def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
finetune_opts(parser)
parser.add_argument("--train_answer_path",
type=str,
required=True,
help="Path of the answers for trainset.")
parser.add_argument("--dev_answer_path",
type=str,
required=True,
help="Path of the answers for devset.")
parser.add_argument(
"--max_choices_num",
default=10,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
args = parser.parse_args()
args.labels_num = args.max_choices_num
if args.output_model_path == None:
args.output_model_path = "./models/chid_model.bin"
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
set_seed(args.seed)
# Build tokenizer.
args.tokenizer = CharTokenizer(args)
# Build multiple choice model.
model = MultipleChoice(args)
# Load or initialize parameters.
load_or_initialize_parameters(args, model)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(args.device)
# Training phase.
trainset = read_dataset(args, args.train_path, args.train_answer_path)
random.shuffle(trainset)
instances_num = len(trainset)
batch_size = args.batch_size
src = torch.LongTensor([example[0] for example in trainset])
tgt = torch.LongTensor([example[1] for example in trainset])
seg = torch.LongTensor([example[2] for example in trainset])
args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
print("Batch size: ", batch_size)
print("The number of training instances:", instances_num)
optimizer, scheduler = build_optimizer(args, model)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
args.amp = amp
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
args.model = model
total_loss, result, best_result = 0., 0., 0.
print("Start training.")
for epoch in range(1, args.epochs_num + 1):
model.train()
for i, (src_batch, tgt_batch, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
loss = train_model(args, model, optimizer, scheduler, src_batch,
tgt_batch, seg_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0:
print("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".
format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.
result = evaluate(
args, read_dataset(args, args.dev_path, args.dev_answer_path))
if result[0] > best_result:
best_result = result[0]
save_model(model, args.output_model_path)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
infer_opts(parser)
parser.add_argument(
"--max_choices_num",
default=4,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
parser.add_argument(
"--tokenizer",
choices=["bert", "char", "space"],
default="bert",
help="Specify the tokenizer."
"Original Google BERT uses bert tokenizer on Chinese corpus."
"Char tokenizer segments sentences into characters."
"Space tokenizer segments sentences into words according to space.")
args = parser.parse_args()
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
# Build tokenizer.
args.tokenizer = str2tokenizer[args.tokenizer](args)
# Build classification model and load parameters.
model = MultipleChoice(args)
model = load_model(model, args.load_model_path)
# For simplicity, we use DataParallel wrapper to use multiple GPUs.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
dataset = read_dataset(args, args.test_path)
src = torch.LongTensor([example[0] for example in dataset])
tgt = torch.LongTensor([example[1] for example in dataset])
seg = torch.LongTensor([example[2] for example in dataset])
batch_size = args.batch_size
instances_num = src.size()[0]
print("The number of prediction instances: ", instances_num)
model.eval()
with open(args.test_path) as f:
data = json.load(f)
question_ids = []
for i in range(len(data)):
questions = data[i][1]
for question in questions:
question_ids.append(question["id"])
index = 0
with open(args.prediction_path, "w") as f:
for i, (src_batch, _, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
src_batch = src_batch.to(device)
seg_batch = seg_batch.to(device)
with torch.no_grad():
_, logits = model(src_batch, None, seg_batch)
pred = torch.argmax(logits, dim=1)
pred = pred.cpu().numpy().tolist()
for j in range(len(pred)):
output = {}
output["id"] = question_ids[index]
index += 1
output["label"] = int(pred[j])
f.write(json.dumps(output))
f.write("\n")
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Path options.
parser.add_argument("--load_model_path",
default=None,
type=str,
help="Path of the multiple choice model.")
parser.add_argument("--vocab_path",
type=str,
required=True,
help="Path of the vocabulary file.")
parser.add_argument("--spm_model_path",
default=None,
type=str,
help="Path of the sentence piece model.")
parser.add_argument("--test_path", type=str, help="Path of the testset.")
parser.add_argument("--prediction_path",
default=None,
type=str,
help="Path of the prediction file.")
parser.add_argument("--config_path",
default="./models/bert_base_config.json",
type=str,
help="Path of the config file.")
