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3334467 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import json
import os
import random
import cv2
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor
from model.llava import conversation as conversation_lib
from model.segment_anything.utils.transforms import ResizeLongestSide, ResizeShortestSide
from .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 + "\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",
image_size: int = 224,
num_classes_per_sample: int = 3,
exclude_val=False,
vqa_data="llava_instruct_150k",
pad_train_clip_images=False,
masks_process_with_clip=False,
preprocessor_config='',
):
self.pad_train_clip_images = pad_train_clip_images
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.image_size = image_size
self.tokenizer = tokenizer
self.precision = precision
self.transform = ResizeLongestSide(image_size)
self.masks_process_with_clip = masks_process_with_clip
self.pad_train_clip_images = pad_train_clip_images
self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) if preprocessor_config == '' else CLIPImageProcessor.from_pretrained(preprocessor_config)
self.transform_clip = ResizeLongestSide(self.clip_image_processor.size['shortest_edge'])
DATA_DIR = os.path.join(base_image_dir, "llava_dataset")
self.vqa_image_root = os.path.join(base_image_dir, "coco/train2017")
with open(os.path.join(DATA_DIR, "{}.json".format(vqa_data))) as f:
vqa_data = json.load(f)
self.vqa_data = vqa_data
print("vqa_data: ", len(self.vqa_data))
def __len__(self):
return self.samples_per_epoch
def preprocess(self, x: torch.Tensor, decoder_image_size) -> torch.Tensor:
"""Normalize pixel values and pad to a square input."""
x = (x - self.pixel_mean) / self.pixel_std
h, w = x.shape[-2:]
padh = decoder_image_size - h
padw = decoder_image_size - w
x = F.pad(x, (0, padw, 0, padh))
return x
def __getitem__(self, idx):
idx = random.randint(0, len(self.vqa_data) - 1)
item = self.vqa_data[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]
if self.pad_train_clip_images:
image_clip = self.transform_clip.apply_image(image)
clip_resize = image_clip.shape[:2]
image_clip = self.preprocess(torch.from_numpy(image_clip).permute(2, 0, 1).contiguous(), self.clip_image_processor.size['shortest_edge'])
else:
image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[
"pixel_values"
][0]
clip_resize = image_clip.shape[-2:]
image = self.transform.apply_image(image)
resize = image.shape[:2]
conv = conversation_lib.default_conversation.copy()
source = item["conversations"]
source = preprocess_multimodal(
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]:
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
image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous(), self.img_size)
masks = torch.rand(0, *ori_size)
label = torch.ones(ori_size) * self.ignore_label
if self.masks_process_with_clip:
mask_shape = image_clip.shape[-1]
if len(masks) == 0:
masks = torch.zeros(0, mask_shape, mask_shape)
else:
masks = transform_mask(masks, mask_shape)
return (
image_path,
image,
image_clip,
conversations,
masks,
label,
resize,
clip_resize,
questions,
sampled_classes,
)
def transform_mask(masks, size):
height, width = masks.shape[-2:]
short, long = (width, height) if width <= height else (height, width)
requested_new_short = size
new_short, new_long = requested_new_short, int(requested_new_short * long / short)
new_shape = (new_long, new_short) if width <= height else (new_short, new_long)
masks = F.interpolate(masks[None].float(), size=new_shape, mode="nearest")[0].bool()
orig_height, orig_width = new_shape
crop_height, crop_width = size, size
crop_height, crop_width = int(crop_height), int(crop_width)
top = (orig_height - crop_height) // 2
bottom = top + crop_height
left = (orig_width - crop_width) // 2
right = left + crop_width
assert top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width
masks = masks[..., top:bottom, left:right]
return masks
def center_crop_image(image, size):
orig_height, orig_width = image.shape[:2]
crop_height, crop_width = size, size
crop_height, crop_width = int(crop_height), int(crop_width)
top = (orig_height - crop_height) // 2
bottom = top + crop_height
left = (orig_width - crop_width) // 2
right = left + crop_width
assert top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width
image = image[top:bottom, left:right]
return image |