File size: 9,620 Bytes
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import os
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor
import transformers
from pycocotools import mask as mask_utils
from model.segment_anything.utils.transforms import ResizeLongestSide
from model.llava import conversation as conversation_lib
from .utils import (
ANSWER_LIST,
DEFAULT_IM_END_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_PATCH_TOKEN,
DEFAULT_IMAGE_TOKEN,
LONG_QUESTION_LIST,
SHORT_QUESTION_LIST,
)
class CustomSegDataset(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,
json_file_path,
samples_per_epoch=500 * 8 * 2 * 10,
precision: str = "fp32",
image_size: int = 1024,
num_classes_per_sample: int = 3,
exclude_val=False,
seg_token_num=1,
pad_train_clip_images=False,
masks_process_with_clip=False,
preprocessor_config='',
inference=False,
):
self.inference = inference
self.pad_train_clip_images = pad_train_clip_images
self.masks_process_with_clip = masks_process_with_clip
self.base_image_dir = base_image_dir
self.image_size = image_size
self.tokenizer = tokenizer
self.precision = precision
self.samples_per_epoch = samples_per_epoch
self.seg_token_num = seg_token_num
self.transform = ResizeLongestSide(image_size)
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'])
self.long_question_list = LONG_QUESTION_LIST
with open(json_file_path, 'r') as f:
self.data = json.load(f)
print(f"Loaded {len(self.data)} custom segmentation samples")
def __len__(self):
if self.samples_per_epoch == 0:
return len(self.data)
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):
if not self.inference:
idx = np.random.randint(0, len(self.data))
image_info = self.data[idx]
image_path = os.path.join(self.base_image_dir, f"{image_info['id']}.jpg")
img = cv2.imread(image_path)
if img is None:
print(f"Warning: Could not read image {image_path}")
if len(self.data) > 1:
return self[(idx + 1) % len(self.data)]
else:
raise FileNotFoundError(f"Cannot load any images from {self.base_image_dir}")
images = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ori_size = images.shape[:2]
if self.pad_train_clip_images:
image_clip = self.transform_clip.apply_image(images)
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(images, return_tensors="pt")["pixel_values"][0]
clip_resize = image_clip.shape[-2:]
images = self.transform.apply_image(images)
resize = images.shape[:2]
segs = image_info['ann_list']
masks = []
if len(segs) == 0:
print(f"Warning: No annotations for {image_path}")
if len(self.data) > 1:
return self[(idx + 1) % len(self.data)]
else:
raise ValueError(f"No valid annotations in dataset")
valid_masks = []
for ann in segs:
points = ann['segmentation']
if isinstance(points[0], list):
points = points[0]
if len(points) < 6:
print(f"Skipping invalid polygon (<3 points): {points}")
continue
xs = points[0::2]
ys = points[1::2]
if (max(xs) - min(xs) < 1) and (max(ys) - min(ys) < 1):
print(f"Skipping degenerate polygon (same point repeated): {points}")
continue
try:
rle = mask_utils.frPyObjects([points], image_info['height'], image_info['width'])
m = mask_utils.decode(rle)
except Exception as e:
print(f"⚠️ Error decoding mask for {image_info['id']}: {e}")
continue
if len(m.shape) > 2:
m = np.sum(m, axis=2)
m = m.astype(np.uint8)
if np.sum(m > 0) == 0:
print(f"⚠️ Skipping empty mask for image {image_info['id']}")
continue
valid_masks.append(m)
if len(valid_masks) == 0:
print(f"⚠️ No valid masks in {image_info['id']}, skipping this sample.")
if len(self.data) > 1:
return self[(idx + 1) % len(self.data)]
else:
raise ValueError(f"No valid masks in dataset for {image_info['id']}")
masks = valid_masks
questions = image_info['questions']
answers = image_info['answers']
reasoning_types = image_info.get('reasoning_types', ['unknown'])
category = reasoning_types[0] if isinstance(reasoning_types, list) and len(reasoning_types) > 0 else (reasoning_types if isinstance(reasoning_types, str) else 'unknown')
conversations = []
conv = conversation_lib.default_conversation.copy()
seg_token = "[SEG]" if self.seg_token_num == 1 else ' '.join([f"[SEG{i}]" for i in range(self.seg_token_num)])
questions = image_info['questions']
answers = image_info['answers']
question = questions[0]
answer = answers[0]
conversations = []
conv = conversation_lib.default_conversation.copy()
seg_token = "[SEG]" if self.seg_token_num == 1 else ' '.join([f"[SEG{i}]" for i in range(self.seg_token_num)])
conv.messages = []
conv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + "\n" + question)
conv.append_message(conv.roles[1], seg_token)
conversations.append(conv.get_prompt())
images = self.preprocess(
torch.from_numpy(images).permute(2, 0, 1).contiguous(),
self.img_size
)
masks = np.stack(masks, axis=0)
masks = torch.from_numpy(masks)
label = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label
if self.masks_process_with_clip:
mask_shape = image_clip.shape[-1]
masks = transform_mask(masks, mask_shape)
if self.inference:
return (
image_path, images, image_clip, conversations,
masks, label, resize, clip_resize,
questions, questions,
False,
True,
category,
answers
)
else:
return (
image_path, images, image_clip, conversations,
masks, label, resize, clip_resize,
questions, questions,
False,
category,
answers
)
def transform_mask(masks, size):
"""与 MultiReasonSegDataset 相同的掩码变换函数"""
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
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 |