def init_network(config, num_classes):
kwargs = {'depth': config.depth, 'num_classes': num_classes}
if config.network == 'vgg':
if 'avg_pool2d' in config:
kwargs.update({'avg_pool2d': config.avg_pool2d})
if 'batch_norm' in config:
kwargs.update({'batch_norm': config.batch_norm})
net = vgg(**kwargs)
elif config.network == 'resnet':
dataset = config.data_dir.split('/')[-1]
if 'cifar' in dataset.lower():
kwargs.update({'dataset': 'cifar'})
net = resnet(**kwargs)
elif config.network == 'reskipnet':
net = reskipnet(**kwargs)
else:
raise NotImplementedError
assert os.path.exists('{}/checkpoint/original'.format(config.result_dir)),\
'No checkpoint directory for original model!'
dataset = config.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, config.network, config.depth)
checkpoint_path, exp_name, epochs = get_best_checkpoint(
'{}/checkpoint/original/{}'.format(config.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
if torch.cuda.is_available():
net.cuda()
return net, exp_name, epochs
def init_network(config, num_classes):
if config.network == 'resnet':
kwargs = {'depth': config.depth, 'num_classes': num_classes}
depth = config.depth
net = reskipnet(**kwargs)
else:
raise NotImplementedError
dataset = config.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, config.network, depth)
assert os.path.exists('{}/checkpoint/sp/{}'.format(config.result_dir, path_to_add)),\
'No checkpoint directory for sp model!'
path_to_add = '{}/{}/{}'.format(dataset, config.network, config.depth)
checkpoint_path, exp_name, epochs = get_best_checkpoint('{}/checkpoint/original/{}'.
format(config.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
if torch.cuda.is_available():
net.cuda()
return net, exp_name, epochs
def init_network(args, num_classes):
kwargs = {'depth': args.depth, 'num_classes': num_classes}
depth = str(args.depth)
if args.network == 'vgg':
kwargs.update({'avg_pool2d': args.avg_pool2d})
kwargs.update({'batch_norm': True})
net = vgg(**kwargs)
elif args.network == 'resnet':
dataset = args.data_dir.split('/')[-1]
if 'cifar' in dataset.lower():
kwargs.update({'dataset': 'cifar'})
net = resnet(**kwargs)
elif args.network == 'reskipnet':
net = reskipnet(**kwargs)
elif args.network == 'resnext':
dataset = args.data_dir.split('/')[-1]
if 'cifar' in dataset.lower():
kwargs.update({'dataset': 'cifar'})
kwargs.update({
'cardinality': args.cardinality,
'base_width': args.base_width
})
net = resnext(**kwargs)
depth = '{}_{}_{}d'.format(depth, args.cardinality, args.base_width)
else:
raise NotImplementedError
if args.resume:
dataset = args.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, args.network, depth)
assert os.path.exists('{}/checkpoint/original/{}'.format(args.result_dir, path_to_add)),\
'No checkpoint directory for original model!'
checkpoint_path, _, previous_epochs = get_best_checkpoint(
'{}/checkpoint/original/{}'.format(args.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
previous_accuracy = checkpoint_path.split('_')[-2]
else:
previous_accuracy = 0
previous_epochs = 0
if torch.cuda.is_available():
net.cuda()
return net, float(previous_accuracy), previous_epochs, depth
def init_network(args, num_classes):
if args.network == 'resnet':
kwargs = {'depth': args.depth, 'num_classes': num_classes}
depth = str(args.depth)
net = reskipnet(**kwargs)
else:
raise NotImplementedError
net.rl_mode = True
dataset = args.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, args.network, depth)
assert os.path.exists('{}/checkpoint/sp/{}'.format(args.result_dir, path_to_add)),\
'No checkpoint directory for sp model!'
checkpoint_path, _, epochs = get_best_checkpoint(
'{}/checkpoint/sp/{}'.format(args.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
if torch.cuda.is_available():
net.cuda()
net.fill_flop_weights()
return net, epochs, depth
def init_network(config, num_classes):
if config.network == 'resnet':
kwargs = {'depth': config.depth, 'num_classes': num_classes}
depth = config.depth
net = reskipnet(**kwargs)
else:
raise NotImplementedError
net.rl_mode = True
dataset = config.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, config.network, depth)
assert os.path.exists('{}/checkpoint/pruned/{}'.format(config.result_dir, path_to_add)),\
'No checkpoint directory for pruned model!'
checkpoint_file = config.checkpoint_file
checkpoint_path = '{}/{}'.format(path_to_add, checkpoint_file)
exp_name = config.exp_name
net = load_checkpoint_pruning(checkpoint_path, net, use_bias=True)
if torch.cuda.is_available():
net.cuda()
net.fill_flop_weights()
compression = checkpoint_file.split('_')[-1].split('.pth')[0]
return net, exp_name, float(compression)
def init_network(args, num_classes):
if args.network == 'resnet':
kwargs = {'depth': args.depth, 'num_classes': num_classes}
depth = str(args.depth)
net = reskipnet(**kwargs)
else:
raise NotImplementedError
if args.resume:
dataset = args.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, args.network, depth)
assert os.path.exists('{}/checkpoint/original/{}'.format(args.result_dir, path_to_add)),\
'No checkpoint directory for original model!'
checkpoint_path, _, previous_epochs = get_best_checkpoint(
'{}/checkpoint/original/{}'.format(args.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
previous_accuracy = checkpoint_path.split('_')[-2]
else:
previous_accuracy = 0
previous_epochs = 0
if torch.cuda.is_available():
net.cuda()
return net, float(previous_accuracy), previous_epochs, depth