Comments (6)
fcjian
commented on June 1, 2024
2
You can try to clamp the value of the box area when computing GIoU loss, e.g.,
TOOD/mmdet/core/bbox/iou_calculators/iou2d_calculator.py
Lines 212 to 215 in 93b3a87
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MeteoriteWeny
commented on June 1, 2024
1
@fcjian Thanks for reply! It solves the CUDA error, but the model can not converge. During training, a problem similar with gradient cutting happened. The log shows a sudden increase of loss. After that, the loss fluctuates in a tiny range. I'll try again with the original TOOD code without transfering to higher mmdet version.
2021-12-29 09:32:52,217 - mmdet - INFO - Epoch [1][600/1162] lr: 2.000e-03, eta: 8:39:18, time: 0.544, data_time: 0.013, memory: 5142, loss_cls: 0.6940, loss_bbox: 1.2061, loss: 1.9001
2021-12-29 09:33:18,832 - mmdet - INFO - Epoch [1][650/1162] lr: 2.000e-03, eta: 8:38:09, time: 0.532, data_time: 0.013, memory: 5142, loss_cls: 0.6794, loss_bbox: 1.1886, loss: 1.8680
2021-12-29 09:33:45,535 - mmdet - INFO - Epoch [1][700/1162] lr: 2.000e-03, eta: 8:37:13, time: 0.534, data_time: 0.013, memory: 5142, loss_cls: 0.6674, loss_bbox: 1.0485, loss: 1.7159
2021-12-29 09:34:12,217 - mmdet - INFO - Epoch [1][750/1162] lr: 2.000e-03, eta: 8:36:19, time: 0.534, data_time: 0.013, memory: 5142, loss_cls: 0.6646, loss_bbox: 1.0119, loss: 1.6765
2021-12-29 09:34:38,781 - mmdet - INFO - Epoch [1][800/1162] lr: 2.000e-03, eta: 8:35:20, time: 0.531, data_time: 0.013, memory: 5142, loss_cls: 0.6487, loss_bbox: 0.9564, loss: 1.6051
2021-12-29 09:35:05,190 - mmdet - INFO - Epoch [1][850/1162] lr: 2.000e-03, eta: 8:34:14, time: 0.528, data_time: 0.013, memory: 5142, loss_cls: 0.6176, loss_bbox: 0.8406, loss: 1.4582
2021-12-29 09:35:31,799 - mmdet - INFO - Epoch [1][900/1162] lr: 2.000e-03, eta: 8:33:26, time: 0.532, data_time: 0.013, memory: 5142, loss_cls: 0.6210, loss_bbox: 0.9229, loss: 1.5439
2021-12-29 09:35:58,144 - mmdet - INFO - Epoch [1][950/1162] lr: 2.000e-03, eta: 8:32:24, time: 0.527, data_time: 0.013, memory: 5142, loss_cls: 1.1693, loss_bbox: 1.1850, loss: 2.3543
2021-12-29 09:36:25,339 - mmdet - INFO - Exp name: tood_r50_fpn_on_input_1x_coco_cloth.py
2021-12-29 09:36:25,340 - mmdet - INFO - Epoch [1][1000/1162] lr: 2.000e-03, eta: 8:32:14, time: 0.544, data_time: 0.013, memory: 5142, loss_cls: 1.2817, loss_bbox: 1.3174, loss: 2.5991
2021-12-29 09:36:52,114 - mmdet - INFO - Epoch [1][1050/1162] lr: 2.000e-03, eta: 8:31:39, time: 0.535, data_time: 0.013, memory: 5142, loss_cls: 1.2358, loss_bbox: 1.2847, loss: 2.5205
2021-12-29 09:37:18,908 - mmdet - INFO - Epoch [1][1100/1162] lr: 2.000e-03, eta: 8:31:07, time: 0.536, data_time: 0.013, memory: 5142, loss_cls: 1.2365, loss_bbox: 1.3173, loss: 2.5538
2021-12-29 09:37:45,867 - mmdet - INFO - Epoch [1][1150/1162] lr: 2.000e-03, eta: 8:30:43, time: 0.539, data_time: 0.013, memory: 5142, loss_cls: 1.2022, loss_bbox: 1.2296, loss: 2.4319
2021-12-29 09:37:52,329 - mmdet - INFO - Saving checkpoint at 1 epochs
2021-12-29 09:38:47,804 - mmdet - INFO - Evaluating bbox...
