def Metric(self):
PCKhinstance = metrics.PCKh(self.opts)
PCKinstance = metrics.PCK(self.opts)
if self.opts.dataset == 'MPII':
return {
'PCK': getattr(PCKinstance, self.opts.model),
'PCKh': getattr(PCKhinstance, self.opts.model)
}
if self.opts.dataset == 'COCO':
return {'PCK': getattr(PCKinstance, self.opts.model)}
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,
shuffle=True)
val_save_loader = torch.utils.data.DataLoader(lsp_val_dataset,
batch_size=args.val_batch_size)
val_eval_loader = torch.utils.data.DataLoader(lsp_val_dataset,
batch_size=args.val_batch_size,
shuffle=True)
#train_eval = torch.utils.data.DataLoader(lsp_train_dataset, batch_size=args.val_batch_size, shuffle=True)
pck = metrics.PCK(metrics.Options(256, 8))
# Loading on GPU, if available
if (args.use_gpu):
generator_model = generator_model.to(fast_device)
discriminator_model_conf = discriminator_model_conf.to(fast_device)
discriminator_model_pose = discriminator_model_pose.to(fast_device)
# Cross entropy loss
#criterion = nn.CrossEntropyLoss()
# Setting the optimizer
if (args.optimizer_type == 'SGD'):
optim_gen = optim.SGD(generator_model.parameters(),
lr=args.lr,
momentum=args.momentum)
# MPII
elif args.dataset == 'mpii':
lsp_train_dataset = MPII('train')
lsp_val_dataset = MPII('val')
# Dataset and the Dataloade
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.val_batch_size)
val_eval_loader = torch.utils.data.DataLoader(lsp_val_dataset,
batch_size=args.val_batch_size,
shuffle=True)
pck = metrics.PCK(metrics.Options(256, config['generator']['num_stacks']))
# Loading on GPU, if available
if (args.use_gpu):
generator_model = generator_model.to(fast_device)
discriminator_model = discriminator_model.to(fast_device)
# Cross entropy loss
criterion = nn.CrossEntropyLoss()
# Setting the optimizer
if (args.optimizer_type == 'SGD'):
optim_gen = optim.SGD(generator_model.parameters(),
lr=args.lr,
momentum=args.momentum)
optim_disc = optim.SGD(discriminator_model.parameters(),