Spaces:
Runtime error
Runtime error
File size: 5,279 Bytes
3672502 |
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 |
import json
import os
import random
import copy
from PIL import Image
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor
from transformers import OwlViTProcessor
from VisualSearch.model.llava import conversation as conversation_lib
from VisualSearch.utils.utils import box_xyxy_to_cxcywh, expand2square
from VisualSearch.utils.utils import DEFAULT_IMAGE_TOKEN
def preprocess_multimodal(source, mm_use_im_start_end):
for sentence in source:
if DEFAULT_IMAGE_TOKEN in sentence["value"]:
sentence["value"] = (
sentence["value"].replace(DEFAULT_IMAGE_TOKEN, "").strip()
)
sentence["value"] = DEFAULT_IMAGE_TOKEN + "[LOC]"+"\n" + sentence["value"]
# sentence["value"] = DEFAULT_IMAGE_TOKEN + "\n" + sentence["value"]
sentence["value"] = sentence["value"].strip()
if "mmtag" in conversation_lib.default_conversation.version:
sentence["value"] = sentence["value"].replace(
DEFAULT_IMAGE_TOKEN, "<Image>" + DEFAULT_IMAGE_TOKEN + "</Image>"
)
return source
class VQADataset(torch.utils.data.Dataset):
pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1)
pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1)
img_size = 1024
ignore_label = 255
def __init__(
self,
base_image_dir,
tokenizer,
vision_tower,
samples_per_epoch=500 * 8 * 2 * 10,
precision: str = "fp32",
num_classes_per_sample: int = 3,
exclude_val=False,
vqa_data="possible_locations_conv_86k||llava_instruct_150k",
vqa_sample_rate=[2,1],
):
self.exclude_val = exclude_val
self.samples_per_epoch = samples_per_epoch
self.num_classes_per_sample = num_classes_per_sample
self.base_image_dir = base_image_dir
self.tokenizer = tokenizer
self.precision = precision
self.transform = OwlViTProcessor.from_pretrained("google/owlvit-base-patch16")
self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
DATA_DIR = os.path.join(base_image_dir, "vsm_vqa_data")
self.vqa_image_root = os.path.join(base_image_dir, "coco2017/train2017")
vqa_datas = vqa_data.split("||")
self.vqa_datas = []
for data in vqa_datas:
with open(os.path.join(DATA_DIR, "{}.json".format(data))) as f:
data = json.load(f)
self.vqa_datas.append(data)
sample_rate = np.array(vqa_sample_rate)
self.sample_rate = sample_rate / sample_rate.sum()
def __len__(self):
return self.samples_per_epoch
def preprocess(self, x: torch.Tensor) -> torch.Tensor:
"""Normalize pixel values and pad to a square input."""
# Normalize colors
x = (x - self.pixel_mean) / self.pixel_std
# Pad
h, w = x.shape[-2:]
padh = self.img_size - h
padw = self.img_size - w
x = F.pad(x, (0, padw, 0, padh))
return x
def __getitem__(self, idx):
ds = np.random.choice(list(range(len(self.vqa_datas))), p=self.sample_rate)
ds = self.vqa_datas[ds]
idx = random.randint(0, len(ds) - 1)
item = ds[idx]
image_path = os.path.join(self.vqa_image_root, item["image"])
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ori_size = image.shape[:2]
image_clip = self.clip_image_processor.preprocess(
expand2square(Image.open(image_path).convert('RGB'), tuple(int(x*255) for x in self.clip_image_processor.image_mean)), return_tensors="pt")["pixel_values"][0]
image = self.transform(images=image, return_tensors="pt")['pixel_values'][0]
resize = image.shape[:2]
conv = conversation_lib.default_conversation.copy()
source = item["conversations"]
source = preprocess_multimodal(
copy.deepcopy(source),
mm_use_im_start_end=conv.sep_style == conversation_lib.SeparatorStyle.TWO,
)
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
conversations = []
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{j}"
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
questions = conversations
sampled_classes = conversations
masks = torch.rand(1, *ori_size)
label = torch.ones(ori_size) * self.ignore_label
bboxes_labels = [torch.tensor([[0.5,0.5,1.0,1.0]])]
bboxes_valid = [0]
masks_valid = [0]
return (
image_path,
image,
image_clip,
conversations,
masks,
label,
bboxes_labels,
bboxes_valid,
masks_valid,
resize,
questions,
sampled_classes,
)
|