LayerType_IMAGE_DATA) {
      ImageDataParameter layer_param = net_param.layers(i).image_data_param();
      if (layer_param.has_scale()) { return true; }
      if (layer_param.has_mean_file()) { return true; }
      if (layer_param.has_crop_size()) { return true; }
      if (layer_param.has_mirror()) { return true; }
    }
    if (net_param.layers(i).type() == V1LayerParameter_LayerType_WINDOW_DATA) {
      WindowDataParameter layer_param = net_param.layers(i).window_data_param();
      if (layer_param.has_scale()) { return true; }
      if (layer_param.has_mean_file()) { return true; }
      if (layer_param.has_crop_size()) { return true; }
      if (layer_param.has_mirror()) { return true; }
    }
  }
  return false;
}

#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \
  do { \
    if (net_param->layers(i).type() == V1LayerParameter_LayerType_##TYPE) { \
      Name##Parameter* layer_param = \
          net_param->mutable_layers(i)->mutable_##param_name##_param(); \
      TransformationParameter* transform_param = \
          net_param->mutable_layers(i)->mutable_transform_param(); \
      if (layer_param->has_scale()) { \
        transform_param->set_scale(layer_param->scale()); \
        layer_param->clear_scale(); \
      } \
      if (layer_param->has_mean_file()) { \
        transform_param->set_mean_file(layer_param->mean_file()); \
        layer_param->clear_mean_file(); \
      } \
      if (layer_param->has_crop_size()) { \
        transform_param->set_crop_size(layer_param->crop_size()); \
        layer_param->clear_crop_size(); \
      } \
      if (layer_param->has_mirror()) { \
        transform_param->set_mirror(layer_param->mirror()); \
        layer_param->clear_mirror(); \
      } \
    } \
  } while (0)

void UpgradeNetDataTransformation(NetParameter* net_param) {
  for (int i = 0; i < net_param->layers_size(); ++i) {
    CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data);
    CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data);
    CONVERT_LAYER_TRANSFORM_PARAM(WINDOW_DATA, WindowData, window_data);
  }
}

bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
  bool success = true;
  if (NetNeedsV0ToV1Upgrade(*param)) {
    // NetParameter was specified using the old style (V0LayerParameter); try to
    // upgrade it.
    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
               << "V0LayerParameter: " << param_file;
    NetParameter original_param(*param);
    if (!UpgradeV0Net(original_param, param)) {
      success = false;
      LOG(ERROR) << "Warning: had one or more problems upgrading "
          << "V0NetParameter to NetParameter (see above); continuing anyway.";
    } else {
      LOG(INFO) << "Successfully upgraded file specified using deprecated "
                << "V0LayerParameter";
    }
    LOG(ERROR) << "Note that future Caffe releases will not support "
        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
        << "weights upgrade this and any other net protos to the new format.";
  }
  // NetParameter uses old style data transformation fields; try to upgrade it.
  if (NetNeedsDataUpgrade(*param)) {
    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
               << "transformation parameters: " << param_file;
    UpgradeNetDataTransformation(param);
    LOG(INFO) << "Successfully upgraded file specified using deprecated "
              << "data transformation parameters.";
    LOG(ERROR) << "Note that future Caffe releases will only support "
               << "transform_param messages for transformation fields.";
  }
  if (NetNeedsV1ToV2Upgrade(*param)) {
    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
               << "V1LayerParameter: " << param_file;
    if (!UpgradeV1Net(param)) {
      success = false;
      LOG(ERROR) << "Warning: had one or more problems upgrading "
          << "V1LayerParameter (see above); continuing anyway.";
    } else {
      LOG(INFO) << "Successfully upgraded file specified using deprecated "
                << "V1LayerParameter";
    }
  }
  // NetParameter uses old style batch norm layers; try to upgrade it.
  if (NetNeedsBatchNormUpgrade(*param)) {
    LOG(INFO) << "Attempting to upgrade batch norm layers using deprecated "
              << "params: " << param_file;
    UpgradeNetBatchNorm(param);
    LOG(INFO) << "Successfully upgraded batch norm layers using deprecated "
              << "params.";
  }
  return success;
}

bool UpgradeV1Net(NetParameter* net_param) {
  // V1LayerParameter layers -> LayerParameter layer
  CV_Assert(net_param != NULL);
  bool is_fully_compatible = true;
  if (net_param->layer_size() > 0) {
    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
               << "fields; these will be ignored for the upgrade.";
    is_fully_compatible = false;
  }
  net_param->clear_layer();
  for (int i = 0; i < net_param->layers_size(); ++i) {
    if (!UpgradeV1LayerParameter(net_param->mutable_layers(i),
                                 net_param->add_layer())) {
      LOG(ERROR) << "Upgrade of input layer " << i << " failed.";
      is_fully_compatible = false;
    }
  }
  net_param->clear_layers();
  return is_fully_compatible;
}

bool NetNeedsBatchNormUpgrade(const NetParameter& net_param) {
  for (int i = 0; i < net_param.layer_size(); ++i) {
    // Check if BatchNorm layers declare three parameters, as required by
    // the previous BatchNorm layer definition.
    if (net_param.layer(i).type() == "BatchNorm"
        && net_param.layer(i).param_size() == 3) {
      return true;
    }
  }
  return false;
}

void UpgradeNetBatchNorm(NetParameter* net_param) {
  for (int i = 0; i < net_param->layer_size(); ++i) {
    // Check if BatchNorm layers declare three parameters, as required by
    // the previous BatchNorm layer definition.
    if (net_param->layer(i).type() == "BatchNorm"
        && net_param->layer(i).param_size() == 3) {
      net_param->mutable_layer(i)->clear_param();
    }
  }
}

bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param_,
                             LayerParameter* layer_param) {
  CV_Assert(v1_layer_param_ != NULL);
  const V1LayerParameter& v1_layer_param = *v1_layer_param_;
  layer_param->Clear();
  bool is_fully_compatible = true;
  for (int i = 0; i < v1_layer_param.bottom_size(); ++i) {
    layer_param->add_bottom(v1_layer_param.bottom(i));
  }
  for (int i = 0; i < v1_layer_param.top_size(); ++i) {
    layer_param->add_top(v1_layer_param.top(i));
  }
  if (v1_layer_param.has_name()) {
    layer_param->set_name(v1_layer_param.name());
  }
  for (int i = 0; i < v1_layer_param.include_size(); ++i) {
    layer_param->add_include()->CopyFrom(v1_layer_param.include(i));
  }
  for (int i = 0; i < v1_layer_param.exclude_size(); ++i) {
    layer_param->add_exclude()->CopyFrom(v1_layer_param.exclude(i));
  }
  if (v1_layer_param.has_type()) {
    layer_param->set_type(UpgradeV1LayerType(v1_layer_param.type()));
  }
  layer_param->mutable_blobs()->Swap(v1_layer_param_->mutable_blobs());
  for (int i = 0; i < v1_layer_param.param_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));
  }
  ParamSpec_DimCheckMode mode = ParamSpec_DimCheckMode_STRICT;
  for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    switch (v1_layer_param.blob_share_mode(i)) {
    case V1LayerParameter_DimCheckMode_STRICT:
      mode = ParamSpec_DimCheckMode_STRICT;
      break;
    case V1LayerParameter_DimCheckMode_PERMISSIVE:
      mode = ParamSpec_DimCheckMode_PERMISSIVE;
      break;
    default:
      LOG(FATAL) << "Unknown blob_share_mode: "
                 << (int)v1_layer_param.blob_share_mode(i);
      CV_Error_(Error::StsError, ("Unknown blob_share_mode: %d", (int)v1_layer_param.blob_share_mode(i)));
    }
    layer_param->mutable_param(i)->set_share_mode(mode);
  }
  for (int i = 0; i < v1_layer_param.blobs_lr_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    layer_param->mutable_param(i)->set_lr_mult(v1_layer_param.blobs_lr(i));
  }
  for (int i = 0; i < v1_layer_param.weight_decay_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    layer_param->mutable_param(i)->set_decay_mult(
        v1_layer_param.weight_decay(i));
  }
  for (int i = 0; i < v1_layer_param.loss_weight_size(); ++i) {
    layer_param->add_loss_weight(v1_layer_param.loss_weight(i));
  }
  if (v1_layer_param.has_accuracy_param()) {
    layer_param->mutable_accuracy_param()->CopyFrom(
        v1_layer_param.accuracy_param());
  }
  if (v1_layer_param.has_argmax_param()) {
    layer_param->mutable_argmax_param()->CopyFrom(
        v1_layer_param.argmax_param());
  }
  if (v1_layer_param.has_concat_param()) {
    layer_param->mutable_concat_param()->CopyFrom(
        v1_layer_param.concat_param());
  }
  if (v1_layer_param.has_contrastive_loss_param()) {
    layer_param->mutable_contrastive_loss_param()->CopyFrom(
        v1_layer_param.contrastive_loss_param());
  }
  if (v1_layer_param.has_convolution_param()) {
    layer_param->mutable_convolution_param()->CopyFrom(
        v1_layer_param.convolution_param());
  }
  if (v1_layer_param.has_data_param()) {
    layer_param->mutable_data_param()->CopyFrom(
        v1_layer_param.data_param());
  }
  if (v1_layer_param.has_dropout_param()) {
    layer_param->mutable_dropout_param()->CopyFrom(
        v1_layer_param.dropout_param());
  }
  if (v1_layer_param.has_dummy_data_param()) {
    layer_param->mutable_dummy_data_param()->CopyFrom(
        v1_layer_param.dummy_data_param());
  }
  if (v1_layer_param.has_eltwise_param()) {
    layer_param->mutable_eltwise_param()->CopyFrom(
        v1_layer_param.eltwise_param());
  }
  if (v1_layer_param.has_exp_param()) {
    layer_param->mutable_exp_param()->CopyFrom(
        v1_layer_param.exp_param());
  }
  if (v1_layer_param.has_hdf5_data_param()) {
    layer_param->mutable_hdf5_data_param()->CopyFrom(
        v1_layer_param.hdf5_data_param());
  }
  if (v1_layer_param.has_hdf5_output_param()) {
    layer_param->mutable_hdf5_output_param()->CopyFrom(
        v1_layer_param.hdf5_output_param());
  }
  if (v1_layer_param.has_hinge_loss_param()) {
    layer_param->mutable_hinge_loss_param()->CopyFrom(
        v1_layer_param.hinge_loss_param());
  }
  if (v1_layer_param.has_image_data_param()) {
    layer_param->mutable_image_data_param()->CopyFrom(
        v1_layer_param.image_data_param());
  }
  if (v1_layer_param.has_infogain_loss_param()) {
    layer_param->mutable_infogain_loss_param()->CopyFrom(
        v1_layer_param.infogain_loss_param());
  }
  if (v1_layer_param.has_inner_product_param()) {
    layer_param->mutable_inner_product_param()->CopyFrom(
        v1_layer_param.inner_product_param());
  }
  if (v1_layer_param.has_lrn_param()) {
    layer_param->mutable_lrn_param()->CopyFrom(
        v1_layer_param.lrn_param());
  }
  if (v1_layer_param.has_memory_data_param()) {
    layer_param->mutable_memory_data_param()->CopyFrom(
        v1_layer_param.memory_data_param());
  }
  if (v1_layer_param.has_mvn_param()) {
    layer_param->mutable_mvn_param()->CopyFrom(
        v1_layer_param.mvn_param());
  }
  if (v1_layer_param.has_pooling_param()) {
    layer_param->mutable_pooling_param()->CopyFrom(
        v1_layer_param.pooling_param());
  }
  if (v1_layer_param.has_power_param()) {
    layer_param->mutable_power_param()->CopyFrom(
        v1_layer_param.power_param());
  }
  if (v1_layer_param.has_relu_param()) {
    layer_param->mutable_relu_param()->CopyFrom(
        v1_layer_param.relu_param());
  }
  if (v1_layer_param.has_sigmoid_param()) {
    layer_param->mutable_sigmoid_param()->CopyFrom(
        v1_layer_param.sigmoid_param());
  }
  if (v1_layer_param.has_softmax_param()) {
    layer_param->mutable_softmax_param()->CopyFrom(
        v1_layer_param.softmax_param());
  }
  if (v1_layer_param.has_slice_param()) {
    layer_param->mutable_slice_param()->CopyFrom(
        v1_layer_param.slice_param());
  }
  if (v1_layer_param.has_tanh_param()) {
    layer_param->mutable_tanh_param()->CopyFrom(
        v1_layer_param.tanh_param());
  }
  if (v1_layer_param.has_threshold_param()) {
    layer_param->mutable_threshold_param()->CopyFrom(
        v1_layer_param.threshold_param());
  }
  if (v1_layer_param.has_window_data_param()) {
    layer_param->mutable_window_data_param()->CopyFrom(
        v1_layer_param.window_data_param());
  }
  if (v1_layer_param.has_transform_param()) {
    layer_param->mutable_transform_param()->CopyFrom(
        v1_layer_param.transform_param());
  }
  if (v1_layer_param.has_loss_param()) {
    layer_param->mutable_loss_param()->CopyFrom(
        v1_layer_param.loss_param());
  }
  if (v1_layer_param.has_layer()) {
    LOG(ERROR) << "Input NetParameter has V0 layer -- ignoring.";
    is_fully_compatible = false;
  }
  return is_fully_compatible;
}

