379 lines
15 KiB
Python
379 lines
15 KiB
Python
|
#!/usr/bin/env python
|
||
|
|
# coding: utf-8
|
||
|
|
|
||
|
|
|
||
|
|
from transformers import VisionEncoderDecoderConfig, DonutProcessor, VisionEncoderDecoderModel
|
||
|
|
from datasets import load_dataset
|
||
|
|
import json
|
||
|
|
import random
|
||
|
|
from typing import Any, List, Tuple
|
||
|
|
import torch
|
||
|
|
from torch.utils.data import Dataset, DataLoader
|
||
|
|
import re
|
||
|
|
from nltk import edit_distance
|
||
|
|
import numpy as np
|
||
|
|
from pytorch_lightning.loggers import WandbLogger
|
||
|
|
from pytorch_lightning.callbacks import Callback, ModelCheckpoint
|
||
|
|
import pytorch_lightning as pl
|
||
|
|
import os
|
||
|
|
from huggingface_hub import login
|
||
|
|
from pytorch_lightning.plugins import CheckpointIO
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
DATASET_PATH = "Zombely/fiszki-ocr-train"
|
||
|
|
PRETRAINED_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
|
||
|
|
START_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
|
||
|
|
OUTPUT_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v3"
|
||
|
|
LOGGING_PATH = "fiszki-ocr-fine-tune"
|
||
|
|
CHECKPOINT_PATH = "./checkpoint"
|
||
|
|
|
||
|
|
|
||
|
|
train_config = {
|
||
|
|
"max_epochs":1,
|
||
|
|
"val_check_interval":0.5, # how many times we want to validate during an epoch
|
||
|
|
"check_val_every_n_epoch":1,
|
||
|
|
"gradient_clip_val":1.0,
|
||
|
|
"num_training_samples_per_epoch": 800,
|
||
|
|
"lr":3e-5,
|
||
|
|
"train_batch_sizes": [8],
|
||
|
|
"val_batch_sizes": [1],
|
||
|
|
"seed":2022,
|
||
|
|
"num_nodes": 1,
|
||
|
|
"warmup_steps": 300, # 800/8*30/10, 10%
|
||
|
|
"result_path": "./result",
|
||
|
|
"verbose": True,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
dataset = load_dataset(DATASET_PATH)
|
||
|
|
|
||
|
|
|
||
|
|
max_length = 768
|
||
|
|
image_size = [1920, 2560]
|
||
|
|
config = VisionEncoderDecoderConfig.from_pretrained(PRETRAINED_MODEL_PATH)
|
||
|
|
config.encoder.image_size = image_size # (height, width)
|
||
|
|
config.decoder.max_length = max_length
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
processor = DonutProcessor.from_pretrained(START_MODEL_PATH)
|
||
|
|
model = VisionEncoderDecoderModel.from_pretrained(PRETRAINED_MODEL_PATH, config=config)
|
||
|
|
|
||
|
|
added_tokens = []
|
||
|
|
|
||
|
|
class CustomCheckpointIO(CheckpointIO):
|
||
|
|
def save_checkpoint(self, checkpoint, path, storage_options=None):
|
||
|
|
del checkpoint["state_dict"]
|
||
|
|
torch.save(checkpoint, path)
|
||
|
|
|
||
|
|
def load_checkpoint(self, path, storage_options=None):
|
||
|
|
checkpoint = torch.load(path + "artifacts.ckpt")
|
||
|
|
state_dict = torch.load(path + "pytorch_model.bin")
|
||
|
|
checkpoint["state_dict"] = {"model." + key: value for key, value in state_dict.items()}
|
||
|
|
return checkpoint
|
||
|
|
|
||
|
|
def remove_checkpoint(self, path) -> None:
|
||
|
|
return super().remove_checkpoint(path)
|
||
|
|
|
||
|
|
class DonutDataset(Dataset):
|
||
|
|
"""
|
||
|
|
DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets)
|
||
|
|
Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt),
|
||
|
|
and it will be converted into input_tensor(vectorized image) and input_ids(tokenized string).