# Model options.
parser.add_argument("--batch_size",
type=int,
default=32,
help="Batch size.")
parser.add_argument("--seq_length",
type=int,
default=64,
help="Sequence length.")
parser.add_argument(
"--max_choices_num",
default=10,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
parser.add_argument("--embedding",
choices=["bert", "word"],
default="bert",
help="Emebdding type.")
parser.add_argument("--encoder", choices=["bert", "lstm", "gru", \
"cnn", "gatedcnn", "attn", "synt", \
"rcnn", "crnn", "gpt", "bilstm"], \
default="bert", help="Encoder type.")
parser.add_argument("--bidirectional",
action="store_true",
help="Specific to recurrent model.")
parser.add_argument("--factorized_embedding_parameterization",
action="store_true",
help="Factorized embedding parameterization.")
parser.add_argument("--parameter_sharing",
action="store_true",
help="Parameter sharing.")
# Tokenizer options.
parser.add_argument(
"--tokenizer",
choices=["bert", "char", "space"],
default="char",
help="Specify the tokenizer."
"Original Google BERT uses bert tokenizer on Chinese corpus."
"Char tokenizer segments sentences into characters."
"Space tokenizer segments sentences into words according to space.")
args = parser.parse_args()
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
# Build tokenizer.
args.tokenizer = globals()[args.tokenizer.capitalize() + "Tokenizer"](args)
# Build classification model and load parameters.
model = MultipleChoice(args)
model = load_model(model, args.load_model_path)
# For simplicity, we use DataParallel wrapper to use multiple GPUs.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
dataset = read_dataset(args, args.test_path, None)
model.eval()
batch_size = args.batch_size
results_final = []
dataset_by_group = {}
print("The number of prediction instances: ", len(dataset))
for example in dataset:
if example[-1] not in dataset_by_group:
dataset_by_group[example[-1]] = [example]
else:
dataset_by_group[example[-1]].append(example)
for group_index, examples in dataset_by_group.items():
src = torch.LongTensor([example[0] for example in examples])
tgt = torch.LongTensor([example[1] for example in examples])
seg = torch.LongTensor([example[2] for example in examples])
index = 0
results = []
for i, (src_batch, _, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
src_batch = src_batch.to(device)
seg_batch = seg_batch.to(device)
with torch.no_grad():
_, logits = model(src_batch, None, seg_batch)
pred = torch.argmax(logits, dim=1)
pred = pred.cpu().numpy().tolist()
for j in range(len(pred)):
results.append(
(examples[index][-2], logits[index].cpu().numpy()))
index += 1
results_final.extend(postprocess_chid_predictions(results))
with open(args.prediction_path, 'w') as f:
json.dump({tag: pred for tag, pred in results_final}, f, indent=2)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
finetune_opts(parser)
parser.add_argument(
"--max_choices_num",
default=4,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
parser.add_argument(
"--tokenizer",
choices=["bert", "char", "space"],
default="bert",
help="Specify the tokenizer."
"Original Google BERT uses bert tokenizer on Chinese corpus."
"Char tokenizer segments sentences into characters."
"Space tokenizer segments sentences into words according to space.")
args = parser.parse_args()
args.labels_num = args.max_choices_num
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
set_seed(args.seed)
# Build tokenizer.
args.tokenizer = str2tokenizer[args.tokenizer](args)
# Build multiple choice model.
model = MultipleChoice(args)
# Load or initialize parameters.
load_or_initialize_parameters(args, model)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(args.device)
# Training phase.
trainset = read_dataset(args, args.train_path)
random.shuffle(trainset)
instances_num = len(trainset)
batch_size = args.batch_size
src = torch.LongTensor([example[0] for example in trainset])
tgt = torch.LongTensor([example[1] for example in trainset])
seg = torch.LongTensor([example[2] for example in trainset])
args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
print("Batch size: ", batch_size)
print("The number of training instances:", instances_num)
optimizer, scheduler = build_optimizer(args, model)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
args.amp = amp
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
args.model = model
total_loss, result, best_result = 0.0, 0.0, 0.0
print("Start training.")
