I am training a model in pytorch. Every 10 eras, I evaluate the train and the test error for the entire train and test data set. For some reason, the rating function causes out of memory on my GPU. This is strange because I have the same package for training and evaluation. I believe this is due to the fact that the net.forward () method is repeated and has all hidden values stored in memory, but I'm not sure how to get around this?
def evaluate(self, data):
correct = 0
total = 0
loader = self.train_loader if data == "train" else self.test_loader
for step, (story, question, answer) in enumerate(loader):
story = Variable(story)
question = Variable(question)
answer = Variable(answer)
_, answer = torch.max(answer, 1)
if self.config.cuda:
story = story.cuda()
question = question.cuda()
answer = answer.cuda()
pred_prob = self.mem_n2n(story, question)[0]
_, output_max_index = torch.max(pred_prob, 1)
toadd = (answer == output_max_index).float().sum().data[0]
correct = correct + toadd
total = total + captions.size(0)
acc = correct / total
return acc
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