train pure function for map, gitignore added vscode
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michal.kozlowski
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.gitignore
vendored
1
.gitignore
vendored
@ -4,3 +4,4 @@ nohup.out
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wandb
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__pycache__/
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checkpoint
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.vscode
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145
train_stream.py
145
train_stream.py
@ -1,3 +1,4 @@
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from typing import Any, List
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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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@ -15,7 +16,7 @@ from utils.callbacks import PushToHubCallback
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import warnings
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from datasets import load_dataset, interleave_datasets
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from torchdata.datapipes.iter import IterableWrapper
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import json
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def main(config, hug_token):
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@ -34,30 +35,128 @@ def main(config, hug_token):
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added_tokens = []
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dataset = load_dataset(config.dataset_path, split="train[:80%]")
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dataset = dataset.train_test_split(test_size=0.1)
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### PROCESS FUNC START ###
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train_dataset_process = DonutDatasetStream(
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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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def add_tokens(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 json2token(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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add_tokens([fr"<s_{k}>", fr"</s_{k}>"])
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output += (
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fr"<s_{k}>"
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+ 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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[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 process(row, split):
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task_start_token, prompt_end_token = "<s_cord-v2>"
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ground_truth = json.loads(row["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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gt_token_sequences = (
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[
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json2token(
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gt_json,
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update_special_tokens_for_json_key=split == "train",
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sort_json_key=False,
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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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val_dataset_process = DonutDatasetStream(
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processor=processor,
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model=model,
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add_tokens([task_start_token, prompt_end_token])
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prompt_end_token_id = processor.tokenizer.convert_tokens_to_ids(prompt_end_token)
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# change if not 3 channels
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if row['image'].mode != "RGB":
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row['image'] = row['image'].convert("RGB")
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# inputs
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pixel_values = processor(row["image"], random_padding=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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input_ids = processor.tokenizer(
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gt_token_sequences,
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add_special_tokens=False,
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max_length=config.max_length,
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split="test",
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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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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] = -100 # model doesn't need to predict pad token
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return {"pixel_values": pixel_values, "labels": labels, 'target_sequence': gt_token_sequences }
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def proces_train(row):
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return process(row, 'train')
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def proces_val(row):
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return process(row, 'validation')
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### PROCESS FUNC END ###
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# train_dataset_process = DonutDatasetStream(
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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_process = DonutDatasetStream(
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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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dataset = load_dataset(config.dataset_path, streaming=True)
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val_dataset = dataset.pop('validation')
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@ -66,8 +165,8 @@ def main(config, hug_token):
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# val_length = list(val_dataset.info.splits.values())[-1].num_examples
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train_dataset = train_dataset.map(lambda x: train_dataset_process.process(x), remove_columns = ['image', 'ground_truth'])
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val_dataset = val_dataset.map(lambda x: val_dataset_process.process(x), remove_columns = ['image', 'ground_truth'])
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train_dataset = train_dataset.map(proces_train, remove_columns = ['image', 'ground_truth'])
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val_dataset = val_dataset.map(proces_val, remove_columns = ['image', 'ground_truth'])
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# train_dataset = train_dataset.with_format('torch')
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# val_dataset = val_dataset.with_format('torch')
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@ -8,19 +8,6 @@ from transformers import DonutProcessor, VisionEncoderDecoderModel
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class DonutDatasetStream:
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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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@ -34,7 +21,6 @@ class DonutDatasetStream:
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sort_json_key: bool = True,
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added_tokens: list = []
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):
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super().__init__()
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self.split = split
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self.max_length = max_length
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