2024-01-15 05:48:36 +00:00
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#!/usr/bin/env python3
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import os
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import argparse
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import torch
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from pathlib import Path
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from PIL import Image
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from .model.Resizer import Resizer
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from .utils import preprocess_fn
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from munch import Munch
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def inference(args):
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img = Image.open(args.image)
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img = img.convert('RGB') if img.format == 'PNG' else img
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processed_img = preprocess_fn({"pixel_values": [img]})
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ckt_path = Path(args.checkpoint).resolve()
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model = Resizer.from_pretrained(ckt_path)
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model.eval()
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inpu = torch.stack(processed_img['pixel_values'])
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2024-01-23 06:07:09 +00:00
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pred = model(inpu) * 1.25
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2024-01-15 05:48:36 +00:00
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print(pred)
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...
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if __name__ == "__main__":
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cur_dirpath = os.getcwd()
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script_dirpath = Path(__file__).resolve().parent
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os.chdir(script_dirpath)
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parser = argparse.ArgumentParser()
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parser.add_argument('-img', '--image', type=str, required=True)
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parser.add_argument('-ckt', '--checkpoint', type=str, required=True)
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args = parser.parse_args([
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'-img', '/home/lhy/code/TeXify/src/models/resizer/foo5_140h.jpg',
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'-ckt', '/home/lhy/code/TeXify/src/models/resizer/train/train_result_pred_height_v5'
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])
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inference(args)
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os.chdir(cur_dirpath)
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