When I trained with the previous version, the code worked fine when the dataset size was 256, but I got an error when my dataset size was 512.How should I solve it? Thank you!
Traceback (most recent call last):
File "scripts/segmentation_train.py", line 119, in
main()
File "scripts/segmentation_train.py", line 88, in main
lr_anneal_steps=args.lr_anneal_steps,
File "./guided_diffusion/train_util.py", line 186, in run_loop
self.run_step(batch, cond)
File "./guided_diffusion/train_util.py", line 207, in run_step
sample = self.forward_backward(batch, cond)
File "./guided_diffusion/train_util.py", line 239, in forward_backward
losses1 = compute_losses()
File "./guided_diffusion/gaussian_diffusion.py", line 1145, in training_losses_segmentation
model_output, cal = model(x_t, self._scale_timesteps(t), model_kwargs)
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, *kwargs)
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 886, in forward
output = self.module(inputs[0], kwargs[0])
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, kwargs)
File "./guided_diffusion/unet.py", line 775, in forward
uemb, cal = self.highway_forward(c, [hs[3],hs[6],hs[9],hs[12]])
File "./guided_diffusion/unet.py", line 744, in highway_forward
return self.hwm(x,hs)
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, *kwargs)
File "./guided_diffusion/unet.py", line 2473, in forward
h = self.ffparserd
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(input, kwargs)
File "./guided_diffusion/unet.py", line 479, in forward
x = x * weight
RuntimeError: The size of tensor a (129) must match the size of tensor b (65) at non-singleton dimension 3
wzx0720
commented
7 months ago
When I trained with the previous version, the code worked fine when the dataset size was 256, but I got an error when my dataset size was 512.How should I solve it? Thank you!
Traceback (most recent call last): File "scripts/segmentation_train.py", line 119, in
main()
File "scripts/segmentation_train.py", line 88, in main
lr_anneal_steps=args.lr_anneal_steps,
File "./guided_diffusion/train_util.py", line 186, in run_loop
self.run_step(batch, cond)
File "./guided_diffusion/train_util.py", line 207, in run_step
sample = self.forward_backward(batch, cond)
File "./guided_diffusion/train_util.py", line 239, in forward_backward
losses1 = compute_losses()
File "./guided_diffusion/gaussian_diffusion.py", line 1145, in training_losses_segmentation
model_output, cal = model(x_t, self._scale_timesteps(t), model_kwargs)
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, *kwargs)
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 886, in forward
output = self.module(inputs[0], kwargs[0])
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, kwargs)
File "./guided_diffusion/unet.py", line 775, in forward
uemb, cal = self.highway_forward(c, [hs[3],hs[6],hs[9],hs[12]])
File "./guided_diffusion/unet.py", line 744, in highway_forward
return self.hwm(x,hs)
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, *kwargs)
File "./guided_diffusion/unet.py", line 2473, in forward
h = self.ffparserd
File "/data0/KongJY/anaconda3/envs/Kong/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(input, kwargs)
File "./guided_diffusion/unet.py", line 479, in forward
x = x * weight
RuntimeError: The size of tensor a (129) must match the size of tensor b (65) at non-singleton dimension 3