| | |
| | | net->layers[i].batch = b; |
| | | #ifdef CUDNN |
| | | if(net->layers[i].type == CONVOLUTIONAL){ |
| | | cudnn_convolutional_setup(net->layers + i, cudnn_fastest); |
| | | /* |
| | | layer *l = net->layers + i; |
| | | cudnn_convolutional_setup(l, cudnn_fastest); |
| | | // check for excessive memory consumption |
| | |
| | | cudnn_convolutional_setup(l, cudnn_smallest); |
| | | l->workspace_size = get_workspace_size(*l); |
| | | } |
| | | */ |
| | | } |
| | | #endif |
| | | } |
| | |
| | | //fflush(stderr); |
| | | for (i = 0; i < net->n; ++i){ |
| | | layer l = net->layers[i]; |
| | | printf(" %d: layer = %d,", i, l.type); |
| | | //printf(" %d: layer = %d,", i, l.type); |
| | | if(l.type == CONVOLUTIONAL){ |
| | | resize_convolutional_layer(&l, w, h); |
| | | }else if(l.type == CROP){ |