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V0.2.2 crash to desktop during tracking. #5

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Seda145 opened this issue May 4, 2023 · 4 comments
Closed

V0.2.2 crash to desktop during tracking. #5

Seda145 opened this issue May 4, 2023 · 4 comments
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@FabianPlum
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help wanted Extra attention is needed

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@Seda145
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Seda145 commented May 4, 2023 •
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Describe the bug
V0.2.2 crash to desktop during tracking.

To Reproduce
Steps to reproduce the behavior:

  1. Install
  2. open up the single ant 1080p video.
    1080p DSLR recording (single-animal pose-estimation)
  3. configure tracking for GPU.
  4. load yolo network with absolute paths YOLOv4-COCO-20230501T235200Z-001
    80 Class model trained on COCO
  5. create simple tracking mask with 4 points around ant.
  6. hit track button.
  7. bbrrrrr crash.

Expected behavior
Just work :)

Screenshots
X

Desktop (please complete the following information):

  • OS: Win 10
  • Version 0.2.2
  • CUDA version 11.2
  • CUDnn version 8.1
  • Hardware GTX 1070, I7

Additional context
Blender refuses to log crashlogs. Open blender through command prompt to keep a console open.
Console notes a hardcoded path to a yolo file which does not exist, user "Plumstation" ?

LOG:

`INFO: successfully loaded OmniTrax
Found computational devices:
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Read blend: K:\Blender3D\OmniTrax\Saved\OmniTrax1.blend
2023-05-05 00:48:18.303125: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-05 00:48:18.775515: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6440 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
Running inference on: [LogicalDevice(name='/device:CPU:0', device_type='CPU'), LogicalDevice(name='/device:GPU:0', device_type='GPU')]

INFO: Initialising darkent network...

<bpy_struct, MaskSpline at 0x000001B09A987308>
0.46663743257522583 0.849280059337616
0.6812906265258789 0.8212818503379822
0.7046225070953369 0.5366330146789551
0.4573046863079071 0.5646312832832336

[[[0.46663743257522583, 0.849280059337616], [0.6812906265258789, 0.8212818503379822], [0.7046225070953369, 0.5366330146789551], [0.4573046863079071, 0.5646312832832336]]]
[[503 162]
[735 193]
[760 500]
[493 470]]
Beginning counting from ID 0
INITIALISED TRACKER!
The imported clip: K:\Blender3D\OmniTrax\Saved..\SourceContent\Recordings\Insect_Ant\single_ant_1080p.mp4 has a total of 2000 frames.

