Table Of Contents
Table Of Contents

Source code for gluoncv.data.ade20k.segmentation

"""Pascal ADE20K Semantic Segmentation Dataset."""
import os
from PIL import Image
import numpy as np
import mxnet as mx
from ..segbase import SegmentationDataset

[docs]class ADE20KSegmentation(SegmentationDataset): """ADE20K Semantic Segmentation Dataset. Parameters ---------- root : string Path to VOCdevkit folder. Default is '$(HOME)/mxnet/datasplits/ade' split: string 'train', 'val' or 'test' transform : callable, optional A function that transforms the image Examples -------- >>> from mxnet.gluon.data.vision import transforms >>> # Transforms for Normalization >>> input_transform = transforms.Compose([ >>> transforms.ToTensor(), >>> transforms.Normalize([.485, .456, .406], [.229, .224, .225]), >>> ]) >>> # Create Dataset >>> trainset = gluoncv.data.ADE20KSegmentation(split='train', transform=input_transform) >>> # Create Training Loader >>> train_data = gluon.data.DataLoader( >>> trainset, 4, shuffle=True, last_batch='rollover', >>> num_workers=4) """ # pylint: disable=abstract-method BASE_DIR = 'ADEChallengeData2016' NUM_CLASS = 150 CLASSES = ("wall", "building, edifice", "sky", "floor, flooring", "tree", "ceiling", "road, route", "bed", "windowpane, window", "grass", "cabinet", "sidewalk, pavement", "person, individual, someone, somebody, mortal, soul", "earth, ground", "door, double door", "table", "mountain, mount", "plant, flora, plant life", "curtain, drape, drapery, mantle, pall", "chair", "car, auto, automobile, machine, motorcar", "water", "painting, picture", "sofa, couch, lounge", "shelf", "house", "sea", "mirror", "rug, carpet, carpeting", "field", "armchair", "seat", "fence, fencing", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "bathtub, bathing tub, bath, tub", "railing, rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace, hearth, open fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs, steps", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table, cocktail table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove, kitchen stove, range, kitchen range, cooking stove", "palm, palm tree", "kitchen island", "computer, computing machine, computing device, data processor, " "electronic computer, information processing system", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus, autobus, coach, charabanc, double-decker, jitney, motorbus, " "motorcoach, omnibus, passenger vehicle", "towel", "light, light source", "truck, motortruck", "tower", "chandelier, pendant, pendent", "awning, sunshade, sunblind", "streetlight, street lamp", "booth, cubicle, stall, kiosk", "television receiver, television, television set, tv, tv set, idiot " "box, boob tube, telly, goggle box", "airplane, aeroplane, plane", "dirt track", "apparel, wearing apparel, dress, clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "swimming pool, swimming bath, natatorium", "stool", "barrel, cask", "basket, handbasket", "waterfall, falls", "tent, collapsible shelter", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name, brand name, brand, marque", "microwave, microwave oven", "pot, flowerpot", "animal, animate being, beast, brute, creature, fauna", "bicycle, bike, wheel, cycle", "lake", "dishwasher, dish washer, dishwashing machine", "screen, silver screen, projection screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light, traffic signal, stoplight", "tray", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, " "dustbin, trash barrel, trash bin", "fan", "pier, wharf, wharfage, dock", "crt screen", "plate", "monitor, monitoring device", "bulletin board, notice board", "shower", "radiator", "glass, drinking glass", "clock", "flag") def __init__(self, root=os.path.expanduser('~/.mxnet/datasets/ade'), split='train', mode=None, transform=None, **kwargs): super(ADE20KSegmentation, self).__init__(root, split, mode, transform, **kwargs) root = os.path.join(root, self.BASE_DIR) assert os.path.exists(root), "Please setup the dataset using" + \ "scripts/datasets/ade20k.py" self.images, self.masks = _get_ade20k_pairs(root, split) assert (len(self.images) == len(self.masks)) if len(self.images) == 0: raise(RuntimeError("Found 0 images in subfolders of: \ " + root + "\n")) def __getitem__(self, index): img = Image.open(self.images[index]).convert('RGB') if self.mode == 'test': img = self._img_transform(img) if self.transform is not None: img = self.transform(img) return img, os.path.basename(self.images[index]) mask = Image.open(self.masks[index]) # synchrosized transform if self.mode == 'train': img, mask = self._sync_transform(img, mask) elif self.mode == 'val': img, mask = self._val_sync_transform(img, mask) else: assert self.mode == 'testval' img, mask = self._img_transform(img), self._mask_transform(mask) # general resize, normalize and toTensor if self.transform is not None: img = self.transform(img) return img, mask def _mask_transform(self, mask): return mx.nd.array(np.array(mask), mx.cpu(0)).astype('int32') - 1 def __len__(self): return len(self.images) @property def classes(self): """Category names.""" return type(self).CLASSES @property def pred_offset(self): return 1
def _get_ade20k_pairs(folder, mode='train'): img_paths = [] mask_paths = [] if mode == 'train': img_folder = os.path.join(folder, 'images/training') mask_folder = os.path.join(folder, 'annotations/training') else: img_folder = os.path.join(folder, 'images/validation') mask_folder = os.path.join(folder, 'annotations/validation') for filename in os.listdir(img_folder): basename, _ = os.path.splitext(filename) if filename.endswith(".jpg"): imgpath = os.path.join(img_folder, filename) maskname = basename + '.png' maskpath = os.path.join(mask_folder, maskname) if os.path.isfile(maskpath): img_paths.append(imgpath) mask_paths.append(maskpath) else: print('cannot find the mask:', maskpath) return img_paths, mask_paths