[chore] exclude paddleocr directory from pre-commit hooks

texteller/models/ocr_model/model/TexTeller.py

@@ -0,0 +1,43 @@
+from pathlib import Path
+
+from ...globals import VOCAB_SIZE, FIXED_IMG_SIZE, IMG_CHANNELS, MAX_TOKEN_SIZE
+
+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.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':
+            if not use_onnx:
+                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",
+                )
+        model_path = Path(model_path).resolve()
+        return VisionEncoderDecoderModel.from_pretrained(str(model_path))
+
+    @classmethod
+    def get_tokenizer(cls, tokenizer_path: str = None) -> RobertaTokenizerFast:
+        if tokenizer_path is None or tokenizer_path == 'default':
+            return RobertaTokenizerFast.from_pretrained(cls.REPO_NAME)
+        tokenizer_path = Path(tokenizer_path).resolve()
+        return RobertaTokenizerFast.from_pretrained(str(tokenizer_path))

texteller/models/ocr_model/model/config.json

@@ -0,0 +1,168 @@
+{
+  "_name_or_path": "OleehyO/TexTeller",
+  "architectures": [
+    "VisionEncoderDecoderModel"
+  ],
+  "decoder": {
+    "_name_or_path": "",
+    "activation_dropout": 0.0,
+    "activation_function": "gelu",
+    "add_cross_attention": true,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "classifier_dropout": 0.0,
+    "cross_attention_hidden_size": 768,
+    "d_model": 1024,
+    "decoder_attention_heads": 16,
+    "decoder_ffn_dim": 4096,
+    "decoder_layerdrop": 0.0,
+    "decoder_layers": 12,
+    "decoder_start_token_id": 2,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.1,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "init_std": 0.02,
+    "is_decoder": true,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layernorm_embedding": true,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 1024,
+    "min_length": 0,
+    "model_type": "trocr",
+    "no_repeat_ngram_size": 0,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "scale_embedding": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": false,
+    "use_learned_position_embeddings": true,
+    "vocab_size": 15000
+  },
+  "encoder": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_probs_dropout_prob": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "encoder_stride": 16,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 448,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-12,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "vit",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 16,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "qkv_bias": false,
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "is_encoder_decoder": true,
+  "model_type": "vision-encoder-decoder",
+  "tie_word_embeddings": false,
+  "transformers_version": "4.41.2",
+  "use_cache": true
+}

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

@@ -0,0 +1,114 @@
+import os
+
+from functools import partial
+from pathlib import Path
+
+from datasets import load_dataset
+from transformers import (
+    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.metrics import bleu_metric
+from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT
+
+
+def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
+    training_args = TrainingArguments(**CONFIG)
+    trainer = Trainer(
+        model,
+        training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        tokenizer=tokenizer,
+        data_collator=collate_fn_with_tokenizer,
+    )
+
+    trainer.train(resume_from_checkpoint=None)
+
+
+def evaluate(model, tokenizer, eval_dataset, collate_fn):
+    eval_config = CONFIG.copy()
+    eval_config['predict_with_generate'] = True
+    generate_config = GenerationConfig(
+        max_new_tokens=MAX_TOKEN_SIZE,
+        num_beams=1,
+        do_sample=False,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+    )
+    eval_config['generation_config'] = generate_config
+    seq2seq_config = Seq2SeqTrainingArguments(**eval_config)
+
+    trainer = Seq2SeqTrainer(
+        model,
+        seq2seq_config,
+        eval_dataset=eval_dataset,
+        tokenizer=tokenizer,
+        data_collator=collate_fn,
+        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 = 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')
+    filter_fn_with_tokenizer = partial(filter_fn, tokenizer=tokenizer)
+    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
+    )
+
+    # Split dataset into train and eval, ratio 9:1
+    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)
+    collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
+
+    # Train from scratch
+    model = TexTeller()
+    # 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'
+    # +)
+
+    enable_train = True
+    enable_evaluate = False
+    if enable_train:
+        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

