train error!

[why228430]

when I use the commend "python tools/train.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py" train DOTA dataset I meet the follow error,how I can do to solve it. Thank you!

ReResNet Orientation: 8 Fix Params: False 2021-03-22 11:10:38,503 - INFO - Distributed training: False /pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. /pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. /pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. 2021-03-22 11:11:08,697 - INFO - load model from: work_dirs/ReResNet_pretrain/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth 2021-03-22 11:11:08,767 - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, backbone.bn1.batch_norm_[8].weight, backbone.bn1.batch_norm_[8].bias, backbone.bn1.batch_norm_[8].running_mean, backbone.bn1.batch_norm_[8].running_var, backbone.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv1.weights, backbone.layer1.0.conv1.filter, backbone.layer1.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn1.indices_8, backbone.layer1.0.bn1.batch_norm_[8].weight, backbone.layer1.0.bn1.batch_norm_[8].bias, backbone.layer1.0.bn1.batch_norm_[8].running_mean, backbone.layer1.0.bn1.batch_norm_[8].running_var, backbone.layer1.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv2.weights, backbone.layer1.0.conv2.filter, backbone.layer1.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn2.indices_8, backbone.layer1.0.bn2.batch_norm_[8].weight, backbone.layer1.0.bn2.batch_norm_[8].bias, backbone.layer1.0.bn2.batch_norm_[8].running_mean, backbone.layer1.0.bn2.batch_norm_[8].running_var, backbone.layer1.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv3.weights, backbone.layer1.0.conv3.filter, backbone.layer1.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn3.indices_8, backbone.layer1.0.bn3.batch_norm_[8].weight, backbone.layer1.0.bn3.batch_norm_[8].bias, backbone.layer1.0.bn3.batch_norm_[8].running_mean, backbone.layer1.0.bn3.batch_norm_[8].running_var, backbone.layer1.0.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.0.downsample.0.weights, backbone.layer1.0.downsample.0.filter, backbone.layer1.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.downsample.1.indices_8, backbone.layer1.0.downsample.1.batch_norm_[8].weight, backbone.layer1.0.downsample.1.batch_norm_[8].bias, backbone.layer1.0.downsample.1.batch_norm_[8].running_mean, backbone.layer1.0.downsample.1.batch_norm_[8].running_var, backbone.layer1.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv1.weights, backbone.layer1.1.conv1.filter, backbone.layer1.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn1.indices_8, backbone.layer1.1.bn1.batch_norm_[8].weight, backbone.layer1.1.bn1.batch_norm_[8].bias, backbone.layer1.1.bn1.batch_norm_[8].running_mean, backbone.layer1.1.bn1.batch_norm_[8].running_var, backbone.layer1.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv2.weights, backbone.layer1.1.conv2.filter, backbone.layer1.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn2.indices_8, backbone.layer1.1.bn2.batch_norm_[8].weight, backbone.layer1.1.bn2.batch_norm_[8].bias, backbone.layer1.1.bn2.batch_norm_[8].running_mean, backbone.layer1.1.bn2.batch_norm_[8].running_var, backbone.layer1.1.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv3.weights, backbone.layer1.1.conv3.filter, backbone.layer1.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn3.indices_8, backbone.layer1.1.bn3.batch_norm_[8].weight, backbone.layer1.1.bn3.batch_norm_[8].bias, backbone.layer1.1.bn3.batch_norm_[8].running_mean, backbone.layer1.1.bn3.batch_norm_[8].running_var, backbone.layer1.1.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv1.weights, backbone.layer1.2.conv1.filter, backbone.layer1.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn1.indices_8, backbone.layer1.2.bn1.batch_norm_[8].weight, backbone.layer1.2.bn1.batch_norm_[8].bias, backbone.layer1.2.bn1.batch_norm_[8].running_mean, backbone.layer1.2.bn1.batch_norm_[8].running_var, 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loading annotations into memory... Done (t=2.77s) creating index... index created! 2021-03-22 11:11:13,786 - INFO - Start running, host: why@why, work_dir: /home/why/DL/ReDet-master/work_dirs/ReDet_re50_refpn_1x_dota15 2021-03-22 11:11:13,787 - INFO - workflow: [('train', 1)], max: 12 epochs Traceback (most recent call last): File "tools/train.py", line 95, in main() File "tools/train.py", line 91, in main logger=logger) File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 61, in train_detector _non_dist_train(model, dataset, cfg, validate=validate) File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 197, in _non_dist_train runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run epoch_runner(data_loaders[i], **kwargs) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 264, in train self.model, data_batch, train_mode=True, **kwargs) File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 39, in batch_processor losses = model(**data) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward return self.module(*inputs[0], **kwargs[0]) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "/home/why/DL/ReDet-master/mmdet/models/detectors/base_new.py", line 95, in forward return self.forward_train(img, img_meta, **kwargs) File "/home/why/DL/ReDet-master/mmdet/models/detectors/ReDet.py", line 143, in forward_train *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) File "/home/why/DL/ReDet-master/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss gt_bboxes_ignore=gt_bboxes_ignore) File "/home/why/DL/ReDet-master/mmdet/models/anchor_heads/anchor_head.py", line 177, in loss sampling=self.sampling) File "/home/why/DL/ReDet-master/mmdet/core/anchor/anchor_target.py", line 63, in anchor_target unmap_outputs=unmap_outputs) File "/home/why/DL/ReDet-master/mmdet/core/utils/misc.py", line 24, in multi_apply return tuple(map(list, zip(*map_results))) File "/home/why/DL/ReDet-master/mmdet/core/anchor/anchor_target.py", line 108, in anchor_target_single cfg.allowed_border) File "/home/why/DL/ReDet-master/mmdet/core/anchor/anchor_target.py", line 173, in anchor_inside_flags (flat_anchors[:, 2] < img_w + allowed_border) &
RuntimeError: Expected object of scalar type Byte but got scalar type Bool for argument #2 'other' in call to _th_and

