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Fix training approach (convolutional layer)

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This commit is contained in:
AlexeyAB
2018-12-27 00:31:28 +03:00
parent dc827f4c1c
commit 64e478db07
4 changed files with 68 additions and 7 deletions
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44
src/convolutional_kernels.cu
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@ -457,7 +457,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
{
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
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_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h);
//#ifndef CUDNN_HALF
//if(l.batch_normalize){
@ -703,6 +704,45 @@ void push_convolutional_layer(convolutional_layer layer)
}
}
void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay)
{
float learning_rate = learning_rate_init*l.learning_rate_scale;
//float momentum = a.momentum;
//float decay = a.decay;
//int batch = a.batch;
int size = l.size*l.size*l.c*l.n; // old
if (l.adam) {
//adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t);
adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch, l.t);
adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
if (l.scales_gpu) {
adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
}
}
else {
//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(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
axpy_ongpu(size, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
scal_ongpu(size, momentum, l.weight_updates_gpu, 1);
axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
scal_ongpu(l.n, momentum, l.bias_updates_gpu, 1);
if (l.scales_gpu) {
axpy_ongpu(l.n, learning_rate / batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
scal_ongpu(l.n, momentum, l.scale_updates_gpu, 1);
}
}
//if (l.clip) {
// constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1);
//}
}
/*
void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
@ -753,5 +793,5 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
//-----------------------------------
}
}
*/

8
src/convolutional_layer.c
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@ -390,6 +390,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.adam = 1;
l.m = calloc(c*n*size*size, sizeof(float));
l.v = calloc(c*n*size*size, sizeof(float));
l.bias_m = calloc(n, sizeof(float));
l.scale_m = calloc(n, sizeof(float));
l.bias_v = calloc(n, sizeof(float));
l.scale_v = calloc(n, sizeof(float));
}
#ifdef GPU
@ -401,6 +405,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
if (adam) {
l.m_gpu = cuda_make_array(l.m, c*n*size*size);
l.v_gpu = cuda_make_array(l.v, c*n*size*size);
l.bias_m_gpu = cuda_make_array(l.bias_m, n);
l.bias_v_gpu = cuda_make_array(l.bias_v, n);
l.scale_m_gpu = cuda_make_array(l.scale_m, n);
l.scale_v_gpu = cuda_make_array(l.scale_v, n);
}
l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);

22
src/layer.h
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@ -100,6 +100,7 @@ struct layer{
float exposure;
float shift;
float ratio;
float learning_rate_scale;
int focal_loss;
int noloss;
int softmax;
@ -122,11 +123,14 @@ struct layer{
float B1;
float B2;
float eps;
float *m_gpu;
float *v_gpu;
int t;
float *m;
float *v;
float * bias_m;
float * bias_v;
float * scale_m;
float * scale_v;
tree *softmax_tree;
int *map;
@ -245,7 +249,7 @@ struct layer{
size_t workspace_size;
#ifdef GPU
#ifdef GPU
float *z_gpu;
float *r_gpu;
float *h_gpu;
@ -263,6 +267,14 @@ struct layer{
float * concat_gpu;
float * concat_delta_gpu;
// adam
float *m_gpu;
float *v_gpu;
float *bias_m_gpu;
float *scale_m_gpu;
float *bias_v_gpu;
float *scale_v_gpu;
float *binary_input_gpu;
float *binary_weights_gpu;
@ -310,8 +322,8 @@ struct layer{
cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16;
cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16;
cudnnPoolingDescriptor_t poolingDesc;
#endif
#endif
#endif // CUDNN
#endif // GPU
};
void free_layer(layer);

1
src/parser.c
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@ -805,6 +805,7 @@ network parse_network_cfg_custom(char *filename, int batch)
l.stopbackward = option_find_int_quiet(options, "stopbackward", 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);
option_unused(options);
net.layers[count] = l;
if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;