YOLOv5-Lite
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Published
20 hours ago •
ppogg
ppogg
batch-size 设置64 报GPU内存不足，调小就报下面的类型错误
GPU是 nvidia 1080 显存 8G
batch-size 设置64 报GPU内存不足，调小就报下面的类型错误
File "C:\ProgramData\Anaconda3\envs\yolov5lite\lib\site-packages\torch\nn\functional.py", line 2438, in batch_norm
return torch.batch_norm(
RuntimeError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 8.00 GiB total capacity; 6.65 GiB already allocated; 0 bytes free; 6.69 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
(yolov5lite) PS C:\Users\Administrator\pyhome\YOLOv5-Lite> python train.py --data data/coco128.yaml --cfg models/v5Lite-g.yaml --weights weights/v5lite-g.pt --batch-size 2
github: skipping check (offline)
YOLOv5 v1.4-35-g5b94a9e torch 1.12.0 CUDA:0 (NVIDIA GeForce GTX 1080, 8191.6875MB)
Namespace(adam=False, artifact_alias='latest', batch_size=2, bbox_interval=-1, bucket='', cache_images=False, cfg='models/v5Lite-g.yaml', data='data/coco128.yaml', device='0', entity=None, epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs\\train\\exp10', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=2, upload_dataset=False, weights='weights/v5lite-g.pt', workers=8, world_size=1)
tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
hyperparameters: lr0=0.001, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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=0.2, mixup=0.0
wandb: Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 20736 models.common.RepVGGBlock [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 82432 models.common.RepVGGBlock [64, 128, 3, 2]
4 -1 1 156928 models.common.C3 [128, 128, 3]
5 -1 1 328704 models.common.RepVGGBlock [128, 256, 3, 2]
6 -1 1 625152 models.common.C3 [256, 256, 3]
7 -1 1 1312768 models.common.RepVGGBlock [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 65792 models.common.Conv [512, 128, 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 189696 models.common.C3 [384, 128, 3, False]
14 -1 1 16640 models.common.Conv [128, 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 173312 models.common.C3 [256, 128, 3, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 173312 models.common.C3 [256, 128, 3, False]
21 -1 1 147712 models.common.Conv [128, 128, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 173312 models.common.C3 [256, 128, 3, False]
24 [17, 20, 23] 1 98685 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 128, 128]]
C:\ProgramData\Anaconda3\envs\yolov5lite\lib\site-packages\torch\functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\TensorShape.cpp:2895.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Model Summary: 375 layers, 5574845 parameters, 5574845 gradients, 16.3 GFLOPS
Transferred 480/482 items from weights/v5lite-g.pt
Scaled weight_decay = 0.0005
Optimizer groups: 82 .bias, 82 conv.weight, 79 other
train: Scanning '..\datasets\coco128\labels\train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [00:00<?, ?it/s]
val: Scanning '..\datasets\coco128\labels\train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [00:00<?, ?it/s]
Plotting labels...
autoanchor: Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946
Image sizes 640 train, 640 test
Using 2 dataloader workers
Logging results to runs\train\exp10
Starting training for 300 epochs...
Epoch gpu_mem box obj cls total labels img_size
0%| | 0/64 [00:03<?, ?it/s]
Traceback (most recent call last):
File "train.py", line 550, in <module>
train(hyp, opt, device, tb_writer)
File "train.py", line 306, in train
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
File "C:\Users\Administrator\pyhome\YOLOv5-Lite\utils\loss.py", line 117, in __call__
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
File "C:\Users\Administrator\pyhome\YOLOv5-Lite\utils\loss.py", line 211, in build_targets
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
RuntimeError: result type Float can't be cast to the desired output type __int64
