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dataset.py
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dataset.py
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import json
import random
import re
import pandas as pd
import numpy as np
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import torch
from model.vision_utils import get_img_process
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
class PretrainDataset(Dataset):
def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024,
prompt_max_len=512,
answer_max_len=256,
image_special_token='<' * 25 + '>' * 25):
super().__init__()
with open(json_path, 'r', encoding='utf-8') as f:
self.data = json.load(f)
self.max_length = max_length
self.prompt_max_len = prompt_max_len
self.answer_max_len = answer_max_len
self.tokenizer = tokenizer
self.vision_model, self.preprocess = vision_model
self.padding = 0
self.bos_id = self.tokenizer('<s>assistant').data['input_ids']
self.dataset_path = './dataset/pretrain_images/'
self.image_special_token = image_special_token
def __len__(self):
return len(self.data)
def find_sublist_index(self, main_list, sub_list) -> int:
last_index = -1
for i in range(len(main_list) - len(sub_list) + 1):
if main_list[i:i + len(sub_list)] == sub_list:
last_index = i
return last_index
def safe_eval(self, s):
try:
res = eval(s)
except Exception as e:
return []
return res
def __getitem__(self, index: int):
sample = self.data[index]
image_name = sample['image']
conversation = sample['conversations']
# minimind-v的image的特殊占位符，对应每张图切分成M个token，和get_img_process中的数量对应
messages = []
# 遍历 conversation 列表
for i in range(0, len(conversation), 2):
# 检查是否有配对的问题和回答
if i + 1 < len(conversation):
q = conversation[i]['value'].replace('<image>', self.image_special_token)
a = conversation[i + 1]['value']
if q and a:
messages.append({"role": "user", "content": q})
messages.append({"role": "assistant", "content": a})
new_prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
padding_len = self.max_length - len(input_id)
input_id = input_id + [self.padding] * padding_len
mask_len = len(input_id) - question_length - padding_len
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
input_id = np.array(input_id)
X = np.array(input_id[:-1]).astype(np.int64)
Y = np.array(input_id[1:]).astype(np.int64)
loss_mask = np.array(loss_mask[1:]).astype(np.int64)
X_tensor = torch.from_numpy(X)
Y_tensor = torch.from_numpy(Y)
loss_mask_tensor = torch.from_numpy(loss_mask)
image = Image.open(f'{self.dataset_path}{image_name}')
image_encoders = get_img_process(image, self.preprocess)
return X_tensor, Y_tensor, loss_mask_tensor, image_encoders
class SFTDataset(Dataset):
def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024,
prompt_max_len=512,
answer_max_len=256,
image_special_token='<' * 25 + '>' * 25):
super().__init__()
with open(json_path, 'r', encoding='utf-8') as f:
self.data = json.load(f)
self.max_length = max_length
self.prompt_max_len = prompt_max_len
self.answer_max_len = answer_max_len
self.tokenizer = tokenizer
self.vision_model, self.preprocess = vision_model
self.padding = 0
self.bos_id = self.tokenizer('<s>assistant').data['input_ids']
self.dataset_path = './dataset/sft_images/'
self.image_special_token = image_special_token
def __len__(self):
return len(self.data)
def find_sublist_index(self, main_list, sub_list) -> int:
last_index = -1
for i in range(len(main_list) - len(sub_list) + 1):
if main_list[i:i + len(sub_list)] == sub_list:
last_index = i
return last_index
def safe_eval(self, s):
try:
res = eval(s)
except Exception as e:
return []
return res
def __getitem__(self, index: int):
sample = self.data[index]
image_name = 'COCO_train2014_' + sample['image']
conversation = sample['conversations']
# minimind-v的image的特殊占位符，对应每张图切分成M个token，和get_img_process中的数量对应
messages = []
# 遍历 conversation 列表
# for i in range(0, len(conversation), 2):
for i in range(0, 1):
# 检查是否有配对的问题和回答
if i + 1 < len(conversation):
q = conversation[i]['value'].replace('<image>', self.image_special_token)
