def create_project_file(config):
"""
Creates a coregen project with settings for this device
Args:
config (dictionary): configuration dictionary
Returns:
(string): filename to the project file
Raises:
Nothing
"""
core_dir = get_coregen_dir(config, absolute = True)
cp_fn = os.path.join(core_dir, COREGEN_PROJECT_NAME)
fp = open(cp_fn, "w")
#Open up the template dictionary
fn = COREGEN_TEMPLATE
fn = os.path.join(os.path.dirname(__file__), fn)
template = json.load(open(fn, "r"))
template["device"] = utils.get_device(config)
template["devicefamily"] = utils.get_family(config)
template["package"] = utils.get_package(config)
template["speedgrade"] = utils.get_speed_grade(config)
template["workingdirectory"] = get_coregen_temp_dir(config, absolute = True)
for t in template:
fp.write("SET %s = %s%s" % (t, template[t], os.linesep))
fp.close()
return cp_fn
def post(self):
# Check arguments
args = user_parser.parse_args()
if not args["email"] or not args["password"]:
abort(406)
# Check for duplicate emails
if User.query.filter(User.email == args["email"]).first() is not None:
abort(409)
# Add user
user = User(args["email"], args["password"])
if args["phone"]:
user.phone = args["phone"]
db.session.add(user)
db.session.commit()
# Add current device for the user
device = Device(user.id, get_device())
device.active = True
db.session.add(device)
db.session.commit()
return user, 201
parser.add_argument('--with_exploration',
action='store_true',
help="See data exploration visualization")
parser.add_argument('--download',
type=str,
default=None,
nargs='+',
choices=dataset_names,
help="Download the specified datasets and quits.")
# 解析参数
args = parser.parse_args()
# 操作参数
CUDA_DEVICE = get_device(args.cuda)
# % of training samples
SAMPLE_PERCENTAGE = args.training_sample
# Data augmentation 数据增强
FLIP_AUGMENTATION = args.flip_augmentation
RADIATION_AUGMENTATION = args.radiation_augmentation
MIXTURE_AUGMENTATION = args.mixture_augmentation
# Dataset name
DATASET = args.dataset
# Model name
MODEL = args.model
# Number of runs (for cross-validation)
N_RUNS = args.runs
# Spatial context size (number of neighbours in each spatial direction)
# 空间上下文大小（每个空间方向上的邻居数）
def train_model(args: dict, hparams:dict):
# Code for this function adopted from https://mccormickml.com/2019/07/22/BERT-fine-tuning/
file = args.dataset_filepath
# pos_file = args.pos_file
# neg_file = args.neg_file
truncation = args.truncation
# n_samples = args.n_samples
seed_val = hparams["seed_val"]
device = utils.get_device(device_no=args.device_no)
saves_dir = "saves/"
Path(saves_dir).mkdir(parents=True, exist_ok=True)
time = datetime.datetime.now()
saves_path = os.path.join(saves_dir, utils.get_filename(time))
Path(saves_path).mkdir(parents=True, exist_ok=True)
log_path = os.path.join(saves_path, "training.log")
logging.basicConfig(filename=log_path, filemode='w', format='%(name)s - %(levelname)s - %(message)s', level=logging.DEBUG)
logger=logging.getLogger()
# logger.setLevel()
logger.info("File: "+str(file))
logger.info("Parameters: "+str(args))
logger.info("Truncation: "+truncation)
# Load the BERT tokenizer.
logger.info('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
max_len = 0
# samples = utils.read_and_sample(file
# # , seed_val=seed_val
# )
samples = utils.read_pairwise(file, first=0, second=2)
random.shuffle(samples)
input_ids = []
attention_masks = []
samples_text = [val[0] for val in samples]
samples_label = [val[1] for val in samples]
print(np.unique(np.array(samples_label)))
max_len = 0
# For every sentence...
for text in samples_text:
# Tokenize the text and add `[CLS]` and `[SEP]` tokens.
input_id = tokenizer(text, add_special_tokens=True)
# Update the maximum sentence length.
max_len = max(max_len, len(input_id['input_ids']))
logger.info('Max text length: ' + str(max_len))
print('Max text length: ' + str(max_len))
for text in samples_text:
input_id = tokenizer(text, add_special_tokens=True)
# print(len(input_id['input_ids']))
# if len(input_id['input_ids']) > 512:
# if truncation == "tail-only":
# input_id = [tokenizer.cls_token_id]+input_id[-511:]
# elif truncation == "head-and-tail":
# input_id = [tokenizer.cls_token_id]+input_id[1:129]+input_id[-382:]+[tokenizer.sep_token_id]
# else:
# input_id = input_id[:511]+[tokenizer.sep_token_id]
# input_ids.append(torch.tensor(input_id).view(1,-1))
# attention_masks.append(torch.ones([1,len(input_id)], dtype=torch.long))
# else:
encoded_dict = tokenizer(
text,
add_special_tokens = True,
truncation=True,
max_length = 512,
padding = 'max_length',
return_attention_mask = True,
return_tensors = 'pt',
)
input_ids.append(encoded_dict['input_ids'])
attention_masks.append(encoded_dict['attention_mask'])
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
samples_label_tensor = torch.tensor(samples_label)
# samples_text_tensor = torch.tensor(samples_text)
dataset = TensorDataset(input_ids, attention_masks, samples_label_tensor)
# dataset = TensorDataset(samples_text_tensor, samples_label_tensor)
train_size = int(0.9 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
logger.info('{:>5,} training samples'.format(train_size))
logger.info('{:>5,} validation samples'.format(val_size))
batch_size = hparams["batch_size"]
train_dataloader = DataLoader(
train_dataset, # The training samples.
sampler = RandomSampler(train_dataset), # Select batches randomly
batch_size = batch_size, # Trains with this batch size.
# collate_fn = collate_fn
)
validation_dataloader = DataLoader(
val_dataset, # The validation samples.
sampler = SequentialSampler(val_dataset), # Pull out batches sequentially.
batch_size = batch_size, # Evaluate with this batch size.
# collate_fn = collate_fn
)
model = BertForSequenceClassification.from_pretrained(
"bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
num_labels = 2, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
model = model.to(device=device)
# model.cuda(device=device)
optimizer = AdamW(model.parameters(),
lr = hparams["learning_rate"], # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps = hparams["adam_epsilon"] # args.adam_epsilon - default is 1e-8.
)
epochs = 4
total_steps = len(train_dataloader) * epochs
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0, # Default value in run_glue.py
num_training_steps = total_steps)
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
training_stats = []
for epoch_i in range(0, epochs):
logger.info("")
logger.info('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
logger.info('Training...')
total_train_loss = 0
model.train()
for step, batch in enumerate(train_dataloader):
print(len(train_dataloader))
if step % 40 == 0 and not step == 0:
logger.info(' Batch {:>5,} of {:>5,}. '.format(step, len(train_dataloader)))
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
model.zero_grad()
loss, logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# print(logits)
# print(loss)
total_train_loss += loss.detach().cpu().numpy()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
avg_train_loss = total_train_loss / len(train_dataloader)
logger.info("")
logger.info("Average training loss: {0:.2f}".format(avg_train_loss))
logger.info("")
logger.info("Running Validation...")
model.eval()
total_eval_accuracy = 0
total_eval_loss = 0
for batch in validation_dataloader:
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
with torch.no_grad():
(loss, logits) = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
total_eval_loss += loss.detach().cpu().numpy()
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
total_eval_accuracy += flat_accuracy(logits, label_ids)
avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)
logger.info("Accuracy: {0:.2f}".format(avg_val_accuracy))
avg_val_loss = total_eval_loss / len(validation_dataloader)
logger.info("Validation Loss: {0:.2f}".format(avg_val_loss))
training_stats.append(
{
'epoch': epoch_i + 1,
'Training Loss': avg_train_loss,
'Valid. Loss': avg_val_loss,
'Valid. Accur.': avg_val_accuracy,
}
)
model_save_path = os.path.join(saves_path, "model_"+str(epoch_i+1)+"epochs")
torch.save(model, model_save_path)
logger.info("")
logger.info("Training complete!")
handlers = logger.handlers[:]
for handler in handlers:
handler.close()
logger.removeHandler(handler)
# Prepare dataset
transformer = transforms.Compose([
transforms.Resize(config["image_size"]),
transforms.CenterCrop(config["image_size"]),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = dset.ImageFolder(root=data_path, transform=transformer)
# Initialize dataloader
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=config["batch_size"],
shuffle=True,
num_workers=2)
device = get_device()
# Show some images from the trainingset
# show_images(dataloader)
# Initialize the model
generator = Generator(config).to(device)
discriminator = Discriminator(config).to(device)
# Initialize custom weights to model
generator.apply(weight_init)
discriminator.apply(weight_init)
# Loss Functions and Optimizers
# BCELoss for Discriminator
def main():
device = utils.get_device()
saves_dir = 'saves'
compare_models(f'{saves_dir}/cae-F-ConvAutoencoder_21-06-01--10-43-39',
f'{saves_dir}/cae-F-ConvAutoencoder_21-06-03--08-16-48',
device)
def main(task='mrpc',
train_cfg='config/train_mrpc.json',
model_cfg='config/bert_base.json',
data_file='../glue/MRPC/train.tsv',
model_file=None,
pretrain_file='../uncased_L-12_H-768_A-12/bert_model.ckpt',
data_parallel=True,
vocab='../uncased_L-12_H-768_A-12/vocab.txt',
save_dir='../exp/bert/mrpc',
max_len=128,
mode='train'):
cfg = train.Config.from_json(train_cfg)
model_cfg = models.Config.from_json(model_cfg)
set_seeds(cfg.seed)
tokenizer = tokenization.FullTokenizer(vocab_file=vocab,
do_lower_case=True)
pipeline = [
Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
AddSpecialTokensWithTruncation(max_len),
TokenIndexing(tokenizer.convert_tokens_to_ids, ('0', '1'), max_len)
]
dataset = CsvDataset(pipeline)
# print(dataset[0])
# pdb.set_trace()
data_iter = DataLoader(dataset, batch_size=1, shuffle=True)
model = Classifier(model_cfg, 1)
criterion = nn.CrossEntropyLoss()
trainer = train.Trainer(cfg, model, data_iter, optim.optim4GPU(cfg, model),
save_dir, get_device())
if mode == 'train':
def get_loss(model, batch,
global_step): # make sure loss is a scalar tensor
# pdb.set_trace()
input_ids, segment_ids, input_mask, label_id = [
b[0] for b in batch
]
# pdb.set_trace()
logits = model(input_ids, segment_ids, input_mask)
# pdb.set_trace()
loss = neg_logloss(logits)
# loss = criterion(logits, label_id)
return loss
trainer.train(get_loss, model_file, pretrain_file, data_parallel)
elif mode == 'eval':
def evaluate(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
_, label_pred = logits.max(1)
result = (label_pred == label_id).float() #.cpu().numpy()
accuracy = result.mean()
return accuracy, result
results = trainer.eval(evaluate, model_file, data_parallel)
total_accuracy = torch.cat(results).mean().item()
print('Accuracy:', total_accuracy)
def main(train_cfg='config/pretrain.json',
model_cfg='config/bert_base.json',
data_file='/root/voucher/dataset/tifu/bert/train.tsv',
model_file=None,
pretrain_file=None,
data_parallel=True,
word_vocab='/root/voucher/dataset/tifu/bert/word_vocab.txt',
pos_vocab='/root/voucher/dataset/tifu/bert/pos_vocab.txt',
dep_vocab='/root/voucher/dataset/tifu/bert/dep_vocab.txt',
pos_dep_word_vocab='/root/voucher/dataset/tifu/bert/pos_dep_word.pkl',
save_dir='../exp/bert/pretrain',
log_dir='../exp/bert/pretrain/runs',
max_len=384,
max_pred=20,
mask_prob=0.15,
mode=train):
if mode == 'train':
pass
elif mode == 'eval':
pass
# max_pred = max_len
# mask_prob = 1
else:
print("please select correct mode")
exit(1)
cfg = train.Config.from_json(train_cfg)
model_cfg = models.Config.from_json(model_cfg)
set_seeds(cfg.seed)
custom_tokenizer = CustomVocabTokenizer(word_vocab_file=word_vocab,
pos_vocab_file=pos_vocab,
dep_vocab_file=dep_vocab,
pos_dep_word_vocab_file=pos_dep_word_vocab)
custom_tokenize = lambda word, pos, dep: custom_tokenizer.tokenize(custom_tokenizer.convert_to_unicode(word),
custom_tokenizer.convert_to_unicode(pos),
custom_tokenizer.convert_to_unicode(dep))
pipeline = [Preprocess4Pretrain(max_pred,
mask_prob,
list(custom_tokenizer.word_tokenizer.vocab.keys()),
list(custom_tokenizer.pos_tokenizer.vocab.keys()),
list(custom_tokenizer.dep_tokenizer.vocab.keys()),
custom_tokenizer.convert_tokens_to_ids,
max_len)]
data_iter = TifuDataLoader(data_file,
cfg.batch_size,
