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# Standard library imports (if any)
import os
# Third-party library imports
import torch
import torch.nn as nn
from transformers import BertForSequenceClassification, BertTokenizerFast
import torch.optim as optim
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
# Local application/library s
from FallingPlanet.orbit.utils.Metrics import AdvancedMetrics
from FallingPlanet.orbit.utils.Metrics import TinyEmoBoard
import torchmetrics
from tqdm import tqdm
from FallingPlanet.orbit.utils.callbacks import EarlyStopping
from FallingPlanet.orbit.models import BertFineTuneTiny
from itertools import islice
class Classifier:
def __init__(self,model, device, num_labels, log_dir):
self.model = model.to(device)
self.device = device
self.loss_criterion = CrossEntropyLoss()
self.writer = TinyEmoBoard(log_dir=log_dir)
self.accuracy = torchmetrics.Accuracy(num_classes=num_labels, task='multiclass').to(device)
self.precision = torchmetrics.Precision(num_classes=num_labels, task='multiclass').to(device)
self.recall = torchmetrics.Recall(num_classes=num_labels, task='multiclass').to(device)
self.f1= torchmetrics.F1Score(num_classes=num_labels, task = 'multiclass').to(device)
self.mcc = torchmetrics.MatthewsCorrCoef(num_classes=num_labels,task = 'multiclass').to(device)
self.top2_acc = torchmetrics.Accuracy(top_k=2, num_classes=num_labels,task='multiclass').to(device)
def compute_loss(self,logits, labels):
loss = self.loss_criterion(logits,labels)
return loss
def train_step(self, dataloader, optimizer, epoch):
self.model.train()
total_loss = 0.0
# Initialize metric accumulators
total_accuracy = 0.0
total_precision = 0.0
total_recall = 0.0
total_f1 = 0.0
total_mcc = 0.0
pbar = tqdm(dataloader, desc=f"Training Epoch {epoch}")
for batch in pbar:
input_ids, attention_masks, labels = [x.to(self.device) for x in batch]
optimizer.zero_grad()
outputs = self.model(input_ids, attention_masks)
loss = self.compute_loss(outputs, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
# Update and accumulate metrics
total_accuracy += self.accuracy(outputs.argmax(dim=1), labels).item()
total_precision += self.precision(outputs.argmax(dim=1), labels).item()
total_recall += self.recall(outputs.argmax(dim=1), labels).item()
total_f1 += self.f1(outputs, labels).item()
total_mcc += self.mcc(outputs.argmax(dim=1), labels).item()
# Update tqdm description with current loss and metrics
pbar.set_postfix(loss=total_loss / (pbar.n + 1))
# Calculate averages
num_batches = len(dataloader)
avg_accuracy = total_accuracy / num_batches
avg_precision = total_precision / num_batches
avg_recall = total_recall / num_batches
avg_f1 = total_f1 / num_batches
avg_mcc = total_mcc / num_batches
avg_train_loss = total_loss / num_batches
# Log metrics to TensorBoard
self.writer.log_scalar('Training/Average Loss', avg_train_loss, epoch)
self.writer.log_scalar('Training/Average Accuracy', avg_accuracy, epoch)
self.writer.log_scalar('Training/Average Precision', avg_precision, epoch)
self.writer.log_scalar('Training/Average Recall', avg_recall, epoch)
self.writer.log_scalar('Training/Average F1', avg_f1, epoch)
self.writer.log_scalar('Training/Average MCC', avg_mcc, epoch)
pbar.close()
def val_step(self, dataloader, epoch):
self.model.eval()
total_loss = 0.0
# Initialize metric accumulators
total_accuracy = 0.0
total_precision = 0.0
total_recall = 0.0
total_f1 = 0.0
total_mcc = 0.0
with torch.no_grad():
pbar = tqdm(dataloader, desc=f"Validation Epoch {epoch}")
for batch in pbar:
input_ids, attention_masks, labels = [x.to(self.device) for x in batch]
outputs = self.model(input_ids, attention_masks)
loss = self.compute_loss(outputs, labels)
total_loss += loss.item()
# Update and accumulate metrics
total_accuracy += self.accuracy(outputs.argmax(dim=1), labels).item()
total_precision += self.precision(outputs.argmax(dim=1), labels).item()
total_recall += self.recall(outputs.argmax(dim=1), labels).item()
total_f1 += self.f1(outputs, labels).item()
total_mcc += self.mcc(outputs.argmax(dim=1), labels).item()
# Update tqdm description with current loss and metrics
pbar.set_postfix(loss=total_loss / (pbar.n + 1))
# Calculate averages
num_batches = len(dataloader)
avg_val_loss = total_loss / num_batches
avg_accuracy = total_accuracy / num_batches
avg_precision = total_precision / num_batches
avg_recall = total_recall / num_batches
avg_f1 = total_f1 / num_batches
avg_mcc = total_mcc / num_batches
# Log metrics to TensorBoard
self.writer.log_scalar('Validation/Average Loss', avg_val_loss, epoch)
self.writer.log_scalar('Validation/Average Accuracy', avg_accuracy, epoch)
self.writer.log_scalar('Validation/Average Precision', avg_precision, epoch)
self.writer.log_scalar('Validation/Average Recall', avg_recall, epoch)
self.writer.log_scalar('Validation/Average F1', avg_f1, epoch)
self.writer.log_scalar('Validation/Average MCC', avg_mcc, epoch)
pbar.close()
return avg_val_loss
def test_step(self, dataloader):
self.model.eval()
# Initialize aggregated metrics
aggregated_metrics = {
'total_accuracy': 0.0,
'total_precision': 0.0,
'total_recall': 0.0,
'total_f1': 0.0,
'total_mcc': 0.0,
'total_top_2_acc': 0.0
}
with torch.no_grad():
pbar = tqdm(dataloader, desc="Testing")
for batch in pbar:
input_ids, attention_masks, labels = [x.to(self.device) for x in batch]
outputs = self.model(input_ids, attention_masks)
# Update and accumulate metrics
aggregated_metrics['total_accuracy'] += self.accuracy(outputs.argmax(dim=1), labels).item()
aggregated_metrics['total_precision'] += self.precision(outputs.argmax(dim=1), labels).item()
aggregated_metrics['total_recall'] += self.recall(outputs.argmax(dim=1), labels).item()
aggregated_metrics['total_f1'] += self.f1(outputs, labels).item()
aggregated_metrics['total_mcc'] += self.mcc(outputs.argmax(dim=1), labels).item()
aggregated_metrics['total_top_2_acc'] += self.top2_acc(outputs, labels).item()
# Update tqdm description with current metrics
pbar.set_postfix({
'Accuracy': aggregated_metrics['total_accuracy'] / (pbar.n + 1),
'MCC': aggregated_metrics['total_mcc'] / (pbar.n + 1)
})
# Calculate average metrics
num_batches = len(dataloader)
for key in aggregated_metrics:
aggregated_metrics[key] /= num_batches
return aggregated_metrics
def main(mode = "full"):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
emotion_data_train = torch.load(r"E:\text_datasets\saved\train_emotion_no_batch_no_batch.pt")
emotion_data_val = torch.load(r"E:\text_datasets\saved\val_emotion_no_batch_no_batch.pt")
emotion_data_test = torch.load(r"E:\text_datasets\saved\test_emotion_no_batch_no_batch.pt")
dataloader_train = DataLoader(emotion_data_train, batch_size=512, shuffle=True)
dataloader_val = DataLoader(emotion_data_val, batch_size=512)
dataloader_test = DataLoader(emotion_data_test, batch_size=512)
NUM_EMOTION_LABELS = 9
LOG_DIR = r"EmoBERTv2-tiny\logging"
model = BertFineTuneTiny(num_tasks=1, num_labels=[9])
optimizer = torch.optim.AdamW(model.parameters(),lr =1e-5, weight_decay=1e-6)
classifier = Classifier(model, device, NUM_EMOTION_LABELS, LOG_DIR)
if mode in ["train", "full"]:
# Your training logic here
early_stopping = EarlyStopping(patience=50, min_delta=1e-8) # Initialize Early Stopping
num_epochs = 75
for epoch in range(num_epochs):
classifier.train_step(dataloader_train, optimizer, epoch)
val_loss = classifier.val_step(dataloader_val, epoch)
if early_stopping.step(val_loss, classifier.model):
print("Early stopping triggered. Restoring best model weights.")
classifier.model.load_state_dict(early_stopping.best_state)
break
if early_stopping.best_state is not None:
torch.save(early_stopping.best_state, 'EmoBERTv2-tiny.pth')
if mode in ["test", "full"]:
if os.path.exists('EmoBERTv2-tiny.pth'):
classifier.model.load_state_dict(torch.load('EmoBERTv2-tiny.pth'))
# Assuming you have test_step implemented in classifier
test_results = classifier.test_step(dataloader_test)
print("Test Results:", test_results)
if __name__ == "__main__":
main(mode="full") # or "train" or "test" |