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Merge pull request #2282 from davidssmith/master

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add LSTM layer
This commit is contained in:
Alexey
2019-01-24 20:19:57 +03:00
committed by GitHub
parent a7366a5a0a 96773df469
commit da044776d1
6 changed files with 762 additions and 2 deletions
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2
Makefile
View File

@ -108,7 +108,7 @@ CFLAGS+= -DCUDNN_HALF
ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]
endif
OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o
OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o
ifeq ($(GPU), 1)
LDFLAGS+= -lstdc++
OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o

79
src/darknet.c
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@ -117,6 +117,26 @@ void operations(char *cfgfile)
ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w;
} else if(l.type == CONNECTED){
ops += 2l * l.inputs * l.outputs;
} else if (l.type == RNN){
ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
} else if (l.type == GRU){
ops += 2l * l.uz->inputs * l.uz->outputs;
ops += 2l * l.uh->inputs * l.uh->outputs;
ops += 2l * l.ur->inputs * l.ur->outputs;
ops += 2l * l.wz->inputs * l.wz->outputs;
ops += 2l * l.wh->inputs * l.wh->outputs;
ops += 2l * l.wr->inputs * l.wr->outputs;
} else if (l.type == LSTM){
ops += 2l * l.uf->inputs * l.uf->outputs;
ops += 2l * l.ui->inputs * l.ui->outputs;
ops += 2l * l.ug->inputs * l.ug->outputs;
ops += 2l * l.uo->inputs * l.uo->outputs;
ops += 2l * l.wf->inputs * l.wf->outputs;
ops += 2l * l.wi->inputs * l.wi->outputs;
ops += 2l * l.wg->inputs * l.wg->outputs;
ops += 2l * l.wo->inputs * l.wo->outputs;
}
}
printf("Floating Point Operations: %ld\n", ops);
@ -220,6 +240,16 @@ void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
denormalize_connected_layer(*l.state_r_layer);
denormalize_connected_layer(*l.state_h_layer);
}
if (l.type == LSTM && l.batch_normalize) {
denormalize_connected_layer(*l.wf);
denormalize_connected_layer(*l.wi);
denormalize_connected_layer(*l.wg);
denormalize_connected_layer(*l.wo);
denormalize_connected_layer(*l.uf);
denormalize_connected_layer(*l.ui);
denormalize_connected_layer(*l.ug);
denormalize_connected_layer(*l.uo);
}
}
save_weights(net, outfile);
}
@ -262,6 +292,17 @@ void normalize_net(char *cfgfile, char *weightfile, char *outfile)
*l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
net.layers[i].batch_normalize=1;
}
if (l.type == LSTM && l.batch_normalize) {
*l.wf = normalize_layer(*l.wf, l.wf->outputs);
*l.wi = normalize_layer(*l.wi, l.wi->outputs);
*l.wg = normalize_layer(*l.wg, l.wg->outputs);
*l.wo = normalize_layer(*l.wo, l.wo->outputs);
*l.uf = normalize_layer(*l.uf, l.uf->outputs);
*l.ui = normalize_layer(*l.ui, l.ui->outputs);
*l.ug = normalize_layer(*l.ug, l.ug->outputs);
*l.uo = normalize_layer(*l.uo, l.uo->outputs);
net.layers[i].batch_normalize=1;
}
}
save_weights(net, outfile);
}
@ -295,6 +336,25 @@ void statistics_net(char *cfgfile, char *weightfile)
printf("State H\n");
statistics_connected_layer(*l.state_h_layer);
}
if (l.type == LSTM && l.batch_normalize) {
printf("LSTM Layer %d\n", i);
printf("wf\n");
statistics_connected_layer(*l.wf);
printf("wi\n");
statistics_connected_layer(*l.wi);
printf("wg\n");
statistics_connected_layer(*l.wg);
printf("wo\n");
statistics_connected_layer(*l.wo);
printf("uf\n");
statistics_connected_layer(*l.uf);
printf("ui\n");
statistics_connected_layer(*l.ui);
printf("ug\n");
statistics_connected_layer(*l.ug);
printf("uo\n");
statistics_connected_layer(*l.uo);
}
printf("\n");
}
}
@ -332,6 +392,25 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
l.state_h_layer->batch_normalize = 0;
net.layers[i].batch_normalize=0;
}
if (l.type == GRU && l.batch_normalize) {
denormalize_connected_layer(*l.wf);
denormalize_connected_layer(*l.wi);
denormalize_connected_layer(*l.wg);
denormalize_connected_layer(*l.wo);
denormalize_connected_layer(*l.uf);
denormalize_connected_layer(*l.ui);
denormalize_connected_layer(*l.ug);
denormalize_connected_layer(*l.uo);
l.wf->batch_normalize = 0;
l.wi->batch_normalize = 0;
l.wg->batch_normalize = 0;
l.wo->batch_normalize = 0;
l.uf->batch_normalize = 0;
l.ui->batch_normalize = 0;
l.ug->batch_normalize = 0;
l.uo->batch_normalize = 0;
net.layers[i].batch_normalize=0;
}
}
save_weights(net, outfile);
}

