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layer_factory.h
/*
Copyright (c) 2013, Taiga Nomi
All rights reserved.
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 the <organization> 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 HOLDER 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.
*/
#pragma once
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>
#include "caffe.pb.h"
#include "tiny_dnn/network.h"
#include "tiny_dnn/lossfunctions/loss_function.h"
#include "tiny_dnn/optimizers/optimizer.h"
#include "tiny_dnn/util/util.h"
#include "tiny_dnn/io/caffe/layer_factory_impl.h"
namespace tiny_dnn {
inline std::shared_ptr<network<sequential>>
create_net_from_caffe_net(const caffe::NetParameter& layer, const shape3d& data_shape)
{
detail::caffe_layer_vector src_net(layer);
shape_t shape;
if (data_shape.size() > 0) {
shape = data_shape;
} else {
if (layer.input_shape_size() > 0) {
// input_shape is deprecated in Caffe
// blob dimensions are ordered by number N x channel K x height H x width W
int depth = static_cast<int>(layer.input_shape(0).dim(1));
int height = static_cast<int>(layer.input_shape(0).dim(2));
int width = static_cast<int>(layer.input_shape(0).dim(3));
shape = shape3d(width, height, depth);
}
else if (src_net[0].has_input_param()) {
// blob dimensions are ordered by number N x channel K x height H x width W
int depth = static_cast<int>(src_net[0].input_param().shape(0).dim(1));
int height = static_cast<int>(src_net[0].input_param().shape(0).dim(2));
int width = static_cast<int>(src_net[0].input_param().shape(0).dim(3));
shape = shape3d(width, height, depth);
}
else {
throw nn_error("input_shape not found in caffemodel. must specify input shape explicitly");
}
}
auto dst_net = std::make_shared<network<sequential>>(layer.name());
for (size_t i = 0; i < src_net.size(); i++) {
auto type = src_net[i].type();
if (detail::layer_skipped(type)) {
continue;
}
if (!detail::layer_supported(type)) {
throw nn_error("error: tiny-dnn does not support this layer type:" + type);
}
shape_t shape_next = shape;
auto layer = detail::create(src_net[i], shape, &shape_next);
nn_info("convert " + type + " => " + typeid(*layer).name());
nn_info("shape:" + to_string(shape_next));
*dst_net << layer;
shape = shape_next;
}
return dst_net;
}
inline std::shared_ptr<network<sequential>>
create_net_from_caffe_protobinary(const std::string& caffebinarymodel, const shape3d& data_shape)
{
caffe::NetParameter np;
detail::read_proto_from_binary(caffebinarymodel, &np);
return create_net_from_caffe_net(np, data_shape);
}
inline std::shared_ptr<network<sequential>>
create_net_from_caffe_prototxt(const std::string& caffeprototxt, const shape3d& shape =shape3d())
{
caffe::NetParameter np;
detail::read_proto_from_text(caffeprototxt, &np);
return create_net_from_caffe_net(np, shape);
}
template <typename N>
inline void reload_weight_from_caffe_net(const caffe::NetParameter& layer, network<N> *net)
{
detail::caffe_layer_vector src_net(layer);
size_t tiny_layer_idx = 0;
for (size_t caffe_layer_idx = 0; caffe_layer_idx < src_net.size(); caffe_layer_idx++) {
auto type = src_net[caffe_layer_idx].type();
size_t next_idx = tiny_layer_idx + 1;
while (next_idx < net->depth() && !detail::layer_match(type, (*net)[next_idx]->layer_type())) {
next_idx++;
}
if (next_idx >= net->depth()) break;
tiny_layer_idx = next_idx;
// load weight
detail::load(src_net[caffe_layer_idx], (*net)[tiny_layer_idx++]);
}
}
template <typename N>
inline void reload_weight_from_caffe_protobinary(const std::string& caffebinary, network<N> *net)
{
caffe::NetParameter np;
detail::read_proto_from_binary(caffebinary, &np);
reload_weight_from_caffe_net(np, net);
}
} // namespace tiny_dnn
tiny_dnn::index3d< serial_size_t >