config = pickle.load(config_f)
assert (config.model_ind == given_config.model_ind)
config.restart = True
config.restart_from_best = given_config.restart_from_best
# copy over new num_epochs and lr schedule
config.num_epochs = given_config.num_epochs
config.lr_schedule = given_config.lr_schedule
else:
print("Config: %s" % config_to_str(config))
# Model ------------------------------------------------------------------------
dataloaders, mapping_assignment_dataloader, mapping_test_dataloader = \
cluster_create_dataloaders(config)
net = archs.__dict__[config.arch](config)
if config.restart:
model_path = os.path.join(config.out_dir, net_name)
net.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
net.cuda()
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart:
optimiser.load_state_dict(
torch.load(os.path.join(config.out_dir, opt_name)))
config = pickle.load(config_f)
assert config.model_ind == given_config.model_ind
config.restart = True
config.restart_from_best = given_config.restart_from_best
# copy over new num_epochs and lr schedule
config.num_epochs = given_config.num_epochs
config.lr_schedule = given_config.lr_schedule
else:
print("Config: %s" % config_to_str(config))
# Model ------------------------------------------------------------------------
dataloaders, mapping_assignment_dataloader, mapping_test_dataloader = cluster_create_dataloaders(
config
)
net = archs.__dict__[config.arch](config) # type: ignore
if config.restart:
assert net_name is not None
model_path = os.path.join(config.out_dir, net_name)
net.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage)
)
net.cuda()
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart: