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import glob
import json
import os
import random
from PIL import Image
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from pycocotools.coco import COCO
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 ANSWER_LIST, SHORT_QUESTION_LIST
parent_dir = os.path.dirname(os.path.abspath(__file__))
def init_objects365(base_dir):
objects365_classes = []
with open(os.path.join(parent_dir, "objects365_classes.txt")) as f:
for line in f.readlines():
objects365_classes.append(line.strip().split(": ")[-1])
objects365_classes = np.array(objects365_classes)
with open(os.path.join(base_dir, "object365", "image2bboxes.json")) as f:
image2bboxes = json.load(f)
objects365_images = list(image2bboxes.keys())
objects365_bboxes = []
for file_name in objects365_images:
bboxes = image2bboxes[file_name]
objects365_bboxes.append(bboxes)
print("objects365: ", len(objects365_images))
objects365_images = [os.path.join(base_dir, 'object365/images/train', file_name) for file_name in objects365_images]
return objects365_classes, objects365_images, objects365_bboxes
def init_cocostuff(base_dir):
cocostuff_classes = []
with open(os.path.join(parent_dir, "cocostuff_classes.txt")) as f:
for line in f.readlines()[1:]:
cocostuff_classes.append(line.strip().split(": ")[-1])
cocostuff_classes = np.array(cocostuff_classes)
cocostuff_images = []
cocostuff_labels = glob.glob(
os.path.join(base_dir, "cocostuff", "train2017", "*.png")
)
cocostuff_images = [
x.replace(".png", ".jpg").replace("cocostuff", "coco2017") for x in cocostuff_labels
]
with open(os.path.join(base_dir, "cocostuff", "annotations", "image2bboxes.json")) as f:
image2bboxes = json.load(f)
cocostuff_bboxes = []
delete_index_list = []
for i, image_path in enumerate(cocostuff_images):
file_name = image_path.split(os.sep)[-1]
if file_name not in image2bboxes:
delete_index_list.append(i)
continue
bboxes = image2bboxes[file_name]
cocostuff_bboxes.append(bboxes)
for index in sorted(delete_index_list, reverse=True):
del cocostuff_labels[index]
del cocostuff_images[index]
print("cocostuff: ", len(cocostuff_images))
return cocostuff_classes, cocostuff_images, cocostuff_labels, cocostuff_bboxes
def init_paco_lvis(base_dir):
coco_api_paco_lvis = COCO(
os.path.join(
base_dir, "vlpart", "paco", "annotations", "paco_lvis_v1_train.json"
)
)
all_classes = coco_api_paco_lvis.loadCats(coco_api_paco_lvis.getCatIds())
class_map_paco_lvis = {}
for cat in all_classes:
cat_split = cat["name"].strip().split(":")
if len(cat_split) == 1:
name = cat_split[0].split("_(")[0]
else:
assert len(cat_split) == 2
obj, part = cat_split
obj = obj.split("_(")[0]
part = part.split("_(")[0]
name = (obj, part)
class_map_paco_lvis[cat["id"]] = name
img_ids = coco_api_paco_lvis.getImgIds()
print("paco_lvis: ", len(img_ids))
return class_map_paco_lvis, img_ids, coco_api_paco_lvis
class SegDetDataset(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_dir,
tokenizer,
vision_tower,
samples_per_epoch=500 * 8 * 2 * 10,
precision: str = "fp32",
num_classes_per_sample: int = 3,
exclude_val=False,
general_segdet_data="objects365||cocostuff||paco_lvis",
general_segdet_sample_rate=[2,1,1]
):
self.exclude_val = exclude_val
self.samples_per_epoch = samples_per_epoch
self.num_classes_per_sample = num_classes_per_sample
self.base_dir = base_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)
self.short_question_list = SHORT_QUESTION_LIST
self.answer_list = ANSWER_LIST
self.data2list = {}
self.data2classes = {}
self.general_segdet_datas = general_segdet_data.split("||")
num_images = []
for ds in self.general_segdet_datas:
if ds == "cocostuff":
classes, images, labels, bboxes = eval("init_{}".format(ds))(base_dir)
self.data2list[ds] = (images, labels, bboxes)
elif ds == "objects365":
classes, images, bboxes = eval("init_{}".format(ds))(base_dir)
self.data2list[ds] = (images, bboxes)
else:
classes, images, labels = eval("init_{}".format(ds))(base_dir)
self.data2list[ds] = (images, labels)
self.data2classes[ds] = classes
num_images.append(len(images))
sample_rate = np.array(general_segdet_sample_rate)
self.sample_rate = sample_rate / sample_rate.sum()
if "cocostuff" in self.general_segdet_datas:
self.cocostuff_class2index = {
c: i for i, c in enumerate(self.data2classes["cocostuff"])
}
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.general_segdet_datas))), p=self.sample_rate)
ds = self.general_segdet_datas[ds]
if ds in ["paco_lvis"]:
class_map = self.data2classes[ds]
img_ids, coco_api = self.data2list[ds]
idx = random.randint(0, len(img_ids) - 1)
img_id = img_ids[idx]
image_info = coco_api.loadImgs([img_id])[0]
file_name = image_info["file_name"]
if ds == "pascal_part":
file_name = os.path.join(
"VOCdevkit", "VOC2010", "JPEGImages", file_name
)
image_path = os.path.join(self.base_dir, "vlpart", ds, file_name)
elif ds == "paco_lvis":
image_path = os.path.join(self.base_dir, "coco2017", file_name)
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# preprocess image for clip
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]
original_size = image.shape[:2]
image = self.transform(images=image, return_tensors="pt")['pixel_values'][0]
resize = image.shape[:2]
annIds = coco_api.getAnnIds(imgIds=image_info["id"])
anns = coco_api.loadAnns(annIds)
anns_category2instances = dict()
for ann in anns:
category_id = ann['category_id']
if category_id not in anns_category2instances:
anns_category2instances[category_id] = []
anns_category2instances[category_id].append(ann)
if len(anns_category2instances) == 0:
return self.__getitem__(0)
if len(anns_category2instances) >= self.num_classes_per_sample:
sampled_anns = np.random.choice(
list(anns_category2instances.keys()), size=self.num_classes_per_sample, replace=False
).tolist()
else:
sampled_anns = list(anns_category2instances.keys())
sampled_classes = []
for category_id in sampled_anns:
sampled_cls = class_map[category_id]
if isinstance(sampled_cls, tuple):
obj, part = sampled_cls
if random.random() < 0.5:
name = obj + " " + part
else:
name = "the {} of the {}".format(part, obj)
else:
name = sampled_cls
name = name.replace('_', ' ')
sampled_classes.append(name)
elif ds in ["cocostuff"]:
image, labels, bboxes_all = self.data2list[ds]
idx = random.randint(0, len(image) - 1)
image_path = image[idx]
label_path = labels[idx]
bboxes = bboxes_all[idx]
label = Image.open(label_path)
label = np.array(label)
if ds == "ade20k":
label[label == 0] = 255
label -= 1
label[label == 254] = 255
elif ds == "cocostuff":
for c, i in self.cocostuff_class2index.items():
if "-" in c:
label[label == i] = 255
img = cv2.imread(image_path)
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# preprocess image for clip
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]
original_size = image.shape[:2]
image = self.transform(images=image, return_tensors="pt")['pixel_values'][0]
resize = image.shape[:2]
unique_label = np.unique(label).tolist()
if 255 in unique_label:
unique_label.remove(255)
if len(unique_label) == 0:
return self.__getitem__(0)
classes = [self.data2classes[ds][class_id] for class_id in unique_label]
if len(classes) >= self.num_classes_per_sample:
sampled_classes = np.random.choice(
classes, size=self.num_classes_per_sample, replace=False
).tolist()
else:
sampled_classes = classes
elif ds in ['objects365']:
image, bboxes_all = self.data2list[ds]
idx = random.randint(0, len(image) - 1)
image_path = image[idx]
bboxes = bboxes_all[idx]
img = cv2.imread(image_path)
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# preprocess image for clip
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]
original_size = image.shape[:2]
image = self.transform(images=image, return_tensors="pt")['pixel_values'][0]
resize = image.shape[:2]
unique_label = set()
for bbox_info in bboxes:
