mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Added grouped convolutional (depth-wise convolutional)

Browse Source
This commit is contained in:
AlexeyAB
2019-05-10 16:46:48 +03:00
parent a7e5976c1b
commit 4f72fcc015
12 changed files with 349 additions and 306 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
src/crnn_layer.c
View File

@ -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, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor)
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor)
{
fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
batch = batch / steps;
@ -40,6 +40,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou
l.h = h;
l.w = w;
l.c = c;
l.groups = groups;
l.out_c = output_filters;
l.inputs = h * w * c;
l.hidden = h * w * hidden_filters;
@ -48,17 +49,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou
l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float));
l.input_layer = (layer*)malloc(sizeof(layer));
*(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0);
*(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 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 = (layer*)malloc(sizeof(layer));
*(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0);
*(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 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 = (layer*)malloc(sizeof(layer));
*(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0);
*(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 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;