import glob
import json
import os
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from pycocotools.coco import COCO
from model.segment_anything.utils.transforms import ResizeLongestSide
from torchvision import transforms
def init_mapillary(base_image_dir):
mapillary_data_root = os.path.join(base_image_dir, "mapillary")
with open(os.path.join(mapillary_data_root, "config_v2.0.json")) as f:
mapillary_classes = json.load(f)["labels"]
mapillary_classes = [x["readable"].lower() for x in mapillary_classes]
mapillary_classes = np.array(mapillary_classes)
mapillary_labels = sorted(
glob.glob(
os.path.join(mapillary_data_root, "training", "v2.0", "labels", "*.png")
)
)
mapillary_images = [
x.replace(".png", ".jpg").replace("v2.0/labels", "images")
for x in mapillary_labels
]
print("mapillary: ", len(mapillary_images))
return mapillary_classes, mapillary_images, mapillary_labels
def init_ade20k(base_image_dir):
with open("utils/ade20k_classes.json", "r") as f:
ade20k_classes = json.load(f)
ade20k_classes = np.array(ade20k_classes)
image_ids = sorted(
os.listdir(os.path.join(base_image_dir, "ade20k/images", "training"))
)
ade20k_image_ids = []
for x in image_ids:
if x.endswith(".jpg"):
ade20k_image_ids.append(x[:-4])
ade20k_images = []
for image_id in ade20k_image_ids: # self.descriptions:
ade20k_images.append(
os.path.join(
base_image_dir,
"ade20k",
"images",
"training",
"{}.jpg".format(image_id),
)
)
ade20k_labels = [
x.replace(".jpg", ".png").replace("images", "annotations")
for x in ade20k_images
]
print("ade20k: ", len(ade20k_images))
return ade20k_classes, ade20k_images, ade20k_labels
def init_paco_lvis(base_image_dir):
coco_api_paco_lvis = COCO(
os.path.join(
base_image_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
def init_pascal_part(base_image_dir):
coco_api_pascal_part = COCO(
os.path.join(base_image_dir, "vlpart", "pascal_part", "train.json")
)
all_classes = coco_api_pascal_part.loadCats(coco_api_pascal_part.getCatIds())
class_map_pascal_part = {}
for cat in all_classes:
cat_main, cat_part = cat["name"].strip().split(":")
name = (cat_main, cat_part)
class_map_pascal_part[cat["id"]] = name
img_ids = coco_api_pascal_part.getImgIds()
print("pascal_part: ", len(img_ids))
return class_map_pascal_part, img_ids, coco_api_pascal_part
class SemSegDataset(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,
samples_per_epoch=500 * 8 * 2 * 10,
precision: str = "fp32",
image_size: int = 224,
num_classes_per_sample: int = 3,
exclude_val=False,
sem_seg_data="ade20k||pascal_part||mapillary",
model_type="ori",
transform=ResizeLongestSide(1024),
):
self.model_type = model_type
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 = transform
self.image_preprocessor = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((image_size, image_size), interpolation=3),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
self.data2list = {}
self.data2classes = {}
self.sem_seg_datas = sem_seg_data.split("||")
for ds in self.sem_seg_datas:
classes, images, labels = eval("init_{}".format(ds))(base_image_dir)
self.data2list[ds] = (images, labels)
self.data2classes[ds] = classes
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."""
if self.model_type=="hq":
h, w = x.shape[-2:]
padh = self.img_size - h
padw = self.img_size - w
x = F.pad(x, (0, padw, 0, padh), value=128)
# Normalize colors
x = (x - self.pixel_mean) / self.pixel_std
if self.model_type=="effi":
x = F.interpolate(x.unsqueeze(0), (self.img_size, self.img_size), mode="bilinear").squeeze(0)
else:
# 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 = random.randint(0, len(self.sem_seg_datas) - 1)
ds = self.sem_seg_datas[ds]
if ds in ["pascal_part"]:
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"]
file_name = os.path.join(
"VOCdevkit", "VOC2010", "JPEGImages", file_name
)
image_path = os.path.join(self.base_image_dir, "vlpart", ds, file_name)
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# preprocess image for evf
image_evf = self.image_preprocessor(image)
image = self.transform.apply_image(image) # preprocess image for sam
resize = image.shape[:2]
annIds = coco_api.getAnnIds(imgIds=image_info["id"])
anns = coco_api.loadAnns(annIds)
if len(anns) == 0:
return self.__getitem__(0)
if len(anns) >= self.num_classes_per_sample:
sampled_anns = np.random.choice(
anns, size=self.num_classes_per_sample, replace=False
).tolist()
else:
sampled_anns = anns
sampled_classes = []
for ann in sampled_anns:
sampled_cls = class_map[ann["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
sampled_classes.append(name)
elif ds in ["ade20k", "mapillary"]:
image, labels = self.data2list[ds]
idx = random.randint(0, len(image) - 1)
image_path = image[idx]
label_path = labels[idx]
label = Image.open(label_path)
label = np.array(label)
if ds == "ade20k":
label[label == 0] = 255
label -= 1
label[label == 254] = 255
img = cv2.imread(image_path)
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# preprocess image for evf
image_evf = self.image_preprocessor(image)
image = self.transform.apply_image(image) # preprocess image for sam
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
class_ids = []
for sampled_cls in sampled_classes:
assert len(sampled_cls.split("||")) == 1
if ds in ["paco_lvis", "pascal_part"]:
continue
class_id = self.data2classes[ds].tolist().index(sampled_cls)
class_ids.append(class_id)
image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous())
if ds in ["pascal_part"]:
masks = []
for ann in sampled_anns:
try:
masks.append(coco_api.annToMask(ann))
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
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)
# sampled_classes = ["all "+_ for _ in sampled_classes]
return (
image_path,
image,
image_evf,
masks,
label,
resize,
sampled_classes,
)