new training script with config, files split
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.gitignore
vendored
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.gitignore
vendored
@ -2,3 +2,4 @@ env
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Donut
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nohup.out
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wandb
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__pycache__/
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22
config-train.yaml
Normal file
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config-train.yaml
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@ -0,0 +1,22 @@
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dataset_path: "Zombely/fiszki-ocr-train"
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pretrained_model_path: "Zombely/plwiki-proto-fine-tuned-v2"
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start_model_path: "Zombely/plwiki-proto-fine-tuned-v2"
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output_model_path: "Zombely/plwiki-proto-fine-tuned-v3"
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wandb_test_name: "fiszki-ocr-fine-tune"
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checkpoint_path: "./checkpoint"
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max_length: 768
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image_size: [1920, 2560]
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train_config:
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max_epochs: 1
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val_check_interval: 0.5
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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: 3.0e-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
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result_path: "./result"
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verbose: True
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@ -21,7 +21,7 @@ def main(config):
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config_vision.decoder.max_length = config.max_dec_length
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processor = DonutProcessor.from_pretrained(config.pretrained_processor_path)
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model = VisionEncoderDecoderModel.from_pretrained(config.pretrained_model_path)
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model = VisionEncoderDecoderModel.from_pretrained(config.pretrained_model_path, config=config_vision if config.use_enc_dec_config else None)
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processor.image_processor.size = config.image_size[::-1] # should be (width, height)
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processor.image_processor.do_align_long_axis = False
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412
donut-train.py
412
donut-train.py
@ -1,379 +1,113 @@
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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 torch.utils.data import DataLoader
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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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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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from pytorch_lightning.plugins import CheckpointIO
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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 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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DATASET_PATH = "Zombely/fiszki-ocr-train"
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PRETRAINED_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
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START_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
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OUTPUT_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v3"
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LOGGING_PATH = "fiszki-ocr-fine-tune"
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CHECKPOINT_PATH = "./checkpoint"
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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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train_config = {
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"max_epochs":1,
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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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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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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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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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+ 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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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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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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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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def __len__(self) -> int:
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return self.dataset_length
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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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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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login(hug_token, True)
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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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model_module = DonutModelPLModule(config.train_config.toDict(), processor, model)
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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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wandb_logger = WandbLogger(project="Donut", name=config.wandb_test_name)
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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}")
|
||||
# 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(
|
||||
checkpoint_callback = ModelCheckpoint(
|
||||
monitor="val_metric",
|
||||
dirpath=CHECKPOINT_PATH,
|
||||
dirpath=config.checkpoint_path,
|
||||
filename="artifacts",
|
||||
save_top_k=1,
|
||||
save_last=False,
|
||||
mode="min",
|
||||
)
|
||||
|
||||
custom_ckpt = CustomCheckpointIO()
|
||||
custom_ckpt = CustomCheckpointIO()
|
||||
|
||||
|
||||
trainer = pl.Trainer(
|
||||
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"),
|
||||
max_epochs=config.train_config.max_epochs,
|
||||
val_check_interval=config.train_config.val_check_interval,
|
||||
check_val_every_n_epoch=config.train_config.check_val_every_n_epoch,
|
||||
gradient_clip_val=config.train_config.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)
|
||||
trainer.fit(model_module)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--config", type=str, required=True)
|
||||
args, left_argv = parser.parse_known_args()
|
||||
config = Config(args.config)
|
||||
config.argv_update(left_argv)
|
||||
|
||||
hug_token = os.environ.get("HUG_TOKEN", None)
|
||||
|
||||
if not torch.cuda.is_available():
|
||||
warnings.warn("You don't have cuda available, training might be taking long time or impossible")
|
||||
|
||||
if not hug_token:
|
||||
raise Exception("You need to set up HUG_TOKKEN in enviroments to push output model to hub")
|
||||
main(config, hug_token)
|
||||
|
||||
378
old/donut-train_old.py
Normal file
378
old/donut-train_old.py
Normal file
@ -0,0 +1,378 @@
|
||||
#!/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)
|
||||
0
utils/__init__.py
Normal file
0
utils/__init__.py
Normal file
21
utils/callbacks.py
Normal file
21
utils/callbacks.py
Normal file
@ -0,0 +1,21 @@
|
||||
from pytorch_lightning.callbacks import Callback
|
||||
|
||||
|
||||
class PushToHubCallback(Callback):
|
||||
def __init__(self, output_model_path) -> None:
|
||||
super().__init__()
|
||||
self.output_model_path = output_model_path
|
||||
|
||||
|
||||
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(self.output_model_path,
|
||||
commit_message=f"Training in progress, epoch {trainer.current_epoch}")
|
||||
# pl_module.processor.push_to_hub(self.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(self.output_model_path,
|
||||
commit_message=f"Training done")
|
||||
pl_module.model.push_to_hub(self.output_model_path,
|
||||
commit_message=f"Training done")
|
||||
17
utils/checkpoint.py
Normal file
17
utils/checkpoint.py
Normal file
@ -0,0 +1,17 @@
|
||||
from pytorch_lightning.plugins import CheckpointIO
|
||||
import torch
|
||||
|
||||
|
||||
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)
|
||||
152
utils/donut_dataset.py
Normal file
152
utils/donut_dataset.py
Normal file
@ -0,0 +1,152 @@
|
||||
from datasets import load_dataset
|
||||
from torch.utils.data import Dataset
|
||||
import json
|
||||
from typing import Any, List, Tuple
|
||||
import random
|
||||
import torch
|
||||
from transformers import DonutProcessor, VisionEncoderDecoderModel
|
||||
|
||||
|
||||
|
||||
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,
|
||||
processor: DonutProcessor,
|
||||
model: VisionEncoderDecoderModel,
|
||||
split: str = "train",
|
||||
ignore_id: int = -100,
|
||||
task_start_token: str = "<s>",
|
||||
prompt_end_token: str = None,
|
||||
sort_json_key: bool = True,
|
||||
added_tokens: list = []
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.max_length = max_length
|
||||
self.split = split
|
||||
self.processor = processor
|
||||
self.model = model
|
||||
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.added_tokens = added_tokens
|
||||
|
||||
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,
|
||||
)
|
||||
+ self.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 = self.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 self.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 = self.processor.tokenizer.add_tokens(list_of_tokens)
|
||||
if newly_added_num > 0:
|
||||
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]
|
||||
|
||||
# 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
|
||||
93
utils/donut_model_pl.py
Normal file
93
utils/donut_model_pl.py
Normal file
@ -0,0 +1,93 @@
|
||||
import torch
|
||||
import pytorch_lightning as pl
|
||||
from nltk import edit_distance
|
||||
import re
|
||||
import numpy as np
|
||||
|
||||
|
||||
class DonutModelPLModule(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
|
||||
|
||||
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=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