crnn_layer.c
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#include "crnn_layer.h"
#include "convolutional_layer.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
#include "gemm.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
static void increment_layer(layer *l, int steps)
{
int num = l->outputs*l->batch*steps;
l->output += num;
l->delta += num;
l->x += num;
l->x_norm += num;
#ifdef GPU
l->output_gpu += num;
l->delta_gpu += num;
l->x_gpu += num;
l->x_norm_gpu += num;
#endif
}
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize)
{
fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
batch = batch / steps;
layer l = {0};
l.batch = batch;
l.type = CRNN;
l.steps = steps;
l.h = h;
l.w = w;
l.c = c;
l.out_h = h;
l.out_w = w;
l.out_c = output_filters;
l.inputs = h*w*c;
l.hidden = h * w * hidden_filters;
l.outputs = l.out_h * l.out_w * l.out_c;
l.state = calloc(l.hidden*batch*(steps+1), sizeof(float));
l.input_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0);
l.input_layer->batch = batch;
l.self_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0);
l.self_layer->batch = batch;
l.output_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0);
l.output_layer->batch = batch;
l.output = l.output_layer->output;
l.delta = l.output_layer->delta;
l.forward = forward_crnn_layer;
l.backward = backward_crnn_layer;
l.update = update_crnn_layer;
#ifdef GPU
l.forward_gpu = forward_crnn_layer_gpu;
l.backward_gpu = backward_crnn_layer_gpu;
l.update_gpu = update_crnn_layer_gpu;
l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
#endif
return l;
}
void update_crnn_layer(layer l, update_args a)
{
update_convolutional_layer(*(l.input_layer), a);
update_convolutional_layer(*(l.self_layer), a);
update_convolutional_layer(*(l.output_layer), a);
}
void forward_crnn_layer(layer l, network net)
{
network s = net;
s.train = net.train;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1);
fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1);
fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1);
if(net.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1);
for (i = 0; i < l.steps; ++i) {
s.input = net.input;
forward_convolutional_layer(input_layer, s);
s.input = l.state;
forward_convolutional_layer(self_layer, s);
float *old_state = l.state;
if(net.train) l.state += l.hidden*l.batch;
if(l.shortcut){
copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
s.input = l.state;
forward_convolutional_layer(output_layer, s);
net.input += l.inputs*l.batch;
increment_layer(&input_layer, 1);
increment_layer(&self_layer, 1);
increment_layer(&output_layer, 1);
}
}
void backward_crnn_layer(layer l, network net)
{
network s = net;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
increment_layer(&input_layer, l.steps-1);
increment_layer(&self_layer, l.steps-1);
increment_layer(&output_layer, l.steps-1);
l.state += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
s.input = l.state;
s.delta = self_layer.delta;
backward_convolutional_layer(output_layer, s);
l.state -= l.hidden*l.batch;
/*
if(i > 0){
copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
*/
s.input = l.state;
s.delta = self_layer.delta - l.hidden*l.batch;
if (i == 0) s.delta = 0;
backward_convolutional_layer(self_layer, s);
copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1);
if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1);
s.input = net.input + i*l.inputs*l.batch;
if(net.delta) s.delta = net.delta + i*l.inputs*l.batch;
else s.delta = 0;
backward_convolutional_layer(input_layer, s);
increment_layer(&input_layer, -1);
increment_layer(&self_layer, -1);
increment_layer(&output_layer, -1);
}
}
#ifdef GPU
void pull_crnn_layer(layer l)
{
pull_convolutional_layer(*(l.input_layer));
pull_convolutional_layer(*(l.self_layer));
pull_convolutional_layer(*(l.output_layer));
}
void push_crnn_layer(layer l)
{
push_convolutional_layer(*(l.input_layer));
push_convolutional_layer(*(l.self_layer));
push_convolutional_layer(*(l.output_layer));
}
void update_crnn_layer_gpu(layer l, update_args a)
{
update_convolutional_layer_gpu(*(l.input_layer), a);
update_convolutional_layer_gpu(*(l.self_layer), a);
update_convolutional_layer_gpu(*(l.output_layer), a);
}
void forward_crnn_layer_gpu(layer l, network net)
{
network s = net;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1);
fill_gpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1);
fill_gpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1);
if(net.train) fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1);
for (i = 0; i < l.steps; ++i) {
s.input_gpu = net.input_gpu;
forward_convolutional_layer_gpu(input_layer, s);
s.input_gpu = l.state_gpu;
forward_convolutional_layer_gpu(self_layer, s);
float *old_state = l.state_gpu;
if(net.train) l.state_gpu += l.hidden*l.batch;
if(l.shortcut){
copy_gpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1);
}else{
fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1);
}
axpy_gpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
s.input_gpu = l.state_gpu;
forward_convolutional_layer_gpu(output_layer, s);
net.input_gpu += l.inputs*l.batch;
increment_layer(&input_layer, 1);
increment_layer(&self_layer, 1);
increment_layer(&output_layer, 1);
}
}
void backward_crnn_layer_gpu(layer l, network net)
{
network s = net;
s.train = net.train;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
increment_layer(&input_layer, l.steps - 1);
increment_layer(&self_layer, l.steps - 1);
increment_layer(&output_layer, l.steps - 1);
l.state_gpu += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_gpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
s.input_gpu = l.state_gpu;
s.delta_gpu = self_layer.delta_gpu;
backward_convolutional_layer_gpu(output_layer, s);
l.state_gpu -= l.hidden*l.batch;
s.input_gpu = l.state_gpu;
s.delta_gpu = self_layer.delta_gpu - l.hidden*l.batch;
if (i == 0) s.delta_gpu = 0;
backward_convolutional_layer_gpu(self_layer, s);
copy_gpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
if (i > 0 && l.shortcut) axpy_gpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
s.input_gpu = net.input_gpu + i*l.inputs*l.batch;
if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch;
else s.delta_gpu = 0;
backward_convolutional_layer_gpu(input_layer, s);
increment_layer(&input_layer, -1);
increment_layer(&self_layer, -1);
increment_layer(&output_layer, -1);
}
}
#endif