@@ -0,0 +1,60 @@
+import torch
+
+from transformers import DataCollatorForLanguageModeling
+from typing import List, Dict, Any
+from .transforms import train_transform, inference_transform
+from ...globals import MIN_HEIGHT, MIN_WIDTH, MAX_TOKEN_SIZE
+
+
+def left_move(x: torch.Tensor, pad_val):
+    assert len(x.shape) == 2, 'x should be 2-dimensional'
+    lefted_x = torch.ones_like(x)
+    lefted_x[:, :-1] = x[:, 1:]
+    lefted_x[:, -1] = pad_val
+    return lefted_x
+
+
+def tokenize_fn(samples: Dict[str, List[Any]], tokenizer=None) -> Dict[str, List[Any]]:
+    assert tokenizer is not None, 'tokenizer should not be None'
+    tokenized_formula = tokenizer(samples['latex_formula'], return_special_tokens_mask=True)
+    tokenized_formula['pixel_values'] = samples['image']
+    return tokenized_formula
+
+
+def collate_fn(samples: List[Dict[str, Any]], tokenizer=None) -> Dict[str, List[Any]]:
+    assert tokenizer is not None, 'tokenizer should not be None'
+    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')
+    batch['decoder_attention_mask'] = batch.pop('attention_mask')
+
+    # 左移labels和decoder_attention_mask
+    batch['labels'] = left_move(batch['labels'], -100)
+
+    # 把list of Image转成一个tensor with (B, C, H, W)
+    batch['pixel_values'] = torch.stack(batch['pixel_values'], dim=0)
+    return batch
+
+
+def img_train_transform(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
+    processed_img = train_transform(samples['pixel_values'])
+    samples['pixel_values'] = processed_img
+    return samples
+
+
+def img_inf_transform(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
+    processed_img = inference_transform(samples['pixel_values'])
+    samples['pixel_values'] = processed_img
+    return samples
+
+
+def filter_fn(sample, tokenizer=None) -> bool:
+    return (
+        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

@@ -0,0 +1,26 @@
+import cv2
+import numpy as np
+from typing import List
+
+
+def convert2rgb(image_paths: List[str]) -> List[np.ndarray]:
+    processed_images = []
+    for path in image_paths:
+        image = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        if image is None:
+            print(f"Image at {path} could not be read.")
+            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))
+
+        channels = 1 if len(image.shape) == 2 else image.shape[2]
+        if channels == 4:
+            image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGB)
+        elif channels == 1:
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+        elif channels == 3:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        processed_images.append(image)
+
+    return processed_images

texteller/models/ocr_model/utils/inference.py

@@ -0,0 +1,49 @@
+import torch
+import numpy as np
+
+from transformers import RobertaTokenizerFast, GenerationConfig
+from typing import List, Union
+
+from .transforms import inference_transform
+from .helpers import convert2rgb
+from ..model.TexTeller import TexTeller
+from ...globals import MAX_TOKEN_SIZE
+
+
+def inference(
+    model: TexTeller,
+    tokenizer: RobertaTokenizerFast,
+    imgs: Union[List[str], List[np.ndarray]],
+    accelerator: str = 'cpu',
+    num_beams: int = 1,
+    max_tokens=None,
+) -> List[str]:
+    if imgs == []:
+        return []
+    if hasattr(model, 'eval'):
+        # not onnx session, turn model.eval()
+        model.eval()
+    if isinstance(imgs[0], str):
+        imgs = convert2rgb(imgs)
+    else:  # already numpy array(rgb format)
+        assert isinstance(imgs[0], np.ndarray)
+        imgs = imgs
+    imgs = inference_transform(imgs)
+    pixel_values = torch.stack(imgs)
+
+    if hasattr(model, 'eval'):
+        # not onnx session, move weights to device
+        model = model.to(accelerator)
+    pixel_values = pixel_values.to(accelerator)
+
+    generate_config = GenerationConfig(
+        max_new_tokens=MAX_TOKEN_SIZE if max_tokens is None else max_tokens,
+        num_beams=num_beams,
+        do_sample=False,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+    )
+    pred = model.generate(pixel_values.to(model.device), generation_config=generate_config)
+    res = tokenizer.batch_decode(pred, skip_special_tokens=True)
+    return res

texteller/models/ocr_model/utils/metrics.py

@@ -0,0 +1,25 @@
+import evaluate
+import numpy as np
+import os
+
+from pathlib import Path
+from typing import Dict
+from transformers import EvalPrediction, RobertaTokenizer
+
+
+def bleu_metric(eval_preds: EvalPrediction, tokenizer: RobertaTokenizer) -> Dict:
+    cur_dir = Path(os.getcwd())
+    os.chdir(Path(__file__).resolve().parent)
+    metric = evaluate.load(
+        'google_bleu'
+    )  # Will download the metric from huggingface if not already downloaded
+    os.chdir(cur_dir)
+
+    logits, labels = eval_preds.predictions, eval_preds.label_ids
+    preds = logits
+
+    labels = np.where(labels == -100, 1, labels)
+
+    preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+    labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+    return metric.compute(predictions=preds, references=labels)

texteller/models/ocr_model/utils/ocr_aug.py

@@ -0,0 +1,152 @@
+from augraphy import *
+import random
+
+
+def ocr_augmentation_pipeline():
+    pre_phase = []
+
+    ink_phase = [
+        InkColorSwap(
+            ink_swap_color="random",
+            ink_swap_sequence_number_range=(5, 10),
+            ink_swap_min_width_range=(2, 3),
+            ink_swap_max_width_range=(100, 120),
+            ink_swap_min_height_range=(2, 3),
+            ink_swap_max_height_range=(100, 120),
+            ink_swap_min_area_range=(10, 20),
+            ink_swap_max_area_range=(400, 500),
+            # p=0.2
+            p=0.4,
+        ),
+        LinesDegradation(
+            line_roi=(0.0, 0.0, 1.0, 1.0),
+            line_gradient_range=(32, 255),
+            line_gradient_direction=(0, 2),
+            line_split_probability=(0.2, 0.4),
+            line_replacement_value=(250, 255),
+            line_min_length=(30, 40),
+            line_long_to_short_ratio=(5, 7),
+            line_replacement_probability=(0.4, 0.5),
+            line_replacement_thickness=(1, 3),
+            # p=0.2
+            p=0.4,
+        ),
+        #  ============================
+        OneOf(
+            [
+                Dithering(
+                    dither="floyd-steinberg",
+                    order=(3, 5),
+                ),
+                InkBleed(
+                    intensity_range=(0.1, 0.2),
+                    kernel_size=random.choice([(7, 7), (5, 5), (3, 3)]),
+                    severity=(0.4, 0.6),
+                ),
+            ],
+            # p=0.2
+            p=0.4,
+        ),
+        #  ============================
+        #  ============================
+        InkShifter(
+            text_shift_scale_range=(18, 27),
+            text_shift_factor_range=(1, 4),
+            text_fade_range=(0, 2),
+            blur_kernel_size=(5, 5),
+            blur_sigma=0,
+            noise_type="perlin",
+            # p=0.2
+            p=0.4,
+        ),
+        #  ============================
+    ]
+
+    paper_phase = [
+        NoiseTexturize(  # tested
+            sigma_range=(3, 10),
+            turbulence_range=(2, 5),
+            texture_width_range=(300, 500),
+            texture_height_range=(300, 500),
+            # p=0.2
+            p=0.4,
+        ),
+        BrightnessTexturize(  # tested
+            texturize_range=(0.9, 0.99),
+            deviation=0.03,
+            # p=0.2
+            p=0.4,
+        ),
+    ]
+
+    post_phase = [
+        ColorShift(  # tested
+            color_shift_offset_x_range=(3, 5),
+            color_shift_offset_y_range=(3, 5),
+            color_shift_iterations=(2, 3),
+            color_shift_brightness_range=(0.9, 1.1),
+            color_shift_gaussian_kernel_range=(3, 3),
+            # p=0.2
+            p=0.4,
+        ),
+        DirtyDrum(  # tested
+            line_width_range=(1, 6),
+            line_concentration=random.uniform(0.05, 0.15),
+            direction=random.randint(0, 2),
+            noise_intensity=random.uniform(0.6, 0.95),
+            noise_value=(64, 224),
+            ksize=random.choice([(3, 3), (5, 5), (7, 7)]),
+            sigmaX=0,
+            # p=0.2
+            p=0.4,
+        ),
+        # =====================================
+        OneOf(
+            [
+                LightingGradient(
+                    light_position=None,
+                    direction=None,
+                    max_brightness=255,
+                    min_brightness=0,
+                    mode="gaussian",
+                    linear_decay_rate=None,
+                    transparency=None,
+                ),
+                Brightness(
+                    brightness_range=(0.9, 1.1),
+                    min_brightness=0,
+                    min_brightness_value=(120, 150),
+                ),
+                Gamma(
+                    gamma_range=(0.9, 1.1),
+                ),
+            ],
+            # p=0.2
+            p=0.4,
+        ),
+        # =====================================
+        # =====================================
+        OneOf(
+            [
+                SubtleNoise(
+                    subtle_range=random.randint(5, 10),
+                ),
+                Jpeg(
+                    quality_range=(70, 95),
+                ),
+            ],
+            # p=0.2
+            p=0.4,
+        ),
+        # =====================================
+    ]
+
+    pipeline = AugraphyPipeline(
+        ink_phase=ink_phase,
+        paper_phase=paper_phase,
+        post_phase=post_phase,
+        pre_phase=pre_phase,
+        log=False,
+    )
+
+    return pipeline

