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- _base_ = [
- '../_base_/datasets/mot_challenge_reid.py', '../_base_/default_runtime.py'
- ]
- model = dict(
- type='BaseReID',
- data_preprocessor=dict(
- type='ReIDDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- backbone=dict(
- type='mmpretrain.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(3, ),
- style='pytorch'),
- neck=dict(type='GlobalAveragePooling', kernel_size=(8, 4), stride=1),
- head=dict(
- type='LinearReIDHead',
- num_fcs=1,
- in_channels=2048,
- fc_channels=1024,
- out_channels=128,
- num_classes=380,
- loss_cls=dict(type='mmpretrain.CrossEntropyLoss', loss_weight=1.0),
- loss_triplet=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),
- norm_cfg=dict(type='BN1d'),
- act_cfg=dict(type='ReLU')),
- init_cfg=dict(
- type='Pretrained',
- checkpoint= # noqa: E251
- 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth' # noqa: E501
- ))
- # optimizer
- optim_wrapper = dict(
- type='OptimWrapper',
- clip_grad=None,
- optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
- # learning policy
- param_scheduler = [
- dict(
- type='LinearLR',
- start_factor=1.0 / 1000,
- by_epoch=False,
- begin=0,
- end=1000),
- dict(
- type='MultiStepLR',
- begin=0,
- end=6,
- by_epoch=True,
- milestones=[5],
- gamma=0.1)
- ]
- # train, val, test setting
- train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=6, val_interval=1)
- val_cfg = dict(type='ValLoop')
- test_cfg = dict(type='TestLoop')
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