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train_ESIM.py
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train_ESIM.py
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"""Defines and trains an ESIM model"""
import torch
import torch.nn.functional as F
import numpy as np
import argparse
import os
from ESIM.ESIM import ESIM
import config
from encoder_models import create_vocab_tensors
import data
from utils import tensorsFromPair
DEVICE = config.DEVICE
GPU_ENABLED = config.GPU_ENABLED
MAX_LENGTH = config.MAX_LENGTH
SOS_token = config.SOS_token
EOS_token = config.EOS_token
# Load Data
if __name__ == '__main__':
train_pairs = data.load_np_data(os.path.join(config.saved_ESIM_model_path, 'aug_train_pairs.npy'))
y_train = data.load_np_data(os.path.join(config.saved_ESIM_model_path, 'aug_train_labels.npy'))
y_train = torch.from_numpy(y_train).to(DEVICE)
val_pairs = data.load_np_data(os.path.join(config.saved_ESIM_model_path, 'aug_val_pairs.npy'))
y_val = data.load_np_data(os.path.join(config.saved_ESIM_model_path, 'aug_val_labels.npy'))
y_val = torch.from_numpy(y_val).to(DEVICE)
test_pairs = data.load_np_data(os.path.join(config.saved_ESIM_model_path, 'aug_test_pairs.npy'))
y_test = data.load_np_data(os.path.join(config.saved_ESIM_model_path, 'aug_test_labels.npy'))
y_test = torch.from_numpy(y_test).to(DEVICE)
vocab_index = data.VOCAB_INDEX
parser = argparse.ArgumentParser(description='Train_Supervised_Model')
parser.add_argument('--train_models', action='store_true',
help='enable training of models')
parser.add_argument('--folder_name', type=str,
help='Brief description of experiment (no spaces)')
parser.add_argument('--n_epochs', type=int, default=1,
help='number of epochs to train online in each loop (default: 1)')
parser.add_argument('--verbose_training', action='store_true',
help='print results during training')
parser.add_argument('--load_models', action='store_true', help='Load pretrained model from prior point')
parser.add_argument('--load_model_folder_name', type=str,
help='folder which contains the saved models to be used')
if __name__ == '__main__':
args = parser.parse_args()
args.save_models = 0
if args.train_models:
args.save_models = 1
saved_ESIM_model_results = data.SaveSupervisedModelResults(args.folder_name)
saved_ESIM_model_results.check_folder_exists()
pretrained_emb_file = 'pretrained_emb_100k.npy'
#%% Manual commands for testing
#args.train_models = 1
#args.verbose_training = 1
#saved_ESIM_model_results = data.SaveSupervisedModelResults('ESIM')
#%%
def mask_batch(input_batch_pairs):
"""Convert batch of sentence pairs to tensors and masks for ESIM model"""
input_tensor = torch.zeros((MAX_LENGTH, len(input_batch_pairs)), dtype=torch.long, device=DEVICE)
target_tensor = torch.zeros((MAX_LENGTH,len(input_batch_pairs)), dtype=torch.long, device=DEVICE)
for idx, pair in enumerate(input_batch_pairs):
encoded_input, encoded_target = tensorsFromPair(pair)
input_tensor[:len(encoded_input), idx], target_tensor[:len(encoded_target), idx] = \
encoded_input.view(-1), encoded_target.view(-1)
input_tensor_mask, target_tensor_mask = input_tensor != 0, target_tensor != 0
input_tensor_mask, target_tensor_mask = input_tensor_mask.float(), target_tensor_mask.float()
return input_tensor, input_tensor_mask, target_tensor, target_tensor_mask
def ESIM_pred(input_pairs, model, temperature=1):
"""Returns probability that sentences are paraphrases from trained ESIM model"""
model.eval()
input_tensor, input_tensor_mask, target_tensor, target_tensor_mask = mask_batch(input_pairs)
with torch.no_grad():
output = model(input_tensor, input_tensor_mask, target_tensor, target_tensor_mask)
probs = F.softmax(output / temperature, dim=1)
return probs[:,1]
def validation_error(val_pairs, y_val, model, temperature=1, batch_size=32, verbose=True):
"""Evalutes the error on a set of input pairs in terms of loss.
