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_base_ = [
'../_base_/models/fast-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadProposals', num_max_proposals=2000),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='ProposalBroadcaster',
transforms=[
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
]),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadProposals', num_max_proposals=None),
dict(
type='ProposalBroadcaster',
transforms=[
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
]),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
dataset=dict(
proposal_file='proposals/rpn_r50_fpn_1x_train2017.pkl',
pipeline=train_pipeline))
val_dataloader = dict(
dataset=dict(
proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl',
pipeline=test_pipeline))
test_dataloader = val_dataloader
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