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import os
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import datasets
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from datasets import load_dataset, ClassLabel, concatenate_datasets
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import torch
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import numpy as np
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import random
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from PIL import Image
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import json
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import copy
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from torchvision import transforms
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import pickle
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import re
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from OmniGen import OmniGenProcessor
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from OmniGen.processor import OmniGenCollator
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class DatasetFromJson(torch.utils.data.Dataset):
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def __init__(
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self,
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json_file: str,
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image_path: str,
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processer: OmniGenProcessor,
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image_transform,
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max_input_length_limit: int = 18000,
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condition_dropout_prob: float = 0.1,
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keep_raw_resolution: bool = True,
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):
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self.image_transform = image_transform
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self.processer = processer
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self.condition_dropout_prob = condition_dropout_prob
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self.max_input_length_limit = max_input_length_limit
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self.keep_raw_resolution = keep_raw_resolution
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self.data = load_dataset('json', data_files=json_file)['train']
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self.image_path = image_path
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def process_image(self, image_file):
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if self.image_path is not None:
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image_file = os.path.join(self.image_path, image_file)
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image = Image.open(image_file).convert('RGB')
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return self.image_transform(image)
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def get_example(self, index):
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example = self.data[index]
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instruction, input_images, output_image = example['instruction'], example['input_images'], example['output_image']
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if random.random() < self.condition_dropout_prob:
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instruction = '<cfg>'
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input_images = None
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if input_images is not None:
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input_images = [self.process_image(x) for x in input_images]
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mllm_input = self.processer.process_multi_modal_prompt(instruction, input_images)
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output_image = self.process_image(output_image)
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return (mllm_input, output_image)
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def __getitem__(self, index):
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return self.get_example(index)
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for _ in range(8):
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try:
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mllm_input, output_image = self.get_example(index)
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if len(mllm_input['input_ids']) > self.max_input_length_limit:
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raise RuntimeError(f"cur number of tokens={len(mllm_input['input_ids'])}, larger than max_input_length_limit={self.max_input_length_limit}")
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return mllm_input, output_image
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except Exception as e:
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print("error when loading data: ", e)
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print(self.data[index])
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index = random.randint(0, len(self.data)-1)
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raise RuntimeError("Too many bad data.")
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def __len__(self):
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return len(self.data)
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class TrainDataCollator(OmniGenCollator):
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def __init__(self, pad_token_id: int, hidden_size: int, keep_raw_resolution: bool):
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self.pad_token_id = pad_token_id
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self.hidden_size = hidden_size
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self.keep_raw_resolution = keep_raw_resolution
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def __call__(self, features):
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mllm_inputs = [f[0] for f in features]
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output_images = [f[1].unsqueeze(0) for f in features]
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target_img_size = [[x.size(-2), x.size(-1)] for x in output_images]
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all_padded_input_ids, all_position_ids, all_attention_mask, all_padding_images, all_pixel_values, all_image_sizes = self.process_mllm_input(mllm_inputs, target_img_size)
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if not self.keep_raw_resolution:
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output_image = torch.cat(output_image, dim=0)
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if len(pixel_values) > 0:
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all_pixel_values = torch.cat(all_pixel_values, dim=0)
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else:
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all_pixel_values = None
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data = {"input_ids": all_padded_input_ids,
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"attention_mask": all_attention_mask,
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"position_ids": all_position_ids,
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"input_pixel_values": all_pixel_values,
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"input_image_sizes": all_image_sizes,
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"padding_images": all_padding_images,
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"output_images": output_images,
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}
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return data
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