2021-12-29 09:38:51,494 - mmdet - INFO - Exp name: tood_r50_fpn_on_input_1x_coco_cloth.py
2021-12-29 09:38:51,495 - mmdet - INFO - Epoch(val) [1][793] bbox_mAP: 0.0170, bbox_mAP_50: 0.0560, bbox_mAP_75: 0.0090, bbox_mAP_s: -1.0000, bbox_mAP_m: 0.0240, bbox_mAP_l: 0.0190, bbox_mAP_copypaste: 0.017 0.056 0.009 -1.000 0.024 0.019
2021-12-29 09:39:21,128 - mmdet - INFO - Epoch [2][50/1162] lr: 2.000e-03, eta: 8:27:14, time: 0.592, data_time: 0.062, memory: 5142, loss_cls: 1.2236, loss_bbox: 1.2423, loss: 2.4659
2021-12-29 09:39:47,839 - mmdet - INFO - Epoch [2][100/1162] lr: 2.000e-03, eta: 8:26:45, time: 0.534, data_time: 0.013, memory: 5142, loss_cls: 1.2410, loss_bbox: 1.2517, loss: 2.4927
2021-12-29 09:40:14,530 - mmdet - INFO - Epoch [2][150/1162] lr: 2.000e-03, eta: 8:26:16, time: 0.534, data_time: 0.013, memory: 5142, loss_cls: 1.2827, loss_bbox: 1.2900, loss: 2.5726
2021-12-29 09:40:41,392 - mmdet - INFO - Epoch [2][200/1162] lr: 2.000e-03, eta: 8:25:54, time: 0.537, data_time: 0.013, memory: 5142, loss_cls: 1.2351, loss_bbox: 1.2374, loss: 2.4725
2021-12-29 09:41:08,168 - mmdet - INFO - Epoch [2][250/1162] lr: 2.000e-03, eta: 8:25:28, time: 0.536, data_time: 0.013, memory: 5142, loss_cls: 1.1736, loss_bbox: 1.1955, loss: 2.3691
2021-12-29 09:41:34,806 - mmdet - INFO - Epoch [2][300/1162] lr: 2.000e-03, eta: 8:24:57, time: 0.533, data_time: 0.013, memory: 5142, loss_cls: 1.2357, loss_bbox: 1.2372, loss: 2.4729
2021-12-29 09:42:01,528 - mmdet - INFO - Epoch [2][350/1162] lr: 2.000e-03, eta: 8:24:29, time: 0.534, data_time: 0.013, memory: 5142, loss_cls: 1.2839, loss_bbox: 1.2587, loss: 2.5425
2021-12-29 09:42:28,154 - mmdet - INFO - Epoch [2][400/1162] lr: 2.000e-03, eta: 8:23:58, time: 0.533, data_time: 0.013, memory: 5142, loss_cls: 1.2595, loss_bbox: 1.2359, loss: 2.4954
2021-12-29 09:42:54,986 - mmdet - INFO - Epoch [2][450/1162] lr: 2.000e-03, eta: 8:23:35, time: 0.537, data_time: 0.013, memory: 5142, loss_cls: 1.2725, loss_bbox: 1.3049, loss: 2.5773
2021-12-29 09:43:21,637 - mmdet - INFO - Epoch [2][500/1162] lr: 2.000e-03, eta: 8:23:05, time: 0.533, data_time: 0.013, memory: 5142, loss_cls: 1.2867, loss_bbox: 1.2862, loss: 2.5730
2021-12-29 09:43:48,377 - mmdet - INFO - Epoch [2][550/1162] lr: 2.000e-03, eta: 8:22:38, time: 0.535, data_time: 0.013, memory: 5142, loss_cls: 1.2554, loss_bbox: 1.2227, loss: 2.4781
2021-12-29 09:44:15,013 - mmdet - INFO - Epoch [2][600/1162] lr: 2.000e-03, eta: 8:22:08, time: 0.533, data_time: 0.013, memory: 5142, loss_cls: 1.2519, loss_bbox: 1.2955, loss: 2.5474
2021-12-29 09:44:42,014 - mmdet - INFO - Epoch [2][650/1162] lr: 2.000e-03, eta: 8:21:49, time: 0.540, data_time: 0.013, memory: 5142, loss_cls: 1.2472, loss_bbox: 1.2727, loss: 2.5199
2021-12-29 09:45:08,675 - mmdet - INFO - Epoch [2][700/1162] lr: 2.000e-03, eta: 8:21:20, time: 0.533, data_time: 0.013, memory: 5142, loss_cls: 1.1740, loss_bbox: 1.2461, loss: 2.4200
2021-12-29 09:45:35,666 - mmdet - INFO - Epoch [2][750/1162] lr: 2.000e-03, eta: 8:21:00, time: 0.540, data_time: 0.013, memory: 5142, loss_cls: 1.2391, loss_bbox: 1.2960, loss: 2.5351
2021-12-29 09:46:02,395 - mmdet - INFO - Epoch [2][800/1162] lr: 2.000e-03, eta: 8:20:33, time: 0.535, data_time: 0.013, memory: 5142, loss_cls: 1.2462, loss_bbox: 1.2470, loss: 2.4933
2021-12-29 09:46:29,543 - mmdet - INFO - Epoch [2][850/1162] lr: 2.000e-03, eta: 8:20:17, time: 0.543, data_time: 0.013, memory: 5142, loss_cls: 1.2525, loss_bbox: 1.3128, loss: 2.5653
2021-12-29 09:46:56,271 - mmdet - INFO - Epoch [2][900/1162] lr: 2.000e-03, eta: 8:19:50, time: 0.535, data_time: 0.013, memory: 5142, loss_cls: 1.2501, loss_bbox: 1.2733, loss: 2.5234
2021-12-29 09:47:22,898 - mmdet - INFO - Epoch [2][950/1162] lr: 2.000e-03, eta: 8:19:19, time: 0.533, data_time: 0.013, memory: 5142, loss_cls: 1.3215, loss_bbox: 1.2575, loss: 2.5790
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one23sunnyQQ
commented on June 1, 2024
Same issue
