[Enhance] LR and Momentum Visualizer (#267)

* impl lr and momentum visualizer

* provide fakerun

mmengine/utils/visualize.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from unittest.mock import patch
+
+import torch
+import torch.nn as nn
+
+from mmengine.model import BaseModel
+from mmengine.registry import MODELS
+
+
+@MODELS.register_module()
+class ToyModel(BaseModel):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        self.conv = nn.Conv2d(1, 1, 1)
+
+    def forward(self, *args, **kwargs):
+        return {'loss': torch.tensor(0.0)}
+
+
+def update_params_step(self, loss):
+    pass
+
+
+def runtimeinfo_step(self, runner, batch_idx, data_batch=None):
+    runner.message_hub.update_info('iter', runner.iter)
+    lr_dict = runner.optim_wrapper.get_lr()
+    for name, lr in lr_dict.items():
+        runner.message_hub.update_scalar(f'train/{name}', lr[0])
+
+    momentum_dict = runner.optim_wrapper.get_momentum()
+    for name, momentum in momentum_dict.items():
+        runner.message_hub.update_scalar(f'train/{name}', momentum[0])
+
+
+@patch('mmengine.optim.optimizer.OptimWrapper.update_params',
+       update_params_step)
+@patch('mmengine.hooks.RuntimeInfoHook.before_train_iter', runtimeinfo_step)
+def fake_run(cfg):
+    from mmengine.runner import Runner
+    cfg.pop('model')
+    cfg.pop('visualizer')
+    cfg.pop('val_dataloader')
+    cfg.pop('val_evaluator')
+    cfg.pop('val_cfg')
+    cfg.pop('test_dataloader')
+    cfg.pop('test_evaluator')
+    cfg.pop('test_cfg')
+    extra_cfg = dict(
+        model=dict(type='ToyModel'),
+        visualizer=dict(
+            type='Visualizer',
+            vis_backends=[
+                dict(type='TensorboardVisBackend', save_dir='temp_dir')
+            ]),
+    )
+    cfg.merge_from_dict(extra_cfg)
+    # build the runner from config
+    runner = Runner.from_cfg(cfg)
+
+    # start training
+    runner.train()