72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
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from functools import partial
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import yaml
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from datasets import load_dataset
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from transformers import (
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Trainer,
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TrainingArguments,
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)
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from texteller import load_model, load_tokenizer
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from texteller.constants import MIN_HEIGHT, MIN_WIDTH
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from examples.train_texteller.utils import (
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collate_fn,
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filter_fn,
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img_inf_transform,
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img_train_transform,
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tokenize_fn,
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)
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def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
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training_args = TrainingArguments(**training_config)
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trainer = Trainer(
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model,
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training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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tokenizer=tokenizer,
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data_collator=collate_fn_with_tokenizer,
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)
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trainer.train(resume_from_checkpoint=None)
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if __name__ == "__main__":
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dataset = load_dataset("imagefolder", data_dir="dataset")["train"]
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dataset = dataset.filter(
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lambda x: x["image"].height > MIN_HEIGHT and x["image"].width > MIN_WIDTH
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)
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dataset = dataset.shuffle(seed=42)
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dataset = dataset.flatten_indices()
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tokenizer = load_tokenizer()
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# If you want use your own tokenizer, please modify the path to your tokenizer
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# tokenizer = load_tokenizer("/path/to/your/tokenizer")
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filter_fn_with_tokenizer = partial(filter_fn, tokenizer=tokenizer)
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dataset = dataset.filter(filter_fn_with_tokenizer, num_proc=8)
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map_fn = partial(tokenize_fn, tokenizer=tokenizer)
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tokenized_dataset = dataset.map(
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map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8
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)
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# Split dataset into train and eval, ratio 9:1
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split_dataset = tokenized_dataset.train_test_split(test_size=0.1, seed=42)
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train_dataset, eval_dataset = split_dataset["train"], split_dataset["test"]
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train_dataset = train_dataset.with_transform(img_train_transform)
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eval_dataset = eval_dataset.with_transform(img_inf_transform)
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collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
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# Train from scratch
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model = load_model()
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# If you want to train from pre-trained model, please modify the path to your pre-trained checkpoint
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# model = load_model("/path/to/your/model_checkpoint")
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enable_train = True
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training_config = yaml.safe_load(open("train_config.yaml"))
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if enable_train:
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train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)
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