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"""
Inference script for trained models
"""
import argparse
import sys
from pathlib import Path
from typing import Dict, List, Optional, Union, Tuple
import json
sys.path.append(str(Path(__file__).parent.parent))
import torch
import torch.nn.functional as F
from PIL import Image
import numpy as np
from src.models.model_factory import ModelFactory
from src.data.transforms import get_transforms_for_model
from src.utils.helpers import get_device
class ModelInference:
"""Class for model inference"""
def __init__(
self,
checkpoint_path: str,
device: Optional[torch.device] = None,
multi_task: bool = False
):
"""
Initialize inference engine
Args:
checkpoint_path: Path to model checkpoint
device: Device to run inference on
multi_task: Whether the model is multi-task
"""
self.checkpoint_path = Path(checkpoint_path)
self.device = device or get_device()
self.multi_task = multi_task
# Load checkpoint (handle PyTorch 2.6+ weights_only change)
try:
self.checkpoint = torch.load(self.checkpoint_path, map_location=self.device, weights_only=True)
except Exception as e:
print(f"Trying with weights_only=False due to: {e}")
self.checkpoint = torch.load(self.checkpoint_path, map_location=self.device, weights_only=False)
self.config = self.checkpoint.get('config', {})
# Check if checkpoint indicates multi-task
if 'multi_task' in self.checkpoint:
self.multi_task = self.checkpoint['multi_task']
# Create model
self._create_model()
# Load transforms
self.transform = get_transforms_for_model(
self.config.get('model_name', 'efficientnet-b0'),
is_training=False
)
# Get class names from checkpoint or use defaults
if 'class_names' in self.checkpoint:
# Use class names from checkpoint if available
if self.multi_task:
self.decades = self.checkpoint['class_names'].get('decade', ['1960s', '1970s', '1980s', '1990s', '2000s'])
self.clusters = self.checkpoint['class_names'].get('cluster', [0, 1, 2, 3, 4])
self.devices = self.checkpoint['class_names'].get('device', ['calculator', 'phone'])
else:
self.decades = self.checkpoint['class_names']
if not isinstance(self.decades, list):
self.decades = ['1960s', '1970s', '1980s', '1990s', '2000s']
self.clusters = [0, 1, 2, 3, 4]
self.devices = ['calculator', 'phone']
else:
# Use default labels
self.decades = ['1960s', '1970s', '1980s', '1990s', '2000s']
self.clusters = [0, 1, 2, 3, 4]
self.devices = ['calculator', 'phone']
print(f"Model configured for {len(self.decades)} decade classes")
print(f"Decade labels: {self.decades}")
if self.multi_task:
print(f"Cluster labels: {self.clusters}")
print(f"Device labels: {self.devices}")
def _create_model(self):
"""Create and load model"""
model_name = self.config.get('model_name', 'efficientnet-b0')
if self.multi_task:
# Multi-task model
num_classes = {
'decade': self.config.get('num_decade_classes', 5),
'cluster': self.config.get('num_cluster_classes', 5),
'device': self.config.get('num_device_classes', 2)
}
self.model = ModelFactory.create_model(
model_name,
num_classes=num_classes,
multi_task=True,
multitask_config={
'hidden_dim': self.config.get('multitask_hidden_dim', 512),
'dropout_rate': self.config.get('multitask_dropout', 0.3),
'num_device_classes': self.config.get('num_device_classes', 2)
},
pretrained=False
)
else:
# Single-task model
num_classes = self.config.get('num_classes', 5)
self.model = ModelFactory.create_model(
model_name,
num_classes=num_classes,
pretrained=False
)
# Load weights
self.model.load_state_dict(self.checkpoint['model_state_dict'])
self.model.to(self.device)
self.model.eval()
print(f"Loaded {model_name} model from {self.checkpoint_path}")
print(f"Multi-task mode: {self.multi_task}")
def predict_image(self, image_path: Union[str, Path]) -> Dict:
"""
Predict on a single image
Args:
image_path: Path to image file
Returns:
Dictionary with predictions
"""
# Load and preprocess image
image = Image.open(image_path).convert('RGB')
image_tensor = self.transform(image).unsqueeze(0).to(self.device)
# Predict
with torch.no_grad():
output = self.model(image_tensor)
if self.multi_task:
# Multi-task predictions
decade_probs = F.softmax(output['decade'], dim=1)
cluster_probs = F.softmax(output['cluster'], dim=1)
# Check if device output exists
if 'device' in output:
device_probs = F.softmax(output['device'], dim=1)
device_prob, device_idx = device_probs.max(1)
else:
device_probs = None
device_prob = None
device_idx = None
# Get top predictions
decade_prob, decade_idx = decade_probs.max(1)
cluster_prob, cluster_idx = cluster_probs.max(1)
# Get top-3 for each task
decade_top3_probs, decade_top3_idx = decade_probs.topk(3, dim=1)
cluster_top3_probs, cluster_top3_idx = cluster_probs.topk(3, dim=1)
result = {
'decade': {
'prediction': self.decades[decade_idx.item()],
'confidence': decade_prob.item(),
'top3': [
{
'class': self.decades[idx],
'confidence': prob
}
for idx, prob in zip(
decade_top3_idx[0].tolist(),
decade_top3_probs[0].tolist()
)
]
},
'cluster': {
'prediction': self.clusters[cluster_idx.item()],
'confidence': cluster_prob.item(),
'top3': [
{
'class': self.clusters[idx],
'confidence': prob
}
for idx, prob in zip(
cluster_top3_idx[0].tolist(),
cluster_top3_probs[0].tolist()
)
]
}
}
# Add device prediction if available
if device_probs is not None:
result['device'] = {
'prediction': self.devices[device_idx.item()],
'confidence': device_prob.item()
}
else:
# Single-task predictions
probs = F.softmax(output, dim=1)
prob, idx = probs.max(1)
# Get top-3 predictions
top3_probs, top3_idx = probs.topk(3, dim=1)
result = {
'decade': {
'prediction': self.decades[idx.item()],
'confidence': prob.item(),
'top3': [
{
'class': self.decades[i],
'confidence': p
}
for i, p in zip(top3_idx[0].tolist(), top3_probs[0].tolist())
],
'all_probabilities': {
self.decades[i]: p for i, p in enumerate(probs[0].tolist())
}
}
}
return result
def predict_batch(self, image_paths: List[Union[str, Path]]) -> List[Dict]:
"""
Predict on multiple images
Args:
image_paths: List of image paths
Returns:
List of prediction dictionaries
"""
results = []
for image_path in image_paths:
try:
result = self.predict_image(image_path)
result['image_path'] = str(image_path)
result['status'] = 'success'
results.append(result)
except Exception as e:
results.append({
'image_path': str(image_path),
'status': 'error',
'error': str(e)
})
return results
def main():
parser = argparse.ArgumentParser(description='Run inference on images')
parser.add_argument('checkpoint', type=str, help='Path to model checkpoint')
parser.add_argument('images', nargs='+', help='Image paths to predict on')
parser.add_argument('--device', type=str, default=None,
help='Device to use (cpu, cuda, cuda:0, etc.)')
parser.add_argument('--output', type=str, default=None,
help='Output JSON file for results')
parser.add_argument('--multi-task', action='store_true',
help='Use multi-task model')
args = parser.parse_args()
# Initialize inference engine
inference = ModelInference(
checkpoint_path=args.checkpoint,
device=get_device(args.device),
multi_task=args.multi_task
)
# Run predictions
if len(args.images) == 1 and Path(args.images[0]).is_dir():
# Directory of images
image_dir = Path(args.images[0])
image_paths = list(image_dir.glob('*.jpg')) + \
list(image_dir.glob('*.jpeg')) + \
list(image_dir.glob('*.png'))
print(f"Found {len(image_paths)} images in {image_dir}")
else:
# Individual images
image_paths = [Path(p) for p in args.images]
# Predict
results = inference.predict_batch(image_paths)
# Display results
for result in results:
print(f"\nImage: {result['image_path']}")
if result['status'] == 'success':
if 'decade' in result:
decade_info = result['decade']
print(f" Decade: {decade_info['prediction']} "
f"(confidence: {decade_info['confidence']:.2%})")
print(" Top 3 decades:")
for i, pred in enumerate(decade_info['top3'], 1):
print(f" {i}. {pred['class']}: {pred['confidence']:.2%}")
if 'cluster' in result:
cluster_info = result['cluster']
print(f" Cluster: {cluster_info['prediction']} "
f"(confidence: {cluster_info['confidence']:.2%})")
else:
print(f" Error: {result['error']}")
# Save results if requested
if args.output:
with open(args.output, 'w') as f:
json.dump(results, f, indent=2)
print(f"\nResults saved to {args.output}")
if __name__ == '__main__':
main() |