for epoch in range(1, args.epochs_num + 1):
model.train()
for i, (src_batch, tgt_batch, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
loss = train_model(args, model, optimizer, scheduler, src_batch,
tgt_batch, seg_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0:
print("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".
format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.0
result = evaluate(args, read_dataset(args, args.dev_path))
if result[0] > best_result:
best_result = result[0]
save_model(model, args.output_model_path)
# Evaluation phase.
if args.test_path is not None:
print("Test set evaluation.")
if torch.cuda.device_count() > 1:
model.module.load_state_dict(torch.load(args.output_model_path))
else:
model.load_state_dict(torch.load(args.output_model_path))
evaluate(args, read_dataset(args, args.test_path))
def evaluate(args, dataset):
src = torch.LongTensor([sample[0] for sample in dataset])
tgt = torch.LongTensor([sample[1] for sample in dataset])
seg = torch.LongTensor([sample[2] for sample in dataset])
batch_size = args.batch_size
instances_num = src.size()[0]
args.model.eval()
for i, (src_batch, tgt_batch, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
src_batch = src_batch.to(args.device)
tgt_batch = tgt_batch.to(args.device)
seg_batch = seg_batch.to(args.device)
with torch.no_grad():
loss, logits = args.model(src_batch, tgt_batch, seg_batch)
if i == 0:
logits_all = logits
if i >= 1:
logits_all = torch.cat((logits_all, logits), 0)
# To calculate MRR, the results are grouped by qid.
dataset_groupby_qid, correct_answer_orders, scores = [], [], []
for i in range(len(dataset)):
label = dataset[i][1]
if i == 0:
qid = dataset[i][3]
# Order of the current sentence in the document.
current_order = 0
scores.append(float(logits_all[i][1].item()))
if label == 1:
# Occasionally, more than one sentences in a document contain answers.
correct_answer_orders.append(current_order)
current_order += 1
continue
if qid == dataset[i][3]:
scores.append(float(logits_all[i][1].item()))
if label == 1:
correct_answer_orders.append(current_order)
current_order += 1
else:
# For each question, we record which sentences contain answers
# and the scores of all sentences in the document.
dataset_groupby_qid.append((qid, correct_answer_orders, scores))
correct_answer_orders, scores, current_order = [], [], 0
qid = dataset[i][3]
scores.append(float(logits_all[i][1].item()))
if label == 1:
correct_answer_orders.append(current_order)
current_order += 1
dataset_groupby_qid.append((qid, correct_answer_orders, scores))
reciprocal_rank = []
for qid, correct_answer_orders, scores in dataset_groupby_qid:
if len(correct_answer_orders) == 1:
sorted_scores = sorted(scores, reverse=True)
for j in range(len(sorted_scores)):
if sorted_scores[j] == scores[correct_answer_orders[0]]:
reciprocal_rank.append(1 / (j + 1))
else:
current_rank = len(scores)
sorted_scores = sorted(scores, reverse=True)
for i in range(len(correct_answer_orders)):
for j in range(len(scores)):
if sorted_scores[j] == scores[
correct_answer_orders[i]] and j < current_rank:
current_rank = j
reciprocal_rank.append(1 / (current_rank + 1))
MRR = sum(reciprocal_rank) / len(reciprocal_rank)
print("Mean Reciprocal Rank: {:.4f}".format(MRR))
return MRR
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Path options.
parser.add_argument("--pretrained_model_path",
default=None,
type=str,
help="Path of the pretrained model.")
parser.add_argument("--output_model_path",
default="./models/multichoice_model.bin",
type=str,
help="Path of the output model.")
