github
/
darknet
mirror of https://github.com/AlexeyAB/darknet.git
1
0
Fork 0
Code Issues Releases Wiki Activity

Added param train_only_bn=1 to train only Batch-normalization for this and previous layers

Browse Source
pull/5817/head
AlexeyAB 5 years ago
parent 342a8d1561
commit d4b2ed9d22
5 changed files with 57 additions and 13 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 3
      build/darknet/x64/partial.cmd
  2. 1
      include/darknet.h
  3. 28
      src/convolutional_kernels.cu
  4. 2
      src/network_kernels.cu
  5. 36
      src/parser.c

3
build/darknet/x64/partial.cmd
Unescape View File

@ -6,6 +6,9 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights
rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13
darknet.exe partial cfg/csdarknet53-omega.cfg csdarknet53-omega_final.weights csdarknet53-omega.conv.105 105
darknet.exe partial cfg/cd53paspp-omega.cfg cd53paspp-omega_final.weights cd53paspp-omega.conv.137 137

1
include/darknet.h
Unescape View File

@ -324,6 +324,7 @@ struct layer {
int onlyforward;
int stopbackward;
int train_only_bn;
int dont_update;
int burnin_update;
int dontload;

28
src/convolutional_kernels.cu
Unescape View File

@ -744,7 +744,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
assert((l.nweights) > 0);
cuda_convert_f32_to_f16(l.weight_updates_gpu, l.nweights, l.weight_updates_gpu16);
if (!state.net.adversarial) {
if (!state.net.adversarial && !l.train_only_bn) {
CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(),
&one,
l.srcTensorDesc16,
@ -796,7 +796,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
backward_batchnorm_layer_gpu(l, state);
}
if (!state.net.adversarial) {
if (!state.net.adversarial && !l.train_only_bn) {
// calculate conv weight updates
// if used: beta=1 then loss decreases faster
CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(),
@ -857,17 +857,19 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w;
//im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
im2col_gpu_ext(im, // input
l.c / l.groups, // input channels
l.h, l.w, // input size (h, w)
l.size, l.size, // kernel size (h, w)
l.pad * l.dilation, l.pad * l.dilation, // padding (h, w)
l.stride_y, l.stride_x, // stride (h, w)
l.dilation, l.dilation, // dilation (h, w)
state.workspace); // output
//gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n);
gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n);
if (!state.net.adversarial && !l.train_only_bn) {
//im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace);
im2col_gpu_ext(im, // input
l.c / l.groups, // input channels
l.h, l.w, // input size (h, w)
l.size, l.size, // kernel size (h, w)
l.pad * l.dilation, l.pad * l.dilation, // padding (h, w)
l.stride_y, l.stride_x, // stride (h, w)
l.dilation, l.dilation, // dilation (h, w)
state.workspace); // output
//gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n);
gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n);
}
if (state.delta) {
if (l.binary || l.xnor) swap_binary(&l);

2
src/network_kernels.cu
Unescape View File

@ -273,6 +273,8 @@ void update_network_gpu(network net)
l.t = get_current_batch(net);
if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0;
if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue;
if (l.train_only_bn) continue;
if(l.update_gpu && l.dont_update < iteration_num){
l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
}

36
src/parser.c
Unescape View File

@ -1190,6 +1190,39 @@ int is_network(section *s)
|| strcmp(s->type, "[network]")==0);
}
void set_train_only_bn(network net)
{
int train_only_bn = 0;
int i;
for (i = net.n - 1; i >= 0; --i) {
if (net.layers[i].train_only_bn) train_only_bn = net.layers[i].train_only_bn; // set l.train_only_bn for all previous layers
if (train_only_bn) {
net.layers[i].train_only_bn = train_only_bn;
if (net.layers[i].type == CONV_LSTM) {
net.layers[i].wf->train_only_bn = train_only_bn;
net.layers[i].wi->train_only_bn = train_only_bn;
net.layers[i].wg->train_only_bn = train_only_bn;
net.layers[i].wo->train_only_bn = train_only_bn;
net.layers[i].uf->train_only_bn = train_only_bn;
net.layers[i].ui->train_only_bn = train_only_bn;
net.layers[i].ug->train_only_bn = train_only_bn;
net.layers[i].uo->train_only_bn = train_only_bn;
if (net.layers[i].peephole) {
net.layers[i].vf->train_only_bn = train_only_bn;
net.layers[i].vi->train_only_bn = train_only_bn;
net.layers[i].vo->train_only_bn = train_only_bn;
}
}
else if (net.layers[i].type == CRNN) {
net.layers[i].input_layer->train_only_bn = train_only_bn;
net.layers[i].self_layer->train_only_bn = train_only_bn;
net.layers[i].output_layer->train_only_bn = train_only_bn;
}
}
}
}
network parse_network_cfg(char *filename)
{
return parse_network_cfg_custom(filename, 0, 0);
@ -1450,6 +1483,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps)
l.dont_update = option_find_int_quiet(options, "dont_update", 0);
l.burnin_update = option_find_int_quiet(options, "burnin_update", 0);
l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
l.train_only_bn = option_find_int_quiet(options, "train_only_bn", 0);
l.dontload = option_find_int_quiet(options, "dontload", 0);
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
@ -1522,6 +1556,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps)
}
#endif
set_train_only_bn(net); // set l.train_only_bn for all required layers
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
avg_outputs = avg_outputs / avg_counter;

Write Preview
Loading…
Cancel
Save