Pytorch tensor data is disturbed when I convert data loader tensor to numpy array

vision
1

Hi,
I am using a simple train loop for a regression task. To make sure that regression ground-truth values are the same as what I expect in the training loop, I decided to plot each batch of data.
However, I see that when I convert the data loader’s tensor to numpy array and plot it, it is disturbed. I am using myTensor.data.cpu().numpy() for the conversion.

My code is as below:

train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size = 32, shuffle = True, num_workers = 0, drop_last = True)


for epoch in range(epochs):
model.train()
for i, (x, y) in enumerate(train_dl):
       x = x.cuda()
       y = y.cuda()
       yy = y.data.cpu().numpy()
       pyplot.plot(yy[0:32,0])
       pyplot.show()

   ...
2

Here are the outputs

3
3857×306 35.2 KB

3

I think it is because I set shuffle = True in data loader.
If I set it to false, it is fine. However, How can I shuffle training batches after each epoch if I set shuffle = False in data loader then?