[chore] exclude paddleocr directory from pre-commit hooks

.pre-commit-config.yaml

@@ -4,8 +4,10 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --respect-gitignore, --config=pyproject.toml]
+        exclude: ^texteller/models/thrid_party/paddleocr/
       - id: ruff-format
         args: [--config=pyproject.toml]
+        exclude: ^texteller/models/thrid_party/paddleocr/
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0

src/models/ocr_model/train/dataset/formulas.jsonl

@@ -1,35 +0,0 @@
-{"img_name": "0.png", "formula": "\\[\\mathbb{C}^{4}\\stackrel{{\\pi_{1}}}{{\\longleftarrow}}\\mathcal{ F}\\stackrel{{\\pi_{2}}}{{\\rightarrow}}\\mathcal{PT},\\]"}
-{"img_name": "1.png", "formula": "\\[W^{*}_{Z}(x_{1},x_{2})=W_{f\\lrcorner Z}(y_{1},y_{2})=\\mathcal{P}\\exp\\left( \\int_{\\gamma}A_{\\mu}dx^{\\mu}\\right).\\]"}
-{"img_name": "2.png", "formula": "\\[G=W^{*}_{Z}(q,p)=\\tilde{H}H^{-1}\\]"}
-{"img_name": "3.png", "formula": "\\[H=W^{*}_{Z}(p,x),\\ \\ \\tilde{H}=W^{*}_{Z}(q,x).\\]"}
-{"img_name": "4.png", "formula": "\\[v\\cdot f^{*}A|_{x}=(f\\lrcorner Z)_{*}v\\cdot A|_{f\\lrcorner Z(x)},\\quad x\\in Z, \\ v\\in T_{x}Z.\\]"}
-{"img_name": "5.png", "formula": "\\[(f\\lrcorner Z)_{*}v\\cdot A|_{f\\lrcorner Z(x)}=v^{\\alpha\\dot{\\alpha}}\\Big{(} \\frac{\\partial y^{\\beta\\dot{\\beta}}}{\\partial x^{\\alpha\\dot{\\alpha}}}A_{\\beta \\dot{\\beta}}\\Big{)}\\Big{|}_{f\\lrcorner Z(x)},\\ x\\in Z,\\ v\\in T_{x}Z,\\]"}
-{"img_name": "6.png", "formula": "\\[\\{T_{i},T_{j}\\}=\\{\\tilde{T}^{i},\\tilde{T}^{j}\\}=0,\\ \\ \\{T_{i},\\tilde{T}^{j}\\}=2i \\delta^{j}_{i}D,\\]"}
-{"img_name": "7.png", "formula": "\\[(\\partial_{s},q_{i},\\tilde{q}^{k})\\rightarrow(D,M^{j}_{i}T_{j},\\tilde{M}^{k}_ {l}\\tilde{T}^{l}),\\]"}
-{"img_name": "8.png", "formula": "\\[M^{i}_{j}\\tilde{M}^{j}_{k}=\\delta^{i}_{k}.\\]"}
-{"img_name": "9.png", "formula": "\\[Q_{i\\alpha}=q_{i\\alpha}+\\omega_{i\\alpha},\\ \\tilde{Q}^{i}_{\\dot{\\alpha}}=q^{i}_{ \\dot{\\alpha}}+\\tilde{\\omega}^{i}_{\\dot{\\alpha}},\\ D_{\\alpha\\dot{\\alpha}}= \\partial_{\\alpha\\dot{\\alpha}}+A_{\\alpha\\dot{\\alpha}}.\\]"}
-{"img_name": "10.png", "formula": "\\[\\hat{f}(g,\\theta^{i\\alpha},\\tilde{\\theta}^{\\dot{\\alpha}}_{j})=(f(g),[V^{-1}]^ {\\alpha}_{\\beta}\\theta^{i\\beta},[\\tilde{V}^{-1}]^{\\dot{\\alpha}}_{\\dot{\\beta}} \\tilde{\\theta}^{\\dot{\\beta}}_{j}),\\ g\\in{\\cal G},\\]"}
-{"img_name": "11.png", "formula": "\\[v^{\\beta\\dot{\\beta}}V^{\\alpha}_{\\beta}\\tilde{V}^{\\dot{\\alpha}}_{\\dot{\\beta}} =((f\\lrcorner L_{0})_{*}v)^{\\alpha\\dot{\\alpha}},\\]"}
-{"img_name": "12.png", "formula": "\\[\\omega_{i\\alpha}=\\tilde{\\theta}^{\\dot{\\alpha}}_{i}h_{\\alpha\\dot{\\alpha}}(x^{ \\beta\\dot{\\beta}},\\tau^{\\beta\\dot{\\beta}}),\\ \\ \\tilde{\\omega}^{i}_{\\alpha}=\\theta^{i\\alpha}\\tilde{h}_{\\alpha\\dot{\\alpha}}(x^{ \\beta\\dot{\\beta}},\\tau^{\\beta\\dot{\\beta}}),\\]"}
-{"img_name": "13.png", "formula": "\\[\\begin{split}&\\lambda^{\\alpha}\\hat{f}^{*}\\omega_{i\\alpha}(z)= \\tilde{\\theta}^{\\dot{\\beta}}_{i}\\lambda^{\\alpha}\\left(V^{\\beta}_{\\alpha}h_{ \\beta\\dot{\\beta}}(x^{\\prime},\\tau^{\\prime})\\right),\\\\ &\\tilde{\\lambda}^{\\dot{\\alpha}}\\hat{f}^{*}\\tilde{\\omega}^{i}_{ \\dot{\\alpha}}(z)=\\theta^{i\\beta}\\tilde{\\lambda}^{\\dot{\\alpha}}\\left(\\tilde{V}^ {\\dot{\\beta}}_{\\dot{\\alpha}}\\tilde{h}_{\\beta\\dot{\\beta}}(x^{\\prime},\\tau^{ \\prime})\\right),\\end{split}\\]"}
-{"img_name": "14.png", "formula": "\\[A_{\\alpha\\dot{\\alpha}}=A_{\\alpha\\dot{\\alpha}}(x^{\\beta\\dot{\\beta}},\\tau^{ \\beta\\dot{\\beta}})\\]"}
-{"img_name": "15.png", "formula": "\\[D=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}D_{\\alpha\\dot{\\alpha}}\\]"}
-{"img_name": "16.png", "formula": "\\[D=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}\\partial_{\\alpha\\dot{\\alpha}}\\]"}
-{"img_name": "17.png", "formula": "\\[[v_{1}\\cdot D^{*},v_{2}\\cdot D^{*}]=0\\]"}
-{"img_name": "18.png", "formula": "\\[\\Phi_{A}=(\\omega_{i\\alpha},\\tilde{\\omega}^{i}_{\\dot{\\alpha}},A_{\\alpha\\dot{ \\alpha}})\\]"}
-{"img_name": "19.png", "formula": "\\[\\hat{f}:{\\cal F}^{6|4N}\\rightarrow{\\cal F}^{6|4N}\\]"}
-{"img_name": "20.png", "formula": "\\[\\sigma=(s,\\xi^{i},\\tilde{\\xi}_{j})\\in\\mathbb{C}^{1|2N}\\]"}
-{"img_name": "21.png", "formula": "\\[\\tau^{\\alpha\\dot{\\alpha}}(h_{\\alpha\\dot{\\alpha}}+\\tilde{h}_{\\alpha\\dot{\\alpha} })=0\\]"}
-{"img_name": "22.png", "formula": "\\[\\tau^{\\alpha\\dot{\\alpha}}\\rightarrow[V^{-1}]^{\\alpha}_{\\beta}[\\tilde{V}^{-1}]^{ \\dot{\\alpha}}_{\\dot{\\beta}}\\tau^{\\beta\\dot{\\beta}}\\]"}
-{"img_name": "23.png", "formula": "\\[\\tau^{\\beta\\dot{\\beta}}=\\sum_{i}\\theta^{i\\beta}\\tilde{\\theta}^{\\dot{\\beta}}_{i}\\]"}
-{"img_name": "24.png", "formula": "\\[\\theta^{i\\alpha}\\omega_{i\\alpha}+\\tilde{\\theta}^{i}_{\\dot{\\alpha}}\\tilde{ \\omega}^{\\dot{\\alpha}}_{i}=0\\]"}
-{"img_name": "25.png", "formula": "\\[\\tilde{T}^{i}=\\tilde{\\lambda}^{\\dot{\\alpha}}\\tilde{Q}^{i}_{\\dot{\\alpha}}\\]"}
-{"img_name": "26.png", "formula": "\\[\\tilde{T}^{i}=\\tilde{\\lambda}^{\\dot{\\alpha}}\\tilde{q}^{i}_{\\dot{\\alpha}}\\]"}
-{"img_name": "27.png", "formula": "\\[\\tilde{\\lambda}^{\\dot{\\alpha}}f^{*}A_{\\alpha\\dot{\\alpha}}=H^{-1}\\tilde{ \\lambda}^{\\dot{\\alpha}}\\partial_{\\alpha\\dot{\\alpha}}H\\]"}
-{"img_name": "28.png", "formula": "\\[\\tilde{q}^{i}=\\partial_{\\tilde{\\xi}_{i}}+i\\xi^{i}\\partial_{s}\\]"}
-{"img_name": "29.png", "formula": "\\[\\tilde{q}^{i}_{\\dot{\\alpha}}=\\frac{\\partial}{\\partial\\tilde{\\theta}^{\\dot{ \\alpha}}_{i}}+i\\theta^{i\\alpha}\\frac{\\partial}{\\partial x^{\\alpha\\dot{\\alpha}}}\\]"}
-{"img_name": "30.png", "formula": "\\[f\\lrcorner L(z)=\\pi_{1}\\circ f(z,\\lambda,\\tilde{\\lambda})\\ \\forall z\\in L\\]"}
-{"img_name": "31.png", "formula": "\\[q_{i\\alpha}=\\frac{\\partial}{\\partial\\theta^{i\\alpha}}+i\\tilde{\\theta}^{\\dot{ \\alpha}}_{i}\\frac{\\partial}{\\partial x^{\\alpha\\dot{\\alpha}}}\\]"}
-{"img_name": "32.png", "formula": "\\[q_{i}=\\partial_{\\xi^{i}}+i\\tilde{\\xi}_{i}\\partial_{s}\\]"}
-{"img_name": "33.png", "formula": "\\[v^{\\alpha\\dot{\\alpha}}=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}\\]"}
-{"img_name": "34.png", "formula": "\\[z^{A}=(x^{\\alpha\\dot{\\alpha}},\\theta^{i\\alpha},\\tilde{\\theta}^{\\dot{\\alpha}}_{ j})\\]"}

