Fix name bug. (#47)

configs/recognition/r2plus1d/r2plus1d_r34_32x2x1_180e_kinetics400_rgb.py

@@ -105,7 +105,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 180
 checkpoint_config = dict(interval=5)
 evaluation = dict(

configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_kinetics400_rgb.py

@@ -105,7 +105,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 180
 checkpoint_config = dict(interval=5)
 evaluation = dict(

configs/recognition/r2plus1d/r2plus1d_r34_video_8x8x1_180e_kinetics400_rgb.py

@@ -116,7 +116,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 180
 checkpoint_config = dict(interval=5)
 evaluation = dict(

configs/recognition/slowfast/slowfast_r50_4x16x1_256e_kinetics400_rgb.py

@@ -111,7 +111,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 256
 checkpoint_config = dict(interval=4)
 workflow = [('train', 1)]

configs/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb.py

@@ -111,7 +111,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 256
 checkpoint_config = dict(interval=4)
 workflow = [('train', 1)]

configs/recognition/slowfast/slowfast_r50_video_4x16x1_256e_kinetics400_rgb.py

@@ -121,7 +121,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 256
 checkpoint_config = dict(interval=4)
 workflow = [('train', 1)]

configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow.py

@@ -102,7 +102,7 @@ optimizer = dict(
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
 lr_config = dict(
-    policy='CosineAnealing',
+    policy='CosineAnnealing',
     min_lr=0,
     warmup='linear',
     warmup_by_epoch=True,

configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py

@@ -94,7 +94,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 256
 checkpoint_config = dict(interval=4)
 workflow = [('train', 1)]

configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow.py

@@ -102,7 +102,7 @@ optimizer = dict(
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
 lr_config = dict(
-    policy='CosineAnealing',
+    policy='CosineAnnealing',
     min_lr=0,
     warmup='linear',
     warmup_by_epoch=True,

configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py

@@ -94,7 +94,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 256
 checkpoint_config = dict(interval=4)
 workflow = [('train', 1)]

configs/recognition/slowonly/slowonly_r50_video_4x16x1_256e_kinetics400_rgb.py

@@ -104,7 +104,7 @@ optimizer = dict(
     weight_decay=0.0001)  # this lr is used for 8 gpus
 optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
 # learning policy
-lr_config = dict(policy='CosineAnealing', min_lr=0)
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
 total_epochs = 256
 checkpoint_config = dict(interval=4)
 workflow = [('train', 1)]

docs/config.md

@@ -158,7 +158,7 @@ optimizer_config = dict(  # Config used to build the optimizer hook
     grad_clip=None)  # Most of the methods do not use gradient clip
 # learning policy
 lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
-    policy='step',  # Policy of scheduler, also support CosineAnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9
+    policy='step',  # 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=7)  # Steps to decay the learning rate
 
 total_epochs = 9  # Total epochs to train the model
@@ -364,7 +364,7 @@ optimizer_config = dict(  # Config used to build the optimizer hook
     grad_clip=dict(max_norm=40, norm_type=2))  # Use gradient clip
 # learning policy
 lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
-    policy='step',  # Policy of scheduler, also support CosineAnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9
+    policy='step',  # 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=[40, 80])  # Steps to decay the learning rate
 total_epochs = 100  # Total epochs to train the model
 checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation