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so much need to commit

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This commit is contained in:
Joseph Redmon
2016-05-06 16:25:16 -07:00
parent 0dff437a69
commit c7b10ceadb
37 changed files with 1502 additions and 438 deletions
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61
src/connected_layer.c
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@ -1,4 +1,5 @@
#include "connected_layer.h"
#include "batchnorm_layer.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
@ -19,6 +20,12 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
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.output = calloc(batch*outputs, sizeof(float));
l.delta = calloc(batch*outputs, sizeof(float));
@ -29,7 +36,6 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
l.weights = calloc(outputs*inputs, sizeof(float));
l.biases = calloc(outputs, sizeof(float));
//float scale = 1./sqrt(inputs);
float scale = sqrt(2./inputs);
for(i = 0; i < outputs*inputs; ++i){
@ -37,7 +43,7 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
}
for(i = 0; i < outputs; ++i){
l.biases[i] = scale;
l.biases[i] = 0;
}
if(batch_normalize){
@ -176,6 +182,19 @@ void backward_connected_layer(connected_layer l, network_state state)
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] + .00001);
for(j = 0; j < l.inputs; ++j){
l.weights[i*l.inputs + j] *= scale;
}
l.biases[i] -= l.rolling_mean[i] * scale;
}
}
#ifdef GPU
void pull_connected_layer(connected_layer l)
@ -223,11 +242,7 @@ void forward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
/*
for(i = 0; i < l.batch; ++i){
copy_ongpu_offset(l.outputs, l.biases_gpu, 0, 1, l.output_gpu, i*l.outputs, 1);
}
*/
int m = l.batch;
int k = l.inputs;
int n = l.outputs;
@ -236,52 +251,26 @@ void forward_connected_layer_gpu(connected_layer l, network_state state)
float * c = l.output_gpu;
gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(l.batch_normalize){
if(state.train){
fast_mean_gpu(l.output_gpu, l.batch, l.outputs, 1, l.mean_gpu);
fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.outputs, 1, l.variance_gpu);
scal_ongpu(l.outputs, .95, l.rolling_mean_gpu, 1);
axpy_ongpu(l.outputs, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
scal_ongpu(l.outputs, .95, l.rolling_variance_gpu, 1);
axpy_ongpu(l.outputs, .05, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.outputs, 1);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
} else {
normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.outputs, 1);
}
scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.outputs, 1);
forward_batchnorm_layer_gpu(l, state);
}
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);
/*
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
float avg = mean_array(l.output, l.outputs*l.batch);
printf("%f\n", avg);
*/
}
void backward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
constrain_ongpu(l.outputs*l.batch, 5, 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_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.outputs, 1, l.scale_updates_gpu);
scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.outputs, 1);
fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.outputs, 1, l.mean_delta_gpu);
fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.outputs, 1, l.variance_delta_gpu);
normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.outputs, 1, l.delta_gpu);
backward_batchnorm_layer_gpu(l, state);
}
int m = l.outputs;