2022-12-14 15:53:27 +01:00
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#!/usr/bin/env python
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# coding: utf-8
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from transformers import VisionEncoderDecoderConfig, DonutProcessor, VisionEncoderDecoderModel
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from datasets import load_dataset
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import json
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import random
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from typing import Any, List, Tuple
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import torch
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from torch.utils.data import Dataset, DataLoader
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import re
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from nltk import edit_distance
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import numpy as np
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from pytorch_lightning.loggers import WandbLogger
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from pytorch_lightning.callbacks import Callback, 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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from pytorch_lightning.plugins import CheckpointIO
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DATASET_PATH = "Zombely/pl-text-images-5000-whole"
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PRETRAINED_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned"
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START_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned"
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OUTPUT_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
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LOGGING_PATH = "plwiki-proto-ft-second-iter"
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CHECKPOINT_PATH = "./checkpoint"
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train_config = {
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"max_epochs":30,
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"val_check_interval":0.5, # how many times we want to validate during an epoch
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"check_val_every_n_epoch":1,
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"gradient_clip_val":1.0,
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"num_training_samples_per_epoch": 800,
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"lr":3e-5,
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"train_batch_sizes": [8],
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"val_batch_sizes": [1],
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"seed":2022,
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"num_nodes": 1,
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"warmup_steps": 300, # 800/8*30/10, 10%
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"result_path": "./result",
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"verbose": True,
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}
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dataset = load_dataset(DATASET_PATH)
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max_length = 768
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image_size = [1920, 2560]
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config = VisionEncoderDecoderConfig.from_pretrained(PRETRAINED_MODEL_PATH)
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config.encoder.image_size = image_size # (height, width)
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config.decoder.max_length = max_length
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processor = DonutProcessor.from_pretrained(START_MODEL_PATH)
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model = VisionEncoderDecoderModel.from_pretrained(PRETRAINED_MODEL_PATH, config=config)
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added_tokens = []
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class CustomCheckpointIO(CheckpointIO):
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def save_checkpoint(self, checkpoint, path, storage_options=None):
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del checkpoint["state_dict"]
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torch.save(checkpoint, path)
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def load_checkpoint(self, path, storage_options=None):
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checkpoint = torch.load(path + "artifacts.ckpt")
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state_dict = torch.load(path + "pytorch_model.bin")
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checkpoint["state_dict"] = {"model." + key: value for key, value in state_dict.items()}
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return checkpoint
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def remove_checkpoint(self, path) -> None:
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return super().remove_checkpoint(path)
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class DonutDataset(Dataset):
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"""
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DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets)
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Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt),
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and it will be converted into input_tensor(vectorized image) and input_ids(tokenized string).
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Args:
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dataset_name_or_path: name of dataset (available at huggingface.co/datasets) or the path containing image files and metadata.jsonl
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max_length: the max number of tokens for the target sequences
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split: whether to load "train", "validation" or "test" split
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ignore_id: ignore_index for torch.nn.CrossEntropyLoss
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task_start_token: the special token to be fed to the decoder to conduct the target task
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prompt_end_token: the special token at the end of the sequences
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sort_json_key: whether or not to sort the JSON keys
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"""
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def __init__(
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self,
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dataset_name_or_path: str,
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max_length: int,
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split: str = "train",
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ignore_id: int = -100,
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task_start_token: str = "<s>",
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prompt_end_token: str = None,
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sort_json_key: bool = True,
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):
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super().__init__()
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self.max_length = max_length
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self.split = split
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self.ignore_id = ignore_id
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self.task_start_token = task_start_token
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self.prompt_end_token = prompt_end_token if prompt_end_token else task_start_token
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self.sort_json_key = sort_json_key
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self.dataset = load_dataset(dataset_name_or_path, split=self.split)
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self.dataset_length = len(self.dataset)
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self.gt_token_sequences = []
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for sample in self.dataset:
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ground_truth = json.loads(sample["ground_truth"])
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if "gt_parses" in ground_truth: # when multiple ground truths are available, e.g., docvqa
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assert isinstance(ground_truth["gt_parses"], list)
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gt_jsons = ground_truth["gt_parses"]
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else:
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assert "gt_parse" in ground_truth and isinstance(ground_truth["gt_parse"], dict)
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gt_jsons = [ground_truth["gt_parse"]]
