Delete clipseg/datasets/pfe_dataset.py

clipseg/datasets/pfe_dataset.py

@@ -1,129 +0,0 @@
-from os.path import expanduser
-import torch
-import json
-from general_utils import get_from_repository
-from datasets.lvis_oneshot3 import blend_image_segmentation
-from general_utils import log
-
-PASCAL_CLASSES = {a['id']: a['synonyms'] for a in json.load(open('datasets/pascal_classes.json'))}
-
-
-class PFEPascalWrapper(object):
-
-    def __init__(self, mode, split, mask='separate', image_size=473, label_support=None, size=None, p_negative=0, aug=None):
-        import sys
-        # sys.path.append(expanduser('~/projects/new_one_shot'))
-        from third_party.PFENet.util.dataset import SemData
-
-        get_from_repository('PascalVOC2012', ['Pascal5i.tar'])
-
-        self.p_negative = p_negative
-        self.size = size
-        self.mode = mode
-        self.image_size = image_size
-        
-        if label_support in {True, False}:
-            log.warning('label_support argument is deprecated. Use mask instead.')
-            #raise ValueError()
-
-        self.mask = mask
-
-        value_scale = 255
-        mean = [0.485, 0.456, 0.406]
-        mean = [item * value_scale for item in mean]
-        std = [0.229, 0.224, 0.225]
-        std = [item * value_scale for item in std]
-
-        import third_party.PFENet.util.transform as transform
-
-        if mode == 'val':
-            data_list = expanduser('~/projects/old_one_shot/PFENet/lists/pascal/val.txt')
-
-            data_transform = [transform.test_Resize(size=image_size)] if image_size != 'original' else []
-            data_transform += [
-                transform.ToTensor(),
-                transform.Normalize(mean=mean, std=std)
-            ]   
-
-
-        elif mode == 'train':
-            data_list =  expanduser('~/projects/old_one_shot/PFENet/lists/pascal/voc_sbd_merge_noduplicate.txt')
-
-            assert image_size != 'original'
-
-            data_transform = [
-                transform.RandScale([0.9, 1.1]),
-                transform.RandRotate([-10, 10], padding=mean, ignore_label=255),
-                transform.RandomGaussianBlur(),
-                transform.RandomHorizontalFlip(),
-                transform.Crop((image_size, image_size), crop_type='rand', padding=mean, ignore_label=255),
-                transform.ToTensor(),
-                transform.Normalize(mean=mean, std=std)
-            ]
-
-        data_transform = transform.Compose(data_transform)
-
-        self.dataset = SemData(split=split, mode=mode, data_root=expanduser('~/datasets/PascalVOC2012/VOC2012'), 
-                               data_list=data_list, shot=1, transform=data_transform, use_coco=False, use_split_coco=False)
-
-        self.class_list = self.dataset.sub_val_list if mode == 'val' else self.dataset.sub_list
-
-        # verify that subcls_list always has length 1
-        # assert len(set([len(d[4]) for d in self.dataset])) == 1
-
-        print('actual length', len(self.dataset.data_list))
-
-    def __len__(self):
-        if self.mode == 'val':
-            return len(self.dataset.data_list)
-        else:
-            return len(self.dataset.data_list)
-
-    def __getitem__(self, index):
-        if self.dataset.mode == 'train':
-            image, label, s_x, s_y, subcls_list = self.dataset[index % len(self.dataset.data_list)]
-        elif self.dataset.mode == 'val':
-            image, label, s_x, s_y, subcls_list, ori_label = self.dataset[index % len(self.dataset.data_list)]
-            ori_label = torch.from_numpy(ori_label).unsqueeze(0)
-            
-            if self.image_size != 'original':
-                longerside = max(ori_label.size(1), ori_label.size(2))
-                backmask = torch.ones(ori_label.size(0), longerside, longerside).cuda()*255
-                backmask[0, :ori_label.size(1), :ori_label.size(2)] = ori_label
-                label = backmask.clone().long()      
-            else:
-                label = label.unsqueeze(0) 
-
-            # assert label.shape == (473, 473)
-
-        if self.p_negative > 0:
-            if torch.rand(1).item() < self.p_negative:
-                while True:
-                    idx = torch.randint(0, len(self.dataset.data_list), (1,)).item()
-                    _, _, s_x, s_y, subcls_list_tmp, _ = self.dataset[idx]
-                    if subcls_list[0] != subcls_list_tmp[0]:
-                        break
-
-        s_x = s_x[0]
-        s_y = (s_y == 1)[0]
-        label_fg = (label == 1).float()
-        val_mask = (label != 255).float()
-
-        class_id = self.class_list[subcls_list[0]]
-
-        label_name = PASCAL_CLASSES[class_id][0]
-        label_add = ()
-        mask = self.mask
-
-        if mask == 'text':
-            support = ('a photo of a ' + label_name + '.',)
-        elif mask == 'separate':
-            support = (s_x, s_y)
-        else:
-            if mask.startswith('text_and_'):
-                label_add = (label_name,)
-                mask = mask[9:]
-
-            support = (blend_image_segmentation(s_x, s_y.float(), mask)[0],)
-
-        return (image,) + label_add + support, (label_fg.unsqueeze(0), val_mask.unsqueeze(0), subcls_list[0])