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- _base_ = [
- '../_base_/models/retinanet_r50_fpn.py',
- '../_base_/schedules/schedule_1x.py',
- '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
- ]
- image_size = (896, 896)
- batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
- norm_cfg = dict(type='BN', requires_grad=True)
- checkpoint = 'https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth' # noqa
- model = dict(
- data_preprocessor=dict(
- type='DetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_size_divisor=32,
- batch_augments=batch_augments),
- backbone=dict(
- _delete_=True,
- type='EfficientNet',
- arch='b3',
- drop_path_rate=0.2,
- out_indices=(3, 4, 5),
- frozen_stages=0,
- norm_cfg=dict(
- type='SyncBN', requires_grad=True, eps=1e-3, momentum=0.01),
- norm_eval=False,
- init_cfg=dict(
- type='Pretrained', prefix='backbone', checkpoint=checkpoint)),
- neck=dict(
- in_channels=[48, 136, 384],
- start_level=0,
- out_channels=256,
- relu_before_extra_convs=True,
- no_norm_on_lateral=True,
- norm_cfg=norm_cfg),
- bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
- # training and testing settings
- train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
- # dataset settings
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='RandomResize',
- scale=image_size,
- ratio_range=(0.8, 1.2),
- keep_ratio=True),
- dict(type='RandomCrop', crop_size=image_size),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='Resize', scale=image_size, keep_ratio=True),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- train_dataloader = dict(
- batch_size=4, num_workers=4, dataset=dict(pipeline=train_pipeline))
- val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
- test_dataloader = val_dataloader
- # optimizer
- optim_wrapper = dict(
- optimizer=dict(lr=0.04),
- paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
- # learning policy
- max_epochs = 12
- param_scheduler = [
- dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
- dict(
- type='MultiStepLR',
- begin=0,
- end=max_epochs,
- by_epoch=True,
- milestones=[8, 11],
- gamma=0.1)
- ]
- train_cfg = dict(max_epochs=max_epochs)
- # cudnn_benchmark=True can accelerate fix-size training
- env_cfg = dict(cudnn_benchmark=True)
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (8 GPUs) x (4 samples per GPU)
- auto_scale_lr = dict(base_batch_size=32)
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