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max_pooling_layer.h
/*
Copyright (c) 2015, 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
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*/
#pragma once
#include <string>
#include <vector>
#include <algorithm>
#include "tiny_dnn/core/backend_tiny.h"
#include "tiny_dnn/core/backend_nnp.h"
#include "tiny_dnn/core/backend_dnn.h"
#ifdef CNN_USE_AVX
#include "tiny_dnn/core/backend_avx.h"
#endif
#include "tiny_dnn/core/kernels/maxpool_op.h"
#include "tiny_dnn/core/kernels/maxpool_grad_op.h"
#include "tiny_dnn/util/util.h"
#include "tiny_dnn/util/image.h"
#include "tiny_dnn/activations/activation_function.h"
namespace tiny_dnn {
template <typename Activation = activation::identity>
class max_pooling_layer : public feedforward_layer<Activation> {
public:
CNN_USE_LAYER_MEMBERS;
typedef feedforward_layer<Activation> Base;
max_pooling_layer(serial_size_t in_width,
serial_size_t in_height,
serial_size_t in_channels,
serial_size_t pooling_size,
backend_t backend_type = core::default_engine())
: max_pooling_layer(in_width, in_height, in_channels, pooling_size,
pooling_size, backend_type) {}
max_pooling_layer(const shape3d& in_shape,
serial_size_t pooling_size,
serial_size_t stride,
backend_t backend_type = core::default_engine())
: max_pooling_layer(in_shape.width_, in_shape.height_, in_shape.depth_,
pooling_size, stride, backend_type) {}
max_pooling_layer(serial_size_t in_width,
serial_size_t in_height,
serial_size_t in_channels,
serial_size_t pooling_size,
serial_size_t stride,
backend_t backend_type = core::default_engine())
: max_pooling_layer(in_width, in_height, in_channels, pooling_size,
pooling_size, stride, stride, padding::valid,
backend_type) {}
max_pooling_layer(serial_size_t in_width,
serial_size_t in_height,
serial_size_t in_channels,
serial_size_t pooling_size_x,
serial_size_t pooling_size_y,
serial_size_t stride_x,
serial_size_t stride_y,
padding pad_type = padding::valid,
backend_t backend_type = core::default_engine())
: Base({ vector_type::data }) {
set_maxpool_params(
shape3d(in_width, in_height, in_channels),
shape3d(conv_out_length(in_width, pooling_size_x, stride_x, pad_type),
conv_out_length(in_height, pooling_size_y, stride_y, pad_type),
in_channels),
pooling_size_x, pooling_size_y, stride_x, stride_y, pad_type);
init_connection();
init_backend(backend_type);
Base::set_backend_type(backend_type);
}
// move constructor
max_pooling_layer(max_pooling_layer&& other) // NOLINT
: Base(std::move(other))
, params_(std::move(other.params_)) {
init_connection();
init_backend(std::move(Base::engine()));
}
serial_size_t fan_in_size() const override {
return static_cast<serial_size_t>(params_.out2in[0].size());
}
serial_size_t fan_out_size() const override {
return 1;
}
void forward_propagation(const std::vector<tensor_t*>& in_data,
std::vector<tensor_t*>& out_data) override {
// forward convolutional op context
auto ctx = OpKernelContext(in_data, out_data);
ctx.setParallelize(layer::parallelize());
ctx.setEngine(layer::engine());
// launch convolutional kernel
kernel_fwd_->compute(ctx);
// activations
this->forward_activation(*out_data[0], *out_data[1]);
}
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 {
// activations
// TODO(edgar/nyanp): refactor and move activations outside
this->backward_activation(*out_grad[0], *out_data[0], *out_grad[1]);
// backward convolutional op context
auto ctx = OpKernelContext(in_data, out_data, out_grad, in_grad);
ctx.setParallelize(layer::parallelize());
ctx.setEngine(layer::engine());
// launch convolutional kernel
kernel_back_->compute(ctx);
}
std::vector<index3d<serial_size_t>>
in_shape() const override { return { params_.in }; }
std::vector<index3d<serial_size_t>>
out_shape() const override { return { params_.out, params_.out }; }
std::string layer_type() const override {
return std::string("max-pool");
}
std::string kernel_file() const override {
return std::string("../tiny_cnn/core/kernels/cl_kernels/pooling.cl");
}
std::pair<serial_size_t, serial_size_t> pool_size() const {
return std::make_pair(params_.pool_size_x, params_.pool_size_y);
}
void set_sample_count(serial_size_t sample_count) override {
Base::set_sample_count(sample_count);
params_.out2inmax.resize(
sample_count, std::vector<serial_size_t>(params_.out.size()));
}
template <class Archive>
static void
load_and_construct(Archive & ar,
cereal::construct<max_pooling_layer> & construct) {
shape3d in;
serial_size_t stride_x, stride_y, pool_size_x, pool_size_y;
padding pad_type;
ar(cereal::make_nvp("in_size", in),
cereal::make_nvp("pool_size_x", pool_size_x),
cereal::make_nvp("pool_size_y", pool_size_y),
cereal::make_nvp("stride_x", stride_x),
cereal::make_nvp("stride_y", stride_y),
cereal::make_nvp("pad_type", pad_type));
construct(in.width_, in.height_, in.depth_, pool_size_x, pool_size_y,
stride_x, stride_y, pad_type);
}
template <class Archive>
void serialize(Archive & ar) {
layer::serialize_prolog(ar);
ar(cereal::make_nvp("in_size", params_.in),