custom_tokenize,
max_len,
pipeline=pipeline)
model = BertModel4Pretrain(model_cfg)
optimizer = optim.optim4GPU(cfg, model)
trainer = train.Trainer(cfg, model, data_iter, optimizer, save_dir, get_device())
if mode == 'eval':
def evaluate(model, batch):
input_word_ids,\
input_segment_ids,\
input_mask,\
target_word_ids,\
target_mask,\
input_len, \
target_len = batch
logits_word = model(input_word_ids,
input_segment_ids,
input_mask,
target_mask)
input_len = input_len.tolist()
target_len = target_len.tolist()
for i in range(len(input_len)):
logits = torch.squeeze(logits_word.narrow(0, i, 1), dim=0)
logits_input = logits.narrow(0, 0, input_len[i])
logits_target = logits.narrow(0, input_len[i], target_len[i])
_, input_ids = logits_input.max(-1)
_, target_ids = logits_target.max(-1)
input_tokens = custom_tokenizer.word_tokenizer.convert_ids_to_tokens(input_ids.tolist())
target_tokens = custom_tokenizer.word_tokenizer.convert_ids_to_tokens(target_ids.tolist())
results = []
input_norm = logits_input / logits_input.norm(dim=1)[:, None]
target_norm = logits_target / logits_target.norm(dim=1)[:, None]
#target_len x input_len
res = torch.mm(target_norm, input_norm.transpose(0, 1))
#target_len x 1
_, sim_idxs = res.max(-1)
for j, sim_idx in enumerate(sim_idxs.tolist()):
results.append([target_tokens[j], input_tokens[sim_idx]])
print(results)
accuracies = [0]
results = [0]
return accuracies, results
results = trainer.eval(evaluate, None, pretrain_file, data_parallel, eval_kind_names=["Word"])
print(results)
def train(flags):
data_root = flags.data
window_size = flags.window_size
pred_size = flags.pred_size
batch_size = flags.batch_size
out_dir = flags.out
num_epochs = flags.epochs
val_every = flags.val_every
classify_thresh = flags.classify_thresh
# optim args
lr = flags.lr
betas = (flags.beta1, flags.beta2)
eps = flags.eps
weight_decay = flags.decay
use_confidence = flags.use_confidence
joint_set = flags.joint_set
if not os.path.exists(data_root):
print('Could not find training data at ' + data_root)
return
if not os.path.exists(out_dir):
os.mkdir(out_dir)
weights_out_path = os.path.join(out_dir, 'op_only_weights.pth')
best_weights_out_path = os.path.join(out_dir, 'op_only_weights_BEST.pth')
# load training and validation data
train_dataset = OpenPoseDataset(data_root,
split='train',
window_size=window_size,
contact_size=pred_size,
use_confidence=use_confidence,
joint_set=joint_set)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
val_dataset = OpenPoseDataset(data_root,
split='val',
window_size=window_size,
contact_size=pred_size,
use_confidence=use_confidence,
joint_set=joint_set)
val_loader = DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=2)
num_joints = len(
openpose_dataset.OP_JOINT_SUBSETS[train_dataset.joint_set])
# create the model and optimizer
device_str = 'cpu' if flags.cpu else None
device = get_device(device_str)
op_model = create_model(window_size,
num_joints,
pred_size,
device,
use_confidence=use_confidence)
op_optim = optim.Adam(op_model.parameters(), lr=lr, betas=betas, \
eps=eps, weight_decay=weight_decay)
model_parameters = filter(lambda p: p.requires_grad, op_model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print('Num model params: ' + str(params))
# viz stats
train_steps = []
train_losses = []
train_accs = []
val_steps = []
val_losses = []
val_accs = []
# train
loss_sum = 0.0
loss_count = 0
best_val_f1 = -float('inf')
confusion_count = np.zeros((4), dtype=int)
for epoch_idx in range(num_epochs):
for batch_idx, batch_data in enumerate(train_loader):
# prepere the data for this batch
input_data = batch_data['joint2d'].to(device)
label_data = batch_data['contacts'].to(device)
# zero the gradients
op_optim.zero_grad()
# forward + backward + optimize
output_data = op_model(input_data)
loss = op_model.loss(output_data, label_data)
n_tp, n_fp, n_fn, n_tn = op_model.accuracy(output_data,
label_data,
thresh=classify_thresh)
loss = torch.mean(loss)
loss.backward()
op_optim.step()
loss_sum += loss.to('cpu').item()
loss_count += 1
confusion_count += np.array([n_tp, n_fp, n_fn, n_tn], dtype=int)
if epoch_idx % 5 == 0:
print('=================== TRAIN (' + str(epoch_idx + 1) +
' epochs) ================================================')
mean_loss = loss_sum / loss_count
print('Mean loss: %0.3f' % (mean_loss))
loss_sum = 0.0
loss_count = 0
metrics = calculate_metrics(confusion_count)
cur_acc, _, _, _, _ = metrics
print_metrics(metrics)
confusion_count = np.zeros((4), dtype=int)
print(
'======================================================================================'
)
train_steps.append(epoch_idx * len(train_loader) + batch_idx)
train_losses.append(mean_loss)
train_accs.append(cur_acc)
# save plot
plot_train_stats((train_steps, train_losses, train_accs), \
(val_steps, val_losses, val_accs), \
out_dir, accuracy_metrics=metrics)
if epoch_idx % val_every == 0:
# run on the validation data
print('==================== VALIDATION (' + str(epoch_idx + 1) +
' epochs) ===========================================')
val_loss, val_metrics = val_epoch(val_loader, op_model, device,
classify_thresh, pred_size)
print('Mean Loss: %0.3f' % (val_loss))
for tgt_frame_idx in range(pred_size):
print('----- Pred Frame ' + str(tgt_frame_idx) + ' ------')
print_metrics(val_metrics[tgt_frame_idx])
val_acc, _, _, _, _ = val_metrics[
pred_size // 2] # only want accuracy for middle target
print(
'======================================================================================'
)
op_model.train()
val_steps.append(epoch_idx * len(train_loader) + batch_idx)
val_losses.append(val_loss)
val_accs.append(val_acc)
# save confusion matrix
for tgt_frame_idx in range(pred_size):
accuracy, precision, recall, f1, cm = val_metrics[
tgt_frame_idx]
plot_confusion_mat(
cm,
os.path.join(
out_dir,
'val_confusion_matrix_%d.png' % (tgt_frame_idx)))
# also save model weights
print('Saving checkpoint...')
torch.save(op_model.state_dict(), weights_out_path)
# check if this is the best so far and save (in terms of f1 score)
if f1 > best_val_f1:
best_val_f1 = f1
print('Saving best model so far...')
torch.save(op_model.state_dict(), best_weights_out_path)
# save final model
print('Saving final checkpoint...')
torch.save(op_model.state_dict(),
os.path.join(out_dir, 'op_only_weights_FINAL.pth'))
# save plot
metrics = calculate_metrics(confusion_count)
plot_train_stats((train_steps, train_losses, train_accs), \
(val_steps, val_losses, val_accs), \
out_dir, accuracy_metrics=metrics)
print('FINISHED Training!')
def load_sagen(name):
device = utils.get_device()
gen = utils.load_model(name,
sagenerator.SelfAttentionGenerator,
save_attention=True)
return gen, device
def validate(loader: DataLoader, model: nn.Module, criterion: Callable,
num_classes: int, num_super_classes: int, maf: torch.FloatTensor,
args: ArgumentParser) -> torch.FloatTensor:
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('MLM Loss', ':.4e')
accuracies = AverageMeter('Acc', ':.4e')
accuracy_deltas = AverageMeter('Acc Delta', ':.4e')
progress = ProgressMeter(len(loader),
[batch_time, losses, accuracies, accuracy_deltas],
prefix="Test: ")
model.eval()
device = get_device(args)
with torch.no_grad():
end = time.time()
for i, (genotypes, labels, super_labels) in enumerate(loader):
### Mask for Masked Language Modeling
mask_num = int((i % 9 + 1) / 10 * genotypes.shape[1])
mask_scores = torch.rand(genotypes.shape[1])
mask_indices = mask_scores.argsort(descending=True)[:mask_num]
masked_genotypes = genotypes[:, mask_indices].reshape(-1)
targets = (masked_genotypes == 1).float().clone().detach()
genotypes[:, mask_indices] = 0
maf_vector = maf[labels[0]]
genotypes = genotypes.to(device)
masked_genotypes = masked_genotypes.to(device)
targets = targets.to(device)
labels = labels.to(device)
super_labels = super_labels.to(device)
maf_vector = maf_vector.to(device)
logits = model(genotypes, labels, super_labels)
logits = logits[:, mask_indices].reshape(-1)
# add weight to nonzero maf snps
weights = torch.ones_like(logits)
weight_coefficients = (maf_vector[mask_indices] > 0).repeat(
genotypes.shape[0]).float() * (args.minor_coefficient - 1) + 1
weights *= weight_coefficients
loss = criterion(logits, targets, weight=weights, reduction='mean')
accuracy = (masked_genotypes * logits.sign()).mean() / 2 + .5
baseline_accuracy = (
masked_genotypes *
(maf_vector[mask_indices].repeat(genotypes.shape[0]) -
.5000001).sign()).mean() / 2 + .5
accuracy_delta = accuracy - baseline_accuracy
losses.update(loss.item(), genotypes.shape[0])
accuracies.update(accuracy.item(), genotypes.shape[0])
accuracy_deltas.update(accuracy_delta.item(), genotypes.shape[0])
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
progress.display(i)
return losses.avg
def main() -> None:
global best_loss
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
start_epoch = 0
vcf_reader = VCFReader(args.train_data, args.classification_map,
args.chromosome, args.class_hierarchy)
vcf_writer = vcf_reader.get_vcf_writer()
train_dataset, validation_dataset = vcf_reader.get_datasets(
args.validation_split)
train_sampler = BatchByLabelRandomSampler(args.batch_size,
train_dataset.labels)
train_loader = DataLoader(train_dataset, batch_sampler=train_sampler)
if args.validation_split != 0:
validation_sampler = BatchByLabelRandomSampler(
args.batch_size, validation_dataset.labels)
validation_loader = DataLoader(validation_dataset,
batch_sampler=validation_sampler)
kwargs = {
'total_size': vcf_reader.positions.shape[0],
'window_size': args.window_size,
'num_layers': args.layers,
'num_classes': len(vcf_reader.label_encoder.classes_),
'num_super_classes': len(vcf_reader.super_label_encoder.classes_)
}
model = WindowedMLP(**kwargs)
model.to(get_device(args))
optimizer = AdamW(model.parameters(), lr=args.learning_rate)
#######
if args.resume_path is not None:
if os.path.isfile(args.resume_path):
print("=> loading checkpoint '{}'".format(args.resume_path))
checkpoint = torch.load(args.resume_path)
if kwargs != checkpoint['model_kwargs']:
raise ValueError(
'The checkpoint\'s kwargs don\'t match the ones used to initialize the model'
)
if vcf_reader.snps.shape[0] != checkpoint['vcf_writer'].snps.shape[
0]:
raise ValueError(
'The data on which the checkpoint was trained had a different number of snp positions'
)
start_epoch = checkpoint['epoch']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume_path, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
#############
if args.validate:
validate(validation_loader, model,
nn.functional.binary_cross_entropy_with_logits,
len(vcf_reader.label_encoder.classes_),
len(vcf_reader.super_label_encoder.classes_), vcf_reader.maf,
args)
return
for epoch in range(start_epoch, args.epochs + start_epoch):
loss = train(train_loader, model,
nn.functional.binary_cross_entropy_with_logits, optimizer,
len(vcf_reader.label_encoder.classes_),
len(vcf_reader.super_label_encoder.classes_),
vcf_reader.maf, epoch, args)
if epoch % args.save_freq == 0 or epoch == args.epochs + start_epoch - 1:
if args.validation_split != 0:
validation_loss = validate(
validation_loader, model,
nn.functional.binary_cross_entropy_with_logits,
len(vcf_reader.label_encoder.classes_),
len(vcf_reader.super_label_encoder.classes_),
vcf_reader.maf, args)
is_best = validation_loss < best_loss
best_loss = min(validation_loss, best_loss)
else:
is_best = loss < best_loss
best_loss = min(loss, best_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'model_kwargs': kwargs,
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'vcf_writer': vcf_writer,
'label_encoder': vcf_reader.label_encoder,
'super_label_encoder': vcf_reader.super_label_encoder,
'maf': vcf_reader.maf
}, is_best, args.chromosome, args.model_name, args.model_dir)
def test():
# get device
device = get_device(0)
# load net
num_classes = 80
anchor_size = config.ANCHOR_SIZE_COCO
if args.dataset == 'COCO':
cfg = config.coco_ab
testset = COCODataset(data_dir=args.dataset_root,
json_file='instances_val2017.json',
name='val2017',
img_size=cfg['min_dim'][0],
debug=args.debug)
mean = config.MEANS
elif args.dataset == 'VOC':
cfg = config.voc_ab
testset = VOCDetection(VOC_ROOT, [('2007', 'test')], None,
VOCAnnotationTransform())
mean = config.MEANS
if args.version == 'yolo_v2':
from models.yolo_v2 import myYOLOv2
net = myYOLOv2(device,
input_size=cfg['min_dim'],
num_classes=num_classes,
anchor_size=config.ANCHOR_SIZE_COCO)
print('Let us test yolo-v2 on the MSCOCO dataset ......')
