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test_bert_mnli.py
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test_bert_mnli.py
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"""
Train the ESIM model on the preprocessed SNLI dataset.
"""
# Aurelien Coet, 2018.
from utils.runned.utils_test_three import validate
from vaa.model_transformer import ESIM
# from vaa.model_bert_transformer import ESIM
import os
import argparse
import json
import numpy as np
import pickle
import torch
import matplotlib
matplotlib.use('Agg')
def transform_batch_data(data, batch_size=64, shuffle=True):
data_batch = dict()
data_batch['premises'] = dict()
data_batch['hypotheses'] = dict()
data_batch['labels'] = dict()
index = np.arange(len(data['labels']))
if shuffle:
np.random.shuffle(index)
idx = -1
for i in range(len(index)):
if i % batch_size == 0:
idx += 1
data_batch['premises'][idx] = []
data_batch['hypotheses'][idx] = []
data_batch['labels'][idx] = []
data_batch['premises'][idx].append(data['premises'][index[i]])
data_batch['hypotheses'][idx].append(data['hypotheses'][index[i]])
data_batch['labels'][idx].append(int(data['labels'][index[i]]))
return data_batch
def main(train_file,
valid_file,
test_file,
target_dir,
embedding_size=512,
hidden_size=512,
dropout=0.5,
num_classes=3,
epochs=64,
batch_size=32,
lr=0.0004,
patience=5,
max_grad_norm=10.0,
checkpoint=None):
"""
Train the ESIM model on the Quora dataset.
Args:
train_file: A path to some preprocessed data that must be used
to train the model.
valid_file: A path to some preprocessed data that must be used
to validate the model.
embeddings_file: A path to some preprocessed word embeddings that
must be used to initialise the model.
target_dir: The path to a directory where the trained model must
be saved.
hidden_size: The size of the hidden layers in the model. Defaults
to 300.
dropout: The dropout rate to use in the model. Defaults to 0.5.
num_classes: The number of classes in the output of the model.
Defaults to 3.
epochs: The maximum number of epochs for training. Defaults to 64.
batch_size: The size of the batches for training. Defaults to 32.
lr: The learning rate for the optimizer. Defaults to 0.0004.
patience: The patience to use for early stopping. Defaults to 5.
checkpoint: A checkpoint from which to continue training. If None,
training starts from scratch. Defaults to None.
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(20 * "=", " Preparing for training ", 20 * "=")
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# -------------------- Data loading ------------------- #
print("\t* Loading training data...")
with open(train_file, "rb") as pkl:
train_data = pickle.load(pkl)
print("\t* Loading validation data...")
with open(valid_file, "rb") as pkl:
valid_data = pickle.load(pkl)
valid_dataloader = transform_batch_data(valid_data, batch_size=batch_size, shuffle=False)
print("\t* Loading test data...")
with open(test_file, "rb") as pkl:
test_data = pickle.load(pkl)
test_dataloader = transform_batch_data(test_data, batch_size=batch_size, shuffle=False)
# -------------------- Model definition ------------------- #
print("\t* Building model...")
model = ESIM(embedding_size,
hidden_size,
dropout=dropout,
num_classes=num_classes,
device=device).to(device)
# -------------------- Preparation for training ------------------- #
# Continuing training from a checkpoint if one was given as argument.
if checkpoint:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint["epoch"] + 1
print("\t* Training will continue on existing model from epoch {}..."
.format(start_epoch))
model.load_state_dict(checkpoint["model"])
# Compute loss and accuracy before starting (or resuming) training.
_, valid_accuracy = validate(model, test_dataloader)
print("\t* Validation accuracy: {:.4f}%".format(valid_accuracy*100))
# _, test_loss, test_accuracy = validate(model,
# test_dataloader,
# criterion)
# print("\t* test loss before training: {:.4f}, accuracy: {:.4f}%"
# .format(test_loss, (test_accuracy*100)))
if __name__ == "__main__":
default_config = "../../config/training/mnli_training_bert.json"
parser = argparse.ArgumentParser(
description="Train the ESIM model on quora")
parser.add_argument("--config",
default=default_config,
help="Path to a json configuration file")
script_dir = os.path.dirname(os.path.realpath(__file__))
script_dir = script_dir + '/scripts/training'
parser.add_argument("--checkpoint",
default=os.path.dirname(os.path.realpath(__file__)) + '/data/checkpoints/MNLI/bert/' +"esim_{}.pth.tar".format(12),
help="Path to a checkpoint file to resume training")
args = parser.parse_args()
if args.config == default_config:
config_path = os.path.join(script_dir, args.config)
else:
config_path = args.config
with open(os.path.normpath(config_path), 'r') as config_file:
config = json.load(config_file)
main(os.path.normpath(os.path.join(script_dir, config["train_data"])),
os.path.normpath(os.path.join(script_dir, config["valid_data_matched"])),
os.path.normpath(os.path.join(script_dir, config["valid_data_mismatched"])),
os.path.normpath(os.path.join(script_dir, config["target_dir"])),
config["embedding_size"],
config["hidden_size"],
0,#config["dropout"],
config["num_classes"],
config["epochs"],
config["batch_size"],
config["lr"],
config["patience"],
config["max_gradient_norm"],
args.checkpoint)