const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
  switch (type) {
  case V1LayerParameter_LayerType_NONE:
    return "";
  case V1LayerParameter_LayerType_ABSVAL:
    return "AbsVal";
  case V1LayerParameter_LayerType_ACCURACY:
    return "Accuracy";
  case V1LayerParameter_LayerType_ARGMAX:
    return "ArgMax";
  case V1LayerParameter_LayerType_BNLL:
    return "BNLL";
  case V1LayerParameter_LayerType_CONCAT:
    return "Concat";
  case V1LayerParameter_LayerType_CONTRASTIVE_LOSS:
    return "ContrastiveLoss";
  case V1LayerParameter_LayerType_CONVOLUTION:
    return "Convolution";
  case V1LayerParameter_LayerType_DECONVOLUTION:
    return "Deconvolution";
  case V1LayerParameter_LayerType_DATA:
    return "Data";
  case V1LayerParameter_LayerType_DROPOUT:
    return "Dropout";
  case V1LayerParameter_LayerType_DUMMY_DATA:
    return "DummyData";
  case V1LayerParameter_LayerType_EUCLIDEAN_LOSS:
    return "EuclideanLoss";
  case V1LayerParameter_LayerType_ELTWISE:
    return "Eltwise";
  case V1LayerParameter_LayerType_EXP:
    return "Exp";
  case V1LayerParameter_LayerType_FLATTEN:
    return "Flatten";
  case V1LayerParameter_LayerType_HDF5_DATA:
    return "HDF5Data";
  case V1LayerParameter_LayerType_HDF5_OUTPUT:
    return "HDF5Output";
  case V1LayerParameter_LayerType_HINGE_LOSS:
    return "HingeLoss";
  case V1LayerParameter_LayerType_IM2COL:
    return "Im2col";
  case V1LayerParameter_LayerType_IMAGE_DATA:
    return "ImageData";
  case V1LayerParameter_LayerType_INFOGAIN_LOSS:
    return "InfogainLoss";
  case V1LayerParameter_LayerType_INNER_PRODUCT:
    return "InnerProduct";
  case V1LayerParameter_LayerType_LRN:
    return "LRN";
  case V1LayerParameter_LayerType_MEMORY_DATA:
    return "MemoryData";
  case V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
    return "MultinomialLogisticLoss";
  case V1LayerParameter_LayerType_MVN:
    return "MVN";
  case V1LayerParameter_LayerType_POOLING:
    return "Pooling";
  case V1LayerParameter_LayerType_POWER:
    return "Power";
  case V1LayerParameter_LayerType_RELU:
    return "ReLU";
  case V1LayerParameter_LayerType_SIGMOID:
    return "Sigmoid";
  case V1LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
    return "SigmoidCrossEntropyLoss";
  case V1LayerParameter_LayerType_SILENCE:
    return "Silence";
  case V1LayerParameter_LayerType_SOFTMAX:
    return "Softmax";
  case V1LayerParameter_LayerType_SOFTMAX_LOSS:
    return "SoftmaxWithLoss";
  case V1LayerParameter_LayerType_SPLIT:
    return "Split";
  case V1LayerParameter_LayerType_SLICE:
    return "Slice";
  case V1LayerParameter_LayerType_TANH:
    return "TanH";
  case V1LayerParameter_LayerType_WINDOW_DATA:
    return "WindowData";
  case V1LayerParameter_LayerType_THRESHOLD:
    return "Threshold";
  default:
    LOG(FATAL) << "Unknown V1LayerParameter layer type: " << (int)type;
    return "";
  }
}

