[Update] update config

configs/_base_/schedules/schedule_adadelta_5e.py

@@ -1,9 +1,8 @@
 optim_wrapper = dict(
     type='OptimWrapper', optimizer=dict(type='Adadelta', lr=1.0))
-train_cfg = dict(by_epoch=True, max_epochs=5)
-val_cfg = dict(interval=1)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=5, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning rate
 param_scheduler = [
     dict(type='ConstantLR', factor=1.0),

configs/_base_/schedules/schedule_adam_600e.py

@@ -1,9 +1,8 @@
 # optimizer
 optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-3))
-train_cfg = dict(by_epoch=True, max_epochs=600)
-val_cfg = dict(interval=20)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=600, val_interval=20)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning rate
 param_scheduler = [
     dict(type='PolyLR', power=0.9, end=600),

configs/_base_/schedules/schedule_adam_step_12e.py

@@ -1,10 +1,9 @@
 # optimizer
 
 optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=4e-4))
-train_cfg = dict(by_epoch=True, max_epochs=12)
-val_cfg = dict(interval=1)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='LinearLR', end=100, by_epoch=False),

configs/_base_/schedules/schedule_adam_step_20e.py

@@ -1,9 +1,8 @@
 # optimizer
 optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-4))
-train_cfg = dict(by_epoch=True, max_epochs=20)
-val_cfg = dict(interval=1)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='LinearLR', end=1, start_factor=0.001),

configs/_base_/schedules/schedule_adam_step_5e.py

@@ -1,9 +1,8 @@
 # optimizer
 optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-3))
-train_cfg = dict(by_epoch=True, max_epochs=5)
-val_cfg = dict(interval=1)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=5, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='MultiStepLR', milestones=[3, 4], end=5),

configs/_base_/schedules/schedule_adam_step_600e.py

@@ -1,9 +1,8 @@
 # optimizer
 optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-4))
-train_cfg = dict(by_epoch=True, max_epochs=600)
-val_cfg = dict(interval=40)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=600, val_interval=40)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='MultiStepLR', milestones=[200, 400], end=600),

configs/_base_/schedules/schedule_adam_step_6e.py

@@ -1,6 +1,6 @@
 _base_ = 'schedule_adam_step_5e.py'
 
-train_cfg = dict(by_epoch=True, max_epochs=6)
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=6, val_interval=1)
 
 # learning policy
 param_scheduler = [

configs/_base_/schedules/schedule_sgd_100k_iters.py

@@ -2,10 +2,10 @@
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001))
-train_cfg = dict(by_epoch=False, max_iters=100000)
-val_cfg = dict(interval=100001)  # Never evaluate
-test_cfg = dict()
 
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=100000)
+test_cfg = dict(type='TestLoop')
+val_cfg = None
 # learning policy
 param_scheduler = [
     dict(type='PolyLR', power=0.9, eta_min=1e-7, by_epoch=False, end=100000),

configs/_base_/schedules/schedule_sgd_1200e.py

@@ -2,10 +2,9 @@
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001))
-train_cfg = dict(by_epoch=True, max_epochs=1200)
-val_cfg = dict(interval=20)  # Never evaluate
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1200, val_interval=20)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='PolyLR', power=0.9, eta_min=1e-7, end=1200),

configs/_base_/schedules/schedule_sgd_1500e.py

@@ -1,7 +1,9 @@
-train_cfg = dict(by_epoch=True, max_epochs=1500)
-val_cfg = dict(interval=20)  # Never evaluate
-test_cfg = dict()
-
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4))
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1500, val_interval=20)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='PolyLR', power=0.9, eta_min=1e-7, end=1500),

configs/_base_/schedules/schedule_sgd_160e.py

@@ -2,10 +2,9 @@
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001))
-train_cfg = dict(by_epoch=True, max_epochs=160)
-val_cfg = dict(interval=20)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=160, val_interval=20)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='LinearLR', end=500, start_factor=0.001, by_epoch=False),

configs/_base_/schedules/schedule_sgd_600e.py

@@ -1,10 +1,9 @@
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='SGD', lr=1e-3, momentum=0.99, weight_decay=5e-4))
-train_cfg = dict(by_epoch=True, max_epochs=600)
-val_cfg = dict(interval=50)
-test_cfg = dict()
-
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=600, val_interval=50)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
     dict(type='MultiStepLR', milestones=[200, 400], end=600),