from src.utils import makedirs, tensor2cuda, load_model, LabelDict
from argument import parser
from src.visualization import VanillaBackprop
from src.attack import FastGradientSignUntargeted
from src.model.madry_model import WideResNet
import matplotlib.pyplot as plt
max_epsilon = 4.7
perturbation_type = 'l2'
out_num = 5
img_folder = 'img'
makedirs(img_folder)
args = parser()
label_dict = LabelDict(args.dataset)
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
def main(args):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
makedirs(args.log_root)
makedirs(args.model_folder)
setattr(args, 'log_folder', args.log_root)
setattr(args, 'model_folder', args.model_folder)
logger = create_logger(args.log_root, args.todo, 'info')
print_args(args, logger)
if args.model == 'enet':
model = EfficientNet.from_pretrained('efficientnet-b5', num_classes=2)
elif args.model == 'xception':
model, *_ = model_selection(modelname='xception',
num_out_classes=2,
init_checkpoint=args.init_load)
else:
raise NotImplementedError
if args.load_checkpoint is not None:
if device.type == 'cpu':
checkpoint = torch.load(args.load_checkpoint,
map_location=torch.device('cpu'))
else:
checkpoint = torch.load(args.load_checkpoint)
model.load_state_dict(checkpoint)
if torch.cuda.device_count() > 1:
print('GPUs: ', torch.cuda.device_count())
model = nn.DataParallel(model)
model = model.to(device)
trainer = Trainer(args, logger)
if args.todo == 'train':
if args.array:
transform = transforms.Normalize(mean=[0.5], std=[0.5])
def npy_loader(path):
sample = torch.from_numpy(np.load(path))
return sample
#BUILD TRAIN SET
print("Initializing Dataset...")
train_set = DatasetFolderWithPaths(root=args.data_root,
loader=npy_loader,
extensions='.npy',
transform=transform)
print("Dataset is successful")
#BUILD VAL SET
val_set = DatasetFolderWithPaths(root=args.val_root,
loader=npy_loader,
extensions='.npy',
transform=transform)
else:
transform = transforms.Compose([
transforms.Resize((299, 299)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
train_set = ImageFolderWithPaths(args.data_root,
transform=transform)
val_set = ImageFolderWithPaths(args.val_root, transform=transform)
logger.info('Train Total: %d' % len(train_set))
logger.info('Val Total: %d' % len(val_set))
#logger.info( "Classes: {}".format(' '.join(map(str, train_set.classes))))
tr_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.nworkers,
pin_memory=torch.cuda.is_available())
te_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.nworkers,
pin_memory=torch.cuda.is_available())
if args.array:
trainer.train(model,
tr_loader,
te_loader,
device,
adv_train=args.adv)
else:
trainer.train(model,
tr_loader,
te_loader,
device,
adv_train=args.adv)
elif args.todo == 'test':
if args.array:
transform = transforms.Normalize(mean=[0.5], std=[0.5])
def npy_loader(path):
sample = torch.from_numpy(np.load(path))
return sample
te_dataset = DatasetFolderWithPaths(root=args.data_root,
loader=npy_loader,
extensions='.npy',
transform=transform)
else:
transform = transforms.Compose([
transforms.Resize((299, 299)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
te_dataset = ImageFolderWithPaths(args.data_root,
transform=transform)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.nworkers,
pin_memory=torch.cuda.is_available())
if args.array:
std_acc, loss = trainer.test(model,
te_loader,
device,
adv_test=args.adv)
else:
std_acc, loss = trainer.test(model,
te_loader,
device,
adv_test=args.adv)
print("std acc: {:4f}".format(std_acc * 100))
else:
raise NotImplementedError
def main(args):
save_folder = '%s_%s' % (args.dataset, args.affix)
log_folder = os.path.join(args.log_root, save_folder)
model_folder = os.path.join(args.model_root, save_folder)
makedirs(log_folder)
makedirs(model_folder)
setattr(args, 'log_folder', log_folder)
setattr(args, 'model_folder', model_folder)
logger = create_logger(log_folder, args.todo, 'info')
print_args(args, logger)
model = WideResNet(depth=34, num_classes=10, widen_factor=10, dropRate=0.0)
attack = FastGradientSignUntargeted(model,
args.epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=args.perturbation_type)
if torch.cuda.is_available():
model.cuda()
trainer = Trainer(args, logger, attack)
if args.todo == 'train':
transform_train = tv.transforms.Compose([
tv.transforms.RandomCrop(32,
padding=4,
fill=0,
padding_mode='constant'),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
])
tr_dataset = tv.datasets.CIFAR10(args.data_root,
train=True,
transform=transform_train,
download=True)
tr_loader = DataLoader(tr_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# evaluation during training
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
trainer.train(model, tr_loader, te_loader, args.adv_train)
elif args.todo == 'test':
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
checkpoint = torch.load(args.load_checkpoint)
model.load_state_dict(checkpoint)
std_acc, adv_acc = trainer.test(model,
te_loader,
adv_test=True,
use_pseudo_label=False)
print(f"std acc: {std_acc * 100:.3f}%, adv_acc: {adv_acc * 100:.3f}%")
else:
raise NotImplementedError
default='single',
choices=['single', 'double'])
parser.add_argument('--approach',
type=str,
default='ocflow',
choices=['ocflow'])
args = parser.parse_args()
if args.prec == 'double':
argPrec = torch.float64
else:
argPrec = torch.float32
# logger
utils.makedirs(args.save)
logger = utils.get_logger(logpath=os.path.join(args.save, 'logs'),
filepath=os.path.abspath(__file__))
logger.info(args)
device = torch.device('cpu') # only supports cpu
figNum = 1
lw = 2 # linewidth
strTitle = 'eval_' + os.path.basename(args.resume)[:-12]
fontsize = 18
title_fontsize = 22
if __name__ == '__main__':
def main(args):
save_folder = '%s_%s' % (args.dataset, args.affix)
log_folder = os.path.join(args.log_root, save_folder)
model_folder = os.path.join(args.model_root, save_folder)
makedirs(log_folder)
makedirs(model_folder)
setattr(args, 'log_folder', log_folder)
setattr(args, 'model_folder', model_folder)
logger = create_logger(log_folder, args.todo, 'info')
print_args(args, logger)
# model = WideResNet(depth=34, num_classes=10, widen_factor=10, dropRate=0.0)
model = models.resnet50(pretrained=True)
num_classes = 10
model.fc = nn.Linear(model.fc.in_features, num_classes)
attack = FastGradientSignUntargeted(model,
args.epsilon,
args.alpha,
min_val=0,
max_val=1,
max_iters=args.k,
_type=args.perturbation_type)
if torch.cuda.is_available():
model.cuda()
trainer = Trainer(args, logger, attack)
if args.todo == 'train':
transform_train = tv.transforms.Compose([
tv.transforms.ToTensor(),
tv.transforms.Lambda(lambda x: F.pad(
x.unsqueeze(0),
(4, 4, 4, 4), mode='constant', value=0).squeeze()),
tv.transforms.ToPILImage(),
tv.transforms.RandomCrop(32),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
])
tr_dataset = tv.datasets.CIFAR10(args.data_root,
train=True,
transform=transform_train,
download=True)
tr_loader = DataLoader(tr_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=48)
# evaluation during training
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=48)
trainer.train(model, tr_loader, te_loader, args.adv_train)
elif args.todo == 'test':
te_dataset = tv.datasets.CIFAR10(args.data_root,
train=False,
transform=tv.transforms.ToTensor(),
download=True)
te_loader = DataLoader(te_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=48)
checkpoint = torch.load(args.load_checkpoint)
model.load_state_dict(checkpoint)
std_acc, adv_acc = trainer.test(model,
te_loader,
adv_test=True,
use_pseudo_label=False)
print("std acc: %.4f, adv_acc: %.4f" % (std_acc * 100, adv_acc * 100))
else:
raise NotImplementedError
def train(train_file, model_path, file_format, train_mode, epochs, batch_size,
d_embed, n_negative, log_every, lr, val_every, gpu,
validation_split):
#print('Configuration:')
torch.cuda.device(gpu)
lhs_field, rhs_field = create_fields(train_mode)
train_dataset, batch_extractor = get_dataset_and_extractor(
train_file, file_format, lhs_field, rhs_field)
train, validation = train_validation_split(
train_dataset, validation_size=validation_split)
print('Num entity pairs train:', len(train))
print('Num entity pairs validation:', len(validation))
lhs_field.build_vocab(train)
rhs_field.build_vocab(train)
n_lhs = len(lhs_field.vocab)
n_rhs = len(rhs_field.vocab)
print('Num LHS features:', n_lhs)
print('Num RHS features:', n_rhs)
train_iter, val_iter = data.BucketIterator.splits((train, validation),
