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darknet
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Fixed Loss scale

This commit is contained in:
AlexeyAB 2020-06-22 14:50:51 +03:00
parent 8f900493c6
commit 17b806f0bd
2 changed files with 4 additions and 15 deletions
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12
src/convolutional_kernels.cu
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@ -1264,18 +1264,12 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init
}
float learning_rate = learning_rate_init*l.learning_rate_scale;
// Loss scale for Mixed-Precision on Tensor-Cores
float learning_rate = learning_rate_init*l.learning_rate_scale / loss_scale;
//float momentum = a.momentum;
//float decay = a.decay;
//int batch = a.batch;
// Loss scale for Mixed-Precision on Tensor-Cores
if (loss_scale != 1.0) {
if (l.weight_updates_gpu && l.nweights > 0) scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1);
if (l.bias_updates_gpu && l.n > 0) scal_ongpu(l.n, 1.0 / loss_scale, l.bias_updates_gpu, 1);
if (l.scale_updates_gpu && l.n > 0) scal_ongpu(l.n, 1.0 / loss_scale, l.scale_updates_gpu, 1);
}
reset_nan_and_inf(l.weight_updates_gpu, l.nweights);
fix_nan_and_inf(l.weights_gpu, l.nweights);
@ -1301,7 +1295,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init
//axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
//axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
//scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1);
axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
axpy_ongpu(l.nweights, -decay*batch*loss_scale, l.weights_gpu, 1, l.weight_updates_gpu, 1);
axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1);

7
src/shortcut_layer.c
View File

@ -243,16 +243,11 @@ void backward_shortcut_layer_gpu(const layer l, network_state state)
void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale)
{
if (l.nweights > 0) {
float learning_rate = learning_rate_init*l.learning_rate_scale;
float learning_rate = learning_rate_init*l.learning_rate_scale / loss_scale;
//float momentum = a.momentum;
//float decay = a.decay;
//int batch = a.batch;
// Loss scale for Mixed-Precision on Tensor-Cores
if (loss_scale != 1.0) {
if(l.weight_updates_gpu && l.nweights > 0) scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1);
}
reset_nan_and_inf(l.weight_updates_gpu, l.nweights);
fix_nan_and_inf(l.weights_gpu, l.nweights);