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darknet
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Minor performance improvement

This commit is contained in:
AlexeyAB 2020-01-09 01:30:57 +03:00
parent 40006b6e9b
commit cfac917b75
3 changed files with 10 additions and 10 deletions
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4
src/convolutional_kernels.cu
View File

@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
//if (state.use_mixed_precision) {
int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions);
if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) &&
(l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8)
(l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8 && l.size > 1)
{
//printf("\n CUDNN_HALF!!! state.index = %d \n", state.index);
@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
//#ifdef CUDNN_HALF
int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions);
if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) &&
(l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8)
(l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8 && l.size > 1)
{
const size_t input16_size = l.batch*l.c*l.w*l.h;
const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h;

4
src/parser.c
View File

@ -865,8 +865,8 @@ layer parse_shortcut(list *options, size_params params, network net)
assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h);
if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c)
fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n",
params.w, net.layers[index].out_w, params.h, net.layers[index].out_h, params.c, params.net.layers[index].out_c);
fprintf(stderr, " (%4d x%4d x%4d) + (%4d x%4d x%4d) \n",
params.w, params.h, params.c, net.layers[index].out_w, net.layers[index].out_h, params.net.layers[index].out_c);
}
return s;

12
src/shortcut_layer.c
View File

@ -196,12 +196,12 @@ void forward_shortcut_layer_gpu(const layer l, network_state state)
//-----------
//if (l.outputs == l.input_sizes[0])
//if(l.n == 1)
//{
// input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c,
// state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu);
//}
//else
if(l.n == 1 && l.nweights == 0)
{
input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c,
state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu);
}
else
{
shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input, l.weights_gpu, l.nweights);
}