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index >= -sizes[i] && index < sizes[i] && "index out of bounds"
你好,麻烦问一下。 我现在可以跑通你的测试代码。 但是,我现在重新训练Youtube-vis时,使用train code会报错。 之前,测试时也会报这个错,但是前天经过你提供的修改版本后,test可以跑通了。但是,现在train会出现这个问题。
rness: 0.6501, loss_mask: 0.6520, loss_match: 0.4234, match_acc: 79.0612, loss: 3.1077 2020-08-08 21:28:59,671 - INFO - Epoch [1][850/15336] lr: 0.00425, eta: 16:25:55, time: 0.316, data_time: 0.015, memory: 3095, loss_cls: 0.7964, loss_bbox: 0.6386, loss_centerness: 0.6634, loss_mask: 0.6462, loss_match: 0.4421, match_acc: 79.8079, loss: 3.1867 2020-08-08 21:29:14,942 - INFO - Epoch [1][900/15336] lr: 0.00450, eta: 16:22:40, time: 0.305, data_time: 0.012, memory: 3095, loss_cls: 0.7723, loss_bbox: 0.6340, loss_centerness: 0.6480, loss_mask: 0.6533, loss_match: 0.4121, match_acc: 80.7472, loss: 3.1197 2020-08-08 21:29:30,913 - INFO - Epoch [1][950/15336] lr: 0.00475, eta: 16:22:01, time: 0.320, data_time: 0.012, memory: 3095, loss_cls: 0.7733, loss_bbox: 0.6439, loss_centerness: 0.6558, loss_mask: 0.6456, loss_match: 0.4613, match_acc: 77.0718, loss: 3.1798 2020-08-08 21:29:47,179 - INFO - Epoch [1][1000/15336] lr: 0.00500, eta: 16:22:17, time: 0.325, data_time: 0.012, memory: 3095, loss_cls: 0.7749, loss_bbox: 0.6194, loss_centerness: 0.6524, loss_mask: 0.6529, loss_match: 0.3811, match_acc: 81.1160, loss: 3.0809 2020-08-08 21:30:02,198 - INFO - Epoch [1][1050/15336] lr: 0.00500, eta: 16:18:52, time: 0.300, data_time: 0.011, memory: 3095, loss_cls: 0.7748, loss_bbox: 0.6533, loss_centerness: 0.6598, loss_mask: 0.6482, loss_match: 0.4230, match_acc: 81.8658, loss: 3.1591 2020-08-08 21:30:17,314 - INFO - Epoch [1][1100/15336] lr: 0.00500, eta: 16:16:01, time: 0.302, data_time: 0.011, memory: 3095, loss_cls: 0.7779, loss_bbox: 0.6308, loss_centerness: 0.6568, loss_mask: 0.6341, loss_match: 0.3886, match_acc: 82.3207, loss: 3.0881 2020-08-08 21:30:33,515 - INFO - Epoch [1][1150/15336] lr: 0.00500, eta: 16:16:16, time: 0.324, data_time: 0.011, memory: 3095, loss_cls: 0.7369, loss_bbox: 0.6382, loss_centerness: 0.6548, loss_mask: 0.6502, loss_match: 0.4324, match_acc: 81.9447, loss: 3.1126 2020-08-08 21:30:49,094 - INFO - Epoch [1][1200/15336] lr: 0.00500, eta: 16:14:51, time: 0.311, data_time: 0.014, memory: 3095, loss_cls: 0.7456, loss_bbox: 0.6051, loss_centerness: 0.6448, loss_mask: 0.6525, loss_match: 0.3567, match_acc: 84.9854, loss: 3.0047 2020-08-08 21:31:04,446 - INFO - Epoch [1][1250/15336] lr: 0.00500, eta: 16:13:03, time: 0.307, data_time: 0.012, memory: 3095, loss_cls: 0.7523, loss_bbox: 0.6132, loss_centerness: 0.6487, loss_mask: 0.6453, loss_match: 0.3773, match_acc: 81.1894, loss: 3.0367 2020-08-08 21:31:20,595 - INFO - Epoch [1][1300/15336] lr: 0.00500, eta: 16:13:13, time: 0.323, data_time: 0.011, memory: 3095, loss_cls: 0.7536, loss_bbox: 0.5968, loss_centerness: 0.6511, loss_mask: 0.6463, loss_match: 0.4068, match_acc: 82.4096, loss: 3.0547 2020-08-08 21:31:36,113 - INFO - Epoch [1][1350/15336] lr: 0.00500, eta: 16:11:54, time: 0.310, data_time: 0.011, memory: 3095, loss_cls: 0.7592, loss_bbox: 0.6125, loss_centerness: 0.6534, loss_mask: 0.6516, loss_match: 0.4166, match_acc: 81.3813, loss: 3.0933 2020-08-08 21:31:51,768 - INFO - Epoch [1][1400/15336] lr: 0.00500, eta: 16:10:58, time: 0.313, data_time: 0.011, memory: 3095, loss_cls: 0.7279, loss_bbox: 0.5773, loss_centerness: 0.6434, loss_mask: 0.6333, loss_match: 0.4268, match_acc: 79.4155, loss: 3.0087 2020-08-08 21:32:07,332 - INFO - Epoch [1][1450/15336] lr: 0.00500, eta: 16:09:53, time: 0.311, data_time: 0.011, memory: 3095, loss_cls: 0.7478, loss_bbox: 0.5974, loss_centerness: 