model/font-classify-main/infer.py

import albumentations as A
import argparse
import numpy as np
import os
import timm
import torch

from albumentations.pytorch import ToTensorV2
from train import CutMax, ResizeWithPad
from PIL import Image


def parse_args():
    # Create an argument parser
    parser = argparse.ArgumentParser(description="Inference script")

    # Add arguments
    parser.add_argument(
        "--model_folder",
        type=str,
        default="sample_data/model",
        help="Path where the trained model was saved",
    )
    parser.add_argument(
        "--data_folder",
        type=str,
        default="sample_data/output/Lato-Regular",
        help="Path to images to run inference on",
    )
    parser.add_argument(
        "-net",
        "--network_type",
        type=str,
        default="resnet50",
        help="Type of network architecture",
    )
    args = parser.parse_args()

    return args


def main(args):
    with open(os.path.join(args.model_folder, "class_names.txt"), "r") as f:
        class_names = f.read().splitlines()

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model = timm.create_model(
        args.network_type, pretrained=False, num_classes=len(class_names)
    )
    model.to(device)

    model_path = os.path.join(args.model_folder, "trained_model.pth")
    checkpoint = torch.load(model_path, map_location=torch.device(device))
    model.load_state_dict(checkpoint)
    model.eval()

    transform = A.Compose(
        [
            A.Lambda(image=CutMax(1024)),
            A.Lambda(image=ResizeWithPad((320, 320))),
            A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            ToTensorV2(),
        ]
    )

    for image_file in os.listdir(args.data_folder):
        image_path = os.path.join(args.data_folder, image_file)
        image = np.array(Image.open(image_path))
        image = transform(image=image)["image"].unsqueeze(0)
        probs = model(image)
        _, prediction = torch.max(probs, 1)
        print(image_file, class_names[prediction])


if __name__ == "__main__":
    args = parse_args()

    main(args)