626
src/lstm_layer.c Normal file
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@ -0,0 +1,626 @@
#include "lstm_layer.h"
#include "connected_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_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize)
{
fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
batch = batch / steps;
layer l = { 0 };
l.batch = batch;
l.type = LSTM;
l.steps = steps;
l.inputs = inputs;
l.uf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.uf->batch = batch;
l.ui = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.ui->batch = batch;
l.ug = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.ug->batch = batch;
l.uo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.uo->batch = batch;
l.wf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wf->batch = batch;
l.wi = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wi->batch = batch;
l.wg = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wg->batch = batch;
l.wo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wo->batch = batch;
l.batch_normalize = batch_normalize;
l.outputs = outputs;
l.output = calloc(outputs*batch*steps, sizeof(float));
l.state = calloc(outputs*batch, sizeof(float));
l.forward = forward_lstm_layer;
l.update = update_lstm_layer;
l.prev_state_cpu = calloc(batch*outputs, sizeof(float));
l.prev_cell_cpu = calloc(batch*outputs, sizeof(float));
l.cell_cpu = calloc(batch*outputs*steps, sizeof(float));
l.f_cpu = calloc(batch*outputs, sizeof(float));
l.i_cpu = calloc(batch*outputs, sizeof(float));
l.g_cpu = calloc(batch*outputs, sizeof(float));
l.o_cpu = calloc(batch*outputs, sizeof(float));
l.c_cpu = calloc(batch*outputs, sizeof(float));
l.h_cpu = calloc(batch*outputs, sizeof(float));
l.temp_cpu = calloc(batch*outputs, sizeof(float));
l.temp2_cpu = calloc(batch*outputs, sizeof(float));
l.temp3_cpu = calloc(batch*outputs, sizeof(float));
l.dc_cpu = calloc(batch*outputs, sizeof(float));
l.dh_cpu = calloc(batch*outputs, sizeof(float));
#ifdef GPU
l.forward_gpu = forward_lstm_layer_gpu;
l.backward_gpu = backward_lstm_layer_gpu;
l.update_gpu = update_lstm_layer_gpu;
l.output_gpu = cuda_make_array(0, batch*outputs*steps);
l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps);
l.prev_state_gpu = cuda_make_array(0, batch*outputs);
l.prev_cell_gpu = cuda_make_array(0, batch*outputs);
l.cell_gpu = cuda_make_array(0, batch*outputs*steps);
l.f_gpu = cuda_make_array(0, batch*outputs);
l.i_gpu = cuda_make_array(0, batch*outputs);
l.g_gpu = cuda_make_array(0, batch*outputs);
l.o_gpu = cuda_make_array(0, batch*outputs);
l.c_gpu = cuda_make_array(0, batch*outputs);
l.h_gpu = cuda_make_array(0, batch*outputs);
l.temp_gpu = cuda_make_array(0, batch*outputs);
l.temp2_gpu = cuda_make_array(0, batch*outputs);
l.temp3_gpu = cuda_make_array(0, batch*outputs);
l.dc_gpu = cuda_make_array(0, batch*outputs);
l.dh_gpu = cuda_make_array(0, batch*outputs);
#ifdef CUDNN
cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w);
cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w);
cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w);
cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w);
cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w);
cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w);
cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w);
cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w);
#endif
#endif
return l;
}
void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay)
{
update_connected_layer(*(l.wf), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.wi), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.wg), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.wo), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.uf), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.ui), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.ug), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.uo), batch, learning_rate, momentum, decay);
}
void forward_lstm_layer(layer l, network_state state)
{
network_state s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1);
if (state.train) {
fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1);
}
for (i = 0; i < l.steps; ++i) {
s.input = l.h_cpu;
forward_connected_layer(wf, s);
forward_connected_layer(wi, s);
forward_connected_layer(wg, s);
forward_connected_layer(wo, s);
s.input = state.input;
forward_connected_layer(uf, s);
forward_connected_layer(ui, s);
forward_connected_layer(ug, s);
forward_connected_layer(uo, s);
copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1);
copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1);
copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1);
copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1);
activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.g_cpu, l.outputs*l.batch, TANH);
activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC);
copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1);
activate_array(l.h_cpu, l.outputs*l.batch, TANH);
mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1);
copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1);
state.input += l.inputs*l.batch;
l.output += l.outputs*l.batch;
l.cell_cpu += l.outputs*l.batch;
increment_layer(&wf, 1);
increment_layer(&wi, 1);
increment_layer(&wg, 1);
increment_layer(&wo, 1);
increment_layer(&uf, 1);
increment_layer(&ui, 1);
increment_layer(&ug, 1);
increment_layer(&uo, 1);
}
}
void backward_lstm_layer(layer l, network_state state)
{
network_state s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
increment_layer(&wf, l.steps - 1);
increment_layer(&wi, l.steps - 1);
increment_layer(&wg, l.steps - 1);
increment_layer(&wo, l.steps - 1);