|
||
|
|
Args:
|
||
|
|
dataset_name_or_path: name of dataset (available at huggingface.co/datasets) or the path containing image files and metadata.jsonl
|
||
|
|
max_length: the max number of tokens for the target sequences
|
||
|
|
split: whether to load "train", "validation" or "test" split
|
||
|
|
ignore_id: ignore_index for torch.nn.CrossEntropyLoss
|
||
|
|
task_start_token: the special token to be fed to the decoder to conduct the target task
|
||
|
|
prompt_end_token: the special token at the end of the sequences
|
||
|
|
sort_json_key: whether or not to sort the JSON keys
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dataset_name_or_path: str,
|
||
|
|
max_length: int,
|
||
|
|
split: str = "train",
|
||
|
|
ignore_id: int = -100,
|
||
|
|
task_start_token: str = "<s>",
|
||
|
|
prompt_end_token: str = None,
|
||
|
|
sort_json_key: bool = True,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.max_length = max_length
|
||
|
|
self.split = split
|
||
|
|
self.ignore_id = ignore_id
|
||
|
|
self.task_start_token = task_start_token
|
||
|
|
self.prompt_end_token = prompt_end_token if prompt_end_token else task_start_token
|
||
|
|
self.sort_json_key = sort_json_key
|
||
|
|
|
||
|
|
self.dataset = load_dataset(dataset_name_or_path, split=self.split)
|
||
|
|
self.dataset_length = len(self.dataset)
|
||
|
|
|
||
|
|
self.gt_token_sequences = []
|
||
|
|
for sample in self.dataset:
|
||
|
|
ground_truth = json.loads(sample["ground_truth"])
|
||
|
|
if "gt_parses" in ground_truth: # when multiple ground truths are available, e.g., docvqa
|
||
|
|
assert isinstance(ground_truth["gt_parses"], list)
|
||
|
|
gt_jsons = ground_truth["gt_parses"]
|
||
|
|
else:
|
||
|
|
assert "gt_parse" in ground_truth and isinstance(ground_truth["gt_parse"], dict)
|
||
|
|
gt_jsons = [ground_truth["gt_parse"]]
|
||
|
|
|
||
|
|
self.gt_token_sequences.append(
|
||
|
|
[
|
||
|
|
self.json2token(
|
||
|
|
gt_json,
|
||
|
|
update_special_tokens_for_json_key=self.split == "train",
|
||
|
|
sort_json_key=self.sort_json_key,
|
||
|
|
)
|
||
|
|
+ processor.tokenizer.eos_token
|
||
|
|
for gt_json in gt_jsons # load json from list of json
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
self.add_tokens([self.task_start_token, self.prompt_end_token])
|
||
|
|
self.prompt_end_token_id = processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)
|
||
|
|
|
||
|
|
def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):
|
||
|
|
"""
|
||
|
|
Convert an ordered JSON object into a token sequence
|
||
|
|
"""
|
||
|
|
if type(obj) == dict:
|
||
|
|
if len(obj) == 1 and "text_sequence" in obj:
|
||
|
|
return obj["text_sequence"]
|
||
|
|
else:
|
||
|
|
output = ""
|
||
|
|
if sort_json_key:
|
||
|
|
keys = sorted(obj.keys(), reverse=True)
|
||
|
|
else:
|
||
|
|
keys = obj.keys()
|
||
|
|
for k in keys:
|
||
|
|
if update_special_tokens_for_json_key:
|
||
|
|
self.add_tokens([fr"<s_{k}>", fr"</s_{k}>"])
|
||
|
|
output += (
|
||
|
|
fr"<s_{k}>"
|
||
|
|
+ self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)
|
||
|
|
+ fr"</s_{k}>"
|
||
|
|
)
|
||
|
|
return output
|
||
|
|
elif type(obj) == list:
|
||
|
|
return r"<sep/>".join(
|
||
|
|
[self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
obj = str(obj)
|
||
|
|
if f"<{obj}/>" in added_tokens:
|
||
|
|
obj = f"<{obj}/>" # for categorical special tokens
|
||
|
|
return obj
|
||
|
|
|
||
|
|
def add_tokens(self, list_of_tokens: List[str]):
|
||
|
|
"""
|
||
|
|
Add special tokens to tokenizer and resize the token embeddings of the decoder
|
||
|
|
"""
|
||
|
|
newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)
|
||
|
|
if newly_added_num > 0:
|
||
|
|
model.decoder.resize_token_embeddings(len(processor.tokenizer))
|
||
|
|
added_tokens.extend(list_of_tokens)
|
||
|
|
|
||
|
|
def __len__(self) -> int:
|
||
|
|
return self.dataset_length
|
||
|
|
|
||
|
|
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||
|
|
"""
|
||
|
|
Load image from image_path of given dataset_path and convert into input_tensor and labels
|
||
|
|
Convert gt data into input_ids (tokenized string)
|
||
|
|
Returns:
|
||
|
|
input_tensor : preprocessed image
|
||
|
|
input_ids : tokenized gt_data
|
||
|
|
labels : masked labels (model doesn't need to predict prompt and pad token)
|
||
|
|
"""
|
||
|
|
sample = self.dataset[idx]
|
||
|
|
|
||
|
|
# inputs
|
||
|
|
pixel_values = processor(sample["image"], random_padding=self.split == "train", return_tensors="pt").pixel_values
|
||
|
|
pixel_values = pixel_values.squeeze()
|
||
|
|
|
||
|
|
# targets
|
||
|
|
target_sequence = random.choice(self.gt_token_sequences[idx]) # can be more than one, e.g., DocVQA Task 1
|
||
|
|
input_ids = processor.tokenizer(
|
||
|
|
target_sequence,
|
||
|
|
add_special_tokens=False,
|
||
|
|
max_length=self.max_length,
|
||
|
|
padding="max_length",
|
||
|
|
truncation=True,
|
||
|
|
return_tensors="pt",
|
||
|
|
)["input_ids"].squeeze(0)
|
||
|
|
|
||
|
|
labels = input_ids.clone()
|
||
|
|
labels[labels == processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token
|
||
|
|
# labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id # model doesn't need to predict prompt (for VQA)
|
||
|
|
return pixel_values, labels, target_sequence
|
||
|
|
|
||
|
|
|
||
|
|
processor.image_processor.size = image_size[::-1] # should be (width, height)
|
||
|
|
processor.image_processor.do_align_long_axis = False
|
||
|
|
|
||
|
|
train_dataset = DonutDataset(DATASET_PATH, max_length=max_length,
|
||
|
|
split="train", task_start_token="<s_cord-v2>", prompt_end_token="<s_cord-v2>",
|
||
|
|
sort_json_key=False, # cord dataset is preprocessed, so no need for this
|
||
|
|
)
|
||
|
|
|
||
|
|
val_dataset = DonutDataset(DATASET_PATH, max_length=max_length,
|
||
|
|
split="validation", task_start_token="<s_cord-v2>", prompt_end_token="<s_cord-v2>",
|
||
|
|
sort_json_key=False, # cord dataset is preprocessed, so no need for this
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
model.config.pad_token_id = processor.tokenizer.pad_token_id
|
||
|
|
model.config.decoder_start_token_id = processor.tokenizer.convert_tokens_to_ids(['<s_cord-v2>'])[0]
|
||
|
|
|
||
|
|
train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=4)
|
||
|
|
val_dataloader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=4)
|
||
|
|
|
||
|
|
|
||
|
|
class DonutModelPLModule(pl.LightningModule):
|
||
|
|
def __init__(self, config, processor, model):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.processor = processor
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
def training_step(self, batch, batch_idx):
|
||
|
|
pixel_values, labels, _ = batch
|
||
|
|
|
||
|
|
outputs = self.model(pixel_values, labels=labels)
|
||
|
|
loss = outputs.loss
|
||
|
|
self.log_dict({"train_loss": loss}, sync_dist=True)
|
||
|
|
return loss
|
||
|
|
|
||
|
|
def validation_step(self, batch, batch_idx, dataset_idx=0):
|
||
|
|
pixel_values, labels, answers = batch
|
||
|
|
batch_size = pixel_values.shape[0]
|
||
|
|
# we feed the prompt to the model
|
||
|
|
decoder_input_ids = torch.full((batch_size, 1), self.model.config.decoder_start_token_id, device=self.device)
|
||
|
|
|
||
|
|
outputs = self.model.generate(pixel_values,
|
||
|
|
decoder_input_ids=decoder_input_ids,
|
||
|
|
max_length=max_length,
|
||
|
|
early_stopping=True,
|
||
|
|
pad_token_id=self.processor.tokenizer.pad_token_id,
|
||
|
|
eos_token_id=self.processor.tokenizer.eos_token_id,
|
||
|
|
use_cache=True,
|
||
|
|
num_beams=1,
|
||
|
|
bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
|
||
|
|
return_dict_in_generate=True,)
|
||
|
|
|
||
|
|
predictions = []
|
||
|
|
for seq in self.processor.tokenizer.batch_decode(outputs.sequences):
|
||
|
|
seq = seq.replace(self.processor.tokenizer.eos_token, "").replace(self.processor.tokenizer.pad_token, "")
|
||
|
|
seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
|
||
|
|
predictions.append(seq)
|
||
|
|
|
||
|
|
scores = list()
|
||
|
|
for pred, answer in zip(predictions, answers):
