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hate warnings

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This commit is contained in:
Joseph Redmon
2016-06-14 11:30:28 -07:00
parent 729ce43e6e
commit 8322a58cf6
9 changed files with 89 additions and 94 deletions
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24
src/convolutional_kernels.cu
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@ -72,10 +72,6 @@ void binarize_filters_gpu(float *filters, int n, int size, float *binary)
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int i;
int m = l.n;
int k = l.size*l.size*l.c;
int n = convolutional_out_height(l)*
convolutional_out_width(l);
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if(l.binary){
@ -109,6 +105,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
l.output_gpu);
#else
int m = l.n;
int k = l.size*l.size*l.c;
int n = l.out_w*l.out_h;
for(i = 0; i < l.batch; ++i){
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
float * a = l.filters_gpu;
@ -121,23 +120,18 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
if (l.batch_normalize) {
forward_batchnorm_layer_gpu(l, state);
}
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
//if(l.dot > 0) dot_error_gpu(l);
if(l.binary || l.xnor) swap_binary(&l);
}
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int m = l.n;
int n = l.size*l.size*l.c;
int k = convolutional_out_height(l)*
convolutional_out_width(l);
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu);
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k);
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
if(l.batch_normalize){
backward_batchnorm_layer_gpu(l, state);
@ -181,6 +175,10 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
}
#else
int m = l.n;
int n = l.size*l.size*l.c;
int k = l.out_w*l.out_h;
int i;
for(i = 0; i < l.batch; ++i){
float * a = l.delta_gpu;