def run(config):
"""Runs the model for either pretrain or segmentation/classifiction tasks
Loads the ResNet backbone and based on task specification performs either
pretrain or segmentation/classification finetuning.
Args:
config: instance of config class
Raises:
ValueError: In segmentation/classification tasks if number of classes are not specified
"""
# load the Resnet backbone
model = load_ResNet(config.model,
config.imagenet_path,
include_top=False,
cifar10=config.cifar10,
weight_decay=config.weight_decay)
if config.task == 'pretrain':
print("Pretraining model")
if config.cifar10:
pretrain_cifar10(model, config)
else:
pretrain(model, config)
elif config.task == 'segmentation' or 'classification':
print("Fine-tuning model")
if config.num_classes is None:
raise ValueError("The number of classes must be set")
if config.cifar10:
finetune_cifar10(model, config)
else:
finetune(model, config)
def main():
args = parse_arguments()
# argument setting
print("=== Argument Setting ===")
print("src: " + args.src)
print("tgt: " + args.tgt)
print("seed: " + str(args.seed))
print("train_seed: " + str(args.train_seed))
print("model_type: " + str(args.model))
print("max_seq_length: " + str(args.max_seq_length))
print("batch_size: " + str(args.batch_size))
print("pre_epochs: " + str(args.pre_epochs))
print("num_epochs: " + str(args.num_epochs))
print("AD weight: " + str(args.alpha))
print("KD weight: " + str(args.beta))
print("temperature: " + str(args.temperature))
set_seed(args.train_seed)
if args.model in ['roberta', 'distilroberta']:
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
else:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# preprocess data
print("=== Processing datasets ===")
if args.src in ['blog', 'airline', 'imdb']:
src_x, src_y = CSV2Array(
os.path.join('data', args.src, args.src + '.csv'))
else:
src_x, src_y = XML2Array(
os.path.join('data', args.src, 'negative.review'),
os.path.join('data', args.src, 'positive.review'))
src_x, src_test_x, src_y, src_test_y = train_test_split(
src_x, src_y, test_size=0.2, stratify=src_y, random_state=args.seed)
if args.tgt in ['blog', 'airline', 'imdb']:
tgt_x, tgt_y = CSV2Array(
os.path.join('data', args.tgt, args.tgt + '.csv'))
else:
tgt_x, tgt_y = XML2Array(
os.path.join('data', args.tgt, 'negative.review'),
os.path.join('data', args.tgt, 'positive.review'))
tgt_train_x, tgt_test_y, tgt_train_y, tgt_test_y = train_test_split(
tgt_x, tgt_y, test_size=0.2, stratify=tgt_y, random_state=args.seed)
if args.model in ['roberta', 'distilroberta']:
src_features = roberta_convert_examples_to_features(
src_x, src_y, args.max_seq_length, tokenizer)
src_test_features = roberta_convert_examples_to_features(
src_test_x, src_test_y, args.max_seq_length, tokenizer)
tgt_features = roberta_convert_examples_to_features(
tgt_x, tgt_y, args.max_seq_length, tokenizer)
tgt_train_features = roberta_convert_examples_to_features(
tgt_train_x, tgt_train_y, args.max_seq_length, tokenizer)
else:
src_features = convert_examples_to_features(src_x, src_y,
args.max_seq_length,
tokenizer)
src_test_features = convert_examples_to_features(
src_test_x, src_test_y, args.max_seq_length, tokenizer)
tgt_features = convert_examples_to_features(tgt_x, tgt_y,
args.max_seq_length,
tokenizer)
tgt_train_features = convert_examples_to_features(
tgt_train_x, tgt_train_y, args.max_seq_length, tokenizer)
# load dataset
src_data_loader = get_data_loader(src_features, args.batch_size)
src_data_eval_loader = get_data_loader(src_test_features, args.batch_size)
tgt_data_train_loader = get_data_loader(tgt_train_features,
args.batch_size)
tgt_data_all_loader = get_data_loader(tgt_features, args.batch_size)
# load models
if args.model == 'bert':
src_encoder = BertEncoder()
tgt_encoder = BertEncoder()
src_classifier = BertClassifier()
elif args.model == 'distilbert':
src_encoder = DistilBertEncoder()
tgt_encoder = DistilBertEncoder()
src_classifier = BertClassifier()
elif args.model == 'roberta':
src_encoder = RobertaEncoder()
tgt_encoder = RobertaEncoder()
src_classifier = RobertaClassifier()
