/
utils.py
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/
utils.py
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import os
import numpy as np
import pickle
import datetime
import time
import torch
import random
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from sklearn import preprocessing
import torch.nn as nn
def load_dataset(dataset_name):
# `dataset_name` can be "train", "valid", or "test".
input_ids = pickle.load(
open(
"input-marginalization/preprocessed_data/SST-2/input_ids_%s" % dataset_name,
"rb",
)
)
attention_masks = pickle.load(
open(
"input-marginalization/preprocessed_data/SST-2/attention_masks_%s"
% dataset_name,
"rb",
)
)
labels = pickle.load(
open(
"input-marginalization/preprocessed_data/SST-2/labels_%s" % dataset_name,
"rb",
)
)
return TensorDataset(input_ids, attention_masks, labels)
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
def format_time(elapsed):
"""
Takes a time in seconds and returns a string hh:mm:ss
"""
# Round to the nearest second.
elapsed_rounded = int(round((elapsed)))
# Format as hh:mm:ss
return str(datetime.timedelta(seconds=elapsed_rounded))
def generate_dataloaders(batch_size):
train_dataset = load_dataset("train")
train_dataloader = DataLoader(
train_dataset, sampler=RandomSampler(train_dataset), batch_size=batch_size
)
print("{:>5,} training samples.".format(len(train_dataset)))
val_dataset = load_dataset("valid")
validation_dataloader = DataLoader(
val_dataset, sampler=SequentialSampler(val_dataset), batch_size=batch_size
)
print("{:>5,} validation samples.".format(len(val_dataset)))
test_dataset = load_dataset("test")
test_dataloader = DataLoader(
test_dataset, sampler=RandomSampler(test_dataset), batch_size=batch_size
)
print("{:>5,} test samples.".format(len(test_dataset)))
return train_dataloader, validation_dataloader, test_dataloader
class SNLIDataset(torch.utils.data.Dataset):
def __init__(self, fname, lencoder=None):
data = pickle.load(open(fname, "rb"))
self.s1, self.s2, self.labels = data["s1"], data["s2"], data["labels"]
self.le = lencoder
if not self.le:
self.le = preprocessing.LabelEncoder()
self.le.fit(self.labels)
self.labels = self.le.transform(self.labels)
def __len__(self):
return len(self.labels)
def __getitem__(self, index):
return (self.s1[index], self.s2[index]), self.labels[index]
def collate_snli(batch):
s1l, s2l, labels = [], [], []
for dp in batch:
X, y = dp
s1, s2 = X
s1l.append(torch.tensor(s1).long())
s2l.append(torch.tensor(s2).long())
labels.append(y)
s1l = nn.utils.rnn.pad_sequence(s1l, batch_first=True)
s2l = nn.utils.rnn.pad_sequence(s2l, batch_first=True)
labels = torch.tensor(labels).long()
return (s1l, s2l), labels
def generate_snli_dataloader(pre, batch_size):
train_dataset = SNLIDataset(os.path.join(pre, "snli_train.pkl"))
dev_dataset = SNLIDataset(os.path.join(pre, "snli_dev.pkl"), train_dataset.le)
test_dataset = SNLIDataset(os.path.join(pre, "snli_test.pkl"), train_dataset.le)
train_dataloader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_snli
)
dev_dataloader = DataLoader(
dev_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_snli
)
test_dataloader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_snli
)
return train_dataloader, dev_dataloader, test_dataloader
def train(
model,
epochs,
train_dataloader,
validation_dataloader,
optimizer,
scheduler,
save_dir,
save_filename,
device,
dataset="sst2",
seed_val=42,
):
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
training_stats = []
total_t0 = time.time()
for epoch_i in range(epochs):
# ========================================
# Training
# ========================================
print("")
print("======== Epoch {:} / {:} ========".format(epoch_i + 1, epochs))
print("Training...")
t0 = time.time()
total_train_loss = 0
model.train()
for step, batch in enumerate(train_dataloader):
if step % 40 == 0 and not step == 0:
elapsed = format_time(time.time() - t0)
print(
" Batch {:>5,} of {:>5,}. Elapsed: {:}.".format(
step, len(train_dataloader), elapsed
)
)
if dataset == "sst2":
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
model.zero_grad()
output = model(
b_input_ids,
attention_mask=b_input_mask,
labels=b_labels,
return_dict=True,
)
loss = output.loss
logits = output.logits
elif dataset == "snli":
X, y = batch[0], batch[1].to(device)
X = (X[0].to(device), X[1].to(device))
model.zero_grad()
output = model(sentences=X, labels=y)
criterion = nn.CrossEntropyLoss()
loss = criterion(output, y)
else:
raise TypeError("Dataset not supported yet")
total_train_loss += loss.item()
loss.backward()
# TODO: See if this is needed.
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
# torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
avg_train_loss = total_train_loss / len(train_dataloader)
training_time = format_time(time.time() - t0)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epcoh took: {:}".format(training_time))
# ========================================
# Validation
# ========================================
print("")
print("Running Validation...")
t0 = time.time()
model.eval()
total_eval_accuracy = 0
total_eval_loss = 0
for batch in validation_dataloader:
if dataset == "sst2":
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
with torch.no_grad():
output = model(
b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels,
)
loss = output.loss
logits = output.logits
elif dataset == "snli":
X, y = batch[0], batch[1].to(device)
X = (X[0].to(device), X[1].to(device))
with torch.no_grad():
output = model(sentences=X, labels=y)
logits = output
criterion = nn.CrossEntropyLoss()
loss = criterion(output, y)
b_labels = y
total_eval_loss += loss.item()
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)
print(" Accuracy: {0:.2f}".format(avg_val_accuracy))
avg_val_loss = total_eval_loss / len(validation_dataloader)
validation_time = format_time(time.time() - t0)
print(" Validation Loss: {0:.2f}".format(avg_val_loss))
print(" Validation took: {:}".format(validation_time))
training_stats.append(
{
"epoch": epoch_i + 1,
"Training Loss": avg_train_loss,
"Valid. Loss": avg_val_loss,
"Valid. Accur.": avg_val_accuracy,
"Training Time": training_time,
"Validation Time": validation_time,
}
)
print("")
print("Training complete!")
print(
"Total training took {:} (h:mm:ss)".format(format_time(time.time() - total_t0))
)
# Save the model.
torch.save(model, f"{save_dir}/{save_filename}.pt")
pickle.dump(
training_stats, open(f"{save_dir}/training_stats_{save_filename}.p", "wb")
)
def test(model, test_dataloader, device, save_dir, save_filename, dataset="sst2"):
# ========================================
# Testing
# ========================================
print("")
print("Testing...")
t0 = time.time()
model.eval()
total_test_accuracy = 0
total_test_loss = 0
for batch in test_dataloader:
if dataset == "sst2":
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
with torch.no_grad():
output = model(
b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels,
)
loss = output.loss
logits = output.logits
elif dataset == "snli":
X, y = batch[0], batch[1].to(device)
X = (X[0].to(device), X[1].to(device))
with torch.no_grad():
output = model(sentences=X, labels=y)
logits = output
criterion = nn.CrossEntropyLoss()
loss = criterion(output, y)
b_labels = y
total_test_loss += loss.item()
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to("cpu").numpy()
total_test_accuracy += flat_accuracy(logits, label_ids)
avg_test_accuracy = total_test_accuracy / len(test_dataloader)
print(" Accuracy: {0:.2f}".format(avg_test_accuracy))
avg_test_loss = total_test_loss / len(test_dataloader)
test_time = format_time(time.time() - t0)
print(" Test Loss: {0:.2f}".format(avg_test_loss))
print(" Test took: {:}".format(test_time))
test_stats = {
"Test Loss": avg_test_loss,
"Test Accur.": avg_test_accuracy,
"Test Time": test_time,
}
pickle.dump(test_stats, open(f"{save_dir}/test_stats_{save_filename}.p", "wb"))