7ce6642f777e9da6249bd5b05330d57fa09ea37a,example/mpii.py,,train,#,131

Before Change

        // measure data loading time
        data_time.update(time.time() - end)

        input_var = torch.autograd.Variable(inputs.cuda())
        target_var = torch.autograd.Variable(target.cuda(async=True))

        // compute output
        output = model(input_var)
        score_map = output[-1].data.cpu()

        loss = criterion(output[0], target_var)
        for j in range(1, len(output)):
            loss += criterion(output[j], target_var)

After Change

        // measure data loading time
        data_time.update(time.time() - end)

        input, target = input.to(device), target.to(device, non_blocking=True)

        // compute output
        output = model(input)

Instances