Requirement: Take MNIST data, apply some distortion, invert that distortion, feed to a cDCGAN and generate samples.
But, the part where I create a custom dataset after applying inversion is erroneous.  The current code written throws an error when I attempt to train the model as given below.
Please advise.
Full code can be found here: https://colab.research.google.com/drive/1zNVxBtnLsmTu6sugQ-D_FDabOIXB-NDC 32
training start!
TypeError                                 Traceback (most recent call last)
 in ()
20     y_fake_ = torch.zeros(batch_size)
21     y_real_, y_fake_ = Variable(y_real_.cuda()), Variable(y_fake_.cuda())
—> 22     for x_, y_ in train_loader:
23         # train discriminator D
24         D.zero_grad()
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in next(self)
613         if self.num_workers == 0:  # same-process loading
614             indices = next(self.sample_iter)  # may raise StopIteration
–> 615             batch = self.collate_fn([self.dataset[i] for i in indices])
616             if self.pin_memory:
617                 batch = pin_memory_batch(batch)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in (.0)
613         if self.num_workers == 0:  # same-process loading
614             indices = next(self.sample_iter)  # may raise StopIteration
–> 615             batch = self.collate_fn([self.dataset[i] for i in indices])
616             if self.pin_memory:
617                 batch = pin_memory_batch(batch)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataset.py in getitem(self, idx)
79         else:
80             sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
—> 81         return self.datasets[dataset_idx][sample_idx]
82
83     @property
 in getitem(self, index)
15         #perform augmentation
16         if self.transform4:
—> 17             img = self.transform4(img, mask) # actually calling the function with necessary attributes
18
19
TypeError: call() takes 2 positional arguments but 3 were given