Try to load cfg: K:\Blender3D\OmniTrax\Networks\YOLOv4-COCO-20230501T235200Z-001\yolov4.cfg, weights: K:\Blender3D\OmniTrax\Networks\YOLOv4-COCO-20230501T235200Z-001\yolov4.weights, clear = 0
0 : compute_capability = 610, cudnn_half = 0, GPU: NVIDIA GeForce GTX 1070 Ti
net.optimized_memory = 0
mini_batch = 1, batch = 8, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 0
conv 32 3 x 3/ 1 512 x 512 x 3 -> 512 x 512 x 32 0.453 BF
1 conv 64 3 x 3/ 2 512 x 512 x 32 -> 256 x 256 x 64 2.416 BF
2 conv 64 1 x 1/ 1 256 x 256 x 64 -> 256 x 256 x 64 0.537 BF
3 route 1 -> 256 x 256 x 64
4 conv 64 1 x 1/ 1 256 x 256 x 64 -> 256 x 256 x 64 0.537 BF
5 conv 32 1 x 1/ 1 256 x 256 x 64 -> 256 x 256 x 32 0.268 BF
6 conv 64 3 x 3/ 1 256 x 256 x 32 -> 256 x 256 x 64 2.416 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 256 x 256 x 64 0.004 BF
8 conv 64 1 x 1/ 1 256 x 256 x 64 -> 256 x 256 x 64 0.537 BF
9 route 8 2 -> 256 x 256 x 128
10 conv 64 1 x 1/ 1 256 x 256 x 128 -> 256 x 256 x 64 1.074 BF
11 conv 128 3 x 3/ 2 256 x 256 x 64 -> 128 x 128 x 128 2.416 BF
12 conv 64 1 x 1/ 1 128 x 128 x 128 -> 128 x 128 x 64 0.268 BF
13 route 11 -> 128 x 128 x 128
14 conv 64 1 x 1/ 1 128 x 128 x 128 -> 128 x 128 x 64 0.268 BF
15 conv 64 1 x 1/ 1 128 x 128 x 64 -> 128 x 128 x 64 0.134 BF
16 conv 64 3 x 3/ 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 128 x 128 x 64 0.001 BF
18 conv 64 1 x 1/ 1 128 x 128 x 64 -> 128 x 128 x 64 0.134 BF
19 conv 64 3 x 3/ 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 128 x 128 x 64 0.001 BF
21 conv 64 1 x 1/ 1 128 x 128 x 64 -> 128 x 128 x 64 0.134 BF
22 route 21 12 -> 128 x 128 x 128
23 conv 128 1 x 1/ 1 128 x 128 x 128 -> 128 x 128 x 128 0.537 BF
24 conv 256 3 x 3/ 2 128 x 128 x 128 -> 64 x 64 x 256 2.416 BF
25 conv 128 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
26 route 24 -> 64 x 64 x 256
27 conv 128 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
28 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
29 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
31 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
32 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
34 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
35 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
37 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
38 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
40 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
41 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
43 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
44 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
46 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
47 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
49 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
50 conv 128 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 64 x 64 x 128 0.001 BF
52 conv 128 1 x 1/ 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BF
53 route 52 25 -> 64 x 64 x 256
54 conv 256 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 256 0.537 BF
55 conv 512 3 x 3/ 2 64 x 64 x 256 -> 32 x 32 x 512 2.416 BF
56 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
57 route 55 -> 32 x 32 x 512
58 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
59 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
60 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
62 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
63 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
65 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
66 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
68 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
69 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
71 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
72 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
74 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
75 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
77 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
78 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
80 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
81 conv 256 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 32 x 32 x 256 0.000 BF
83 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF
84 route 83 56 -> 32 x 32 x 512
85 conv 512 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 512 0.537 BF
86 conv 1024 3 x 3/ 2 32 x 32 x 512 -> 16 x 16 x1024 2.416 BF
87 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
88 route 86 -> 16 x 16 x1024
89 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
90 conv 512 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BF
91 conv 512 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 16 x 16 x 512 0.000 BF
93 conv 512 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BF
94 conv 512 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 16 x 16 x 512 0.000 BF
96 conv 512 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BF
97 conv 512 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 16 x 16 x 512 0.000 BF
99 conv 512 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BF
100 conv 512 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 16 x 16 x 512 0.000 BF
102 conv 512 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BF
103 route 102 87 -> 16 x 16 x1024
104 conv 1024 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x1024 0.537 BF
105 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
106 conv 1024 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x1024 2.416 BF
107 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
108 max 5x 5/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.003 BF
109 route 107 -> 16 x 16 x 512
110 max 9x 9/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.011 BF
111 route 107 -> 16 x 16 x 512
112 max 13x13/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.022 BF
113 route 112 110 108 107 -> 16 x 16 x2048
114 conv 512 1 x 1/ 1 16 x 16 x2048 -> 16 x 16 x 512 0.537 BF
115 conv 1024 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x1024 2.416 BF
116 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
117 conv 256 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BF
118 upsample 2x 16 x 16 x 256 -> 32 x 32 x 256
119 route 85 -> 32 x 32 x 512
120 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
121 route 120 118 -> 32 x 32 x 512
122 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
123 conv 512 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
124 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
125 conv 512 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
126 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
127 conv 128 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BF
128 upsample 2x 32 x 32 x 128 -> 64 x 64 x 128
129 route 54 -> 64 x 64 x 256
130 conv 128 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
131 route 130 128 -> 64 x 64 x 256
132 conv 128 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
133 conv 256 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 256 2.416 BF
134 conv 128 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
135 conv 256 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 256 2.416 BF
136 conv 128 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
137 conv 256 3 x 3/ 1 64 x 64 x 128 -> 64 x 64 x 256 2.416 BF
138 conv 255 1 x 1/ 1 64 x 64 x 256 -> 64 x 64 x 255 0.535 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000
140 route 136 -> 64 x 64 x 128
141 conv 256 3 x 3/ 2 64 x 64 x 128 -> 32 x 32 x 256 0.604 BF
142 route 141 126 -> 32 x 32 x 512
143 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
144 conv 512 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
145 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
146 conv 512 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
147 conv 256 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BF
148 conv 512 3 x 3/ 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
149 conv 255 1 x 1/ 1 32 x 32 x 512 -> 32 x 32 x 255 0.267 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000
151 route 147 -> 32 x 32 x 256
152 conv 512 3 x 3/ 2 32 x 32 x 256 -> 16 x 16 x 512 0.604 BF
153 route 152 116 -> 16 x 16 x1024
154 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
155 conv 1024 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x1024 2.416 BF
156 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
157 conv 1024 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x1024 2.416 BF
158 conv 512 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BF
159 conv 1024 3 x 3/ 1 16 x 16 x 512 -> 16 x 16 x1024 2.416 BF
160 conv 255 1 x 1/ 1 16 x 16 x1024 -> 16 x 16 x 255 0.134 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 91.095
avg_outputs = 757643
Allocate additional workspace_size = 9.44 MB
Try to load weights: K:\Blender3D\OmniTrax\Networks\YOLOv4-COCO-20230501T235200Z-001\yolov4.weights
Loading weights from K:\Blender3D\OmniTrax\Networks\YOLOv4-COCO-20230501T235200Z-001\yolov4.weights...
seen 64, trained: 0 K-images (0 Kilo-batches_64)
Done! Loaded 162 layers from weights-file
Couldn't open file: C:/Users/PlumStation/Desktop/OmniTrax_Testing/YOLOv4-COCO/coco.names
Error: Not freed memory blocks: 56843, total unfreed memory 21.423847 MB
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.
Freeing memory after the leak detector has run. This can happen when using static variables in C++ that are defined outside of functions. To fix this error, use the 'construct on first use' idiom.`
@Seda145 Seda145 changed the title V2.2 crash to desktop during tracking. V0.2.2 crash to desktop during tracking. May 4, 2023
@FabianPlum
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Could you check whether your sytem might be running out of VRAM?
Please upload your config file here, perhaps lowering the input size would suffice.