unique_label.add(bbox_info['category_id'])
unique_label = list(unique_label)
if len(unique_label) == 0:
return self.__getitem__(0)
classes = [self.data2classes[ds][class_id] for class_id in unique_label]
if len(classes) >= self.num_classes_per_sample:
sampled_classes = np.random.choice(
classes, size=self.num_classes_per_sample, replace=False
).tolist()
else:
sampled_classes = classes
questions = []
answers = []
class_ids = []
bboxes_labels = []
for i, sampled_cls in enumerate(sampled_classes):
text = sampled_cls
if ds in ['objects365']:
text = random.sample(text.split('/'), 1)[0]
assert len(text.split("||")) == 1
question_template = random.choice(self.short_question_list)
questions.append(question_template.format(class_name=text.lower()))
answers.append(random.choice(self.answer_list))
if ds in ["paco_lvis", "pascal_part"]:
category_id = sampled_anns[i]
cur_bboxes = [instance['bbox'] for instance in anns_category2instances[category_id]]
cur_bboxes = torch.tensor(cur_bboxes).view(-1, 4)
# xywh to x1y1x2y2
cur_bboxes[:, 2:] += cur_bboxes[:, :2]
cur_bboxes[:, 0::2].clamp_(min=0, max=original_size[1])
cur_bboxes[:, 1::2].clamp_(min=0, max=original_size[0])
keep = (cur_bboxes[:, 3] > cur_bboxes[:, 1]) & (cur_bboxes[:, 2] > cur_bboxes[:, 0])
cur_bboxes = cur_bboxes[keep]
cur_bboxes = box_xyxy_to_cxcywh(cur_bboxes)
cur_bboxes = cur_bboxes / torch.tensor([original_size[1], original_size[0], original_size[1], original_size[0]], dtype=torch.float32)
if len(cur_bboxes) == 0:
return self.__getitem__(0)
bboxes_labels.append(cur_bboxes)
continue
class_id = self.data2classes[ds].tolist().index(sampled_cls)
class_ids.append(class_id)
if ds in ['objects365']:
cur_bboxes = [bbox['bbox'] for bbox in bboxes if bbox['category_id'] == class_id]
else:
cur_bboxes = [bbox['bbox'] for bbox in bboxes if bbox['category_id']-1 == class_id]
cur_bboxes = cur_bboxes[:100]
assert len(cur_bboxes) > 0
cur_bboxes = torch.tensor(cur_bboxes).view(-1, 4)
# xywh to x1y1x2y2
cur_bboxes[:, 2:] += cur_bboxes[:, :2]
cur_bboxes[:, 0::2].clamp_(min=0, max=original_size[1])
cur_bboxes[:, 1::2].clamp_(min=0, max=original_size[0])
keep = (cur_bboxes[:, 3] > cur_bboxes[:, 1]) & (cur_bboxes[:, 2] > cur_bboxes[:, 0])
cur_bboxes = cur_bboxes[keep]
cur_bboxes = box_xyxy_to_cxcywh(cur_bboxes)
cur_bboxes = cur_bboxes / torch.tensor([original_size[1], original_size[0], original_size[1], original_size[0]], dtype=torch.float32)
if len(cur_bboxes) == 0:
return self.__getitem__(0)
bboxes_labels.append(cur_bboxes)
bboxes_valid = [1]*len(bboxes_labels)
masks_valid = [1]*len(bboxes_labels)
conversations = []
conv = conversation_lib.default_conversation.copy()
i = 0
while i < len(questions):
conv.messages = []
conv.append_message(conv.roles[0], questions[i])
conv.append_message(conv.roles[1], answers[i])
conversations.append(conv.get_prompt())
i += 1
if ds in ["paco_lvis", "pascal_part"]:
masks = []
for category_id in sampled_anns:
try:
cur_anns = anns_category2instances[category_id]
cur_mask = None
for ann in cur_anns:
if cur_mask is None:
cur_mask = coco_api.annToMask(ann)
else:
cur_mask = cur_mask | coco_api.annToMask(ann)
assert cur_mask is not None
masks.append(cur_mask)
except Exception as e:
print(e)
return self.__getitem__(0)
masks = np.stack(masks, axis=0)
masks = torch.from_numpy(masks)
label = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label
elif ds in ['objects365']:
masks = torch.rand(len(bboxes_labels), *original_size)
label = torch.ones(original_size) * self.ignore_label
masks_valid = [0]*len(bboxes_labels)
else:
label = torch.from_numpy(label).long()
masks = []
for class_id in class_ids:
masks.append(label == class_id)
masks = torch.stack(masks, dim=0)
return (
image_path,
image,
image_clip,
conversations,
masks,
label,
bboxes_labels,
bboxes_valid,
masks_valid,
resize,
questions,
sampled_classes,
)
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