elif args.version == 'yolo_v3':
from models.yolo_v3 import myYOLOv3
net = myYOLOv3(device,
input_size=cfg['min_dim'],
num_classes=num_classes,
anchor_size=config.MULTI_ANCHOR_SIZE_COCO)
elif args.version == 'tiny_yolo_v2':
from models.tiny_yolo_v2 import YOLOv2tiny
net = YOLOv2tiny(device,
input_size=cfg['min_dim'],
num_classes=num_classes,
anchor_size=config.ANCHOR_SIZE_COCO)
elif args.version == 'tiny_yolo_v3':
from models.tiny_yolo_v3 import YOLOv3tiny
net = YOLOv3tiny(device,
input_size=cfg['min_dim'],
num_classes=num_classes,
anchor_size=config.MULTI_ANCHOR_SIZE_COCO)
net.load_state_dict(torch.load(args.trained_model, map_location='cuda'))
net.to(device).eval()
print('Finished loading model!')
# evaluation
test_net(net,
device,
testset,
BaseTransform(net.input_size,
mean=(0.406, 0.456, 0.485),
std=(0.225, 0.224, 0.229)),
thresh=args.visual_threshold)
def main(with_gui=None, check_stop=None):
device_idx, model_name, generator_cfg, _, train_cfg = load_config()
device = get_device()
if with_gui is None:
with_gui = train_cfg.get('with_gui')
model = None
loss_f = BCELoss(reduction='none')
pause = False
images, count, loss_sum, epoch, acc_sum, acc_sum_p = 0, 0, 0, 1, 0, 0
generator, generator_cfg = None, None
optimizer, optimizer_cfg = None, None
best_loss = None
while check_stop is None or not check_stop():
# Check config:
if images == 0:
cfg = load_config()
if model_name != cfg.model or model is None:
model_name = cfg.model
model, best_loss, epoch = load_model(model_name, train=True, device=device)
log(model_name, 'Loaded model %s' % model_name)
optimizer_cfg = None
if optimizer_cfg != cfg.optimizer:
optimizer_cfg = cfg.optimizer
optimizer = create_optimizer(optimizer_cfg, model)
log(model_name, 'Created optimizer %s' % str(optimizer))
if generator_cfg != cfg.generator:
generator_cfg = cfg.generator
generator = create_generator(generator_cfg, device=device)
log(model_name, 'Created generator')
train_cfg = cfg.train
# Run:
x, target = next(generator)
mask, _ = target.max(dim=1, keepdim=True)
optimizer.zero_grad()
y = model(x)
# Save for debug:
if train_cfg.get('save', False):
show_images(x, 'input', save_dir='debug')
show_images(y, 'output', mask=True, save_dir='debug')
show_images(target, 'target', mask=mask, save_dir='debug')
# GUI:
if with_gui:
if not pause:
show_images(x, 'input')
show_images(y, 'output', mask=True, step=2)
show_images(target, 'target', mask=mask, step=2)
key = cv2.waitKey(1)
if key == ord('s'):
torch.save(model.state_dict(), 'models/unet2.pt')
elif key == ord('p'):
pause = not pause
elif key == ord('q'):
break
# Optimize:
acc_sum += check_accuracy(y, target)
loss = (loss_f(y, target) * mask).mean()
loss_item = loss.item()
loss_sum += loss_item
count += 1
images += len(x)
loss.backward()
optimizer.step()
# Complete epoch:
if images >= train_cfg['epoch_images']:
acc_total, names = acc_sum, channel_names
msg = 'Epoch %d: train loss %f, acc %s' % (
epoch, loss_sum / count,
acc_to_str(acc_total, names=names)
)
log(model_name, msg)
count = 0
images = 0
loss_sum = 0
epoch += 1
acc_sum[:] = 0
save_model(model_name, model, best_loss, epoch)
log(model_name, 'Stopped\n')
def get_default_run_options(model, dataset, runs, sampling_mode):
"""Setup general experiment options, irrespective of the model and data.
Parameters:
model (str): name of model to use. Available:
SVM (linear), SVM_grid (grid search on linear, poly and RBF),
baseline (fully connected NN), hu (1D CNN),
hamida (3D CNN + 1D classifier), lee (3D FCN), chen (3D CNN),
li (3D CNN), he (3D CNN), luo (3D CNN), sharma (2D CNN),
mou (1D RNN) boulch (1D semi-supervised CNN),
liu (3D semi-supervised CNN)
dataset (str): hyperspectral image name.
runs (int): number of runs.
sampling_mode ('all' 'fixed'): how to select pixels for train/test.
Returns:
options (dict): set of options.
"""
options = {
'model': model,
'runs': runs,
'sampling_mode': sampling_mode,
'dataset': dataset,
'device': get_device(0), # (defaults to -1, which learns on CPU)
'dataset_path': PATH_DATA,
'sample_path': PATH_SAMPLES,
'rdir': 'work/',
'preprocessing': {
'type': 'division'
}
}
if model == 'hu':
options['batch_size'], options['epoch'] = 50, 400
elif model == 'li' or model == 'lee':
options['batch_size'], options['epoch'] = 100, 200
else:
options['batch_size'], options['epoch'] = 100, 100
# DeepHyperX default options:
options['svm_grid_params'] = [{
'kernel': ['rbf'],
'gamma': [1e-1, 1e-2, 1e-3],
'C': [1, 10, 100, 1000]
}, {
'kernel': ['linear'],
'C': [0.1, 1, 10, 100, 1000]
}, {
'kernel': ['poly'],
'degree': [3],
'gamma': [1e-1, 1e-2, 1e-3]
}]
options.update({
'class_balancing': False,
'flip_augmentation': False,
'mixture_augmentation': False,
'multi_class': 1,
'path': './predictions/',
'radiation_augmentation': False,
'test_stride': 1,
'training_sample': 10,
'with_exploration': False
})
# DeepHyperX handy, but unused options
options.update({
'checkpoint':
None, # option to load state dict instead of train from scratch
'train_gt': None, # train GT filename, not used
'test_gt': None, # test GT filename, not used
})
return options
def test_get_device(self):
config = utils.read_config(self.env)
config["device"] = "xc6slx9-tqg144-3"
device = utils.get_device(config)
self.assertEqual("xc6slx9", device)
from captum.attr import (
GradientShap,
DeepLift,
DeepLiftShap,
IntegratedGradients,
LayerConductance,
NeuronConductance,
NoiseTunnel,
)
import tqdm
from utils import get_device, isnotebook, ensure_arr, is_numeric
import metrics
DEVICE = get_device()
# Combination of
# https://www.nature.com/articles/s41598-020-59827-1#MOESM1
# - this is generally a superset of what Seurat uses
# https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html
PBMC_MARKER_GENES = {
"CD4+ T cells": ["IL7R", "CD3D", "CD4", "CTLA4"],
"IL7RCD4+ T Cells": ["CD8A", "IL7R", "CD3D"], # Nature paper only
"CD8+ T cells": ["CD8A", "GZMB", "CD3D", "CD8B"], # Common both
"B cells": ["CD19", "MS4A1", "CD79A", "CD79B", "BLNK"], # Common both
"Natural Killer cells": [
"FCGR3A",
"NCAM1",
"KLRB1",
"KLRC1",
params["reg_ratio"] = np.random.rand() * 0.0015
params["batch_size"] = np.random.randint(26, 256)
params["bidirectional"] = bool(np.random.randint(0, 2))
cfg = AcousticLLDConfig(**params)
model = RNN(cfg)
elif args.model_type == "acoustic-spectrogram":
test_features, test_labels, val_features, val_labels, train_features, train_labels = load_spectrogram_dataset(
)
params["fc_size"] = np.random.randint(10, 200)
params["dropout"] = 0.3 + np.random.rand() * 0.6
cfg = AcousticSpectrogramConfig(**params)
model = CNN(cfg)
else:
raise Exception(
"model_type parameter has to be one of [linguistic|acoustic-lld|acoustic-spectrogram]"
)
print(
"Subsets sizes: test_features:{}, test_labels:{}, val_features:{}, val_labels:{}, train_features:{}, train_labels:{}"
.format(test_features.shape[0], test_labels.shape[0],
val_features.shape[0], val_labels.shape[0],
train_features.shape[0], train_labels.shape[0]))
"""Converting model to specified hardware and format"""
model.float()
model = model.to(get_device())
run_training(model, cfg, test_features, test_labels, train_features,
train_labels, val_features, val_labels)
from utils import get_args, get_device, get_class_names
from modeler import load_model, predict
## Get the arguments
args = get_args('predict')
## Get the device
device = get_device(args.gpu)
## Get the model saved as checkpoint
trained_validated_model = load_model(model_dir=args.model_dir)
probs, classes = predict(device=device,
image_path=args.image_path,
model=trained_validated_model,
topx=args.topk)
## Get names of the classes
class_names = get_class_names(classes=classes, cat_names=args.cat_names)
## Print prediction(s)
print(('AI Model\'s top {} prediction(s) are:').format(args.topk))
print('Rank'.ljust(5) + 'Predicted Name'.ljust(25) + 'Probability')
for i, (prob, class_name) in enumerate(zip(probs, class_names)):
print('{}. {} {}%'.format(
str(i + 1).rjust(3), class_name.ljust(25),
("%.2f" % round(prob * 100, 2)).rjust(6)))
param_group['lr'] = lr
return lr
def warmup_strategy(optimizer, epoch_size, iteration):
lr = 1e-6 + (args.lr - 1e-6) * iteration / (epoch_size * (args.wp_epoch))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
if __name__ == '__main__':
global hr, cfg
hr = False
device = get_device(args.gpu_ind)
if args.high_resolution == 1:
hr = True
cfg = voc_ab
if args.version == 'fcos_lite':
from models.fcos_lite import FCOS_LITE
fcos_lite = FCOS_LITE(device,
input_size=cfg['min_dim'],
num_classes=args.num_classes,
trainable=True,
hr=hr)
print('Let us train FCOS-LITE on the VOC0712 dataset ......')