else:
src_encoder = DistilRobertaEncoder()
tgt_encoder = DistilRobertaEncoder()
src_classifier = RobertaClassifier()
discriminator = Discriminator()
if args.load:
src_encoder = init_model(args,
src_encoder,
restore=param.src_encoder_path)
src_classifier = init_model(args,
src_classifier,
restore=param.src_classifier_path)
tgt_encoder = init_model(args,
tgt_encoder,
restore=param.tgt_encoder_path)
discriminator = init_model(args,
discriminator,
restore=param.d_model_path)
else:
src_encoder = init_model(args, src_encoder)
src_classifier = init_model(args, src_classifier)
tgt_encoder = init_model(args, tgt_encoder)
discriminator = init_model(args, discriminator)
# train source model
print("=== Training classifier for source domain ===")
if args.pretrain:
src_encoder, src_classifier = pretrain(args, src_encoder,
src_classifier, src_data_loader)
# eval source model
print("=== Evaluating classifier for source domain ===")
evaluate(src_encoder, src_classifier, src_data_loader)
evaluate(src_encoder, src_classifier, src_data_eval_loader)
evaluate(src_encoder, src_classifier, tgt_data_all_loader)
for params in src_encoder.parameters():
params.requires_grad = False
for params in src_classifier.parameters():
params.requires_grad = False
# train target encoder by GAN
print("=== Training encoder for target domain ===")
if args.adapt:
tgt_encoder.load_state_dict(src_encoder.state_dict())
tgt_encoder = adapt(args, src_encoder, tgt_encoder, discriminator,
src_classifier, src_data_loader,
tgt_data_train_loader, tgt_data_all_loader)
# eval target encoder on lambda0.1 set of target dataset
print("=== Evaluating classifier for encoded target domain ===")
print(">>> source only <<<")
evaluate(src_encoder, src_classifier, tgt_data_all_loader)
print(">>> domain adaption <<<")
evaluate(tgt_encoder, src_classifier, tgt_data_all_loader)
def main(args, f):
set_seed(args.train_seed)
if args.model in ['roberta', 'distilroberta']:
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
else:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# preprocess data
src_eval_loader, src_loader, tgt_all_loader, tgt_train_loader, tgt_te = get_all_dataloader(
args, tokenizer)
# load models
if args.model == 'bert':
src_encoder = BertEncoder()
# encoder = BertEncoder()
classifier = BertClassifier()
elif args.model == 'distilbert':
src_encoder = DistilBertEncoder()
# encoder = DistilBertEncoder()
classifier = BertClassifier()
elif args.model == 'roberta':
src_encoder = RobertaEncoder()
# encoder = RobertaEncoder()
classifier = RobertaClassifier()
else:
src_encoder = DistilRobertaEncoder()
# encoder = DistilRobertaEncoder()
classifier = RobertaClassifier()
discriminator = Discriminator()
# parallel models
if torch.cuda.device_count() > 1:
print('Let\'s use {} GPUs!'.format(torch.cuda.device_count()))
src_encoder = nn.DataParallel(src_encoder)
classifier = nn.DataParallel(classifier)
# encoder = nn.DataParallel(encoder)
discriminator = nn.DataParallel(discriminator)
if args.load:
src_encoder = init_model(args,
src_encoder,
restore_path=param.src_encoder_path)
classifier = init_model(args,
classifier,
restore_path=param.src_classifier_path)
# encoder = init_model(args, encoder, restore_path=param.tgt_encoder_path)
# discriminator = init_model(args, discriminator, restore_path=param.d_model_path)
else:
src_encoder = init_model(args, src_encoder)
classifier = init_model(args, classifier)
# encoder = init_model(args, encoder)
discriminator = init_model(args, discriminator)
# train source model
if args.pretrain:
print("=== Training classifier for source domain ===")
src_encoder, classifier = pretrain(args, src_encoder, classifier,
src_loader)
# save pretrained model
# save_model(args, src_encoder, param.src_encoder_path)
# save_model(args, classifier, param.src_classifier_path)
# eval source model
print("=== Evaluating classifier for source domain ===")
evaluate(args, src_encoder, classifier, src_loader)
src_acc = evaluate(args, src_encoder, classifier, tgt_all_loader)
f.write(f'{args.src} -> {args.tgt}: No adapt acc on src data: {src_acc}\n')
