--- a
+++ b/tests/test_runtime/test_lr.py
@@ -0,0 +1,121 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os.path as osp
+import shutil
+import sys
+import tempfile
+from unittest.mock import MagicMock, call
+
+import torch
+import torch.nn as nn
+from mmcv.runner import IterTimerHook, PaviLoggerHook, build_runner
+from torch.utils.data import DataLoader
+
+
+def test_tin_lr_updater_hook():
+    sys.modules['pavi'] = MagicMock()
+    loader = DataLoader(torch.ones((10, 2)))
+    runner = _build_demo_runner()
+
+    hook_cfg = dict(type='TINLrUpdaterHook', min_lr=0.1)
+    runner.register_hook_from_cfg(hook_cfg)
+
+    hook_cfg = dict(
+        type='TINLrUpdaterHook',
+        by_epoch=False,
+        min_lr=0.1,
+        warmup='exp',
+        warmup_iters=2,
+        warmup_ratio=0.9)
+    runner.register_hook_from_cfg(hook_cfg)
+    runner.register_hook_from_cfg(dict(type='IterTimerHook'))
+    runner.register_hook(IterTimerHook())
+
+    hook_cfg = dict(
+        type='TINLrUpdaterHook',
+        by_epoch=False,
+        min_lr=0.1,
+        warmup='constant',
+        warmup_iters=2,
+        warmup_ratio=0.9)
+    runner.register_hook_from_cfg(hook_cfg)
+    runner.register_hook_from_cfg(dict(type='IterTimerHook'))
+    runner.register_hook(IterTimerHook())
+
+    hook_cfg = dict(
+        type='TINLrUpdaterHook',
+        by_epoch=False,
+        min_lr=0.1,
+        warmup='linear',
+        warmup_iters=2,
+        warmup_ratio=0.9)
+    runner.register_hook_from_cfg(hook_cfg)
+    runner.register_hook_from_cfg(dict(type='IterTimerHook'))
+    runner.register_hook(IterTimerHook())
+    # add pavi hook
+    hook = PaviLoggerHook(interval=1, add_graph=False, add_last_ckpt=True)
+    runner.register_hook(hook)
+    runner.run([loader], [('train', 1)])
+    shutil.rmtree(runner.work_dir)
+
+    assert hasattr(hook, 'writer')
+    calls = [
+        call('train', {
+            'learning_rate': 0.028544155877284292,
+            'momentum': 0.95
+        }, 1),
+        call('train', {
+            'learning_rate': 0.04469266270539641,
+            'momentum': 0.95
+        }, 6),
+        call('train', {
+            'learning_rate': 0.09695518130045147,
+            'momentum': 0.95
+        }, 10)
+    ]
+    hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
+
+
+def _build_demo_runner(runner_type='EpochBasedRunner',
+                       max_epochs=1,
+                       max_iters=None):
+
+    class Model(nn.Module):
+
+        def __init__(self):
+            super().__init__()
+            self.linear = nn.Linear(2, 1)
+
+        def forward(self, x):
+            return self.linear(x)
+
+        def train_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+        def val_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+    model = Model()
+
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.95)
+
+    log_config = dict(
+        interval=1, hooks=[
+            dict(type='TextLoggerHook'),
+        ])
+
+    tmp_dir = tempfile.mkdtemp()
+    tmp_dir = osp.join(tmp_dir, '.test_lr_tmp')
+
+    runner = build_runner(
+        dict(type=runner_type),
+        default_args=dict(
+            model=model,
+            work_dir=tmp_dir,
+            optimizer=optimizer,
+            logger=logging.getLogger(),
+            max_epochs=max_epochs,
+            max_iters=max_iters))
+    runner.register_checkpoint_hook(dict(interval=1))
+    runner.register_logger_hooks(log_config)
+    return runner