Files
darknet/src/connected_layer.c

437 lines
14 KiB
C

#include "connected_layer.h"
#include "batchnorm_layer.h"
#include "convolutional_layer.h"
#include "utils.h"
#include "dark_cuda.h"
#include "blas.h"
#include "gemm.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
size_t get_connected_workspace_size(layer l)
{
#ifdef CUDNN
return get_convolutional_workspace_size(l);
/*
if (gpu_index >= 0) {
size_t most = 0;
size_t s = 0;
CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
l.srcTensorDesc,
l.weightDesc,
l.convDesc,
l.dstTensorDesc,
l.fw_algo,
&s));
if (s > most) most = s;
CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
l.srcTensorDesc,
l.ddstTensorDesc,
l.convDesc,
l.dweightDesc,
l.bf_algo,
&s));
if (s > most) most = s;
CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
l.weightDesc,
l.ddstTensorDesc,
l.convDesc,
l.dsrcTensorDesc,
l.bd_algo,
&s));
if (s > most) most = s;
return most;
}
*/
#endif
return 0;
}
connected_layer make_connected_layer(int batch, int steps, int inputs, int outputs, ACTIVATION activation, int batch_normalize)
{
int total_batch = batch*steps;
int i;
connected_layer l = { (LAYER_TYPE)0 };
l.type = CONNECTED;
l.inputs = inputs;
l.outputs = outputs;
l.batch= batch;
l.batch_normalize = batch_normalize;
l.h = 1;
l.w = 1;
l.c = inputs;
l.out_h = 1;
l.out_w = 1;
l.out_c = outputs;
l.n = l.out_c;
l.size = 1;
l.stride = 1;
l.pad = 0;
l.activation = activation;
l.learning_rate_scale = 1;
l.output = (float*)calloc(total_batch * outputs, sizeof(float));
l.delta = (float*)calloc(total_batch * outputs, sizeof(float));
l.weight_updates = (float*)calloc(inputs * outputs, sizeof(float));
l.bias_updates = (float*)calloc(outputs, sizeof(float));
l.weights = (float*)calloc(outputs * inputs, sizeof(float));
l.biases = (float*)calloc(outputs, sizeof(float));
l.forward = forward_connected_layer;
l.backward = backward_connected_layer;
l.update = update_connected_layer;
//float scale = 1./sqrt(inputs);
float scale = sqrt(2.f/inputs);
for(i = 0; i < outputs*inputs; ++i){
l.weights[i] = scale*rand_uniform(-1, 1);
}
for(i = 0; i < outputs; ++i){
l.biases[i] = 0;
}
if(batch_normalize){
l.scales = (float*)calloc(outputs, sizeof(float));
l.scale_updates = (float*)calloc(outputs, sizeof(float));
for(i = 0; i < outputs; ++i){
l.scales[i] = 1;
}
l.mean = (float*)calloc(outputs, sizeof(float));
l.mean_delta = (float*)calloc(outputs, sizeof(float));
l.variance = (float*)calloc(outputs, sizeof(float));
l.variance_delta = (float*)calloc(outputs, sizeof(float));
l.rolling_mean = (float*)calloc(outputs, sizeof(float));
l.rolling_variance = (float*)calloc(outputs, sizeof(float));
l.x = (float*)calloc(total_batch * outputs, sizeof(float));
l.x_norm = (float*)calloc(total_batch * outputs, sizeof(float));
}
#ifdef GPU
l.forward_gpu = forward_connected_layer_gpu;
l.backward_gpu = backward_connected_layer_gpu;
l.update_gpu = update_connected_layer_gpu;
l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
l.biases_gpu = cuda_make_array(l.biases, outputs);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
l.output_gpu = cuda_make_array(l.output, outputs*total_batch);
l.delta_gpu = cuda_make_array(l.delta, outputs*total_batch);
if (batch_normalize) {
l.scales_gpu = cuda_make_array(l.scales, outputs);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs);
l.mean_gpu = cuda_make_array(l.mean, outputs);
l.variance_gpu = cuda_make_array(l.variance, outputs);
l.rolling_mean_gpu = cuda_make_array(l.mean, outputs);
l.rolling_variance_gpu = cuda_make_array(l.variance, outputs);
l.mean_delta_gpu = cuda_make_array(l.mean, outputs);
l.variance_delta_gpu = cuda_make_array(l.variance, outputs);
l.x_gpu = cuda_make_array(l.output, total_batch*outputs);
l.x_norm_gpu = cuda_make_array(l.output, total_batch*outputs);
}
#ifdef CUDNN
create_convolutional_cudnn_tensors(&l);
cudnn_convolutional_setup(&l, cudnn_fastest); // cudnn_fastest, cudnn_smallest
l.workspace_size = get_connected_workspace_size(l);
#endif // CUDNN
#endif // GPU
fprintf(stderr, "connected %4d -> %4d\n", inputs, outputs);
return l;
}
void update_connected_layer(connected_layer l, int batch, float learning_rate, float momentum, float decay)
{
axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
scal_cpu(l.outputs, momentum, l.bias_updates, 1);
if(l.batch_normalize){
axpy_cpu(l.outputs, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
scal_cpu(l.outputs, momentum, l.scale_updates, 1);
}
axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
}
void forward_connected_layer(connected_layer l, network_state state)
{
int i;
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
int m = l.batch;
int k = l.inputs;
int n = l.outputs;
float *a = state.input;
float *b = l.weights;
float *c = l.output;
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(l.batch_normalize){
if(state.train){
mean_cpu(l.output, l.batch, l.outputs, 1, l.mean);
variance_cpu(l.output, l.mean, l.batch, l.outputs, 1, l.variance);
scal_cpu(l.outputs, .95f, l.rolling_mean, 1);
axpy_cpu(l.outputs, .05f, l.mean, 1, l.rolling_mean, 1);
scal_cpu(l.outputs, .95f, l.rolling_variance, 1);
axpy_cpu(l.outputs, .05f, l.variance, 1, l.rolling_variance, 1);
copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1);
normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1);
copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1);