increment_layer(&uf, l.steps - 1);
increment_layer(&ui, l.steps - 1);
increment_layer(&ug, l.steps - 1);
increment_layer(&uo, l.steps - 1);
state.input += l.inputs*l.batch*(l.steps - 1);
if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1);
l.output += l.outputs*l.batch*(l.steps - 1);
l.cell_cpu += l.outputs*l.batch*(l.steps - 1);
l.delta += l.outputs*l.batch*(l.steps - 1);
for (i = l.steps - 1; i >= 0; --i) {
if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1);
copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1);
if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1);
copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1);
l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch;
copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1);
copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1);
copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1);
copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1);
activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.g_cpu, l.outputs*l.batch, TANH);
activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC);
copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1);
activate_array(l.temp_cpu, l.outputs*l.batch, TANH);
copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1);
mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1);
gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu);
axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1);
activate_array(l.temp_cpu, l.outputs*l.batch, TANH);
mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1);
gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wo, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(uo, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1);
gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wg, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(ug, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1);
gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wi, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(ui, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1);
gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wf, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(uf, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1);
state.input -= l.inputs*l.batch;
if (state.delta) state.delta -= l.inputs*l.batch;
l.output -= l.outputs*l.batch;
l.cell_cpu -= l.outputs*l.batch;
l.delta -= l.outputs*l.batch;
increment_layer(&wf, -1);
increment_layer(&wi, -1);
increment_layer(&wg, -1);
increment_layer(&wo, -1);
increment_layer(&uf, -1);
increment_layer(&ui, -1);
increment_layer(&ug, -1);
increment_layer(&uo, -1);
}
}
#ifdef GPU
void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay)
{
update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay);
}
void forward_lstm_layer_gpu(layer l, network_state state)
{
network_state s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1);
if (state.train) {
fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
}
for (i = 0; i < l.steps; ++i) {
s.input = l.state_gpu;
forward_connected_layer_gpu(wf, s);
forward_connected_layer_gpu(wi, s);
forward_connected_layer_gpu(wg, s);
forward_connected_layer_gpu(wo, s);
s.input = state.input;
forward_connected_layer_gpu(uf, s);
forward_connected_layer_gpu(ui, s);
forward_connected_layer_gpu(ug, s);
forward_connected_layer_gpu(uo, s);
copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1);
activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1);
state.input += l.inputs*l.batch;
l.output_gpu += l.outputs*l.batch;
l.cell_gpu += l.outputs*l.batch;
increment_layer(&wf, 1);
increment_layer(&wi, 1);
increment_layer(&wg, 1);
increment_layer(&wo, 1);
increment_layer(&uf, 1);
increment_layer(&ui, 1);
increment_layer(&ug, 1);
increment_layer(&uo, 1);
}
}
void backward_lstm_layer_gpu(layer l, network_state state)
{
network_state s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
increment_layer(&wf, l.steps - 1);
increment_layer(&wi, l.steps - 1);
increment_layer(&wg, l.steps - 1);
increment_layer(&wo, l.steps - 1);
increment_layer(&uf, l.steps - 1);
increment_layer(&ui, l.steps - 1);
increment_layer(&ug, l.steps - 1);
increment_layer(&uo, l.steps - 1);
state.input += l.inputs*l.batch*(l.steps - 1);
if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1);
l.output_gpu += l.outputs*l.batch*(l.steps - 1);
l.cell_gpu += l.outputs*l.batch*(l.steps - 1);
l.delta_gpu += l.outputs*l.batch*(l.steps - 1);
for (i = l.steps - 1; i >= 0; --i) {
if (i != 0) copy_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1);
if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1);
l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1);
gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu);
axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wo, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(uo, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wg, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(ug, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wi, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(ui, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wf, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(uf, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1);
state.input -= l.inputs*l.batch;
if (state.delta) state.delta -= l.inputs*l.batch;
l.output_gpu -= l.outputs*l.batch;
l.cell_gpu -= l.outputs*l.batch;
l.delta_gpu -= l.outputs*l.batch;
increment_layer(&wf, -1);
increment_layer(&wi, -1);
increment_layer(&wg, -1);
increment_layer(&wo, -1);
increment_layer(&uf, -1);
increment_layer(&ui, -1);
increment_layer(&ug, -1);
increment_layer(&uo, -1);
}
}
#endif

