 b/dataprocess/segdataloader.py
+import os
+import numpy as np
+from glob import glob
+from PIL import Image
+import torch
+from torchvision.transforms import Compose, Normalize, ToTensor
+from Transforms import Scale
+import cv2
+# width, height = 96, 96
+def att_compare(a, b=3):
+    if a > b:
+        return np.array([1])
+    else:
+        return np.array([0])
+def makedirs(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+def normalazation(image_array):
+    max = image_array.max()
+    min = image_array.min()
+    image_array = (image_array-min)/(max-min)  # float cannot apply the compute,or array error will occur
+    avg = image_array.mean()
+    image_array = image_array-avg
+    return image_array   # a bug here, a array must be returned,directly appling function did't work
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self, datas, lungs, medias, inters, unions, masks, label=None, width=64, height=64):
+        self.size = (width, height)
+        self.datas = datas
+        self.lungs = lungs
+        self.medias = medias
+        self.inters = inters
+        self.unions = unions
+        self.masks = masks
+        self.img_resize = Compose([
+            Scale(self.size, Image.BILINEAR),
+        ])
+        self.label_resize = Compose([
+            Scale(self.size, Image.NEAREST),
+        ])
+        self.img_transform_gray = Compose([
+            ToTensor(),
+            Normalize(mean=[0.448749], std=[0.399953])  # 这个归一化方式可以提升近1个点的dice
+        ])
+        self.img_transform_rgb = Compose([
+            ToTensor(),
+            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+        self.input_paths = self.datas
+        self.lung_paths = self.lungs
+        self.media_paths = self.medias
+        self.inter_path = self.inters
+        self.union_path = self.unions
+        self.mask_path = self.masks
+        print('Training data:')
+        print(len(self.datas))
+    def __getitem__(self, index):
+        input = np.load(self.input_paths[index])
+        lung = np.load(self.lung_paths[index])
+        media = np.load(self.media_paths[index])
+        lung = normalazation(lung)
+        media = normalazation(media)
+        inter = Image.open(self.inter_path[index])
+        union = Image.open(self.union_path[index])
+        mask = Image.open(self.mask_path[index])
+        input = cv2.resize(input, self.size)
+        lung = cv2.resize(lung, self.size)
+        media = cv2.resize(media, self.size)
+        inter = self.img_resize(inter)
+        union = self.img_resize(union)
+        mask = self.img_resize(mask)
+        torch.from_numpy(input)
+        torch.from_numpy(lung)
+        torch.from_numpy(media)
+        # 归一化
+        input = self.img_transform_gray(input)
+        lung = self.img_transform_gray(lung)
+        media = self.img_transform_gray(media)
+        inter = self.img_transform_gray(inter)
+        union = self.img_transform_gray(union)
+        mask = self.img_transform_gray(mask)
+        # 二值化
+        inter[inter > 0.5] = 1
+        inter[inter < 0.5] = 0
+        union[union > 0.5] = 1
+        union[union < 0.5] = 0
+        mask[mask > 0.5] = 1
+        mask[mask < 0.5] = 0
+        return input, lung, media, union, inter, mask
+    def __len__(self):
+        return len(self.input_paths)
+class Dataset_val(torch.utils.data.Dataset):
+    def __init__(self, datas, medias, inters, unions, masks, label=None, width=64, height=64):
+        self.size = (width, height)
+        self.data = datas
+        self.medias = medias
+        self.inters = inters
+        self.unions = unions
+        self.mask = masks
+        self.img_resize = Compose([
+            Scale(self.size, Image.BILINEAR),
+        ])
+        self.label_resize = Compose([
+            Scale(self.size, Image.NEAREST),
+        ])
+        self.img_transform_gray = Compose([
+            ToTensor(),
+            Normalize(mean=[0.448749], std=[0.399953])  # 这个归一化方式可以提升近1个点的dice
+        ])
+        self.img_transform_rgb = Compose([
+            ToTensor(),
+            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+        self.input_paths = self.data
+        self.media_paths = self.medias
+        self.inter_path = self.inters
+        self.union_path = self.unions
+        self.mask_paths = self.mask
+        print('Testing data:')
+        print(len(self.input_paths))
+    def __getitem__(self, index):
+        input = np.load(self.input_paths[index])
+        media = np.load(self.media_paths[index])
+        inter = Image.open(self.inter_path[index])
+        union = Image.open(self.union_path[index])
+        mask = Image.open(self.mask_paths[index])
+        input = cv2.resize(input, self.size)
+        media = cv2.resize(media, self.size)
+        inter = self.img_resize(inter)
+        union = self.img_resize(union)
+        mask = self.img_resize(mask)
+        torch.from_numpy(input)
+        torch.from_numpy(media)
+        input = self.img_transform_gray(input)
+        media = self.img_transform_gray(media)
+        inter = self.img_transform_gray(inter)
+        union = self.img_transform_gray(union)
+        mask = self.img_transform_gray(mask)
+        # 二值化
+        inter[inter > 0.5] = 1
+        inter[inter < 0.5] = 0
+        union[union > 0.5] = 1
+        union[union < 0.5] = 0
+        mask[mask > 0.5] = 1
+        mask[mask < 0.5] = 0
+        return input, media, union, inter, mask
+    def __len__(self):
+        return len(self.input_paths)
+class RowDataset(torch.utils.data.Dataset):
+    def __init__(self, data, mask, label=None, width=64, height=64):
+        self.size = (width, height)
+        self.data = data
+        self.mask = mask
+        self.label = label
+        self.img_resize = Compose([
+            Scale(self.size, Image.BILINEAR),
+        ])
+        self.label_resize = Compose([
+            Scale(self.size, Image.NEAREST),
+        ])
+        self.img_transform_gray = Compose([
+            ToTensor(),
+            Normalize(mean=[0.448749], std=[0.399953])  # 这个归一化方式可以提升近1个点的dice
+        ])
+        self.img_transform_rgb = Compose([
+            ToTensor(),
+            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+        self.input_paths = self.data
+        self.mask_paths = self.mask
+        self.label_paths = self.label
+    def __getitem__(self, index):
+        image = Image.open(self.input_paths[index])
+        mask = Image.open(self.mask_paths[index])
+        # mask.save('image1.jpg')
+        image = self.img_resize(image)
+        mask = self.img_resize(mask)
+        # mask.save('image2.jpg')
+        image = self.img_transform_gray(image)
+        mask = self.img_transform_gray(mask)
+        # mask = mask.squeeze()
+        # mask = np.transpose(mask.cpu().detach().numpy(), (0,1))
+        # cv2.imwrite('image3.jpg', mask)
+        return image, mask
+    def __len__(self):
+        return len(self.input_paths)
+def loader(dataset, batch_size, num_workers=8, shuffle=False):
+    input_images = dataset
+    input_loader = torch.utils.data.DataLoader(dataset=input_images,
+                                               batch_size=batch_size,
+                                               shuffle=shuffle,
+                                               num_workers=num_workers)
+    return input_loader