natanielruiz
2017-10-30 af51d0ecb51ad4d6c8ed086855bd3c411ebc4aa0
code/train_alexnet.py
@@ -129,6 +129,9 @@
    # Regression loss coefficient
    alpha = args.alpha
    idx_tensor = [idx for idx in xrange(66)]
    idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
    optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
                                  {'params': get_non_ignored_params(model), 'lr': args.lr},
                                  {'params': get_fc_params(model), 'lr': args.lr * 5}],
@@ -150,17 +153,21 @@
            label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
            # Forward pass
            yaw, pitch, roll, angles = model(images)
            pre_yaw, pre_pitch, pre_roll = model(images)
            # Cross entropy loss
            loss_yaw = criterion(yaw, label_yaw)
            loss_pitch = criterion(pitch, label_pitch)
            loss_roll = criterion(roll, label_roll)
            loss_yaw = criterion(pre_yaw, label_yaw)
            loss_pitch = criterion(pre_pitch, label_pitch)
            loss_roll = criterion(pre_roll, label_roll)
            # MSE loss
            yaw_predicted = angles[:,0]
            pitch_predicted = angles[:,1]
            roll_predicted = angles[:,2]
            yaw_predicted = softmax(pre_yaw)
            pitch_predicted = softmax(pre_pitch)
            roll_predicted = softmax(pre_roll)
            yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99
            pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99
            roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99
            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)
            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
@@ -173,7 +180,6 @@
            loss_seq = [loss_yaw, loss_pitch, loss_roll]
            grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
            optimizer.zero_grad()
            torch.autograd.backward(loss_seq, grad_seq)
            optimizer.step()