parser.add_argument("--vocab_path",
default=None,
type=str,
help="Path of the vocabulary file.")
parser.add_argument("--spm_model_path",
default=None,
type=str,
help="Path of the sentence piece model.")
parser.add_argument("--train_data_path",
type=str,
required=True,
help="Path of the trainset.")
parser.add_argument("--train_label_path",
type=str,
required=True,
help="Path of the trainset.")
parser.add_argument("--dev_data_path",
type=str,
required=True,
help="Path of the devset.")
parser.add_argument("--dev_label_path",
type=str,
required=True,
help="Path of the devset.")
parser.add_argument("--config_path",
default="./models/bert_base_config.json",
type=str,
help="Path of the config file.")
# Model options.
parser.add_argument("--batch_size",
type=int,
default=32,
help="Batch size.")
parser.add_argument("--seq_length",
type=int,
default=512,
help="Sequence length.")
parser.add_argument("--embedding",
choices=["bert", "word"],
default="bert",
help="Emebdding type.")
parser.add_argument("--encoder", choices=["bert", "lstm", "gru", \
"cnn", "gatedcnn", "attn", "synt", \
"rcnn", "crnn", "gpt", "bilstm"], \
default="bert", help="Encoder type.")
parser.add_argument("--bidirectional",
action="store_true",
help="Specific to recurrent model.")
parser.add_argument("--factorized_embedding_parameterization",
action="store_true",
help="Factorized embedding parameterization.")
parser.add_argument("--parameter_sharing",
action="store_true",
help="Parameter sharing.")
parser.add_argument(
"--max_choices_num",
default=10,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
# Tokenizer options.
parser.add_argument(
"--tokenizer",
choices=["bert", "char", "space"],
default="bert",
help="Specify the tokenizer."
"Original Google BERT uses bert tokenizer on Chinese corpus."
"Char tokenizer segments sentences into characters."
"Space tokenizer segments sentences into words according to space.")
# Optimizer options.
parser.add_argument("--learning_rate",
type=float,
default=2e-5,
help="Learning rate.")
parser.add_argument("--warmup",
type=float,
default=0.1,
help="Warm up value.")
parser.add_argument(
"--fp16",
action='store_true',
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit."
)
parser.add_argument(
"--fp16_opt_level",
choices=["O0", "O1", "O2", "O3"],
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
# Training options.
parser.add_argument("--dropout", type=float, default=0.2, help="Dropout.")
parser.add_argument("--epochs_num",
type=int,
default=8,
help="Number of epochs.")
parser.add_argument("--report_steps",
type=int,
default=100,
help="Specific steps to print prompt.")
parser.add_argument("--seed", type=int, default=7, help="Random seed.")
args = parser.parse_args()
args.labels_num = args.max_choices_num
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
set_seed(args.seed)
# Build tokenizer.
args.tokenizer = globals()[args.tokenizer.capitalize() + "Tokenizer"](args)
# Build multiple choice model.
model = MultipleChoice(args)
# Load or initialize parameters.
load_or_initialize_parameters(args, model)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(args.device)
# Training phase.
trainset = read_dataset(args, args.train_data_path, args.train_label_path)
random.shuffle(trainset)
instances_num = len(trainset)
batch_size = args.batch_size
src = torch.LongTensor([example[0] for example in trainset])
tgt = torch.LongTensor([example[1] for example in trainset])
seg = torch.LongTensor([example[2] for example in trainset])
args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
print("Batch size: ", batch_size)
print("The number of training instances:", instances_num)
optimizer, scheduler = build_optimizer(args, model)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
args.amp = amp
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
args.model = model
total_loss, result, best_result = 0., 0., 0.
print("Start training.")
for epoch in range(1, args.epochs_num + 1):
model.train()
for i, (src_batch, tgt_batch, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
loss = train_model(args, model, optimizer, scheduler, src_batch,
tgt_batch, seg_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0:
print("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".
