Source code for

"""Pascal Augmented VOC Semantic Segmentation Dataset."""
import os
from PIL import Image
from ..segbase import SegmentationDataset

[docs]class VOCAugSegmentation(SegmentationDataset): """Pascal VOC Augmented Semantic Segmentation Dataset. Parameters ---------- root : string Path to VOCdevkit folder. Default is '$(HOME)/mxnet/datasplits/voc' split: string 'train' or 'val' transform : callable, optional A function that transforms the image Examples -------- >>> from import transforms >>> # Transforms for Normalization >>> input_transform = transforms.Compose([ >>> transforms.ToTensor(), >>> transforms.Normalize([.485, .456, .406], [.229, .224, .225]), >>> ]) >>> # Create Dataset >>> trainset ='train', transform=input_transform) >>> # Create Training Loader >>> train_data = >>> trainset, 4, shuffle=True, last_batch='rollover', >>> num_workers=4) """ TRAIN_BASE_DIR = 'VOCaug/dataset/' NUM_CLASS = 21 CLASSES = ("background", "airplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorcycle", "person", "potted-plant", "sheep", "sofa", "train", "tv") def __init__(self, root=os.path.expanduser('~/.mxnet/datasets/voc'), split='train', mode=None, transform=None, **kwargs): super(VOCAugSegmentation, self).__init__(root, split, mode, transform, **kwargs) # train/val/test splits are pre-cut _voc_root = os.path.join(root, self.TRAIN_BASE_DIR) _mask_dir = os.path.join(_voc_root, 'cls') _image_dir = os.path.join(_voc_root, 'img') if split == 'train': _split_f = os.path.join(_voc_root, 'trainval.txt') elif split == 'val': _split_f = os.path.join(_voc_root, 'val.txt') else: raise RuntimeError('Unknown dataset split: {}'.format(split)) self.images = [] self.masks = [] with open(os.path.join(_split_f), "r") as lines: for line in lines: _image = os.path.join(_image_dir, line.rstrip('\n')+".jpg") assert os.path.isfile(_image) self.images.append(_image) _mask = os.path.join(_mask_dir, line.rstrip('\n')+".mat") assert os.path.isfile(_mask) self.masks.append(_mask) assert (len(self.images) == len(self.masks)) def __getitem__(self, index): img =[index]).convert('RGB') target = self._load_mat(self.masks[index]) # synchrosized transform if self.mode == 'train': img, target = self._sync_transform(img, target) elif self.mode == 'val': img, target = self._val_sync_transform(img, target) else: raise RuntimeError('unknown mode for dataloader: {}'.format(self.mode)) # general resize, normalize and toTensor if self.transform is not None: img = self.transform(img) return img, target def _load_mat(self, filename): mat =, mat_dtype=True, squeeze_me=True, struct_as_record=False) mask = mat['GTcls'].Segmentation return Image.fromarray(mask) def __len__(self): return len(self.images) @property def classes(self): """Category names.""" return type(self).CLASSES