-
Notifications
You must be signed in to change notification settings - Fork 0
/
nohup.out
110 lines (102 loc) · 11.2 KB
/
nohup.out
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 1639680 ultralytics.nn.modules.GAM_Attention.GAM_Attention[256, 256]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 156416 ultralytics.nn.modules.block.C2f [448, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [15, 18, 21] 1 3527104 ultralytics.nn.modules.head.Detect [80, [192, 448, 384]]
YOLOv8n-addatt summary: 236 layers, 7459088 parameters, 7459072 gradients
New https://pypi.org/project/ultralytics/8.0.117 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.0.110 🚀 Python-3.8.16 torch-1.12.1+cu102 CUDA:0 (Tesla T4, 15110MiB)
WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.
[34m[1myolo/engine/trainer: [0mtask=detect, mode=train, model=yolov8n-addatt.yaml, data=/home/whut4/zwj/0426underwater/dataset2_copy_2/data.yaml, epochs=20, patience=50, batch=64, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=cfg, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/cfg5
Overriding model.yaml nc=80 with nc=31
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 1639680 ultralytics.nn.modules.GAM_Attention.GAM_Attention[256, 256]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 156416 ultralytics.nn.modules.block.C2f [448, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [15, 18, 21] 1 3498733 ultralytics.nn.modules.head.Detect [31, [192, 448, 384]]
YOLOv8n-addatt summary: 236 layers, 7430717 parameters, 7430701 gradients
[34m[1mAMP: [0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...
[34m[1mAMP: [0mchecks passed ✅
[34m[1moptimizer:[0m SGD(lr=0.01) with parameter groups 59 weight(decay=0.0), 68 weight(decay=0.0005), 69 bias
[34m[1mtrain: [0mScanning /home/whut4/zwj/0426underwater/dataset2_copy_2/train/labels.cache... 4846 images, 24 backgrounds, 0 corrupt: 100%|██████████| 4846/4846 [00:00<?, ?it/s][34m[1mtrain: [0mScanning /home/whut4/zwj/0426underwater/dataset2_copy_2/train/labels.cache... 4846 images, 24 backgrounds, 0 corrupt: 100%|██████████| 4846/4846 [00:00<?, ?it/s]
[34m[1mval: [0mScanning /home/whut4/zwj/0426underwater/dataset2_copy_2/valid/labels.cache... 605 images, 5 backgrounds, 0 corrupt: 100%|██████████| 605/605 [00:00<?, ?it/s][34m[1mval: [0mScanning /home/whut4/zwj/0426underwater/dataset2_copy_2/valid/labels.cache... 605 images, 5 backgrounds, 0 corrupt: 100%|██████████| 605/605 [00:00<?, ?it/s]
Plotting labels to runs/detect/cfg5/labels.jpg...
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to [1mruns/detect/cfg5[0m
Starting training for 20 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/76 [00:00<?, ?it/s] 0%| | 0/76 [00:00<?, ?it/s]
Traceback (most recent call last):
File "mytrain.py", line 7, in <module>
results = model.train(data="/home/whut4/zwj/0426underwater/dataset2_copy_2/data.yaml", epochs=20, batch=64, workers=8, close_mosaic=0, name='cfg') # 训练模型
File "/home/whut4/zwj/ultralytics/ultralytics/yolo/engine/model.py", line 371, in train
self.trainer.train()
File "/home/whut4/zwj/ultralytics/ultralytics/yolo/engine/trainer.py", line 192, in train
self._do_train(world_size)
File "/home/whut4/zwj/ultralytics/ultralytics/yolo/engine/trainer.py", line 328, in _do_train
preds = self.model(batch['img'])
File "/home/whut4/anaconda3/envs/yolov8add_att/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/whut4/zwj/ultralytics/ultralytics/nn/tasks.py", line 215, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "/home/whut4/zwj/ultralytics/ultralytics/nn/tasks.py", line 64, in _forward_once
x = m(x) # run
File "/home/whut4/anaconda3/envs/yolov8add_att/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/whut4/zwj/ultralytics/ultralytics/nn/modules/block.py", line 181, in forward
y = list(self.cv1(x).chunk(2, 1))
File "/home/whut4/anaconda3/envs/yolov8add_att/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/whut4/zwj/ultralytics/ultralytics/nn/modules/conv.py", line 39, in forward
return self.act(self.bn(self.conv(x)))
File "/home/whut4/anaconda3/envs/yolov8add_att/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/whut4/anaconda3/envs/yolov8add_att/lib/python3.8/site-packages/torch/nn/modules/activation.py", line 391, in forward
return F.silu(input, inplace=self.inplace)
File "/home/whut4/anaconda3/envs/yolov8add_att/lib/python3.8/site-packages/torch/nn/functional.py", line 2047, in silu
return torch._C._nn.silu_(input)
RuntimeError: CUDA out of memory. Tried to allocate 50.00 MiB (GPU 0; 14.76 GiB total capacity; 2.59 GiB already allocated; 12.44 MiB free; 2.65 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF