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import argparse
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from models.classifier import DogBreedClassifier
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input_folder", type=str, required=True)
parser.add_argument("--ckpt_path", type=str, required=True)
args = parser.parse_args()
# Load model
model = DogBreedClassifier.load_from_checkpoint(args.ckpt_path)
model.eval()
# Create dataset
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
dataset = ImageFolder(root=args.input_folder, transform=transform)
dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
# Evaluate
model.val_acc.reset()
for batch in dataloader:
images, labels = batch
with torch.no_grad():
outputs = model(images)
model.val_acc(outputs, labels)
print(f"Validation Accuracy: {model.val_acc.compute():.4f}")
if __name__ == "__main__":
main()
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