From 98ceffda7bb163d5957108aa3a508f566db7b71d Mon Sep 17 00:00:00 2001
From: AmirMasoud Nourollah <61701369+Nourollah@users.noreply.github.com>
Date: Wed, 12 Oct 2022 20:50:56 -0700
Subject: [PATCH] [Docs] Logger Hook Config Updated to Add WandB (#1345)

* [Docs] Logger Hook Config Updated to Add WandB

* [Docs] WandB init_kwargs comment added

* [Docs] WandbLoggerHook Details Added To Config Doc File

* [Docs] WandbLoggerHook Details Added To Config Doc File (Pass lint test)

* fix comment

Co-authored-by: liukuikun <liukuikun@sensetime.com>
---
 docs/en/tutorials/config.md | 33 +++++++++++++++++++--------------
 1 file changed, 19 insertions(+), 14 deletions(-)

diff --git a/docs/en/tutorials/config.md b/docs/en/tutorials/config.md
index ac8faee9..3442d1e3 100644
--- a/docs/en/tutorials/config.md
+++ b/docs/en/tutorials/config.md
@@ -223,16 +223,16 @@ Mainly include optimizer settings, `optimizer hook` settings, learning rate sche
 ```python
 # The configuration file used to build the optimizer, support all optimizers in PyTorch.
 optimizer = dict(type='SGD',         # Optimizer type
-                lr=0.1,              # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch
-                momentum=0.9,        # Momentum
-                weight_decay=0.0001) # Weight decay of SGD
+                 lr=0.1,              # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch
+                 momentum=0.9,        # Momentum
+                 weight_decay=0.0001) # Weight decay of SGD
 # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details.
 optimizer_config = dict(grad_clip=None)  # Most of the methods do not use gradient clip
 # Learning rate scheduler config used to register LrUpdater hook
 lr_config = dict(policy='step',          # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.
                  step=[30, 60, 90])      # Steps to decay the learning rate
 runner = dict(type='EpochBasedRunner',   # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner)
-            max_epochs=100)    # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`
+              max_epochs=100)    # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`
 ```
 
 ### Runtime Setting
@@ -243,11 +243,16 @@ This part mainly includes saving the checkpoint strategy, log configuration, tra
 # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
 checkpoint_config = dict(interval=1)    # The save interval is 1
 # config to register logger hook
-log_config = dict(
-    interval=100,                       # Interval to print the log
+log_config = dict(  # Config to register logger hook
+    interval=50,  # Interval to print the log
     hooks=[
-        dict(type='TextLoggerHook'),           # The Tensorboard logger is also supported
-        # dict(type='TensorboardLoggerHook')
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook', by_epoch=False),
+        dict(type='WandbLoggerHook', by_epoch=False, # The Wandb logger is also supported, It requires `wandb` to be installed.
+             init_kwargs={
+                          'project': "MMOCR", # Project name in WandB
+                          }), # Check https://docs.wandb.ai/ref/python/init for more init arguments.
+        # ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation.
     ])
 
 dist_params = dict(backend='nccl')   # Parameters to setup distributed training, the port can also be set.
@@ -337,8 +342,8 @@ The `train_cfg` and `test_cfg` are deprecated in config file, please specify the
 ```python
 # deprecated
 model = dict(
-   type=...,
-   ...
+    type=...,
+    ...
 )
 train_cfg=dict(...)
 test_cfg=dict(...)
@@ -349,9 +354,9 @@ The migration example is as below.
 ```python
 # recommended
 model = dict(
-   type=...,
-   ...
-   train_cfg=dict(...),
-   test_cfg=dict(...),
+    type=...,
+    ...
+    train_cfg=dict(...),
+    test_cfg=dict(...),
 )
 ```