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import argparse
from pathlib import Path
import torch
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from models.classifier import DogBreedClassifier
def get_transform():
return transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input_folder", type=str, required=True)
parser.add_argument("--output_folder", type=str, required=True)
parser.add_argument("--ckpt_path", type=str, required=True)
args = parser.parse_args()
# Create output directory
Path(args.output_folder).mkdir(exist_ok=True)
# Load model
model = DogBreedClassifier.load_from_checkpoint(args.ckpt_path)
model.eval()
# Process each image
transform = get_transform()
class_labels = ['Beagle', 'Boxer', 'Bulldog', 'Dachshund', 'German Shepherd',
'Golden Retriever', 'Labrador Retriever', 'Poodle', 'Rottweiler',
'Yorkshire Terrier']
for img_path in Path(args.input_folder).glob("*"):
if img_path.suffix.lower() not in ['.jpg', '.jpeg', '.png']:
continue
# Load and preprocess image
img = Image.open(img_path).convert('RGB')
img_tensor = transform(img).unsqueeze(0)
# Inference
with torch.no_grad():
output = model(img_tensor)
probs = F.softmax(output, dim=1)
pred_idx = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred_idx].item()
# Save results
result = f"{img_path.name}: {class_labels[pred_idx]} ({confidence:.2f})\n"
with open(Path(args.output_folder) / "predictions.txt", "a") as f:
f.write(result)
if __name__ == "__main__":
main()
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