Closed tuteming closed 2 years ago
maybe need to check the anchors.
我仔細檢查整個過程:(該改的我之前都改了)image size=800x800, net size=800x800, classess=6. yolov4.cfg 和我的一樣,除了net size=608x608改成800x800 主要差異在於你的yolov4.cpp define each layer. Line244-471 中很多層的padding=0, 在yolov4.cfg 他們的padding=1, 我修正yolov4.cpp後, run yolov4.exe -s 出現 Loading weights: ../yolov4.wts [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). Building tensorrt engine, please wait for a while... [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]). [03/02/2022-14:40:10] [E] [TRT] Could not compute dimensions for (Unnamed Layer 18) [ElementWise]_output, because the network is not valid. [03/02/2022-14:40:10] [E] [TRT] Network validation failed. Build engine successfully!
run yolov4.exe -d samples [03/02/2022-15:11:16] [E] [TRT] Parameter check failed at: runtime.cpp::nvinfer1::Runtime::deserializeCudaEngine::30, condition: (blob) != nullptr
is 400x400, neither 404x404 nor 402x402 請教一下,謝謝
yolov4.cpp padding=0, 與 yolov4.cfg padding=1,的定義已經不一樣 為何產生的yolov4.engine可以跑? 只是正確率很差. 改過的yolov4.cpp padding=1, 與 yolov4.cfg padding=1,的定義一樣 產生的yolov4.engine反而出現上面的錯誤.
請賜教 再次感謝
which yolov4.cfg are you checking? can you share the link?
a -version : https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/cfg/yolov4.cfg u -version : https://github.com/ultralytics/yolov3/blob/archive/cfg/yolov4.cfg 2個版本網路結構都一樣差在a -version的3個[yolo]的最後多個max_delta=5,這只跟訓練有關,無關測試 python gen_wts.py yolov4.weights時,須將max_delta=5 #掉,因為不支援. 無論任一版本都是padding=1(重頭到尾), 而yolov4.cpp 部分 padding=0 , 這是我奇怪的地方. 同時, tensorrtx/scaled-yolov4/ yolov4_csp.cpp 與https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp 也是一樣padding=0的情形. yolo結構我研究了3年,padding size 不一樣就大有問題
由於tensort我初接觸, 請賜教 再次感謝
makaveli10 gitub 我已檢查過,跟你一樣,也是padding=0
padding=0 in .cfg doesn't mean that the conv layer has padding=0,
can you check the conv layer
"padding=0 in .cfg doesn't mean that the conv layer has padding=0," =>yolov4.cpp 的line 242 // define each layer. 不是要根據yolov4.cfg 每一層來定義嗎? "can you check the conv layer" 請問the conv layer指的是yolov4.cpp? 我上述的錯誤中 Loading weights: ../yolov4.wts [03/02/2022-14:40:10] [E] [TRT] (Unnamed Layer* 18) [ElementWise]: elementwise inputs must have same dimensions or follow broadcast rules (input dimensions were [64,404,404] and [64,402,402]).
(Unnamed Layer* 18指的是哪一層(line num )? 64,404,404] and [64,402,402]).顯然就是route那一層發現維度不等
再次打擾你,真是抱歉.請再次賜教 感謝
You can use this repo https://github.com/ultralytics/yolov3/blob/archive/cfg/yolov4.cfg to load pytorch model and print models, then check each conv layer
這是結果,除了size=608外,其他都一樣
無論任一版本都是padding=1(重頭到尾), 而yolov4.cpp 部分 padding=0 , 這是我奇怪的地方.
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 255 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BF
139 yolo
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 255 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BF
150 yolo
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 255 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BF
161 yolo
我又做了個實驗
我將yolov4.cpp的 padding=0 不變,把// define each layer. 放進原來的yolov4.cfg(padding=1 )
去跑darknet.exe(a 版) 發現結構(padding=0與padding=1)都一樣,再去跑我的程式正確率都一樣.
顯然padding沒有作用, 難道是USE_FP16造成的錯誤嗎?(應該沒那麼離譜)
所以 //#define USE_FP16 // comment out this if want to use FP32
FP32時間多了一倍 but 錯誤一樣
yolov5.cpp我測試得很好. 但它無須到u-版去轉換darknet的config 及weight 因為gen_wts.py 轉換出現警告訊息 smart bias initialization failure. 你碰過嗎?
For this [convolution] https://github.com/ultralytics/yolov3/blob/98068efebc699e7a652fb495f3e7a23bf296affd/cfg/yolov4.cfg#L48
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish
The pad=1, but the conv layer will have padding=0.
Because https://github.com/ultralytics/yolov3/blob/98068efebc699e7a652fb495f3e7a23bf296affd/models.py#L31
padding=k // 2 if mdef['pad'] else 0,
你的意思是將pad=1->pad=0 就不會出現smart bias initialization failure. 轉出的yolov4.wts再用yolov4.exe->yolov4.engine就ok嗎? 謝謝 我試試
同樣出現3次 WARNING: smart bias initialization failure. WARNING: smart bias initialization failure. WARNING: smart bias initialization failure. 結果一樣 總之謝謝你
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/////////////////////////////// yolov4 result is very poor when compare the original yolov4 [AlexeyAB], the yolov4 results is very poor.(by custom data). when the trained weight can obtain 99.96% in original yolov4. in yolov4 results only about 70%. //////////////////////////////////////
sudo ./yolov5 -s