def load_execution_engine(path, verbose=True):
checkpoint = load_cpu(path)
model_type = checkpoint['args']['model_type']
kwargs = checkpoint['execution_engine_kwargs']
state = checkpoint['execution_engine_state']
kwargs['verbose'] = verbose
if model_type == 'FiLM':
kwargs = get_updated_args(kwargs, FiLMedNet)
model = FiLMedNet(**kwargs)
elif model_type == 'EE':
model = ModuleNet(**kwargs)
elif model_type == 'MAC':
kwargs.setdefault('write_unit', 'original')
kwargs.setdefault('read_connect', 'last')
kwargs.setdefault('noisy_controls', False)
kwargs.pop('sharing_params_patterns', None)
model = MAC(**kwargs)
elif model_type == 'RelNet':
model = RelationNet(**kwargs)
elif model_type == 'SHNMN':
model = SHNMN(**kwargs)
else:
raise ValueError()
cur_state = model.state_dict()
model.load_state_dict(state)
return model, kwargs
def load_execution_engine(path, verbose=True):
checkpoint = load_cpu(path)
if checkpoint['args'].get('symbolic_ee'):
vocab = load_vocab(checkpoint['args']['vocab_json'])
ee = ClevrExecutor(vocab)
return ee, {}
model_type = checkpoint['args']['model_type']
kwargs = checkpoint['execution_engine_kwargs']
state = checkpoint['execution_engine_state']
kwargs['verbose'] = verbose
if model_type == 'FiLM':
model = FiLMedNet(**kwargs)
elif model_type in ['PG+EE', 'EE', 'Control-EE']:
kwargs.pop('sharing_patterns', None)
kwargs.setdefault('module_pool', 'mean')
kwargs.setdefault('module_use_gammas', 'linear')
model = ModuleNet(**kwargs)
elif model_type == 'MAC':
kwargs.setdefault('write_unit', 'original')
kwargs.setdefault('read_connect', 'last')
kwargs.setdefault('read_unit', 'original')
kwargs.setdefault('noisy_controls', False)
kwargs.pop('sharing_params_patterns', None)
model = MAC(**kwargs)
elif model_type == 'RelNet':
model = RelationNet(**kwargs)
elif model_type == 'SHNMN':
model = SHNMN(**kwargs)
elif model_type == 'SimpleNMN':
model = SimpleModuleNet(**kwargs)
else:
raise ValueError()
cur_state = model.state_dict()
model.load_state_dict(state)
return model, kwargs
def load_execution_engine(path, verbose=True, model_type='PG+EE'):
checkpoint = load_cpu(path)
kwargs = checkpoint['execution_engine_kwargs']
state = checkpoint['execution_engine_state']
kwargs['verbose'] = verbose
if model_type == 'FiLM':
print('Loading FiLMedNet from ' + path)
kwargs = get_updated_args(kwargs, FiLMedNet)
model = FiLMedNet(**kwargs)
else:
print('Loading EE from ' + path)
model = ModuleNet(**kwargs)
cur_state = model.state_dict()
model.load_state_dict(state)
return model, kwargs
def load_execution_engine(path, verbose=True, model_type="PG+EE"):
checkpoint = load_cpu(path)
kwargs = checkpoint["execution_engine_kwargs"]
state = checkpoint["execution_engine_state"]
kwargs["verbose"] = verbose
if model_type == "FiLM":
print("Loading FiLMedNet from " + path)
kwargs = get_updated_args(kwargs, FiLMedNet)
model = FiLMedNet(**kwargs)
else:
print("Loading EE from " + path)
model = ModuleNet(**kwargs)
cur_state = model.state_dict()
model.load_state_dict(state)
return model, kwargs
def load_execution_engine(path, verbose=True, model_type='PG+EE'):
checkpoint = load_cpu(path)
kwargs = checkpoint['execution_engine_kwargs']
state = checkpoint['execution_engine_state']
kwargs['verbose'] = verbose
if model_type == 'FiLM':
print('Loading FiLMedNet from ' + path)
kwargs = get_updated_args(kwargs, FiLMedNet)
model = FiLMedNet(**kwargs)
else:
print('Loading EE from ' + path)
model = ModuleNet(**kwargs)
cur_state = model.state_dict()
# cur_params = dict(model.named_parameters())
# print cur_params.keys()
state_stemed = {}
for k, v in state.iteritems():
k_new = '.'.join(k.split('.')[1:])
state_stemed[k_new] = v
model.load_state_dict(state_stemed)
return model, kwargs
'batch_size': args.batch_size,
'max_samples': args.num_val_samples,
'num_workers': args.loader_num_workers,
}
with ClevrDataLoader(**train_loader_kwargs) as train_loader, \
ClevrDataLoader(**val_loader_kwargs) as val_loader:
best_val_acc = 0.0
for i in range(30):
logger.info('Epoch ' + str(i))
train_loss, train_acc = eval_epoch(train_loader,
film_gen,
filmed_net,
opt=opt)
valid_loss, valid_acc = eval_epoch(val_loader, film_gen,
filmed_net)
if train_loss.ndim == 1:
train_loss = train_loss[0]
valid_loss = valid_loss[0]
logger.info("{}, {}, {}, {}".format(train_loss, train_acc,
valid_loss, valid_acc))
if valid_acc > best_val_acc:
best_val_acc = valid_acc
state = dict()
state['film_gen'] = film_gen.state_dict()
state['filmed_net'] = filmed_net.state_dict()
state['args'] = args
torch.save(state, os.path.join(exp_dir, 'model.pt'))