Is intended to be used on a validation or test set to evaluate performance"""
model.eval()
total_val_loss = 0
val_sents_scanned = 0
val_num_correct = 0
batch_counter = 0
batch_size = min(len(val_pairs), batch_size)
output_probs = torch.zeros((len(val_pairs),2), device=DEVICE)
for idx in range(len(val_pairs) // batch_size):
input_tensor, input_tensor_mask, target_tensor, target_tensor_mask = mask_batch(
val_pairs[idx*batch_size:(idx+1)*batch_size])
batch_labels = y_val[idx*batch_size:(idx+1)*batch_size]
with torch.no_grad():
output = model(input_tensor, input_tensor_mask, target_tensor, target_tensor_mask)
probs = F.softmax(output / temperature, dim=1)
loss = criterion(output, batch_labels)
output_probs[idx*batch_size:(idx+1)*batch_size,:] = probs
result = output.detach().cpu().numpy()
a = np.argmax(result, axis=1)
b = batch_labels.data.cpu().numpy()
val_num_correct += np.sum(a == b)
val_sents_scanned += len(batch_labels)
batch_counter += 1
batch_loss = loss.data.item()
total_val_loss += batch_loss
val_loss = total_val_loss / batch_counter
val_accuracy = (val_num_correct / val_sents_scanned)
if verbose:
print('{} batches | validation loss: {:.3} | validation accuracy: {:.3}'.format(
batch_counter, val_loss, val_accuracy))
return val_loss, val_accuracy, output_probs
def model_pipeline(model, criterion, optimizer, batch_size=32, num_epochs=1,
report_interval=10, early_stopping_interval=100, verbose=True):
"""Model pipeline which trains model and also generates examples while training and evaluation
on the validation set for potential early stopping"""
batch_counter = 0
print('start training...')
model.train()
for epoch in range(num_epochs):
model.train()
print('--' * 20)
train_sents_scanned = 0
train_num_correct = 0
batch_counter = 0
for idx in range(len(train_pairs) // batch_size):
input_tensor, input_tensor_mask, target_tensor, target_tensor_mask = mask_batch(
train_pairs[idx*batch_size:(idx+1)*batch_size])
batch_labels = y_train[idx*batch_size:(idx+1)*batch_size]
optimizer.zero_grad()
output = model(input_tensor, input_tensor_mask, target_tensor, target_tensor_mask)
loss = criterion(output, batch_labels)
loss.backward()
result = output.detach().cpu().numpy()
a = np.argmax(result, axis=1)
b = batch_labels.data.cpu().numpy()
train_num_correct += np.sum(a == b)
train_sents_scanned += len(batch_labels)
optimizer.step()
training_loss = loss.detach().item()
batch_counter += 1
saved_ESIM_model_results.train_loss.append(np.around(training_loss, 4))
if batch_counter % report_interval == 0 and verbose == True:
print('{} epochs, {} batches | training batch loss: {:.3} | train accuracy: {:.3}'.format(
epoch, batch_counter, training_loss, train_num_correct / train_sents_scanned))
if batch_counter % early_stopping_interval == 0:
val_prop = int(0.05 * len(val_pairs))
random_idx = np.random.choice(val_prop, val_prop, replace=False)
sample_val_pairs, sample_y_train = val_pairs[random_idx], y_val[random_idx]
val_loss, val_accuracy, _ = validation_error(
sample_val_pairs, sample_y_train, model,
temperature=1, batch_size=32, verbose=verbose)
model.train()
saved_ESIM_model_results.val_loss.append(np.around(val_accuracy, 4))
saved_ESIM_model_results.save_top_models(model, 'ESIM_{:.3f}.pt'.format(
val_accuracy))
saved_ESIM_model_results.export_loss('training_loss.txt', 'val_loss.txt')
class HyperParams(object):
"""Sets the experiment hyperparameters"""
def __init__(self, print_every=10):
self.print_every = print_every
self.early_stopping_interval = 150
self.dim_word = 300
self.batch_size = 32
self.n_words = vocab_index.n_words
self.n_classes = 2
def load_pretrained_emb(input_path=None):
"""Loads word embeddings for vocabulary or creates new vocabulary"""
if input_path is not None:
return data.load_np_data(input_path)
else:
return create_vocab_tensors(vocab_index)[0].cpu().numpy()
