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RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED
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YOLOV5 + torch1.8.0 +cuda10.2+GTX1650 OS:Windows 10 python 3.9
(mmdeploy) D:\widows_mm\yolov5-7.0>python segment/train.py --weights yolov5n-seg.pt --img 640 --batch-size 2 --data data.yaml segment\train: weights=yolov5n-seg.pt, cfg=, data=data.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 YOLOv5 2022-11-22 Python-3.9.12 torch-1.8.0 CUDA:0 (NVIDIA GeForce GTX 1650, 4096MiB)
hyperparameters: 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=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=1.0, mixup=0.0, copy_paste=0.0 TensorBoard: Start with 'tensorboard --logdir runs\train-seg', view at http://localhost:6006/ Overriding model.yaml nc=80 with nc=2
from n params module arguments
0 -1 1 1760 models.common.Conv [3, 16, 6, 2, 2] 1 -1 1 4672 models.common.Conv [16, 32, 3, 2] 2 -1 1 4800 models.common.C3 [32, 32, 1] 3 -1 1 18560 models.common.Conv [32, 64, 3, 2] 4 -1 2 29184 models.common.C3 [64, 64, 2] 5 -1 1 73984 models.common.Conv [64, 128, 3, 2] 6 -1 3 156928 models.common.C3 [128, 128, 3] 7 -1 1 295424 models.common.Conv [128, 256, 3, 2] 8 -1 1 296448 models.common.C3 [256, 256, 1] 9 -1 1 164608 models.common.SPPF [256, 256, 5] 10 -1 1 33024 models.common.Conv [256, 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 90880 models.common.C3 [256, 128, 1, False] 14 -1 1 8320 models.common.Conv [128, 64, 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 22912 models.common.C3 [128, 64, 1, False] 18 -1 1 36992 models.common.Conv [64, 64, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 74496 models.common.C3 [128, 128, 1, False] 21 -1 1 147712 models.common.Conv [128, 128, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 296448 models.common.C3 [256, 256, 1, False] 24 [17, 20, 23] 1 128863 models.yolo.Segment [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 64, [64, 128, 256]] Model summary: 225 layers, 1886015 parameters, 1886015 gradients, 6.9 GFLOPs
Transferred 361/367 items from yolov5n-seg.pt AMP: checks passed optimizer: SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias train: Scanning D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels\train2017... 1000 images, 0 train: WARNING Cache directory D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels is not writeable: [WinError 183] : 'D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels\train2017.cache.npy' -> 'D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels\train2017.cache' val: Scanning D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels\train2017... 1000 images, 0 ba val: WARNING Cache directory D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels is not writeable: [WinError 183] : 'D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels\train2017.cache.npy' -> 'D:\widows_mm\yolov5-7.0\labelmedata\json2yolo-master\new_dir_shuang\labels\train2017.cache'
AutoAnchor: 5.55 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset Plotting labels to runs\train-seg\exp3\labels.jpg... Image sizes 640 train, 640 val Using 2 dataloader workers Logging results to runs\train-seg\exp3 Starting training for 100 epochs...
Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size
0%| | 0/500 00:00
Traceback (most recent call last):
File "D:\widows_mm\yolov5-7.0\segment\train.py", line 658, in cublasGemmEx( handle, opa, opb, m, n, k, &falpha, a, CUDA_R_16F, lda, b, CUDA_R_16F, ldb, &fbeta, c, CUDA_R_16F, ldc, CUDA_R_32F, CUBLAS_GEMM_DFALT_TENSOR_OP)
Hello, when I was training my own dataset, I reported an error when calculating mask loss. Is it related to @? The environment can be tested
Additional
No response
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@jiaqizhang123-stack 👋 Hello! Thanks for asking about CUDA issues. You may simply be out of memory.
YOLOv5 🚀 can be trained on CPU, single-GPU, or multi-GPU. When training on GPU it is important to keep your batch-size small enough that you do not use all of your GPU memory, otherwise you will see a CUDA Out Of Memory (OOM) Error and your training will crash. You can observe your CUDA memory utilization using either the nvidia-smi command or by viewing your console output:
CUDA Out of Memory Solutions
If you encounter a CUDA OOM error, the steps you can take to reduce your memory usage are:
- Reduce
--batch-size - Reduce
--img-size - Reduce model size, i.e. from YOLOv5x -> YOLOv5l -> YOLOv5m -> YOLOv5s > YOLOv5n
- Train with multi-GPU at the same
--batch-size - Upgrade your hardware to a larger GPU
- Train on free GPU backends with up to 16GB of CUDA memory:
AutoBatch
You can use YOLOv5 AutoBatch (NEW) to find the best batch size for your training by passing --batch-size -1. AutoBatch will solve for a 90% CUDA memory-utilization batch-size given your training settings. AutoBatch is experimental, and only works for Single-GPU training. It may not work on all systems, and is not recommended for production use.
Good luck 🍀 and let us know if you have any other questions!
Hello, my training model is yolov5n seg, and the above error will also occur when the batch size is 1. I think this error has nothing to do with the back size, because it is an error when calculating the loss of a single image. When calculating a large matrix, the pred and proto have large dimensions, leading to cuda deficiency,
And when I replace “pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])” with "pred_mask = torch.tensor(np.matmul(pred.cpu().detach().numpy(), proto.view(self.nm, -1).cpu().detach().numpy())).cuda().view(-1, *proto.shape[1:])", there will be no error.
At present, the GPU resources are sufficient, but an error occurs when calculating the matrix multiplication. Is this a bug in the GPU or is it caused by something? Thank you for your answer
@jiaqizhang123-stack there's no bug in the code, it's likely you are simply out of CUDA memory and this may be the highest-memory bottleneck that first trips an error. All Segmentation models were of course trained with GPUs without issue.
Is it necessary to train on a larger GPU? This code cannot train on a smaller GPU
@jiaqizhang123-stack yes I think you might need a larger GPU, or just to reduce memory usage using some of the tips above like smaller --imgsz.
I don't know if your fix will work as torch won't be able to calculate gradients after your .detach() and numpy ops.
👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
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@jiaqizhang123-stack you're very welcome! If you have any further questions or need assistance, feel free to ask. Good luck with your training!