yolof_r50-c5_8xb8-1x_coco.py

_base_ = [
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
    type='YOLOF',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.530, 116.280, 123.675],
        std=[1.0, 1.0, 1.0],
        bgr_to_rgb=False,
        pad_size_divisor=32),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(3, ),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='caffe',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron/resnet50_caffe')),
    neck=dict(
        type='DilatedEncoder',
        in_channels=2048,
        out_channels=512,
        block_mid_channels=128,
        num_residual_blocks=4,
        block_dilations=[2, 4, 6, 8]),
    bbox_head=dict(
        type='YOLOFHead',
        num_classes=80,
        in_channels=512,
        reg_decoded_bbox=True,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            scales=[1, 2, 4, 8, 16],
            strides=[32]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1., 1., 1., 1.],
            add_ctr_clamp=True,
            ctr_clamp=32),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))
# optimizer
optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=0.0001),
    paramwise_cfg=dict(
        norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)}))

# learning rate
param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=0.00066667,
        by_epoch=False,
        begin=0,
        end=1500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=12,
        by_epoch=True,
        milestones=[8, 11],
        gamma=0.1)
]

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='RandomShift', prob=0.5, max_shift_px=32),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='Resize', scale=(1333, 800), 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=8, num_workers=8, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

# 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)