[Python] Traceback (most recent call last) in Colab when looping through dataloader in pytorch

I'm working on a project to classify flower images using a pre-trained model vgg19 using pytorch.

I'm relying on the model features only and using a custom classifier.

However on starting a for-loop to feed images to the model classifier and calculate accuracy through epochs I get an error.

I'm not sure what's the problem as the error is a traceback (most recent call last)

Below is my notebook.

The cell throwing the error is below

#training the classifier
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.classifier.parameters(),lr=0.01)

steps = 0
running_loss = 0
epochs = 5
print_every = 5

for epoch in range(epochs):
for images,labels in train_dataloader:
steps += 1
optimizer.zero_grad()

logps = model.forward(images)
loss = criterion(logps,labels)
loss.backward()
optimizer.step()

running_loss += loss.item()

if steps % print_every == 0:
test_loss = 0
accuracy = 0
model.eval()

with torch.no_grad():
for images, labels in valid_dataloader:
logps = model.forward(images)
batch_loss = criterion(logps, labels)

test_loss += batch_loss.item()

#Calculate accuracy
ps = torch.exp(logps)
top_p, top_class = ps.topk(5,dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()

print(f"Epoch {epoch+1}/{epochs}.."
f"Train loss: {running_loss/print_every: .3f}.."
f"Test loss: {test_loss/len(valid_loader):.3f}.."
f"Test accuracy: {accuracy/len(valid_loader):.3f}")
running_loss = 0
model.train()


The error I get on running the notebook

AttributeError Traceback (most recent call last)
<ipython-input-11-c218f8f2b72e> in <module>()
8
9 for epoch in range(epochs):
---> 10 for images,labels in train_dataloader:
11 steps += 1
12 optimizer.zero_grad()

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
312 if self.num_workers == 0: # same-process loading
313 indices = next(self.sample_iter) # may raise StopIteration
--> 314 batch = self.collate_fn([self.dataset for i in indices])
315 if self.pin_memory:
316 batch = pin_memory_batch(batch)

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in <listcomp>(.0)
312 if self.num_workers == 0: # same-process loading
313 indices = next(self.sample_iter) # may raise StopIteration
--> 314 batch = self.collate_fn([self.dataset for i in indices])
315 if self.pin_memory:
316 batch = pin_memory_batch(batch)

/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py in __getitem__(self, index)
99 """
100 path, target = self.samples[index]
--> 101 sample = self.loader(path)
102 if self.transform is not None:
103 sample = self.transform(sample)

/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py in default_loader(path)
145 return accimage_loader(path)
146 else:
--> 147 return pil_loader(path)
148
149

/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py in pil_loader(path)
127 # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
128 with open(path, 'rb') as f:
--> 129 img = Image.open(f)
130 return img.convert('RGB')
131

/usr/local/lib/python3.6/dist-packages/PIL/Image.py in open(fp, mode)
2319 return True
2320
-> 2321
2322 def new(mode, size, color=0):
2323 """

/usr/local/lib/python3.6/dist-packages/PIL/Image.py in preinit()
368
369
--> 370 def preinit():
371 """Explicitly load standard file format drivers."""
372

/usr/local/lib/python3.6/dist-packages/PIL/PpmImagePlugin.py in <module>()
156 Image.register_save(PpmImageFile.format, _save)
157
--> 158 Image.register_extensions(PpmImageFile.format, [".pbm", ".pgm", ".ppm"])

AttributeError: module 'PIL.Image' has no attribute 'register_extensions'

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