fix bug

data/dataset.py

@@ -46,7 +46,7 @@ class EpisodicDataset(Dataset):
         self.origin = origin_dataset
         self.num_class = num_class
         assert self.num_class < len(self.origin.classes_list)
-        self.num_set = num_set*2  # 2*K
+        self.num_set = num_set  # K
         self.num_episodes = num_episodes
 
     def __len__(self):
@@ -54,15 +54,23 @@ class EpisodicDataset(Dataset):
 
     def __getitem__(self, _):
         random_classes = torch.randint(high=len(self.origin.classes_list), size=(self.num_class,)).tolist()
-        item = {}
+        support_set_list = []
-        for i in random_classes:
+        query_set_list = []
-            image_list = self.origin.classes_list[i]
+        target_list = []
-            if len(image_list) > self.num_set:
+        for i, c in enumerate(random_classes):
-                idx_list = torch.randperm(len(image_list))[:self.num_set].tolist()
+            image_list = self.origin.classes_list[c]
+            if len(image_list) > self.num_set * 2:
+                idx_list = torch.randperm(len(image_list))[:self.num_set*2].tolist()
             else:
-                idx_list = torch.randint(high=len(image_list), size=(self.num_set,)).tolist()
+                idx_list = torch.randint(high=len(image_list), size=(self.num_set*2,)).tolist()
-            item[i] = [self.origin[idx] for idx in idx_list]
+            support_set_list.extend([self.origin[idx] for idx in idx_list[:self.num_set]])
-        return item
+            query_set_list.extend([self.origin[idx] for idx in idx_list[self.num_set:]])
+            target_list.extend([i]*self.num_set)
+        return {
+            "support": torch.stack(support_set_list),
+            "query": torch.stack(query_set_list),
+            "target": torch.tensor(target_list)
+        }
 
     def __repr__(self):
         return "<EpisodicDataset N={} K={} NUM={}>".format(self.num_class, self.num_set, self.num_episodes)

test.py

@@ -3,6 +3,7 @@ from torch.utils.data import DataLoader
 import torchvision
 from data import dataset
 import torch.nn as nn
+from ignite.utils import convert_tensor
 
 
 def setup_seed(seed):
@@ -31,15 +32,17 @@ def euclidean_dist(x, y):
 
 def evaluate(query, target, support):
     """
-    :param query: NK x D vector
+    :param query: B x NK x D vector
-    :param target: NK x 1 vector
+    :param target: B x NK x 1 vector
-    :param support: N x K x D vector
+    :param support: B x N x K x D vector
     :return:
     """
+    K = support.size(1)
     prototypes = support.mean(1)
     distance = euclidean_dist(query, prototypes)
     indices = distance.argmin(1)
-    return torch.eq(target, indices).float().mean()
+    y_hat = torch.tensor([target[i*K-1] for i in indices])
+    return torch.eq(target.to("cpu"), y_hat).float().mean()
 
 
 class Flatten(nn.Module):
@@ -49,6 +52,7 @@ class Flatten(nn.Module):
     def forward(self, x):
         return x.view(x.size(0), -1)
 
+
 # def make_extractor():
 #     resnet50 = torchvision.models.resnet50(pretrained=True)
 #     resnet50.to(torch.device("cuda"))
@@ -97,41 +101,32 @@ def test():
         torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
     ])
     origin_dataset = dataset.CARS("/data/few-shot/STANFORD-CARS/", transform=data_transform)
+    #origin_dataset = dataset.ImprovedImageFolder("/data/few-shot/mini_imagenet_full_size/train", transform=data_transform)
 
+    N = torch.randint(5, 10, (1,)).tolist()[0]
+    K = torch.randint(1, 10, (1,)).tolist()[0]
     episodic_dataset = dataset.EpisodicDataset(
         origin_dataset,  # 抽取数据集
-        torch.randint(5, 10, (1,)).tolist()[0],  # N
+        N,  # N
-        torch.randint(1, 10, (1,)).tolist()[0],  # K
+        K,  # K
-        5  # 任务数目
+        100  # 任务数目
     )
     print(episodic_dataset)
 
-    data_loader = DataLoader(episodic_dataset)
+    data_loader = DataLoader(episodic_dataset, batch_size=2)
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     extractor = make_extractor()
     accs = []
     for item in data_loader:
-        support_list = []
-        query_list = []
-        class_id_list = []
-        for class_id in item:
-            for i in range(len(item[class_id])):
-                item[class_id][i] = item[class_id][i].to(device)
-            num_support_set = len(item[class_id]) // 2
-            num_query_set = len(item[class_id]) - num_support_set
-            support_list.append(torch.stack([extractor(pair) for pair in item[class_id][:num_support_set]]))
-            query_list.append(torch.stack([extractor(pair) for pair in item[class_id][num_support_set:]]))
-            class_id_list.extend([class_id]*num_query_set)
 
-        query = torch.squeeze(torch.cat(query_list)).to(device)
+        item = convert_tensor(item, device)
-        support = torch.squeeze(torch.stack(support_list)).to(device)
+        query_batch = [extractor(images) for images in item["query"]]
-        target = torch.squeeze(torch.tensor(class_id_list)).to(device)
+        support_batch = [torch.stack(torch.split(extractor(images), K)) for images in item["support"]]
-
+        for i in range(len(query_batch)):
-        accs.append(evaluate(query, target, support))
+            accs.append(evaluate(query_batch[i], item["target"][i], support_batch[i]))
-
+    print(torch.tensor(accs).mean().item())
-    print(accs)
 
 
 if __name__ == '__main__':
-    setup_seed(10)
+    setup_seed(100)
     test()