def main(train_cfg='config/pretrain.json',
model_cfg='config/bert_base.json',
data_file='/root/voucher/dataset/tifu/bert/train.tsv',
model_file=None,
pretrain_file=None,
data_parallel=True,
word_vocab='/root/voucher/dataset/tifu/bert/word_vocab.txt',
pos_vocab='/root/voucher/dataset/tifu/bert/pos_vocab.txt',
dep_vocab='/root/voucher/dataset/tifu/bert/dep_vocab.txt',
pos_dep_word_vocab='/root/voucher/dataset/tifu/bert/pos_dep_word.pkl',
save_dir='../exp/bert/pretrain',
log_dir='../exp/bert/pretrain/runs',
max_len=384,
max_pred=20,
mask_prob=0.15,
mode=train):
if mode == 'train':
pass
elif mode == 'eval':
pass
# max_pred = max_len
# mask_prob = 1
else:
print("please select correct mode")
exit(1)
cfg = train.Config.from_json(train_cfg)
model_cfg = models.Config.from_json(model_cfg)
set_seeds(cfg.seed)
custom_tokenizer = CustomVocabTokenizer(word_vocab_file=word_vocab,
pos_vocab_file=pos_vocab,
dep_vocab_file=dep_vocab,
pos_dep_word_vocab_file=pos_dep_word_vocab)
custom_tokenize = lambda word, pos, dep: custom_tokenizer.tokenize(custom_tokenizer.convert_to_unicode(word),
custom_tokenizer.convert_to_unicode(pos),
custom_tokenizer.convert_to_unicode(dep))
pipeline = [Preprocess4Pretrain(max_pred,
mask_prob,
list(custom_tokenizer.word_tokenizer.vocab.keys()),
list(custom_tokenizer.pos_tokenizer.vocab.keys()),
list(custom_tokenizer.dep_tokenizer.vocab.keys()),
custom_tokenizer.convert_tokens_to_ids,
max_len)]
data_iter = TifuDataLoader(data_file,
cfg.batch_size,
custom_tokenize,
max_len,
pipeline=pipeline)
model = BertModel4Pretrain(model_cfg)
criterion3 = nn.CrossEntropyLoss(reduction='none')
optimizer = optim.optim4GPU(cfg, model)
trainer = train.Trainer(cfg, model, data_iter, optimizer, save_dir, get_device())
writer = SummaryWriter(log_dir=log_dir) # for tensorboardX
if mode == 'train':
def get_loss(model, batch, global_step): # make sure loss is tensor
input_word_ids,\
input_segment_ids,\
input_mask,\
target_word_ids,\
target_mask = batch
logits_word = model(input_word_ids,
input_segment_ids,
input_mask,
target_mask)
loss_word = criterion3(logits_word.transpose(1, 2), target_word_ids) # for masked word
loss_word = (loss_word*target_mask.float()).mean()
print(loss_word.item())
writer.add_scalars('data/scalar_group',
{'loss_word': loss_word.item(),
'loss_total': loss_word.item(),
'lr': optimizer.get_lr()[0],
},
global_step)
return loss_word
trainer.train(get_loss, model_file, pretrain_file, data_parallel)
elif mode == 'eval':
def evaluate(model, batch):
input_word_ids,\
input_segment_ids,\
input_mask,\
target_word_ids,\
target_mask = batch
logits_word = model(input_word_ids,
input_segment_ids,
input_mask,
target_mask)
_, label_word = logits_word.max(-1)
result_word = (label_word == target_word_ids).float()
word_accuracy = result_word.mean()
accuracies = [word_accuracy]
results = [result_word]
return accuracies, results
results = trainer.eval(evaluate, model_file, data_parallel, eval_kind_names=["Word"])
print(results)
def __init__(self, alpha: float = 1.0, use_cuda: bool = True):
self.alpha = alpha
self.device = get_device(use_cuda)
def train_rank_net(start_epoch=0,
additional_epoch=100,
lr=0.0001,
optim="adam",
train_algo=SUM_SESSION,
double_precision=False,
standardize=False,
small_dataset=False,
debug=False):
"""
:param start_epoch: int
:param additional_epoch: int
:param lr: float
:param optim: str
:param train_algo: str
:param double_precision: boolean
:param standardize: boolean
:param small_dataset: boolean
:param debug: boolean
:return:
"""
print("start_epoch:{}, additional_epoch:{}, lr:{}".format(
start_epoch, additional_epoch, lr))
precision = torch.float64 if double_precision else torch.float32
# get training and validation data:
data_fold = 'Fold1'
train_loader, df_train, valid_loader, df_valid = load_train_vali_data(
data_fold, small_dataset)
if standardize:
df_train, scaler = train_loader.train_scaler_and_transform()
df_valid = valid_loader.apply_scaler(scaler)
net, net_inference, ckptfile = get_train_inference_net(
train_algo, train_loader.num_features, start_epoch, double_precision)
device = get_device()
net.to(device)
net_inference.to(device)
# initialize to make training faster
net.apply(init_weights)
if optim == "adam":
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
elif optim == "sgd":
optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)
else:
raise ValueError(
"Optimization method {} not implemented".format(optim))
print(optimizer)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=10,
gamma=0.75)
loss_func = None
if train_algo == BASELINE:
loss_func = torch.nn.BCELoss()
loss_func.to(device)
losses = []
for i in range(start_epoch, start_epoch + additional_epoch):
scheduler.step()
net.zero_grad()
net.train()
if train_algo == BASELINE:
epoch_loss = baseline_pairwise_training_loop(i,
net,
loss_func,
optimizer,
train_loader,
precision=precision,
device=device,
debug=debug)
elif train_algo in [SUM_SESSION, ACC_GRADIENT]:
epoch_loss = factorized_training_loop(i,
net,
None,
optimizer,
train_loader,
training_algo=train_algo,
precision=precision,
device=device,
debug=debug)
losses.append(epoch_loss)
print('=' * 20 + '\n', get_time(),
'Epoch{}, loss : {}'.format(i, losses[-1]), '\n' + '=' * 20)
# save to checkpoint every 5 step, and run eval
if i % 5 == 0 and i != start_epoch:
save_to_ckpt(ckptfile, i, net, optimizer, scheduler)
net_inference.load_state_dict(net.state_dict())
eval_model(net_inference, device, df_valid, valid_loader)
# save the last ckpt
save_to_ckpt(ckptfile, start_epoch + additional_epoch, net, optimizer,
scheduler)
# final evaluation
net_inference.load_state_dict(net.state_dict())
ndcg_result = eval_model(net_inference, device, df_valid, valid_loader)
# save the final model
torch.save(net.state_dict(), ckptfile)
print(
get_time(), "finish training " + ", ".join(
["NDCG@{}: {:.5f}".format(k, ndcg_result[k])
for k in ndcg_result]), '\n\n')
def __init__(
self,
policy,
env,
transform_func,
gamma,
learning_rate,
buffer_size,
exploration_type,
exploration_frac,
exploration_ep,
exploration_initial_eps,
exploration_final_eps,
double_q,
policy_kwargs,
seed,
device
):
super(DeepRLModel, self).__init__(
policy=policy, env=env,
policy_kwargs=policy_kwargs,
seed=seed
)
self.gamma = gamma
self.learning_rate = learning_rate
self.buffer_size = buffer_size
self.exploration_type = exploration_type
self.exploration_frac = exploration_frac
self.exploration_ep = exploration_ep
self.exploration_initial_eps = exploration_initial_eps
self.exploration_final_eps = exploration_final_eps
self.double_q = double_q
# self.policy_kwargs = {} if policy_kwargs is None else policy_kwargs
if device is None:
self.device = get_device(device)
else:
self.device = device
self.policy_kwargs = get_default_args(self.policy)
self.policy_kwargs['ob_space'] = self.observation_space
self.policy_kwargs['ac_space'] = self.action_space
self.policy_kwargs['device'] = self.device
self.policy_kwargs['learning_rate'] = self.learning_rate
if policy_kwargs is not None:
for key, val in policy_kwargs.items():
self.policy_kwargs[key] = val
# self.policy_kwargs['transform_func'] = transform_func
# if policy_kwargs is None:
# self.policy = policy(self.observation_space, self.action_space,
# intent=True, device=self.device)
# else:
self.policy = policy(**self.policy_kwargs)
if self.buffer_size is None:
self.replay_buffer = None
else:
self.replay_buffer = ReplayBuffer(self.buffer_size, device=self.device, torch=True)
def main(task_name='qqp',
base_train_cfg='config/QDElectra_pretrain.json',
train_cfg='config/train_mrpc.json',
model_cfg='config/QDElectra_base.json',
train_data_file='GLUE/glue_data/QQP/train.tsv',
eval_data_file='GLUE/glue_data/QQP/eval.tsv',
model_file=None,
data_parallel=True,
vocab='../uncased_L-12_H-768_A-12/vocab.txt',
log_dir='../exp/electra/pretrain/runs',
save_dir='../exp/bert/mrpc',
distill=True,
quantize=True,
gradually_distill=False,
imitate_tinybert=False,
pred_distill=True):
check_dirs_exist([log_dir, save_dir])
train_cfg_dict = json.load(open(base_train_cfg, "r"))
train_cfg_dict.update(json.load(open(train_cfg, "r")))
train_cfg = ElectraConfig().from_dict(train_cfg_dict)
model_cfg = ElectraConfig().from_json_file(model_cfg)
output_mode, train_cfg.n_epochs, max_len = get_task_params(task_name)
set_seeds(train_cfg.seed)
tokenizer = tokenization.FullTokenizer(vocab_file=vocab,
do_lower_case=True)
TaskDataset = dataset_class(
task_name) # task dataset class according to the task name
model_cfg.num_labels = len(TaskDataset.labels)
pipeline = [
Tokenizing(task_name, tokenizer.convert_to_unicode,
tokenizer.tokenize),
AddSpecialTokensWithTruncation(max_len),
TokenIndexing(tokenizer.convert_tokens_to_ids, TaskDataset.labels,
output_mode, max_len)
]
train_data_set = TaskDataset(train_data_file, pipeline)
eval_data_set = TaskDataset(eval_data_file, pipeline)
train_data_iter = DataLoader(train_data_set,
batch_size=train_cfg.batch_size,
shuffle=True)
eval_data_iter = DataLoader(eval_data_set,
batch_size=train_cfg.batch_size,
shuffle=False)
generator = ElectraForSequenceClassification.from_pretrained(
'google/electra-small-generator')
t_discriminator = ElectraForSequenceClassification.from_pretrained(
'google/electra-base-discriminator')
s_discriminator = QuantizedElectraForSequenceClassification if quantize else ElectraForSequenceClassification
s_discriminator = s_discriminator.from_pretrained(
'google/electra-small-discriminator', config=model_cfg)
model = DistillElectraForSequenceClassification(generator, t_discriminator,
s_discriminator, model_cfg)
optimizer = optim.optim4GPU(train_cfg, model)
writer = SummaryWriter(log_dir=log_dir) # for tensorboardX
base_trainer_args = (train_cfg, model_cfg, model, train_data_iter,
eval_data_iter, optimizer, save_dir, get_device())
trainer = QuantizedDistillElectraTrainer(task_name, output_mode, distill,
gradually_distill,
imitate_tinybert, pred_distill,
len(TaskDataset.labels), writer,
*base_trainer_args)
trainer.train(model_file, None, data_parallel)
trainer.eval(model_file, data_parallel)
def train_network(model,
x,
y,
x_test=None,
y_test=None,
epochs=50,
batch_size=64,
loss_f=BCELoss,
optimizer=Adam,
lr=0.001,
y_postprocessing=utils.y_to_one_hot,
weight_decay: float = 0.0000001,
verbose: int = 1):
print("Started training")
best_epoch = (0, 0, 0)
if y_postprocessing is not None:
y = y_postprocessing(y)
y_test = y_postprocessing(y_test)
y, y_test = y.astype(np.float), y_test.astype(np.float)
loss_f = loss_f()
optimizer = optimizer(model.parameters(), lr, weight_decay=weight_decay)
model.train()
for epoch in range(epochs):
for i in range((x.shape[0] // batch_size) + 1):
x_for_network = x[i * batch_size:(i + 1) * batch_size]
y_for_network = y[i * batch_size:(i + 1) * batch_size]
if x_for_network.size == 0:
break
if verbose > 0:
print("batch {} of {}".format(i + 1,
x.shape[0] // batch_size + 1))
optimizer.zero_grad()
pred = model(x_for_network)
loss = loss_f(
pred,
from_numpy(y_for_network).float().to(utils.get_device()))
loss.backward()
optimizer.step()
train_performance = evaluation.evaluate_model(
model,
x,
y,
pred_postprocessing=utils.softmax_to_one_hot,
out_dim=2,
batch_size=batch_size)
print("train performance is {}".format(train_performance))
if x_test is not None:
performance = evaluation.evaluate_model(
model,
x_test,
y_test,
pred_postprocessing=utils.softmax_to_one_hot,
out_dim=2,
batch_size=batch_size)
print("test performance is {}".format(performance))
if performance > best_epoch[-1]:
best_epoch = (epoch, train_performance, performance)
print("Finished epoch {}".format(epoch))
if x_test is not None:
print(
"Finished training. Best epoch is {} with training performance {} "
"and test performance {}".format(*best_epoch))
def main(cfg: DictConfig) -> None:
"The entry point for testing"
assert cfg.model_path is not None, "Need to specify model_path for testing."