# adapt
print("=== Adapt tgt encoder ===")
# encoder.load_state_dict(src_encoder.state_dict())
# if args.src_free:
# s_res_features = src_gmm(args, src_encoder, src_loader)
# src_loader = s_numpy_dataloader(s_res_features, args.batch_size)
# encoder = aad_adapt_src_free(args, src_encoder, encoder, discriminator,
# classifier, src_loader, tgt_train_loader, tgt_all_loader)
# else:
if args.adapt:
encoder, classifier = shot_adapt(args, src_encoder, classifier,
tgt_train_loader, tgt_all_loader,
tgt_te)
# save_model(args, encoder, param.tgt_encoder_path)
# argument setting
# print("=== Argument Setting ===")
print(
f"model_type: {args.model}; max_seq_len: {args.max_seq_length}; batch_size: {args.batch_size}; "
f"pre_epochs: {args.pre_epochs}; num_epochs: {args.num_epochs}; src: {args.src}; tgt: {args.tgt}; "
f'src_free: {args.src_free}; dp: {args.dp}')
# eval target encoder on lambda0.1 set of target dataset
print("=== Evaluating classifier for encoded target domain ===")
print(">>> domain adaption <<<")
tgt_acc = evaluate(args, encoder, classifier, tgt_all_loader)
f.write(f'{args.src} -> {args.tgt}: DA acc on tgt data: {tgt_acc}\n')
f.write(
f"model_type: {args.model}; batch_size: {args.batch_size}; pre_epochs: {args.pre_epochs}; "
f"num_epochs: {args.num_epochs}; src_free: {args.src_free}; src: {args.src}; "
f"tgt: {args.tgt}; dp: {args.dp}\n\n")
def main():
""" Main """
# Arguments
args = parser.parse_args()
# Setup Distributed Training
device, local_rank = setup(distributed=args.distributed)
# Get Dataloaders for Dataset of choice
dataloaders, args = get_dataloaders(args)
# Setup logging, saving models, summaries
args = experiment_config(parser, args)
# Get available models from /model/network.py
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
# If model exists
if any(args.model in model_name for model_name in model_names):
# Load model
base_encoder = getattr(models, args.model)(
args, num_classes=args.n_classes) # Encoder
proj_head = models.projection_MLP(args)
sup_head = models.Sup_Head(args)
else:
raise NotImplementedError("Model Not Implemented: {}".format(
args.model))
# Remove last FC layer from resnet
base_encoder.fc = nn.Sequential()
# Place model onto GPU(s)
if args.distributed:
torch.cuda.set_device(device)
torch.set_num_threads(6) # n cpu threads / n processes per node
base_encoder = DistributedDataParallel(base_encoder.cuda(),
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
broadcast_buffers=False)
proj_head = DistributedDataParallel(proj_head.cuda(),
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
broadcast_buffers=False)
sup_head = DistributedDataParallel(sup_head.cuda(),
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
broadcast_buffers=False)
# Only print from process (rank) 0
args.print_progress = True if int(
os.environ.get('RANK')) == 0 else False
else:
# If non Distributed use DataParallel
if torch.cuda.device_count() > 1:
base_encoder = nn.DataParallel(base_encoder)
proj_head = nn.DataParallel(proj_head)
sup_head = nn.DataParallel(sup_head)
print('\nUsing', torch.cuda.device_count(), 'GPU(s).\n')
base_encoder.to(device)
proj_head.to(device)
sup_head.to(device)
args.print_progress = True
# Print Network Structure and Params
if args.print_progress:
print_network(base_encoder,
args) # prints out the network architecture etc
logging.info('\npretrain/train: {} - valid: {} - test: {}'.format(
len(dataloaders['train'].dataset),
len(dataloaders['valid'].dataset),
len(dataloaders['test'].dataset)))
# launch model training or inference
if not args.finetune:
''' Pretraining / Finetuning / Evaluate '''
if not args.supervised:
# Pretrain the encoder and projection head
proj_head.apply(init_weights)
pretrain(base_encoder, proj_head, dataloaders, args)
else:
supervised(base_encoder, sup_head, dataloaders, args)
print("\n\nLoading the model: {}\n\n".format(args.load_checkpoint_dir))
# Load the pretrained model
checkpoint = torch.load(args.load_checkpoint_dir)
# Load the encoder parameters
base_encoder.load_state_dict(checkpoint['encoder'])
