File size: 7,043 Bytes
4eedc1a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | from qwen_vl_utils import process_vision_info
from decord import VideoReader, cpu
import argparse
import numpy as np
from tqdm import tqdm
import torch
import transformers
import math
from PIL import Image
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from torchvision import io, transforms
from torchvision.transforms import InterpolationMode
import os
import json
IMAGE_FACTOR = 28
MIN_PIXELS = 144 * 28 * 28
MAX_PIXELS = 144 * 28 * 28
MAX_RATIO = 200
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
def read_json_file(file_path):
"""
读取JSON文件并返回数据作为字典。
参数:
file_path (str): JSON文件的路径。
返回:
dict: JSON文件中的数据。
"""
try:
# 打开文件并读取数据
with open(file_path, 'r', encoding='utf-8') as file:
# 将JSON数据解析为字典
data = json.load(file)
return data
except FileNotFoundError:
print(f"The file {file_path} was not found.")
except json.JSONDecodeError:
print(f"Error decoding JSON from file {file_path}.")
except Exception as e:
print(f"An error occurred: {e}")
def fetch_image(ele, size_factor: int = IMAGE_FACTOR) -> Image.Image:
if "image" in ele:
image = ele["image"]
else:
image = ele["image_url"]
image_obj = None
if isinstance(image, Image.Image):
image_obj = image
elif image.startswith("http://") or image.startswith("https://"):
image_obj = Image.open(requests.get(image, stream=True).raw)
elif image.startswith("file://"):
image_obj = Image.open(image[7:])
elif image.startswith("data:image"):
if "base64," in image:
_, base64_data = image.split("base64,", 1)
data = base64.b64decode(base64_data)
image_obj = Image.open(BytesIO(data))
else:
image_obj = Image.open(image)
if image_obj is None:
raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
image = image_obj.convert("RGB")
## resize
if "resized_height" in ele and "resized_width" in ele:
resized_height, resized_width = 252, 448
# resized_height, resized_width = smart_resize(
# ele["resized_height"],
# ele["resized_width"],
# factor=size_factor,
# )
else:
width, height = image.size
min_pixels = ele.get("min_pixels", MIN_PIXELS)
max_pixels = ele.get("max_pixels", MAX_PIXELS)
# resized_height, resized_width = smart_resize(
# height,
# width,
# factor=size_factor,
# min_pixels=min_pixels,
# max_pixels=max_pixels,
# )
resized_height, resized_width = 252, 448
image = image.resize((resized_width, resized_height))
return image
def main(args):
model_path = args.model
model = Qwen2VLForConditionalGeneration.from_pretrained(model_path,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2"
)
processor = AutoProcessor.from_pretrained("/mnt/petrelfs/weixilin/cache/Qwen2-VL-7B-Instruct")
del model.model.layers
# dataset = load_dataset(args.needle_dataset)["test"]
# dataset = load_dataset('json', '/mnt/petrelfs/weixilin/projects/MLLM/LongVA/vision_niah/needle_datasets/dataset.json')
dataset = read_json_file(args.needle_dataset)
for index, instance in enumerate(dataset):
# image = instance["image"].convert("RGB")
img = fetch_image({"image": os.path.join('/mnt/petrelfs/weixilin/projects/MLLM/Qwen2-VL/vision_niah/needle_datasets/images', instance['path']), "resized_height": 252, "resized_width": 448})
image_single = processor.image_processor(images=[img], videos=None)
merge_length = processor.image_processor.merge_size**2
pixel_values, image_grid_thw=torch.from_numpy(image_single['pixel_values']), torch.from_numpy(image_single['image_grid_thw']).to(model.device)
# import pdb; pdb.set_trace()
pixel_values = pixel_values.type(model.visual.get_dtype()).to(model.device)
image_embed = model.visual(pixel_values, grid_thw=image_grid_thw).to(model.device)
print(image_embed.shape)
os.makedirs(args.output_dir, exist_ok=True)
torch.save(image_embed, f"{args.output_dir}/{index}.pt")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="/mnt/petrelfs/weixilin/cache/Qwen2-VL-7B-Instruct")
parser.add_argument("--needle_dataset", type=str, default="/mnt/petrelfs/weixilin/projects/MLLM/Qwen2-VL/vision_niah/needle_datasets/dataset_change_format_debug.json")
parser.add_argument("--output_dir", type=str, default="/mnt/petrelfs/weixilin/projects/MLLM/Qwen2-VL/vision_niah/video_needle_haystack/data/needle_vicuna_embeddings_144tokens-tune_projector_interrupt_debug")
args = parser.parse_args()
main(args)
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