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Hey, @ardeal I want to produce similar profiling as you could see below that tells that how much time is taken by each layer in the yolov5. The ultralytics/yolo5 repo has a prebuilt profiling tool inside yolo.py itself which on running creates this type of profiling but I couldn't find any similar thing in yolo_nano. Can you please tell me how would that be possible in this case?
The thing to produce for yolo_nano?
/content/yolov5
YOLOv5 :rocket: v5.0-44-g5afe783 torch 1.8.1+cu101 CPU
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 156928 models.common.C3 [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1182720 models.common.C3 [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 229245 Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS
time (ms) GFLOPS params module
11.90 0.18 3520 models.common.Focus
8.65 0.24 18560 models.common.Conv
15.68 0.24 18816 models.common.C3
6.20 0.24 73984 models.common.Conv
20.03 0.50 156928 models.common.C3
5.97 0.24 295424 models.common.Conv
17.69 0.50 625152 models.common.C3
8.36 0.24 1180672 models.common.Conv
15.80 0.13 656896 models.common.SPP
10.73 0.24 1182720 models.common.C3
1.35 0.03 131584 models.common.Conv
0.70 0.00 0 torch.nn.modules.upsampling.Upsample
0.16 0.00 0 models.common.Concat
11.79 0.29 361984 models.common.C3
1.22 0.03 33024 models.common.Conv
1.33 0.00 0 torch.nn.modules.upsampling.Upsample
0.29 0.00 0 models.common.Concat
11.75 0.29 90880 models.common.C3
3.47 0.12 147712 models.common.Conv
0.10 0.00 0 models.common.Concat
8.68 0.24 296448 models.common.C3
3.87 0.12 590336 models.common.Conv
0.04 0.00 0 models.common.Concat
10.87 0.24 1182720 models.common.C3
5.37 0.18 229245 Detect
182.0ms total
So @ardeal can you please help me know that how it can be possible in this case? Looking forward to hearing from you.
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