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self.gt_token_sequences.append(
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[
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self.json2token(
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gt_json,
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update_special_tokens_for_json_key=self.split == "train",
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sort_json_key=self.sort_json_key,
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)
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+ processor.tokenizer.eos_token
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for gt_json in gt_jsons # load json from list of json
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]
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)
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self.add_tokens([self.task_start_token, self.prompt_end_token])
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self.prompt_end_token_id = processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)
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def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):
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"""
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Convert an ordered JSON object into a token sequence
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"""
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if type(obj) == dict:
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if len(obj) == 1 and "text_sequence" in obj:
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return obj["text_sequence"]
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else:
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output = ""
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if sort_json_key:
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keys = sorted(obj.keys(), reverse=True)
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else:
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keys = obj.keys()
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for k in keys:
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if update_special_tokens_for_json_key:
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self.add_tokens([fr"<s_{k}>", fr"</s_{k}>"])
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output += (
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fr"<s_{k}>"
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+ self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)
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+ fr"</s_{k}>"
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)
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return output
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elif type(obj) == list:
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return r"<sep/>".join(
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[self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]
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)
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else:
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obj = str(obj)
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if f"<{obj}/>" in added_tokens:
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obj = f"<{obj}/>" # for categorical special tokens
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return obj
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def add_tokens(self, list_of_tokens: List[str]):
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"""
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Add special tokens to tokenizer and resize the token embeddings of the decoder
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"""
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newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)
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if newly_added_num > 0:
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model.decoder.resize_token_embeddings(len(processor.tokenizer))
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added_tokens.extend(list_of_tokens)
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def __len__(self) -> int:
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return self.dataset_length
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def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Load image from image_path of given dataset_path and convert into input_tensor and labels
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Convert gt data into input_ids (tokenized string)
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Returns:
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input_tensor : preprocessed image
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input_ids : tokenized gt_data
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labels : masked labels (model doesn't need to predict prompt and pad token)
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"""
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sample = self.dataset[idx]
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# inputs
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pixel_values = processor(sample["image"], random_padding=self.split == "train", return_tensors="pt").pixel_values
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pixel_values = pixel_values.squeeze()
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# targets
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target_sequence = random.choice(self.gt_token_sequences[idx]) # can be more than one, e.g., DocVQA Task 1
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input_ids = processor.tokenizer(
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target_sequence,
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add_special_tokens=False,
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max_length=self.max_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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)["input_ids"].squeeze(0)
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labels = input_ids.clone()
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labels[labels == processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token
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# labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id # model doesn't need to predict prompt (for VQA)
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return pixel_values, labels, target_sequence
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processor.image_processor.size = image_size[::-1] # should be (width, height)
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processor.image_processor.do_align_long_axis = False
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train_dataset = DonutDataset(DATASET_PATH, max_length=max_length,
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split="train", task_start_token="<s_cord-v2>", prompt_end_token="<s_cord-v2>",
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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(DATASET_PATH, max_length=max_length,
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split="validation", task_start_token="<s_cord-v2>", prompt_end_token="<s_cord-v2>",
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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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class DonutModelPLModule(pl.LightningModule):
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def __init__(self, config, processor, model):
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super().__init__()
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self.config = config
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self.processor = processor
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self.model = model
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def training_step(self, batch, batch_idx):
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pixel_values, labels, _ = batch
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outputs = self.model(pixel_values, labels=labels)
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loss = outputs.loss
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self.log_dict({"train_loss": loss}, sync_dist=True)
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return loss
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def validation_step(self, batch, batch_idx, dataset_idx=0):
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pixel_values, labels, answers = batch
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batch_size = pixel_values.shape[0]
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# we feed the prompt to the model
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decoder_input_ids = torch.full((batch_size, 1), self.model.config.decoder_start_token_id, device=self.device)
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outputs = self.model.generate(pixel_values,
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decoder_input_ids=decoder_input_ids,
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max_length=max_length,
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early_stopping=True,
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pad_token_id=self.processor.tokenizer.pad_token_id,
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eos_token_id=self.processor.tokenizer.eos_token_id,
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use_cache=True,
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num_beams=1,
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bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
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return_dict_in_generate=True,)
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predictions = []
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for seq in self.processor.tokenizer.batch_decode(outputs.sequences):
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seq = seq.replace(self.processor.tokenizer.eos_token, "").replace(self.processor.tokenizer.pad_token, "")
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seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
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predictions.append(seq)
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scores = list()
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for pred, answer in zip(predictions, answers):
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pred = re.sub(r"(?:(?<=>) | (?=</s_))", "", pred)
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# NOT NEEDED ANYMORE
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# answer = re.sub(r"<.*?>", "", answer, count=1)
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answer = answer.replace(self.processor.tokenizer.eos_token, "")
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scores.append(edit_distance(pred, answer) / max(len(pred), len(answer)))
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if self.config.get("verbose", False) and len(scores) == 1:
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print(f"Prediction: {pred}")
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print(f" Answer: {answer}")
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print(f" Normed ED: {scores[0]}")
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return scores
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def validation_epoch_end(self, validation_step_outputs):
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# I set this to 1 manually
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# (previously set to len(self.config.dataset_name_or_paths))
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num_of_loaders = 1
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if num_of_loaders == 1:
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validation_step_outputs = [validation_step_outputs]
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assert len(validation_step_outputs) == num_of_loaders
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cnt = [0] * num_of_loaders
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total_metric = [0] * num_of_loaders
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val_metric = [0] * num_of_loaders
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for i, results in enumerate(validation_step_outputs):
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for scores in results:
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cnt[i] += len(scores)
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total_metric[i] += np.sum(scores)
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val_metric[i] = total_metric[i] / cnt[i]
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val_metric_name = f"val_metric_{i}th_dataset"
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self.log_dict({val_metric_name: val_metric[i]}, sync_dist=True)
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self.log_dict({"val_metric": np.sum(total_metric) / np.sum(cnt)}, sync_dist=True)
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def configure_optimizers(self):
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# TODO add scheduler
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optimizer = torch.optim.Adam(self.parameters(), lr=self.config.get("lr"))
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return optimizer
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def train_dataloader(self):
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return train_dataloader
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def val_dataloader(self):
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return val_dataloader
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class PushToHubCallback(Callback):
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def on_train_epoch_end(self, trainer, pl_module):
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print(f"Pushing model to the hub, epoch {trainer.current_epoch}")
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pl_module.model.push_to_hub(OUTPUT_MODEL_PATH,
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commit_message=f"Training in progress, epoch {trainer.current_epoch}")
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2022-12-14 20:43:43 +01:00
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# pl_module.processor.push_to_hub(OUTPUT_MODEL_PATH, commit_message=f"Training in progress, epoch {trainer.current_epoch}")
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2022-12-14 15:53:27 +01:00
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def on_train_end(self, trainer, pl_module):
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print(f"Pushing model to the hub after training")
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pl_module.processor.push_to_hub(OUTPUT_MODEL_PATH,
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commit_message=f"Training done")
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pl_module.model.push_to_hub(OUTPUT_MODEL_PATH,
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commit_message=f"Training done")
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2022-12-14 20:53:34 +01:00
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login(os.environ.get("HUG_TOKKEN", None), True)
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2022-12-14 15:53:27 +01:00
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# ### Wandb.ai link: https://wandb.ai/michalkozlowski936/Donut?workspace=user-michalkozlowski936
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# ### Hugging_face link https://huggingface.co/Zombely
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model_module = DonutModelPLModule(train_config, processor, model)
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wandb_logger = WandbLogger(project="Donut", name=LOGGING_PATH)
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2022-12-14 16:26:35 +01:00
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checkpoint_callback = ModelCheckpoint(
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monitor="val_metric",
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dirpath=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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2022-12-14 15:53:27 +01:00
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trainer = pl.Trainer(
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accelerator="gpu", # change to gpu
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devices=1,
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max_epochs=train_config.get("max_epochs"),
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val_check_interval=train_config.get("val_check_interval"),
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check_val_every_n_epoch=train_config.get("check_val_every_n_epoch"),
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gradient_clip_val=train_config.get("gradient_clip_val"),
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precision=16, # we'll use mixed precision
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2022-12-14 16:26:35 +01:00
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plugins=custom_ckpt,
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2022-12-14 15:53:27 +01:00
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num_sanity_val_steps=0,
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logger=wandb_logger,
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2022-12-14 16:26:35 +01:00
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callbacks=[PushToHubCallback(), checkpoint_callback],
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2022-12-14 15:53:27 +01:00
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)
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trainer.fit(model_module)
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