format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.
result = evaluate(
args, read_dataset(args, args.dev_data_path, args.dev_label_path))
if result > best_result:
best_result = result
save_model(model, args.output_model_path)
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
infer_opts(parser)
parser.add_argument("--max_choices_num", default=10, type=int,
help="The maximum number of cadicate answer, shorter than this will be padded.")
args = parser.parse_args()
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
# Build tokenizer.
args.tokenizer = CharTokenizer(args)
# Build classification model and load parameters.
model = MultipleChoice(args)
model = load_model(model, args.load_model_path)
# For simplicity, we use DataParallel wrapper to use multiple GPUs.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
dataset = read_dataset(args, args.test_path, None)
model.eval()
batch_size = args.batch_size
results_final = []
dataset_by_group = {}
print("The number of prediction instances: ", len(dataset))
for example in dataset:
if example[-1] not in dataset_by_group:
dataset_by_group[example[-1]] = [example]
else:
dataset_by_group[example[-1]].append(example)
for group_index, examples in dataset_by_group.items():
src = torch.LongTensor([example[0] for example in examples])
tgt = torch.LongTensor([example[1] for example in examples])
seg = torch.LongTensor([example[2] for example in examples])
index = 0
results = []
for i, (src_batch, _, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
src_batch = src_batch.to(device)
seg_batch = seg_batch.to(device)
with torch.no_grad():
_, logits = model(src_batch, None, seg_batch)
pred = torch.argmax(logits, dim=1)
pred = pred.cpu().numpy().tolist()
for j in range(len(pred)):
results.append((examples[index][-2], logits[index].cpu().numpy()))
index += 1
results_final.extend(postprocess_chid_predictions(results))
with open(args.prediction_path, 'w') as f:
json.dump({tag: pred for tag, pred in results_final}, f, indent=2)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
finetune_opts(parser)
parser.add_argument("--pooling",
choices=["mean", "max", "first", "last"],
default="first",
help="Pooling type.")
parser.add_argument(
"--tokenizer",
choices=["bert", "char", "space"],
default="bert",
help="Specify the tokenizer."
"Original Google BERT uses bert tokenizer on Chinese corpus."
"Char tokenizer segments sentences into characters."
"Space tokenizer segments sentences into words according to space.")
parser.add_argument("--soft_targets",
action='store_true',
help="Train model with logits.")
parser.add_argument("--soft_alpha",
type=float,
default=0.5,
help="Weight of the soft targets loss.")
args = parser.parse_args()
if args.output_model_path == None:
args.output_model_path = "./models/dbqa_model.bin"
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
set_seed(args.seed)
# Count the number of labels.
args.labels_num = count_labels_num(args.train_path)
# Build tokenizer.
args.tokenizer = globals()[args.tokenizer.capitalize() + "Tokenizer"](args)
# Build classification model.
model = Classifier(args)
# Load or initialize parameters.
load_or_initialize_parameters(args, model)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(args.device)
# Training phase.
trainset = read_dataset(args, args.train_path)
random.shuffle(trainset)
instances_num = len(trainset)
batch_size = args.batch_size
src = torch.LongTensor([example[0] for example in trainset])
tgt = torch.LongTensor([example[1] for example in trainset])
seg = torch.LongTensor([example[2] for example in trainset])
args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
print("Batch size: ", batch_size)
print("The number of training instances:", instances_num)
optimizer, scheduler = build_optimizer(args, model)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
args.amp = amp
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
args.model = model
total_loss, result, best_result = 0., 0., 0.
print("Start training.")
for epoch in range(1, args.epochs_num + 1):
model.train()
for i, (src_batch, tgt_batch, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
loss = train_model(args, model, optimizer, scheduler, src_batch,
tgt_batch, seg_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0:
print("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".
format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.