src/models/ocr_model/train/dataset/loader.py

@@ -1,50 +0,0 @@
-from PIL import Image
-from pathlib import Path
-import datasets
-import json
-
-DIR_URL = Path('absolute/path/to/dataset/directory')
-# e.g. DIR_URL = Path('/home/OleehyO/TeXTeller/src/models/ocr_model/train/dataset')
-
-
-class LatexFormulas(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = []
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features({
-                "image": datasets.Image(),
-                "latex_formula": datasets.Value("string")
-            })
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        dir_path = Path(dl_manager.download(str(DIR_URL)))
-        assert dir_path.is_dir()
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    'dir_path': dir_path,
-                }
-            )
-        ]
-
-    def _generate_examples(self, dir_path: Path):
-        images_path   = dir_path / 'images'
-        formulas_path = dir_path / 'formulas.jsonl'
-
-        img2formula = {}
-        with formulas_path.open('r', encoding='utf-8') as f:
-            for line in f:
-                single_json = json.loads(line)
-                img2formula[single_json['img_name']] = single_json['formula']
-
-        for img_path in images_path.iterdir():
-            if img_path.suffix not in ['.jpg', '.png']:
-                continue
-            yield str(img_path), {
-                "image": Image.open(img_path),
-                "latex_formula": img2formula[img_path.name]
-            }

src/models/ocr_model/train/training_args.py

@@ -1,38 +0,0 @@
-CONFIG = {
-    "seed": 42,                            # Random seed for reproducibility
-    "use_cpu": False,                      # Whether to use CPU (it's easier to debug with CPU when starting to test the code)
-    "learning_rate": 5e-5,                 # Learning rate
-    "num_train_epochs": 10,                # Total number of training epochs
-    "per_device_train_batch_size": 4,      # Batch size per GPU for training
-    "per_device_eval_batch_size": 8,       # Batch size per GPU for evaluation
-
-    "output_dir": "train_result",          # Output directory
-    "overwrite_output_dir": False,         # If the output directory exists, do not delete its content
-    "report_to": ["tensorboard"],          # Report logs to TensorBoard
-
-    "save_strategy": "steps",              # Strategy to save checkpoints
-    "save_steps": 500,                     # Interval of steps to save checkpoints, can be int or a float (0~1), when float it represents the ratio of total training steps (e.g., can set to 1.0 / 2000)
-    "save_total_limit": 5,                 # Maximum number of models to save. The oldest models will be deleted if this number is exceeded
-
-    "logging_strategy": "steps",           # Log every certain number of steps
-    "logging_steps": 500,                  # Number of steps between each log
-    "logging_nan_inf_filter": False,       # Record logs for loss=nan or inf
-
-    "optim": "adamw_torch",                # Optimizer
-    "lr_scheduler_type": "cosine",         # Learning rate scheduler
-    "warmup_ratio": 0.1,                   # Ratio of warmup steps in total training steps (e.g., for 1000 steps, the first 100 steps gradually increase lr from 0 to the set lr)
-    "max_grad_norm": 1.0,                  # For gradient clipping, ensure the norm of the gradients does not exceed 1.0 (default 1.0)
-    "fp16": False,                         # Whether to use 16-bit floating point for training (generally not recommended, as loss can easily explode)
-    "bf16": False,                         # Whether to use Brain Floating Point (bfloat16) for training (recommended if architecture supports it)
-    "gradient_accumulation_steps": 1,      # Gradient accumulation steps, consider this parameter to achieve large batch size effects when batch size cannot be large
-    "jit_mode_eval": False,                # Whether to use PyTorch jit trace during eval (can speed up the model, but the model must be static, otherwise will throw errors)
-    "torch_compile": False,                # Whether to use torch.compile to compile the model (for better training and inference performance)
-
-    "dataloader_pin_memory": True,         # Can speed up data transfer between CPU and GPU
-    "dataloader_num_workers": 1,           # Default is not to use multiprocessing for data loading, usually set to 4*number of GPUs used
-
-    "evaluation_strategy": "steps",        # Evaluation strategy, can be "steps" or "epoch"
-    "eval_steps": 500,                     # If evaluation_strategy="step"
-
-    "remove_unused_columns": False,        # Don't change this unless you really know what you are doing.
-}

src/models/utils/__init__.py

@@ -1 +0,0 @@
-from .mix_inference import mix_inference

src/rec_infer_from_crop_imgs.py

@@ -1,65 +0,0 @@
-import os
-import argparse
-import cv2 as cv
-from pathlib import Path
-from models.ocr_model.utils.to_katex import to_katex
-from models.ocr_model.utils.inference import inference as latex_inference
-from models.ocr_model.model.TexTeller import TexTeller
-
-
-if __name__ == '__main__':
-    os.chdir(Path(__file__).resolve().parent)
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '-img_dir', 
-        type=str, 
-        help='path to the input image',
-        default='./detect_results/subimages'
-    )
-    parser.add_argument(
-        '-output_dir', 
-        type=str, 
-        help='path to the output dir',
-        default='./rec_results'
-    )
-    parser.add_argument(
-        '--inference-mode', 
-        type=str,
-        default='cpu',
-        help='Inference mode, select one of cpu, cuda, or mps'
-    )
-    parser.add_argument(
-        '--num-beam', 
-        type=int,
-        default=1,
-        help='number of beam search for decoding'
-    )
-
-    args = parser.parse_args()
-
-    print('Loading model and tokenizer...')
-    latex_rec_model = TexTeller.from_pretrained()
-    tokenizer = TexTeller.get_tokenizer()
-    print('Model and tokenizer loaded.')
-
-    # Create the output directory if it doesn't exist
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    # Loop through all images in the input directory
-    for filename in os.listdir(args.img_dir):
-        img_path = os.path.join(args.img_dir, filename)
-        img = cv.imread(img_path)
-
-        if img is not None:
-            print(f'Inference for {filename}...')
-            res = latex_inference(latex_rec_model, tokenizer, [img], accelerator=args.inference_mode, num_beams=args.num_beam)
-            res = to_katex(res[0])
-
-            # Save the recognition result to a text file
-            output_file = os.path.join(args.output_dir, os.path.splitext(filename)[0] + '.txt')
-            with open(output_file, 'w') as f:
-                f.write(res)
-
-            print(f'Result saved to {output_file}')
-        else:
-            print(f"Warning: Could not read image {img_path}. Skipping...")

