Delete clipseg/datasets/pascal_zeroshot.py

clipseg/datasets/pascal_zeroshot.py

@@ -1,60 +0,0 @@
-from os.path import expanduser
-import torch
-import json
-import torchvision
-from general_utils import get_from_repository
-from general_utils import log
-from torchvision import transforms
-
-PASCAL_VOC_CLASSES_ZS = [['cattle.n.01', 'motorcycle.n.01'], ['aeroplane.n.01', 'sofa.n.01'], 
-                         ['cat.n.01', 'television.n.03'], ['train.n.01', 'bottle.n.01'],
-                          ['chair.n.01', 'pot_plant.n.01']]
-
-
-class PascalZeroShot(object):
-
-    def __init__(self, split, n_unseen, image_size=224) -> None:
-        super().__init__()
-
-        import sys
-        sys.path.append('third_party/JoEm')
-        from third_party.JoEm.data_loader.dataset import VOCSegmentation
-        from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
-
-        self.pascal_classes = VOC
-        self.image_size = image_size
-
-        self.transform = transforms.Compose([
-            transforms.Resize((image_size, image_size)),
-        ])
-
-        if split == 'train':
-            self.voc = VOCSegmentation(get_unseen_idx(n_unseen), get_seen_idx(n_unseen), 
-                                       split=split, transform=True, transform_args=dict(base_size=312, crop_size=312), 
-                                       ignore_bg=False, ignore_unseen=False, remv_unseen_img=True)
-        elif split == 'val':
-            self.voc = VOCSegmentation(get_unseen_idx(n_unseen), get_seen_idx(n_unseen), 
-                                       split=split, transform=False, 
-                                       ignore_bg=False, ignore_unseen=False)
-
-        self.unseen_idx = get_unseen_idx(n_unseen)
-
-    def __len__(self):
-        return len(self.voc)
-
-    def __getitem__(self, i):
-
-        sample = self.voc[i]
-        label = sample['label'].long()
-        all_labels = [l for l in torch.where(torch.bincount(label.flatten())>0)[0].numpy().tolist() if l != 255]
-        class_indices = [l for l in all_labels]
-        class_names = [self.pascal_classes[l] for l in all_labels]
-
-        image = self.transform(sample['image'])
-
-        label = transforms.Resize((self.image_size, self.image_size), 
-            interpolation=torchvision.transforms.InterpolationMode.NEAREST)(label.unsqueeze(0))[0]
-
-        return (image,), (label, )
-
-