} else {
normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.outputs, 1);
}
scale_bias(l.output, l.scales, l.batch, l.outputs, 1);
}
for(i = 0; i < l.batch; ++i){
axpy_cpu(l.outputs, 1, l.biases, 1, l.output + i*l.outputs, 1);
}
activate_array(l.output, l.outputs*l.batch, l.activation);
}
void backward_connected_layer(connected_layer l, network_state state)
{
int i;
gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
for(i = 0; i < l.batch; ++i){
axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
}
if(l.batch_normalize){
backward_scale_cpu(l.x_norm, l.delta, l.batch, l.outputs, 1, l.scale_updates);
scale_bias(l.delta, l.scales, l.batch, l.outputs, 1);
mean_delta_cpu(l.delta, l.variance, l.batch, l.outputs, 1, l.mean_delta);
variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.outputs, 1, l.variance_delta);
normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.outputs, 1, l.delta);
}
int m = l.outputs;
int k = l.batch;
int n = l.inputs;
float *a = l.delta;
float *b = state.input;
float *c = l.weight_updates;
gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = l.batch;
k = l.outputs;
n = l.inputs;
a = l.delta;
b = l.weights;
c = state.delta;
if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
void denormalize_connected_layer(layer l)
{
int i, j;
for(i = 0; i < l.outputs; ++i){
float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001f);
for(j = 0; j < l.inputs; ++j){
l.weights[i*l.inputs + j] *= scale;
}
l.biases[i] -= l.rolling_mean[i] * scale;
l.scales[i] = 1;
l.rolling_mean[i] = 0;
l.rolling_variance[i] = 1;
}
}
void statistics_connected_layer(layer l)
{
if(l.batch_normalize){
printf("Scales ");
print_statistics(l.scales, l.outputs);
/*
printf("Rolling Mean ");
print_statistics(l.rolling_mean, l.outputs);
printf("Rolling Variance ");
print_statistics(l.rolling_variance, l.outputs);
*/
}
printf("Biases ");
print_statistics(l.biases, l.outputs);
printf("Weights ");
print_statistics(l.weights, l.outputs);
}
#ifdef GPU
void pull_connected_layer(connected_layer l)
{
cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
if (l.batch_normalize){
cuda_pull_array(l.scales_gpu, l.scales, l.outputs);
cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
}
CHECK_CUDA(cudaPeekAtLastError());
}
void push_connected_layer(connected_layer l)
{
cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
cuda_push_array(l.biases_gpu, l.biases, l.outputs);
cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
if (l.batch_normalize){
cuda_push_array(l.scales_gpu, l.scales, l.outputs);
cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
}
CHECK_CUDA(cudaPeekAtLastError());
}
void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay)
{
axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1);
if(l.batch_normalize){
axpy_ongpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
scal_ongpu(l.outputs, momentum, l.scale_updates_gpu, 1);
}
axpy_ongpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
axpy_ongpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
scal_ongpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
}
void forward_connected_layer_gpu(connected_layer l, network_state state)
{
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
int m = l.batch;
int k = l.inputs;
int n = l.outputs;
float * a = state.input;
float * b = l.weights_gpu;
float * c = l.output_gpu;
#ifdef CUDNN
float one = 1; // alpha[0], beta[0]
float alpha = 1, beta = 0;
CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(),
&alpha, //&one,
l.srcTensorDesc,
state.input,
l.weightDesc,
l.weights_gpu,
l.convDesc,
l.fw_algo,
state.workspace,
l.workspace_size,
&beta, //&one,
l.dstTensorDesc,
l.output_gpu));
#else // CUDNN
gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
#endif // CUDNN
if (l.batch_normalize) {
forward_batchnorm_layer_gpu(l, state);
}
else {
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1);
}
//for(i = 0; i < l.batch; ++i) axpy_ongpu(l.outputs, 1, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1);
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
}
void backward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
for(i = 0; i < l.batch; ++i){
axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
}
if(l.batch_normalize){
backward_batchnorm_layer_gpu(l, state);
}
#ifdef CUDNN_DISABLED
float one = 1;
// calculate conv weight updates
// if used: beta=1 then loss decreases faster
CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(),
&one,
l.srcTensorDesc,
state.input,
l.ddstTensorDesc,
l.delta_gpu,
l.convDesc,
l.bf_algo,
state.workspace,
l.workspace_size,
&one,
l.dweightDesc,
l.weight_updates_gpu));
if (state.delta) {
// http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
// calculate delta for the next layer
CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(),
&one,
l.weightDesc,
l.weights_gpu,
l.ddstTensorDesc,
l.delta_gpu,
l.convDesc,
l.bd_algo,
state.workspace,
l.workspace_size,
&one,
l.dsrcTensorDesc,
state.delta));
}
#else // CUDNN
int m = l.outputs;
int k = l.batch;
int n = l.inputs;
float * a = l.delta_gpu;
float * b = state.input;
float * c = l.weight_updates_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = l.batch;
k = l.outputs;
n = l.inputs;
a = l.delta_gpu;
b = l.weights_gpu;
c = state.delta;
if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
#endif // CUDNN
}
#endif