v1

test.py

@@ -4,6 +4,7 @@ import torchvision
 from data import dataset
 import torch.nn as nn
 from ignite.utils import convert_tensor
+import time
 
 
 def setup_seed(seed):
@@ -37,12 +38,9 @@ def evaluate(query, target, support):
     :param support: B x N x K x D vector
     :return:
     """
-    K = support.size(-2)
     prototypes = support.mean(-2)  # B x N x D
     distance = euclidean_dist(query, prototypes)  # B x NK x N
-    print(distance.shape)
     indices = distance.argmin(-1)  # B x NK
-    print(indices, target)
     return torch.eq(target, indices).float().mean()
 
 
@@ -54,29 +52,29 @@ class Flatten(nn.Module):
         return x.view(x.size(0), -1)
 
 
-# def make_extractor():
-#     resnet50 = torchvision.models.resnet50(pretrained=True)
-#     resnet50.to(torch.device("cuda"))
-#     resnet50.fc = torch.nn.Identity()
-#     resnet50.eval()
-#
-#     def extract(images):
-#         with torch.no_grad():
-#             return resnet50(images)
-#     return extract
-
-
 def make_extractor():
-    model = resnet18()
-    model.to(torch.device("cuda"))
-    model.eval()
+    resnet50 = torchvision.models.resnet50(pretrained=True)
+    resnet50.to(torch.device("cuda"))
+    resnet50.fc = torch.nn.Identity()
+    resnet50.eval()
 
     def extract(images):
         with torch.no_grad():
-            return model(images)
+            return resnet50(images)
     return extract
 
 
+# def make_extractor():
+#     model = resnet18()
+#     model.to(torch.device("cuda"))
+#     model.eval()
+#
+#     def extract(images):
+#         with torch.no_grad():
+#             return model(images)
+#     return extract
+
+
 def resnet18(model_path="ResNet18Official.pth"):
     """Constructs a ResNet-18 model.
     Args:
@@ -115,20 +113,22 @@ def test():
     )
     print(episodic_dataset)
 
-    data_loader = DataLoader(episodic_dataset, batch_size=batch_size)
+    data_loader = DataLoader(episodic_dataset, batch_size=batch_size, pin_memory=True)
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     extractor = make_extractor()
     accs = []
+    st = time.time()
     for item in data_loader:
-        item = convert_tensor(item, device)
+        item = convert_tensor(item, device, non_blocking=True)
         # item["query"]: B x NK x 3 x W x H
         # item["support"]: B x NK x 3 x W x H
         # item["target"]: B x NK
-        print(item["support"].shape, item["target"].shape)
         query_batch = extractor(item["query"].view([-1, *item["query"].shape[-3:]])).view(batch_size, N*K, -1)
         support_batch = extractor(item["support"].view([-1, *item["query"].shape[-3:]])).view(batch_size, N, K, -1)
 
         accs.append(evaluate(query_batch, item["target"], support_batch))
+        print("time: ", time.time()-st)
+        st = time.time()
     print(torch.tensor(accs).mean().item())