def get_execution_engine(args):
vocab = utils.load_vocab(args.vocab_json)
if args.execution_engine_start_from is not None:
ee, kwargs = utils.load_execution_engine(
args.execution_engine_start_from, model_type=args.model_type)
else:
kwargs = {
'vocab': vocab,
'feature_dim': parse_int_list(args.feature_dim),
'stem_batchnorm': args.module_stem_batchnorm == 1,
'stem_num_layers': args.module_stem_num_layers,
'module_dim': args.module_dim,
'module_residual': args.module_residual == 1,
'module_batchnorm': args.module_batchnorm == 1,
'classifier_proj_dim': args.classifier_proj_dim,
'classifier_downsample': args.classifier_downsample,
'classifier_fc_layers': parse_int_list(args.classifier_fc_dims),
'classifier_batchnorm': args.classifier_batchnorm == 1,
'classifier_dropout': args.classifier_dropout,
'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['num_modules'] = args.num_modules
kwargs['stem_kernel_size'] = args.module_stem_kernel_size
kwargs['stem_stride'] = args.module_stem_stride
kwargs['stem_padding'] = args.module_stem_padding
kwargs['module_num_layers'] = args.module_num_layers
kwargs['module_batchnorm_affine'] = args.module_batchnorm_affine == 1
kwargs['module_dropout'] = args.module_dropout
kwargs['module_input_proj'] = args.module_input_proj
kwargs['module_kernel_size'] = args.module_kernel_size
kwargs['use_gamma'] = args.use_gamma == 1
kwargs['use_beta'] = args.use_beta == 1
kwargs['use_coords'] = args.use_coords
kwargs['debug_every'] = args.debug_every
kwargs['print_verbose_every'] = args.print_verbose_every
kwargs['condition_method'] = args.condition_method
kwargs['condition_pattern'] = parse_int_list(args.condition_pattern)
kwargs['parameter_efficient'] = args.program_generator_parameter_efficient == 1
kwargs['output_batchnorm'] = args.rnn_output_batchnorm == 1
kwargs['bidirectional'] = args.bidirectional == 1
kwargs['rnn_time_step'] = args.rnn_time_step
kwargs['encoder_type'] = args.encoder_type
kwargs['decoder_type'] = args.decoder_type
kwargs['gamma_option'] = args.gamma_option
kwargs['gamma_baseline'] = args.gamma_baseline # 1
ee = FiLMedNet(**kwargs)
else:
ee = ModuleNet(**kwargs)
ee.cuda()
ee.train()
return ee, kwargs
def get_execution_engine(args):
vocab = utils.load_vocab(args.vocab_json)
if args.execution_engine_start_from is not None:
ee, kwargs = utils.load_execution_engine(
args.execution_engine_start_from, model_type=args.model_type)
else:
kwargs = {
'vocab': vocab,
'feature_dim': parse_int_list(args.feature_dim),
'stem_batchnorm': args.module_stem_batchnorm == 1,
'stem_num_layers': args.module_stem_num_layers,
'module_dim': args.module_dim,
'module_residual': args.module_residual == 1,
'module_batchnorm': args.module_batchnorm == 1,
'classifier_proj_dim': args.classifier_proj_dim,
'classifier_downsample': args.classifier_downsample,
'classifier_fc_layers': parse_int_list(args.classifier_fc_dims),
'classifier_batchnorm': args.classifier_batchnorm == 1,
'classifier_dropout': args.classifier_dropout,
}
if args.model_type == 'FiLM':
kwargs['num_modules'] = args.num_modules
kwargs['stem_kernel_size'] = args.module_stem_kernel_size
kwargs['stem_stride'] = args.module_stem_stride
kwargs['stem_padding'] = args.module_stem_padding
kwargs['module_num_layers'] = args.module_num_layers
kwargs[
'module_batchnorm_affine'] = args.module_batchnorm_affine == 1
kwargs['module_dropout'] = args.module_dropout
kwargs['module_input_proj'] = args.module_input_proj