texteller/models/ocr_model/utils/to_katex.py

@@ -0,0 +1,184 @@
+import re
+
+
+def change(input_str, old_inst, new_inst, old_surr_l, old_surr_r, new_surr_l, new_surr_r):
+    result = ""
+    i = 0
+    n = len(input_str)
+
+    while i < n:
+        if input_str[i : i + len(old_inst)] == old_inst:
+            # check if the old_inst is followed by old_surr_l
+            start = i + len(old_inst)
+        else:
+            result += input_str[i]
+            i += 1
+            continue
+
+        if start < n and input_str[start] == old_surr_l:
+            # found an old_inst followed by old_surr_l, now look for the matching old_surr_r
+            count = 1
+            j = start + 1
+            escaped = False
+            while j < n and count > 0:
+                if input_str[j] == '\\' and not escaped:
+                    escaped = True
+                    j += 1
+                    continue
+                if input_str[j] == old_surr_r and not escaped:
+                    count -= 1
+                    if count == 0:
+                        break
+                elif input_str[j] == old_surr_l and not escaped:
+                    count += 1
+                escaped = False
+                j += 1
+
+            if count == 0:
+                assert j < n
+                assert input_str[start] == old_surr_l
+                assert input_str[j] == old_surr_r
+                inner_content = input_str[start + 1 : j]
+                # Replace the content with new pattern
+                result += new_inst + new_surr_l + inner_content + new_surr_r
+                i = j + 1
+                continue
+            else:
+                assert count >= 1
+                assert j == n
+                print("Warning: unbalanced surrogate pair in input string")
+                result += new_inst + new_surr_l
+                i = start + 1
+                continue
+        else:
+            result += input_str[i:start]
+            i = start
+
+    if old_inst != new_inst and (old_inst + old_surr_l) in result:
+        return change(result, old_inst, new_inst, old_surr_l, old_surr_r, new_surr_l, new_surr_r)
+    else:
+        return result
+
+
+def find_substring_positions(string, substring):
+    positions = [match.start() for match in re.finditer(re.escape(substring), string)]
+    return positions
+
+
+def rm_dollar_surr(content):
+    pattern = re.compile(r'\\[a-zA-Z]+\$.*?\$|\$.*?\$')
+    matches = pattern.findall(content)
+
+    for match in matches:
+        if not re.match(r'\\[a-zA-Z]+', match):
+            new_match = match.strip('$')
+            content = content.replace(match, ' ' + new_match + ' ')
+
+    return content
+
+
+def change_all(input_str, old_inst, new_inst, old_surr_l, old_surr_r, new_surr_l, new_surr_r):
+    pos = find_substring_positions(input_str, old_inst + old_surr_l)
+    res = list(input_str)
+    for p in pos[::-1]:
+        res[p:] = list(
+            change(
+                ''.join(res[p:]), old_inst, new_inst, old_surr_l, old_surr_r, new_surr_l, new_surr_r
+            )
+        )
+    res = ''.join(res)
+    return res
+
+
+def to_katex(formula: str) -> str:
+    res = formula
+    # remove mbox surrounding
+    res = change_all(res, r'\mbox ', r' ', r'{', r'}', r'', r'')
+    res = change_all(res, r'\mbox', r' ', r'{', r'}', r'', r'')
+    # remove hbox surrounding
+    res = re.sub(r'\\hbox to ?-? ?\d+\.\d+(pt)?\{', r'\\hbox{', res)
+    res = change_all(res, r'\hbox', r' ', r'{', r'}', r'', r' ')
+    # remove raise surrounding
+    res = re.sub(r'\\raise ?-? ?\d+\.\d+(pt)?', r' ', res)
+    # remove makebox
+    res = re.sub(r'\\makebox ?\[\d+\.\d+(pt)?\]\{', r'\\makebox{', res)
+    res = change_all(res, r'\makebox', r' ', r'{', r'}', r'', r' ')
+    # remove vbox surrounding, scalebox surrounding
+    res = re.sub(r'\\raisebox\{-? ?\d+\.\d+(pt)?\}\{', r'\\raisebox{', res)
+    res = re.sub(r'\\scalebox\{-? ?\d+\.\d+(pt)?\}\{', r'\\scalebox{', res)
+    res = change_all(res, r'\scalebox', r' ', r'{', r'}', r'', r' ')
+    res = change_all(res, r'\raisebox', r' ', r'{', r'}', r'', r' ')
+    res = change_all(res, r'\vbox', r' ', r'{', r'}', r'', r' ')
+
+    origin_instructions = [
+        r'\Huge',
+        r'\huge',
+        r'\LARGE',
+        r'\Large',
+        r'\large',
+        r'\normalsize',
+        r'\small',
+        r'\footnotesize',
+        r'\tiny',
+    ]
+    for old_ins, new_ins in zip(origin_instructions, origin_instructions):
+        res = change_all(res, old_ins, new_ins, r'$', r'$', '{', '}')
+    res = change_all(res, r'\boldmath ', r'\bm', r'{', r'}', r'{', r'}')
+    res = change_all(res, r'\boldmath', r'\bm', r'{', r'}', r'{', r'}')
+    res = change_all(res, r'\boldmath ', r'\bm', r'$', r'$', r'{', r'}')
+    res = change_all(res, r'\boldmath', r'\bm', r'$', r'$', r'{', r'}')
+    res = change_all(res, r'\scriptsize', r'\scriptsize', r'$', r'$', r'{', r'}')
+    res = change_all(res, r'\emph', r'\textit', r'{', r'}', r'{', r'}')
+    res = change_all(res, r'\emph ', r'\textit', r'{', r'}', r'{', r'}')
+
+    origin_instructions = [
+        r'\left',
+        r'\middle',
+        r'\right',
+        r'\big',
+        r'\Big',
+        r'\bigg',
+        r'\Bigg',
+        r'\bigl',
+        r'\Bigl',
+        r'\biggl',
+        r'\Biggl',
+        r'\bigm',
+        r'\Bigm',
+        r'\biggm',
+        r'\Biggm',
+        r'\bigr',
+        r'\Bigr',
+        r'\biggr',
+        r'\Biggr',
+    ]
+    for origin_ins in origin_instructions:
+        res = change_all(res, origin_ins, origin_ins, r'{', r'}', r'', r'')
+
+    res = re.sub(r'\\\[(.*?)\\\]', r'\1\\newline', res)
+
+    if res.endswith(r'\newline'):
+        res = res[:-8]
+
+    # remove multiple spaces
+    res = re.sub(r'(\\,){1,}', ' ', res)
+    res = re.sub(r'(\\!){1,}', ' ', res)
+    res = re.sub(r'(\\;){1,}', ' ', res)
+    res = re.sub(r'(\\:){1,}', ' ', res)
+    res = re.sub(r'\\vspace\{.*?}', '', res)
+
+    # merge consecutive text
+    def merge_texts(match):
+        texts = match.group(0)
+        merged_content = ''.join(re.findall(r'\\text\{([^}]*)\}', texts))
+        return f'\\text{{{merged_content}}}'
+
+    res = re.sub(r'(\\text\{[^}]*\}\s*){2,}', merge_texts, res)
+
+    res = res.replace(r'\bf ', '')
+    res = rm_dollar_surr(res)
+
+    # remove extra spaces (keeping only one)
+    res = re.sub(r' +', ' ', res)
+
+    return res.strip()

texteller/models/ocr_model/utils/transforms.py

@@ -0,0 +1,177 @@
+import torch
+import random
+import numpy as np
+import cv2
+
+from torchvision.transforms import v2
+from typing import List, Union
+from PIL import Image
+from collections import Counter
+
+from ...globals import (
+    IMG_CHANNELS,
+    FIXED_IMG_SIZE,
+    IMAGE_MEAN,
+    IMAGE_STD,
+    MAX_RESIZE_RATIO,
+    MIN_RESIZE_RATIO,
+)
+from .ocr_aug import ocr_augmentation_pipeline
+
+# train_pipeline = default_augraphy_pipeline(scan_only=True)
+train_pipeline = ocr_augmentation_pipeline()
+
+general_transform_pipeline = v2.Compose(
+    [
+        v2.ToImage(),
+        v2.ToDtype(torch.uint8, scale=True),  # optional, most input are already uint8 at this point
+        v2.Grayscale(),
+        v2.Resize(
+            size=FIXED_IMG_SIZE - 1,
+            interpolation=v2.InterpolationMode.BICUBIC,
+            max_size=FIXED_IMG_SIZE,
+            antialias=True,
+        ),
+        v2.ToDtype(torch.float32, scale=True),  # Normalize expects float input
+        v2.Normalize(mean=[IMAGE_MEAN], std=[IMAGE_STD]),
+        # v2.ToPILImage()
+    ]
+)
+
+
+def trim_white_border(image: np.ndarray):
+    if len(image.shape) != 3 or image.shape[2] != 3:
+        raise ValueError("Image is not in RGB format or channel is not in third dimension")
+
+    if image.dtype != np.uint8:
+        raise ValueError(f"Image should stored in uint8")
+
+    corners = [tuple(image[0, 0]), tuple(image[0, -1]), tuple(image[-1, 0]), tuple(image[-1, -1])]
+    bg_color = Counter(corners).most_common(1)[0][0]
+    bg_color_np = np.array(bg_color, dtype=np.uint8)
+
+    h, w = image.shape[:2]
+    bg = np.full((h, w, 3), bg_color_np, dtype=np.uint8)
+
+    diff = cv2.absdiff(image, bg)
+    mask = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
+
+    threshold = 15
+    _, diff = cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)
+
+    x, y, w, h = cv2.boundingRect(diff)
+
+    trimmed_image = image[y : y + h, x : x + w]
+
+    return trimmed_image
+
+
+def add_white_border(image: np.ndarray, max_size: int) -> np.ndarray:
+    randi = [random.randint(0, max_size) for _ in range(4)]
+    pad_height_size = randi[1] + randi[3]
+    pad_width_size = randi[0] + randi[2]
+    if pad_height_size + image.shape[0] < 30:
+        compensate_height = int((30 - (pad_height_size + image.shape[0])) * 0.5) + 1
+        randi[1] += compensate_height
+        randi[3] += compensate_height
+    if pad_width_size + image.shape[1] < 30:
+        compensate_width = int((30 - (pad_width_size + image.shape[1])) * 0.5) + 1
+        randi[0] += compensate_width
+        randi[2] += compensate_width
+    return v2.functional.pad(
+        torch.from_numpy(image).permute(2, 0, 1),
+        padding=randi,
+        padding_mode='constant',
+        fill=(255, 255, 255),
+    )
+
+
+def padding(images: List[torch.Tensor], required_size: int) -> List[torch.Tensor]:
+    images = [
+        v2.functional.pad(
+            img, padding=[0, 0, required_size - img.shape[2], required_size - img.shape[1]]
+        )
+        for img in images
+    ]