log.info("\n" + OmegaConf.to_yaml(cfg))
# restore the hyperparameters used for training
model_path = hydra.utils.to_absolute_path(cfg.model_path)
log.info("Loading the model from %s" % model_path)
checkpoint = load_model(model_path)
restore_hyperparams(checkpoint["cfg"], cfg)
# create dataloaders for validation and testing
vocabs = checkpoint["vocabs"]
loader_val, _ = create_dataloader(
hydra.utils.to_absolute_path(cfg.path_val),
"val",
cfg.encoder,
vocabs,
cfg.eval_batch_size,
cfg.num_workers,
)
loader_test, _ = create_dataloader(
hydra.utils.to_absolute_path(cfg.path_test),
"test",
cfg.encoder,
vocabs,
cfg.eval_batch_size,
cfg.num_workers,
)
# restore the trained model checkpoint
model = Parser(vocabs, cfg)
model.load_state_dict(checkpoint["model_state"])
device, _ = get_device()
model.to(device)
log.info("\n" + str(model))
log.info("#parameters = %d" % sum([p.numel() for p in model.parameters()]))
# validation
log.info("Validating..")
f1_score = validate(loader_val, model, cfg)
log.info(
"Validation F1 score: %.03f, Exact match: %.03f, Precision: %.03f, Recall: %.03f"
% (
f1_score.fscore,
f1_score.complete_match,
f1_score.precision,
f1_score.recall,
))
# testing
log.info("Testing..")
if cfg.beam_size > 1:
log.info("Performing beam search..")
f1_score = beam_search(loader_test, model, cfg)
else:
log.info("Running without beam search..")
f1_score = validate(loader_test, model, cfg)
log.info(
"Testing F1 score: %.03f, Exact match: %.03f, Precision: %.03f, Recall: %.03f"
% (
f1_score.fscore,
f1_score.complete_match,
f1_score.precision,
f1_score.recall,
))
sampler=train_sampler)
valid_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
num_workers=4,
collate_fn=collate_fn,
sampler=valid_sampler)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=1,
num_workers=4,
shuffle=False)
config = {
"epochs": 100,
"device": get_device(),
"sampling": True,
"temperature": 1.0,
"max_sentence_length": 18
}
embedding_dim = 256
hidden_dim = 512
vocab_size = len(vocab)
model = Baseline(embedding_dim, hidden_dim, vocab_size, vanilla=False)
criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=5e-4)
model.cuda()
train(model, optimizer, criterion, train_loader, valid_loader, vocab, config)
import torch,data
import utils
device = utils.get_device()
def evaluate(seq2seq,test_generator,vocab):
#cnt = 0
acc_cnt = 0
total_examples = 0
for (num1s,ops,num2s,l1s,l2s,mask1s,mask2s),(answers,ans_lens,ans_masks) in test_generator.get_batches():
batch_size = len(num1s)
context_vector = seq2seq.encode(num1s,ops,num2s,l1s,l2s,mask1s,mask2s)
decoder_input = torch.tensor([data.BOS_ID]*batch_size).view(-1,1).to(device)
current_input = seq2seq.embeddings(decoder_input).view(-1,1,seq2seq.embedding_dim)
current_hidden = context_vector.unsqueeze(1).transpose(0,1)
max_decode_size = answers.size(1)
preidct_each_t = []
for t in range(max_decode_size):
ot,ht,probs,predicts = seq2seq.predict_step(current_input ,current_hidden)
preidct_each_t.append(predicts)
current_input = seq2seq.embeddings(predicts)
current_hidden = ht
preidct_each_t = torch.cat(preidct_each_t,1)
import re
predict_numbers = seq2Number(preidct_each_t, vocab,True)
answer_numbers = seq2Number(answers, vocab,False)
answer_numbers = [re.findall('[\-0-9]+',n)[0] for n in answer_numbers]
def main(task='mrpc',
train_cfg='./model/config/train_mrpc.json',
model_cfg='./model/config/bert_base.json',
data_train_file='total_data/imdbtrain.tsv',
data_test_file='total_data/IMDB_test.tsv',
model_file=None,
pretrain_file='./model/uncased_L-12_H-768_A-12/bert_model.ckpt',
data_parallel=False,
vocab='./model/uncased_L-12_H-768_A-12/vocab.txt',
dataName='IMDB',
stopNum=250,
max_len=300,
mode='train'):
if mode == 'train':
def get_loss_CNN(model, batch,
global_step): # make sure loss is a scalar tensor
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
logits = model(input_ids, segment_ids, input_mask)
loss = criterion(logits, label_id)
return loss
def evalute_CNN(model, batch):
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
logits = model(input_ids, segment_ids, input_mask)
return label_id, logits
def get_loss_Attn_LSTM(
model, batch,
global_step): # make sure loss is a scalar tensor
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
input_ids = input_ids[perm_idx]
label_id = label_id[perm_idx]
token1 = embedding(input_ids.long())
logits, attention_score = model(token1.cuda(), input_ids,
segment_ids, input_mask,
seq_lengths)
loss1 = criterion(logits, label_id)
return loss1
def evalute_Attn_LSTM(model, batch, global_step, ls):
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
input_ids = input_ids[perm_idx]
label_id = label_id[perm_idx]
token1 = embedding(input_ids.long())
logits, attention_score = model(token1.cuda(), input_ids,
segment_ids, input_mask,
seq_lengths)
logits = F.softmax(logits)
y_pred11, y_pred1 = logits.max(1)
return label_id, logits
def generating_lexiocn(model2, batch, global_step, ls, e):
if (global_step == 0):
result3.clear()
result_label.clear()
bb_11.clear()
bb_22.clear()
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
input_ids = input_ids[perm_idx]
label_id = label_id[perm_idx]
token1 = embedding(input_ids.long())
#logits = model(input_ids, segment_ids, input_mask)
logits2, attention_score2 = model2(token1.cuda(), input_ids,
segment_ids, input_mask,
seq_lengths)
#logits=F.softmax(logits)
logits = F.softmax(logits2)
# y_pred11, y_pred1 = logits.max(1)
y_pred22, y_pred2 = logits2.max(1)
atten, attn_s1 = attention_score2.max(1)
atte2, attn_s2 = torch.topk(attention_score2, 4)
for i in range(0, len(input_ids)):
split_tokens = []
att_index = []
for token in tokenizer.tokenize(data0[global_step * 64 +
perm_idx[i]]):
split_tokens.append(token)
if (len(split_tokens) <= attn_s1[i].item()):
attn_index3 = attention_score2[i][:len(split_tokens) - 1]
attn_num, attn_index2 = attn_index3.max(0)
attn_index = attn_index2.item()
else:
for j in range(0, 4):
att_index.append(attn_s2[i][j].item())
tok = []
if (atten[i].item() <= 0):
token_ab = split_tokens[0]
else:
for j in range(0, len(att_index)):
if (att_index[j] >= len(split_tokens)):
continue
tok.append(split_tokens[att_index[j]])
token_temp = data0[global_step * 64 + perm_idx[i]].split(' ')
token2 = []
for kk in range(0, len(tok)):
token_ab = tok[kk]
token_ab = token_ab.replace(".", "")
token_ab = token_ab.replace(",", "")
token_ab = token_ab.replace("'", "")
token_ab = token_ab.replace("!", "")
token_ab = token_ab.replace("?", "")
token_ab = token_ab.replace("'", "")
token_ab = token_ab.replace('"', "")
if (token_ab == '' or token_ab == ' ' or token_ab == ','
or token_ab == '.' or token_ab == 'from'
or token_ab == 'are' or token_ab == 'is'
or token_ab == 'and' or token_ab == 'with'
or token_ab == 'may' or token_ab == 'would'
or token_ab == 'could' or token_ab == 'have'
or token_ab == 'has' or token_ab == 'had'
or token_ab == 'was' or token_ab == 'were'
or token_ab == 'this' or token_ab == 'who'
or token_ab == 'that' or token_ab == 'www'
or token_ab == 'http' or token_ab == 'com'
or token_ab == 'those' or token_ab == 'your'
or token_ab == 'not' or token_ab == 'seem'
or token_ab == 'too' or token_ab == 'lol'
or token_ab == 'but' or token_ab == 'these'
or token_ab == 'their' or token_ab == 'can'
or token_ab == 'there' or token_ab == 'gave'
or token_ab == 'his' or token_ab == 'etc'
or token_ab == 'thats' or token_ab == 'though'
or token_ab == 'off' or token_ab == 'she'
or token_ab == 'them' or token_ab == 'huh'
or token_ab == 'why' or token_ab == 'wont'
or token_ab == 'any' or token_ab == 'some'
or token_ab == 'its' or token_ab == 'yeah'
or token_ab == 'yes' or token_ab == 'you'
or token_ab == 'should' or token_ab == 'dont'
or token_ab == 'anybody' or token_ab == 'than'
or token_ab == 'where' or token_ab == 'for'
or token_ab == 'more' or token_ab == 'will'
or token_ab == 'him' or token_ab == 'its'
or token_ab == 'your' or token_ab == 'wii'
or token_ab == 'having' or token_ab == 'just'
or token_ab == 'help' or token_ab == 'helps'
or token_ab == 'all' or token_ab == 'they'
or token_ab == 'take' or token_ab == 'the'
or token_ab == 'what' or token_ab == 'need'
or token_ab == 'make' or token_ab == 'about'
or token_ab == 'then' or token_ab == 'when'
or token_ab == 'does' or token_ab == 'ask'
or token_ab == 'much' or token_ab == 'man'
or token_ab == 'know' or token_ab == 'how'
or token_ab == 'look' or token_ab == 'like'
or token_ab == 'one' or token_ab == 'think'
or token_ab == 'tell' or token_ab == 'find'
or token_ab == 'cant' or token_ab == 'now'
or token_ab == 'try' or token_ab == 'give'
or token_ab == 'answer' or token_ab == 'her'
or token_ab == 'out' or token_ab == 'get'
or token_ab == 'because' or token_ab == 'myself'
or token_ab == 'wants' or token_ab == 'movie'
or token_ab == 'film' or token_ab == 'films'):
continue
if (len(token_ab) < 2):
continue
for gge, input_word in enumerate(token_temp):
if (token_ab.lower() in input_word.lower()):
input_word = input_word.replace(".", "")
input_word = input_word.replace(",", "")
input_word = input_word.replace("'", "")
input_word = input_word.replace("!", "")
input_word = input_word.replace("?", "")
input_word = input_word.replace("'", "")
input_word = input_word.replace('"', "")
token2.append(input_word.lower())
break
token2 = list(set(token2))
if (len(token2) < 3):
continue