static const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.

bool ReadProtoFromBinary(ZeroCopyInputStream* input, Message *proto) {
    CodedInputStream coded_input(input);
#if GOOGLE_PROTOBUF_VERSION >= 3006000
    coded_input.SetTotalBytesLimit(kProtoReadBytesLimit);
#else
    coded_input.SetTotalBytesLimit(kProtoReadBytesLimit, 536870912);
#endif

    return proto->ParseFromCodedStream(&coded_input);
}

bool ReadProtoFromTextFile(const char* filename, Message* proto) {
    std::ifstream fs(filename, std::ifstream::in);
    CHECK(fs.is_open()) << "Can't open \"" << filename << "\"";
    IstreamInputStream input(&fs);
    google::protobuf::TextFormat::Parser parser;
#ifndef OPENCV_DNN_EXTERNAL_PROTOBUF
    parser.AllowUnknownField(true);
    parser.SetRecursionLimit(1000);
#endif
    return parser.Parse(&input, proto);
}

bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
    std::ifstream fs(filename, std::ifstream::in | std::ifstream::binary);
    CHECK(fs.is_open()) << "Can't open \"" << filename << "\"";
    IstreamInputStream raw_input(&fs);

    return ReadProtoFromBinary(&raw_input, proto);
}

bool ReadProtoFromTextBuffer(const char* data, size_t len, Message* proto) {
    ArrayInputStream input(data, len);
    google::protobuf::TextFormat::Parser parser;
#ifndef OPENCV_DNN_EXTERNAL_PROTOBUF
    parser.AllowUnknownField(true);
    parser.SetRecursionLimit(1000);
#endif
    return parser.Parse(&input, proto);
}


bool ReadProtoFromBinaryBuffer(const char* data, size_t len, Message* proto) {
    ArrayInputStream raw_input(data, len);
    return ReadProtoFromBinary(&raw_input, proto);
}

void ReadNetParamsFromTextFileOrDie(const char* param_file,
                                    NetParameter* param) {
  CHECK(ReadProtoFromTextFile(param_file, param))
      << "Failed to parse NetParameter file: " << param_file;
  UpgradeNetAsNeeded(param_file, param);
}

void ReadNetParamsFromTextBufferOrDie(const char* data, size_t len,
                                      NetParameter* param) {
  CHECK(ReadProtoFromTextBuffer(data, len, param))
      << "Failed to parse NetParameter buffer";
  UpgradeNetAsNeeded("memory buffer", param);
}

void ReadNetParamsFromBinaryFileOrDie(const char* param_file,
                                      NetParameter* param) {
  CHECK(ReadProtoFromBinaryFile(param_file, param))
      << "Failed to parse NetParameter file: " << param_file;
  UpgradeNetAsNeeded(param_file, param);
}

void ReadNetParamsFromBinaryBufferOrDie(const char* data, size_t len,
                                        NetParameter* param) {
  CHECK(ReadProtoFromBinaryBuffer(data, len, param))
      << "Failed to parse NetParameter buffer";
  UpgradeNetAsNeeded("memory buffer", param);
}

}
}
#endif