0 conv 32 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF
输出位置:
darknet-master\src\convolutional_layer.c(564): fprintf(stderr, "%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
2.Crop Layer的输出位置
C:\darknet-master\src\crop_layer.c(18): fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c);
3.反卷积层的输出
C:\darknet-master\src\deconvolutional_layer.c(101): fprintf(stderr, "Deconvolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
4.Local Layer
C:\darknet-master\src\local_layer.c(86): fprintf(stderr, "Local Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
5.LSTM Layer
C:\darknet-master\src\lstm_layer.c(31): fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
C:\darknet-master\src\maxpool_layer.c(90): fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
7.reorg
C:\darknet-master\src\reorg_layer.c(26): fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
8.downsample 与upsample
C:\darknet-master\src\upsample_layer.c(39): if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
C:\darknet-master\src\upsample_layer.c(40): else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
82 yolo
C:\darknet-master\src\yolo_layer.c(72): fprintf(stderr, "yolo\n");
4 Shortcut Layer: 1
C:\darknet-master\src\shortcut_layer.c(9): fprintf(stderr,"Shortcut Layer: %d\n", index);
83 route 79
C:\darknet-master\src\route_layer.c(8): fprintf(stderr,"route ");
C:\darknet-master\src\route_layer.c(18): fprintf(stderr," %d", input_layers[i]);
C:\darknet-master\src\route_layer.c(21): fprintf(stderr, "\n");
C:\darknet-master\src\parser.c(881): fprintf(stderr, " layer filters size/strd(dil) input output\n");
C:\darknet-master\src\parser.c(892): fprintf(stderr, "%4d ", count);
13.分配内存
C:\darknet-master\src\parser.c(1021): fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000);
C:\darknet-master\src\parser.c(358): fprintf(stderr, "[yolo] params: iou loss: %s, iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, l.scale_x_y);
C:\darknet-master\src\network.c(446): printf("\n try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
C:\darknet-master\src\network.c(448): printf(" CUDA allocate done! \n");
C:\darknet-master\src\network.c(547): fprintf(stderr, "Resizing type %d \n", (int)l.type);
C:\darknet-master\src\network.c(560): printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
C:\darknet-master\src\network.c(570): printf(" CUDA allocate done! \n");
C:\darknet-master\src\parser.c(999): fprintf(stderr, "Total BFLOPS %5.3f \n", bflops);
C:\darknet-master\src\parser.c(1394): printf("\n seen 64 \n");
C:\darknet-master\src\parser.c(1475): fprintf(stderr, "Done!\n");
C:\darknet-master\src\parser.c(782): fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half);