Hi,
Frankly speaking, I am a newbie to Pycharm and familiar with Tensorflow. While reproducing the code available at Here using Pycharm as the IDE, I am facing the following error. Can you please help me to resolve it? I would really appreciate your help. Thanks in advance.
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 1, 256, 256]], which is output 0 of ReluBackward0, is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
I have just edited the dataset file (to load the dataset in an unsupervised way from the directories) and created a new train.py file to run the code in Pycharm. All of the remaining code is exactly the same as in the mentioned repository.
The dataset.py file is edited to this:
import os
import glob
import torch
import random
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class Images_with_Names(data.Dataset):
""" can act both as Supervised or Un-supervised """
def __init__(self, directory_A, directory_B, unsupervised=True, transform=None):
self.directory_A = directory_A
self.directory_B = directory_B
self.unsupervised = unsupervised
self.transform = transform
self.imageList_A = sorted(glob.glob(f"{directory_A}/*.jpg*"))
self.imageList_B = sorted(glob.glob(f"{directory_B}/*.jpg*"))
def __getitem__(self, index):
image_A = Image.open(self.imageList_A[index])
if self.unsupervised:
image_B = Image.open(self.imageList_B[random.randint(0, len(self.imageList_B) - 1)])
else:
image_B = Image.open(self.imageList_B[index])
if self.transform is not None:
image_A = self.transform(image_A)
image_B = self.transform(image_B)
return image_A, image_B
def __len__(self):
return max(len(self.imageList_A), len(self.imageList_B))
def preprocessing(x):
x = (x / 127.5) - 1
x = torch.reshape(x, (-1, x.shape[0], x.shape[1], x.shape[2]))
return x
The train.py file is:
import os
import torch
import torchvision.transforms as transforms
from torchsummary import summary
from utils import train_UGAC
from dataset import Images_with_Names
from dataset import preprocessing
from Networks import CasUNet_3head, NLayerDiscriminator
# First instantiate the generators and discriminators
netG_A = CasUNet_3head(3, 3)
netD_A = NLayerDiscriminator(3, n_layers=4)
netG_B = CasUNet_3head(3, 3)
netD_B = NLayerDiscriminator(3, n_layers=4)
data_directory = "../code/UncertaintyAwareCycleConsistency/data/"
directory_A = os.path.join(data_directory, "A")
directory_B = os.path.join(data_directory, "B")
data_transformer = transforms.Compose([transforms.PILToTensor(),
transforms.Lambda(lambda x: preprocessing(x))])
train_loader = Images_with_Names(directory_A=directory_A, directory_B=directory_B, unsupervised=True,
transform=data_transformer)
# summary(netG_A.cuda(), input_size=(3, 256, 256))
train_UGAC(netG_A, netG_B, netD_A, netD_B, train_loader, dtype=torch.cuda.FloatTensor, device='cuda',
num_epochs=10, init_lr=1e-5, ckpt_path='..saved_models/checkpoints/UGAC',
list_of_hp=[1, 0.015, 0.01, 0.001, 1, 0.015, 0.01, 0.001, 0.05, 0.05, 0.01])
Attempts that I have tried to resolve the issue are:
- Setting
inplace=Falseto allReluandLeakyReluactivations following this but failed. - Tried to get traceback of forward call that caused the error with
torch.autograd.set_detect_anomaly(True), it says the following:
[W python_anomaly_mode.cpp:104] Warning: Error detected in ReluBackward0. Traceback of forward call that caused the error:
File "/home/xyz/code/UncertaintyAwareCycleConsistency/src/train.py", line 29, in <module>
netG_A, netG_B, netD_A, netD_B = train_UGAC(netG_A, netG_B, netD_A, netD_B, train_loader, dtype=torch.cuda.FloatTensor,
File "/home/xyz/code/UncertaintyAwareCycleConsistency/src/utils.py", line 69, in train_UGAC
t0, t0_alpha, t0_beta = netG_B(xA)
File "/home/xyz/.conda/envs/pytorch/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/xyz/code/UncertaintyAwareCycleConsistency/src/Networks.py", line 205, in forward
y = self.unet_list[i](y + x)
File "/home/xyz/.conda/envs/pytorch/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/xyz/code/UncertaintyAwareCycleConsistency/src/Networks.py", line 181, in forward
y_mean, y_alpha, y_beta = self.out_mean(x), self.out_alpha(x), self.out_beta(x)
Looking forward to hearing from you soon. Thanks.