utils/sem_seg_dataset.py

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,
        )