[fix]

data_loader.py

@@ -4,9 +4,11 @@ from PIL import Image
 import torch.utils.data as data
 from torchvision import datasets, transforms
 
+
 def My_loader(path):
     return PIL.Image.open(path).convert('RGB')
 
+
 class MyDataset(torch.utils.data.Dataset):
 
     def __init__(self, txt_dir, image_path, transform=None, target_transform=None, loader=My_loader):
@@ -15,8 +17,14 @@ class MyDataset(torch.utils.data.Dataset):
         for line in data_txt:
             line = line.strip()
             words = line.split(' ')
-            print(words)
-            imgs.append((words[0], int(words[1].strip())))
+            p = ''
+            for i, word in enumerate(words):
+                if i < len(words) - 1:
+                    if i > 0:
+                        p += ' '
+                    p += word
+
+            imgs.append((p, int(words[-1].strip())))
         self.imgs = imgs
         self.transform = transform
         self.target_transform = target_transform
@@ -32,7 +40,7 @@ class MyDataset(torch.utils.data.Dataset):
         # label = list(map(int, label))
         # print label
         # print type(label)
-        #img = self.loader('/home/vipl/llh/food101_finetuning/food101_vgg/origal_data/images/'+img_name.replace("\\","/"))
+        # img = self.loader('/home/vipl/llh/food101_finetuning/food101_vgg/origal_data/images/'+img_name.replace("\\","/"))
         img = self.loader(self.image_path + img_name)
 
         # print img
@@ -46,6 +54,7 @@ class MyDataset(torch.utils.data.Dataset):
         # if the label is the single-label it can be the int
         # if the multilabel can be the list to torch.tensor
 
+
 def load_data(image_path, train_dir, test_dir, batch_size):
     normalize = transforms.Normalize(mean=[0.5457954, 0.44430383, 0.34424934],
                                      std=[0.23273608, 0.24383051, 0.24237761])
@@ -68,6 +77,6 @@ def load_data(image_path, train_dir, test_dir, batch_size):
     ])
     train_dataset = MyDataset(txt_dir=train_dir, image_path=image_path, transform=train_transforms)
     test_dataset = MyDataset(txt_dir=test_dir, image_path=image_path, transform=test_transforms)
-    train_loader  = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True,  num_workers=0)
-    test_loader   = torch.utils.data.DataLoader(dataset=test_dataset,  batch_size=batch_size//2,  shuffle=False, num_workers=0)
+    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
+    test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size // 2, shuffle=False, num_workers=0)
     return train_dataset, train_loader, test_dataset, test_loader