# def my data loader, return the data and corresponding label
def default_loader(path):
return Image.open(path).convert('RGB') # operation object is the PIL image object
class myImageFloder(data.Dataset): # Class inheritance，继承Ｄａｔａｓｅｔ类
def __init__(self, root, label, transform=None, target_transform=None, loader=default_loader):
# fh = open(label)
c = 0
imgs = []
class_names = ['regression']
for line in label: # label is a list
cls = line.split() # cls is a list
fn = cls.pop(0)
if os.path.isfile(os.path.join(root, fn)):
imgs.append((fn, tuple([float(v) for v in cls[:len(cls)-1]])))
# access the last label
# images is the list,and the content is the tuple, every image corresponds to a label
# despite the label's dimension
# we can use the append way to append the element for list
c = c + 1
print('the total image is',c)
print(class_names)
self.root = root
self.imgs = imgs
self.classes = class_names
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self, index):
fn, label = self.imgs[index] # even though the imgs is just a list, it can return the elements of it
# in a proper way
img = self.loader(os.path.join(self.root, fn))
if self.transform is not None:
img = self.transform(img)
return img, torch.Tensor(label), fn # 在这里返回图像数据以及对应的ｌａｂｅｌ以及对应的名称
def __len__(self):
return len(self.imgs)
def getName(self):
return self.classes
实际上是继承Ｄａｔａｓｅｔ这个类中的两个函数__getitem__与__len__，并且返回的变量类型是ｔｏｒｃｈ．Ｔｅｎｓｏｒ即可
看ｄａｔａｌｏａｄｅｒ定义方式以及如何在ｄａｔａｌｏａｄｅｒ中加载数据
mytransform = transforms.Compose([transforms.ToTensor()]) # transform [0,255] to [0,1]
test_data_root = "/home/ying/data/google_streetview_train_test1"
data_label = "/home/ying/data/google_streetview_train_test1/label.txt"
# test_label="/home/ying/data/google_streetview_train_test1/label.txt"
train_label, test_label = random_choose_data(data_label)
test_loader = torch.utils.data.DataLoader(
myImageFloder(root=test_data_root, label=test_label, transform=mytransform),batch_size=batch_size, shuffle=True, **kwargs)
．．．
for i, (test_images, test_labels, fn) in enumerate(test_loader): # the first i in index, and the () is the content
test_images = Variable(test_images.cuda())
test_labels = Variable(test_labels.cuda())
outputs = cnn(test_images)
print(outputs.data[0])
print(fn)
loss = criterion(outputs, test_labels)
print("Iter [%d/%d] Test_Loss: %.4f" % (i + 1, 781, loss.data[0]))
实际上刚刚在myImageFloder中定义的__getitem__实际上就是i, (test_images, test_labels, fn) in enumerate(test_loader): 中返回的对象， 其中第一个ｉ是与ｅｎｕｍｂｅｒａｔｅ相关的ｉｎｄｅｘ
这样就能够在模型ｔｅｓｔ的时候观察哪些数据误差比较大并且进行输出