def main():
parser = argparse.ArgumentParser()
model_config(parser)
set_config(parser)
train_config(parser)
args = parser.parse_args()
layer_indexes = [int(x) for x in args.layers.split(",")]
set_environment(args.seed)
# process data
data, is_single_sentence = process_data(args)
data_type = DataFormat.PremiseOnly if is_single_sentence else DataFormat.PremiseAndOneHypothesis
collater = Collater(gpu=args.cuda, is_train=False, data_type=data_type)
batcher = DataLoader(data, batch_size=args.batch_size, collate_fn=collater.collate_fn, pin_memory=args.cuda)
opt = vars(args)
# load model
if os.path.exists(args.checkpoint):
state_dict = torch.load(args.checkpoint)
config = state_dict['config']
config['dump_feature'] = True
opt.update(config)
else:
logger.error('#' * 20)
logger.error(
'Could not find the init model!\n The parameters will be initialized randomly!')
logger.error('#' * 20)
return
num_all_batches = len(batcher)
model = MTDNNModel(
opt,
state_dict=state_dict,
num_train_step=num_all_batches)
if args.cuda:
model.cuda()
features_dict = {}
for batch_meta, batch_data in batcher:
batch_meta, batch_data = Collater.patch_data(args.cuda, batch_meta, batch_data)
all_encoder_layers, _ = model.extract(batch_meta, batch_data)
embeddings = [all_encoder_layers[idx].detach().cpu().numpy()
for idx in layer_indexes]
uids = batch_meta['uids']
masks = batch_data[batch_meta['mask']].detach().cpu().numpy().tolist()
for idx, uid in enumerate(uids):
slen = sum(masks[idx])
features = {}
for yidx, layer in enumerate(layer_indexes):
features[layer] = str(embeddings[yidx][idx][:slen].tolist())
features_dict[uid] = features
# save features
with open(args.foutput, 'w', encoding='utf-8') as writer:
for sample in data:
uid = sample['uid']
tokens = sample['tokens']
feature = features_dict[uid]
feature['tokens'] = tokens
feature['uid'] = uid
writer.write('{}\n'.format(json.dumps(feature)))
parser = argparse.ArgumentParser()
parser = data_config(parser)
parser = model_config(parser)
parser = train_config(parser)
args = parser.parse_args()
output_dir = args.output_dir
data_dir = args.data_dir
args.train_datasets = args.train_datasets.split(',')
args.test_datasets = args.test_datasets.split(',')
pprint(args)
os.makedirs(output_dir, exist_ok=True)
output_dir = os.path.abspath(output_dir)
set_environment(args.seed, args.cuda)
log_path = args.log_file
logger = create_logger(__name__, to_disk=True, log_file=log_path)
logger.info(args.answer_opt)
task_defs = TaskDefs(args.task_def)
encoder_type = task_defs.encoderType
args.encoder_type = encoder_type
def dump(path, data):
with open(path, 'w') as f:
json.dump(data, f)
def generate_decoder_opt(enable_san, max_opt):
def main():
task_def_path = 'data_complex/lcp.yml'
task = os.path.splitext(os.path.basename(task_def_path))[0]
task_defs = TaskDefs(task_def_path)
prefix = task.split('_')[0]
task_def = task_defs.get_task_def(prefix)
parser = argparse.ArgumentParser()
model_config(parser)
set_config(parser)
train_config(parser)
args = parser.parse_args()
encoder_type = args.encoder_type
layer_indexes = [int(x) for x in args.layers.split(",")]
set_environment(args.seed)
# process data
data, is_single_sentence = process_data(args)
data_type = DataFormat.PremiseOnly if is_single_sentence else DataFormat.PremiseAndOneHypothesis
fout_temp = '{}.tmp'.format(args.finput)
dump_data(data, fout_temp)
collater = Collater(is_train=False, encoder_type=encoder_type)
dataset = SingleTaskDataset(fout_temp, False, maxlen=args.max_seq_length, task_def=task_def)#, data_type=data_type)
batcher = DataLoader(dataset, batch_size=args.batch_size, collate_fn=collater.collate_fn, pin_memory=args.cuda)
opt = vars(args)
# load model
if os.path.exists(args.checkpoint):
state_dict = torch.load(args.checkpoint)
config = state_dict['config']
config['dump_feature'] = True
config['local_rank'] = -1
opt.update(config)
else:
logger.error('#' * 20)
logger.error(
'Could not find the init model!\n The parameters will be initialized randomly!')