a = conversation[i + 1]['value']
if q and a:
messages.append({"role": "user", "content": q})
messages.append({"role": "assistant", "content": a})
new_prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
padding_len = self.max_length - len(input_id)
input_id = input_id + [self.padding] * padding_len
mask_len = len(input_id) - question_length - padding_len
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
input_id = np.array(input_id)
X = np.array(input_id[:-1]).astype(np.int64)
Y = np.array(input_id[1:]).astype(np.int64)
loss_mask = np.array(loss_mask[1:]).astype(np.int64)
X_tensor = torch.from_numpy(X)
Y_tensor = torch.from_numpy(Y)
loss_mask_tensor = torch.from_numpy(loss_mask)
image = Image.open(f'{self.dataset_path}{image_name}')
image_encoders = get_img_process(image, self.preprocess)
return X_tensor, Y_tensor, loss_mask_tensor, image_encoders
class SFTDataset_multi(Dataset):
def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024,
prompt_max_len=512,
answer_max_len=256,
image_special_token='<' * 25 + '>' * 25):
super().__init__()
with open(json_path, 'r', encoding='utf-8') as f:
self.data = json.load(f)
self.max_length = max_length
self.prompt_max_len = prompt_max_len
self.answer_max_len = answer_max_len
self.tokenizer = tokenizer
self.vision_model, self.preprocess = vision_model
self.padding = 0
self.bos_id = self.tokenizer('<s>assistant').data['input_ids']
self.dataset_path = './dataset/sft_multi_images/'
self.image_special_token = image_special_token
def __len__(self):
return len(self.data)
def find_sublist_index(self, main_list, sub_list) -> int:
last_index = -1
for i in range(len(main_list) - len(sub_list) + 1):
if main_list[i:i + len(sub_list)] == sub_list:
last_index = i
return last_index
def safe_eval(self, s):
try:
res = eval(s)
except Exception as e:
return []
return res
def __getitem__(self, index: int):
sample = self.data[index]
image_names = sample['image'].split(', ')
conversation = sample['conversations']
messages = []
for i in range(0, 1):
if i + 1 < len(conversation):
q = conversation[i]['value'].replace('<image>', self.image_special_token)
a = conversation[i + 1]['value']
if q and a:
messages.append({"role": "user", "content": q})
messages.append({"role": "assistant", "content": a})
# print(messages) # [{'role': 'user', 'content': '<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\nAre the two images below that resemble each other described by the same term? You must choose your answer from the Choice List. Choice_List: True, False.'}, {'role': 'assistant', 'content': 'False'}][{'role': 'user', 'content': '<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\nDo these four pictures fall into the same category? You must choose your answer from the Choice List. Choice_List: True, False.'}, {'role': 'assistant', 'content': 'False'}]
new_prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
# print(len(input_id)) # 165 or 263 or 259
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
padding_len = self.max_length - len(input_id)
input_id = input_id + [self.padding] * padding_len
mask_len = len(input_id) - question_length - padding_len
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
input_id = np.array(input_id)
X = np.array(input_id[:-1]).astype(np.int64)
Y = np.array(input_id[1:]).astype(np.int64)
loss_mask = np.array(loss_mask[1:]).astype(np.int64)
X_tensor = torch.from_numpy(X)
Y_tensor = torch.from_numpy(Y)
loss_mask_tensor = torch.from_numpy(loss_mask)
# 读取多张图像
image_encoders = []
for image_name in image_names:
image = Image.open(f'{self.dataset_path}{image_name.strip()}') # 去掉可能的空格
image_encoders.append(get_img_process(image, self.preprocess)['pixel_values'])
# 确定目标形状
max_images = 2 # 根据你的需求设置 一次性最大输入几张图片
target_shape = (max_images, 3, 224, 224)
# 创建填充张量
padded_image_encoders = torch.zeros(target_shape, dtype=torch.float32)
# 填充图像编码
for i, img_enc in enumerate(image_encoders):
if i < max_images:
padded_image_encoders[i] = img_enc.squeeze(0)
return X_tensor, Y_tensor, loss_mask_tensor, padded_image_encoders