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import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from torch.utils.data import Dataset
from transformers import (
AutoImageProcessor,
AutoModelForImageClassification,
TrainingArguments,
Trainer
)
from torchvision.transforms import (
Compose,
Normalize,
RandomRotation,
RandomHorizontalFlip,
Resize,
ToTensor
)
from cnnClassifier.entity.config_entity import MultiTaskModelTrainerConfig
from cnnClassifier import logger
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
class MultiTaskEfficientNet(nn.Module):
def __init__(self, model_name, num_labels_age, num_labels_gender, num_labels_race):
super().__init__()
self.efficientnet_base = AutoModelForImageClassification.from_pretrained(model_name, ignore_mismatched_sizes=True)
original_classifier = self.efficientnet_base.classifier
feature_dim = original_classifier.in_features
self.efficientnet_base.classifier = nn.Identity()
self.age_classifier = nn.Linear(feature_dim, num_labels_age)
self.gender_classifier = nn.Linear(feature_dim, num_labels_gender)
self.race_classifier = nn.Linear(feature_dim, num_labels_race)
def forward(self, pixel_values, labels=None):
features = self.efficientnet_base.efficientnet(pixel_values)
pooled_features = features.last_hidden_state.mean(dim=[2, 3])
age_logits = self.age_classifier(pooled_features)
gender_logits = self.gender_classifier(pooled_features)
race_logits = self.race_classifier(pooled_features)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
age_loss = loss_fct(age_logits, labels[:, 0])
gender_loss = loss_fct(gender_logits, labels[:, 1])
race_loss = loss_fct(race_logits, labels[:, 2])
loss = (2.0 * age_loss) + gender_loss + race_loss
return {"loss": loss, "age_logits": age_logits, "gender_logits": gender_logits, "race_logits": race_logits}
class FairFaceDataset(Dataset):
def __init__(self, dataframe, processor, transforms):
self.dataframe = dataframe
self.processor = processor
self.transforms = transforms
self.normalize = Normalize(mean=processor.image_mean, std=processor.image_std)
def __len__(self):
return len(self.dataframe)
def __getitem__(self, idx):
row = self.dataframe.iloc[idx]
image_path = row['image_file_path']
image = Image.open(image_path).convert("RGB")
pixel_values = self.transforms(image)
pixel_values = self.normalize(pixel_values)
labels = torch.tensor([row['age_id'], row['gender_id'], row['race_id']], dtype=torch.long)
return {"pixel_values": pixel_values, "labels": labels}
def compute_multitask_metrics(eval_pred):
predictions, labels = eval_pred
age_logits, gender_logits, race_logits = predictions['age_logits'], predictions['gender_logits'], predictions['race_logits']
age_preds = np.argmax(age_logits, axis=1)
gender_preds = np.argmax(gender_logits, axis=1)
race_preds = np.argmax(race_logits, axis=1)
age_labels, gender_labels, race_labels = labels[:, 0], labels[:, 1], labels[:, 2]
age_acc = accuracy_score(age_labels, age_preds)
gender_acc = accuracy_score(gender_labels, gender_preds)
race_acc = accuracy_score(race_labels, race_preds)
overall_acc = (age_acc + gender_acc + race_acc) / 3.0
return {"age_accuracy": age_acc, "gender_accuracy": gender_acc, "race_accuracy": race_acc, "overall_accuracy": overall_acc}
class MultiTaskModelTrainer:
def __init__(self, config: MultiTaskModelTrainerConfig):
self.config = config
self.processor = AutoImageProcessor.from_pretrained(config.model_name)
def train(self):
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {device}")
logger.info("Loading and preparing dataset from cleaned CSV...")
df = pd.read_csv(self.config.data_path)
label_maps = {}
for task in ['age', 'gender', 'race']:
labels = sorted(df[task].unique())
label_maps[f'{task}_label2id'] = {label: i for i, label in enumerate(labels)}
df[f'{task}_id'] = df[task].map(label_maps[f'{task}_label2id'])
num_classes_age = len(label_maps['age_label2id'])
num_classes_gender = len(label_maps['gender_label2id'])
num_classes_race = len(label_maps['race_label2id'])
train_df, test_df = train_test_split(df, test_size=self.config.test_split_size, random_state=self.config.random_state, stratify=df['age'])
train_transforms = Compose([
Resize((self.config.image_size, self.config.image_size)),
RandomHorizontalFlip(),
RandomRotation(10),
ToTensor(), # Normalization is now in the Dataset
])
val_transforms = Compose([
Resize((self.config.image_size, self.config.image_size)),
ToTensor(),
])
train_dataset = FairFaceDataset(dataframe=train_df, processor=self.processor, transforms=train_transforms)
test_dataset = FairFaceDataset(dataframe=test_df, processor=self.processor, transforms=val_transforms)
model = MultiTaskEfficientNet(model_name=self.config.model_name, num_labels_age=num_classes_age, num_labels_gender=num_classes_gender, num_labels_race=num_classes_race).to(device)
args = TrainingArguments(
output_dir=self.config.root_dir,
logging_dir=f'{self.config.root_dir}/logs',
evaluation_strategy="epoch",
learning_rate=self.config.learning_rate,
per_device_train_batch_size=self.config.batch_size,
per_device_eval_batch_size=self.config.batch_size,
num_train_epochs=self.config.num_train_epochs,
weight_decay=self.config.weight_decay,
save_strategy='epoch',
load_best_model_at_end=True,
metric_for_best_model="eval_overall_accuracy",
dataloader_num_workers=4,
lr_scheduler_type='cosine',
report_to="none"
)
class EvalTrainer(Trainer):
def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None):
has_labels = "labels" in inputs
inputs = self._prepare_inputs(inputs)
with torch.no_grad():
outputs = model(**inputs)
loss = outputs.get("loss")
predictions = {"age_logits": outputs["age_logits"], "gender_logits": outputs["gender_logits"], "race_logits": outputs["race_logits"]}
return (loss, predictions, inputs["labels"] if has_labels else None)
trainer = EvalTrainer(model=model, args=args, train_dataset=train_dataset, eval_dataset=test_dataset, compute_metrics=compute_multitask_metrics)
trainer.train()
logger.info(f"Saving final model and processor to {self.config.trained_model_path}")
trainer.save_model(self.config.trained_model_path)
self.processor.save_pretrained(self.config.trained_model_path) |