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Bo396543018
commented on June 1, 2024
Same issue. My mmdet's version is 2.19.0 and raise error during training the 3rd epoch
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GloriaHM
commented on June 1, 2024
You can try to clamp the value of the box area when computing GIoU loss, e.g.,
TOOD/mmdet/core/bbox/iou_calculators/iou2d_calculator.py
Lines 212 to 215 in 93b3a87
hello sir，i have clamp the value of box area as you show ,but still crash at the 5rd epoch. My mmdet's version is 2.14.0+d3e713d.
Error Report:
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [508,0,0], thread: [26,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [508,0,0], thread: [27,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [508,0,0], thread: [28,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [508,0,0], thread: [29,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [508,0,0], thread: [30,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [508,0,0], thread: [31,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [32,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [33,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [34,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [35,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [36,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [37,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [38,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [39,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [40,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [41,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [42,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [43,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [44,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [45,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [46,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [47,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [48,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [49,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [50,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [51,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [52,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [53,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [54,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [55,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [56,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [57,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [58,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [59,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [60,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [61,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [62,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [238,0,0], thread: [63,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [0,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [1,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [2,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [3,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [4,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [5,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [6,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [7,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [8,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [9,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [10,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [11,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [12,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [13,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [14,0,0] Assertion input_val >= zero && input_val <= one failed.
/opt/conda/conda-bld/pytorch_1614378098133/work/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [328,0,0], thread: [15,0,0] Assertion input_val >= zero && input_val <= one failed.