+    return images
+
+
+def random_resize(images: List[np.ndarray], minr: float, maxr: float) -> List[np.ndarray]:
+    if len(images[0].shape) != 3 or images[0].shape[2] != 3:
+        raise ValueError("Image is not in RGB format or channel is not in third dimension")
+
+    ratios = [random.uniform(minr, maxr) for _ in range(len(images))]
+    return [
+        cv2.resize(
+            img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LANCZOS4
+        )  # 抗锯齿
+        for img, r in zip(images, ratios)
+    ]
+
+
+def rotate(image: np.ndarray, min_angle: int, max_angle: int) -> np.ndarray:
+    # Get the center of the image to define the point of rotation
+    image_center = tuple(np.array(image.shape[1::-1]) / 2)
+
+    # Generate a random angle within the specified range
+    angle = random.randint(min_angle, max_angle)
+
+    # Get the rotation matrix for rotating the image around its center
+    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
+
+    # Determine the size of the rotated image
+    cos = np.abs(rotation_mat[0, 0])
+    sin = np.abs(rotation_mat[0, 1])
+    new_width = int((image.shape[0] * sin) + (image.shape[1] * cos))
+    new_height = int((image.shape[0] * cos) + (image.shape[1] * sin))
+
+    # Adjust the rotation matrix to take into account translation
+    rotation_mat[0, 2] += (new_width / 2) - image_center[0]
+    rotation_mat[1, 2] += (new_height / 2) - image_center[1]
+
+    # Rotate the image with the specified border color (white in this case)
+    rotated_image = cv2.warpAffine(
+        image, rotation_mat, (new_width, new_height), borderValue=(255, 255, 255)
+    )
+
+    return rotated_image
+
+
+def ocr_aug(image: np.ndarray) -> np.ndarray:
+    if random.random() < 0.2:
+        image = rotate(image, -5, 5)
+    image = add_white_border(image, max_size=25).permute(1, 2, 0).numpy()
+    image = train_pipeline(image)
+    return image
+
+
+def train_transform(images: List[Image.Image]) -> List[torch.Tensor]:
+    assert IMG_CHANNELS == 1, "Only support grayscale images for now"
+
+    images = [np.array(img.convert('RGB')) for img in images]
+    # random resize first
+    images = random_resize(images, MIN_RESIZE_RATIO, MAX_RESIZE_RATIO)
+    images = [trim_white_border(image) for image in images]
+
+    # OCR augmentation
+    images = [ocr_aug(image) for image in images]
+
+    # general transform pipeline
+    images = [general_transform_pipeline(image) for image in images]
+    # padding to fixed size
+    images = padding(images, FIXED_IMG_SIZE)
+    return images
+
+
+def inference_transform(images: List[Union[np.ndarray, Image.Image]]) -> List[torch.Tensor]:
+    assert IMG_CHANNELS == 1, "Only support grayscale images for now"
+    images = [
+        np.array(img.convert('RGB')) if isinstance(img, Image.Image) else img for img in images
+    ]
+    images = [trim_white_border(image) for image in images]
+    # general transform pipeline
+    images = [general_transform_pipeline(image) for image in images]  # imgs: List[PIL.Image.Image]
+    # padding to fixed size
+    images = padding(images, FIXED_IMG_SIZE)
+
+    return images