diff --git a/tests/test_models/test_utils/test_extractor.py b/tests/test_models/test_utils/test_extractor.py
index 4a3c606d..54876960 100644
--- a/tests/test_models/test_utils/test_extractor.py
+++ b/tests/test_models/test_utils/test_extractor.py
@@ -1,6 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from unittest.mock import MagicMock
-
 import torch
 import torch.nn as nn
 from torch.utils.data import Dataset
@@ -8,41 +6,52 @@ from torch.utils.data import Dataset
 from mmselfsup.models.utils import Extractor
 
 
-class ExampleDataset(Dataset):
-
-    def __getitem__(self, idx):
-        results = dict(img=torch.tensor([1]), img_metas=dict())
-        return results
+class DummyDataset(Dataset):
+    METAINFO = dict()  # type: ignore
+    data = torch.randn(12, 2)
+    label = torch.ones(12)
 
     def __len__(self):
-        return 1
+        return self.data.size(0)
+
+    def __getitem__(self, index):
+        return dict(inputs=self.data[index], data_sample=self.label[index])
 
 
-class ExampleModel(nn.Module):
+class ToyModel(nn.Module):
 
     def __init__(self):
-        super(ExampleModel, self).__init__()
-        self.test_cfg = None
-        self.conv = nn.Conv2d(3, 3, 3)
-        self.neck = nn.Identity()
+        super().__init__()
+        self.loss_lambda = 0.5
+        self.linear = nn.Linear(2, 1)
 
-    def forward(self, img, test_mode=False, **kwargs):
-        return img
+    def forward(self, data_batch, return_loss=False):
+        inputs, labels = [], []
+        for x in data_batch:
+            inputs.append(x['inputs'])
+            labels.append(x['data_sample'])
 
-    def train_step(self, data_batch, optimizer):
-        loss = self.forward(**data_batch)
-        return dict(loss=loss)
+        device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+        inputs = torch.stack(inputs).to(device)
+        labels = torch.stack(labels).to(device)
+        outputs = self.linear(inputs)
+        if return_loss:
+            loss = (labels - outputs).sum()
+            outputs = dict(loss=loss, log_vars=dict(loss=loss.item()))
+            return outputs
+        else:
+            outputs = dict(log_vars=dict(a=1, b=0.5))
+            return outputs
 
 
 def test_extractor():
-    test_dataset = ExampleDataset()
-    test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+    dummy_dataset = DummyDataset()
 
     extract_dataloader = dict(
         batch_size=1,
         num_workers=1,
         sampler=dict(type='DefaultSampler', shuffle=False),
-        dataset=test_dataset)
+        dataset=dummy_dataset)
 
     # test init
     extractor = Extractor(
@@ -51,7 +60,9 @@ def test_extractor():
 
     # test init
     extractor = Extractor(
-        extract_dataloader=extract_dataloader, dist_mode=False)
+        extract_dataloader=extract_dataloader,
+        dist_mode=False,
+        pool_cfg=dict(type='AvgPool2d', output_size=1))
 
     # TODO: test runtime
     # As the BaseModel is not defined finally, I will add it later.