_base_ = [

'../_base_/models/mask-rcnn_r50_fpn.py',

'../common/lsj-100e_coco-instance.py'

]

image_size = (1024, 1024)

batch_augments = [

dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)

]

model = dict(data_preprocessor=dict(batch_augments=batch_augments))

train_dataloader = dict(batch_size=8, num_workers=4)

# Enable automatic-mixed-precision training with AmpOptimWrapper.

optim_wrapper = dict(

type='AmpOptimWrapper',

optimizer=dict(

type='SGD', lr=0.02 * 4, momentum=0.9, weight_decay=0.00004))

# NOTE: `auto_scale_lr` is for automatically scaling LR,

# USER SHOULD NOT CHANGE ITS VALUES.

# base_batch_size = (8 GPUs) x (8 samples per GPU)

auto_scale_lr = dict(base_batch_size=64)