def load_program_generator(path, model_type="PG+EE"):
checkpoint = load_cpu(path)
kwargs = checkpoint["program_generator_kwargs"]
state = checkpoint["program_generator_state"]
if model_type == "FiLM":
print("Loading FiLMGen from " + path)
kwargs = get_updated_args(kwargs, FiLMGen)
model = FiLMGen(**kwargs)
else:
print("Loading PG from " + path)
model = Seq2Seq(**kwargs)
model.load_state_dict(state)
return model, kwargs
def load_program_generator(path, model_type='PG+EE'):
checkpoint = load_cpu(path)
kwargs = checkpoint['program_generator_kwargs']
state = checkpoint['program_generator_state']
if model_type == 'FiLM':
print('Loading FiLMGen from ' + path)
kwargs = get_updated_args(kwargs, FiLMGen)
model = FiLMGen(**kwargs)
else:
print('Loading PG from ' + path)
model = Seq2Seq(**kwargs)
model.load_state_dict(state)
return model, kwargs
def load_program_generator(path, model_type='PG+EE'):
checkpoint = load_cpu(path)
kwargs = checkpoint['program_generator_kwargs']
state = checkpoint['program_generator_state']
if model_type == 'FiLM':
print('Loading FiLMGen from ' + path)
kwargs = get_updated_args(kwargs, FiLMGen)
model = FiLMGen(**kwargs)
else:
print('Loading PG from ' + path)
model = Seq2Seq(**kwargs)
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
def load_program_generator(path, model_type='PG+EE'):
checkpoint = load_cpu(path)
kwargs = checkpoint['program_generator_kwargs']
state = checkpoint['program_generator_state']
if model_type == 'FiLM':
print('Loading FiLMGen from ' + path)
kwargs = get_updated_args(kwargs, FiLMGen)
model = FiLMGen(**kwargs)
new_state_dict = OrderedDict()
for k, v in state.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
state = new_state_dict
else:
print('Loading PG from ' + path)
model = Seq2Seq(**kwargs)
model.load_state_dict(state)
return model, kwargs
def load_program_generator(path):
checkpoint = load_cpu(path)
model_type = checkpoint['args']['model_type']
kwargs = checkpoint['program_generator_kwargs']
state = checkpoint['program_generator_state']
if model_type in ['FiLM', 'MAC', 'RelNet']:
kwargs = get_updated_args(kwargs, FiLMGen)
model = FiLMGen(**kwargs)
elif model_type == 'PG+EE':
if kwargs.rnn_attention:
model = Seq2SeqAtt(**kwargs)
else:
model = Seq2Seq(**kwargs)
else:
model = None
if model is not None:
model.load_state_dict(state)
return model, kwargs
def get_program_generator(args):
vocab = utils.load_vocab(args.vocab_json)
if args.program_generator_start_from is not None: # it is None
pg, kwargs = utils.load_program_generator(
args.program_generator_start_from, model_type=args.model_type)
cur_vocab_size = pg.encoder_embed.weight.size(0)
if cur_vocab_size != len(vocab['question_token_to_idx']):
print('Expanding vocabulary of program generator')
pg.expand_encoder_vocab(vocab['question_token_to_idx'])
kwargs['encoder_vocab_size'] = len(vocab['question_token_to_idx'])
else:
kwargs = {
'encoder_vocab_size': len(vocab['question_token_to_idx']),
'decoder_vocab_size': len(vocab['program_token_to_idx']),
'wordvec_dim': args.rnn_wordvec_dim,
'hidden_dim': args.rnn_hidden_dim,
'rnn_num_layers': args.rnn_num_layers,
'rnn_dropout': args.rnn_dropout, # 0e-2
}
if args.model_type == 'FiLM':
kwargs[
'parameter_efficient'] = args.program_generator_parameter_efficient == 1
kwargs['output_batchnorm'] = args.rnn_output_batchnorm == 1
kwargs['bidirectional'] = args.bidirectional == 1
kwargs['encoder_type'] = args.encoder_type
kwargs['decoder_type'] = args.decoder_type
kwargs['gamma_option'] = args.gamma_option
kwargs['gamma_baseline'] = args.gamma_baseline
kwargs['num_modules'] = args.num_modules
kwargs['module_num_layers'] = args.module_num_layers
kwargs['module_dim'] = args.module_dim
kwargs['debug_every'] = args.debug_every
pg = FiLMGen(**kwargs)
else:
pg = Seq2Seq(**kwargs)
pg.cuda()
pg.encoder_rnn.flatten_parameters()
if args.gpu_devices:
gpu_id = parse_int_list(args.gpu_devices)
pg = DataParallel(pg, device_ids=gpu_id)
pg.train()
pg.module.encoder_rnn.flatten_parameters()
return pg, kwargs
def get_program_generator(args):
vocab = utils.load_vocab(args.vocab_json)
if args.program_generator_start_from is not None:
pg, kwargs = utils.load_program_generator(
args.program_generator_start_from, model_type=args.model_type)
cur_vocab_size = pg.encoder_embed.weight.size(0)
if cur_vocab_size != len(vocab['question_token_to_idx']):
print('Expanding vocabulary of program generator')
pg.expand_encoder_vocab(vocab['question_token_to_idx'])
kwargs['encoder_vocab_size'] = len(vocab['question_token_to_idx'])
else:
kwargs = {
'encoder_vocab_size': len(vocab['question_token_to_idx']),
'decoder_vocab_size': len(vocab['program_token_to_idx']),
'wordvec_dim': args.rnn_wordvec_dim,
'hidden_dim': args.rnn_hidden_dim,
'rnn_num_layers': args.rnn_num_layers,
'rnn_dropout': args.rnn_dropout,
}
if args.model_type.startswith('FiLM'):
kwargs['parameter_efficient'] = args.program_generator_parameter_efficient == 1
kwargs['output_batchnorm'] = args.rnn_output_batchnorm == 1
kwargs['bidirectional'] = args.bidirectional == 1
kwargs['encoder_type'] = args.encoder_type
kwargs['decoder_type'] = args.decoder_type
kwargs['gamma_option'] = args.gamma_option
kwargs['gamma_baseline'] = args.gamma_baseline
kwargs['num_modules'] = args.num_modules
kwargs['module_num_layers'] = args.module_num_layers
kwargs['module_dim'] = args.module_dim
kwargs['debug_every'] = args.debug_every
if args.model_type == 'FiLM+BoW':
kwargs['encoder_type'] = 'bow'
pg = FiLMGen(**kwargs)
else:
pg = Seq2Seq(**kwargs)
if torch.cuda.is_available():
pg.cuda()
else:
pg.cpu()
pg.train()
return pg, kwargs