def load_ESIM_model(folder_name, file_name='best', path_override=None):
"""Instantiates and loads ESIM model"""
hp = HyperParams()
pretrained_emb = load_pretrained_emb(os.path.join(config.saved_ESIM_model_path, pretrained_emb_file))
ESIM_model = ESIM(hp.dim_word, hp.n_classes, hp.n_words, hp.dim_word, pretrained_emb).to(DEVICE)
if path_override is not None:
data.load_model(ESIM_model, os.path.join(path_override, file_name))
else:
if file_name == 'best':
file_name = 'ESIM_{:.3f}.pt'.format(data.get_top_n_models(
os.path.join(config.saved_ESIM_model_path, folder_name), 'ESIM', n=1, descending=True)[0])
data.load_model(ESIM_model, os.path.join(config.saved_ESIM_model_path, folder_name, file_name))
return ESIM_model
#%%
class RLAdversary():
"""Defines RL adversary model for use as reward function"""
def __init__(self, folder_name, file_name='best'):
super(RLAdversary, self).__init__()
self.name = 'ESIM RL Adversary'
self.folder_name = folder_name
self.file_name = file_name
self.model, self.criterion, self.optimizer = self.init_model()
self.pred_pairs = []
self.target_pairs = []
self.batch_size = 32
self.num_epochs = 1
self.update_iter = 0
self.training_accuracy = {}
def init_model(self):
model = load_ESIM_model(self.folder_name, self.file_name)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
return model, criterion, optimizer
def create_update_data(self):
update_pairs = np.concatenate([self.pred_pairs, self.target_pairs])
y_update = torch.zeros((len(update_pairs),), dtype=torch.long, device=DEVICE)
y_update[len(self.pred_pairs):] = 1
random_idx = np.random.choice(len(y_update), len(y_update), replace=False)
update_pairs, y_update = update_pairs[random_idx], y_update[random_idx]
return update_pairs, y_update
def update_model(self):
self.update_iter += 1
self.training_accuracy[self.update_iter] = []
update_pairs, y_update = self.create_update_data()
self.model.train()
batch_size = min(len(update_pairs), self.batch_size)
for epoch in range(self.num_epochs):
self.model.train()
train_sents_scanned = 0
train_num_correct = 0
for idx in range(len(update_pairs) // batch_size):
input_tensor, input_tensor_mask, target_tensor, target_tensor_mask = mask_batch(
update_pairs[idx*batch_size:(idx+1)*batch_size])
batch_labels = y_update[idx*batch_size:(idx+1)*batch_size]
self.optimizer.zero_grad()
output = self.model(input_tensor, input_tensor_mask, target_tensor, target_tensor_mask)
loss = self.criterion(output, batch_labels)
loss.backward()
self.optimizer.step()
result = output.detach().cpu().numpy()
a = np.argmax(result, axis=1)
b = batch_labels.data.cpu().numpy()
train_num_correct += np.sum(a == b)
train_sents_scanned += len(batch_labels)
self.training_accuracy[self.update_iter].append(train_num_correct / train_sents_scanned)
self.pred_pairs = []
self.target_pairs = []
def reset(self):
self.model, self.criterion, self.optimizer = self.init_model()
self.pred_pairs = []
self.target_pairs = []
self.batch_size=32
self.num_epochs=1
self.update_iter = 0
self.training_accuracy = {}
#%%
if (__name__ == '__main__') and args.train_models:
"""Initializes models subject to cmd line args and then trains and evaluates performance"""
# Set the hyperparameters
hp = HyperParams()
# Load pretrained embeddings and model
pretrained_emb = load_pretrained_emb(os.path.join(config.saved_ESIM_model_path, pretrained_emb_file))
model = ESIM(hp.dim_word, hp.n_classes, hp.n_words, hp.dim_word, pretrained_emb).to(DEVICE)
# Set criterion and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
# Create folder if saving
if args.save_models:
saved_ESIM_model_results.init_folder(args, None, None)
# Train model
model_pipeline(model=model, criterion=criterion, optimizer=optimizer, batch_size=hp.batch_size,
num_epochs=args.n_epochs, report_interval=hp.print_every,
early_stopping_interval = hp.early_stopping_interval, verbose=args.verbose_training)