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#!/usr/bin/env python3
"""
Validation Runner: Runs trained model on validation set and saves predictions
"""
import os
import json
import torch
import numpy as np
from PIL import Image
from pathlib import Path
import logging
from transformers import AutoProcessor
from stage_4 import MultiHeadSiglipClassifier, CKPT, load_task_config
import pandas as pd
from tqdm import tqdm
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def _is_incomplete_classification(classification: dict, task_config: dict) -> bool:
"""Check if classification contains incomplete data (empty or '...' values)"""
if not task_config or 'tasks' not in task_config:
return True
required_tasks = [task['key'] for task in 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 load_trained_model(checkpoint_dir: str = './checkpoints'):
"""Load the trained model and processor"""
checkpoint_path = Path(checkpoint_dir)
# Load task configuration
task_config_path = checkpoint_path / 'task_config.json'
if not task_config_path.exists():
# Fallback to root directory
task_config_path = './task_config.json'
task_config = load_task_config(str(task_config_path))
# Load processor
processor = AutoProcessor.from_pretrained(CKPT)
# Load model with task config
model = MultiHeadSiglipClassifier(task_config)
model_state = torch.load(checkpoint_path / 'multi_head_siglip2_classifier.pth', map_location='cpu')
model.load_state_dict(model_state)
# Set to evaluation mode
model.eval()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
logger.info(f"Model loaded successfully on device: {device}")
return model, processor, device, task_config
def load_validation_data(data_dir: str = './data', task_config: dict = None):
"""Load validation samples from stage 2 metadata files"""
data_path = Path(data_dir)
# Load from stage 2 metadata files
metadata_dir = data_path / 'metadata'
if not metadata_dir.exists():
logger.error("Metadata directory not found. Run stages 1 and 2 first.")
return []
metadata_files = list(metadata_dir.glob('meta_*_stage2.json'))
if not metadata_files:
logger.error("No stage 2 metadata files found. Run stage 2 first.")
return []
samples = []
skipped_incomplete = 0
for meta_file in tqdm(metadata_files, desc="Loading validation data"):
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
classification = metadata.get('classification', {})
if not classification or _is_incomplete_classification(classification, task_config):
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
samples.append({
'idx': metadata['idx'],
'image_path': metadata['image_path'],
'caption': metadata['caption'],
'url': metadata['url'],
'hash': metadata['hash'],
'ground_truth': metadata['classification']
})
except Exception as e:
logger.warning(f"Error loading {meta_file}: {e}")
skipped_incomplete += 1
if skipped_incomplete > 0:
logger.warning(f"Skipped {skipped_incomplete} incomplete samples")
logger.info(f"Loaded {len(samples)} valid validation samples")
return samples
def predict_batch(model, processor, images, device, task_config, batch_size=8):
"""Run predictions on a batch of images"""
predictions = []
tasks = {task['key']: task for task in task_config['tasks']}
for i in range(0, len(images), batch_size):
batch_images = images[i:i+batch_size]
# Process images
inputs = processor(images=batch_images, return_tensors="pt")
pixel_values = inputs['pixel_values'].to(device)
with torch.no_grad():
outputs = model(pixel_values)
# Convert outputs to probabilities and predictions
batch_preds = []
for j in range(len(batch_images)):
pred = {}
# Process each task dynamically
for task_key, task_info in tasks.items():
logits = outputs[task_key][j]
probs = torch.softmax(logits, dim=0)
pred_class = torch.argmax(logits).item()
confidence = probs[pred_class].item()
if task_info['type'] == 'binary':
# Binary classification
pred[f'{task_key}_prediction'] = 'yes' if pred_class == 1 else 'no'
pred[f'{task_key}_confidence'] = confidence
pred[f'{task_key}_prob_yes'] = probs[1].item()
pred[f'{task_key}_prob_no'] = probs[0].item()
elif task_info['type'] == 'multi_class':
# Multi-class classification
pred_label = task_info['labels'][pred_class]
pred[f'{task_key}_prediction'] = pred_label
pred[f'{task_key}_confidence'] = confidence
# Add probabilities for all classes
for idx, label in enumerate(task_info['labels']):
pred[f'{task_key}_prob_{label}'] = probs[idx].item()
batch_preds.append(pred)
predictions.extend(batch_preds)
return predictions
def calculate_accuracies(predictions, ground_truths, task_config):
"""Calculate accuracies for each task"""
accuracies = {}
tasks = {task['key']: task for task in task_config['tasks']}
for task_key, task_info in tasks.items():
pred_key = f'{task_key}_prediction'
correct = sum(1 for pred, gt in zip(predictions, ground_truths)
if pred[pred_key] == gt[task_key])
total = len(predictions)
accuracies[f'{task_key}_accuracy'] = correct / total if total > 0 else 0
return accuracies
def run_validation(data_dir: str = './data', checkpoint_dir: str = './checkpoints',
output_file: str = './validation_results.json'):
"""Run complete validation and save results"""
logger.info("Starting validation run...")
# Load model and data
model, processor, device, task_config = load_trained_model(checkpoint_dir)
samples = load_validation_data(data_dir, task_config)
if not samples:
logger.error("No validation samples found!")
return
# Prepare images for batch processing
images = []
for sample in tqdm(samples, desc="Loading images"):
try:
img = Image.open(sample['image_path']).convert('RGB')
images.append(img)
except Exception as e:
logger.error(f"Error loading image {sample['image_path']}: {e}")
images.append(None)
# Filter out failed images
valid_samples = []
valid_images = []
for sample, img in zip(samples, images):
if img is not None:
valid_samples.append(sample)
valid_images.append(img)
logger.info(f"Running predictions on {len(valid_samples)} valid samples...")
# Run predictions
predictions = predict_batch(model, processor, valid_images, device, task_config)
# Calculate accuracies
ground_truths = [sample['ground_truth'] for sample in valid_samples]
accuracies = calculate_accuracies(predictions, ground_truths, task_config)
# Combine results
validation_results = []
for sample, prediction in zip(valid_samples, predictions):
result = {
**sample,
'predictions': prediction
}
validation_results.append(result)
# Create final output
output_data = {
'metadata': {
'total_samples': len(validation_results),
'model_checkpoint': checkpoint_dir,
'validation_accuracies': accuracies,
'task_config': task_config,
'timestamp': pd.Timestamp.now().isoformat()
},
'results': validation_results
}
# Save results
output_path = Path(output_file)
with open(output_path, 'w') as f:
json.dump(output_data, f, indent=2)
logger.info(f"Validation results saved to {output_path}")
logger.info("Validation Accuracies:")
for key, value in accuracies.items():
logger.info(f" {key}: {value:.4f}")
return output_data
def main():
"""Main execution"""
logger.info("Starting validation runner...")
# Check if model exists
if not Path('./checkpoints/multi_head_siglip2_classifier.pth').exists():
logger.error("Trained model not found! Run stage 4 first.")
return
# Run validation
results = run_validation()
if results:
logger.info("Validation completed successfully!")
else:
logger.error("Validation failed!")
if __name__ == "__main__":
main() |