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XPixelGroup / RankSRGAN

ICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution. PyTorch implementation
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How to train this model in one node multi-gpus mode? #14

Open trillionpowers opened 4 years ago

trillionpowers commented 4 years ago

Thanks for your project.

My eviroument is Ubuntu16.04+Python3.6 +Pytorch1.1+CUDA10.0

I try to use this code to train distributed python -m torch.distributed.launch --nproc_per_node=2 --master_port=4321 train_niqe.py -opt options/train/train_AdaGrowingNet.yml --launcher pytorch

First, for VGGFeatureExtractor, I got this error: RuntimeError: replicas_[0].size() >= 1 ASSERT FAILED at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:53, please report a bug to PyTorch. Expected at least one parameter. (Reducer at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:53) frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f27c47be441 in /home/wangzhan/anaconda3/envs/py36_pt10_tf14/lib/python3.6/site-packages/torch/lib/libc10.so) frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f27c47bdd7a in /home/wangzhan/anaconda3/envs/py36_pt10_tf14/lib/python3.6/site-packages/torch/lib/libc10.so) frame #2: c10d::Reducer::Reducer(std::vector<std::vector<torch::autograd::Variable, std::allocator<torch::autograd::Variable> >, std::allocator<std::vector<torch::autograd::Variable, std::allocator<torch::autograd::Variable> > > >, std::vector<std::vector<unsigned long, std::allocator<unsigned long> >, std::allocator<std::vector<unsigned long, std::allocator<unsigned long> > > >, std::shared_ptr<c10d::ProcessGroup>) + 0x199c (0x7f280405fc1c in /home/wangzhan/anaconda3/envs/py36_pt10_tf14/lib/python3.6/site-packages/torch/lib/libtorch_python.so)

Then I set the parameters of netF: v.requires_grad = False; After self.netF = DistributedDataParallel(self.netF, device_ids=[torch.cuda.current_device()]). While this code is first at the define of the VGGFeatureExtractor. So, this error disappeared.

Then I still run this code, But it got RuntimeError. Traceback (most recent call last): File "train_niqe.py", line 260, in <module> main() File "train_niqe.py", line 172, in main model.optimize_parameters(current_step) File "/home/wangzhan/SRtask/data_augment/RankSRGAN-master/codes/models/RankSRGAN_model.py", line 215, in optimize_parameters l_d_total.backward() File "/home/wangzhan/anaconda3/envs/py36_pt10_tf14/lib/python3.6/site-packages/torch/tensor.py", line 107, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/wangzhan/anaconda3/envs/py36_pt10_tf14/lib/python3.6/site-packages/torch/autograd/__init__.py", line 93, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [512]] is at version 4; expected version 3 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

Do you encounter this problem? How to fix it?

Som5ra commented 2 years ago

Hi mate, how did u fix this? I just met the same problem with multi-GPUs mode but the code ran well in single GPU mode instead.