texteller/infer_det.py

@@ -1,85 +1,96 @@
-import os
-import argparse
-import glob
-import subprocess
-
-import onnxruntime
-from pathlib import Path
-
-from models.det_model.inference import PredictConfig, predict_image
-
-
-parser = argparse.ArgumentParser(description=__doc__)
-parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml",
-                    default="./models/det_model/model/infer_cfg.yml")
-parser.add_argument('--onnx_file', type=str, help="onnx model file path",
-                    default="./models/det_model/model/rtdetr_r50vd_6x_coco.onnx")
-parser.add_argument("--image_dir", type=str, default='./testImgs')
-parser.add_argument("--image_file", type=str)
-parser.add_argument("--imgsave_dir", type=str, default="./detect_results")
-parser.add_argument('--use_gpu', action='store_true', help='Whether to use GPU for inference', default=True)
-
-
-def get_test_images(infer_dir, infer_img):
-    """
-    Get image path list in TEST mode
-    """
-    assert infer_img is not None or infer_dir is not None, \
-        "--image_file or --image_dir should be set"
-    assert infer_img is None or os.path.isfile(infer_img), \
-            "{} is not a file".format(infer_img)
-    assert infer_dir is None or os.path.isdir(infer_dir), \
-            "{} is not a directory".format(infer_dir)
-
-    # infer_img has a higher priority
-    if infer_img and os.path.isfile(infer_img):
-        return [infer_img]
-
-    images = set()
-    infer_dir = os.path.abspath(infer_dir)
-    assert os.path.isdir(infer_dir), \
-        "infer_dir {} is not a directory".format(infer_dir)
-    exts = ['jpg', 'jpeg', 'png', 'bmp']
-    exts += [ext.upper() for ext in exts]
-    for ext in exts:
-        images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
-    images = list(images)
-
-    assert len(images) > 0, "no image found in {}".format(infer_dir)
-    print("Found {} inference images in total.".format(len(images)))
-
-    return images
-
-def download_file(url, filename):
-    print(f"Downloading {filename}...")
-    subprocess.run(["wget", "-q", "--show-progress", "-O", filename, url], check=True)
-    print("Download complete.")
-
-if __name__ == '__main__':
-    cur_path = os.getcwd()
-    script_dirpath = Path(__file__).resolve().parent
-    os.chdir(script_dirpath)
-
-    FLAGS = parser.parse_args()
-
-    if not os.path.exists(FLAGS.infer_cfg):
-        infer_cfg_url = "https://huggingface.co/TonyLee1256/texteller_det/resolve/main/infer_cfg.yml?download=true"
-        download_file(infer_cfg_url, FLAGS.infer_cfg)
-
-    if not os.path.exists(FLAGS.onnx_file):
-        onnx_file_url = "https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true"
-        download_file(onnx_file_url, FLAGS.onnx_file)
-    
-    # load image list
-    img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
-
-    if FLAGS.use_gpu:
-        predictor = onnxruntime.InferenceSession(FLAGS.onnx_file, providers=['CUDAExecutionProvider'])
-    else:
-        predictor = onnxruntime.InferenceSession(FLAGS.onnx_file, providers=['CPUExecutionProvider'])
-    # load infer config
-    infer_config = PredictConfig(FLAGS.infer_cfg)
-
-    predict_image(FLAGS.imgsave_dir, infer_config, predictor, img_list)
-
-    os.chdir(cur_path)
+import os
+import argparse
+import glob
+import subprocess
+
+import onnxruntime
+from pathlib import Path
+
+from models.det_model.inference import PredictConfig, predict_image
+
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    "--infer_cfg", type=str, help="infer_cfg.yml", default="./models/det_model/model/infer_cfg.yml"
+)
+parser.add_argument(
+    '--onnx_file',
+    type=str,
+    help="onnx model file path",
+    default="./models/det_model/model/rtdetr_r50vd_6x_coco.onnx",
+)
+parser.add_argument("--image_dir", type=str, default='./testImgs')
+parser.add_argument("--image_file", type=str)
+parser.add_argument("--imgsave_dir", type=str, default="./detect_results")
+parser.add_argument(
+    '--use_gpu', action='store_true', help='Whether to use GPU for inference', default=True
+)
+
+
+def get_test_images(infer_dir, infer_img):
+    """
+    Get image path list in TEST mode
+    """
+    assert (
+        infer_img is not None or infer_dir is not None
+    ), "--image_file or --image_dir should be set"
+    assert infer_img is None or os.path.isfile(infer_img), "{} is not a file".format(infer_img)
+    assert infer_dir is None or os.path.isdir(infer_dir), "{} is not a directory".format(infer_dir)
+
+    # infer_img has a higher priority
+    if infer_img and os.path.isfile(infer_img):
+        return [infer_img]
+
+    images = set()
+    infer_dir = os.path.abspath(infer_dir)
+    assert os.path.isdir(infer_dir), "infer_dir {} is not a directory".format(infer_dir)
+    exts = ['jpg', 'jpeg', 'png', 'bmp']
+    exts += [ext.upper() for ext in exts]
+    for ext in exts:
+        images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
+    images = list(images)
+
+    assert len(images) > 0, "no image found in {}".format(infer_dir)
+    print("Found {} inference images in total.".format(len(images)))
+
+    return images
+
+
+def download_file(url, filename):
+    print(f"Downloading {filename}...")
+    subprocess.run(["wget", "-q", "--show-progress", "-O", filename, url], check=True)
+    print("Download complete.")
+
+
+if __name__ == '__main__':
+    cur_path = os.getcwd()
+    script_dirpath = Path(__file__).resolve().parent
+    os.chdir(script_dirpath)
+
+    FLAGS = parser.parse_args()
+
+    if not os.path.exists(FLAGS.infer_cfg):
+        infer_cfg_url = "https://huggingface.co/TonyLee1256/texteller_det/resolve/main/infer_cfg.yml?download=true"
+        download_file(infer_cfg_url, FLAGS.infer_cfg)
+
+    if not os.path.exists(FLAGS.onnx_file):
+        onnx_file_url = "https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true"
+        download_file(onnx_file_url, FLAGS.onnx_file)
+
+    # load image list
+    img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
+
+    if FLAGS.use_gpu:
+        predictor = onnxruntime.InferenceSession(
+            FLAGS.onnx_file, providers=['CUDAExecutionProvider']
+        )
+    else:
+        predictor = onnxruntime.InferenceSession(
+            FLAGS.onnx_file, providers=['CPUExecutionProvider']
+        )
+    # load infer config
+    infer_config = PredictConfig(FLAGS.infer_cfg)
+
+    predict_image(FLAGS.imgsave_dir, infer_config, predictor, img_list)
+
+    os.chdir(cur_path)

texteller/inference.py

@@ -18,32 +18,20 @@ from models.det_model.inference import PredictConfig
 if __name__ == '__main__':
     os.chdir(Path(__file__).resolve().parent)
     parser = argparse.ArgumentParser()
+    parser.add_argument('-img', type=str, required=True, help='path to the input image')
     parser.add_argument(
-        '-img', 
-        type=str, 
-        required=True,
-        help='path to the input image'
-    )
-    parser.add_argument(
-        '--inference-mode', 
+        '--inference-mode',
         type=str,
         default='cpu',
-        help='Inference mode, select one of cpu, cuda, or mps'
+        help='Inference mode, select one of cpu, cuda, or mps',
     )
     parser.add_argument(
-        '--num-beam', 
-        type=int,
-        default=1,
-        help='number of beam search for decoding'
+        '--num-beam', type=int, default=1, help='number of beam search for decoding'
     )
-    parser.add_argument(
-        '-mix', 
-        action='store_true',
-        help='use mix mode'
-    )
-    
+    parser.add_argument('-mix', action='store_true', help='use mix mode')
+
     args = parser.parse_args()
-    
+
     # You can use your own checkpoint and tokenizer path.
     print('Loading model and tokenizer...')
     latex_rec_model = TexTeller.from_pretrained()
@@ -63,8 +51,8 @@ if __name__ == '__main__':
 
         use_gpu = args.inference_mode == 'cuda'
         SIZE_LIMIT = 20 * 1024 * 1024
-        det_model_dir =  "./models/thrid_party/paddleocr/checkpoints/det/default_model.onnx"
-        rec_model_dir =  "./models/thrid_party/paddleocr/checkpoints/rec/default_model.onnx"
+        det_model_dir = "./models/thrid_party/paddleocr/checkpoints/det/default_model.onnx"
+        rec_model_dir = "./models/thrid_party/paddleocr/checkpoints/rec/default_model.onnx"
         # The CPU inference of the detection model will be faster than the GPU inference (in onnxruntime)
         det_use_gpu = False
         rec_use_gpu = use_gpu and not (os.path.getsize(rec_model_dir) < SIZE_LIMIT)
@@ -78,8 +66,16 @@ if __name__ == '__main__':
         detector = predict_det.TextDetector(paddleocr_args)
         paddleocr_args.use_gpu = rec_use_gpu
         recognizer = predict_rec.TextRecognizer(paddleocr_args)
-        
+
         lang_ocr_models = [detector, recognizer]
         latex_rec_models = [latex_rec_model, tokenizer]
-        res = mix_inference(img_path, infer_config, latex_det_model, lang_ocr_models, latex_rec_models, args.inference_mode, args.num_beam)
+        res = mix_inference(
+            img_path,
+            infer_config,
+            latex_det_model,
+            lang_ocr_models,
+            latex_rec_models,
+            args.inference_mode,
+            args.num_beam,
+        )
         print(res)

texteller/models/det_model/Bbox.py

@@ -9,7 +9,7 @@ class Point:
     def __init__(self, x: int, y: int):
         self.x = int(x)
         self.y = int(y)
-    
+
     def __repr__(self) -> str:
         return f"Point(x={self.x}, y={self.y})"
 
@@ -28,30 +28,28 @@ class Bbox:
     @property
     def ul_point(self) -> Point:
         return self.p
-    
+
     @property
     def ur_point(self) -> Point:
         return Point(self.p.x + self.w, self.p.y)
-    
+
     @property
     def ll_point(self) -> Point:
         return Point(self.p.x, self.p.y + self.h)
-    
+
     @property
     def lr_point(self) -> Point:
         return Point(self.p.x + self.w, self.p.y + self.h)
-    
-    
+
     def same_row(self, other) -> bool:
-        if (
-            (self.p.y >= other.p.y and self.ll_point.y <= other.ll_point.y)
-            or (self.p.y <= other.p.y and self.ll_point.y >= other.ll_point.y)
+        if (self.p.y >= other.p.y and self.ll_point.y <= other.ll_point.y) or (
+            self.p.y <= other.p.y and self.ll_point.y >= other.ll_point.y
         ):
             return True
         if self.ll_point.y <= other.p.y or self.p.y >= other.ll_point.y:
             return False
         return 1.0 * abs(self.p.y - other.p.y) / max(self.h, other.h) < self.THREADHOLD
-    
+
     def __lt__(self, other) -> bool:
         '''
         from top to bottom, from left to right
@@ -60,7 +58,7 @@ class Bbox:
             return self.p.y < other.p.y
         else:
             return self.p.x < other.p.x
-    
+
     def __repr__(self) -> str:
         return f"Bbox(upper_left_point={self.p}, h={self.h}, w={self.w}), label={self.label}, confident={self.confidence}, content={self.content})"
 
@@ -76,16 +74,16 @@ def draw_bboxes(img: Image.Image, bboxes: List[Bbox], name="annotated_image.png"
         top = bbox.p.y
         right = bbox.p.x + bbox.w
         bottom = bbox.p.y + bbox.h
-        
+
         # Draw the rectangle on the image
         drawer.rectangle([left, top, right, bottom], outline="green", width=1)
-        
+
         # Optionally, add text label if it exists
         if bbox.label:
             drawer.text((left, top), bbox.label, fill="blue")
-        
+
         if bbox.content:
             drawer.text((left, bottom - 10), bbox.content[:10], fill="red")
 
     # Save the image with drawn rectangles
-    img.save(log_dir / name)
+    img.save(log_dir / name)

texteller/models/det_model/inference.py

@@ -12,10 +12,28 @@ from .Bbox import Bbox
 
 # Global dictionary
 SUPPORT_MODELS = {
-    'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
-    'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
-    'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet', 
-    'DETR'
+    'YOLO',
+    'PPYOLOE',
+    'RCNN',
+    'SSD',
+    'Face',
+    'FCOS',
+    'SOLOv2',
+    'TTFNet',
+    'S2ANet',
+    'JDE',
+    'FairMOT',
+    'DeepSORT',
+    'GFL',
+    'PicoDet',
+    'CenterNet',
+    'TOOD',
+    'RetinaNet',
+    'StrongBaseline',
+    'STGCN',
+    'YOLOX',
+    'HRNet',
+    'DETR',
 }
 
 
@@ -42,12 +60,12 @@ class PredictConfig(object):
         self.fpn_stride = yml_conf.get("fpn_stride", None)
 
         color_pool = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
-        self.colors = {label: color_pool[i % len(color_pool)] for i, label in enumerate(self.label_list)}
+        self.colors = {
+            label: color_pool[i % len(color_pool)] for i, label in enumerate(self.label_list)
+        }
 
         if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
-            print(
-                'The RCNN export model is used for ONNX and it only supports batch_size = 1'
-            )
+            print('The RCNN export model is used for ONNX and it only supports batch_size = 1')
         self.print_config()
 
     def check_model(self, yml_conf):
@@ -58,8 +76,7 @@ class PredictConfig(object):
         for support_model in SUPPORT_MODELS:
             if support_model in yml_conf['arch']:
                 return True
-        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
-            'arch'], SUPPORT_MODELS))
+        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf['arch'], SUPPORT_MODELS))
 
     def print_config(self):
         print('-----------  Model Configuration -----------')
@@ -77,8 +94,15 @@ def draw_bbox(image, outputs, infer_config):
             label = infer_config.label_list[int(cls_id)]
             color = infer_config.colors[label]
             cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), color, 2)
-            cv2.putText(image, "{}: {:.2f}".format(label, score),
-                        (int(xmin), int(ymin - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+            cv2.putText(
+                image,
+                "{}: {:.2f}".format(label, score),
+                (int(xmin), int(ymin - 5)),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                color,
+                2,
+            )
     return image
 
 
@@ -104,7 +128,7 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
 
         inputs = transforms(img_path)
         inputs_name = [var.name for var in predictor.get_inputs()]
-        inputs = {k: inputs[k][None, ] for k in inputs_name}
+        inputs = {k: inputs[k][None,] for k in inputs_name}
 
         # Start timing
         start_time = time.time()
@@ -119,7 +143,9 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
         else:
             total_time += inference_time
             num_images += 1
-        print(f"ONNXRuntime predict time for {os.path.basename(img_path)}: {inference_time:.4f} seconds")
+        print(
+            f"ONNXRuntime predict time for {os.path.basename(img_path)}: {inference_time:.4f} seconds"
+        )
 
         print("ONNXRuntime predict: ")
         if infer_config.arch in ["HRNet"]:
@@ -128,8 +154,7 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
             bboxes = np.array(outputs[0])
             for bbox in bboxes:
                 if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
-                    print(f"{int(bbox[0])} {bbox[1]} "
-                          f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
+                    print(f"{int(bbox[0])} {bbox[1]} " f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
 
         # Save the subimages (crop from the original image)
         subimg_counter = 1
@@ -137,7 +162,7 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
             cls_id, score, xmin, ymin, xmax, ymax = output
             if score > infer_config.draw_threshold:
                 label = infer_config.label_list[int(cls_id)]
-                subimg = img[int(max(ymin, 0)):int(ymax), int(max(xmin, 0)):int(xmax)]
+                subimg = img[int(max(ymin, 0)) : int(ymax), int(max(xmin, 0)) : int(xmax)]
                 if len(subimg) == 0:
                     continue
 
@@ -151,8 +176,14 @@ def predict_image(imgsave_dir, infer_config, predictor, img_list):
         for output in np.array(outputs[0]):
             cls_id, score, xmin, ymin, xmax, ymax = output
             if score > infer_config.draw_threshold:
-                cv2.rectangle(img_with_mask, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (255, 255, 255), -1) # 盖白
-        
+                cv2.rectangle(
+                    img_with_mask,
+                    (int(xmin), int(ymin)),
+                    (int(xmax), int(ymax)),
+                    (255, 255, 255),
+                    -1,
+                )  # 盖白
+
         img_with_bbox = draw_bbox(img, np.array(outputs[0]), infer_config)
 
         output_dir = imgsave_dir
@@ -178,7 +209,7 @@ def predict(img_path: str, predictor, infer_config) -> List[Bbox]:
     transforms = Compose(infer_config.preprocess_infos)
     inputs = transforms(img_path)
     inputs_name = [var.name for var in predictor.get_inputs()]
-    inputs = {k: inputs[k][None, ] for k in inputs_name}
+    inputs = {k: inputs[k][None,] for k in inputs_name}
 
     outputs = predictor.run(output_names=None, input_feed=inputs)[0]
     res = []

texteller/models/det_model/preprocess.py

@@ -15,10 +15,8 @@ def decode_image(img_path):
     im = cv2.imdecode(data, 1)  # BGR mode, but need RGB mode
     im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
     img_info = {
-        "im_shape": np.array(
-            im.shape[:2], dtype=np.float32),
-        "scale_factor": np.array(
-            [1., 1.], dtype=np.float32)
+        "im_shape": np.array(im.shape[:2], dtype=np.float32),
+        "scale_factor": np.array([1.0, 1.0], dtype=np.float32),
     }
     return im, img_info
 
@@ -51,16 +49,9 @@ class Resize(object):
         assert self.target_size[0] > 0 and self.target_size[1] > 0
         im_channel = im.shape[2]
         im_scale_y, im_scale_x = self.generate_scale(im)
-        im = cv2.resize(
-            im,
-            None,
-            None,
-            fx=im_scale_x,
-            fy=im_scale_y,
-            interpolation=self.interp)
+        im = cv2.resize(im, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=self.interp)
         im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
-        im_info['scale_factor'] = np.array(
-            [im_scale_y, im_scale_x]).astype('float32')
+        im_info['scale_factor'] = np.array([im_scale_y, im_scale_x]).astype('float32')
         return im, im_info
 
     def generate_scale(self, im):
@@ -134,7 +125,9 @@ class Permute(object):
         channel_first (bool): whether convert HWC to CHW
     """
 
-    def __init__(self, ):
+    def __init__(
+        self,
+    ):
         super(Permute, self).__init__()
 
     def __call__(self, im, im_info):
@@ -151,7 +144,7 @@ class Permute(object):
 
 
 class PadStride(object):
-    """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
+    """padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
     Args:
         stride (bool): model with FPN need image shape % stride == 0
     """
@@ -198,18 +191,16 @@ class LetterBoxResize(object):
         ratio_h = float(height) / shape[0]
         ratio_w = float(width) / shape[1]
         ratio = min(ratio_h, ratio_w)
-        new_shape = (round(shape[1] * ratio),
-                     round(shape[0] * ratio))  # [width, height]
+        new_shape = (round(shape[1] * ratio), round(shape[0] * ratio))  # [width, height]
         padw = (width - new_shape[0]) / 2
         padh = (height - new_shape[1]) / 2
         top, bottom = round(padh - 0.1), round(padh + 0.1)
         left, right = round(padw - 0.1), round(padw + 0.1)
 
-        img = cv2.resize(
-            img, new_shape, interpolation=cv2.INTER_AREA)  # resized, no border
+        img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA)  # resized, no border
         img = cv2.copyMakeBorder(
-            img, top, bottom, left, right, cv2.BORDER_CONSTANT,
-            value=color)  # padded rectangular
+            img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color
+        )  # padded rectangular
         return img, ratio, padw, padh
 
     def __call__(self, im, im_info):
@@ -302,12 +293,7 @@ def _get_3rd_point(a, b):
     return third_pt
 
 
-def get_affine_transform(center,
-                         input_size,
-                         rot,
-                         output_size,
-                         shift=(0., 0.),
-                         inv=False):
+def get_affine_transform(center, input_size, rot, output_size, shift=(0.0, 0.0), inv=False):
     """Get the affine transform matrix, given the center/scale/rot/output_size.
 
     Args:
@@ -337,8 +323,8 @@ def get_affine_transform(center,
     dst_h = output_size[1]
 
     rot_rad = np.pi * rot / 180
-    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
-    dst_dir = np.array([0., dst_w * -0.5])
+    src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)
+    dst_dir = np.array([0.0, dst_w * -0.5])
 
     src = np.zeros((3, 2), dtype=np.float32)
     src[0, :] = center + scale_tmp * shift
@@ -359,16 +345,9 @@ def get_affine_transform(center,
 
 
 class WarpAffine(object):
-    """Warp affine the image
-    """
+    """Warp affine the image"""
 
-    def __init__(self,
-                 keep_res=False,
-                 pad=31,
-                 input_h=512,
-                 input_w=512,
-                 scale=0.4,
-                 shift=0.1):
+    def __init__(self, keep_res=False, pad=31, input_h=512, input_w=512, scale=0.4, shift=0.1):
         self.keep_res = keep_res
         self.pad = pad
         self.input_h = input_h
@@ -398,12 +377,11 @@ class WarpAffine(object):
         else:
             s = max(h, w) * 1.0
             input_h, input_w = self.input_h, self.input_w
-            c = np.array([w / 2., h / 2.], dtype=np.float32)
+            c = np.array([w / 2.0, h / 2.0], dtype=np.float32)
 
         trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
         img = cv2.resize(img, (w, h))
-        inp = cv2.warpAffine(
-            img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
+        inp = cv2.warpAffine(img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
         return inp, im_info
 
 
@@ -432,13 +410,17 @@ def get_warp_matrix(theta, size_input, size_dst, size_target):
     matrix[0, 0] = np.cos(theta) * scale_x
     matrix[0, 1] = -np.sin(theta) * scale_x
     matrix[0, 2] = scale_x * (
-        -0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] *
-        np.sin(theta) + 0.5 * size_target[0])
+        -0.5 * size_input[0] * np.cos(theta)
+        + 0.5 * size_input[1] * np.sin(theta)
+        + 0.5 * size_target[0]
+    )
     matrix[1, 0] = np.sin(theta) * scale_y
     matrix[1, 1] = np.cos(theta) * scale_y
     matrix[1, 2] = scale_y * (
-        -0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] *
-        np.cos(theta) + 0.5 * size_target[1])
+        -0.5 * size_input[0] * np.sin(theta)
+        - 0.5 * size_input[1] * np.cos(theta)
+        + 0.5 * size_target[1]
+    )
     return matrix
 
