0.01

test.py

@@ -1,10 +1,11 @@
 import torch
 from torch.utils.data import DataLoader
-import torchvision
+
 from data import dataset
-import torch.nn as nn
+
 from ignite.utils import convert_tensor
 import time
+from tqdm import tqdm
 
 
 def setup_seed(seed):
@@ -44,67 +45,11 @@ def evaluate(query, target, support):
     return torch.eq(target, indices).float().mean()
 
 
-class Flatten(nn.Module):
-    def __init__(self):
-        super(Flatten, self).__init__()
-
-    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"))
-    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()
-#
-#     def extract(images):
-#         with torch.no_grad():
-#             return model(images)
-#     return extract
-
-
-def resnet18(model_path="ResNet18Official.pth"):
-    """Constructs a ResNet-18 model.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model_w_fc = torchvision.models.resnet18(pretrained=False)
-    seq = list(model_w_fc.children())[:-1]
-    seq.append(Flatten())
-    model = torch.nn.Sequential(*seq)
-    # model.load_state_dict(torch.load(model_path), strict=False)
-    model.load_state_dict(torch.load(model_path, map_location ='cpu'), strict=False)
-    # model.load_state_dict(torch.load(model_path))
-    model.eval()
-
-    return model
-
-
-def test():
-    data_transform = torchvision.transforms.Compose([
-        torchvision.transforms.Resize([int(224*1.15), int(224*1.15)]),
-        torchvision.transforms.CenterCrop(224),
-        torchvision.transforms.ToTensor(),
-        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)
+def test(lmdb_path):
+    origin_dataset = dataset.LMDBDataset(lmdb_path)
 
     N = torch.randint(5, 10, (1,)).tolist()[0]
     K = torch.randint(1, 10, (1,)).tolist()[0]
-    batch_size = 2
     episodic_dataset = dataset.EpisodicDataset(
         origin_dataset,  # 抽取数据集
         N,  # N
@@ -113,25 +58,34 @@ def test():
     )
     print(episodic_dataset)
 
-    data_loader = DataLoader(episodic_dataset, batch_size=batch_size, pin_memory=True)
+    data_loader = DataLoader(episodic_dataset, batch_size=4, pin_memory=False)
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    extractor = make_extractor()
+
+    from submit import make_model
+    extractor = make_model()
+    extractor.to(device)
+
     accs = []
     st = time.time()
-    for item in data_loader:
-        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
-        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)
+    with torch.no_grad():
+        for item in tqdm(data_loader, nrows=80):
+            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
+            batch_size = item["target"].size(0)
+            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())
+    print("time: ", time.time() - st)
 
 
 if __name__ == '__main__':
     setup_seed(100)
-    test()
+    for path in ["/data/few-shot/lmdb/CUB_200_2011/data.lmdb",
+                 "/data/few-shot/lmdb/mini-imagenet/train.lmdb",
+                 "/data/few-shot/lmdb/STANFORD-CARS/train.lmdb"]:
+        print(path)
+        test(path)