ssdlite_mobilemamba_b1_8gpu_2lr_coco.py

_base_ = [
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

# model settings
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=1)
model = dict(
    type='SingleStageDetector',
    data_preprocessor=data_preprocessor,
    backbone=dict(
        type='MobileMamba',
        img_size=224,
        in_chans=3,
        num_classes=80,
        stages=['s', 's', 's'],
        embed_dim=[200, 376, 448],
        global_ratio=[0.8, 0.7, 0.6],
        local_ratio=[0.2, 0.2, 0.3],
        depth=[2, 3, 2],
        kernels=[7, 5, 3],
        down_ops=[['subsample', 2], ['subsample', 2], ['']],
        distillation=False, drop_path=0.03, ssm_ratio=2, forward_type="v052d",
        sync_bn=False, out_indices=(1, 2), pretrained='../../weights/MobileMamba_B1/mobilemamba_b1.pth', frozen_stages=-1,
        norm_eval=True, ),
    neck=dict(
        type='SSDNeck',
        in_channels=(200, 376),
        out_channels=(200, 376, 512, 256, 256, 128),
        level_strides=(2, 2, 2, 2),
        level_paddings=(1, 1, 1, 1),
        l2_norm_scale=None,
        use_depthwise=True,
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
        act_cfg=dict(type='ReLU6'),
        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
    bbox_head=dict(
        type='SSDHead',
        in_channels=(200, 376, 512, 256, 256, 128),
        num_classes=80,
        use_depthwise=True,
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
        act_cfg=dict(type='ReLU6'),
        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),

        # set anchor size manually instead of using the predefined
        # SSD300 setting.
        anchor_generator=dict(
            type='SSDAnchorGenerator',
            scale_major=False,
            strides=[16, 32, 64, 107, 160, 320],
            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
            min_sizes=[48, 100, 150, 202, 253, 304],
            max_sizes=[100, 150, 202, 253, 304, 320]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2])),
    # model training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.5,
            min_pos_iou=0.,
            ignore_iof_thr=-1,
            gt_max_assign_all=False),
        sampler=dict(type='PseudoSampler'),
        smoothl1_beta=1.,
        allowed_border=-1,
        pos_weight=-1,
        neg_pos_ratio=3,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        nms=dict(type='nms', iou_threshold=0.45),
        min_bbox_size=0,
        score_thr=0.02,
        max_per_img=200))
env_cfg = dict(cudnn_benchmark=True)

# dataset settings
input_size = 320
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Expand',
        mean=data_preprocessor['mean'],
        to_rgb=data_preprocessor['bgr_to_rgb'],
        ratio_range=(1, 4)),
    dict(
        type='MinIoURandomCrop',
        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
        min_crop_size=0.3),
    dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
    dict(type='RandomFlip', prob=0.5),
    dict(
        type='PhotoMetricDistortion',
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
ratio = 1
bs_ratio = 1
train_dataloader = dict(
    batch_size=24 * bs_ratio * ratio,
    num_workers=12,
    batch_sampler=None,
    dataset=dict(
        _delete_=True,
        type='RepeatDataset',
        times=5,
        dataset=dict(
            type={{_base_.dataset_type}},
            data_root={{_base_.data_root}},
            ann_file='annotations/instances_train2017.json',
            data_prefix=dict(img='train2017/'),
            filter_cfg=dict(filter_empty_gt=True, min_size=32),
            pipeline=train_pipeline)))
val_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

# training schedule
max_epochs = 120
train_cfg = dict(max_epochs=max_epochs, val_interval=5)

# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(_delete_=True, type='AdamW', lr=0.0002 * 6 * ratio * 2, betas=(0.9, 0.999), weight_decay=0.05),
    paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
                                                   'relative_position_bias_table': dict(decay_mult=0.),
                                                   'norm': dict(decay_mult=0.)}),
    clip_grad=dict(max_norm=0.1, norm_type=2), )

param_scheduler = [
    dict(
        type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0, end=500),
    dict(
        type='CosineAnnealingLR',
        begin=max_epochs // 2,
        T_max=max_epochs // 2,
        end=max_epochs,
        by_epoch=True,
        eta_min=0)
]


custom_hooks = [
    dict(type='NumClassCheckHook'),
    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
]

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (24 samples per GPU)
auto_scale_lr = dict(base_batch_size=192)