#print(token2)
sen = ""
for l in range(0, len(token2) - 1):
sen += token2[l] + ' '
sen += token2[len(token2) - 1]
if (y_pred2[i] == 0):
try:
bb_11[sen] += y_pred22[i]
except KeyError:
bb_11[sen] = y_pred22[i]
if (y_pred2[i] == 1):
try:
bb_22[sen] += y_pred22[i]
except KeyError:
bb_22[sen] = y_pred22[i]
if (global_step == ls - 1):
abusive_11.clear()
abusive_22.clear()
bb_11_up = sorted(bb_11.items(),
key=lambda x: x[1],
reverse=True)
bb_22_up = sorted(bb_22.items(),
key=lambda x: x[1],
reverse=True)
lexicon_size = 50
bb_11_up = bb_11_up[:lexicon_size]
bb_22_up = bb_22_up[:lexicon_size]
for i in bb_11_up:
flag = 0
for j in bb_22_up:
if ((i[0].lower() in j[0].lower())
or (j[0].lower() in i[0].lower())):
if (i[1] < j[1]):
flag = 1
break
if (flag == 0):
abusive_11.append(i[0])
for i in bb_22_up:
flag = 0
for j in bb_11_up:
if ((i[0].lower() in j[0].lower())
or (j[0].lower() in i[0].lower())):
if (i[1] < j[1]):
flag = 1
break
if (flag == 0):
abusive_22.append(i[0])
ddf = open("./IMDB_Lexicon/imdbLexicon_1.txt",
'w',
encoding='UTF8')
for i in range(0, len(abusive_11)):
ddf.write(abusive_11[i] + '\n')
ddf.close()
ddf = open("./IMDB_Lexicon/imdbLexicon_2.txt",
'w',
encoding='UTF8')
for i in range(0, len(abusive_22)):
ddf.write(abusive_22[i] + '\n')
ddf.close()
return label_id, logits
def evalute_CNN_SSL(model, batch, global_step):
if (global_step == 0):
result5.clear()
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
logits = model(input_ids, segment_ids, input_mask)
logits = F.softmax(logits)
y_pred11, y_pred1 = logits.max(1)
for i in range(0, len(input_ids)):
result5.append([y_pred1[i].item(), y_pred11[i].item()])
return label_id, logits
def pseudo_labeling(model2, batch, global_step, ls, e):
if (global_step == 0):
result3.clear()
result4.clear()
label_0.clear()
label_1.clear()
result_label.clear()
abusive_11.clear()
abusive_22.clear()
abusive_dic_file = open("./IMDB_Lexicon/imdbLexicon_1.txt",
'r',
encoding='UTF8')
for line in abusive_dic_file.read().split('\n'):
if (len(line) <= 3):
continue
abusive_11.append(line)
abusive_dic_file.close()
abusive_dic_file = open("./IMDB_Lexicon/imdbLexicon_2.txt",
'r',
encoding='UTF8')
for line in abusive_dic_file.read().split('\n'):
if (len(line) <= 3):
continue
abusive_22.append(line)
abusive_dic_file.close()
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
input_ids = input_ids[perm_idx]
label_id = label_id[perm_idx]
token1 = embedding(input_ids.long())
logits2, attention_score2 = model2(token1.cuda(), input_ids,
segment_ids, input_mask,
seq_lengths)
logits2 = F.softmax(logits2)
y_pred22, y_pred2 = logits2.max(1)
label_id2 = []
for i in range(0, len(input_ids)):
input_sentence = data0[global_step * 64 + perm_idx[i]]
input_sentence = re.sub("[!@#$%^&*().?\"~/<>:;'{}]", "",
input_sentence)
matching_word1 = 3
matching_word2 = 4
abusive_word_list_neg11 = list()
abusive_word_list_neg11 += matching_blacklist2(
abusive_11, input_sentence, matching_word1)
abusive_word_list_neg11 = list((set(abusive_word_list_neg11)))
abusive_word_list_neg22 = list()
abusive_word_list_neg22 += matching_blacklist2(
abusive_22, input_sentence, matching_word1)
abusive_word_list_neg22 = list((set(abusive_word_list_neg22)))
abusive_word_list_neg111 = list()
abusive_word_list_neg111 += matching_blacklist2(
abusive_11, input_sentence, matching_word2)
abusive_word_list_neg111 = list(
(set(abusive_word_list_neg111)))
abusive_word_list_neg222 = list()
abusive_word_list_neg222 += matching_blacklist2(
abusive_22, input_sentence, matching_word2)
abusive_word_list_neg222 = list(
(set(abusive_word_list_neg222)))
a = max(len(abusive_word_list_neg11),
len(abusive_word_list_neg22))
aa = max(len(abusive_word_list_neg111),
len(abusive_word_list_neg222))
if ((len(abusive_word_list_neg11) >
len(abusive_word_list_neg22)
and result5[global_step * 64 + perm_idx[i]][0] == 0
and result5[global_step * 64 + perm_idx[i]][1] >= 0.9) or
(len(abusive_word_list_neg11) >
len(abusive_word_list_neg22) and y_pred2[i].item() == 0
and y_pred22[i].item() >= 0.9)):
label_0.append(0)
result4.append([
global_step * 64 + perm_idx[i], 0,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
elif ((len(abusive_word_list_neg11) <
len(abusive_word_list_neg22)
and result5[global_step * 64 + perm_idx[i]][0] == 1
and result5[global_step * 64 + perm_idx[i]][1] >= 0.9)
or
(len(abusive_word_list_neg11) <
len(abusive_word_list_neg22) and y_pred2[i].item() == 1
and y_pred22[i].item() >= 0.9)):
label_1.append(1)
result4.append([
global_step * 64 + perm_idx[i], 1,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
elif (aa >= 1 and len(abusive_word_list_neg111) >
len(abusive_word_list_neg222)):
label_0.append(0)
result4.append([
global_step * 64 + perm_idx[i], 0,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
elif (aa >= 1 and len(abusive_word_list_neg111) <
len(abusive_word_list_neg222)):
label_1.append(1)
result4.append([
global_step * 64 + perm_idx[i], 1,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
elif (result5[global_step * 64 + perm_idx[i]][1]
and y_pred22[i].item() >= 0.9
and result5[global_step * 64 + perm_idx[i]][0]
== y_pred2[i].item()):
if (result5[global_step * 64 + perm_idx[i]][0] == 0):
label_0.append(0)
result4.append([
global_step * 64 + perm_idx[i], 0,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
elif (result5[global_step * 64 + perm_idx[i]][0] == 1):
label_1.append(1)
result4.append([
global_step * 64 + perm_idx[i], 1,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
else:
result4.append([
global_step * 64 + perm_idx[i], -1,
data0[global_step * 64 + perm_idx[i]],
label_id[perm_idx[i]].item()
])
if (global_step == ls - 1):
result_label.clear()
result3.clear()
print("###result3[i] ###:", len(result3))
a = min(len(label_0), len(label_1))
la_0 = 0
la_1 = 0
la_2 = 0
la_3 = 0
random.shuffle(result4)
for i in range(0, len(result4)):
if (result4[i][1] == 0 and la_0 < a):
if (temp_check[result4[i][0]][0] == 0):
temp_check[result4[i][0]][0] = 1
temp_check[result4[i][0]][1] = 0
la_0 += 1
continue
elif (result4[i][1] == 1 and la_1 < a):
if (temp_check[result4[i][0]][0] == 0):
temp_check[result4[i][0]][0] = 1
temp_check[result4[i][0]][1] = 1
la_1 += 1
continue
result_label.clear()
result3.clear()
fw = open('./temp_data/temp_train_IMDB.tsv',
'a',
encoding='utf-8',
newline='')
wr = csv.writer(fw, delimiter='\t')
fww = open('./temp_data/temp_train_na_IMDB.tsv',
'w',
encoding='utf-8',
newline='')
wrr = csv.writer(fww, delimiter='\t')
for i in range(0, len(temp_check)):
if (temp_check[i][0] == 1):
result_label.append(str(temp_check[i][3]))
result3.append(str(temp_check[i][1]))
wr.writerow(
[str(temp_check[i][1]),
str(temp_check[i][2])])
else:
wrr.writerow(
[str(temp_check[i][3]),
str(temp_check[i][2])])
fw.close()
fww.close()
data0.clear()
temp_check.clear()
with open('./temp_data/temp_train_na_IMDB.tsv',
"r",
encoding='utf-8') as f:
lines = csv.reader(f, delimiter='\t')
for i in lines:
a = ''
lines2 = i[1].split(' ')
b = 0
for j in range(0, len(lines2)):
a += lines2[j] + ' '
b += 1
data0.append(a)
temp_check.append([0, -1, a, i[0]])
print("################;", len(data0))
f.close()
dataset_temp = TaskDataset('./temp_data/temp_train_IMDB.tsv',
pipeline)
data_iter_temp = DataLoader(dataset_temp,
batch_size=64,
shuffle=True)
dataset_temp_b = TaskDataset('./temp_data/temp_train_IMDB.tsv',
pipeline1)
data_iter_temp_b = DataLoader(dataset_temp_b,
batch_size=64,
shuffle=True)
dataset_temp_na = TaskDataset(
'./temp_data/temp_train_na_IMDB.tsv', pipeline)
data_iter_temp_na = DataLoader(dataset_temp_na,
batch_size=64,
shuffle=False)
dataset_temp_na_b = TaskDataset(
'./temp_data/temp_train_na_IMDB.tsv', pipeline1)
data_iter_temp_na_b = DataLoader(dataset_temp_na_b,
batch_size=64,
shuffle=False)
if (global_step != ls - 1):
dataset_temp = TaskDataset(data_dev_file, pipeline)
data_iter_temp = DataLoader(dataset_temp,
batch_size=cfg.batch_size,
shuffle=True)
dataset_temp_b = TaskDataset(data_dev_file, pipeline1)
data_iter_temp_b = DataLoader(dataset_temp_b,
batch_size=64,
shuffle=True)
dataset_temp_na = TaskDataset(data_dev_file, pipeline)
data_iter_temp_na = DataLoader(dataset_temp_na,
batch_size=cfg.batch_size,
shuffle=False)
dataset_temp_na_b = TaskDataset(data_dev_file, pipeline1)
data_iter_temp_na_b = DataLoader(dataset_temp_na_b,
batch_size=64,
shuffle=False)
return label_id, logits2, result_label, result3, data_iter_temp, data_iter_temp_b, data_iter_temp_na, data_iter_temp_na_b
def evalute_Attn_LSTM_SSL(model, batch):
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
input_ids = input_ids[perm_idx]
label_id = label_id[perm_idx]
token1 = embedding(input_ids.long())
logits, attention_score = model2(token1.cuda(), input_ids,
segment_ids, input_mask,
seq_lengths)
return label_id, logits
curNum = 1
print("###########################################")
print(model_cfg)
print(model_cfg)
#kkk+=1
cfg = train.Config.from_json(train_cfg)
model_cfg = models.Config.from_json(model_cfg)
for kkk in range(0, 5):
print("###########################################")
tokenizer = tokenization.FullTokenizer(do_lower_case=True)
tokenizer1 = tokenization.FullTokenizer1(vocab_file=vocab,
do_lower_case=True)
TaskDataset = dataset_class(
task) # task dataset class according to the task
pipeline = [
Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