All the best
Fabi

@FabianPlum FabianPlum self-assigned this May 5, 2023
@FabianPlum FabianPlum added the help wanted Extra attention is needed label May 5, 2023
@Seda145
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Seda145 commented May 5, 2023

Monitored the VRam while tracking, it never reached more than 2GB total for the entire system. Can't be running out of VRam. Which parameter is the input size? the network size is 512 to match the network configuration of 512.

@Seda145
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Seda145 commented May 5, 2023

Here's the blend file if it helps. The environment is 100% clean just blender + omnitrax.

OmniTrax1.zip

@Seda145
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Seda145 commented May 5, 2023

Just to test I tried with and without a DLC path, nothing changes.

Another note about the log, the logged path is wrong:
"The imported clip: K:\Blender3D\OmniTrax\Saved..\SourceContent\Recordings\Insect_Ant\single_ant_1080p.mp4"
Probably a mistake when appending the path string to something else in the logger. It should have said
"K:\Blender3D\OmniTrax\SourceContent\Recordings\Insect_Ant\single_ant_1080p.mp4"

This path doesn't exist at all:
C:/Users/PlumStation/Desktop/OmniTrax_Testing/YOLOv4-COCO/coco.names

I'll keep it on the PC for a while so I can assist in finding a crash fix, but I'm actually looking for a production ready solution that animates my skeleton rig from video data XD

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