logger.error('#' * 20)
return
num_all_batches = len(batcher)
model = MTDNNModel(
opt,
state_dict=state_dict,
num_train_step=num_all_batches)
if args.cuda:
model.cuda()
features_dict = {}
for batch_meta, batch_data in batcher:
batch_meta, batch_data = Collater.patch_data(args.cuda, batch_meta, batch_data)
all_encoder_layers, _ = model.extract(batch_meta, batch_data)
embeddings = [all_encoder_layers[idx].detach().cpu().numpy()
for idx in layer_indexes]
#import pdb; pdb.set_trace()
uids = batch_meta['uids']
masks = batch_data[batch_meta['mask']].detach().cpu().numpy().tolist()
for idx, uid in enumerate(uids):
slen = sum(masks[idx])
features = {}
for yidx, layer in enumerate(layer_indexes):
features[layer] = str(embeddings[yidx][idx][:slen].tolist())
features_dict[uid] = features
# save features
with open(args.foutput, 'w', encoding='utf-8') as writer:
for sample in data:
uid = sample['uid']
tokens = sample['tokens']
feature = features_dict[uid]
feature['tokens'] = tokens
feature['uid'] = uid
writer.write('{}\n'.format(json.dumps(feature)))
def main():
parser = argparse.ArgumentParser()
model_config(parser)
set_config(parser)
train_config(parser)
args = parser.parse_args()
encoder_type = args.encoder_type
layer_indexes = [int(x) for x in args.layers.split(",")]
set_environment(args.seed)
# process data
data, is_single_sentence = process_data(args)
data_type = (DataFormat.PremiseOnly
if is_single_sentence else DataFormat.PremiseAndOneHypothesis)
fout_temp = "{}.tmp".format(args.finput)
dump_data(data, fout_temp)
collater = Collater(is_train=False, encoder_type=encoder_type)
dataset = SingleTaskDataset(
fout_temp,
False,
maxlen=args.max_seq_length,
)
batcher = DataLoader(
dataset,
batch_size=args.batch_size,
collate_fn=collater.collate_fn,
pin_memory=args.cuda,
)
opt = vars(args)
# load model
if os.path.exists(args.checkpoint):
state_dict = torch.load(args.checkpoint)
config = state_dict["config"]
config["dump_feature"] = True
opt.update(config)
else:
logger.error("#" * 20)
logger.error(
"Could not find the init model!\n The parameters will be initialized randomly!"
)
logger.error("#" * 20)
return
num_all_batches = len(batcher)
model = MTDNNModel(opt,
state_dict=state_dict,
num_train_step=num_all_batches)
if args.cuda:
model.cuda()
features_dict = {}
for batch_meta, batch_data in batcher:
batch_meta, batch_data = Collater.patch_data(args.cuda, batch_meta,
batch_data)
all_encoder_layers, _ = model.extract(batch_meta, batch_data)
embeddings = [
all_encoder_layers[idx].detach().cpu().numpy()
for idx in layer_indexes
]
uids = batch_meta["uids"]
masks = batch_data[batch_meta["mask"]].detach().cpu().numpy().tolist()
for idx, uid in enumerate(uids):
slen = sum(masks[idx])
features = {}
for yidx, layer in enumerate(layer_indexes):
features[layer] = str(embeddings[yidx][idx][:slen].tolist())
features_dict[uid] = features
# save features
with open(args.foutput, "w", encoding="utf-8") as writer:
for sample in data:
uid = sample["uid"]
feature = features_dict[uid]
feature["uid"] = uid
writer.write("{}\n".format(json.dumps(feature)))