# Initalize weights of the supervised / classification head
sup_head.apply(init_weights)
# Supervised Finetuning of the supervised classification head
finetune(base_encoder, sup_head, dataloaders, args)
# Evaluate the pretrained model and trained supervised head
test_loss, test_acc, test_acc_top5 = evaluate(base_encoder, sup_head,
dataloaders, 'test',
args.finetune_epochs,
args)
print('[Test] loss {:.4f} - acc {:.4f} - acc_top5 {:.4f}'.format(
test_loss, test_acc, test_acc_top5))
if args.distributed: # cleanup
torch.distributed.destroy_process_group()
else:
''' Finetuning / Evaluate '''
# Do not Pretrain, just finetune and inference
print("\n\nLoading the model: {}\n\n".format(args.load_checkpoint_dir))
# Load the pretrained model
checkpoint = torch.load(args.load_checkpoint_dir)
# Load the encoder parameters
base_encoder.load_state_dict(checkpoint['encoder']) # .cuda()
# Initalize weights of the supervised / classification head
sup_head.apply(init_weights)
# Supervised Finetuning of the supervised classification head
finetune(base_encoder, sup_head, dataloaders, args)
# Evaluate the pretrained model and trained supervised head
test_loss, test_acc, test_acc_top5 = evaluate(base_encoder, sup_head,
dataloaders, 'test',
args.finetune_epochs,
args)
print('[Test] loss {:.4f} - acc {:.4f} - acc_top5 {:.4f}'.format(
test_loss, test_acc, test_acc_top5))
if args.distributed: # cleanup
torch.distributed.destroy_process_group()
def main(args, f):
# args = parse_arguments()
set_seed(args.train_seed)
if args.model in ['roberta', 'distilroberta']:
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
else:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# preprocess data
src_eval_loader, src_loader, tgt_all_loader, tgt_train_loader = get_all_dataloader(
args, tokenizer)
# load models
if args.model == 'bert':
encoder = BertEncoder()
src_encoder = BertEncoder()
classifier = BertClassifier()
elif args.model == 'distilbert':
encoder = DistilBertEncoder()
src_encoder = DistilBertEncoder()
classifier = BertClassifier()
elif args.model == 'roberta':
encoder = RobertaEncoder()
src_encoder = RobertaEncoder()
classifier = RobertaClassifier()
else:
encoder = DistilRobertaEncoder()
src_encoder = DistilRobertaEncoder()
classifier = RobertaClassifier()
# domain discriminator
discriminator = AdversarialNetworkCdan(param.input_dim * param.num_labels,
param.hidden_dim)
# parallel models
if torch.cuda.device_count() > 1:
print('Let\'s use {} GPUs!'.format(torch.cuda.device_count()))
encoder = nn.DataParallel(encoder)
src_encoder = nn.DataParallel(src_encoder)
classifier = nn.DataParallel(classifier)
discriminator = nn.DataParallel(discriminator)
if args.load:
encoder = init_model(args,
encoder,
restore_path=param.src_encoder_path)
src_encoder = init_model(args,
src_encoder,
restore_path=param.tgt_encoder_path)
classifier = init_model(args,
classifier,
restore_path=param.src_classifier_path)
# discriminator = init_model(args, discriminator, restore_path=param.d_model_path)
else:
encoder = init_model(args, encoder)
src_encoder = init_model(args, src_encoder)
classifier = init_model(args, classifier)
discriminator = init_model(args, discriminator)
# train source model
print("=== Pretrain encoder for source domain ===")
if args.pretrain:
encoder, classifier = pretrain(args, encoder, classifier, src_loader)
# eval source model
print("=== Evaluating classifier for source domain ===")
evaluate(args, encoder, classifier, src_loader)
src_acc = evaluate(args, encoder, classifier, tgt_all_loader)
f.write(f'{args.src} -> {args.tgt} no adapt acc on src data: {src_acc}\n')
# x, y = save_features(args, encoder, src_loader)
# np.savez(os.path.join(param.model_root, 's_feat_pretrain'), x, y)
# x, y = save_features(args, encoder, tgt_all_loader)
# np.savez(os.path.join(param.model_root, 't_feat_pretrain'), x, y)
# adapt
print("=== Adapt encoder for target domain ===")
src_encoder.load_state_dict(encoder.state_dict())
if args.src_free:
# use the same encoder and copy encoder to src_encoder have different baseline results
s_res_features = src_gmm(args, encoder, src_loader)
