From 0b8e19c1cc8ad03805d4ca68f32df6e4806a36e8 Mon Sep 17 00:00:00 2001 From: natanielruiz <nataniel777@hotmail.com> Date: 星期五, 08 九月 2017 11:15:10 +0800 Subject: [PATCH] Finetune layer working --- code/train.py | 137 +++++++++++++++++++++++++++++++-------------- 1 files changed, 95 insertions(+), 42 deletions(-) diff --git a/code/train.py b/code/train.py index f98bbc3..826793d 100644 --- a/code/train.py +++ b/code/train.py @@ -33,6 +33,8 @@ default=0, type=int) parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.', default=5, type=int) + parser.add_argument('--num_epochs_ft', dest='num_epochs_ft', help='Maximum number of finetuning epochs.', + default=5, type=int) parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', default=16, type=int) parser.add_argument('--lr', dest='lr', help='Base learning rate.', @@ -41,9 +43,7 @@ default='', type=str) parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', default='', type=str) - args = parser.parse_args() - return args def get_ignored_params(model): @@ -66,6 +66,7 @@ b.append(model.fc_yaw) b.append(model.fc_pitch) b.append(model.fc_roll) + b.append(model.fc_finetune) for i in range(len(b)): for j in b[i].modules(): for k in j.parameters(): @@ -86,6 +87,7 @@ cudnn.enabled = True num_epochs = args.num_epochs + num_epochs_ft = args.num_epochs_ft batch_size = args.batch_size gpu = args.gpu_id @@ -95,17 +97,22 @@ # ResNet101 with 3 outputs # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) # ResNet50 - # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) + model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) # ResNet18 - model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) - load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet18'])) + # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) + load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50'])) print 'Loading data.' - transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224), - transforms.ToTensor()]) + # transformations = transforms.Compose([transforms.Scale(224), + # transforms.RandomCrop(224), + # transforms.ToTensor()]) - pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list, + transformations = transforms.Compose([transforms.Scale(250), + transforms.RandomCrop(224), transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) + + pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations) train_loader = torch.utils.data.DataLoader(dataset=pose_dataset, batch_size=batch_size, @@ -113,11 +120,10 @@ num_workers=2) model.cuda(gpu) - criterion = nn.CrossEntropyLoss() - reg_criterion = nn.MSELoss() + criterion = nn.CrossEntropyLoss().cuda() + reg_criterion = nn.MSELoss().cuda() # Regression loss coefficient alpha = 0.01 - lsm = nn.Softmax() idx_tensor = [idx for idx in xrange(66)] idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) @@ -125,38 +131,31 @@ optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr}, {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], lr = args.lr) - # optimizer = torch.optim.SGD([{'params': get_ignored_params(model), 'lr': args.lr}, - # {'params': get_non_ignored_params(model), 'lr': args.lr}], - # lr = args.lr, momentum=0.9) - # optimizer = torch.optim.RMSprop([{'params': get_ignored_params(model), 'lr': args.lr}, - # {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], - # lr = args.lr) print 'Ready to train network.' + print 'First phase of training.' for epoch in range(num_epochs): for i, (images, labels, name) in enumerate(train_loader): - images = Variable(images).cuda(gpu) - label_yaw = Variable(labels[:,0]).cuda(gpu) - label_pitch = Variable(labels[:,1]).cuda(gpu) - label_roll = Variable(labels[:,2]).cuda(gpu) + images = Variable(images.cuda(gpu)) + label_yaw = Variable(labels[:,0].cuda(gpu)) + label_pitch = Variable(labels[:,1].cuda(gpu)) + label_roll = Variable(labels[:,2].cuda(gpu)) optimizer.zero_grad() - yaw, pitch, roll = model(images) + model.zero_grad() - loss_yaw = criterion(yaw, label_yaw) - loss_pitch = criterion(pitch, label_pitch) - loss_roll = criterion(roll, label_roll) + pre_yaw, pre_pitch, pre_roll, angles = model(images) - # loss_seq = [loss_yaw, loss_pitch, loss_roll] - # grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] - # torch.autograd.backward(loss_seq, grad_seq) - # optimizer.step() + # Cross entropy loss + 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 = F.softmax(yaw) - pitch_predicted = F.softmax(pitch) - roll_predicted = F.softmax(roll) + yaw_predicted = F.softmax(pre_yaw) + pitch_predicted = F.softmax(pre_pitch) + roll_predicted = F.softmax(pre_roll) yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) @@ -166,33 +165,87 @@ loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) - # print yaw_predicted[0], label_yaw.data[0] - + # Total loss loss_yaw += alpha * loss_reg_yaw loss_pitch += alpha * loss_reg_pitch loss_roll += alpha * loss_reg_roll loss_seq = [loss_yaw, loss_pitch, loss_roll] grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] - model.zero_grad() torch.autograd.backward(loss_seq, grad_seq) optimizer.step() - - # print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' - # %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) if (i+1) % 100 == 0: print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) # if epoch == 0: # torch.save(model.state_dict(), - # 'output/snapshots/resnet18_sgd_iter_'+ str(i+1) + '.pkl') + # 'output/snapshots/hopenet50_epoch_'+ str(i+1) + '.pkl') # Save models at numbered epochs. - if epoch % 1 == 0 and epoch < num_epochs - 1: + if epoch % 1 == 0 and epoch < num_epochs: print 'Taking snapshot...' torch.save(model.state_dict(), - 'output/snapshots/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl') + 'output/snapshots/hopenet50_epoch_'+ str(epoch+1) + '.pkl') + + print 'Second phase of training (finetuning layer).' + for epoch in range(num_epochs_ft): + for i, (images, labels, name) in enumerate(train_loader): + images = Variable(images.cuda(gpu)) + label_yaw = Variable(labels[:,0].cuda(gpu)) + label_pitch = Variable(labels[:,1].cuda(gpu)) + label_roll = Variable(labels[:,2].cuda(gpu)) + label_angles = Variable(labels[:,:3].cuda(gpu)) + + optimizer.zero_grad() + model.zero_grad() + + pre_yaw, pre_pitch, pre_roll, angles = model(images) + + # Cross entropy loss + 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 = F.softmax(pre_yaw) + pitch_predicted = F.softmax(pre_pitch) + roll_predicted = F.softmax(pre_roll) + + yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) + pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) + roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1) + + loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float()) + loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) + loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) + + # Total loss + loss_yaw += alpha * loss_reg_yaw + loss_pitch += alpha * loss_reg_pitch + loss_roll += alpha * loss_reg_roll + + # Finetuning loss + loss_angles = reg_criterion(angles[0], label_angles.float()) + + loss_seq = [loss_yaw, loss_pitch, loss_roll, loss_angles] + grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] + torch.autograd.backward(loss_seq, grad_seq) + optimizer.step() + + if (i+1) % 100 == 0: + print ('Epoch [%d/%d], Iter [%d/%d] Losses: pre-yaw %.4f, pre-pitch %.4f, pre-roll %.4f, finetuning %.4f' + %(epoch+1, num_epochs_ft, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0], loss_angles.data[0])) + # if epoch == 0: + # torch.save(model.state_dict(), + # 'output/snapshots/hopenet50_iter_'+ str(i+1) + '.pkl') + + # Save models at numbered epochs. + if epoch % 1 == 0 and epoch < num_epochs_ft - 1: + print 'Taking snapshot...' + torch.save(model.state_dict(), + 'output/snapshots/hopenet50_epoch_'+ str(num_epochs+epoch+1) + '.pkl') + # Save the final Trained Model - torch.save(model.state_dict(), 'output/snapshots/resnet18_sgd_epoch_' + str(epoch+1) + '.pkl') + torch.save(model.state_dict(), 'output/snapshots/hopenet50_epoch_' + str(num_epochs+epoch+1) + '.pkl') -- Gitblit v1.8.0