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| | | from torchvision import transforms |
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| | | import torchvision |
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| | | import torch.backends.cudnn as cudnn |
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| | | import torch.nn.functional as F |
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| | | |
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| | | import cv2 |
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| | | import matplotlib.pyplot as plt |
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| | | model.cuda(gpu) |
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| | | criterion = nn.CrossEntropyLoss() |
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| | | reg_criterion = nn.MSELoss() |
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| | | # Regression loss coefficient |
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| | | alpha = 0.01 |
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| | | lsm = nn.Softmax() |
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| | | |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
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| | | |
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| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr}, |
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| | | {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], |
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| | | lr = args.lr) |
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| | | # optimizer = torch.optim.SGD([{'params': get_ignored_params(model), 'lr': args.lr}, |
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| | | # {'params': get_non_ignored_params(model), 'lr': args.lr}], |
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| | | # {'params': get_non_ignored_params(model), 'lr': args.lr}], |
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| | | # lr = args.lr, momentum=0.9) |
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| | | # optimizer = torch.optim.RMSprop([{'params': get_ignored_params(model), 'lr': args.lr}, |
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| | | # {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], |
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| | | optimizer.zero_grad() |
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| | | yaw, pitch, roll = model(images) |
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| | | |
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| | | loss_yaw = criterion(yaw, label_yaw) |
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| | | loss_pitch = criterion(pitch, label_pitch) |
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| | | loss_roll = criterion(roll, label_roll) |
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| | | |
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| | | # loss_seq = [loss_yaw, loss_pitch, loss_roll] |
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| | | # grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] |
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| | | # torch.autograd.backward(loss_seq, grad_seq) |
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| | | # optimizer.step() |
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| | | |
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| | | # MSE loss |
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| | | yaw_predicted = F.softmax(yaw) |
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| | | pitch_predicted = F.softmax(pitch) |
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| | | roll_predicted = F.softmax(roll) |
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| | | |
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| | | yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) |
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| | | pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) |
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| | | roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1) |
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| | | |
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| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float()) |
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| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) |
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| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) |
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| | | |
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| | | # print yaw_predicted[0], label_yaw.data[0] |
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| | | |
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| | | loss_yaw += alpha * loss_reg_yaw |
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| | | loss_pitch += alpha * loss_reg_pitch |
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| | | loss_roll += alpha * loss_reg_roll |
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| | | |
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| | | loss_seq = [loss_yaw, loss_pitch, loss_roll] |
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| | | grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] |
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| | | model.zero_grad() |
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| | | torch.autograd.backward(loss_seq, grad_seq) |
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| | | optimizer.step() |
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| | | |
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| | | # print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
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| | | # %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
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| | | |
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| | | if (i+1) % 100 == 0: |
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| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
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| | | %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
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| | | # if epoch == 0: |
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| | | # torch.save(model.state_dict(), |
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| | | # 'output/snapshots/resnet18_sgd_iter_'+ str(i+1) + '.pkl') |
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| | | |
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| | | # Save models at numbered epochs. |
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| | | if epoch % 1 == 0 and epoch < num_epochs - 1: |
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| | | print 'Taking snapshot...' |
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| | | torch.save(model.state_dict(), |
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| | | 'output/snapshots/resnet18_cr_epoch_'+ str(epoch+1) + '.pkl') |
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| | | 'output/snapshots/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl') |
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| | | |
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| | | # Save the final Trained Model |
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| | | torch.save(model.state_dict(), 'output/snapshots/resnet18_cr_epoch_' + str(epoch+1) + '.pkl') |
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| | | torch.save(model.state_dict(), 'output/snapshots/resnet18_sgd_epoch_' + str(epoch+1) + '.pkl') |
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