src_loader = s_numpy_dataloader(s_res_features, args.batch_size)
encoder, classifier = cdan_adapt_src_free(args, encoder, src_encoder,
discriminator, classifier,
src_loader, tgt_train_loader,
tgt_all_loader)
elif args.data_free:
s_res_features = src_gmm(args, encoder, src_loader)
t_res_features = tgt_gmm(encoder, tgt_all_loader, 1)
src_loader = s_numpy_dataloader(s_res_features, args.batch_size)
tgt_train_loader = t_numpy_dataloader(t_res_features, args.batch_size)
encoder, classifier = cdan_adapt_data_free(args, encoder,
discriminator, classifier,
src_loader,
tgt_train_loader,
tgt_all_loader)
else:
encoder, classifier = cdan_adapt(args, encoder, discriminator,
classifier, src_loader,
tgt_train_loader, tgt_all_loader)
# x, y = save_features(args, encoder, src_loader)
# np.savez(os.path.join(param.model_root, 's_feat_adapt_cdan'), x, y)
# x, y = save_features(args, encoder, tgt_all_loader)
# np.savez(os.path.join(param.model_root, 't_feat_adapt_cdan'), x, y)
# argument setting
print(
f"model_type: {args.model}; batch_size: {args.batch_size}; data_free: {args.data_free}; "
f"src_free: {args.src_free}; pre_epochs: {args.pre_epochs}; num_epochs: {args.num_epochs}; "
f"src: {args.src}; tgt: {args.tgt}; kd: {args.kd}; dp: {args.dp}; ent: {args.ent}"
)
# eval target encoder on lambda0.1 set of target dataset
print("=== Evaluating classifier for encoded target domain ===")
print(">>> domain adaption <<<")
tgt_acc = evaluate(args, encoder, classifier, tgt_all_loader)
f.write(f'{args.src} -> {args.tgt}: DA acc on tgt data: {tgt_acc}\n')
f.write(
f"model_type: {args.model}; batch_size: {args.batch_size}; data_free: {args.data_free}; "
f"src_free: {args.src_free}; pre_epochs: {args.pre_epochs}; num_epochs: {args.num_epochs}; "
f"src: {args.src}; tgt: {args.tgt}; kd: {args.kd}; dp: {args.dp}; ent: {args.ent}\n\n"
)
elif args.mode == 'urdf':
object_paths = [
str(path) for path in Path(args.objects).rglob('*.obj')
]
object_paths = list(
filter(lambda path: not path.endswith("collision.obj"),
object_paths))
with Pool(48) as p:
rv = list(
tqdm(p.imap(create_grasp_object_urdf, object_paths),
total=len(object_paths)))
exit()
config = load_config(args.config)
if args.mode == 'cotrain':
cotrain(*setup(args, config))
elif args.mode == 'vae':
train_vae(*setup_train_vae(args, config))
elif args.mode == 'pretrain':
pretrain(args, config)
elif args.mode == 'pretrain_gn':
pretrain_gn(args, config)
elif args.mode == 'pretrain_dataset':
generate_pretrain_data(*setup(args, config))
elif args.mode == 'pretrain_imprint_dataset':
generate_pretrain_imprint_data(*setup(args, config))
elif args.mode == 'grasp_objects':
create_grasp_objects(*setup(args, config))
elif args.mode == 'imprint_baseline':
generate_imprints(*setup(args, config))
def main():
""" Main """
# Arguments
args = parser.parse_args()
# Setup Distributed Training
device, local_rank = setup(distributed=args.distributed)
# Get Dataloaders for Dataset of choice
dataloaders, args = get_dataloaders(args)
# Setup logging, saving models, summaries
args = experiment_config(parser, args)
''' Base Encoder '''
# Get available models from /model/network.py
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
# If model exists
if any(args.model in model_name for model_name in model_names):
# Load model
base_encoder = getattr(models, args.model)(
args, num_classes=args.n_classes) # Encoder
else:
raise NotImplementedError("Model Not Implemented: {}".format(
args.model))
if not args.supervised:
# freeze all layers but the last fc
for name, param in base_encoder.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
# init the fc layer
init_weights(base_encoder)
''' MoCo Model '''
moco = MoCo_Model(args,
queue_size=args.queue_size,
momentum=args.queue_momentum,
temperature=args.temperature)
# Place model onto GPU(s)
if args.distributed:
torch.cuda.set_device(device)
torch.set_num_threads(6) # n cpu threads / n processes per node
moco = DistributedDataParallel(moco.cuda(),
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
