mmdetection/configs/yolact/yolact_r50_1xb8-55e_coco.py

_base_ = [
    '../_base_/datasets/coco_instance.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
    mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
# model settings
input_size = 550
model = dict(
    type='YOLACT',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=img_norm_cfg['mean'],
        std=img_norm_cfg['std'],
        bgr_to_rgb=img_norm_cfg['to_rgb'],
        pad_mask=True),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,  # do not freeze stem
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=False,  # update the statistics of bn
        zero_init_residual=False,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5,
        upsample_cfg=dict(mode='bilinear')),
    bbox_head=dict(
        type='YOLACTHead',
        num_classes=80,
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            octave_base_scale=3,
            scales_per_octave=1,
            base_sizes=[8, 16, 32, 64, 128],
            ratios=[0.5, 1.0, 2.0],
            strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
            centers=[(550 * 0.5 / x, 550 * 0.5 / x)
                     for x in [69, 35, 18, 9, 5]]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=False,
            reduction='none',
            loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
        num_head_convs=1,
        num_protos=32,
        use_ohem=True),
    mask_head=dict(
        type='YOLACTProtonet',
        in_channels=256,
        num_protos=32,
        num_classes=80,
        max_masks_to_train=100,
        loss_mask_weight=6.125,
        with_seg_branch=True,
        loss_segm=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0.,
            ignore_iof_thr=-1,
            gt_max_assign_all=False),
        sampler=dict(type='PseudoSampler'),  # YOLACT should use PseudoSampler
        # smoothl1_beta=1.,
        allowed_border=-1,
        pos_weight=-1,
        neg_pos_ratio=3,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        mask_thr=0.5,
        iou_thr=0.5,
        top_k=200,
        max_per_img=100,
        mask_thr_binary=0.5))
# dataset settings
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)),
    dict(
        type='Expand',
        mean=img_norm_cfg['mean'],
        to_rgb=img_norm_cfg['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', backend_args={{_base_.backend_args}}),
    dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=8,
    num_workers=4,
    batch_sampler=None,
    dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

max_epochs = 55
# training schedule for 55e
train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

# learning rate
param_scheduler = [
    dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[20, 42, 49, 52],
        gamma=0.1)
]

# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4))

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

env_cfg = dict(cudnn_benchmark=True)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (1 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)