result = evaluate(args, read_dataset(args, args.dev_path))
if result > best_result:
best_result = result
save_model(model, args.output_model_path)
# Evaluation phase.
if args.test_path is not None:
print("Test set evaluation.")
if torch.cuda.device_count() > 1:
model.module.load_state_dict(torch.load(args.output_model_path))
else:
model.load_state_dict(torch.load(args.output_model_path))
evaluate(args, read_dataset(args, args.test_path))
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Path options.
parser.add_argument("--load_model_path",
default=None,
type=str,
help="Path of the multiple choice model.")
parser.add_argument("--vocab_path",
type=str,
required=True,
help="Path of the vocabulary file.")
parser.add_argument("--test_path", type=str, help="Path of the testset.")
parser.add_argument("--prediction_path",
default=None,
type=str,
help="Path of the prediction file.")
parser.add_argument("--config_path",
default="./models/bert_base_config.json",
type=str,
help="Path of the config file.")
# Model options.
parser.add_argument("--batch_size",
type=int,
default=32,
help="Batch size.")
parser.add_argument("--seq_length",
type=int,
default=512,
help="Sequence length.")
parser.add_argument(
"--max_choices_num",
default=4,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
parser.add_argument("--embedding",
choices=["bert", "word"],
default="bert",
help="Emebdding type.")
parser.add_argument("--encoder", choices=["bert", "lstm", "gru", \
"cnn", "gatedcnn", "attn", "synt", \
"rcnn", "crnn", "gpt", "bilstm"], \
default="bert", help="Encoder type.")
parser.add_argument("--bidirectional",
action="store_true",
help="Specific to recurrent model.")
parser.add_argument("--pooling",
choices=["mean", "max", "first", "last"],
default="first",
help="Pooling type.")
parser.add_argument("--factorized_embedding_parameterization",
action="store_true",
help="Factorized embedding parameterization.")
parser.add_argument("--parameter_sharing",
action="store_true",
help="Parameter sharing.")
# Tokenizer options.
parser.add_argument(
"--tokenizer",
choices=["bert", "char", "space"],
default="bert",
help="Specify the tokenizer."
"Original Google BERT uses bert tokenizer on Chinese corpus."
"Char tokenizer segments sentences into characters."
"Space tokenizer segments sentences into words according to space.")
args = parser.parse_args()
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
# Load vocabulary.
vocab = Vocab()
vocab.load(args.vocab_path)
args.vocab = vocab
# Build classification model and load parameters.
args.soft_targets = False
model = MultipleChoice(args)
model = load_model(model, args.load_model_path)
# For simplicity, we use DataParallel wrapper to use multiple GPUs.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
if torch.cuda.device_count() > 1:
print("{} GPUs are available. Let's use them.".format(
torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
# Build tokenizer.
args.tokenizer = globals()[args.tokenizer.capitalize() + "Tokenizer"](args)
dataset = read_dataset(args, args.test_path)
src = torch.LongTensor([example[0] for example in dataset])
tgt = torch.LongTensor([example[1] for example in dataset])
seg = torch.LongTensor([example[2] for example in dataset])
batch_size = args.batch_size
instances_num = src.size()[0]
print("The number of prediction instances: ", instances_num)
model.eval()
with open(args.test_path) as f:
data = json.load(f)
question_ids = []
for i in range(len(data)):
questions = data[i][1]
for question in questions:
question_ids.append(question['id'])
index = 0
with open(args.prediction_path, 'w') as f:
for i, (src_batch, _, seg_batch,
_) in enumerate(batch_loader(batch_size, src, tgt, seg)):
src_batch = src_batch.to(device)
seg_batch = seg_batch.to(device)
with torch.no_grad():
_, logits = model(src_batch, None, seg_batch)
pred = torch.argmax(logits, dim=1)
pred = pred.cpu().numpy().tolist()
for j in range(len(pred)):
output = {}
output['id'] = question_ids[index]
index += 1
output['label'] = int(pred[j])
f.write(json.dumps(output))
f.write('\n')
