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Fixed CRNN (RNN based on Convolution) layer

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AlexeyAB
2019-02-01 01:30:02 +03:00
parent bd91d0a908
commit c7309c1fdb
7 changed files with 38 additions and 25 deletions
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24
src/crnn_layer.c
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@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps)
#endif
}
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize)
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize)
{
fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
batch = batch / steps;
@ -47,20 +47,20 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou
l.state = calloc(l.hidden*batch*(steps+1), sizeof(float));
l.input_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0);
fprintf(stderr, "");
*(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0);
l.input_layer->batch = batch;
if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size;
l.self_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0);
fprintf(stderr, "");
*(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0);
l.self_layer->batch = batch;
if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size;
l.output_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0);
fprintf(stderr, "");
*(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0);
l.output_layer->batch = batch;
if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size;
@ -75,8 +75,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou
l.forward_gpu = forward_crnn_layer_gpu;
l.backward_gpu = backward_crnn_layer_gpu;
l.update_gpu = update_crnn_layer_gpu;
l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1));
l.state_gpu = cuda_make_array(l.state, batch*l.hidden*(steps + 1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
#endif
@ -263,8 +262,8 @@ void backward_crnn_layer_gpu(layer l, network_state state)
increment_layer(&output_layer, l.steps - 1);
l.state_gpu += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
//copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN
//axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN
s.input = l.state_gpu;
s.delta = self_layer.delta_gpu;
@ -272,12 +271,13 @@ void backward_crnn_layer_gpu(layer l, network_state state)
l.state_gpu -= l.hidden*l.batch;
copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
s.input = l.state_gpu;
s.delta = self_layer.delta_gpu - l.hidden*l.batch;
if (i == 0) s.delta = 0;
backward_convolutional_layer_gpu(self_layer, s);
copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
s.input = state.input + i*l.inputs*l.batch;
if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;