cereal::make_nvp("pool_size_x", params_.pool_size_x),
cereal::make_nvp("pool_size_y", params_.pool_size_y),
cereal::make_nvp("stride_x", params_.stride_x),
cereal::make_nvp("stride_y", params_.stride_y),
cereal::make_nvp("pad_type", params_.pad_type));
}
private:
/* The Max Poling operation params */
maxpool_params params_;
/* Forward and backward ops */
std::shared_ptr<core::OpKernel> kernel_fwd_;
std::shared_ptr<core::OpKernel> kernel_back_;
void connect_kernel(serial_size_t pooling_size_x,
serial_size_t pooling_size_y,
serial_size_t outx,
serial_size_t outy,
serial_size_t c) {
serial_size_t dxmax = static_cast<serial_size_t>(
std::min(static_cast<serial_size_t>(pooling_size_x),
params_.in.width_ - outx * params_.stride_x));
serial_size_t dymax = static_cast<serial_size_t>(
std::min(static_cast<serial_size_t>(pooling_size_y),
params_.in.height_ - outy * params_.stride_y));
for (serial_size_t dy = 0; dy < dymax; dy++) {
for (serial_size_t dx = 0; dx < dxmax; dx++) {
serial_size_t in_index = params_.in.get_index(
static_cast<serial_size_t>(outx * params_.stride_x + dx),
static_cast<serial_size_t>(outy * params_.stride_y + dy), c);
serial_size_t out_index = params_.out.get_index(outx, outy, c);
if (in_index >= params_.in2out.size()) {
throw nn_error("index overflow");
}
if (out_index >= params_.out2in.size()) {
throw nn_error("index overflow");
}
params_.in2out[in_index] = out_index;
params_.out2in[out_index].push_back(in_index);
}
}
}
void init_connection() {
params_.in2out.resize(params_.in.size());
params_.out2in.resize(params_.out.size());
for (serial_size_t c = 0; c < params_.in.depth_; ++c) {
for (serial_size_t y = 0; y < params_.out.height_; ++y) {
for (serial_size_t x = 0; x < params_.out.width_; ++x) {
connect_kernel(params_.pool_size_x,
params_.pool_size_y,
x, y, c);
}
}
}
}
void init_backend(backend_t backend_type) {
core::OpKernelConstruction ctx =
core::OpKernelConstruction(layer::device(), &params_);
if (backend_type == backend_t::internal ||
backend_type == backend_t::nnpack ||
backend_type == backend_t::avx) {
kernel_fwd_.reset(new MaxPoolOp(ctx));
kernel_back_.reset(new MaxPoolGradOp(ctx));
return;
}
else {
throw nn_error("Not supported engine: " + to_string(backend_type));
}
}
void set_maxpool_params(const shape3d& in,
const shape3d& out,
serial_size_t pooling_size_x,
serial_size_t pooling_size_y,
serial_size_t stride_x,
serial_size_t stride_y,
padding pad_type) {
params_.in = in;
params_.out = out;
params_.pool_size_x = pooling_size_x;
params_.pool_size_y = pooling_size_y;
params_.stride_x = stride_x;
params_.stride_y = stride_y;
params_.pad_type = pad_type;
}
};
} // namespace tiny_dnn
tiny_dnn::feedforward_layer
single-input, single-output network with activation function
Definition feedforward_layer.h:37
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::layer::in_channels
serial_size_t in_channels() const
number of outgoing edges in this layer
Definition layer.h:146
tiny_dnn::max_pooling_layer
applies max-pooing operaton to the spatial data
Definition max_pooling_layer.h:53
tiny_dnn::max_pooling_layer::max_pooling_layer
max_pooling_layer(serial_size_t in_width, serial_size_t in_height, serial_size_t in_channels, serial_size_t pooling_size, backend_t backend_type=core::default_engine())
Definition max_pooling_layer.h:64
tiny_dnn::max_pooling_layer::fan_out_size
serial_size_t fan_out_size() const override
number of outgoing connections for each input unit used only for weight/bias initialization methods w...
Definition max_pooling_layer.h:130
tiny_dnn::max_pooling_layer::layer_type
std::string layer_type() const override
name of layer, should be unique for each concrete class
Definition max_pooling_layer.h:171
tiny_dnn::max_pooling_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 max_pooling_layer.h:148
tiny_dnn::max_pooling_layer::out_shape
std::vector< index3d< serial_size_t > > out_shape() const override
array of output shapes (width x height x depth)
Definition max_pooling_layer.h:169
tiny_dnn::max_pooling_layer::max_pooling_layer
max_pooling_layer(serial_size_t in_width, serial_size_t in_height, serial_size_t in_channels, serial_size_t pooling_size_x, serial_size_t pooling_size_y, serial_size_t stride_x, serial_size_t stride_y, padding pad_type=padding::valid, backend_t backend_type=core::default_engine())
Definition max_pooling_layer.h:96
tiny_dnn::max_pooling_layer::in_shape
std::vector< index3d< serial_size_t > > in_shape() const override
array of input shapes (width x height x depth)
Definition max_pooling_layer.h:166
tiny_dnn::max_pooling_layer::fan_in_size
serial_size_t fan_in_size() const override
number of incoming connections for each output unit used only for weight/bias initialization methods ...
Definition max_pooling_layer.h:126
tiny_dnn::max_pooling_layer::forward_propagation
void forward_propagation(const std::vector< tensor_t * > &in_data, std::vector< tensor_t * > &out_data) override
Definition max_pooling_layer.h:134
tiny_dnn::index3d< serial_size_t >