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slice_layer.h
/*
Copyright (c) 2016, 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 "tiny_dnn/util/util.h"
#include "tiny_dnn/layers/layer.h"
namespace tiny_dnn {
enum class slice_type {
slice_samples,
slice_channels
};
class slice_layer : public layer {
public:
typedef layer Base;
slice_layer(const shape3d& in_shape, slice_type slice_type, serial_size_t num_outputs)
: layer(std::vector<vector_type>(1, vector_type::data), std::vector<vector_type>(num_outputs, vector_type::data)),
in_shape_(in_shape), slice_type_(slice_type), num_outputs_(num_outputs) {
set_shape();
}
slice_layer(const layer& prev_layer, slice_type slice_type, serial_size_t num_outputs)
: layer(std::vector<vector_type>(1, vector_type::data), std::vector<vector_type>(num_outputs, vector_type::data)),
in_shape_(prev_layer.out_shape()[0]), slice_type_(slice_type), num_outputs_(num_outputs) {
set_shape();
}
std::string layer_type() const override {
return "slice";
}
std::vector<shape3d> in_shape() const override {
return {in_shape_};
}
std::vector<shape3d> out_shape() const override {
return out_shapes_;
}
void forward_propagation(const std::vector<tensor_t*>& in_data,
std::vector<tensor_t*>& out_data) override {
switch (slice_type_) {
case slice_type::slice_samples:
slice_data_forward(*in_data[0], out_data);
break;
case slice_type::slice_channels:
slice_channels_forward(*in_data[0], out_data);
break;
default:
throw nn_not_implemented_error();
}
}
void back_propagation(const std::vector<tensor_t*>& in_data,
const std::vector<tensor_t*>& out_data,
std::vector<tensor_t*>& out_grad,
std::vector<tensor_t*>& in_grad) override {
CNN_UNREFERENCED_PARAMETER(in_data);
CNN_UNREFERENCED_PARAMETER(out_data);
switch (slice_type_) {
case slice_type::slice_samples:
slice_data_backward(out_grad, *in_grad[0]);
break;
case slice_type::slice_channels:
slice_channels_backward(out_grad, *in_grad[0]);
break;
default:
throw nn_not_implemented_error();
}
}
template <class Archive>
static void load_and_construct(Archive & ar, cereal::construct<slice_layer> & construct) {
shape3d in_shape;
slice_type slice_type;
serial_size_t num_outputs;
ar(cereal::make_nvp("in_size", in_shape), cereal::make_nvp("slice_type", slice_type), cereal::make_nvp("num_outputs", num_outputs));
construct(in_shape, slice_type, num_outputs);
}
template <class Archive>
void serialize(Archive & ar) {
layer::serialize_prolog(ar);
ar(cereal::make_nvp("in_size", in_shape_), cereal::make_nvp("slice_type", slice_type_), cereal::make_nvp("num_outputs", num_outputs_));
}
private:
void slice_data_forward(const tensor_t& in_data,
std::vector<tensor_t*>& out_data) {
const vec_t* in = &in_data[0];
for (serial_size_t i = 0; i < num_outputs_; i++) {
tensor_t& out = *out_data[i];
std::copy(in, in + slice_size_[i], &out[0]);
in += slice_size_[i];
}
}
void slice_data_backward(std::vector<tensor_t*>& out_grad,
tensor_t& in_grad) {
vec_t* in = &in_grad[0];
for (serial_size_t i = 0; i < num_outputs_; i++) {
tensor_t& out = *out_grad[i];
std::copy(&out[0], &out[0] + slice_size_[i], in);
in += slice_size_[i];
}
}
void slice_channels_forward(const tensor_t& in_data,
std::vector<tensor_t*>& out_data) {
serial_size_t num_samples = static_cast<serial_size_t>(in_data.size());
serial_size_t channel_idx = 0;
serial_size_t spatial_dim = in_shape_.area();
for (serial_size_t i = 0; i < num_outputs_; i++) {
for (serial_size_t s = 0; s < num_samples; s++) {
float_t *out = &(*out_data[i])[s][0];
