所以，我正在尝试使用ade20k数据集进行语义分割，并且已经使用gluon-cv和github的base编写了一些代码。当我将拆分模式设置为train和val.但为了测试，它总是失败
这是我的完整代码：
import os
import random
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image, ImageOps, ImageFilter
class ADE20KSegmentation(data.Dataset):
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, split='train',
base_size=256, crop_size=224, mode=None, transform=None, target_transform=None, **kwargs):
super(ADE20KSegmentation, self).__init__()
# assert exists and prepare dataset automatically
root = os.path.join(root, self.BASE_DIR)
self.mode = mode if mode is not None else split
self.transform = transform
self.target_transform = target_transform
self.base_size = base_size
self.crop_size = crop_size
assert os.path.exists(root), "Please setup the dataset using" +
"encoding/scripts/prepare_ade20k.py"
self.images, self.masks = _get_ade20k_pairs(root, split)
if split != 'test':
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 = PIL.Image.open(self.images[index]).convert('RGB')
if self.mode == 'test':
if self.transform is not None:
img = self.transform(img)
return img, os.path.basename(self.images[index])
mask = PIL.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'
mask = self._mask_transform(mask)
# general resize, normalize and toTensor
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
mask = self.target_transform(mask)
return img, mask
def _val_sync_transform(self, img, mask):
outsize = self.crop_size
short_size = outsize
w, h = img.size
if w > h:
oh = short_size
ow = int(1.0 * w * oh / h)
else:
ow = short_size
oh = int(1.0 * h * ow / w)
img = img.resize((ow, oh), PIL.Image.BILINEAR)
mask = mask.resize((ow, oh), PIL.Image.NEAREST)
# center crop
w, h = img.size
x1 = int(round((w - outsize) / 2.))
y1 = int(round((h - outsize) / 2.))
img = img.crop((x1, y1, x1 + outsize, y1 + outsize))
mask = mask.crop((x1, y1, x1 + outsize, y1 + outsize))
# final transform
img, mask = self._img_transform(img), self._mask_transform(mask)
return img, mask
def _sync_transform(self, img, mask):
# random mirror
if random.random() < 0.5:
img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
mask = mask.transpose(PIL.Image.FLIP_LEFT_RIGHT)
crop_size = self.crop_size
# random scale (short edge)
w, h = img.size
long_size = random.randint(int(self.base_size*0.5), int(self.base_size*2.0))
if h > w:
oh = long_size
ow = int(1.0 * w * long_size / h + 0.5)
short_size = ow
else:
ow = long_size
oh = int(1.0 * h * long_size / w + 0.5)
short_size = oh
img = img.resize((ow, oh), PIL.Image.BILINEAR)
mask = mask.resize((ow, oh), PIL.Image.NEAREST)
# pad crop
if short_size < crop_size:
padh = crop_size - oh if oh < crop_size else 0
padw = crop_size - ow if ow < crop_size else 0
img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=0)
# random crop crop_size
w, h = img.size
x1 = random.randint(0, w - crop_size)
y1 = random.randint(0, h - crop_size)
img = img.crop((x1, y1, x1+crop_size, y1+crop_size))
mask = mask.crop((x1, y1, x1+crop_size, y1+crop_size))
# gaussian blur as in PSP
if random.random() < 0.5:
img = img.filter(ImageFilter.GaussianBlur(
radius=random.random()))
# final transform
return img, self._mask_transform(mask)
def _img_transform(self, img):
return np.array(img)
def _mask_transform(self, mask):
return torch.from_numpy(np.array(mask)).long()
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, split='train'):
def get_path_pairs(img_folder, mask_folder):
img_paths = []
mask_paths = []
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
if split == 'train':
img_folder = os.path.join(folder, 'images/training')
mask_folder = os.path.join(folder, 'annotations/training')
img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
print('len(img_paths):', len(img_paths))
assert len(img_paths) == 20210
elif split == 'val':
img_folder = os.path.join(folder, 'images/validation')
mask_folder = os.path.join(folder, 'annotations/validation')
img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
print('len(img_paths):', len(img_paths))
assert len(img_paths) == 2000
else:
assert split == 'trainval'
train_img_folder = os.path.join(folder, 'images/training')
train_mask_folder = os.path.join(folder, 'annotations/training')
val_img_folder = os.path.join(folder, 'images/validation')
val_mask_folder = os.path.join(folder, 'annotations/validation')
train_img_paths, train_mask_paths = get_path_pairs(train_img_folder, train_mask_folder)
val_img_paths, val_mask_paths = get_path_pairs(val_img_folder, val_mask_folder)
img_paths = train_img_paths + val_img_paths
mask_paths = train_mask_paths + val_mask_paths
assert len(img_paths) == 22210
return img_paths, mask_paths
if __name__ == '__main__':
dataset = ADE20KSegmentation(root='/content/dataset', split='test')
img, label = dataset[0]
这就是错误
AssertionError Traceback (most recent call last)
<ipython-input-6-75127ee3fbef> in <module>()
228
229 if __name__ == '__main__':
--> 230 dataset = ADE20KSegmentation(root='/content/dataset', split='test')
231 img, label = dataset[0]
1 frames
<ipython-input-6-75127ee3fbef> in __init__(self, root, split, base_size, crop_size, mode, transform, target_transform, **kwargs)
82 self.crop_size = crop_size
83 assert os.path.exists(root), "Please setup the dataset using" + "encoding/scripts/prepare_ade20k.py"
---> 84 self.images, self.masks = _get_ade20k_pairs(root, split)
85 if split != 'test':
86 assert (len(self.images) == len(self.masks))
<ipython-input-6-75127ee3fbef> in _get_ade20k_pairs(folder, split)
215 assert len(img_paths) == 2000
216 else:
--> 217 assert split == 'trainval'
218 train_img_folder = os.path.join(folder, 'images/training')
219 train_mask_folder = os.path.join(folder, 'annotations/training')
AssertionError:
我认为主要的错误是在_mask_transform函数上。但它让我感到困惑，因为它对训练和val都很有效，但在测试中失败了。我已经改变了定义上的al分割模式进行测试，但仍然不能很好地工作
没有为测试拆分编写代码。
if split == 'train':
img_folder = os.path.join(folder, 'images/training')
mask_folder = os.path.join(folder, 'annotations/training')
img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
print('len(img_paths):', len(img_paths))
assert len(img_paths) == 20210
elif split == 'val':
img_folder = os.path.join(folder, 'images/validation')
mask_folder = os.path.join(folder, 'annotations/validation')
img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
print('len(img_paths):', len(img_paths))
assert len(img_paths) == 2000
else:
assert split == 'trainval'
train_img_folder = os.path.join(folder, 'images/training')
train_mask_folder = os.path.join(folder, 'annotations/training')
val_img_folder = os.path.join(folder, 'images/validation')
val_mask_folder = os.path.join(folder, 'annotations/validation')
train_img_paths, train_mask_paths = get_path_pairs(train_img_folder, train_mask_folder)
val_img_paths, val_mask_paths = get_path_pairs(val_img_folder, val_mask_folder)
img_paths = train_img_paths + val_img_paths
mask_paths = train_mask_paths + val_mask_paths
assert len(img_paths) == 22210
return img_paths, mask_paths
将此设置模式检查为None通过
dataset = ADE20KSegmentation(root='/content/dataset', split='test',mode='test')
img, label = dataset[0]