[Refactor] default runtime config (#447)

* init

* Update default_runtime.py

configs/_base_/default_runtime.py

@@ -1,19 +1,24 @@
-# yapf:disable
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        # dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
+default_scope = 'mmrotate'
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'))
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+# TODO: Visualizer is not ready.
+# vis_backends = [dict(type='LocalVisBackend')]
+# visualizer = dict(
+#     type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
 
-dist_params = dict(backend='nccl')
 log_level = 'INFO'
 load_from = None
-resume_from = None
-workflow = [('train', 1)]
-
-# disable opencv multithreading to avoid system being overloaded
-opencv_num_threads = 0
-# set multi-process start method as `fork` to speed up the training
-mp_start_method = 'fork'
+resume = False

configs/_base_/schedules/schedule_1x.py

@@ -1,14 +1,27 @@
-# evaluation
-evaluation = dict(interval=1, metric='mAP')
+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0 / 3,
+        by_epoch=False,
+        begin=0,
+        end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=12,
+        by_epoch=True,
+        milestones=[8, 11],
+        gamma=0.1)
+]
+
 # optimizer
-optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
-checkpoint_config = dict(interval=1)
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001),
+    clip_grad=dict(max_norm=35, norm_type=2))

configs/_base_/schedules/schedule_3x.py

@@ -1,14 +1,27 @@
-# evaluation
-evaluation = dict(interval=1, metric='mAP')
+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0 / 3,
+        by_epoch=False,
+        begin=0,
+        end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=36,
+        by_epoch=True,
+        milestones=[24, 33],
+        gamma=0.1)
+]
+
 # optimizer
-optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[24, 33])
-runner = dict(type='EpochBasedRunner', max_epochs=36)
-checkpoint_config = dict(interval=1)
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001),
+    clip_grad=dict(max_norm=35, norm_type=2))

configs/_base_/schedules/schedule_40e.py

@@ -1,14 +1,27 @@
-# evaluation
-evaluation = dict(interval=1, metric='mAP')
+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=40, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0 / 3,
+        by_epoch=False,
+        begin=0,
+        end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=40,
+        by_epoch=True,
+        milestones=[24, 32, 38],
+        gamma=0.1)
+]
+
 # optimizer
-optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[24, 32, 38])
-runner = dict(type='EpochBasedRunner', max_epochs=40)
-checkpoint_config = dict(interval=1)
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001),
+    clip_grad=dict(max_norm=35, norm_type=2))

configs/_base_/schedules/schedule_6x.py

@@ -1,14 +1,27 @@
-# evaluation
-evaluation = dict(interval=1, metric='mAP')
+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=72, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0 / 3,
+        by_epoch=False,
+        begin=0,
+        end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=72,
+        by_epoch=True,
+        milestones=[48, 66],
+        gamma=0.1)
+]
+
 # optimizer
-optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[48, 66])
-runner = dict(type='EpochBasedRunner', max_epochs=72)
-checkpoint_config = dict(interval=1)
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001),
+    clip_grad=dict(max_norm=35, norm_type=2))