def run():
# load source dataset
src_data_loader = get_data_loader(params.src_dataset)
src_data_loader_eval = get_data_loader(params.src_dataset, train=False)
# load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore)
src_classifier = init_model(net=LeNetClassifier(),
restore=params.src_classifier_restore)
# pre-train source model
print("=== Training classifier for source domain ===")
print(">>> Source Encoder <<<")
im, _ = next(iter(src_data_loader))
summary(src_encoder, input_size=im[0].size())
print(">>> Source Classifier <<<")
print(src_classifier)
if not (src_encoder.restored and src_classifier.restored and
params.src_model_trained):
src_encoder, src_classifier = train_src(
src_encoder, src_classifier, src_data_loader)
# eval source model
print("=== Evaluating classifier for source domain ===")
eval_src(src_encoder, src_classifier, src_data_loader_eval)
def run():
# load dataset
src_data_loader = get_data_loader(params.src_dataset)
tgt_data_loader = get_data_loader(params.tgt_dataset)
# load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore)
tgt_encoder = init_model(net=LeNetEncoder(),
restore=params.tgt_encoder_restore)
critic = init_model(Discriminator(input_dims=params.d_input_dims,
hidden_dims=params.d_hidden_dims,
output_dims=params.d_output_dims),
restore=params.d_model_restore)
# Adapt target encoder by GAN
print("=== Training encoder for target domain ===")
print(">>> Target Encoder <<<")
im, _ = next(iter(tgt_data_loader))
summary(tgt_encoder, input_size=im[0].size())
print(">>> Critic <<<")
print(critic)
# init weights of target encoder with those of source encoder
if not tgt_encoder.restored:
tgt_encoder.load_state_dict(src_encoder.state_dict())
# Train target
if not (tgt_encoder.restored and critic.restored
and params.tgt_model_trained):
tgt_encoder = train_tgt(src_encoder, tgt_encoder, critic,
src_data_loader, tgt_data_loader)
def main():
args = parse_arguments()
n_vocab = params.n_vocab
n_layer = params.n_layer
n_hidden = params.n_hidden
n_embed = params.n_embed
n_batch = args.n_batch
temperature = params.temperature
train_path = params.train_path
assert torch.cuda.is_available()
print("loading_data...")
# 训练时加载处理好的词典（如果有的话）
if os.path.exists("vocab.json"):
vocab = Vocabulary()
with open('vocab.json', 'r') as fp:
vocab.stoi = json.load(fp)
for key, value in vocab.stoi.items():
vocab.itos.append(key)
else:
vocab = build_vocab(train_path, n_vocab)
# save vocab
with open('vocab.json', 'w') as fp:
json.dump(vocab.stoi, fp)
train_X, train_y, train_K = load_data(train_path, vocab)
train_loader = get_data_loader(train_X, train_y, train_K, n_batch)
print("successfully loaded")
encoder = Encoder(n_vocab, n_embed, n_hidden, n_layer, vocab).cuda()
Kencoder = KnowledgeEncoder(n_vocab, n_embed, n_hidden, n_layer,
vocab).cuda()
manager = Manager(n_hidden, n_vocab, temperature).cuda()
decoder = Decoder(n_vocab, n_embed, n_hidden, n_layer, vocab).cuda()
if args.restore:
encoder = init_model(encoder, restore=params.encoder_restore)
Kencoder = init_model(Kencoder, restore=params.Kencoder_restore)
manager = init_model(manager, restore=params.manager_restore)
decoder = init_model(decoder, restore=params.decoder_restore)
# ToDo：目前的代码所有的embedding都是独立的，可以参考transformer源码使用直接赋值的方法共享参数：
#if emb_src_trg_weight_sharing:
# self.encoder.src_word_emb.weight = self.decoder.trg_word_emb.weight
model = [encoder, Kencoder, manager, decoder]
parameters = list(encoder.parameters()) + list(Kencoder.parameters()) + \
list(manager.parameters()) + list(decoder.parameters())
optimizer = optim.Adam(parameters, lr=args.lr)
# pre_train knowledge manager
print("start pre-training")
pre_train(model, optimizer, train_loader, args)
print("start training")
train(model, optimizer, train_loader, args)
# save final model
save_models(model, params.all_restore)
def train(args):
"""Train."""
start_time = time.time()
if args.one_per_line:
corpus: Corpus = ClassificationCorpus(
args.data_dir,
train_file=args.train_file,
dev_file=args.dev_file,
)
else:
assert args.label_symbol is not None
corpus: Corpus = FlyClassificationCorpus(
args.data_dir,
train_file=args.train_file,
dev_file=args.dev_file,
comment_symbol=args.comment_symbol,
label_symbol=args.label_symbol,
)
label_dict = corpus.make_label_dictionary()
vocab = corpus.make_vocab_dictionary().get_items()
embeddings = utils.init_embeddings(vocab, args)
document_embeddings = DocumentRNNEmbeddings(
[embeddings],
hidden_size=args.hidden_size,
use_attn=args.use_attn,
num_heads=args.num_heads,
scaling=args.scaling,
pooling_operation=args.pooling_operation,
use_sent_query=args.use_sent_query,
)
model = TextClassifier(document_embeddings, label_dictionary=label_dict)
utils.init_model(model, args)
trainer: ModelTrainer = ModelTrainer(model, corpus,
utils.optim_method(args.optim))
trainer.train(
args.model_dir,
mini_batch_size=args.mini_batch_size,
max_epochs=args.max_epochs,
anneal_factor=args.anneal_factor,
learning_rate=args.learning_rate,
patience=args.patience,
min_learning_rate=args.min_learning_rate,
embeddings_storage_mode=args.embeddings_storage_mode,
)
logger.info("End of training: time %.1f min",
(time.time() - start_time) / 60)
def train(args):
"""Train."""
start_time = time.time()
column_format = {i: col for i, col in enumerate(args.data_columns)}
corpus: Corpus = ColumnCorpus(
args.data_dir,
column_format,
train_file=args.train_file,
dev_file=args.dev_file,
comment_symbol=args.comment_symbol,
)
tag_type = args.data_columns[-1]
tag_dict = corpus.make_tag_dictionary(tag_type=tag_type)
vocab = corpus.make_vocab_dictionary().get_items()
embeddings = utils.init_embeddings(vocab, args)
model: SequenceTagger = SequenceTagger(
hidden_size=args.hidden_size,
embeddings=embeddings,
tag_dictionary=tag_dict,
tag_type=tag_type,
column_format=column_format,
use_crf=True,
use_attn=args.use_attn,
attn_type=args.attn_type,
num_heads=args.num_heads,
scaling=args.scaling,
pooling_operation=args.pooling_operation,
use_sent_query=args.use_sent_query,
)
utils.init_model(model, args)
trainer: ModelTrainer = ModelTrainer(model, corpus,
utils.optim_method(args.optim))
trainer.train(
args.model_dir,
mini_batch_size=args.mini_batch_size,
max_epochs=args.max_epochs,
anneal_factor=args.anneal_factor,
learning_rate=args.learning_rate,
patience=args.patience,
min_learning_rate=args.min_learning_rate,
embeddings_storage_mode=args.embeddings_storage_mode,
)
logger.info("End of training: time %.1f min",
(time.time() - start_time) / 60)
def __init__(
self,
net_G=None,
net_D=None,
opt_G=None,
opt_D=None,
scaler_G=None,
scaler_D=None,
device=None,
lambda_L1=100.0,
):
super().__init__()
self.device = device
if net_G:
self.net_G = net_G.to(self.device)
else:
self.net_G = init_model(Generator_Res_Unet().get_model(), self.device)
if net_D:
self.net_D = net_D.to(self.device)
else:
self.net_D = init_model(Discriminator(input_channels=3), self.device)
if scaler_G:
self.scaler_G = scaler_G
else:
self.scaler_G = amp.GradScaler()
if scaler_D:
self.scaler_D = scaler_D
else:
self.scaler_D = amp.GradScaler()
if opt_G:
self.opt_G = opt_G
else:
self.opt_G = optim.Adam(
self.net_G.parameters(), lr=lr_G, betas=(beta1, beta2)
)
if opt_D:
self.opt_D = opt_D
else:
self.opt_D = optim.Adam(
self.net_D.parameters(), lr=lr_D, betas=(beta1, beta2)
)
self.GANcriterion = GANLoss(gan_mode="vanilla").to(self.device)
self.L1criterion = nn.L1Loss().to(self.device)
self.lambda_L1 = lambda_L1
def main():
args = arguments()
# init random seed
init_random_seed(manual_seed)
src_train_loader, src_test_loader, tgt_train_loader, tgt_test_loader = get_dataset(
args)
print("=== Datasets successfully loaded ===")
src_encoder_restore = "snapshots/src-encoder-{}.pt".format(args.src)
src_classifier_restore = "snapshots/src-classifier-{}.pt".format(args.src)
# load models
src_encoder = init_model(BERTEncoder(), restore=src_encoder_restore)
src_classifier = init_model(BERTClassifier(),
restore=src_classifier_restore)
# 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)
# argument setting
print("=== Argument Setting ===")
print("src: " + args.src)
print("tgt: " + args.tgt)
print("seqlen: " + str(args.seqlen))
print("num_epochs: " + str(args.num_epochs))
print("batch_size: " + str(args.batch_size))
print("learning_rate: " + str(args.lr))
if args.enc_train:
for param in src_encoder.parameters():
param.requires_grad = True
# train source model
print("=== Training classifier for source domain ===")
src_encoder, src_classifier = train_no_da(args, src_encoder,
src_classifier,
src_train_loader,
src_test_loader)
# eval source model
print("Evaluate classifier for source domain: {}".format(args.src))
eval_src(src_encoder, src_classifier, src_test_loader)
# eval target encoder on test set of target dataset
print("Evaluate classifier for encoded target domain: {}".format(args.tgt))
eval_tgt(src_encoder, src_classifier, tgt_test_loader)
def office():
init_random_seed(params.manual_seed)
# load dataset
src_data_loader = get_data_loader(params.src_dataset)
src_data_loader_eval = get_data_loader(params.src_dataset, train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, train=False)
# load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore)
src_classifier = init_model(net=LeNetClassifier(),
restore=params.src_classifier_restore)
tgt_encoder = init_model(net=LeNetEncoder(),
restore=params.tgt_encoder_restore)
critic = init_model(Discriminator(input_dims=params.d_input_dims,
hidden_dims=params.d_hidden_dims,
output_dims=params.d_output_dims),
restore=params.d_model_restore)
if not (src_encoder.restored and src_classifier.restored and
params.src_model_trained):
src_encoder, src_classifier = train_src(
src_encoder, src_classifier, src_data_loader)
# eval source model
# print("=== Evaluating classifier for source domain ===")
# eval_src(src_encoder, src_classifier, src_data_loader_eval)
# train target encoder by GAN
# init weights of target encoder with those of source encoder
if not tgt_encoder.restored:
tgt_encoder.load_state_dict(src_encoder.state_dict())
if not (tgt_encoder.restored and critic.restored and
params.tgt_model_trained):
tgt_encoder = train_tgt(src_encoder, tgt_encoder, critic,
src_data_loader, tgt_data_loader)
# eval target encoder on test set of target dataset
print(">>> domain adaption <<<")
acc = eval_tgt(tgt_encoder, src_classifier, tgt_data_loader_eval)
return acc
def main():
args = parse_arguments()
n_vocab = params.n_vocab
n_layer = params.n_layer
n_hidden = params.n_hidden
n_embed = params.n_embed
n_batch = args.n_batch
temperature = params.temperature
train_path = params.train_path
assert torch.cuda.is_available()
print("loading_data...")
vocab = build_vocab(train_path, n_vocab)
# save vocab
with open('vocab.json', 'w') as fp:
json.dump(vocab.stoi, fp)
train_X, train_y, train_K = load_data(train_path, vocab)
train_loader = get_data_loader(train_X, train_y, train_K, n_batch)
print("successfully loaded")
encoder = Encoder(n_vocab, n_embed, n_hidden, n_layer, vocab).cuda()
Kencoder = KnowledgeEncoder(n_vocab, n_embed, n_hidden, n_layer,
vocab).cuda()
manager = Manager(n_hidden, n_vocab, temperature).cuda()
decoder = Decoder(n_vocab, n_embed, n_hidden, n_layer, vocab).cuda()
if args.restore:
encoder = init_model(encoder, restore=params.encoder_restore)
Kencoder = init_model(Kencoder, restore=params.Kencoder_restore)
manager = init_model(manager, restore=params.manager_restore)
decoder = init_model(decoder, restore=params.decoder_restore)
model = [encoder, Kencoder, manager, decoder]
parameters = list(encoder.parameters()) + list(Kencoder.parameters()) + \
list(manager.parameters()) + list(decoder.parameters())
optimizer = optim.Adam(parameters, lr=args.lr)
# pre_train knowledge manager
print("start pre-training")
pre_train(model, optimizer, train_loader, args)
print("start training")
train(model, optimizer, train_loader, args)
# save final model
save_models(model, params.all_restore)
def trio(tgt_classifier_net, tgt_encoder_net, src_dataset, tgt_dataset, conv):
print('loading pretrained trio after conv ' + str(conv) + '...')
tgt_classifier = init_model(net=tgt_classifier_net,
restore= str(conv) + "_snapshots/" + \
src_dataset + "-ADDA-target-classifier-final.pt")
tgt_encoder = init_model(net=tgt_encoder_net,
restore= str(conv) + "_snapshots/" + \
tgt_dataset + "-ADDA-target-classifier-final.pt")
critic = init_model(Discriminator(input_dims=params.d_input_dims,
hidden_dims=params.d_hidden_dims,
output_dims=params.d_output_dims),
restore= str(conv) + "_snapshots/" + \
tgt_dataset + "-ADDA-target-classifier-final.pt")
return tgt_classifier, tgt_encoder, critic
def main():
args = parse_arguments()
n_vocab = params.n_vocab
n_layer = params.n_layer
n_hidden = params.n_hidden
n_embed = params.n_embed
n_batch = args.n_batch
temperature = params.temperature
test_path = params.test_path
vocab_path = params.vocab_path
assert torch.cuda.is_available()
print("loading the vocab...")
vocab = Vocabulary()
with open(vocab_path, 'r',encoding='utf-8') as fp:
vocab.stoi = json.load(fp)
for key, value in vocab.stoi.items():
vocab.itos.append(key)
# load data and change to id
print("loading_data...")
test_X, test_y, test_K = load_data(test_path, vocab)
test_loader = get_data_loader(test_X, test_y, test_K, n_batch,False)
print("successfully loaded test data")
encoder = Encoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
Kencoder = KnowledgeEncoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
manager = Manager(n_hidden, n_vocab, temperature).cuda()
decoder = Decoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
encoder = init_model(encoder, restore=params.encoder_restore)
Kencoder = init_model(Kencoder, restore=params.Kencoder_restore)
manager = init_model(manager, restore=params.manager_restore)
decoder = init_model(decoder, restore=params.decoder_restore)
print("models successfully loaded!\n")
model = [encoder, Kencoder, manager, decoder]
#evaluate_loss(model, 0, test_loader)
evaluate_sample(model, vocab,test_X, test_y, test_K, test_loader)
def run():
# load dataset
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, train=False)
# Load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore)
src_classifier = init_model(net=LeNetClassifier(),
restore=params.src_classifier_restore)
tgt_encoder = init_model(net=LeNetEncoder(),
restore=params.tgt_encoder_restore)
# Evalute target encoder on test set of target dataset
print("=== Evaluating classifier for encoded target domain ===")
print(">>> source only <<<")
eval_tgt(src_encoder, src_classifier, tgt_data_loader_eval)
print(">>> domain adaption <<<")
eval_tgt(tgt_encoder, src_classifier, tgt_data_loader_eval)
def main():
logger.info('=> PyTorch Version: {}'.format(torch.__version__))
# Environment initialization
device, pin_memory = init_device(args.seed, args.cpu, args.gpu,
args.cpu_affinity)
# Create the data loader
train_loader, val_loader, test_loader = Cost2100DataLoader(
root=args.data_dir,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=pin_memory,
scenario=args.scenario)()
# Define model
model = init_model(args)
model.to(device)
# Define loss function
criterion = nn.MSELoss().to(device)
# Inference mode
if args.evaluate:
Tester(model, device, criterion)(test_loader)
return
# Define optimizer and scheduler
lr_init = 1e-3 if args.scheduler == 'const' else 2e-3
optimizer = torch.optim.Adam(model.parameters(), lr_init)
if args.scheduler == 'const':
scheduler = FakeLR(optimizer=optimizer)
else:
scheduler = WarmUpCosineAnnealingLR(optimizer=optimizer,
T_max=args.epochs *
len(train_loader),
T_warmup=30 * len(train_loader),
eta_min=5e-5)
# Define the training pipeline
trainer = Trainer(model=model,
device=device,
optimizer=optimizer,
criterion=criterion,
scheduler=scheduler,
resume=args.resume)
# Start training
trainer.loop(args.epochs, train_loader, val_loader, test_loader)
# Final testing
loss, rho, nmse = Tester(model, device, criterion)(test_loader)
print(f"\n=! Final test loss: {loss:.3e}"
f"\n test rho: {rho:.3e}"
f"\n test NMSE: {nmse:.3e}\n")
def main():
args = parse_arguments()
n_vocab = params.n_vocab
n_layer = params.n_layer
n_hidden = params.n_hidden
n_embed = params.n_embed
n_batch = args.n_batch
temperature = params.temperature
test_path = params.test_path
assert torch.cuda.is_available()
print("loading_data...")
if os.path.exists("vocab.json"):
vocab = Vocabulary()
with open('vocab.json', 'r') as fp:
vocab.stoi = json.load(fp)
for key, value in vocab.stoi.items():
vocab.itos.append(key)
else:
train_path = params.train_path
vocab = build_vocab(train_path, n_vocab)
test_X, test_y, test_K = load_data(test_path, vocab)
test_loader = get_data_loader(test_X, test_y, test_K, n_batch)
print("successfully loaded")
encoder = Encoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
Kencoder = KnowledgeEncoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
manager = Manager(n_hidden, n_vocab, temperature).cuda()
decoder = Decoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
encoder = init_model(encoder, restore=params.encoder_restore)
Kencoder = init_model(Kencoder, restore=params.Kencoder_restore)
manager = init_model(manager, restore=params.manager_restore)
decoder = init_model(decoder, restore=params.decoder_restore)
model = [encoder, Kencoder, manager, decoder]
print("start evaluating")
evaluate(model, test_loader)
def main():
logger.info('=> PyTorch Version: {}'.format(torch.__version__))
# Environment initialization
device = init_device(args.seed, args.cpu, args.gpu, args.cpu_affinity)
# Create the test data loader
test_loader = Cost2100DataLoader(root=args.data_dir,
batch_size=args.batch_size,
num_workers=args.workers,
scenario=args.scenario)()
# Define model
model = init_model(args)
model.to(device)
# Define loss function
criterion = nn.MSELoss().to(device)
# Inference
Tester(model, device, criterion, print_freq=20)(test_loader)
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"
)
def experiments(exp):
#print(exp, case, affine, num_epochs)
# init random seed
#params.d_learning_rate = lr_d
#params.c_learning_rate = lr_c
init_random_seed(params.manual_seed)
# load dataset
src_dataset, tgt_dataset = exp.split('_')
src_data_loader = get_data_loader(src_dataset)
src_data_loader_eval = get_data_loader(src_dataset, train=False)
tgt_data_loader = get_data_loader(tgt_dataset)
tgt_data_loader_eval = get_data_loader(tgt_dataset, train=False)
# load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore,
exp=exp)
src_classifier = init_model(net=LeNetClassifier(),
restore=params.src_classifier_restore,
exp=exp)
tgt_encoder = init_model(net=LeNetEncoder(),
restore=params.tgt_encoder_restore,
exp=exp)
critic = init_model(Discriminator(input_dims=params.d_input_dims,
hidden_dims=params.d_hidden_dims,
output_dims=params.d_output_dims),
exp=exp,
restore=params.d_model_restore)
# train source model
print("=== Training classifier for source domain ===")
print(">>> Source Encoder <<<")
print(src_encoder)
print(">>> Source Classifier <<<")
print(src_classifier)
if not (src_encoder.restored and src_classifier.restored
and params.src_model_trained):
src_encoder, src_classifier = train_src(exp, src_encoder,
src_classifier,
src_data_loader,
src_data_loader_eval)
# eval source model
print("=== Evaluating classifier for source domain ===")
evaluation(src_encoder, src_classifier, src_data_loader_eval)
# train target encoder by GAN
print("=== Training encoder for target domain ===")
print(">>> Target Encoder <<<")
print(tgt_encoder)
print(">>> Critic <<<")
print(critic)
# init weights of target encoder with those of source encoder
if not tgt_encoder.restored:
tgt_encoder.load_state_dict(src_encoder.state_dict())
if not (tgt_encoder.restored and critic.restored
and params.tgt_model_trained):
tgt_encoder = train_tgt(exp, src_encoder, tgt_encoder, critic,
src_classifier, src_data_loader,
tgt_data_loader, tgt_data_loader_eval)
# eval target encoder on test set of target dataset
print("=== Evaluating classifier for encoded target domain ===")
print(">>> source only <<<")
evaluation(src_encoder, src_classifier, tgt_data_loader_eval)
print(">>> domain adaption <<<")
evaluation(tgt_encoder, src_classifier, tgt_data_loader_eval)
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
if __name__ == '__main__':
# init random seed
init_random_seed(params.manual_seed)
# load dataset
src_data_loader = get_visda(root=params.data_root, sub_dir='train', split='train')
src_data_loader_eval = get_visda(root=params.data_root, sub_dir='train', split='test')
tgt_data_loader = get_visda(root=params.data_root, sub_dir='validation', split='train')
tgt_data_loader_eval = get_visda(root=params.data_root, sub_dir='validation', split='test')
# load models
src_encoder = init_model(net=ResNet34Encoder(),
restore=params.src_encoder_restore)
src_classifier = init_model(net=Classifier(),
restore=params.src_classifier_restore)
# train source model
# print("=== Training classifier for source domain ===")
# print(">>> Source Encoder <<<")
# print(src_encoder)
# print(">>> Source Classifier <<<")
# print(src_classifier)
# eval source model
print("=== Evaluating classifier for source domain ===")
eval_src(src_encoder, src_classifier, src_data_loader_eval)
# eval target encoder on test set of target dataset
"Target RUBV3D2: Verification IOU Precision = {:.4f}%, F1 IOU= {:.4f}%"
.format(iou_acc_v, f1_v))
(self.store_learning).write_file(
(self.store_learning).IOU_acc_file_verif, iou_acc_v)
(self.store_learning).write_file(
(self.store_learning).f1_IOU_file_verif, f1_v)
if __name__ == '__main__':
##Final Model Classification
from RUBV3D2 import UNet
src_encoder = init_model(net=UNet(params.input_channels, params.nb_classes,
params.default_layers,
params.default_features_root,
params.default_filter_width,
params.distance_unet, params.bins,
params.default_bn),
restore=params.src_encoder_restore)
tgt_encoder = init_model(net=UNet(params.input_channels, params.nb_classes,
params.default_layers,
params.default_features_root,
params.default_filter_width,
params.distance_unet, params.bins,
params.default_bn),
restore=params.tgt_encoder_restore)
discri_net = init_model(net=Discriminator(
input_dims=params.d_input_dims,
output_dims=params.d_output_dims,
len_feature_map=params.d_len_feature_maps,
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")
parser.add_argument('--out_dir', type=str, default=None)
parser.add_argument('--fig_save_path', type=str, default=None)
parser.add_argument('--num', type=int, default=10)
parser.add_argument('--sample_h', type=int, default=256)
parser.add_argument('--sample_w', type=int, default=256)
parser.add_argument('--lower', type=float, default=-3)
parser.add_argument('--upper', type=float, default=3)
parser.add_argument('--starter_im1_path', type=str)
parser.add_argument('--starter_im2_path', type=str)
args = parser.parse_args()
print(args)
print('----------')
latent_dim = 512
model = init_model(args.model_class, args.restore_path, restore_required=True, latent_dim=latent_dim)
model.eval()
lower = args.lower
upper = args.upper
num = args.num
sample_h = args.sample_h
sample_w = args.sample_w
fig_save_path = args.fig_save_path
out_dir = args.out_dir
starter_im1_path = args.starter_im1_path
starter_im2_path = args.starter_im2_path
# generate samples
with torch.no_grad():
opts.train_opts(parser)
opt = parser.parse_args()
location = opt.gpu if torch.cuda.is_available(
) and opt.gpu != -1 else 'cpu'
device = torch.device(location)
TXT, train_iter, valid_iter = \
preproc.build_iters(ftrain=opt.ftrain,
fvalid=opt.fvalid,
bsz=opt.bsz,
min_freq=opt.min_freq,
device=opt.gpu)
model = nets.BaseRNN(voc_size=len(TXT.vocab.itos),
edim=opt.edim,
hdim=opt.hdim,
padding_idx=TXT.vocab.stoi[PAD]).to(device)
utils.init_model(model)
optimizer = optim.Adam(params=filter(lambda p: p.requires_grad,
model.parameters()),
lr=opt.lr)
criterion = nn.CrossEntropyLoss(reduce=False)
train(model, {
'train': train_iter,
'valid': valid_iter
}, opt, criterion, optimizer)
data_sequences = []
for i in range(len(data.review)):
tokenized_text = tokenizer.tokenize(data.review[i])
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
data_sequences.append(indexed_tokens)
data_loader = get_data_loader(data_sequences, data.label,
args.batch_size, args.seqlen)
encoder = BERTEncoder()
if torch.cuda.device_count() > 1:
encoder = torch.nn.DataParallel(encoder)
if args.tgt:
encoder = init_model(encoder, restore=param.tgt_encoder_restore)
else:
encoder = init_model(encoder, restore=param.src_encoder_restore)
feats = []
labels = []
encoder.eval()
print("=== start encoding data ===")
for step, (reviews, label) in enumerate(data_loader):
mask = (reviews != 0).long()
feat = encoder(reviews, mask)
feats.extend(feat.cpu().detach().numpy())
labels.extend(label.cpu().numpy())
print("Step [%.2d/%.2d]" % (step + 1, len(data_loader)), feat.size())
feats = np.array(feats)
edge = {}
edge['data'] = result
scipy.io.savemat(join(save_dir, "%s.mat" % filename), edge)
#result = Image.fromarray((result * 255).astype(np.uint8))
#result.save(join(save_dir, "%s.png" % filename))
print("Running test [%d/%d]" % (idx + 1, len(test_loader)))
test_dataset = BSDS_Loader(root='../DATA/data/HED-BSDS/')
test_loader = DataLoader(test_dataset,
batch_size=1,
num_workers=0,
drop_last=True,
shuffle=False)
with open('../DATA/data/HED-BSDS/test.lst', 'r') as f:
test_list = f.readlines()
test_list = [split(i.rstrip())[1] for i in test_list]
assert len(test_list) == len(test_loader), "%d vs %d" % (len(test_list),
len(test_loader))
print('Test size : %d' % len(test_loader))
save_dir = 'bsds'
model = RCF()
init_model(model)
#print(state_dict['conv1_1.weight'].shape)
#quit()
model.cuda()
test_model(model, test_loader, test_list, save_dir)
from utils import get_data_loader, init_model, init_random_seed
from test import eval_tgt
from adopt import train_target
if __name__ == '__main__':
# init random seed
init_random_seed(params.manual_seed)
# load dataset
src_data_loader = get_data_loader(params.src_dataset)
src_data_loader_eval = get_data_loader(params.src_dataset, train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, train=False)
# load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore)
src_classifier = init_model(net=LeNetClassifier(),
restore=params.src_classifier_restore)
tgt_encoder = init_model(net=LeNetEncoder(),
restore=params.tgt_encoder_restore)
critic = init_model(Discriminator(input_dim=params.d_input_dims,
hidden_dim=params.d_hidden_dims,
output_dim=params.d_output_dims),
restore=params.d_model_restore)
# train source model
print("=== Training classifier for source domain ===")
print(">>> Source Encoder <<<")
print(src_encoder)
print(">>> Source Classifier <<<")
print(src_classifier)
def main(data, val_data, config):
wandb.init(project="prescalenorm")
config = load_config(config)
dataset = init_dataset(data)
dataset.train_percent = config.train_data_percent
dataset.set_data_source(config.data_source)
loader = init_dataloader(dataset, batch_size=config.batch_size)
model = init_model(type_vocab_size=config.type_vocab_size)
print(f'from_scratch: {config.from_scratch}', f'prenorm: {config.prenorm}',
f'tie_qk: {config.tie_query_key}', f'norm_type: {config.norm_type}')
model = configure_model(model, config)
val_dataset = init_dataset(val_data)
val_dataset.train_percent = config.val_data_percent
val_dataset.to_val_mode('scientsbank', 'answer')
val_loader = init_dataloader(val_dataset,
batch_size=config.batch_size,
random=False)
wandb.watch(model)
model.train()
cuda = torch.cuda.is_available()
if cuda:
model.cuda()
optimizer = init_optimizer(model, config)
lr_scheduler = transformers.get_cosine_schedule_with_warmup(
optimizer, config.warmup_steps, config.total_steps)
# best_val_acc = 0.0
# torch.save(config, os.path.join(wandb.run.dir, 'model.config'))
json.dump(config.__dict__,
open(os.path.join(wandb.run.dir, 'model_config.json'), 'w'))
wandb.save('*.config')
best_f1 = 0.0
patience = 0
try:
while lr_scheduler.last_epoch <= config.total_steps:
av_epoch_loss = train_epoch(loader, model, optimizer, lr_scheduler,
config, cuda)
#tidy stuff up every epoch
gc.collect()
torch.cuda.empty_cache()
p, r, f1, val_acc = val_loop(model, val_loader, cuda)
log_line = f'precision: {p:.5f} | recall: {r:.5f} | f1: {f1:.5f} | accuracy: {val_acc:.5f}\n'
print(log_line[:-1])
print('av_epoch_loss', av_epoch_loss)
if f1 > best_f1:
print(
"saving to: ",
os.path.join(wandb.run.dir,
f'full_bert_model_best_acc.pt'))
torch.save([model.state_dict(), config.__dict__],
os.path.join(wandb.run.dir,
f'full_bert_model_best_f1.pt'))
wandb.save('*.pt')
best_f1 = f1
patience = max((0, patience - 1))
else:
patience += 1
if patience >= 3:
break
if av_epoch_loss < .2:
break
torch.save([model.state_dict(), config.__dict__],
os.path.join(
wandb.run.dir,
f'full_bert_model_{lr_scheduler.last_epoch}_steps.pt'))
#Move stuff off the gpu
model.cpu()
#This is for sure a kinda dumb way of doing it, but the least mentally taxing right now
optimizer = init_optimizer(model, config)
gc.collect()
torch.cuda.empty_cache()
return model
except KeyboardInterrupt:
wandb.save('*.pt')
#Move stuff off the gpu
model.cpu()
#This is for sure a kinda dumb way of doing it, but the least mentally taxing right now
optimizer = init_optimizer(model, config)
gc.collect()
torch.cuda.empty_cache()
return model
def main():
max_len = 50
n_vocab = params.n_vocab
n_layer = params.n_layer
n_hidden = params.n_hidden
n_embed = params.n_embed
temperature = params.temperature
assert torch.cuda.is_available()
if os.path.exists("data/vocab.json"):
vocab = Vocabulary()
with open('data/vocab.json', 'r') as fp:
vocab.stoi = json.load(fp)
for key, value in vocab.stoi.items():
vocab.itos.append(key)
else:
print("vocabulary doesn't exist!")
return
print("loading model...")
encoder = Encoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
Kencoder = KnowledgeEncoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
manager = Manager(n_hidden, n_vocab, temperature).cuda()
decoder = Decoder(n_vocab, n_embed, n_hidden, n_layer).cuda()
encoder = init_model(encoder, restore=params.encoder_restore)
Kencoder = init_model(Kencoder, restore=params.Kencoder_restore)
manager = init_model(manager, restore=params.manager_restore)
decoder = init_model(decoder, restore=params.decoder_restore)
print("successfully loaded!\n")
utterance = ""
while True:
if utterance == "exit":
break
k1 = input("Type first Knowledge: ").lower()
while not k1:
print("Please type first Knowledge.\n")
k1 = input("Type first Knowledge: ").lower()
k2 = input("Type second Knowledge: ").lower()
while not k2:
print("Please type second Knowledge.\n")
k2 = input("Type second Knowledge: ").lower()
k3 = input("Type third Knowledge: ").lower()
while not k3:
print("Please type third Knowledge.\n")
k3 = input("Type third Knowledge: ").lower()
K = [k1, k2, k3]
K = knowledgeToIndex(K, vocab)
K = Kencoder(K)
print()
while True:
utterance = input("you: ").lower()
while not utterance:
print("Please type utterance.\n")
utterance = input("you: ")
if utterance == "change knowledge" or utterance == "exit":
print()
break
X = []
tokens = nltk.word_tokenize(utterance)
for word in tokens:
if word in vocab.stoi:
X.append(vocab.stoi[word])
else:
X.append(vocab.stoi["<UNK>"])
X = torch.LongTensor(X).unsqueeze(0).cuda() # X: [1, x_seq_len]
encoder_outputs, hidden, x = encoder(X)
k_i = manager(x, None, K)
outputs = torch.zeros(
max_len, 1, n_vocab).cuda() # outputs: [max_len, 1, n_vocab]
hidden = hidden[decoder.n_layer:]
output = torch.LongTensor([params.SOS]).cuda()
for t in range(max_len):
output, hidden, attn_weights = decoder(output, k_i, hidden,
encoder_outputs)
outputs[t] = output
output = output.data.max(1)[1]
outputs = outputs.max(2)[1]
answer = ""
for idx in outputs:
if idx == params.EOS:
break
answer += vocab.itos[idx] + " "
print("bot:", answer[:-1], "\n")
import gym
import world
import utils
from Buffer import ReplayBuffer
from models import DQN
from world import Print, ARGS
from wrapper import WrapIt
from procedure import train_DQN
# ------------------------------------------------
env = gym.make('RiverraidNoFrameskip-v4')
env = WrapIt(env)
Print('ENV action', env.unwrapped.get_action_meanings())
Print('ENV observation', f"Image: {ARGS.imgDIM} X {ARGS.imgDIM} X {1}"
) # we assert to use gray image
# ------------------------------------------------
Optimizer = utils.getOptimizer()
schedule = utils.LinearSchedule(1000000, 0.1)
Game_buffer = ReplayBuffer(ARGS.buffersize, ARGS.framelen)
Q = utils.init_model(env, DQN).train().to(world.DEVICE)
Q_target = utils.init_model(env, DQN).eval().to(world.DEVICE)
# ------------------------------------------------
train_DQN(env,
Q=Q,
Q_target=Q_target,
optimizer=Optimizer,
replay_buffer=Game_buffer,
exploration=schedule)
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)
train=True)
src_data_loader_eval = get_data_loader(params.src_dataset,
dataset_root=params.dataset_root,
batch_size=params.batch_size,
train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset,
dataset_root=params.dataset_root,
batch_size=params.batch_size,
train=True)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset,
dataset_root=params.dataset_root,
batch_size=params.batch_size,
train=False)
# load models
src_encoder = init_model(net=LeNetEncoder(),
restore=params.src_encoder_restore)
src_classifier = init_model(net=LeNetClassifier(),
restore=params.src_classifier_restore)
tgt_encoder = init_model(net=LeNetEncoder(),
restore=params.tgt_encoder_restore)
critic = init_model(Discriminator(), restore=params.d_model_restore)
# train source model
print("=== Training classifier for source domain ===")
if not (src_encoder.restored and src_classifier.restored
and params.src_model_trained):
src_encoder, src_classifier = train_src(src_encoder, src_classifier,
src_data_loader, params)
# eval source model