batch_size=batch_size,
device=gpu)
# TODO: implement loading from snapshot
model = StarSpace(d_embed=d_embed,
n_input=n_lhs,
n_output=n_rhs,
similarity=InnerProductSimilarity(),
max_norm=20,
aggregate=torch.sum)
model.cuda()
neg_sampling = NegativeSampling(n_output=n_rhs, n_negative=n_negative)
criterion = MarginRankingLoss(margin=1., aggregate=torch.mean)
opt = torch.optim.Adam(model.parameters(), lr=lr)
logger = TableLogger(headers=[
'time', 'epoch', 'iterations', 'loss', 'accuracy', 'val_accuracy'
])
makedirs(model_path)
iterations = 0
start = time.time()
train_iter.repeat = False
best_val_acc = -1
for epoch in range(epochs):
n_correct, n_total = 0, 0
for batch in train_iter:
model.train()
opt.zero_grad()
iterations += 1
# TODO: add input/output extractor function for different tasks
# TODO: add prediction candidate generator function for different tasks
# TODO: add correctness/accuracy function for different tasks
# TODO: clean up accuracy computation
# extract entity pairs from batch
lhs, rhs = batch_extractor(batch)
# get similarity for positive entity pairs
lhs_repr, pos_rhs_repr = model(lhs, rhs) # B x dim, B x dim
positive_similarity = model.similarity(
lhs_repr, pos_rhs_repr).squeeze(1) # B x 1
# get similarity for negative entity pairs
n_samples = batch.batch_size * n_negative
neg_rhs = neg_sampling.sample(n_samples)
if lhs.is_cuda:
neg_rhs = neg_rhs.cuda()
_, neg_rhs_repr = model(output=neg_rhs) # (B * n_negative) x dim
neg_rhs_repr = neg_rhs_repr.view(batch.batch_size, n_negative,
-1) # B x n_negative x dim
negative_similarity = model.similarity(
lhs_repr, neg_rhs_repr).squeeze(1) # B x n_negative
# calculate accuracy of predictions in the current batch
candidate_rhs = torch.autograd.Variable(
torch.arange(0, n_rhs).long().expand(batch.batch_size,
-1)) # B x n_output
if lhs.is_cuda:
candidate_rhs = candidate_rhs.cuda()
_, candidate_rhs_repr = model(output=candidate_rhs.view(
batch.batch_size * n_rhs)) # B x dim, (B * n_output) x dim
candidate_rhs_repr = candidate_rhs_repr.view(
batch.batch_size, n_rhs, -1) # B x n_output x dim
similarity = model.similarity(
lhs_repr, candidate_rhs_repr).squeeze(1) # B x n_output
n_correct += (torch.max(similarity, dim=-1)[1].view(
rhs.size()).data == rhs.data).sum()
n_total += batch.batch_size
train_acc = 100. * n_correct / n_total
# calculate loss
loss = criterion(positive_similarity, negative_similarity)
loss.backward()
opt.step()
# evaluate performance on validation set
if iterations % val_every == 0:
model.eval()
# calculate accuracy on validation set
n_val_correct = 0
for val_batch_idx, val_batch in enumerate(val_iter):
val_lhs, val_rhs = batch_extractor(val_batch)
val_candidate_rhs = torch.autograd.Variable(
torch.arange(0, n_rhs).long().expand(
val_batch.batch_size, -1)) # B x n_output
if val_lhs.is_cuda:
val_candidate_rhs = val_candidate_rhs.cuda()
val_lhs_repr, val_candidate_rhs_repr = model(
val_lhs,
val_candidate_rhs.view(
val_batch.batch_size *
n_rhs)) # B x dim, (B * n_output) x dim
val_candidate_rhs_repr = val_candidate_rhs_repr.view(
val_batch.batch_size, n_rhs, -1) # B x n_output x dim
similarity = model.similarity(
val_lhs_repr,
val_candidate_rhs_repr).squeeze(1) # B x n_output
n_val_correct += (torch.max(similarity, dim=-1)[1].view(
val_rhs.size()).data == val_rhs.data).sum()
val_acc = 100. * n_val_correct / len(validation)
# log progress, including validation metrics
logger.log(time=time.time() - start,
epoch=epoch,
iterations=iterations,
loss=loss.data[0],
accuracy=(100. * n_correct / n_total),
val_accuracy=val_acc)
# found a better model
if val_acc > best_val_acc:
best_val_acc = val_acc
# save model
torch.save(model, os.path.join(model_path, 'model.pt'))
# save vocabulary for both entity fields
save_vocab(lhs_field,
os.path.join(model_path, 'lhs_vocab.pkl'))
save_vocab(rhs_field,
os.path.join(model_path, 'rhs_vocab.pkl'))
elif iterations % log_every == 0:
# log training progress
logger.log(time=time.time() - start,
epoch=epoch,
iterations=iterations,
loss=loss.data[0],
accuracy=(100. * n_correct / n_total))
def main():
makedirs(config.temp_dir)
makedirs(config.result_dir)
makedirs(config.train_log_dir)
makedirs(config.valid_log_dir)
print('preparing data...')