|
||
|
|
pred = re.sub(r"(?:(?<=>) | (?=</s_))", "", pred)
|
||
|
|
# NOT NEEDED ANYMORE
|
||
|
|
# answer = re.sub(r"<.*?>", "", answer, count=1)
|
||
|
|
answer = answer.replace(self.processor.tokenizer.eos_token, "")
|
||
|
|
scores.append(edit_distance(pred, answer) / max(len(pred), len(answer)))
|
||
|
|
|
||
|
|
if self.config.get("verbose", False) and len(scores) == 1:
|
||
|
|
print(f"Prediction: {pred}")
|
||
|
|
print(f" Answer: {answer}")
|
||
|
|
print(f" Normed ED: {scores[0]}")
|
||
|
|
|
||
|
|
return scores
|
||
|
|
|
||
|
|
def validation_epoch_end(self, validation_step_outputs):
|
||
|
|
# I set this to 1 manually
|
||
|
|
# (previously set to len(self.config.dataset_name_or_paths))
|
||
|
|
num_of_loaders = 1
|
||
|
|
if num_of_loaders == 1:
|
||
|
|
validation_step_outputs = [validation_step_outputs]
|
||
|
|
assert len(validation_step_outputs) == num_of_loaders
|
||
|
|
cnt = [0] * num_of_loaders
|
||
|
|
total_metric = [0] * num_of_loaders
|
||
|
|
val_metric = [0] * num_of_loaders
|
||
|
|
for i, results in enumerate(validation_step_outputs):
|
||
|
|
for scores in results:
|
||
|
|
cnt[i] += len(scores)
|
||
|
|
total_metric[i] += np.sum(scores)
|
||
|
|
val_metric[i] = total_metric[i] / cnt[i]
|
||
|
|
val_metric_name = f"val_metric_{i}th_dataset"
|
||
|
|
self.log_dict({val_metric_name: val_metric[i]}, sync_dist=True)
|
||
|
|
self.log_dict({"val_metric": np.sum(total_metric) / np.sum(cnt)}, sync_dist=True)
|
||
|
|
|
||
|
|
def configure_optimizers(self):
|
||
|
|
# TODO add scheduler
|
||
|
|
optimizer = torch.optim.Adam(self.parameters(), lr=self.config.get("lr"))
|
||
|
|
|
||
|
|
return optimizer
|
||
|
|
|
||
|
|
def train_dataloader(self):
|
||
|
|
return train_dataloader
|
||
|
|
|
||
|
|
def val_dataloader(self):
|
||
|
|
return val_dataloader
|
||
|
|
|
||
|
|
|
||
|
|
class PushToHubCallback(Callback):
|
||
|
|
def on_train_epoch_end(self, trainer, pl_module):
|
||
|
|
print(f"Pushing model to the hub, epoch {trainer.current_epoch}")
|
||
|
|
pl_module.model.push_to_hub(OUTPUT_MODEL_PATH,
|
||
|
|
commit_message=f"Training in progress, epoch {trainer.current_epoch}")
|
||
|
|
# pl_module.processor.push_to_hub(OUTPUT_MODEL_PATH, commit_message=f"Training in progress, epoch {trainer.current_epoch}")
|
||
|
|
|
||
|
|
def on_train_end(self, trainer, pl_module):
|
||
|
|
print(f"Pushing model to the hub after training")
|
||
|
|
pl_module.processor.push_to_hub(OUTPUT_MODEL_PATH,
|
||
|
|
commit_message=f"Training done")
|
||
|
|
pl_module.model.push_to_hub(OUTPUT_MODEL_PATH,
|
||
|
|
commit_message=f"Training done")
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
login(os.environ.get("HUG_TOKKEN", None), True)
|
||
|
|
|
||
|
|
|
||
|
|
# ### Wandb.ai link: https://wandb.ai/michalkozlowski936/Donut?workspace=user-michalkozlowski936
|
||
|
|
# ### Hugging_face link https://huggingface.co/Zombely
|
||
|
|
|
||
|
|
model_module = DonutModelPLModule(train_config, processor, model)
|
||
|
|
|
||
|
|
wandb_logger = WandbLogger(project="Donut", name=LOGGING_PATH)
|
||
|
|
|
||
|
|
checkpoint_callback = ModelCheckpoint(
|
||
|
|
monitor="val_metric",
|
||
|
|
dirpath=CHECKPOINT_PATH,
|
||
|
|
filename="artifacts",
|
||
|
|
save_top_k=1,
|
||
|
|
save_last=False,
|
||
|
|
mode="min",
|
||
|
|
)
|
||
|
|
|
||
|
|
custom_ckpt = CustomCheckpointIO()
|
||
|
|
|
||
|
|
|
||
|
|
trainer = pl.Trainer(
|
||
|
|
accelerator="gpu" if torch.cuda.is_available() else 'cpu', # change to gpu
|
||
|
|
devices=1,
|
||
|
|
max_epochs=train_config.get("max_epochs"),
|
||
|
|
val_check_interval=train_config.get("val_check_interval"),
|
||
|
|
check_val_every_n_epoch=train_config.get("check_val_every_n_epoch"),
|
||
|
|
gradient_clip_val=train_config.get("gradient_clip_val"),
|
||
|
|
precision=16, # we'll use mixed precision
|
||
|
|
plugins=custom_ckpt,
|
||
|
|
num_sanity_val_steps=0,
|
||
|
|
logger=wandb_logger,
|
||
|
|
callbacks=[PushToHubCallback(), checkpoint_callback],
|
||
|
|
)
|
||
|
|
|
||
|
|
trainer.fit(model_module)
|