--- a
+++ b/tests/test_eval_hook.py
@@ -0,0 +1,204 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import tempfile
+from unittest.mock import MagicMock, patch
+
+import mmcv.runner
+import pytest
+import torch
+import torch.nn as nn
+from mmcv.runner import obj_from_dict
+from torch.utils.data import DataLoader, Dataset
+
+from mmseg.apis import single_gpu_test
+from mmseg.core import DistEvalHook, EvalHook
+
+
+class ExampleDataset(Dataset):
+
+    def __getitem__(self, idx):
+        results = dict(img=torch.tensor([1]), img_metas=dict())
+        return results
+
+    def __len__(self):
+        return 1
+
+
+class ExampleModel(nn.Module):
+
+    def __init__(self):
+        super(ExampleModel, self).__init__()
+        self.test_cfg = None
+        self.conv = nn.Conv2d(3, 3, 3)
+
+    def forward(self, img, img_metas, test_mode=False, **kwargs):
+        return img
+
+    def train_step(self, data_batch, optimizer):
+        loss = self.forward(**data_batch)
+        return dict(loss=loss)
+
+
+def test_iter_eval_hook():
+    with pytest.raises(TypeError):
+        test_dataset = ExampleModel()
+        data_loader = [
+            DataLoader(
+                test_dataset,
+                batch_size=1,
+                sampler=None,
+                num_worker=0,
+                shuffle=False)
+        ]
+        EvalHook(data_loader)
+
+    test_dataset = ExampleDataset()
+    test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
+    test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = ExampleModel()
+    data_loader = DataLoader(
+        test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
+    optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+    optimizer = obj_from_dict(optim_cfg, torch.optim,
+                              dict(params=model.parameters()))
+
+    # test EvalHook
+    with tempfile.TemporaryDirectory() as tmpdir:
+        eval_hook = EvalHook(data_loader, by_epoch=False, efficient_test=True)
+        runner = mmcv.runner.IterBasedRunner(
+            model=model,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logging.getLogger())
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 1)
+        test_dataset.evaluate.assert_called_with([torch.tensor([1])],
+                                                 logger=runner.logger)
+
+
+def test_epoch_eval_hook():
+    with pytest.raises(TypeError):
+        test_dataset = ExampleModel()
+        data_loader = [
+            DataLoader(
+                test_dataset,
+                batch_size=1,
+                sampler=None,
+                num_worker=0,
+                shuffle=False)
+        ]
+        EvalHook(data_loader, by_epoch=True)
+
+    test_dataset = ExampleDataset()
+    test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
+    test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = ExampleModel()
+    data_loader = DataLoader(
+        test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
+    optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+    optimizer = obj_from_dict(optim_cfg, torch.optim,
+                              dict(params=model.parameters()))
+
+    # test EvalHook with interval
+    with tempfile.TemporaryDirectory() as tmpdir:
+        eval_hook = EvalHook(data_loader, by_epoch=True, interval=2)
+        runner = mmcv.runner.EpochBasedRunner(
+            model=model,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logging.getLogger())
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 2)
+        test_dataset.evaluate.assert_called_once_with([torch.tensor([1])],
+                                                      logger=runner.logger)
+
+
+def multi_gpu_test(model,
+                   data_loader,
+                   tmpdir=None,
+                   gpu_collect=False,
+                   pre_eval=False):
+    # Pre eval is set by default when training.
+    results = single_gpu_test(model, data_loader, pre_eval=True)
+    return results
+
+
+@patch('mmseg.apis.multi_gpu_test', multi_gpu_test)
+def test_dist_eval_hook():
+    with pytest.raises(TypeError):
+        test_dataset = ExampleModel()
+        data_loader = [
+            DataLoader(
+                test_dataset,
+                batch_size=1,
+                sampler=None,
+                num_worker=0,
+                shuffle=False)
+        ]
+        DistEvalHook(data_loader)
+
+    test_dataset = ExampleDataset()
+    test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
+    test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = ExampleModel()
+    data_loader = DataLoader(
+        test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
+    optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+    optimizer = obj_from_dict(optim_cfg, torch.optim,
+                              dict(params=model.parameters()))
+
+    # test DistEvalHook
+    with tempfile.TemporaryDirectory() as tmpdir:
+        eval_hook = DistEvalHook(
+            data_loader, by_epoch=False, efficient_test=True)
+        runner = mmcv.runner.IterBasedRunner(
+            model=model,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logging.getLogger())
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 1)
+        test_dataset.evaluate.assert_called_with([torch.tensor([1])],
+                                                 logger=runner.logger)
+
+
+@patch('mmseg.apis.multi_gpu_test', multi_gpu_test)
+def test_dist_eval_hook_epoch():
+    with pytest.raises(TypeError):
+        test_dataset = ExampleModel()
+        data_loader = [
+            DataLoader(
+                test_dataset,
+                batch_size=1,
+                sampler=None,
+                num_worker=0,
+                shuffle=False)
+        ]
+        DistEvalHook(data_loader)
+
+    test_dataset = ExampleDataset()
+    test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
+    test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = ExampleModel()
+    data_loader = DataLoader(
+        test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
+    optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+    optimizer = obj_from_dict(optim_cfg, torch.optim,
+                              dict(params=model.parameters()))
+
+    # test DistEvalHook
+    with tempfile.TemporaryDirectory() as tmpdir:
+        eval_hook = DistEvalHook(data_loader, by_epoch=True, interval=2)
+        runner = mmcv.runner.EpochBasedRunner(
+            model=model,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logging.getLogger())
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 2)
+        test_dataset.evaluate.assert_called_with([torch.tensor([1])],
+                                                 logger=runner.logger)