kwargs['module_kernel_size'] = args.module_kernel_size
kwargs['use_gamma'] = args.use_gamma == 1
kwargs['use_beta'] = args.use_beta == 1
kwargs['use_coords'] = args.use_coords
kwargs['debug_every'] = args.debug_every
kwargs['print_verbose_every'] = args.print_verbose_every
kwargs['condition_method'] = args.condition_method
kwargs['with_cbn'] = args.with_cbn
kwargs['final_resblock_with_cbn'] = args.final_resblock_with_cbn
kwargs['condition_pattern'] = parse_int_list(
args.condition_pattern)
ee = FiLMedNet(**kwargs)
else:
ee = ModuleNet(**kwargs)
# if cuda.device_count() > 1:
# ee = nn.DataParallel(ee)
ee.cuda()
ee.train()
return ee, kwargs
def get_execution_engine(args):
vocab = utils.load_vocab(args.vocab_json)
if args.execution_engine_start_from is not None:
ee, kwargs = utils.load_execution_engine(
args.execution_engine_start_from, model_type=args.model_type)
else:
kwargs = {
'vocab': vocab,
'feature_dim': parse_int_list(args.feature_dim),
'stem_batchnorm': args.module_stem_batchnorm == 1,
'stem_num_layers': args.module_stem_num_layers,
'module_dim': args.module_dim,
'module_residual': args.module_residual == 1,
'module_batchnorm': args.module_batchnorm == 1,
'classifier_proj_dim': args.classifier_proj_dim,
'classifier_downsample': args.classifier_downsample,
'classifier_fc_layers': parse_int_list(args.classifier_fc_dims),
'classifier_batchnorm': args.classifier_batchnorm == 1,
'classifier_dropout': args.classifier_dropout,
}
if args.model_type.startswith('FiLM'):
kwargs['num_modules'] = args.num_modules
kwargs['stem_use_resnet'] = (args.model_type == 'FiLM+ResNet1' or args.model_type == 'FiLM+ResNet0')
kwargs['stem_resnet_fixed'] = args.model_type == 'FiLM+ResNet0'
kwargs['stem_kernel_size'] = args.module_stem_kernel_size
kwargs['stem_stride2_freq'] = args.module_stem_stride2_freq
kwargs['stem_padding'] = args.module_stem_padding
kwargs['module_num_layers'] = args.module_num_layers
kwargs['module_batchnorm_affine'] = args.module_batchnorm_affine == 1
kwargs['module_dropout'] = args.module_dropout
kwargs['module_input_proj'] = args.module_input_proj
kwargs['module_kernel_size'] = args.module_kernel_size
kwargs['use_gamma'] = args.use_gamma == 1
kwargs['use_beta'] = args.use_beta == 1
kwargs['use_coords'] = args.use_coords
kwargs['debug_every'] = args.debug_every
kwargs['print_verbose_every'] = args.print_verbose_every
kwargs['condition_method'] = args.condition_method
kwargs['condition_pattern'] = parse_int_list(args.condition_pattern)
ee = FiLMedNet(**kwargs)
else:
ee = ModuleNet(**kwargs)
if torch.cuda.is_available():
ee.cuda()
else:
ee.cpu()
ee.train()
return ee, kwargs
module_dropout=args.module_dropout,
module_input_proj=args.module_input_proj,
module_kernel_size=args.module_kernel_size,
classifier_proj_dim=args.classifier_proj_dim,
classifier_downsample='maxpoolfull',
classifier_fc_layers=(1024, ),
classifier_batchnorm=args.classifier_batchnorm,
classifier_dropout=0,
condition_method='bn-film',
condition_pattern=[],
use_gamma=True,
use_beta=True,
use_coords=args.use_coords,
late_fusion_question=(args.classifier_late_fusion == 1),
rnn_hidden_dim=args.rnn_hidden_dim)
filmed_net = filmed_net.cuda()
params = list()
for p in filmed_net.parameters():
params.append({
'params': p,
'weight_decay': args.filmed_net_weight_decay
})
for p in film_gen.parameters():
params.append({
'params': p,
'weight_decay': args.film_gen_weight_decay
})
opt = optim.Adam(params, lr=args.learning_rate)
train_loader_kwargs = {
