Resume training from a checkpoint

[itouchzh]

Hi.
How can I resume training from the breakpoint without starting over when the training is interrupted unexpectedly?
Is modifying the load_step option in the code and loading the desired checkpoint a correct approach?
Like this：
parser.add_argument("--load_step", type=int, default=70000) parser.add_argument('--gen_checkpoint', type=str, default='./checkpoints/gen_step_070000.pth', help='gen checkpoint') parser.add_argument('--dis_checkpoint', type=str, default='./checkpoints/dis_step_070000.pth', help='dis checkpoint')
Is there anything else that needs to be done besides this?
Thanks.

[24thTinyGiant]

@itouchzh can you you tell what steps you carried out to start training and did you keep the dataset as it is for training or made some changes. Please Help

[24thTinyGiant]

File "/content/TryYours-Virtual-Try-On/HR-VITON-main/train_condition.py", line 503, in
main()
File "/content/TryYours-Virtual-Try-On/HR-VITON-main/train_condition.py", line 494, in main
train(opt, train_loader, val_loader, test_loader, board, tocg, D)
File "/content/TryYours-Virtual-Try-On/HR-VITON-main/train_condition.py", line 162, in train
flow_list, fake_segmap, warped_cloth_paired, warped_clothmask_paired = tocg(opt,input1=input2, input2=input2)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/TryYours-Virtual-Try-On/HR-VITON-main/networks.py", line 107, in forward
E1_list.append(self.ClothEncoderi)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/TryYours-Virtual-Try-On/HR-VITON-main/networks.py", line 197, in forward
residual = self.scale(x)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [96, 4, 3, 3], expected input[8, 16, 256, 192] to have 4 channels, but got 16 channels instead "I'm getting this error"