0.6457, loss_mask: 0.6373, loss_match: 0.3742, match_acc: 82.5788, loss: 3.0025 2020-08-08 21:32:23,535 - INFO - Epoch [1][1500/15336] lr: 0.00500, eta: 16:10:10, time: 0.324, data_time: 0.025, memory: 3095, loss_cls: 0.7332, loss_bbox: 0.6111, loss_centerness: 0.6494, loss_mask: 0.6385, loss_match: 0.3091, match_acc: 83.7850, loss: 2.9413 2020-08-08 21:32:38,794 - INFO - Epoch [1][1550/15336] lr: 0.00500, eta: 16:08:33, time: 0.305, data_time: 0.012, memory: 3095, loss_cls: 0.7310, loss_bbox: 0.5800, loss_centerness: 0.6470, loss_mask: 0.6450, loss_match: 0.3471, match_acc: 85.5443, loss: 2.9502 2020-08-08 21:32:54,288 - INFO - Epoch [1][1600/15336] lr: 0.00500, eta: 16:07:28, time: 0.310, data_time: 0.011, memory: 3095, loss_cls: 0.7496, loss_bbox: 0.5906, loss_centerness: 0.6543, loss_mask: 0.6394, loss_match: 0.3909, match_acc: 84.7658, loss: 3.0248 2020-08-08 21:33:10,256 - INFO - Epoch [1][1650/15336] lr: 0.00500, eta: 16:07:19, time: 0.319, data_time: 0.012, memory: 3095, loss_cls: 0.7324, loss_bbox: 0.5914, loss_centerness: 0.6515, loss_mask: 0.6451, loss_match: 0.3490, match_acc: 83.4625, loss: 2.9694 2020-08-08 21:33:25,515 - INFO - Epoch [1][1700/15336] lr: 0.00500, eta: 16:05:52, time: 0.305, data_time: 0.011, memory: 3095, loss_cls: 0.7436, loss_bbox: 0.6063, loss_centerness: 0.6452, loss_mask: 0.6301, loss_match: 0.3348, match_acc: 84.6653, loss: 2.9600 2020-08-08 21:33:41,107 - INFO - Epoch [1][1750/15336] lr: 0.00500, eta: 16:05:05, time: 0.312, data_time: 0.012, memory: 3095, loss_cls: 0.7260, loss_bbox: 0.5829, loss_centerness: 0.6478, loss_mask: 0.6330, loss_match: 0.3358, match_acc: 84.9732, loss: 2.9256 2020-08-08 21:33:56,003 - INFO - Epoch [1][1800/15336] lr: 0.00500, eta: 16:03:10, time: 0.298, data_time: 0.011, memory: 3095, loss_cls: 0.7380, loss_bbox: 0.5682, loss_centerness: 0.6520, loss_mask: 0.6421, loss_match: 0.4252, match_acc: 81.2897, loss: 3.0255 2020-08-08 21:34:11,874 - INFO - Epoch [1][1850/15336] lr: 0.00500, eta: 16:02:55, time: 0.317, data_time: 0.011, memory: 3095, loss_cls: 0.7461, loss_bbox: 0.5867, loss_centerness: 0.6488, loss_mask: 0.6300, loss_match: 0.3946, match_acc: 81.6873, loss: 3.0062 2020-08-08 21:34:27,096 - INFO - Epoch [1][1900/15336] lr: 0.00500, eta: 16:01:39, time: 0.305, data_time: 0.011, memory: 3095, loss_cls: 0.7425, loss_bbox: 0.5659, loss_centerness: 0.6462, loss_mask: 0.6339, loss_match: 0.3349, match_acc: 83.1951, loss: 2.9234 2020-08-08 21:34:42,450 - INFO - Epoch [1][1950/15336] lr: 0.00500, eta: 16:00:38, time: 0.307, data_time: 0.011, memory: 3095, loss_cls: 0.7156, loss_bbox: 0.5853, loss_centerness: 0.6458, loss_mask: 0.6365, loss_match: 0.4714, match_acc: 81.8768, loss: 3.0547 2020-08-08 21:34:57,208 - INFO - Epoch [1][2000/15336] lr: 0.00500, eta: 15:58:45, time: 0.295, data_time: 0.011, memory: 3095, loss_cls: 0.7369, loss_bbox: 0.5948, loss_centerness: 0.6464, loss_mask: 0.6382, loss_match: 0.3108, match_acc: 86.0842, loss: 2.9271 2020-08-08 21:35:12,865 - INFO - Epoch [1][2050/15336] lr: 0.00500, eta: 15:58:13, time: 0.313, data_time: 0.011, memory: 3095, loss_cls: 0.7298, loss_bbox: 0.5639, loss_centerness: 0.6446, loss_mask: 0.6384, loss_match: 0.3719, match_acc: 82.5019, loss: 2.9486 2020-08-08 21:35:27,859 - INFO - Epoch [1][2100/15336] lr: 0.00500, eta: 15:56:51, time: 0.301, data_time: 0.012, memory: 3095, loss_cls: 0.7314, loss_bbox: 0.5772, loss_centerness: 0.6464, loss_mask: 0.6288, loss_match: 0.4115, match_acc: 84.3310, loss: 2.9952 2020-08-08 21:35:43,216 - INFO - Epoch [1][2150/15336] lr: 0.00500, eta: 15:55:57, time: 0.306, data_time: 0.011, memory: 3095, loss_cls: 0.7277, loss_bbox: 0.5800, loss_centerness: 0.6488, loss_mask: 0.6266, loss_match: 0.4211, match_acc: 82.1631, loss: 3.0040 2020-08-08 21:35:59,211 - INFO - Epoch [1][2200/15336] lr: 0.00500, eta: 15:56:01, time: 0.320, data_time: 0.012, memory: 3095, loss_cls: 0.7260, loss_bbox: 0.5557, loss_centerness: 0.6417, loss_mask: 0.6312, loss_match: 0.3349, match_acc: 83.9794, loss: 2.8894 2020-08-08 21:36:14,111 - INFO - Epoch [1][2250/15336] lr: 0.00500, eta: 15:54:34, time: 0.298, data_time: 0.011, memory: 3095, loss_cls: 0.7008, loss_bbox: 0.5723, loss_centerness: 0.6455, loss_mask: 0.6160, loss_match: 0.3169, match_acc: 86.5696, loss: 2.8514 2020-08-08 21:36:29,251 - INFO - Epoch [1][2300/15336] lr: 0.00500, eta: 15:53:31, time: 0.303, data_time: 0.012, memory: 3095, loss_cls: 0.7353, loss_bbox: 0.5662, loss_centerness: 0.6443, loss_mask: 0.6367, loss_match: 0.3508, match_acc: 84.5352, loss: 2.9333 2020-08-08 21:36:44,466 - INFO - Epoch [1][2350/15336] lr: 0.00500, eta: 15:52:34, time: 0.304, data_time: 0.011, memory: 3095, loss_cls: 0.7200, loss_bbox: 0.5537, loss_centerness: 0.6419, loss_mask: 0.6401, loss_match: 0.3553, match_acc: 84.2998, loss: 2.9111 2020-08-08 21:37:00,215 - INFO - Epoch [1][2400/15336] lr: 0.00500, eta: 15:52:20, time: 0.315, data_time: 0.012, memory: 3095, loss_cls: 0.7434, loss_bbox: 0.5692, loss_centerness: 0.6481, loss_mask: 0.6277, loss_match: 0.3681, match_acc: 83.9581, loss: 2.9566 2020-08-08 21:37:15,480 - INFO - Epoch [1][2450/15336] lr: 0.00500, eta: 15:51:30, time: 0.306, data_time: 0.011, memory: 3095, loss_cls: 0.7208, loss_bbox: 0.5515, loss_centerness: 0.6454, loss_mask: 0.6220, loss_match: 0.3207, match_acc: 85.4823, loss: 2.8605 2020-08-08 21:37:30,535 - INFO - Epoch [1][2500/15336] lr: 0.00500, eta: 15:50:25, time: 0.301, data_time: 0.011, memory: 3095, loss_cls: 0.7279, loss_bbox: 0.5622, loss_centerness: 0.6490, loss_mask: 0.6312, loss_match: 0.3531, match_acc: 84.4417, loss: 2.9234 2020-08-08 21:37:45,963 - INFO - Epoch [1][2550/15336] lr: 0.00500, eta: 15:49:51, time: 0.309, data_time: 0.011, memory: 3095, loss_cls: 0.7056, loss_bbox: 0.5183, loss_centerness: 0.6353, loss_mask: 0.6171, loss_match: 0.2979, match_acc: 85.8967, loss: 2.7742 2020-08-08 21:38:01,695 - INFO - Epoch [1][2600/15336] lr: 0.00500, eta: 15:49:37, time: 0.315, data_time: 0.012, memory: 3095, loss_cls: 0.7048, loss_bbox: 0.5487, loss_centerness: 0.6448, loss_mask: 0.6169, loss_match: 0.3339, match_acc: 86.0841, loss: 2.8492 2020-08-08 21:38:17,266 - INFO - Epoch [1][2650/15336] lr: 0.00500, eta: 15:49:12, time: 0.311, data_time: 0.012, memory: 3095, loss_cls: 0.6899, loss_bbox: 0.5335, loss_centerness: 0.6436, loss_mask: 0.6148, loss_match: 0.3327, match_acc: 85.6113, loss: 2.8145 /tmp/pip-req-build-xea91aoh/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [23,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. /tmp/pip-req-build-xea91aoh/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [23,0,0], thread: [33,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. /tmp/pip-req-build-xea91aoh/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [23,0,0], thread: [34,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. /tmp/pip-req-build-xea91aoh/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [23,0,0], thread: [35,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. /tmp/pip-req-build-xea91aoh/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [23,0,0], thread: [36,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. 