broadcast_buffers=False)
base_encoder = DistributedDataParallel(base_encoder.cuda(),
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
broadcast_buffers=False)
# Only print from process (rank) 0
args.print_progress = True if int(
os.environ.get('RANK')) == 0 else False
else:
# If non Distributed use DataParallel
if torch.cuda.device_count() > 1:
moco = nn.DataParallel(moco)
base_encoder = nn.DataParallel(base_encoder)
print('\nUsing', torch.cuda.device_count(), 'GPU(s).\n')
moco.to(device)
base_encoder.to(device)
args.print_progress = True
# Print Network Structure and Params
if args.print_progress:
print_network(moco, args) # prints out the network architecture etc
logging.info('\npretrain/train: {} - valid: {} - test: {}'.format(
len(dataloaders['train'].dataset),
len(dataloaders['valid'].dataset),
len(dataloaders['test'].dataset)))
# launch model training or inference
if not args.finetune:
''' Pretraining / Finetuning / Evaluate '''
if not args.supervised:
# Pretrain the encoder and projection head
pretrain(moco, dataloaders, args)
# Load the state_dict from query encoder and load it on finetune net
base_encoder = load_moco(base_encoder, args)
else:
supervised(base_encoder, dataloaders, args)
# Load the state_dict from query encoder and load it on finetune net
base_encoder = load_sup(base_encoder, args)
# Supervised Finetuning of the supervised classification head
finetune(base_encoder, dataloaders, args)
# Evaluate the pretrained model and trained supervised head
test_loss, test_acc, test_acc_top5 = evaluate(base_encoder,
dataloaders, 'test',
args.finetune_epochs,
args)
print('[Test] loss {:.4f} - acc {:.4f} - acc_top5 {:.4f}'.format(
test_loss, test_acc, test_acc_top5))
if args.distributed: # cleanup
torch.distributed.destroy_process_group()
else:
''' Finetuning / Evaluate '''
# Do not Pretrain, just finetune and inference
# Load the state_dict from query encoder and load it on finetune net
base_encoder = load_moco(base_encoder, args)
# Supervised Finetuning of the supervised classification head
finetune(base_encoder, dataloaders, args)
# Evaluate the pretrained model and trained supervised head
test_loss, test_acc, test_acc_top5 = evaluate(base_encoder,
dataloaders, 'test',
args.finetune_epochs,
args)
print('[Test] loss {:.4f} - acc {:.4f} - acc_top5 {:.4f}'.format(
test_loss, test_acc, test_acc_top5))
if args.distributed: # cleanup
torch.distributed.destroy_process_group()
def main():
parser = argparse.ArgumentParser(
description='Argument Parser for SERIL.')
parser.add_argument('--logdir', default='log',
help='Name of current experiment.')
parser.add_argument('--n_jobs', default=2, type=int)
parser.add_argument(
'--do', choices=['train', 'test'], default='train', type=str)
parser.add_argument(
'--mode', choices=['seril', 'finetune'], default='seril', type=str)
parser.add_argument(
'--model', choices=['LSTM', 'Residual', 'IRM'], default='LSTM', type=str)
# Options
parser.add_argument(
'--config', default='config/config.yaml', required=False)
parser.add_argument('--seed', default=1126, type=int,
help='Random seed for reproducable results.', required=False)
parser.add_argument('--gpu', default='2', type=int,
help='Assigning GPU id.')
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# build log directory
os.makedirs(args.logdir, exist_ok=True)
# load configure
config = yaml.load(open(args.config, 'r'), Loader=yaml.FullLoader)
if config['train']['loss'] == 'sisdr':
loss_func = SingleSrcNegSDR("sisdr", zero_mean=False,
reduction='mean')
if args.do == 'train':
torch.cuda.set_device(args.gpu)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
assert len(config['dataset']['train']['clean']) == len(
config['dataset']['train']['noisy']) and len(config['dataset']['train']['clean']) >= 1
model_path = f'{args.logdir}/pretrain/{args.model}_model_T0.pth'
lifelong_agent_path = f'{args.logdir}/pretrain/{args.model}_synapses_T0.pth'
if os.path.exists(model_path) and os.path.exists(lifelong_agent_path):
print(f'[Runner] - pretrain model has already existed!')
