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_base_ = [
'_base_master_resnet31.py',
'../_base_/datasets/toy_data.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_adam_base.py',
]
optim_wrapper = dict(optimizer=dict(lr=4e-4))
train_cfg = dict(max_epochs=12)
# learning policy
param_scheduler = [
dict(type='LinearLR', end=100, by_epoch=False),
dict(type='MultiStepLR', milestones=[11], end=12),
]
# dataset settings
train_list = [_base_.toy_rec_train]
test_list = [_base_.toy_rec_test]
train_dataset = dict(
type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
test_dataset = dict(
type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)
train_dataloader = dict(
batch_size=2,
num_workers=1,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=train_dataset)
val_dataloader = dict(
batch_size=2,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=test_dataset)
test_dataloader = val_dataloader
val_evaluator = dict(dataset_prefixes=['Toy'])
test_evaluator = val_evaluator
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