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refactoring and added DARK ZONE

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This commit is contained in:
Joseph Redmon
2015-03-11 22:20:15 -07:00
parent f047cfff99
commit dcb000b553
37 changed files with 640 additions and 918 deletions
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32
src/deconvolutional_layer.c
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@ -43,15 +43,11 @@ image get_deconvolutional_delta(deconvolutional_layer layer)
return float_to_image(h,w,c,layer.delta);
}
deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation, float learning_rate, float momentum, float decay)
deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
deconvolutional_layer *layer = calloc(1, sizeof(deconvolutional_layer));
layer->learning_rate = learning_rate;
layer->momentum = momentum;
layer->decay = decay;
layer->h = h;
layer->w = w;
layer->c = c;
@ -120,7 +116,7 @@ void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w)
#endif
}
void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in)
void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state)
{
int i;
int out_h = deconvolutional_out_height(layer);
@ -135,7 +131,7 @@ void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in)
for(i = 0; i < layer.batch; ++i){
float *a = layer.filters;
float *b = in + i*layer.c*layer.h*layer.w;
float *b = state.input + i*layer.c*layer.h*layer.w;
float *c = layer.col_image;
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
@ -145,7 +141,7 @@ void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in)
activate_array(layer.output, layer.batch*layer.n*size, layer.activation);
}
void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, float *delta)
void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int out_h = deconvolutional_out_height(layer);
@ -156,14 +152,14 @@ void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, floa
gradient_array(layer.output, size*layer.n*layer.batch, layer.activation, layer.delta);
backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, size);
if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
int m = layer.c;
int n = layer.size*layer.size*layer.n;
int k = layer.h*layer.w;
float *a = in + i*m*n;
float *a = state.input + i*m*n;
float *b = layer.col_image;
float *c = layer.filter_updates;
@ -171,29 +167,29 @@ void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, floa
layer.size, layer.stride, 0, b);
gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
if(delta){
if(state.delta){
int m = layer.c;
int n = layer.h*layer.w;
int k = layer.size*layer.size*layer.n;
float *a = layer.filters;
float *b = layer.col_image;
float *c = delta + i*n*m;
float *c = state.delta + i*n*m;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
}
void update_deconvolutional_layer(deconvolutional_layer layer)
void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
axpy_cpu(layer.n, learning_rate, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.n, momentum, layer.bias_updates, 1);
axpy_cpu(size, -layer.decay, layer.filters, 1, layer.filter_updates, 1);
axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
scal_cpu(size, layer.momentum, layer.filter_updates, 1);
axpy_cpu(size, -decay, layer.filters, 1, layer.filter_updates, 1);
axpy_cpu(size, learning_rate, layer.filter_updates, 1, layer.filters, 1);
scal_cpu(size, momentum, layer.filter_updates, 1);
}