Delete EndoScene.py

datasets/EndoScene.py

-#Adopted from ACSNet
-
-import torch
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms.functional as F
-from torchvision import transforms
-import os
-from PIL import Image
-import os.path as osp
-from utils.transform import *
-
-
-# EndoScene Dataset
-class EndoScene(Dataset):
-    def __init__(self, root, data_dir, mode='train', transform=None):
-        super(EndoScene, self).__init__()
-        data_path1 = osp.join(root, data_dir)
-        #data_path2 = osp.join(root, data_dir) + '/CVC-612'
-        self.imglist = []
-        self.gtlist = []
-
-        datalist1 = os.listdir(osp.join(data_path1, 'image'))
-        for data1 in datalist1:
-            self.imglist.append(osp.join(data_path1 + '/image', data1))
-            self.gtlist.append(osp.join(data_path1 + '/gtpolyp', data1))
-
-        #datalist2 = os.listdir(osp.join(data_path2, 'image'))
-        #for data2 in datalist2:
-            #self.imglist.append(osp.join(data_path2 + '/image', data2))
-            #self.gtlist.append(osp.join(data_path2 + '/gtpolyp', data2))
-
-        if transform is None:
-            if mode == 'train':
-               transform = transforms.Compose([
-                   Resize((320, 320)),
-                   RandomHorizontalFlip(),
-                   RandomVerticalFlip(),
-                   RandomRotation(90),
-                   RandomZoom((0.9, 1.1)),
-                   #Translation(10),
-                   RandomCrop((256, 256)),
-                   #transforms.Normalize([0.485, 0.456, 0.406],
-                                 #[0.229, 0.224, 0.225])]
-                   ToTensor(),
-
-               ])
-            elif mode == 'valid' or mode == 'test':
-                transform = transforms.Compose([
-                   Resize((320, 320)),
-                   ToTensor(),
-               ])
-        self.transform = transform
-
-    def __getitem__(self, index):
-        img_path = self.imglist[index]
-        gt_path = self.gtlist[index]
-        img = Image.open(img_path).convert('RGB')
-        gt = Image.open(gt_path).convert('L')
-        data = {'image': img, 'label': gt}
-        if self.transform:
-            data = self.transform(data)
-
-        return data
-
-    def __len__(self):
-        return len(self.imglist)