config.word_2_id, config.id_2_word = read_json_dict(config.vocab_dict)
config.vocab_size = min(config.vocab_size, len(config.word_2_id))
config.oov_vocab_size = min(config.oov_vocab_size,
len(config.word_2_id) - config.vocab_size)
embedding_matrix = None
if args.do_train:
if os.path.exists(config.glove_file):
print('loading embedding matrix from file: {}'.format(
config.glove_file))
embedding_matrix, config.word_em_size = load_glove_embedding(
config.glove_file, list(config.word_2_id.keys()))
print('shape of embedding matrix: {}'.format(
embedding_matrix.shape))
else:
if os.path.exists(config.glove_file):
with open(config.glove_file, 'r', encoding='utf-8') as fin:
line = fin.readline()
config.word_em_size = len(line.strip().split()) - 1
data_reader = DataReader(config)
evaluator = Evaluator('tgt')
print('building model...')
model = get_model(config, embedding_matrix)
saver = tf.train.Saver(max_to_keep=10)
if args.do_train:
print('loading data...')
train_data = data_reader.read_train_data()
valid_data = data_reader.read_valid_data()
print_title('Trainable Variables')
for v in tf.trainable_variables():
print(v)
print_title('Gradients')
for g in model.gradients:
print(g)
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(config.result_dir)
if model_file is not None:
print('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
else:
print('initializing from scratch...')
tf.global_variables_initializer().run()
train_writer = tf.summary.FileWriter(config.train_log_dir,
sess.graph)
valid_writer = tf.summary.FileWriter(config.valid_log_dir,
sess.graph)
valid_log_history = run_train(sess,
model,
train_data,
valid_data,
saver,
evaluator,
train_writer,
valid_writer,
verbose=True)
save_json(
valid_log_history,
os.path.join(config.result_dir, 'valid_log_history.json'))
if args.do_eval:
print('loading data...')
valid_data = data_reader.read_valid_data()
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(config.result_dir)
if model_file is not None:
print('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
predicted_ids, alignment_history, valid_loss, valid_accu = run_evaluate(
sess, model, valid_data, verbose=True)
print(
'average valid loss: {:>.4f}, average valid accuracy: {:>.4f}'
.format(valid_loss, valid_accu))
print_title('Saving Result')
if not config.beam_search:
save_result_v1(predicted_ids, alignment_history,
config.id_2_word, config.valid_data,
config.valid_result)
else:
save_result_v2(predicted_ids, config.id_2_word,
config.valid_result)
evaluator.evaluate(config.valid_data, config.valid_result,
config.to_lower)
else:
print('model not found!')
if args.do_test:
print('loading data...')
test_data = data_reader.read_test_data()
with tf.Session(config=sess_config) as sess:
model_file = args.model_file
if model_file is None:
model_file = tf.train.latest_checkpoint(config.result_dir)
if model_file is not None:
print('loading model from {}...'.format(model_file))
saver.restore(sess, model_file)
predicted_ids, alignment_history = run_test(sess,
model,
test_data,
verbose=True)
print_title('Saving Result')
if not config.beam_search:
save_result_v1(predicted_ids, alignment_history,
config.id_2_word, config.test_data,
config.test_result)
else:
save_result_v2(predicted_ids, config.id_2_word,
config.test_result)
evaluator.evaluate(config.test_data, config.test_result,
config.to_lower)
else:
print('model not found!')