 
@@ -462,22 +444,26 @@ class TopDownEvalAffine(object):
     def __call__(self, image, im_info):
         rot = 0
         imshape = im_info['im_shape'][::-1]
-        center = im_info['center'] if 'center' in im_info else imshape / 2.
+        center = im_info['center'] if 'center' in im_info else imshape / 2.0
         scale = im_info['scale'] if 'scale' in im_info else imshape
         if self.use_udp:
             trans = get_warp_matrix(
-                rot, center * 2.0,
-                [self.trainsize[0] - 1.0, self.trainsize[1] - 1.0], scale)
+                rot, center * 2.0, [self.trainsize[0] - 1.0, self.trainsize[1] - 1.0], scale
+            )
             image = cv2.warpAffine(
                 image,
-                trans, (int(self.trainsize[0]), int(self.trainsize[1])),
-                flags=cv2.INTER_LINEAR)
+                trans,
+                (int(self.trainsize[0]), int(self.trainsize[1])),
+                flags=cv2.INTER_LINEAR,
+            )
         else:
             trans = get_affine_transform(center, scale, rot, self.trainsize)
             image = cv2.warpAffine(
                 image,
-                trans, (int(self.trainsize[0]), int(self.trainsize[1])),
-                flags=cv2.INTER_LINEAR)
+                trans,
+                (int(self.trainsize[0]), int(self.trainsize[1])),
+                flags=cv2.INTER_LINEAR,
+            )
 
         return image, im_info
 

texteller/models/globals.py

@@ -1,6 +1,6 @@
 # Formula image(grayscale) mean and variance
 IMAGE_MEAN = 0.9545467
-IMAGE_STD  = 0.15394445
+IMAGE_STD = 0.15394445
 
 # Vocabulary size for TexTeller
 VOCAB_SIZE = 15000
@@ -20,4 +20,4 @@ MIN_RESIZE_RATIO = 0.75
 
 # Minimum height and width for input image for TexTeller
 MIN_HEIGHT = 12
-MIN_WIDTH  = 30
+MIN_WIDTH = 30

texteller/models/ocr_model/model/TexTeller.py

@@ -1,30 +1,24 @@
 from pathlib import Path
 
-from ...globals import (
-    VOCAB_SIZE,
-    FIXED_IMG_SIZE,
-    IMG_CHANNELS,
-    MAX_TOKEN_SIZE
-)
+from ...globals import VOCAB_SIZE, FIXED_IMG_SIZE, IMG_CHANNELS, MAX_TOKEN_SIZE
 
-from transformers import (
-    RobertaTokenizerFast,
-    VisionEncoderDecoderModel,
-    VisionEncoderDecoderConfig
-)
+from transformers import RobertaTokenizerFast, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
 
 
 class TexTeller(VisionEncoderDecoderModel):
     REPO_NAME = 'OleehyO/TexTeller'
+
     def __init__(self):
-        config = VisionEncoderDecoderConfig.from_pretrained(Path(__file__).resolve().parent / "config.json")
-        config.encoder.image_size              = FIXED_IMG_SIZE
-        config.encoder.num_channels            = IMG_CHANNELS
-        config.decoder.vocab_size              = VOCAB_SIZE
+        config = VisionEncoderDecoderConfig.from_pretrained(
+            Path(__file__).resolve().parent / "config.json"
+        )
+        config.encoder.image_size = FIXED_IMG_SIZE
+        config.encoder.num_channels = IMG_CHANNELS
+        config.decoder.vocab_size = VOCAB_SIZE
         config.decoder.max_position_embeddings = MAX_TOKEN_SIZE
 
         super().__init__(config=config)
-    
+
     @classmethod
     def from_pretrained(cls, model_path: str = None, use_onnx=False, onnx_provider=None):
         if model_path is None or model_path == 'default':
@@ -32,8 +26,12 @@ class TexTeller(VisionEncoderDecoderModel):
                 return VisionEncoderDecoderModel.from_pretrained(cls.REPO_NAME)
             else:
                 from optimum.onnxruntime import ORTModelForVision2Seq
+
                 use_gpu = True if onnx_provider == 'cuda' else False
-                return ORTModelForVision2Seq.from_pretrained(cls.REPO_NAME, provider="CUDAExecutionProvider" if use_gpu else "CPUExecutionProvider")
+                return ORTModelForVision2Seq.from_pretrained(
+                    cls.REPO_NAME,
+                    provider="CUDAExecutionProvider" if use_gpu else "CPUExecutionProvider",
+                )
         model_path = Path(model_path).resolve()
         return VisionEncoderDecoderModel.from_pretrained(str(model_path))
 

texteller/models/ocr_model/train/dataset/train/metadata.jsonl

@@ -0,0 +1,35 @@
+{"file_name": "0.png", "latex_formula": "\\[\\mathbb{C}^{4}\\stackrel{{\\pi_{1}}}{{\\longleftarrow}}\\mathcal{ F}\\stackrel{{\\pi_{2}}}{{\\rightarrow}}\\mathcal{PT},\\]"}
+{"file_name": "1.png", "latex_formula": "\\[W^{*}_{Z}(x_{1},x_{2})=W_{f\\lrcorner Z}(y_{1},y_{2})=\\mathcal{P}\\exp\\left( \\int_{\\gamma}A_{\\mu}dx^{\\mu}\\right).\\]"}
+{"file_name": "2.png", "latex_formula": "\\[G=W^{*}_{Z}(q,p)=\\tilde{H}H^{-1}\\]"}
+{"file_name": "3.png", "latex_formula": "\\[H=W^{*}_{Z}(p,x),\\ \\ \\tilde{H}=W^{*}_{Z}(q,x).\\]"}
+{"file_name": "4.png", "latex_formula": "\\[v\\cdot f^{*}A|_{x}=(f\\lrcorner Z)_{*}v\\cdot A|_{f\\lrcorner Z(x)},\\quad x\\in Z, \\ v\\in T_{x}Z.\\]"}
+{"file_name": "5.png", "latex_formula": "\\[(f\\lrcorner Z)_{*}v\\cdot A|_{f\\lrcorner Z(x)}=v^{\\alpha\\dot{\\alpha}}\\Big{(} \\frac{\\partial y^{\\beta\\dot{\\beta}}}{\\partial x^{\\alpha\\dot{\\alpha}}}A_{\\beta \\dot{\\beta}}\\Big{)}\\Big{|}_{f\\lrcorner Z(x)},\\ x\\in Z,\\ v\\in T_{x}Z,\\]"}
+{"file_name": "6.png", "latex_formula": "\\[\\{T_{i},T_{j}\\}=\\{\\tilde{T}^{i},\\tilde{T}^{j}\\}=0,\\ \\ \\{T_{i},\\tilde{T}^{j}\\}=2i \\delta^{j}_{i}D,\\]"}
+{"file_name": "7.png", "latex_formula": "\\[(\\partial_{s},q_{i},\\tilde{q}^{k})\\rightarrow(D,M^{j}_{i}T_{j},\\tilde{M}^{k}_ {l}\\tilde{T}^{l}),\\]"}
+{"file_name": "8.png", "latex_formula": "\\[M^{i}_{j}\\tilde{M}^{j}_{k}=\\delta^{i}_{k}.\\]"}
+{"file_name": "9.png", "latex_formula": "\\[Q_{i\\alpha}=q_{i\\alpha}+\\omega_{i\\alpha},\\ \\tilde{Q}^{i}_{\\dot{\\alpha}}=q^{i}_{ \\dot{\\alpha}}+\\tilde{\\omega}^{i}_{\\dot{\\alpha}},\\ D_{\\alpha\\dot{\\alpha}}= \\partial_{\\alpha\\dot{\\alpha}}+A_{\\alpha\\dot{\\alpha}}.\\]"}
+{"file_name": "10.png", "latex_formula": "\\[\\hat{f}(g,\\theta^{i\\alpha},\\tilde{\\theta}^{\\dot{\\alpha}}_{j})=(f(g),[V^{-1}]^ {\\alpha}_{\\beta}\\theta^{i\\beta},[\\tilde{V}^{-1}]^{\\dot{\\alpha}}_{\\dot{\\beta}} \\tilde{\\theta}^{\\dot{\\beta}}_{j}),\\ g\\in{\\cal G},\\]"}
+{"file_name": "11.png", "latex_formula": "\\[v^{\\beta\\dot{\\beta}}V^{\\alpha}_{\\beta}\\tilde{V}^{\\dot{\\alpha}}_{\\dot{\\beta}} =((f\\lrcorner L_{0})_{*}v)^{\\alpha\\dot{\\alpha}},\\]"}
+{"file_name": "12.png", "latex_formula": "\\[\\omega_{i\\alpha}=\\tilde{\\theta}^{\\dot{\\alpha}}_{i}h_{\\alpha\\dot{\\alpha}}(x^{ \\beta\\dot{\\beta}},\\tau^{\\beta\\dot{\\beta}}),\\ \\ \\tilde{\\omega}^{i}_{\\alpha}=\\theta^{i\\alpha}\\tilde{h}_{\\alpha\\dot{\\alpha}}(x^{ \\beta\\dot{\\beta}},\\tau^{\\beta\\dot{\\beta}}),\\]"}
+{"file_name": "13.png", "latex_formula": "\\[\\begin{split}&\\lambda^{\\alpha}\\hat{f}^{*}\\omega_{i\\alpha}(z)= \\tilde{\\theta}^{\\dot{\\beta}}_{i}\\lambda^{\\alpha}\\left(V^{\\beta}_{\\alpha}h_{ \\beta\\dot{\\beta}}(x^{\\prime},\\tau^{\\prime})\\right),\\\\ &\\tilde{\\lambda}^{\\dot{\\alpha}}\\hat{f}^{*}\\tilde{\\omega}^{i}_{ \\dot{\\alpha}}(z)=\\theta^{i\\beta}\\tilde{\\lambda}^{\\dot{\\alpha}}\\left(\\tilde{V}^ {\\dot{\\beta}}_{\\dot{\\alpha}}\\tilde{h}_{\\beta\\dot{\\beta}}(x^{\\prime},\\tau^{ \\prime})\\right),\\end{split}\\]"}
+{"file_name": "14.png", "latex_formula": "\\[A_{\\alpha\\dot{\\alpha}}=A_{\\alpha\\dot{\\alpha}}(x^{\\beta\\dot{\\beta}},\\tau^{ \\beta\\dot{\\beta}})\\]"}
+{"file_name": "15.png", "latex_formula": "\\[D=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}D_{\\alpha\\dot{\\alpha}}\\]"}
+{"file_name": "16.png", "latex_formula": "\\[D=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}\\partial_{\\alpha\\dot{\\alpha}}\\]"}
+{"file_name": "17.png", "latex_formula": "\\[[v_{1}\\cdot D^{*},v_{2}\\cdot D^{*}]=0\\]"}
+{"file_name": "18.png", "latex_formula": "\\[\\Phi_{A}=(\\omega_{i\\alpha},\\tilde{\\omega}^{i}_{\\dot{\\alpha}},A_{\\alpha\\dot{ \\alpha}})\\]"}
+{"file_name": "19.png", "latex_formula": "\\[\\hat{f}:{\\cal F}^{6|4N}\\rightarrow{\\cal F}^{6|4N}\\]"}
+{"file_name": "20.png", "latex_formula": "\\[\\sigma=(s,\\xi^{i},\\tilde{\\xi}_{j})\\in\\mathbb{C}^{1|2N}\\]"}
+{"file_name": "21.png", "latex_formula": "\\[\\tau^{\\alpha\\dot{\\alpha}}(h_{\\alpha\\dot{\\alpha}}+\\tilde{h}_{\\alpha\\dot{\\alpha} })=0\\]"}
+{"file_name": "22.png", "latex_formula": "\\[\\tau^{\\alpha\\dot{\\alpha}}\\rightarrow[V^{-1}]^{\\alpha}_{\\beta}[\\tilde{V}^{-1}]^{ \\dot{\\alpha}}_{\\dot{\\beta}}\\tau^{\\beta\\dot{\\beta}}\\]"}
+{"file_name": "23.png", "latex_formula": "\\[\\tau^{\\beta\\dot{\\beta}}=\\sum_{i}\\theta^{i\\beta}\\tilde{\\theta}^{\\dot{\\beta}}_{i}\\]"}
+{"file_name": "24.png", "latex_formula": "\\[\\theta^{i\\alpha}\\omega_{i\\alpha}+\\tilde{\\theta}^{i}_{\\dot{\\alpha}}\\tilde{ \\omega}^{\\dot{\\alpha}}_{i}=0\\]"}
+{"file_name": "25.png", "latex_formula": "\\[\\tilde{T}^{i}=\\tilde{\\lambda}^{\\dot{\\alpha}}\\tilde{Q}^{i}_{\\dot{\\alpha}}\\]"}
+{"file_name": "26.png", "latex_formula": "\\[\\tilde{T}^{i}=\\tilde{\\lambda}^{\\dot{\\alpha}}\\tilde{q}^{i}_{\\dot{\\alpha}}\\]"}
+{"file_name": "27.png", "latex_formula": "\\[\\tilde{\\lambda}^{\\dot{\\alpha}}f^{*}A_{\\alpha\\dot{\\alpha}}=H^{-1}\\tilde{ \\lambda}^{\\dot{\\alpha}}\\partial_{\\alpha\\dot{\\alpha}}H\\]"}
+{"file_name": "28.png", "latex_formula": "\\[\\tilde{q}^{i}=\\partial_{\\tilde{\\xi}_{i}}+i\\xi^{i}\\partial_{s}\\]"}
+{"file_name": "29.png", "latex_formula": "\\[\\tilde{q}^{i}_{\\dot{\\alpha}}=\\frac{\\partial}{\\partial\\tilde{\\theta}^{\\dot{ \\alpha}}_{i}}+i\\theta^{i\\alpha}\\frac{\\partial}{\\partial x^{\\alpha\\dot{\\alpha}}}\\]"}
+{"file_name": "30.png", "latex_formula": "\\[f\\lrcorner L(z)=\\pi_{1}\\circ f(z,\\lambda,\\tilde{\\lambda})\\ \\forall z\\in L\\]"}
+{"file_name": "31.png", "latex_formula": "\\[q_{i\\alpha}=\\frac{\\partial}{\\partial\\theta^{i\\alpha}}+i\\tilde{\\theta}^{\\dot{ \\alpha}}_{i}\\frac{\\partial}{\\partial x^{\\alpha\\dot{\\alpha}}}\\]"}
+{"file_name": "32.png", "latex_formula": "\\[q_{i}=\\partial_{\\xi^{i}}+i\\tilde{\\xi}_{i}\\partial_{s}\\]"}
+{"file_name": "33.png", "latex_formula": "\\[v^{\\alpha\\dot{\\alpha}}=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}\\]"}
+{"file_name": "34.png", "latex_formula": "\\[z^{A}=(x^{\\alpha\\dot{\\alpha}},\\theta^{i\\alpha},\\tilde{\\theta}^{\\dot{\\alpha}}_{ j})\\]"}

texteller/models/ocr_model/train/train.py

@@ -5,18 +5,24 @@ from pathlib import Path
 
 from datasets import load_dataset
 from transformers import (
-    Trainer, 
-    TrainingArguments, 
-    Seq2SeqTrainer, 
-    Seq2SeqTrainingArguments, 
-    GenerationConfig
+    Trainer,
+    TrainingArguments,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    GenerationConfig,
 )
 
 from .training_args import CONFIG
 from ..model.TexTeller import TexTeller
-from ..utils.functional import tokenize_fn, collate_fn, img_train_transform, img_inf_transform, filter_fn
+from ..utils.functional import (
+    tokenize_fn,
+    collate_fn,
+    img_train_transform,
+    img_inf_transform,
+    filter_fn,
+)
 from ..utils.metrics import bleu_metric
-from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT   
+from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT
 
 
 def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
@@ -24,11 +30,9 @@ def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokeniz
     trainer = Trainer(
         model,
         training_args,
-
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
-
-        tokenizer=tokenizer, 
+        tokenizer=tokenizer,
         data_collator=collate_fn_with_tokenizer,
     )
 