model = torch.load(model_path).to(device)
lifelong_agent = torch.load(lifelong_agent_path).to(device)
lifelong_agent.load_config(**config['train']['strategies'])
else:
print(f'[Runner] - run pretrain process!')
preprocessor = OnlinePreprocessor(feat_list=feat_list).to(device)
model = eval(f'{args.model}')(loss_func, preprocessor, **config['model']).to(device)
lifelong_agent = LifeLongAgent(model, **config['train']['strategies'])
pretrain(args, config, model, lifelong_agent)
print(f'[Runner] - run adaptation process!')
args.logdir = f'{args.logdir}/{args.mode}'
if args.mode == 'seril':
adapt(args, config, model, lifelong_agent)
elif args.mode == 'finetune':
adapt(args, config, model)
if args.do == 'test':
test(args, config)
def main(args, f):
# args = parse_arguments()
set_seed(args.train_seed)
if args.model in ['roberta', 'distilroberta']:
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
else:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# preprocess data
src_eval_loader, src_loader, tgt_all_loader, tgt_train_loader = get_all_dataloader(
args, tokenizer)
# load models
if args.model == 'bert':
src_encoder = BertEncoder()
tgt_encoder = BertEncoder()
src_classifier = BertClassifier()
elif args.model == 'distilbert':
src_encoder = DistilBertEncoder()
tgt_encoder = DistilBertEncoder()
src_classifier = BertClassifier()
elif args.model == 'roberta':
src_encoder = RobertaEncoder()
tgt_encoder = RobertaEncoder()
src_classifier = RobertaClassifier()
else:
src_encoder = DistilRobertaEncoder()
tgt_encoder = DistilRobertaEncoder()
src_classifier = RobertaClassifier()
discriminator = Discriminator() # output dims is 2 instead of 1
if args.load:
src_encoder = init_model(args,
src_encoder,
restore_path=param.src_encoder_path)
src_classifier = init_model(args,
src_classifier,
restore_path=param.src_classifier_path)
tgt_encoder = init_model(args,
tgt_encoder,
restore_path=param.tgt_encoder_path)
discriminator = init_model(args,
discriminator,
restore_path=param.d_model_path)
else:
src_encoder = init_model(args, src_encoder)
src_classifier = init_model(args, src_classifier)
tgt_encoder = init_model(args, tgt_encoder)
discriminator = init_model(args, discriminator)
# parallel models
if torch.cuda.device_count() > 1:
print('Let\'s use {} GPUs!'.format(torch.cuda.device_count()))
src_encoder = nn.DataParallel(src_encoder)
src_classifier = nn.DataParallel(src_classifier)
tgt_encoder = nn.DataParallel(tgt_encoder)
discriminator = nn.DataParallel(discriminator)
# train source model
print("=== Training classifier for source domain ===")
if args.pretrain:
src_encoder, src_classifier = pretrain(args, src_encoder,
src_classifier, src_loader)
# eval source model
print("=== Evaluating classifier for source domain ===")
evaluate(args, src_encoder, src_classifier, src_loader)
src_acc = evaluate(args, src_encoder, src_classifier, tgt_all_loader)
f.write(f'{args.src} -> {args.tgt}: No adapt acc on src data: {src_acc}\n')
for params in src_encoder.parameters():
params.requires_grad = False
# train target encoder by ADDA
print("=== Training encoder for target domain ===")
if args.adapt:
tgt_encoder.load_state_dict(src_encoder.state_dict())
tgt_encoder = adda_adapt(args, src_encoder, tgt_encoder, discriminator,
src_loader, tgt_train_loader)
# argument setting
print(
f"model_type: {args.model}; max_seq_len: {args.max_seq_length}; batch_size: {args.batch_size}; "
f"pre_epochs: {args.pre_epochs}; num_epochs: {args.num_epochs}; src: {args.src}; tgt: {args.tgt}"
)
# eval target encoder on lambda0.1 set of target dataset
print("=== Evaluating classifier for encoded target domain ===")
print(">>> domain adaption <<<")
tgt_acc = evaluate(args, tgt_encoder, src_classifier, tgt_all_loader)
f.write(f'{args.src} -> {args.tgt}: DA acc on tgt data: {tgt_acc}\n')
f.write(
f"model_type: {args.model}; batch_size: {args.batch_size}; pre_epochs: {args.pre_epochs}; "
f"num_epochs: {args.num_epochs}; src_free: {args.src_free}; src: {args.src}; "
f"tgt: {args.tgt};\n\n")