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#!/usr/bin/env python3
"""
Stage 4: SigLIP v2 Multi-Head Classifier Training
Trains a SigLIP v2-based multi-head classifier on pseudo-labeled data
"""
import os
import json
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from transformers import SiglipModel, AutoProcessor
import numpy as np
from PIL import Image
from pathlib import Path
import logging
from typing import Dict, List, Any
import pickle
import matplotlib.pyplot as plt
from torch.optim.lr_scheduler import LambdaLR
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
CKPT = "google/siglip-base-patch16-256"
def load_task_config(config_path: str = './task_config.json'):
"""Load task configuration from JSON file"""
if not os.path.exists(config_path):
raise FileNotFoundError(f"Task configuration not found: {config_path}")
with open(config_path, 'r') as f:
config = json.load(f)
logger.info(f"Loaded task configuration with {len(config['tasks'])} tasks")
return config
class MultiHeadDataset(Dataset):
"""Dataset for multi-head classification with configurable tasks"""
def __init__(self, data_dir: str, processor, task_config: Dict):
self.data_dir = Path(data_dir)
self.processor = processor
self.task_config = task_config
# Load all metadata files from stage 2 (with _stage2 suffix)
metadata_dir = self.data_dir / 'metadata'
if not metadata_dir.exists():
raise FileNotFoundError("Metadata directory not found. Run stages 1 and 2 first.")
metadata_files = list(metadata_dir.glob('meta_*_stage2.json'))
if not metadata_files:
raise FileNotFoundError("No stage 2 metadata files found. Run stage 2 first.")
# Load all samples
self.samples = []
skipped_incomplete = 0
for meta_file in metadata_files:
try:
with open(meta_file, 'r') as f:
metadata = json.load(f)
# Check if classification is complete
if not metadata.get('stage2_complete', False):
logger.warning(f"Skipping {meta_file} - classification not complete")
skipped_incomplete += 1
continue
# Check if classification contains incomplete data (empty or "..." values)
classification = metadata.get('classification', {})
if not classification or self._is_incomplete_classification(classification):
logger.warning(f"Skipping {meta_file} - incomplete classification data")
skipped_incomplete += 1
continue
# Check if image exists
image_path = metadata['image_path']
if not os.path.exists(image_path):
logger.warning(f"Image not found: {image_path}")
skipped_incomplete += 1
continue
self.samples.append(metadata)
except Exception as e:
logger.error(f"Error loading {meta_file}: {e}")
skipped_incomplete += 1
# Create label mappings from task config
self.label_mappings = {}
for task in self.task_config['tasks']:
if task['type'] == 'multi_class':
self.label_mappings[task['key']] = {
label: idx for idx, label in enumerate(task['labels'])
}
if skipped_incomplete > 0:
logger.warning(f"Skipped {skipped_incomplete} incomplete samples")
logger.info(f"Loaded {len(self.samples)} valid samples for training")
def _is_incomplete_classification(self, classification: Dict) -> bool:
"""Check if classification contains incomplete data (empty or '...' values)"""
required_tasks = [task['key'] for task in self.task_config['tasks']]
for task_key in required_tasks:
if task_key not in classification:
return True
value = classification[task_key]
# Check for incomplete markers
if not value or value == "..." or value == "" or value is None:
return True
return False
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
sample = self.samples[idx]
# Load image
image = Image.open(sample['image_path']).convert('RGB')
# Process image only
inputs = self.processor(
images=image,
return_tensors="pt"
)
# Convert classifications to labels based on task config
classification = sample['classification']
labels = {}
for task in self.task_config['tasks']:
task_key = task['key']
if task['type'] == 'binary':
# Binary tasks: convert yes/no to 1/0
labels[task_key] = 1 if classification[task_key] == 'yes' else 0
elif task['type'] == 'multi_class':
# Multi-class tasks: convert to index
label_str = classification[task_key]
labels[task_key] = self.label_mappings[task_key].get(label_str, 0) # default to first class
return {
'pixel_values': inputs['pixel_values'].squeeze(0),
'labels': labels,
'metadata': {
'idx': sample['idx'],
'caption': sample['caption'],
'image_path': sample['image_path']
}
}
class MultiHeadSiglipClassifier(nn.Module):
"""SigLIP-based multi-head classifier with configurable tasks"""
def __init__(self, task_config: Dict, model_name: str = CKPT):
super().__init__()
self.task_config = task_config
self.siglip = SiglipModel.from_pretrained(model_name)
# Freeze SigLIP parameters initially
for param in self.siglip.parameters():
param.requires_grad = False
# Create classification heads dynamically based on task config
hidden_size = self.siglip.config.vision_config.hidden_size
self.classification_heads = nn.ModuleDict()
for task in task_config['tasks']:
task_key = task['key']
num_classes = len(task['labels'])
# Create linear layer for this task
head = nn.Linear(hidden_size, num_classes)
# Initialize with zeros
head.weight.data.zero_()
head.bias.data.zero_()
self.classification_heads[task_key] = head
logger.info(f"Created {len(self.classification_heads)} classification heads")
def forward(self, pixel_values):
# Get SigLIP image embeddings only
combined_embeds = self.siglip.get_image_features(pixel_values=pixel_values)
# Apply all classification heads
outputs = {}
for task_key, head in self.classification_heads.items():
outputs[task_key] = head(combined_embeds)
return outputs
def calculate_accuracy(predictions, labels):
"""Calculate accuracy for binary/multi-class predictions"""
pred_classes = torch.argmax(predictions, dim=1)
correct = (pred_classes == labels).float()
return correct.mean().item()
def plot_validation_accuracies(history, task_config, save_path='./checkpoints/validation_accuracies.png'):
"""Create and save validation accuracy plots"""
tasks = [task['key'] for task in task_config['tasks']]
task_names = [task['name'] for task in task_config['tasks']]
# Calculate grid size
n_tasks = len(tasks)
n_cols = 3
n_rows = (n_tasks + n_cols - 1) // n_cols # Ceiling division
fig, axes = plt.subplots(n_rows, n_cols, figsize=(18, 6 * n_rows))
fig.suptitle('Training Progress Dashboard', fontsize=16, fontweight='bold')
# Flatten axes for easier indexing
if n_rows == 1:
axes = [axes] if n_cols == 1 else axes
else:
axes = axes.flatten()
epochs = range(1, len(history['val_accuracy'][tasks[0]]) + 1)
colors = plt.cm.Set1(np.linspace(0, 1, n_tasks))
# Plot individual validation accuracies
for i, (task_key, task_name, color) in enumerate(zip(tasks, task_names, colors)):
if i < len(axes):
axes[i].plot(epochs, history['val_accuracy'][task_key],
label=task_name, marker='o', color=color, linewidth=2, markersize=4)
axes[i].set_xlabel('Epoch')
axes[i].set_ylabel('Validation Accuracy')
axes[i].set_title(f'{task_name} Validation Accuracy')
axes[i].grid(True, alpha=0.3)
axes[i].set_ylim(0, 1)
# Hide unused subplots
for i in range(n_tasks, len(axes)):
axes[i].set_visible(False)
plt.tight_layout()
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.close()
logger.info(f"Validation accuracy plots saved to {save_path}")
# Calculate summary statistics
best_accs = [max(history['val_accuracy'][task]) for task in tasks]
final_accs = [history['val_accuracy'][task][-1] for task in tasks]
return best_accs, final_accs
def train_multi_head_classifier(data_dir: str, task_config_path: str = './task_config.json',
epochs: int = 30, batch_size: int = 4):
"""Train the multi-head SigLIP v2 classifier"""
logger.info("Starting multi-head classifier training...")
# Load task configuration
task_config = load_task_config(task_config_path)
# Create checkpoints directory
checkpoint_dir = Path('./checkpoints')
checkpoint_dir.mkdir(exist_ok=True)
logger.info(f"Checkpoints will be saved to: {checkpoint_dir}")
# Save task config to checkpoints for inference
with open(checkpoint_dir / 'task_config.json', 'w') as f:
json.dump(task_config, f, indent=2)
# Load processor and model
processor = AutoProcessor.from_pretrained(CKPT)
model = MultiHeadSiglipClassifier(task_config, model_name=CKPT)
# Dataset and dataloader
dataset = MultiHeadDataset(data_dir, processor, task_config)
if len(dataset) == 0:
logger.error("No training data found!")
return
# Split dataset (simple train/val split)
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
# Setup training
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f"Using device: {device}")
model.to(device)
# Optimizer and loss functions
# Get model parameters that require gradients (only classification heads)
params = []
for name, param in model.named_parameters():
if param.requires_grad:
params.append(param)
optimizer = optim.AdamW(params, lr=1e-2)
# Linear cooldown LR scheduler
def linear_cooldown(epoch):
return max(0.1, 1.0 - (epoch / epochs))
scheduler = LambdaLR(optimizer, lr_lambda=linear_cooldown)
criterion = nn.CrossEntropyLoss()
# Initialize training history
history = {
'train_loss': [],
'val_loss': [],
'learning_rates': [],
'val_accuracy': {task['key']: [] for task in task_config['tasks']},
'epoch_val_accuracy': []
}
# Training loop
for epoch in range(epochs):
# Training phase
model.train()
total_train_loss = 0
for batch_idx, batch in enumerate(train_loader):
optimizer.zero_grad()
# Move to device
pixel_values = batch['pixel_values'].to(device)
# Forward pass
outputs = model(pixel_values)
# Calculate losses for each task
losses = []
for task in task_config['tasks']:
task_key = task['key']
labels = batch['labels'][task_key].to(device)
loss = criterion(outputs[task_key], labels)
losses.append(loss)
# Total loss
total_batch_loss = sum(losses)
total_batch_loss.backward()
optimizer.step()
total_train_loss += total_batch_loss.item()
if batch_idx % 10 == 0:
logger.info(f"Epoch {epoch+1}/{epochs}, Batch {batch_idx}/{len(train_loader)}, Loss: {total_batch_loss.item():.4f}")
avg_train_loss = total_train_loss / len(train_loader)
history['train_loss'].append(avg_train_loss)
# Record learning rate
current_lr = optimizer.param_groups[0]['lr']
history['learning_rates'].append(current_lr)
# Validation phase
model.eval()
total_val_loss = 0
val_accuracies = {task['key']: [] for task in task_config['tasks']}
with torch.no_grad():
for batch in val_loader:
pixel_values = batch['pixel_values'].to(device)
outputs = model(pixel_values)
# Calculate validation losses and accuracies
losses = []
for task in task_config['tasks']:
task_key = task['key']
labels = batch['labels'][task_key].to(device)
loss = criterion(outputs[task_key], labels)
losses.append(loss)
# Calculate accuracy
acc = calculate_accuracy(outputs[task_key], labels)
val_accuracies[task_key].append(acc)
total_val_loss += sum(losses).item()
avg_val_loss = total_val_loss / len(val_loader)
history['val_loss'].append(avg_val_loss)
# Calculate average accuracies
epoch_accuracies = {}
for task in task_config['tasks']:
task_key = task['key']
avg_acc = np.mean(val_accuracies[task_key])
epoch_accuracies[task_key] = avg_acc
history['val_accuracy'][task_key].append(avg_acc)
history['epoch_val_accuracy'].append(epoch_accuracies.copy())
logger.info(f"Epoch {epoch+1}/{epochs}")
logger.info(f" Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}")
logger.info(f" Learning Rate: {current_lr:.6f}")
logger.info(f" Val Accuracies: {epoch_accuracies}")
# Step the learning rate scheduler
scheduler.step()
# Create comprehensive checkpoint
checkpoint = {
'epoch': epochs,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'history': history,
'final_accuracies': epoch_accuracies,
'task_config': task_config
}
# Save the trained model and checkpoint
torch.save(model.state_dict(), checkpoint_dir / 'multi_head_siglip2_classifier.pth')
torch.save(checkpoint, checkpoint_dir / 'training_checkpoint.pth')
logger.info(f"Model saved to {checkpoint_dir / 'multi_head_siglip2_classifier.pth'}")
logger.info(f"Full checkpoint saved to {checkpoint_dir / 'training_checkpoint.pth'}")
# Save processor for inference
processor.save_pretrained(checkpoint_dir / 'siglip2_processor')
logger.info(f"Processor saved to {checkpoint_dir / 'siglip2_processor'}")
# Save training history as JSON
with open(checkpoint_dir / 'training_history.json', 'w') as f:
json_history = {}
for key, value in history.items():
if key == 'val_accuracy':
json_history[key] = {task: [float(acc) for acc in accs] for task, accs in value.items()}
elif key == 'epoch_val_accuracy':
json_history[key] = [{task: float(acc) for task, acc in epoch.items()} for epoch in value]
else:
json_history[key] = [float(x) for x in value]
json.dump(json_history, f, indent=2)
logger.info(f"Training history saved to {checkpoint_dir / 'training_history.json'}")
# Generate and save validation accuracy plots
best_accs, final_accs = plot_validation_accuracies(history, task_config, checkpoint_dir / 'validation_accuracies.png')
# Save detailed validation accuracy summary
val_summary = {
'best_accuracies': {
task['key']: float(max(history['val_accuracy'][task['key']]))
for task in task_config['tasks']
},
'final_accuracies': {task: float(acc) for task, acc in epoch_accuracies.items()},
'average_best_accuracy': float(np.mean(best_accs)),
'average_final_accuracy': float(np.mean(final_accs)),
'improvement_per_task': {
task['key']: float(history['val_accuracy'][task['key']][-1] - history['val_accuracy'][task['key']][0])
for task in task_config['tasks']
}
}
with open(checkpoint_dir / 'validation_summary.json', 'w') as f:
json.dump(val_summary, f, indent=2)
logger.info(f"Validation summary saved to {checkpoint_dir / 'validation_summary.json'}")
# Save final training summary
final_summary = {
"model_type": "SigLIP2 Multi-Head Classifier",
"training_samples": len(train_dataset),
"validation_samples": len(val_dataset),
"epochs": epochs,
"final_train_loss": avg_train_loss,
"final_val_loss": avg_val_loss,
"final_accuracies": epoch_accuracies,
"task_config": task_config,
"classification_heads": {
task['key']: f"{task['type']} - {task['description']}"
for task in task_config['tasks']
}
}
with open(checkpoint_dir / 'stage4_summary.json', 'w') as f:
json.dump(final_summary, f, indent=2)
logger.info(f"Stage 4 summary saved to {checkpoint_dir / 'stage4_summary.json'}")
# Log summary of saved artifacts
logger.info("="*60)
logger.info("TRAINING COMPLETE - ARTIFACTS SAVED:")
logger.info(f"π Checkpoint Directory: {checkpoint_dir}")
logger.info(f"π€ Model Weights: multi_head_siglip2_classifier.pth")
logger.info(f"πΎ Full Checkpoint: training_checkpoint.pth")
logger.info(f"π§ Processor: siglip2_processor/")
logger.info(f"βοΈ Task Config: task_config.json")
logger.info(f"π Training History: training_history.json")
logger.info(f"π Validation Plots: validation_accuracies.png")
logger.info(f"π Validation Summary: validation_summary.json")
logger.info(f"π Stage Summary: stage4_summary.json")
logger.info("="*60)
def main():
"""Main execution for Stage 4"""
logger.info("Starting Stage 4: SigLIP v2 Multi-Head Training...")
# Train classifier
train_multi_head_classifier('./data', epochs=10, batch_size=2)
logger.info("Stage 4 completed successfully!")
logger.info("π Complete pipeline finished! Check ./checkpoints/ for all training artifacts.")
if __name__ == "__main__":
main() |