20
src/lstm_layer.h Normal file
View File

@ -0,0 +1,20 @@
#ifndef LSTM_LAYER_H
#define LSTM_LAYER_H
#include "activations.h"
#include "layer.h"
#include "network.h"
#define USET
layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize);
void forward_lstm_layer(layer l, network_state state);
void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay);
#ifdef GPU
void forward_lstm_layer_gpu(layer l, network_state state);
void backward_lstm_layer_gpu(layer l, network_state state);
void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
#endif
#endif

2
src/network.c
View File

@ -140,6 +140,8 @@ char *get_layer_string(LAYER_TYPE a)
return "rnn";
case GRU:
return "gru";
case LSTM:
return "lstm";
case CRNN:
return "crnn";
case MAXPOOL:

35
src/parser.c
View File

@ -18,6 +18,7 @@
#include "gru_layer.h"
#include "list.h"
#include "local_layer.h"
#include "lstm_layer.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "option_list.h"
@ -58,6 +59,7 @@ LAYER_TYPE string_to_layer_type(char * type)
|| strcmp(type, "[network]")==0) return NETWORK;
if (strcmp(type, "[crnn]")==0) return CRNN;
if (strcmp(type, "[gru]")==0) return GRU;
if (strcmp(type, "[lstm]")==0) return LSTM;
if (strcmp(type, "[rnn]")==0) return RNN;
if (strcmp(type, "[conn]")==0
|| strcmp(type, "[connected]")==0) return CONNECTED;
@ -219,6 +221,16 @@ layer parse_gru(list *options, size_params params)
return l;
}
layer parse_lstm(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
return l;
}
connected_layer parse_connected(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
@ -755,6 +767,8 @@ network parse_network_cfg_custom(char *filename, int batch)
l = parse_rnn(options, params);
}else if(lt == GRU){
l = parse_gru(options, params);
}else if(lt == LSTM){
l = parse_lstm(options, params);
}else if(lt == CRNN){
l = parse_crnn(options, params);
}else if(lt == CONNECTED){
@ -1025,6 +1039,15 @@ void save_weights_upto(network net, char *filename, int cutoff)
save_connected_weights(*(l.state_z_layer), fp);
save_connected_weights(*(l.state_r_layer), fp);
save_connected_weights(*(l.state_h_layer), fp);
} if(l.type == LSTM){
save_connected_weights(*(l.wf), fp);
save_connected_weights(*(l.wi), fp);
save_connected_weights(*(l.wg), fp);
save_connected_weights(*(l.wo), fp);
save_connected_weights(*(l.uf), fp);
save_connected_weights(*(l.ui), fp);
save_connected_weights(*(l.ug), fp);
save_connected_weights(*(l.uo), fp);
} if(l.type == CRNN){
save_convolutional_weights(*(l.input_layer), fp);
save_convolutional_weights(*(l.self_layer), fp);
@ -1236,6 +1259,16 @@ void load_weights_upto(network *net, char *filename, int cutoff)
load_connected_weights(*(l.state_r_layer), fp, transpose);
load_connected_weights(*(l.state_h_layer), fp, transpose);
}
if(l.type == LSTM){
load_connected_weights(*(l.wf), fp, transpose);
load_connected_weights(*(l.wi), fp, transpose);
load_connected_weights(*(l.wg), fp, transpose);
load_connected_weights(*(l.wo), fp, transpose);
load_connected_weights(*(l.uf), fp, transpose);
load_connected_weights(*(l.ui), fp, transpose);
load_connected_weights(*(l.ug), fp, transpose);
load_connected_weights(*(l.uo), fp, transpose);
}
if(l.type == LOCAL){
int locations = l.out_w*l.out_h;
int size = l.size*l.size*l.c*l.n*locations;
@ -1281,4 +1314,4 @@ network *load_network(char *cfg, char *weights, int clear)
}
if (clear) (*net->seen) = 0;
return net;
}
}