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