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Traceback (most recent call last): File "./tools/train.py", line 110, in <module> main() File "./tools/train.py", line 106, in main logger=logger) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/apis/train.py", line 60, in train_detector _non_dist_train(model, dataset, cfg, validate=validate) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/apis/train.py", line 221, in _non_dist_train runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/mmcv/runner/runner.py", line 358, in run epoch_runner(data_loaders[i], **kwargs) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/mmcv/runner/runner.py", line 264, in train self.model, data_batch, train_mode=True, **kwargs) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/apis/train.py", line 38, in batch_processor losses = model(**data) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 152, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 162, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 83, in parallel_apply raise output File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 59, in _worker output = module(*input, **kwargs) File "/home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/core/fp16/decorators.py", line 49, in new_func return old_func(*args, **kwargs) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/models/detectors/base.py", line 86, in forward return self.forward_train(img, img_meta, **kwargs) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/models/detectors/single_stage.py", line 69, in forward_train *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore, gt_masks_list=gt_masks, ref_bboxes_list = ref_bboxes, gt_pids_list=gt_pids) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/core/fp16/decorators.py", line 127, in new_func return old_func(*args, **kwargs) File "/home/zxq/code/SipMask-master(1)/SipMask-master/SipMask-VIS/tools/../mmdet/models/anchor_heads/sipmask_head.py", line 506, in loss gt_mask_new[:gt_mask.shape[0], :shape[1], :shape[2]] = gt_mask[:gt_mask.shape[0], :shape[1], :shape[2]] RuntimeError: CUDA error: device-side assert triggered terminate called after throwing an instance of 'c10::Error' what(): CUDA error: device-side assert triggered (insert_events at ../c10/cuda/CUDACachingAllocator.cpp:564) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x6d (0x7fa5fb1ea33d in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libc10.so) frame #1: <unknown function> + 0x11ea1 (0x7fa5fb20eea1 in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libc10_cuda.so) frame #2: c10::TensorImpl::release_resources() + 0x5c (0x7fa5fb1dc91c in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libc10.so) frame #3: torch::autograd::Variable::Impl::release_resources() + 0x7c (0x7fa5febec17c in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libtorch.so.1) frame #4: <unknown function> + 0x124c84 (0x7fa6234b8c84 in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libtorch_python.so) frame #5: <unknown function> + 0x35467c (0x7fa6236e867c in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libtorch_python.so) frame #6: <unknown function> + 0x3546b2 (0x7fa6236e86b2 in /home/zxq/anaconda3/envs/sipmask/lib/python3.6/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame #27: __libc_start_main + 0xe7 (0x7fa631940b97 in /lib/x86_64-linux-gnu/libc.so.6) Aborted (core dumped)
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@Xiaoqi-Zhao-DLUT 你的batch size比较小且用了两个GPU,可能默认的学习率偏大。可以把学习率改小试试,例如0.0002。建议用大点的batch size。
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你好,麻烦问一下。 我现在可以跑通你的测试代码。 但是,我现在重新训练Youtube-vis时,使用train code会报错。
之前,测试时也会报这个错,但是前天经过你提供的修改版本后,test可以跑通了。但是,现在train会出现这个问题。
报错信息
The text was updated successfully, but these errors were encountered: