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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a29bcadb",
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'train'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[1], line 10\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01myaml\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mPIL\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtrain\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m CutMax, ResizeWithPad\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'train'"
]
}
],
"source": [
"import argparse\n",
"import albumentations as A\n",
"import csv\n",
"import numpy as np\n",
"import onnxruntime as ort\n",
"import os\n",
"import yaml\n",
"\n",
"from PIL import Image\n",
"from train import CutMax, ResizeWithPad"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "133ad4f9",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"\n",
"# =========================\n",
"# PATH LOKAL MODEL & CONFIG\n",
"# =========================\n",
"BASE_PATH = r\"C:\\Users\\fmaul\\Documents\\KULIAH\\COMPRO\\Font Classify\\Checkpoint\"\n",
"\n",
"CONFIG_PATH = os.path.join(BASE_PATH, \"model_config.yaml\")\n",
"MODEL_PATH = os.path.join(BASE_PATH, \"model.onnx\")\n",
"\n",
"MAPPING_PATH = r\"C:\\Users\\fmaul\\Documents\\KULIAH\\COMPRO\\Font Classify\\font-classify-main\\google_fonts_mapping.tsv\"\n",
"\n",
"\n",
"def parse_args():\n",
" parser = argparse.ArgumentParser(\n",
" description=\"Inference with pretrained Storia model (local)\"\n",
" )\n",
" parser.add_argument(\n",
" \"--data_folder\",\n",
" type=str,\n",
" default=r\"C:\\Users\\fmaul\\Documents\\KULIAH\\COMPRO\\Font Classify\\font-classify-main\\sample_data\\fonts\",\n",
" help=\"Path to images to run inference on\",\n",
" )\n",
" return parser.parse_args()\n",
"\n",
"\n",
"def softmax(x):\n",
" e_x = np.exp(x - np.max(x))\n",
" return e_x / e_x.sum(axis=0)\n",
"\n",
"\n",
"def main(args):\n",
" # ===== Load config =====\n",
" with open(CONFIG_PATH, \"r\") as f:\n",
" config = yaml.safe_load(f)\n",
"\n",
" input_size = config[\"size\"]\n",
"\n",
" # ===== Load font mapping =====\n",
" google_font_mapping = {}\n",
" with open(MAPPING_PATH, \"r\", encoding=\"utf-8\") as f:\n",
" tsv_file = csv.reader(f, delimiter=\"\\t\")\n",
" for i, row in enumerate(tsv_file):\n",
" if i > 0:\n",
" filename, font_name, version = row\n",
" google_font_mapping[filename] = (font_name, version)\n",
"\n",
" # ===== Load ONNX model =====\n",
" session = ort.InferenceSession(MODEL_PATH, providers=[\"CPUExecutionProvider\"])\n",
"\n",
" # ===== Preprocessing =====\n",
" transform = A.Compose(\n",
" [\n",
" A.Lambda(image=CutMax(1024)),\n",
" A.Lambda(image=ResizeWithPad((input_size, input_size))),\n",
" A.Normalize(mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]),\n",
" ]\n",
" )\n",
"\n",
" # ===== Inference =====\n",
" for image_file in os.listdir(args.data_folder):\n",
" image_path = os.path.join(args.data_folder, image_file)\n",
"\n",
" image = np.array(Image.open(image_path).convert(\"RGB\"))\n",
" image = transform(image=image)[\"image\"]\n",
"\n",
" image = np.transpose(image, (2, 0, 1)) # HWC → CHW\n",
" image = np.expand_dims(image, 0) # Add batch dim\n",
"\n",
" logits = session.run(None, {\"input\": image})[0][0]\n",
" probs = softmax(logits)\n",
"\n",
" predicted = config[\"classnames\"][probs.argmax()]\n",
" font_name, version = google_font_mapping.get(predicted, (\"Unknown\", \"-\"))\n",
"\n",
" print(image_file, font_name, version)\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" args = parse_args()\n",
" main(args)\n"
]
}
],
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"language_info": {
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"file_extension": ".py",
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