116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
from transformers import VisionEncoderDecoderConfig, DonutProcessor, VisionEncoderDecoderModel
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import torch
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from torch.utils.data import DataLoader
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from pytorch_lightning.loggers import WandbLogger
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from pytorch_lightning.callbacks import ModelCheckpoint
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import pytorch_lightning as pl
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import os
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from huggingface_hub import login
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import argparse
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from sconf import Config
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from utils.checkpoint import CustomCheckpointIO
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from utils.donut_dataset_stream import DonutDataset
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from utils.donut_model_pl import DonutModelPLModule
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from utils.callbacks import PushToHubCallback
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import warnings
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from datasets import load_dataset
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def main(config, hug_token):
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config_vision = VisionEncoderDecoderConfig.from_pretrained(
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config.pretrained_model_path)
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config_vision.encoder.image_size = config.image_size
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config_vision.decoder.max_length = config.max_length
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processor = DonutProcessor.from_pretrained(config.start_model_path)
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model = VisionEncoderDecoderModel.from_pretrained(
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config.pretrained_model_path, config=config_vision)
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processor.image_processor.size = config.image_size[::-1]
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processor.image_processor.do_align_long_axis = False
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added_tokens = []
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train_dataset = DonutDataset(
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config.dataset_path,
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processor=processor,
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model=model,
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max_length=config.max_length,
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split="train",
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task_start_token="<s_cord-v2>",
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prompt_end_token="<s_cord-v2>",
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added_tokens=added_tokens,
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sort_json_key=False, # cord dataset is preprocessed, so no need for this
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)
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val_dataset = DonutDataset(
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config.dataset_path,
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processor=processor,
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model=model,
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max_length=config.max_length,
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split="validation",
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task_start_token="<s_cord-v2>",
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prompt_end_token="<s_cord-v2>",
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added_tokens=added_tokens,
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sort_json_key=False, # cord dataset is preprocessed, so no need for this
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)
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model.config.pad_token_id = processor.tokenizer.pad_token_id
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model.config.decoder_start_token_id = processor.tokenizer.convert_tokens_to_ids(['<s_cord-v2>'])[0]
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train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=4)
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val_dataloader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=4)
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login(hug_token, True)
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model_module = DonutModelPLModule(config.train_config.toDict(), processor, model, max_length=config.max_length, train_dataloader=train_dataloader, val_dataloader=val_dataloader)
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wandb_logger = WandbLogger(project="Donut", name=config.wandb_test_name)
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checkpoint_callback = ModelCheckpoint(
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monitor="val_metric",
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dirpath=config.checkpoint_path,
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filename="artifacts",
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save_top_k=1,
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save_last=False,
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mode="min",
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)
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custom_ckpt = CustomCheckpointIO()
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trainer = pl.Trainer(
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accelerator="gpu" if torch.cuda.is_available() else 'cpu', # change to gpu
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devices=1,
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max_epochs=config.train_config.max_epochs,
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val_check_interval=config.train_config.val_check_interval,
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check_val_every_n_epoch=config.train_config.check_val_every_n_epoch,
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gradient_clip_val=config.train_config.gradient_clip_val,
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precision=16, # we'll use mixed precision
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plugins=custom_ckpt,
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num_sanity_val_steps=0,
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logger=wandb_logger,
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callbacks=[PushToHubCallback(output_model_path=config.output_model_path), checkpoint_callback],
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)
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trainer.fit(model_module)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str, required=True)
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args, left_argv = parser.parse_known_args()
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config = Config(args.config)
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config.argv_update(left_argv)
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hug_token = os.environ.get("HUG_TOKEN", None)
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if not torch.cuda.is_available():
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warnings.warn("You don't have cuda available, training might be taking long time or impossible")
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if not hug_token:
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raise Exception("You need to set up HUG_TOKEN in enviroments to push output model to hub")
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main(config, hug_token)
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