stream trainning
This commit is contained in:
mkozlowskiAzimuthe
parent
93a231a477
commit
debc55fc4d
@ -1,14 +1,14 @@
|
||||
dataset_path: "Zombely/wikisource-small"
|
||||
dataset_path: "Zombely/wikisource-yellow"
|
||||
pretrained_model_path: "Zombely/plwiki-proto-fine-tuned-v3.2"
|
||||
start_model_path: "Zombely/plwiki-proto-fine-tuned-v3.2"
|
||||
output_model_path: "Zombely/pl-donut"
|
||||
wandb_test_name: "wikisource-small"
|
||||
checkpoint_path: "./checkpoint"
|
||||
max_length: 768
|
||||
image_size: [2560, 1920]
|
||||
image_size: [1280, 960]
|
||||
train_config:
|
||||
max_epochs: 1
|
||||
val_check_interval: 0.3
|
||||
val_check_interval: 1.0
|
||||
check_val_every_n_epoch: 1
|
||||
gradient_clip_val: 1.0
|
||||
num_training_samples_per_epoch: 800
|
||||
|
||||
@ -9,12 +9,12 @@ from huggingface_hub import login
|
||||
import argparse
|
||||
from sconf import Config
|
||||
from utils.checkpoint import CustomCheckpointIO
|
||||
from utils.donut_dataset_stream import DonutDataset
|
||||
from utils.donut_model_pl import DonutModelPLModule
|
||||
from utils.donut_dataset_stream import DonutDatasetStream
|
||||
from utils.donut_model_pl_stream import DonutModelPLModuleStream
|
||||
from utils.callbacks import PushToHubCallback
|
||||
import warnings
|
||||
from datasets import load_dataset
|
||||
|
||||
from datasets import load_dataset, interleave_datasets
|
||||
from torchdata.datapipes.iter import IterableWrapper
|
||||
|
||||
|
||||
|
||||
@ -34,8 +34,7 @@ def main(config, hug_token):
|
||||
|
||||
added_tokens = []
|
||||
|
||||
train_dataset = DonutDataset(
|
||||
config.dataset_path,
|
||||
train_dataset_process = DonutDatasetStream(
|
||||
processor=processor,
|
||||
model=model,
|
||||
max_length=config.max_length,
|
||||
@ -46,8 +45,7 @@ def main(config, hug_token):
|
||||
sort_json_key=False, # cord dataset is preprocessed, so no need for this
|
||||
)
|
||||
|
||||
val_dataset = DonutDataset(
|
||||
config.dataset_path,
|
||||
val_dataset_process = DonutDatasetStream(
|
||||
processor=processor,
|
||||
model=model,
|
||||
max_length=config.max_length,
|
||||
@ -58,18 +56,37 @@ def main(config, hug_token):
|
||||
sort_json_key=False, # cord dataset is preprocessed, so no need for this
|
||||
)
|
||||
|
||||
dataset = load_dataset(config.dataset_path, streaming=True)
|
||||
val_dataset = dataset.pop('validation')
|
||||
train_dataset = interleave_datasets(list(dataset.values()))
|
||||
# train_length = sum(split.num_examples for split in dataset[list(dataset.keys())[0]].info.splits.values() if split.name != 'validation')
|
||||
# val_length = list(val_dataset.info.splits.values())[-1].num_examples
|
||||
|
||||
|
||||
train_dataset = train_dataset.map(lambda x: train_dataset_process.process(x), remove_columns = ['image', 'ground_truth'])
|
||||
val_dataset = val_dataset.map(lambda x: val_dataset_process.process(x), remove_columns = ['image', 'ground_truth'])
|
||||
|
||||
# train_dataset = train_dataset.with_format('torch')
|
||||
# val_dataset = val_dataset.with_format('torch')
|
||||
|
||||
train_dataset = IterableWrapper(train_dataset)
|
||||
val_dataset = IterableWrapper(val_dataset)
|
||||
|
||||
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=1)
|
||||
val_dataloader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=1)
|
||||
train_dataloader = DataLoader(train_dataset, batch_size=1, num_workers=0)
|
||||
val_dataloader = DataLoader(val_dataset, batch_size=1, num_workers=0)
|
||||
|
||||
login(hug_token, True)
|
||||
|
||||
model_module = DonutModelPLModule(config.train_config.toDict(), processor, model, max_length=config.max_length, train_dataloader=train_dataloader, val_dataloader=val_dataloader)
|
||||
model_module = DonutModelPLModuleStream(config.train_config.toDict(), processor, model, max_length=config.max_length, train_dataloader=train_dataloader, val_dataloader=val_dataloader)
|
||||
|
||||
wandb_logger = WandbLogger(project="Donut", name=config.wandb_test_name)
|
||||
|
||||
if not os.path.exists(config.checkpoint_path):
|
||||
os.mkdir(config.checkpoint_path)
|
||||
|
||||
checkpoint_callback = ModelCheckpoint(
|
||||
monitor="val_metric",
|
||||
dirpath=config.checkpoint_path,
|
||||
@ -105,7 +122,7 @@ if __name__ == "__main__":
|
||||
config = Config(args.config)
|
||||
config.argv_update(left_argv)
|
||||
|
||||
hug_token = os.environ.get("HUG_TOKEN", None)
|
||||
hug_token = os.environ.get("HUG_TOKEN", "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")
|
||||
|
||||
if not torch.cuda.is_available():
|
||||
warnings.warn("You don't have cuda available, training might be taking long time or impossible")
|
||||
|
||||
@ -116,8 +116,8 @@ class DonutDataset(Dataset):
|
||||
self.model.decoder.resize_token_embeddings(len(self.processor.tokenizer))
|
||||
self.added_tokens.extend(list_of_tokens)
|
||||