[csuhan]

We have tested on Pytorch1.1/1.3. Try lower version Pytorch or see here

[why228430]

Thanks for your replay! I have update my Pytorch==1.1,CUDA=10.0, the up problem have solved,but I meet a new problem inside_flags4 = torch.tensor(valid_flags, dtype=torch.uint8) THCudaCheck FAIL file=src/riroi_align_kernel.cu line=389 error=7 : too many resources requested for launch Traceback (most recent call last): File "tools/train.py", line 95, in main() File "tools/train.py", line 91, in main logger=logger) File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 61, in train_detector _non_dist_train(model, dataset, cfg, validate=validate) File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 197, in _non_dist_train runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run epoch_runner(data_loaders[i], **kwargs) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 271, in train self.call_hook('after_train_iter') File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 229, in call_hook getattr(hook, fn_name)(self) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/hooks/optimizer.py", line 17, in after_train_iter runner.outputs['loss'].backward() File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/tensor.py", line 107, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/autograd/init.py", line 93, in backward allow_unreachable=True) # allow_unreachable flag File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/autograd/function.py", line 77, in apply return self._forward_cls.backward(self, *args) File "/home/why/DL/ReDet-master/mmdet/ops/riroi_align/functions/riroi_align.py", line 58, in backward grad_input) RuntimeError: cuda runtime error (7) : too many resources requested for launch at src/riroi_align_kernel.cu:389

How I can do to solve it. Thank you!

[AlisLi]

@why228430 How do you solve the first problem? How I can do to solve it. Thank you!

[csuhan]

@why228430 Have you re-compiled the ops after your re-installation of Pytorch? Please del all *.so and re-compile the ops.

[1127244933]

@why228430 Have you re-compiled the ops after your re-installation of Pytorch? Please del all *.so and re-compile the ops.

del all *.so and re-compile the ops. but the problem still exists How I can do Thank you!

[csuhan]

@1127244933 What is your GPU? I have tested on V100 and Titan Xp with Pytorch=1.3.1, cuda=10.1. Besides, is the inference (demo_large_image.py) ok?

[why228430]

Thank you so much for answering my question in the night! I did as you said, but the problem still exists ! My GPU is 2080ti with Pytorch=1.1.0, cuda=10.0. When I try inference demo_large_image.py, I meet the "No module '_polyiou'"!

[csuhan]

For the polyiou, you need:

sudo apt-get install swig
cd DOTA_devkit
swig -c++ -python polyiou.i
python setup.py build_ext --inplace

For the riroi_align, I suggest to change THREADS_PER_BLOCK from 1024 to 512. Whether it's ok or not, please infer me to fix the bug. https://github.com/csuhan/ReDet/blob/7430068dbffa008bf5db158d225e87de4f2bbfa5/mmdet/ops/riroi_align/src/riroi_align_kernel.cu#L11

[why228430]

First I update the demo! then I del all *.so and re-compile the ops. finally I use recommend "python setup.py develop" the "polyiou" has solved ! but the " RuntimeError: cuda runtime error (7) : too many resources requested for launch at src/riroi_align_kernel.cu:389" problem still exists Thank you!

[why228430]

I find from the issue that someone has run code .but I also meet the " RuntimeError: cuda runtime error (7) : too many resources requested for launch at src/riroi_align_kernel.cu:389" problem. Is it problem with my environment?

[wangchengtza]

@why228430 try lower like 256. I met same problem on 2080ti, 768 is okay on my platform

[polar99]

@why228430 @wangchengtza I met same problem on Tesla T4. And I have tried even 64 for THREADS_PER_BLOCK . Problem still exists. My env: GPU: Tesla T4 Pytorch: 1.3.1 with cudatoolkit=10.0

How I can do... Thank you!

[why228430]

After change THREADS_PER_BLOCK, you should re-compile "bash compile.sh"!

[polar99]

Yes, I did re-compile "bash compile.sh". But problem still exists. Did my environment cause the error? I use docker to impl this project. docker: pytorch/pytorch:1.3-cuda10.1-cudnn7-devel I have installed ReDet following official instructions.

[why228430]

I don't use docker! I am not sure if your environment problem! My environment is 2080ti with Pytorch=1.1.0, cuda=10.0. In my platform the project is okay!

[polar99]

@why228430 Thanks a lot. I'll try new env. What is the training speed in your platform (2080Ti)?

[why228430]

{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00465, "time": 1.45851, "data_time": 0.21686, "memory": 4806, "loss_rpn_cls": 0.29471, "loss_rpn_bbox": 0.11749, "s0.rbbox_loss_cls": 0.48488, "s0.rbbox_acc": 88.70508, "s0.rbbox_loss_bbox": 0.71225, "s1.rbbox_loss_cls": 0.32175, "s1.rbbox_acc": 92.81233, "s1.rbbox_loss_bbox": 0.22031, "loss": 2.15139} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00532, "time": 1.49202, "data_time": 0.11837, "memory": 4806, "loss_rpn_cls": 0.20144, "loss_rpn_bbox": 0.08253, "s0.rbbox_loss_cls": 0.47648, "s0.rbbox_acc": 88.93945, "s0.rbbox_loss_bbox": 0.51546, "s1.rbbox_loss_cls": 0.35854, "s1.rbbox_acc": 91.87267, "s1.rbbox_loss_bbox": 0.39948, "loss": 2.03393} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00599, "time": 1.77337, "data_time": 0.17864, "memory": 4806, "loss_rpn_cls": 0.18083, "loss_rpn_bbox": 0.09426, "s0.rbbox_loss_cls": 0.47266, "s0.rbbox_acc": 88.01562, "s0.rbbox_loss_bbox": 0.47843, "s1.rbbox_loss_cls": 0.3567, "s1.rbbox_acc": 90.61376, "s1.rbbox_loss_bbox": 0.42733, "loss": 2.01021} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00665, "time": 1.44145, "data_time": 0.19534, "memory": 4806, "loss_rpn_cls": 0.15557, "loss_rpn_bbox": 0.09309, "s0.rbbox_loss_cls": 0.49047, "s0.rbbox_acc": 86.23047, "s0.rbbox_loss_bbox": 0.52675, "s1.rbbox_loss_cls": 0.40528, "s1.rbbox_acc": 88.74186, "s1.rbbox_loss_bbox": 0.5826, "loss": 2.25377} Here are some logs in my training process ！

[csuhan]

As I am not familiar with CUDA programming, the current code is not so efficient and robust. I will continue to optimize the code, and pulling requests are welcomed!

[jiarenyf]

We have tested on Pytorch1.1/1.3. Try lower version Pytorch or see here

Using pytorch 1.3.1, met the same problem ...

[lyccol]

您好，我是用v100 cuda10.0 pytorch13.1 和 pytorch1.1.0 都出现了这个问题，请问该如何解决。

[initxuan]

I suggest to install pytorch1.1.0 for 2080Ti.

# CUDA 10.0
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

[initxuan]

To solve the " RuntimeError: cuda runtime error (7) : too many resources requested for launch at src/riroi_align_kernel.cu:389" problem. I suggest to reduce the value of THREADS_PER_BLOCK https://github.com/csuhan/ReDet/blob/7430068dbffa008bf5db158d225e87de4f2bbfa5/mmdet/ops/riroi_align/src/riroi_align_kernel.cu#L11

[luoz66]

@why228430 你的第一个问题，我是这样解决的，你可以试一下 把出错地方的代码改成这样

[luoz66]

RuntimeError: cuda runtime error (701) : too many resources requested for launch at src/riroi_align_kernel.cu:389 这个错误可以修改ReDet/mmdet/ops/riroi_align/src/riroi_align_kernel.cu里的 #define THREADS_PER_BLOCK 1024，改成256 然后删掉所有的*.so文件，然后执行下面的命令 bash compile.sh python setup.py develop swig -c++ -python polyiou.i python setup.py build_ext --inplace 然后再重新训练模型