AddSpecialTokensWithTruncation(max_len),
TokenIndexing(tokenizer.convert_tokens_to_ids,
TaskDataset.labels, max_len)
]
pipeline1 = [
Tokenizing(tokenizer1.convert_to_unicode, tokenizer1.tokenize),
AddSpecialTokensWithTruncation(max_len),
TokenIndexing(tokenizer1.convert_tokens_to_ids1,
TaskDataset.labels, max_len)
]
fd = open("./total_data/imdbtrain.tsv", 'r', encoding='utf-8')
rdr = csv.reader(fd, delimiter='\t')
res = []
num_a = 0
num_b = 0
for line in rdr:
#print(line)
num_a += 1
res.append([line[0], line[1]])
print("curNum#:", curNum)
#print(res)
fw = open('./data/IMDB_temp_short.tsv',
'w',
encoding='utf-8',
newline='')
wr = csv.writer(fw, delimiter='\t')
for i in range(0, curNum):
random.shuffle(res)
#print(res[1][0])
print("########")
curNum += 100
num_data = len(res)
num_data_dev_temp = int(num_data * 0.01)
num_data_dev = int(num_data_dev_temp * 0.15)
num_data_short = int(num_data_dev_temp * 0.85)
num_data_train = num_data - num_data_dev_temp
fd.close()
num = 0
data_train_file = "./data/IMDB_train" + str(kkk + 1) + ".tsv"
data_dev_file = "./data/IMDB_dev" + str(kkk + 1) + ".tsv"
data_short_file = "./data/IMDB_short" + str(kkk + 1) + ".tsv"
print("num_data_dev#:", num_data_dev)
print("num_data_short#:", num_data_short)
print("num_data_train#:", num_data_train)
fw = open('./data/IMDB_temp_short.tsv',
'w',
encoding='utf-8',
newline='')
wr = csv.writer(fw, delimiter='\t')
fe = open(data_train_file, 'w', encoding='utf-8', newline='')
we = csv.writer(fe, delimiter='\t')
res2 = []
num_pos = 0
num_neg = 0
for line in res:
#print(line[0])
#print(line[1])
if (line[0] == '0' and num_pos <= (num_data_dev_temp / 2)):
num_pos += 1
wr.writerow(['0', line[1]])
elif (line[0] == '1' and num_neg <= (num_data_dev_temp / 2)):
num_neg += 1
wr.writerow(['1', line[1]])
else:
num += 1
we.writerow([line[0], line[1]])
fw.close()
fe.close()
print("num_pos #:", num_pos, " num_neg:", num_neg)
f = open('./data/IMDB_temp_short.tsv', 'r', encoding='utf-8')
rdr = csv.reader(f, delimiter='\t')
num_pos = 0
num_neg = 0
num = 0
fw = open(data_dev_file, 'w', encoding='utf-8', newline='')
wr = csv.writer(fw, delimiter='\t')
fe = open(data_short_file, 'w', encoding='utf-8', newline='')
we = csv.writer(fe, delimiter='\t')
for line in rdr:
#print(line[0])
if (line[0] == '0' and num_pos <= (num_data_dev / 2)):
num_pos += 1
wr.writerow(['0', line[1]])
elif (line[0] == '1' and num_neg <= (num_data_dev / 2)):
num_neg += 1
wr.writerow(['1', line[1]])
else:
num += 1
we.writerow([line[0], line[1]])
print("num_pos #:", num_pos, " num_neg:", num_neg)
f.close()
fw.close()
fe.close()
dataset = TaskDataset(data_train_file, pipeline)
data_iter = DataLoader(dataset, batch_size=64, shuffle=False)
dataset_b = TaskDataset(data_train_file, pipeline1)
data_iter_b = DataLoader(dataset_b, batch_size=64, shuffle=False)
dataset2 = TaskDataset(data_test_file, pipeline)
data_iter2 = DataLoader(dataset2, batch_size=64, shuffle=False)
dataset2_b = TaskDataset(data_test_file, pipeline1)
data_iter2_b = DataLoader(dataset2_b, batch_size=64, shuffle=False)
dataset_dev = TaskDataset(data_dev_file, pipeline)
data_iter_dev = DataLoader(dataset_dev,
batch_size=64,
shuffle=False)
dataset_dev_b = TaskDataset(data_dev_file, pipeline1)
data_iter_dev_b = DataLoader(dataset_dev_b,
batch_size=64,
shuffle=False)
dataset3 = TaskDataset(data_short_file, pipeline)
data_iter3 = DataLoader(dataset3, batch_size=64, shuffle=True)
dataset3_b = TaskDataset(data_short_file, pipeline1)
data_iter3_b = DataLoader(dataset3_b, batch_size=64, shuffle=True)
print("###########################################")
print(model_cfg)
weights = tokenization.embed_lookup2()
print("#train_set:", len(data_iter))
print("#test_set:", len(data_iter2))
print("#short_set:", len(data_iter3))
print("#dev_set:", len(data_iter_dev))
curNum += 1
embedding = nn.Embedding.from_pretrained(weights).cuda()
criterion = nn.CrossEntropyLoss()
model = Classifier(model_cfg, 2)
model2 = Classifier_Attention_LSTM(2)
trainer = train.Trainer(
cfg, dataName, stopNum, model, model2, data_iter, data_iter_b,
data_iter2, data_iter2_b, data_iter3, data_iter3_b,
data_iter_dev, data_iter_dev_b,
optim.optim4GPU(cfg, model,
len(data_iter) * 10),
torch.optim.Adam(model2.parameters(),
lr=0.005), get_device(), kkk + 1)
label_0 = []
label_1 = []
result3 = []
result4 = []
result5 = []
bb_11 = {}
bb_22 = {}
abusive_11 = []
abusive_22 = []
result_label = []
fw = open('./temp_data/temp_train_IMDB.tsv',
'w',
encoding='utf-8',
newline='')
wr = csv.writer(fw, delimiter='\t')
fr = open(data_short_file, 'r', encoding='utf-8')
rdrr = csv.reader(fr, delimiter='\t')
for line in rdrr:
wr.writerow([line[0], line[1]])
fw.close()
fr.close()
data0 = []
temp_check = []
temp_label = []
with open(data_train_file, "r", encoding='utf-8') as f:
lines = csv.reader(f, delimiter='\t')
for i in lines:
a = ''
lines2 = i[1].split(' ')
for j in range(0, len(lines2)):
a += lines2[j] + ' '
data0.append(a)
temp_check.append([0, -1, a, i[0]])
temp_label.append([0, 0])
f.close()
trainer.train(model_file, pretrain_file, get_loss_CNN,
get_loss_Attn_LSTM, evalute_CNN_SSL, pseudo_labeling,
evalute_Attn_LSTM, evalute_CNN,
evalute_Attn_LSTM_SSL, generating_lexiocn,
data_parallel)
elif mode == 'eval':
def evalute_Attn_LSTM_SSL(model, batch):
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
input_ids = input_ids[perm_idx]
label_id = label_id[perm_idx]
token1 = embedding(input_ids.long())
logits, attention_score = model2(token1.cuda(), input_ids,
segment_ids, input_mask,
seq_lengths)
return label_id, logits
def evalute_CNN_SSL(model, batch):
input_ids, segment_ids, input_mask, label_id, seq_lengths = batch
token1 = embedding(input_ids.long())
logits, attention_score = model(token1.cuda(), input_ids,
segment_ids, input_mask)
return label_id, logits
weights = tokenization.embed_lookup2()
embedding = nn.Embedding.from_pretrained(weights).cuda()
criterion = nn.CrossEntropyLoss()
model = Classifier_CNN(2)
model2 = Classifier_Attention_LSTM(2)
trainer = train.Eval(cfg, model, model2, data_iter, save_dir,
get_device())
embedding = nn.Embedding.from_pretrained(weights).cuda()
results = trainer.eval(evalute_CNN_SSL, evalute_Attn_LSTM_SSL,
data_parallel)
def customize_core(config, coregen_filename):
"""
Reads in the xco from the the core directory and customize it for this
Architecture
Args:
config (dictionary): configuration dictionary
coregen_filename (string): filename of the coregen file to work on
Returns:
(string): filename to the custom core path
Raises:
Nothing
"""
#Open the coregen file
fp = open(coregen_filename)
core_in = fp.read()
fp.close()
#open a reference to the output file
c_fn = os.path.split(coregen_filename)[1]
c_fn = os.path.join(get_coregen_dir(config, absolute = True), c_fn)
#Open up the template dictionary
fn = COREGEN_TEMPLATE
fn = os.path.join(os.path.dirname(__file__), fn)
template = json.load(open(fn, "r"))
template["device"] = utils.get_device(config)
template["devicefamily"] = utils.get_family(config)
template["package"] = utils.get_package(config)
template["speedgrade"] = utils.get_speed_grade(config)
template["workingdirectory"] = get_coregen_temp_dir(config, absolute = True)
#print "Open: %s" % c_fn
fp = open(c_fn, "w")
#Break this into lines
core_in_lines = core_in.splitlines()
for line in core_in_lines:
line = line.strip()
if re.search('BEGIN.*Project.*Options', line, re.I):
#print "\tFound the beginning of the project"
fp.write("%s%s" % (line, os.linesep))
#Copy all the objects into the new file
for key in template:
fp.write("SET %s = %s%s" % (key, template[key], os.linesep))
continue
if "CRC" in line:
#Don't write the CRC
continue
#if line.startswith("#"):
#print "line: %s" % line
items = line.split(' ')
if "set" == items[0].lower():
#print "Line: %s" % line
#Now we have a line we might need to modify
if items[1].lower() in template.keys():
#Skip it, cause we already wrote what we wanted into the new xco
continue
fp.write("%s%s" % (line, os.linesep))
fp.close()
char_to_idx, idx_to_char = char_mapping()
config = {
"VOCAB_SIZE": len(char_to_idx.keys()),
"HIDDEN": 100,
# For songs sampling
"TEMPERATURE": 1,
"TAKE_MAX_PROBABLE": False,
"LIMIT_LEN": 440
}
MODEL_INPUT = "$\nX:3"
model = LSTMSimple(config["VOCAB_SIZE"], config["HIDDEN"],
config["VOCAB_SIZE"]).to(get_device())
model.init_state()
model.load_state_dict(
torch.load("trained_models/model2019-11-26-03-35.pth", map_location='cpu'))
model.eval()
text = """$
X:3
T:Trow Faicstieu
C:Itt
R:polka
Z:id:hn-hornpipe-59
M:C|
K:A
^GG|B2B B2c BGA|B2d c2c d2B|g6 A3|BdB dBA B2d|edf ecA Bdg|gdc AAF |1 dfdd g2ge ||
def train(
start_epoch=0,
additional_epoch=100,
lr=0.0001,
optim="adam",
leaky_relu=False,
ndcg_gain_in_train="exp2",
sigma=1.0,
double_precision=False,
standardize=False,
small_dataset=False,
debug=False,
output_dir="/tmp/ranking_output/",
):
print("start_epoch:{}, additional_epoch:{}, lr:{}".format(
start_epoch, additional_epoch, lr))
writer = SummaryWriter(output_dir)
precision = torch.float64 if double_precision else torch.float32
# get training and validation data:
data_fold = 'Fold1'
train_loader, df_train, valid_loader, df_valid = load_train_vali_data(
data_fold, small_dataset)
if standardize:
df_train, scaler = train_loader.train_scaler_and_transform()
df_valid = valid_loader.apply_scaler(scaler)