const float_t *in = &in_data[s][0] + channel_idx*spatial_dim;
std::copy(in, in + slice_size_[i] * spatial_dim, out);
}
channel_idx += slice_size_[i];
}
}
void slice_channels_backward(std::vector<tensor_t*>& out_grad,
tensor_t& in_grad) {
serial_size_t num_samples = static_cast<serial_size_t>(in_grad.size());
serial_size_t channel_idx = 0;
serial_size_t spatial_dim = in_shape_.area();
for (serial_size_t i = 0; i < num_outputs_; i++) {
for (serial_size_t s = 0; s < num_samples; s++) {
const float_t *out = &(*out_grad[i])[s][0];
float_t *in = &in_grad[s][0] + channel_idx*spatial_dim;
std::copy(out, out + slice_size_[i] * spatial_dim, in);
}
channel_idx += slice_size_[i];
}
}
void set_sample_count(serial_size_t sample_count) override {
if (slice_type_ == slice_type::slice_samples) {
if (num_outputs_ == 0)
throw nn_error("num_outputs must be positive integer");
serial_size_t sample_per_out = sample_count / num_outputs_;
slice_size_.resize(num_outputs_, sample_per_out);
slice_size_.back() = sample_count - (sample_per_out*(num_outputs_-1));
}
Base::set_sample_count(sample_count);
}
void set_shape() {
switch (slice_type_) {
case slice_type::slice_samples:
set_shape_data();
break;
case slice_type::slice_channels:
set_shape_channels();
break;
default:
throw nn_not_implemented_error();
}
}
void set_shape_data() {
out_shapes_.resize(num_outputs_, in_shape_);
}
void set_shape_channels() {
serial_size_t channel_per_out = in_shape_.depth_ / num_outputs_;
out_shapes_.clear();
for (serial_size_t i = 0; i < num_outputs_; i++) {
serial_size_t ch = channel_per_out;
if (i == num_outputs_ - 1) {
assert(in_shape_.depth_ >= i * channel_per_out);
ch = in_shape_.depth_ - i * channel_per_out;
}
slice_size_.push_back(ch);
out_shapes_.push_back(shape3d(in_shape_.width_, in_shape_.height_, ch));
}
}
shape3d in_shape_;
slice_type slice_type_;
serial_size_t num_outputs_;
std::vector<shape3d> out_shapes_;
std::vector<serial_size_t> slice_size_;
};
} // namespace tiny_dnn
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::layer
base class of all kind of NN layers
Definition layer.h:62
tiny_dnn::nn_not_implemented_error
Definition nn_error.h:83
tiny_dnn::slice_layer
slice an input data into multiple outputs along a given slice dimension.
Definition slice_layer.h:42
tiny_dnn::slice_layer::forward_propagation
void forward_propagation(const std::vector< tensor_t * > &in_data, std::vector< tensor_t * > &out_data) override
Definition slice_layer.h:93
tiny_dnn::slice_layer::slice_layer
slice_layer(const shape3d &in_shape, slice_type slice_type, serial_size_t num_outputs)
Definition slice_layer.h:69
tiny_dnn::slice_layer::in_shape
std::vector< shape3d > in_shape() const override
array of input shapes (width x height x depth)
Definition slice_layer.h:85
tiny_dnn::slice_layer::layer_type
std::string layer_type() const override
name of layer, should be unique for each concrete class
Definition slice_layer.h:81
tiny_dnn::slice_layer::out_shape
std::vector< shape3d > out_shape() const override
array of output shapes (width x height x depth)
Definition slice_layer.h:89
tiny_dnn::slice_layer::back_propagation
void back_propagation(const std::vector< tensor_t * > &in_data, const std::vector< tensor_t * > &out_data, std::vector< tensor_t * > &out_grad, std::vector< tensor_t * > &in_grad) override
return delta of previous layer (delta=\frac{dE}{da}, a=wx in fully-connected layer)
Definition slice_layer.h:107
tiny_dnn::index3d< serial_size_t >