[Refactor] refactor EvalHook (#395)

* polish eval hooks

* update unittest

* add by_epoch

* correct unittest

* correct unittest

* use runner.hook

* add comment

* polish

* polish again

* fix

* polish

* polish logger info

* fix unittest

* BC

* add deprecated class

* add deprecated info

configs/detection/ava/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py

@@ -159,7 +159,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

configs/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py

@@ -158,7 +158,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

configs/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py

@@ -158,7 +158,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

configs/detection/ava/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb.py

@@ -143,7 +143,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py

@@ -142,7 +142,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py

@@ -142,7 +142,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

configs/detection/ava/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb.py

@@ -143,7 +143,7 @@
 total_epochs = 20
 checkpoint_config = dict(interval=1)
 workflow = [('train', 1)]
-evaluation = dict(interval=1, key_indicator='[email protected]')
+evaluation = dict(interval=1, save_best='[email protected]')
 log_config = dict(
     interval=20, hooks=[
         dict(type='TextLoggerHook'),

docs/tutorials/7_customize_runtime.md

@@ -191,7 +191,7 @@ We support many other learning rate schedule [here](https://github.com/open-mmla
 
 ## Customize Workflow
 
-By default, we recommend users to use `EpochEvalHook` to do evaluation after training epoch, but they can still use `val` workflow as an alternative.
+By default, we recommend users to use `EvalHook` to do evaluation after training epoch, but they can still use `val` workflow as an alternative.
 
 Workflow is a list of (phase, epochs) to specify the running order and epochs. By default it is set to be
 
@@ -213,7 +213,7 @@ so that 1 epoch for training and 1 epoch for validation will be run iteratively.
 
 1. The parameters of model will not be updated during val epoch.
 2. Keyword `total_epochs` in the config only controls the number of training epochs and will not affect the validation workflow.
-3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EpochEvalHook` because `EpochEvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`.
+3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`.
    Therefore, the only difference between `[('train', 1), ('val', 1)]` and ``[('train', 1)]`` is that the runner will calculate losses on validation set after each training epoch.
 
 ## Customize Hooks
@@ -344,7 +344,7 @@ log_config = dict(
 
 #### Evaluation config
 
-The config of `evaluation` will be used to initialize the [`EpochEvalHook`](https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/eval_hooks.py#L12).
+The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/eval_hooks.py#L12).
 Except the key `interval`, other arguments such as `metrics` will be passed to the `dataset.evaluate()`
 
 ```python

mmaction/apis/train.py

@@ -6,8 +6,8 @@
                          build_optimizer)
 from mmcv.runner.hooks import Fp16OptimizerHook
 
-from ..core import (DistEpochEvalHook, EpochEvalHook,
-                    OmniSourceDistSamplerSeedHook, OmniSourceRunner)
+from ..core import (DistEvalHook, EvalHook, OmniSourceDistSamplerSeedHook,
+                    OmniSourceRunner)
 from ..datasets import build_dataloader, build_dataset
 from ..utils import PreciseBNHook, get_root_logger
 
@@ -143,7 +143,7 @@ def train_model(model,
         dataloader_setting = dict(dataloader_setting,
                                   **cfg.data.get('val_dataloader', {}))
         val_dataloader = build_dataloader(val_dataset, **dataloader_setting)
-        eval_hook = DistEpochEvalHook if distributed else EpochEvalHook
+        eval_hook = DistEvalHook if distributed else EvalHook
         runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
 
     if cfg.resume_from:

mmaction/core/evaluation/__init__.py

@@ -5,13 +5,12 @@
                        mmit_mean_average_precision, pairwise_temporal_iou,
                        softmax, top_k_accuracy)
 from .eval_detection import ActivityNetLocalization
-from .eval_hooks import DistEpochEvalHook, EpochEvalHook
+from .eval_hooks import DistEvalHook, EvalHook
 
 __all__ = [
-    'DistEpochEvalHook', 'EpochEvalHook', 'top_k_accuracy',
-    'mean_class_accuracy', 'confusion_matrix', 'mean_average_precision',
-    'get_weighted_score', 'average_recall_at_avg_proposals',
-    'pairwise_temporal_iou', 'average_precision_at_temporal_iou',
-    'ActivityNetLocalization', 'softmax', 'interpolated_precision_recall',
-    'mmit_mean_average_precision'
+    'DistEvalHook', 'EvalHook', 'top_k_accuracy', 'mean_class_accuracy',
+    'confusion_matrix', 'mean_average_precision', 'get_weighted_score',
+    'average_recall_at_avg_proposals', 'pairwise_temporal_iou',
+    'average_precision_at_temporal_iou', 'ActivityNetLocalization', 'softmax',
+    'interpolated_precision_recall', 'mmit_mean_average_precision'
 ]