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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
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",
):
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.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
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) -> 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):
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]
image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[
"pixel_values"
][
0
] # preprocess image for clip
image = self.transform.apply_image(image) # preprocess image for sam
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]:
# 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"{i}"
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())
masks = torch.rand(0, *ori_size)
label = torch.ones(ori_size) * self.ignore_label
return (
image_path,
image,
image_clip,
conversations,
masks,
label,
resize,
questions,
sampled_classes,
)
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