Delete datasets directory

datasets/__init__.py

-from .EndoScene import EndoScene
-from .kvasir_SEG import kvasir_SEG

datasets/kvasir_SEG.py

-#Adopted from ACSNet
-import os
-import os.path as osp
-from utils.transform import *
-from torch.utils.data import Dataset
-from torchvision import transforms
-
-
-# KavSir-SEG Dataset
-class kvasir_SEG(Dataset):
-    def __init__(self, root, data2_dir, mode='train', transform=None):
-        super(kvasir_SEG, self).__init__()
-        data_path = osp.join(root, data2_dir)
-        self.imglist = []
-        self.gtlist = []
-
-        datalist = os.listdir(osp.join(data_path, 'images'))
-        for data in datalist:
-            self.imglist.append(osp.join(data_path+'/images', data))
-            self.gtlist.append(osp.join(data_path+'/masks', data))
-
-        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)),
-                   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)
-
-
-class test_dataset:
-    def __init__(self, image_root, gt_root, testsize):
-        self.testsize = testsize
-        self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
-        self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.tif') or f.endswith('.png')]
-        self.images = sorted(self.images)
-        self.gts = sorted(self.gts)
-        self.transform = transforms.Compose([
-            transforms.Resize((self.testsize, self.testsize)),
-            transforms.ToTensor(),
-            #transforms.Normalize([0.485, 0.456, 0.406],
-                                 #[0.229, 0.224, 0.225])])
-        self.gt_transform = transforms.ToTensor()
-        self.size = len(self.images)
-        self.index = 0
-
-    def load_data(self):
-        image = self.rgb_loader(self.images[self.index])
-        image = self.transform(image).unsqueeze(0)
-        gt = self.binary_loader(self.gts[self.index])
-        name = self.images[self.index].split('/')[-1]
-        if name.endswith('.jpg'):
-            name = name.split('.jpg')[0] + '.png'
-        self.index += 1
-        return image, gt, name
-
-    def rgb_loader(self, path):
-        with open(path, 'rb') as f:
-            img = Image.open(f)
-            return img.convert('RGB')
-
-    def binary_loader(self, path):
-        with open(path, 'rb') as f:
-            img = Image.open(f)
-            return img.convert('L')