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_base_ = [
'../_base_/datasets/sunrgbd-3d.py', '../_base_/default_runtime.py',
'../_base_/models/imvotenet.py'
]
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_bbox_3d=False,
with_label_3d=False),
dict(
type='RandomChoiceResize',
scales=[(1333, 480), (1333, 504), (1333, 528), (1333, 552),
(1333, 576), (1333, 600)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pack3DDetInputs', keys=['img', 'gt_bboxes', 'gt_bboxes_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 600), keep_ratio=True),
dict(
type='Pack3DDetInputs',
keys=(['img']),
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset', times=1, dataset=dict(pipeline=train_pipeline)))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=8, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=8,
by_epoch=True,
milestones=[6],
gamma=0.1)
]
val_evaluator = dict(type='Indoor2DMetric')
test_evaluator = val_evaluator
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
load_from = 'http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' # noqa
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