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Traceback (most recent call last):
File "./tools/train.py", line 188, in
main()
File "./tools/train.py", line 184, in main
meta=meta)
File "/mnt/mhm/project/TODO/TOOD/mmdet/apis/train.py", line 170, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter
**kwargs)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/parallel/distributed.py", line 52, in train_step
output = self.module.train_step(*inputs[0], **kwargs[0])
File "/mnt/mhm/project/TODO/TOOD/mmdet/models/detectors/base.py", line 237, in train_step
losses = self(**data)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func
return old_func(*args, **kwargs)
File "/mnt/mhm/project/TODO/TOOD/mmdet/models/detectors/base.py", line 171, in forward
return self.forward_train(img, img_metas, **kwargs)
File "/mnt/mhm/project/TODO/TOOD/mmdet/models/detectors/single_stage.py", line 83, in forward_train
gt_labels, gt_bboxes_ignore)
File "/mnt/mhm/project/TODO/TOOD/mmdet/models/dense_heads/base_dense_head.py", line 54, in forward_train
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 186, in new_func
return old_func(*args, kwargs)
File "/mnt/mhm/project/TODO/TOOD/mmdet/models/dense_heads/tood_head.py", line 447, in loss
num_total_samples=num_total_samples)
File "/mnt/mhm/project/TODO/TOOD/mmdet/core/utils/misc.py", line 29, in multi_apply
return tuple(map(list, zip(map_results)))
File "/mnt/mhm/project/TODO/TOOD/mmdet/models/dense_heads/tood_head.py", line 354, in loss_single
& (labels < bg_class_ind)).nonzero().squeeze(1)
RuntimeError: CUDA error: device-side assert triggered
terminate called after throwing an instance of 'c10::Error'
what(): CUDA error: device-side assert triggered
Exception raised from create_event_internal at /opt/conda/conda-bld/pytorch_1614378098133/work/c10/cuda/CUDACachingAllocator.cpp:733 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7fdf062a32f2 in /root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const, char const, unsigned int, std::string const&) + 0x5b (0x7fdf062a067b in /root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::CUDACachingAllocator::raw_delete(void) + 0x809 (0x7fdf064fc219 in /root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::TensorImpl::release_resources() + 0x54 (0x7fdf0628b3a4 in /root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #4: + 0x6e6a3a (0x7fdf5d204a3a in /root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: + 0x6e6ae1 (0x7fdf5d204ae1 in /root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #6: + 0x1817da (0x55b94fc797da in /root/anaconda3/envs/open-mmlab/bin/python)
frame #7: + 0xfbfa9 (0x55b94fbf3fa9 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #8: + 0xfa8c8 (0x55b94fbf28c8 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #9: + 0xfa8c8 (0x55b94fbf28c8 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #10: + 0xfa2d8 (0x55b94fbf22d8 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #11: + 0xfad68 (0x55b94fbf2d68 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #12: + 0xfad7c (0x55b94fbf2d7c in /root/anaconda3/envs/open-mmlab/bin/python)
frame #13: + 0xfad7c (0x55b94fbf2d7c in /root/anaconda3/envs/open-mmlab/bin/python)
frame #14: + 0xfad7c (0x55b94fbf2d7c in /root/anaconda3/envs/open-mmlab/bin/python)
frame #15: + 0xfad7c (0x55b94fbf2d7c in /root/anaconda3/envs/open-mmlab/bin/python)
frame #16: + 0xfad7c (0x55b94fbf2d7c in /root/anaconda3/envs/open-mmlab/bin/python)
frame #17: + 0xfad7c (0x55b94fbf2d7c in /root/anaconda3/envs/open-mmlab/bin/python)
frame #18: + 0x12b327 (0x55b94fc23327 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #19: PyDict_SetItemString + 0x89 (0x55b94fc2fe59 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #20: PyImport_Cleanup + 0xab (0x55b94fca4d0b in /root/anaconda3/envs/open-mmlab/bin/python)
frame #21: Py_FinalizeEx + 0x64 (0x55b94fd19304 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #22: + 0x232960 (0x55b94fd2a960 in /root/anaconda3/envs/open-mmlab/bin/python)
frame #23: _Py_UnixMain + 0x3c (0x55b94fd2accc in /root/anaconda3/envs/open-mmlab/bin/python)
frame #24: __libc_start_main + 0xf0 (0x7fdf9851e830 in /lib/x86_64-linux-gnu/libc.so.6)
frame #25: + 0x1d7555 (0x55b94fccf555 in /root/anaconda3/envs/open-mmlab/bin/python)
Killing subprocess 19911
Traceback (most recent call last):
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in
main()
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main
sigkill_handler(signal.SIGTERM, None) # not coming back
File "/root/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/root/anaconda3/envs/open-mmlab/bin/python', '-u', './tools/train.py', '--local_rank=0',
Thank you for your reply.
from tood.
beeper00
commented on June 1, 2024
i meet the same issue , my code is
"area1 = fp16_clamp((bboxes1[..., 2] - bboxes1[..., 0]), min=0) * fp16_clamp((
bboxes1[..., 3] - bboxes1[..., 1]), min=0) "
since i clone the code, so i don't have to modify it.
but the bug still happens. and it happens randomly each time when i train it.
from tood.
Related Issues (20)
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from tood.