Execute code command --continue_train --which_epoch 35 --epoch_count 36,it comes an error.It can not found _net_D.pth.coule you tell me how to solve it?

Hello, I trained a model on the CVUSA dataset, but the IS value and KL result are slightly worse during the evaluation. The following are my running commands. Are there any problems that I have not noticed? python train.py --dataroot ./datasets/cvusa/
--name cvusa_selectiongan
--model selectiongan
--which_model_netG unet_256
--which_direction AtoB
--dataset_mode aligned
--norm batch
--gpu_ids 0,1
--batchSize 4
--loadSize 286
--fineSize 256
--no_flip
--display_id 1
--lambda_L1 100
--lambda_L1_seg 1

Where should we run this code .In collab and kaggle notebook , we are running out of space , to download data sets ,with given command for pose transfer (person transfer).

Hello. I've been trying to download your pretrained models but neither the Google Drive, nor the Baidu link works. Could you please check this? Thanks.

in the paper, page 5, the Fm is said to be provided into 3 line, where a matrix multiplication operation was perform between 2 of them to create channel attention map A, but i can't see that in the paper. is that a new change? or it's a more effective way?

self.padding, self.dilation, self.groups) RuntimeError: Calculated padded input size per channel: (3 x 3). Kernel size: (4 x 4). Kernel size can't be greater than actual input size

Hello Sir,

Using my-datasets, I tried to train your code. But I met CUDNN-ERROR.

...
/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:312: operator(): block: [374,0,0], thread: [62,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:312: operator(): block: [374,0,0], thread: [63,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "index out of bounds"` failed.
Traceback (most recent call last):
  File "/data1/TESTBOARD/additional_networks/generation/SelectionGAN_Ha0Tang/semantic_synthesis/train.py", line 40, in <module>
    trainer.run_generator_one_step(data_i)
  File "/data1/TESTBOARD/additional_networks/generation/SelectionGAN_Ha0Tang/semantic_synthesis/trainers/pix2pix_trainer.py", line 35, in run_generator_one_step
    g_losses, generated = self.pix2pix_model(data, mode='generator')
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data1/TESTBOARD/additional_networks/generation/SelectionGAN_Ha0Tang/semantic_synthesis/models/pix2pix_model.py", line 46, in forward
    input_semantics, real_image)
  File "/data1/TESTBOARD/additional_networks/generation/SelectionGAN_Ha0Tang/semantic_synthesis/models/pix2pix_model.py", line 136, in compute_generator_loss
    input_semantics, real_image, compute_kld_loss=self.opt.use_vae)
  File "/data1/TESTBOARD/additional_networks/generation/SelectionGAN_Ha0Tang/semantic_synthesis/models/pix2pix_model.py", line 198, in generate_fake
    fake_image = self.netG(input_semantics, z=z)
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data1/TESTBOARD/additional_networks/generation/SelectionGAN_Ha0Tang/semantic_synthesis/models/networks/generator.py", line 90, in forward
    x = self.fc(x)
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 443, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/itsme/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 440, in _conv_forward
    self.padding, self.dilation, self.groups)
RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED
terminate called after throwing an instance of 'c10::CUDAError'
  what():  CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Exception raised from create_event_internal at /pytorch/c10/cuda/CUDACachingAllocator.cpp:1055 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7f44b3256a22 in /home/itsme/anaconda3/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x10aa3 (0x7f44b34b7aa3 in /home/itsme/anaconda3/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10::cuda::CUDACachingAllocator::raw_delete(void*) + 0x1a7 (0x7f44b34b9147 in /home/itsme/anaconda3/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::TensorImpl::release_resources() + 0x54 (0x7f44b32405a4 in /home/itsme/anaconda3/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #4: <unknown function> + 0xa2f382 (0x7f4558065382 in /home/itsme/anaconda3/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: <unknown function> + 0xa2f421 (0x7f4558065421 in /home/itsme/anaconda3/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #21: __libc_start_main + 0xe7 (0x7f455add0b97 in /lib/x86_64-linux-gnu/libc.so.6)

How to solve it??

Thanks, Edward Cho.

Hi,

I have tried to duplicate the result based on your dayton_a2g_256_pretrained model. However, there are many checkers in the generated images which is not clearly shown in Fig.4 of you paper. I followed all the hyperparameters as you mentioned in readme file

I am wondering if you encountered the same issue in your experiments or do I miss anything?

The attached is one of the screenshots. Thank you in advance!