| | |
|---|
| | | transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224), |
|---|
| | | transforms.ToTensor()]) |
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| | | |
|---|
| | | pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list, |
|---|
| | | 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, |
|---|
| | |
|---|
| | | reg_criterion = nn.MSELoss().cuda(gpu) |
|---|
| | | # Regression loss coefficient |
|---|
| | | alpha = 0.1 |
|---|
| | | lsm = nn.Softmax() |
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| | | |
|---|
| | | idx_tensor = [idx for idx in xrange(66)] |
|---|
| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
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| | | |
|---|
| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr}, |
|---|
| | | {'params': get_non_ignored_params(model), 'lr': args.lr}], |
|---|
| | | {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], |
|---|
| | | lr = args.lr) |
|---|
| | | |
|---|
| | | print 'Ready to train network.' |
|---|