|
||||
# def __len__(self) -> int:
|
||||
# return self.dataset_length
|
||||
def __len__(self) -> int:
|
||||
return self.dataset_length
|
||||
|
||||
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
|
||||
@ -7,7 +7,7 @@ import torch
|
||||
from transformers import DonutProcessor, VisionEncoderDecoderModel
|
||||
|
||||
|
||||
class DonutDataset(Dataset):
|
||||
class DonutDatasetStream:
|
||||
"""
|
||||
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),
|
||||
@ -24,12 +24,11 @@ class DonutDataset(Dataset):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dataset_name_or_path: str,
|
||||
max_length: int,
|
||||
processor: DonutProcessor,
|
||||
model: VisionEncoderDecoderModel,
|
||||
split: str = "train",
|
||||
max_length: int,
|
||||
ignore_id: int = -100,
|
||||
split: str = 'train',
|
||||
task_start_token: str = "<s>",
|
||||
prompt_end_token: str = None,
|
||||
sort_json_key: bool = True,
|
||||
@ -37,8 +36,8 @@ class DonutDataset(Dataset):
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.max_length = max_length
|
||||
self.split = split
|
||||
self.max_length = max_length
|
||||
self.processor = processor
|
||||
self.model = model
|
||||
self.ignore_id = ignore_id
|
||||
@ -47,13 +46,10 @@ class DonutDataset(Dataset):
|
||||
self.sort_json_key = sort_json_key
|
||||
self.added_tokens = added_tokens
|
||||
|
||||
self.dataset = load_dataset(dataset_name_or_path, split=self.split, streaming=True).with_format("torch")
|
||||
print(self.dataset)
|
||||
self.dataset_length = len(self.dataset)
|
||||
def process(self, row):
|
||||
|
||||
self.gt_token_sequences = []
|
||||
for sample in self.dataset:
|
||||
ground_truth = json.loads(sample["ground_truth"])
|
||||
|
||||
ground_truth = json.loads(row["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"]
|
||||
@ -61,7 +57,7 @@ class DonutDataset(Dataset):
|
||||
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.gt_token_sequences = (
|
||||
[
|
||||
self.json2token(
|
||||
gt_json,
|
||||
@ -76,6 +72,30 @@ class DonutDataset(Dataset):
|
||||
self.add_tokens([self.task_start_token, self.prompt_end_token])
|
||||
self.prompt_end_token_id = self.processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)
|
||||
|
||||
|
||||
# change if not 3 channels
|
||||
if row['image'].mode != "RGB":
|
||||
row['image'] = row['image'].convert("RGB")
|
||||
|
||||
# inputs
|
||||
pixel_values = self.processor(row["image"], random_padding=self.split == "train", return_tensors="pt").pixel_values
|
||||
pixel_values = pixel_values.squeeze()
|
||||
|
||||
# targets
|
||||
target_sequence = self.gt_token_sequences # can be more than one, e.g., DocVQA Task 1
|
||||
input_ids = self.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 == self.processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token
|
||||
return {"pixel_values": pixel_values, "labels": labels, 'target_sequence': target_sequence }
|
||||
|
||||
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
|
||||
@ -117,40 +137,3 @@ class DonutDataset(Dataset):
|
||||
self.model.decoder.resize_token_embeddings(len(self.processor.tokenizer))
|
||||
self.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]
|
||||
|
||||
# change if not 3 channels
|
||||
if sample['image'].mode != "RGB":
|
||||
sample['image'] = sample['image'].convert("RGB")
|
||||
|
||||
# inputs
|
||||
pixel_values = self.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 = self.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 == self.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
|
||||
|
||||
98
utils/donut_model_pl_stream.py
Normal file
98
utils/donut_model_pl_stream.py
Normal file
@ -0,0 +1,98 @@
|
||||
import torch
|
||||
import pytorch_lightning as pl
|
||||
from nltk import edit_distance
|
||||
import re
|
||||
import numpy as np
|
||||
|
||||
|
||||
class DonutModelPLModuleStream(pl.LightningModule):
|
||||
def __init__(self, config, processor, model, max_length, train_dataloader, val_dataloader):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.processor = processor
|
||||
self.model = model
|
||||
self.max_length = max_length
|
||||
self._train_dataloader = train_dataloader
|
||||
self._val_dataloader = val_dataloader
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
# pixel_values, labels, _ = batch
|
||||
pixel_values = batch['pixel_values']
|
||||
labels = batch['labels']
|
||||
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
|
||||
|
||||
pixel_values = batch['pixel_values']
|
||||
labels = batch['labels']
|
||||
answers = batch['target_sequence']
|
||||
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=self.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 self._train_dataloader
|
||||
|
||||
def val_dataloader(self):
|
||||
return self._val_dataloader
|
||||
Loading…
Reference in New Issue
Block a user