@@ -52,43 +56,44 @@ def evaluate(model, tokenizer, eval_dataset, collate_fn):
     trainer = Seq2SeqTrainer(
         model,
         seq2seq_config,
-
         eval_dataset=eval_dataset,
-        tokenizer=tokenizer, 
+        tokenizer=tokenizer,
         data_collator=collate_fn,
-        compute_metrics=partial(bleu_metric, tokenizer=tokenizer)
+        compute_metrics=partial(bleu_metric, tokenizer=tokenizer),
     )
 
     eval_res = trainer.evaluate()
     print(eval_res)
-    
+
 
 if __name__ == '__main__':
     script_dirpath = Path(__file__).resolve().parent
     os.chdir(script_dirpath)
 
-    dataset = load_dataset(str(Path('./dataset/loader.py').resolve()))['train']
-    dataset = dataset.filter(lambda x: x['image'].height > MIN_HEIGHT and x['image'].width > MIN_WIDTH)
+    # dataset = load_dataset(str(Path('./dataset/loader.py').resolve()))['train']
+    dataset = load_dataset("imagefolder", data_dir=str(script_dirpath / 'dataset'))['train']
+    dataset = dataset.filter(
+        lambda x: x['image'].height > MIN_HEIGHT and x['image'].width > MIN_WIDTH
+    )
     dataset = dataset.shuffle(seed=42)
     dataset = dataset.flatten_indices()
 
     tokenizer = TexTeller.get_tokenizer()
     # If you want use your own tokenizer, please modify the path to your tokenizer
-    #+tokenizer = TexTeller.get_tokenizer('/path/to/your/tokenizer')
+    # +tokenizer = TexTeller.get_tokenizer('/path/to/your/tokenizer')
     filter_fn_with_tokenizer = partial(filter_fn, tokenizer=tokenizer)
-    dataset = dataset.filter(
-        filter_fn_with_tokenizer,
-        num_proc=8
-    )
+    dataset = dataset.filter(filter_fn_with_tokenizer, num_proc=8)
 
     map_fn = partial(tokenize_fn, tokenizer=tokenizer)
-    tokenized_dataset = dataset.map(map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8)
+    tokenized_dataset = dataset.map(
+        map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8
+    )
 
     # Split dataset into train and eval, ratio 9:1
-    split_dataset = tokenized_dataset.train_test_split(test_size=0.1, seed=42)    
+    split_dataset = tokenized_dataset.train_test_split(test_size=0.1, seed=42)
     train_dataset, eval_dataset = split_dataset['train'], split_dataset['test']
     train_dataset = train_dataset.with_transform(img_train_transform)
-    eval_dataset  = eval_dataset.with_transform(img_inf_transform)
+    eval_dataset = eval_dataset.with_transform(img_inf_transform)
     collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
 
     # Train from scratch
@@ -96,14 +101,14 @@ if __name__ == '__main__':
     # or train from TexTeller pre-trained model: model = TexTeller.from_pretrained()
 
     # If you want to train from pre-trained model, please modify the path to your pre-trained checkpoint
-    #+e.g.
-    #+model = TexTeller.from_pretrained(
-    #+    '/path/to/your/model_checkpoint'
-    #+)
+    # +e.g.
+    # +model = TexTeller.from_pretrained(
+    # +    '/path/to/your/model_checkpoint'
+    # +)
 
     enable_train = True
     enable_evaluate = False
     if enable_train:
-        train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)  
+        train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)
     if enable_evaluate and len(eval_dataset) > 0:
         evaluate(model, tokenizer, eval_dataset, collate_fn_with_tokenizer)

texteller/models/ocr_model/train/training_args.py

@@ -0,0 +1,31 @@
+CONFIG = {
+    "seed": 42,  # Random seed for reproducibility
+    "use_cpu": False,  # Whether to use CPU (it's easier to debug with CPU when starting to test the code)
+    "learning_rate": 5e-5,  # Learning rate
+    "num_train_epochs": 10,  # Total number of training epochs
+    "per_device_train_batch_size": 4,  # Batch size per GPU for training
+    "per_device_eval_batch_size": 8,  # Batch size per GPU for evaluation
+    "output_dir": "train_result",  # Output directory
+    "overwrite_output_dir": False,  # If the output directory exists, do not delete its content
+    "report_to": ["tensorboard"],  # Report logs to TensorBoard
+    "save_strategy": "steps",  # Strategy to save checkpoints
+    "save_steps": 500,  # Interval of steps to save checkpoints, can be int or a float (0~1), when float it represents the ratio of total training steps (e.g., can set to 1.0 / 2000)
+    "save_total_limit": 5,  # Maximum number of models to save. The oldest models will be deleted if this number is exceeded
+    "logging_strategy": "steps",  # Log every certain number of steps
+    "logging_steps": 500,  # Number of steps between each log
+    "logging_nan_inf_filter": False,  # Record logs for loss=nan or inf
+    "optim": "adamw_torch",  # Optimizer
+    "lr_scheduler_type": "cosine",  # Learning rate scheduler
+    "warmup_ratio": 0.1,  # Ratio of warmup steps in total training steps (e.g., for 1000 steps, the first 100 steps gradually increase lr from 0 to the set lr)
+    "max_grad_norm": 1.0,  # For gradient clipping, ensure the norm of the gradients does not exceed 1.0 (default 1.0)
+    "fp16": False,  # Whether to use 16-bit floating point for training (generally not recommended, as loss can easily explode)
+    "bf16": False,  # Whether to use Brain Floating Point (bfloat16) for training (recommended if architecture supports it)
+    "gradient_accumulation_steps": 1,  # Gradient accumulation steps, consider this parameter to achieve large batch size effects when batch size cannot be large
+    "jit_mode_eval": False,  # Whether to use PyTorch jit trace during eval (can speed up the model, but the model must be static, otherwise will throw errors)
+    "torch_compile": False,  # Whether to use torch.compile to compile the model (for better training and inference performance)
+    "dataloader_pin_memory": True,  # Can speed up data transfer between CPU and GPU
+    "dataloader_num_workers": 1,  # Default is not to use multiprocessing for data loading, usually set to 4*number of GPUs used
+    "evaluation_strategy": "steps",  # Evaluation strategy, can be "steps" or "epoch"
+    "eval_steps": 500,  # If evaluation_strategy="step"
+    "remove_unused_columns": False,  # Don't change this unless you really know what you are doing.
+}

texteller/models/ocr_model/utils/functional.py

@@ -26,7 +26,7 @@ def collate_fn(samples: List[Dict[str, Any]], tokenizer=None) -> Dict[str, List[
     pixel_values = [dic.pop('pixel_values') for dic in samples]
 
     clm_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-    
+
     batch = clm_collator(samples)
     batch['pixel_values'] = pixel_values
     batch['decoder_input_ids'] = batch.pop('input_ids')
@@ -54,6 +54,7 @@ def img_inf_transform(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
 
 def filter_fn(sample, tokenizer=None) -> bool:
     return (
-        sample['image'].height > MIN_HEIGHT and sample['image'].width > MIN_WIDTH
+        sample['image'].height > MIN_HEIGHT
+        and sample['image'].width > MIN_WIDTH
         and len(tokenizer(sample['latex_formula'])['input_ids']) < MAX_TOKEN_SIZE - 10
     )

texteller/models/ocr_model/utils/helpers.py

@@ -12,7 +12,7 @@ def convert2rgb(image_paths: List[str]) -> List[np.ndarray]:
             continue
         if image.dtype == np.uint16:
             print(f'Converting {path} to 8-bit, image may be lossy.')
-            image = cv2.convertScaleAbs(image, alpha=(255.0/65535.0))
+            image = cv2.convertScaleAbs(image, alpha=(255.0 / 65535.0))
 
         channels = 1 if len(image.shape) == 2 else image.shape[2]
         if channels == 4: