Yes it is training loss as far as I understand.
Below you can see my codes in order to calculate the loss:
#training loop
for epoch in range(num_epochs):
for data in dataloader:
img = data
img = Variable(img)
output = model (img)
loss = criterion (output,img)

optimizer.zero_grad()
loss.backward()
optimizer.step()

if epoch%10 == 1:
  print ('\r Train Epoch : {}/{} \tLoss : {:.6f}'.format (epoch+1,num_epochs,loss))

I also wrote you down the loss of the last epoch :
Train Epoch : 72/80 	Loss : 1088451712.000000
Train Epoch : 72/80 	Loss : 1088443520.000000
Train Epoch : 72/80 	Loss : 1088414208.000000
Train Epoch : 72/80 	Loss : 1088391808.000000
Train Epoch : 72/80 	Loss : 1088355584.000000
Train Epoch : 72/80 	Loss : 1088334464.000000