lambdarank_structure = [136, 64, 16]
net = LambdaRank(lambdarank_structure,
leaky_relu=leaky_relu,
double_precision=double_precision,
sigma=sigma)
device = get_device()
net.to(device)
net.apply(init_weights)
print(net)
ckptfile = get_ckptdir('lambdarank', lambdarank_structure, sigma)
if optim == "adam":
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
elif optim == "sgd":
optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)
else:
raise ValueError(
"Optimization method {} not implemented".format(optim))
print(optimizer)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=10,
gamma=0.75)
ideal_dcg = NDCG(2**9, ndcg_gain_in_train)
for i in range(start_epoch, start_epoch + additional_epoch):
net.train()
net.zero_grad()
count = 0
batch_size = 200
grad_batch, y_pred_batch = [], []
for X, Y in train_loader.generate_batch_per_query():
if np.sum(Y) == 0:
# negative session, cannot learn useful signal
continue
N = 1.0 / ideal_dcg.maxDCG(Y)
X_tensor = torch.tensor(X, dtype=precision, device=device)
y_pred = net(X_tensor)
y_pred_batch.append(y_pred)
# compute the rank order of each document
rank_df = pd.DataFrame({"Y": Y, "doc": np.arange(Y.shape[0])})
rank_df = rank_df.sort_values("Y").reset_index(drop=True)
rank_order = rank_df.sort_values("doc").index.values + 1
with torch.no_grad():
pos_pairs_score_diff = 1.0 + torch.exp(sigma *
(y_pred - y_pred.t()))
Y_tensor = torch.tensor(Y, dtype=precision,
device=device).view(-1, 1)
rel_diff = Y_tensor - Y_tensor.t()
pos_pairs = (rel_diff > 0).type(precision)
neg_pairs = (rel_diff < 0).type(precision)
Sij = pos_pairs - neg_pairs
if ndcg_gain_in_train == "exp2":
gain_diff = torch.pow(2.0, Y_tensor) - torch.pow(
2.0, Y_tensor.t())
elif ndcg_gain_in_train == "identity":
gain_diff = Y_tensor - Y_tensor.t()
else:
raise ValueError(
"ndcg_gain method not supported yet {}".format(
ndcg_gain_in_train))
rank_order_tensor = torch.tensor(rank_order,
dtype=precision,
device=device).view(-1, 1)
decay_diff = 1.0 / torch.log2(rank_order_tensor +
1.0) - 1.0 / torch.log2(
rank_order_tensor.t() + 1.0)
delta_ndcg = torch.abs(N * gain_diff * decay_diff)
lambda_update = sigma * (0.5 * (1 - Sij) -
1 / pos_pairs_score_diff) * delta_ndcg
lambda_update = torch.sum(lambda_update, 1, keepdim=True)
assert lambda_update.shape == y_pred.shape
check_grad = torch.sum(lambda_update, (0, 1)).item()
if check_grad == float('inf') or np.isnan(check_grad):
import ipdb
ipdb.set_trace()
grad_batch.append(lambda_update)
# optimization is to similar to RankNetListWise, but to maximize NDCG.
# lambda_update scales with gain and decay
count += 1
if count % batch_size == 0:
for grad, y_pred in zip(grad_batch, y_pred_batch):
y_pred.backward(grad / batch_size)
if count % (4 * batch_size) == 0 and debug:
net.dump_param()
optimizer.step()
net.zero_grad()
grad_batch, y_pred_batch = [], [
] # grad_batch, y_pred_batch used for gradient_acc
# optimizer.step()
print(
get_time(),
"training dataset at epoch {}, total queries: {}".format(i, count))
if debug:
eval_cross_entropy_loss(net,
device,
train_loader,
i,
writer,
phase="Train")
# eval_ndcg_at_k(net, device, df_train, train_loader, 100000, [10, 30, 50])
if i % 5 == 0 and i != start_epoch:
print(get_time(), "eval for epoch: {}".format(i))
eval_cross_entropy_loss(net, device, valid_loader, i, writer)
eval_ndcg_at_k(net, device, df_valid, valid_loader, 100000,
[10, 30], i, writer)
if i % 10 == 0 and i != start_epoch:
save_to_ckpt(ckptfile, i, net, optimizer, scheduler)
scheduler.step()
# save the last ckpt
save_to_ckpt(ckptfile, start_epoch + additional_epoch, net, optimizer,
scheduler)
# save the final model
torch.save(net.state_dict(), ckptfile)
ndcg_result = eval_ndcg_at_k(net, device, df_valid, valid_loader, 100000,
[10, 30], start_epoch + additional_epoch,
writer)
print(
get_time(), "finish training " + ", ".join(
["NDCG@{}: {:.5f}".format(k, ndcg_result[k])
for k in ndcg_result]), '\n\n')
def train(args):
# Init wandb
run = wandb.init(name=args.save_dir[len('../runs/'):],
config=args,
project='sign-language-recognition')
# Create directory for model checkpoints and log
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Save args
with open(os.path.join(args.save_dir, 'args.json'), 'w') as f:
json.dump(vars(args), f, sort_keys=True, indent=2)
# Logger
logger = create_logger(args.save_dir)
# Set gpu
if torch.cuda.is_available():
i = get_free_gpu()
device = get_device(gpu=i)
else:
device = 'cpu'
logger.info('using device: {}'.format(device))
# Prepare early stop
stopped = False
best_epoch = 0
best_loss = torch.Tensor([float('Inf')])
# Data
if args.freeze_vgg:
real_batch_size = 3
else:
real_batch_size = 2 # can't fit more into gpu memory
json_file = os.path.join(args.data_path, 'WLASL_v0.3.json')
videos_folder = os.path.join(args.data_path, 'videos')
keypoints_folder = os.path.join(args.data_path, 'keypoints')
train_transforms = transforms.Compose([videotransforms.RandomCrop(224)])
val_transforms = train_transforms
# Debug data
if args.debug_dataset:
train_dataset = WLASL(json_file=json_file,
videos_folder=videos_folder,
keypoints_folder=keypoints_folder,
transforms=train_transforms,
split='train',
subset=args.subset)
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=real_batch_size,
sampler=DebugSampler(
args.debug_dataset,
len(train_dataset)))
val_dl = train_dl
else:
train_dataset = WLASL(json_file=json_file,
videos_folder=videos_folder,
keypoints_folder=keypoints_folder,
transforms=train_transforms,
split='train',
subset=args.subset)
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=real_batch_size,
shuffle=True)
val_dataset = WLASL(json_file=json_file,
videos_folder=videos_folder,
keypoints_folder=keypoints_folder,
transforms=val_transforms,
split='val',
subset=args.subset)
val_dl = torch.utils.data.DataLoader(val_dataset,
batch_size=real_batch_size,
shuffle=True)
logger.info('data loaded')
# Model, loss, optimizer
m = Conv2dRNN(args).to(device)
optimizer = torch.optim.Adam(m.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss()
# Resume train
start_epoch = 0
if args.resume_train:
checkpoint = torch.load(os.path.join(args.save_dir,
'checkpoint.pt.tar'),
map_location=torch.device('cpu'))
best_epoch = checkpoint['epoch']
m.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
m = m.to(device)
best_loss = checkpoint['best_val_loss']
start_epoch = best_epoch + 1
# Change learning rate
for g in optimizer.param_groups:
g['lr'] = args.lr
logger.info(
'Resuming training from epoch {} with best loss {:.4f}'.format(
start_epoch, best_loss))
# learning rate scheduler
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=args.lr_schedule_factor,
patience=args.lr_schedule_patience,
threshold=args.lr_schedule_threshold)
# Watch model with wandb
run.watch(m, log='all', log_freq=5)
# Print args
logger.info('using args: \n' +
json.dumps(vars(args), sort_keys=True, indent=2))
# Train loop
for t in range(args.n_epochs):
t += start_epoch
# Train
losses = AverageMeter()
batch_time = AverageMeter()
m.train()
start_t = time.time()
for i, batch in enumerate(train_dl):
# Run the forward pass multiple times and accumulate gradient (to be able to use large batch size)
X = batch['X'].to(device)
label = batch['label'].to(device)
# [per frame logits, mean of all frames logits]
logits = m(X)
# Create label for each logit
label = torch.cat([l.repeat(logits.shape[1], 1) for l in label],
dim=0)
# Squeeze time sequence and batch into one dimension
logits = logits.reshape(logits.shape[0] * logits.shape[1],
logits.shape[2])
loss = criterion(logits, label.squeeze())
loss.backward()
losses.update(loss.item())
if (i % (args.batch_size // real_batch_size)) == 0:
# Optimize with accumulated gradient
optimizer.step()
optimizer.zero_grad()
batch_time.update(time.time() - start_t)
start_t = time.time()
train_loss = losses.avg
# Validate
with torch.no_grad():
top1 = AverageMeter()
top5 = AverageMeter()
top10 = AverageMeter()
losses = AverageMeter()
m.eval()
for batch in val_dl:
X = batch['X'].to(device)
label = batch['label'].to(device)
# [per frame logits, mean of all frames logits]
logits = m(X)
# Create label for each logit
label = torch.cat(
[l.repeat(logits.shape[1], 1) for l in label], dim=0)
# Squeeze time sequence and batch into one dimension
logits = logits.reshape(logits.shape[0] * logits.shape[1],
logits.shape[2])
losses.update(criterion(logits, label.squeeze()).item())
# Update metrics
acc1, acc5, acc10 = topk_accuracy(logits,
label,
topk=(1, 5, 10))
top1.update(acc1.item())
top5.update(acc5.item())
top10.update(acc10.item())
val_loss = losses.avg
# Save best model
if val_loss < best_loss:
best_loss, best_epoch = val_loss, t
save_best(args, t, m, optimizer, best_loss)
# Check early stop
if t >= best_epoch + args.early_stop:
logger.info('EARLY STOP')
break
# Log info
logger.info(
'epoch: {} train loss: {:.4f} val loss: {:.4f} top1acc {:.4f} top5acc {:.4f} top10acc {:.4f} lr: {:.2e} time per batch {:.1f} s'
.format(t + 1, train_loss, val_loss, top1.avg, top5.avg, top10.avg,
optimizer.param_groups[0]['lr'], batch_time.avg))
# Wandb log
run.log({
'train_loss': train_loss,
'val_loss': val_loss,
'top1_acc': top1.avg,
'top5_acc': top5.avg,
'top10_acc': top10.avg,
'lr': optimizer.param_groups[0]['lr']
})
# Scheduler step
if args.use_lr_scheduler:
scheduler.step(val_loss)