Initial loop
parent
a89c1b812f
commit
0b622f29c2
|
@ -1 +1,2 @@
|
|||
.venv/
|
||||
__pycache__/
|
||||
|
|
|
@ -1,78 +0,0 @@
|
|||
import tensorflow as tf
|
||||
import nlpaug.augmenter.word as naw
|
||||
|
||||
|
||||
class DataLoader:
|
||||
def __init__(self, path, buffer_size, batch_size, max_length, test_ratio=0.2):
|
||||
self.path = path
|
||||
self.buffer_size = buffer_size
|
||||
self.batch_size = batch_size
|
||||
self.max_length = max_length
|
||||
self.test_ratio = test_ratio
|
||||
self.aug = naw.SynonymAug(aug_src="wordnet")
|
||||
|
||||
def _split_input_target(self, sequence):
|
||||
parts = tf.strings.split(sequence, "\t")
|
||||
index = int(parts[0])
|
||||
sentence = tf.strings.reduce_join(parts[1:], separator=" ")
|
||||
return sentence, index
|
||||
|
||||
def augment_data(self, sentence, index):
|
||||
aug_sentence = self.aug.augment(sentence.numpy().decode())
|
||||
return sentence, aug_sentence, index
|
||||
|
||||
def tf_augment_data(self, sentence, index):
|
||||
sentence, aug_sentence, index = tf.py_function(
|
||||
self.augment_data, [sentence, index], [tf.string, tf.string, tf.int32]
|
||||
)
|
||||
return sentence, aug_sentence, index
|
||||
|
||||
def load_dataset(self):
|
||||
lines_dataset = tf.data.TextLineDataset(self.path)
|
||||
dataset = lines_dataset.map(self._split_input_target)
|
||||
dataset = dataset.map(self.tf_augment_data)
|
||||
|
||||
# Split dataset into train and test
|
||||
dataset_size = tf.data.experimental.cardinality(dataset).numpy()
|
||||
test_size = int(dataset_size * self.test_ratio)
|
||||
train_size = dataset_size - test_size
|
||||
train_dataset = dataset.take(train_size)
|
||||
test_dataset = dataset.skip(train_size)
|
||||
|
||||
# Shuffle and batch
|
||||
train_dataset = train_dataset.shuffle(self.buffer_size).batch(self.batch_size)
|
||||
test_dataset = test_dataset.shuffle(self.buffer_size).batch(self.batch_size)
|
||||
|
||||
return train_dataset, test_dataset
|
||||
|
||||
|
||||
def test():
|
||||
# Hyperparameters
|
||||
buffer_size = 10000
|
||||
batch_size = 64
|
||||
max_length = 100 # Or any other value depending on your data
|
||||
|
||||
# Create DataLoader
|
||||
data_loader = DataLoader(
|
||||
"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
|
||||
buffer_size,
|
||||
batch_size,
|
||||
max_length,
|
||||
)
|
||||
|
||||
# Load the datasets
|
||||
train_dataset, test_dataset = data_loader.load_dataset()
|
||||
|
||||
# Test the data loader on the training dataset
|
||||
print("First 5 batches from the training dataset:")
|
||||
for sent, aug, indxs in train_dataset.take(1):
|
||||
print(f"Indices: {indxs}, Sentences: {sent}, Augmented: {aug}")
|
||||
|
||||
# Test the data loader on the test dataset
|
||||
# print("\nFirst 5 batches from the test dataset:")
|
||||
# for sentences, indices in test_dataset.take(5):
|
||||
# print(f"Indices: {indices}, Sentences: {sentences}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
|
@ -0,0 +1,75 @@
|
|||
#! /usr/bin/env python3
|
||||
|
||||
import torch
|
||||
from torch.utils.data import Dataset, DataLoader, random_split
|
||||
from nlpaug.augmenter.char import OcrAug
|
||||
from nlpaug.augmenter.word import RandomWordAug
|
||||
from sklearn.model_selection import train_test_split
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class TextDataset(Dataset):
|
||||
def __init__(self, path, max_length, buffer_size):
|
||||
self.data = pd.read_csv(path, delimiter="\t", header=None)
|
||||
self.max_length = max_length
|
||||
self.buffer_size = buffer_size
|
||||
|
||||
# Augmentations
|
||||
self.aug_char = OcrAug(
|
||||
name="OCR_Aug",
|
||||
aug_char_min=2,
|
||||
aug_char_max=10,
|
||||
aug_char_p=0.3,
|
||||
aug_word_p=0.3,
|
||||
aug_word_min=1,
|
||||
aug_word_max=10,
|
||||
)
|
||||
self.aug_delete = RandomWordAug(
|
||||
action="delete", name="RandomWord_Aug", aug_min=0, aug_max=1, aug_p=0.1
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
index, sentence = self.data.iloc[idx]
|
||||
aug_sentence = self.aug_char.augment(sentence)
|
||||
aug_sentence = self.aug_delete.augment(aug_sentence)
|
||||
aug_sentence = aug_sentence[0]
|
||||
return sentence, aug_sentence
|
||||
|
||||
|
||||
def load_dataset(path, max_length, buffer_size, batch_size, test_ratio=0.2):
|
||||
# Create dataset
|
||||
dataset = TextDataset(path, max_length, buffer_size)
|
||||
|
||||
# Calculate split sizes
|
||||
total_size = len(dataset)
|
||||
test_size = int(total_size * test_ratio)
|
||||
train_size = total_size - test_size
|
||||
|
||||
# Split dataset into train and test
|
||||
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
|
||||
|
||||
# Create dataloaders
|
||||
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
|
||||
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
|
||||
|
||||
return train_dataloader, test_dataloader
|
||||
|
||||
|
||||
def test():
|
||||
train_dataloader, test_dataloader = load_dataset(
|
||||
"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
|
||||
100,
|
||||
100,
|
||||
32,
|
||||
test_ratio=0.2,
|
||||
)
|
||||
for batch in train_dataloader:
|
||||
for sentence, aug_sentence in zip(batch[0], batch[1]):
|
||||
print(f"sentence: {sentence} | aug_sentence: {aug_sentence}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
87
ft.py
87
ft.py
|
@ -1 +1,88 @@
|
|||
#! /usr/bin/env python3
|
||||
from transformers import BartForConditionalGeneration, BartTokenizer, AdamW
|
||||
import torch
|
||||
from dl import load_dataset
|
||||
from tqdm import tqdm
|
||||
|
||||
# Enable cudnn optimizations
|
||||
torch.backends.cudnn.benchmark = True
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
# load tokenizer and model
|
||||
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
|
||||
model = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
|
||||
model.to(device)
|
||||
|
||||
# set up optimizer
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
|
||||
|
||||
# Initialize Amp. This should be optional and should not affect computation if not available
|
||||
try:
|
||||
from torch.cuda.amp import GradScaler, autocast
|
||||
scaler = GradScaler()
|
||||
except ImportError:
|
||||
# If Amp is not available, we'll simply define a dummy context manager
|
||||
class autocast:
|
||||
def __enter__(self):
|
||||
pass
|
||||
def __exit__(self, *args):
|
||||
pass
|
||||
scaler = None # We won't use a scaler if we don't have Amp
|
||||
|
||||
def train_model(dataloader):
|
||||
model.train()
|
||||
total_loss = 0
|
||||
print("Training model...")
|
||||
for batch in tqdm(dataloader):
|
||||
optimizer.zero_grad()
|
||||
|
||||
inputs = tokenizer(batch[1], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
||||
inputs.to(device)
|
||||
labels = tokenizer(batch[0], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
||||
labels.to(device)
|
||||
|
||||
outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels["input_ids"])
|
||||
|
||||
loss = outputs.loss
|
||||
loss.backward()
|
||||
|
||||
optimizer.step()
|
||||
total_loss += loss.item()
|
||||
|
||||
avg_train_loss = total_loss / len(dataloader)
|
||||
return avg_train_loss
|
||||
|
||||
def test_model(dataloader):
|
||||
model.eval()
|
||||
total_loss = 0
|
||||
print("Testing model...")
|
||||
for batch in tqdm(dataloader):
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(batch[1], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
||||
inputs.to(device)
|
||||
labels = tokenizer(batch[0], return_tensors="pt", padding=True, truncation=True, max_length=512)
|
||||
labels.to(device)
|
||||
outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels["input_ids"])
|
||||
loss = outputs.loss
|
||||
total_loss += loss.item()
|
||||
|
||||
avg_test_loss = total_loss / len(dataloader)
|
||||
return avg_test_loss
|
||||
|
||||
def train():
|
||||
train_dataloader, test_dataloader = load_dataset(
|
||||
"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
|
||||
100, 100, 1, test_ratio=0.2
|
||||
)
|
||||
num_epochs = 3
|
||||
for epoch in range(num_epochs):
|
||||
avg_train_loss = train_model(train_dataloader)
|
||||
print(f"Train loss for epoch {epoch+1}: {avg_train_loss}")
|
||||
|
||||
avg_test_loss = test_model(test_dataloader)
|
||||
print(f"Test loss for epoch {epoch+1}: {avg_test_loss}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
|
||||
|
|
Loading…
Reference in New Issue