for params in generator_model.parameters():
params.requires_grad = True
for params in discriminator_model_pose.parameters():
params.requires_grad = True
for params in discriminator_model_conf.parameters():
params.requires_grad = True
# Use dataparallel
generator_model = nn.DataParallel(generator_model)
discriminator_model_conf = nn.DataParallel(discriminator_model_conf)
discriminator_model_pose = nn.DataParallel(discriminator_model_pose)
# Datasets
if args.dataset == 'lsp':
lsp_train_dataset = LSP(args)
args.mode = 'val'
lsp_val_dataset = LSP(args)
# medical
if args.dataset == 'medical':
lsp_train_dataset = HANDXRAY(args)
args.mode = 'val'
lsp_val_dataset = HANDXRAY(args)
# MPII
elif args.dataset == 'mpii':
lsp_train_dataset = MPII('train')
lsp_val_dataset = MPII('valid') ## MPII('val') was present originally
# Dataset and the Dataloade
train_loader = torch.utils.data.DataLoader(lsp_train_dataset,
batch_size=args.batch_size,
config['dataset']['num_joints'],
config['discriminator']['num_residuals'])
# Load
model_data = torch.load(args.modelName)
generator_model = model_data['generator_model']
# Use dataparallel
generator_model = nn.DataParallel(generator_model)
discriminator_model = nn.DataParallel(discriminator_model)
generator_model = (generator_model).module
discriminator_model = (discriminator_model).module
# Dataset and the Dataloader
lsp_train_dataset = LSP(args)
args.mode = 'val'
lsp_val_dataset = MPII('val') #LSP(args)
train_loader = torch.utils.data.DataLoader(lsp_train_dataset,
batch_size=args.batch_size,
shuffle=True)
val_save_loader = torch.utils.data.DataLoader(lsp_val_dataset,
batch_size=args.batch_size)
val_eval_loader = torch.utils.data.DataLoader(lsp_val_dataset,
batch_size=args.batch_size,
shuffle=True)
# Loading on GPU, if available
if (args.use_gpu):
generator_model = generator_model.to(fast_device)
discriminator_model = discriminator_model.to(fast_device)