mask2former_r50_lsj_8x2_50e_coco-panoptic.py

_base_ = [
    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
num_things_classes = 80
num_stuff_classes = 53
num_classes = num_things_classes + num_stuff_classes
model = dict(
    type='Mask2Former',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    panoptic_head=dict(
        type='Mask2FormerHead',
        in_channels=[256, 512, 1024, 2048],  # pass to pixel_decoder inside
        strides=[4, 8, 16, 32],
        feat_channels=256,
        out_channels=256,
        num_things_classes=num_things_classes,
        num_stuff_classes=num_stuff_classes,
        num_queries=100,
        num_transformer_feat_level=3,
        pixel_decoder=dict(
            type='MSDeformAttnPixelDecoder',
            num_outs=3,
            norm_cfg=dict(type='GN', num_groups=32),
            act_cfg=dict(type='ReLU'),
            encoder=dict(
                type='DetrTransformerEncoder',
                num_layers=6,
                transformerlayers=dict(
                    type='BaseTransformerLayer',
                    attn_cfgs=dict(
                        type='MultiScaleDeformableAttention',
                        embed_dims=256,
                        num_heads=8,
                        num_levels=3,
                        num_points=4,
                        im2col_step=64,
                        dropout=0.0,
                        batch_first=False,
                        norm_cfg=None,
                        init_cfg=None),
                    ffn_cfgs=dict(
                        type='FFN',
                        embed_dims=256,
                        feedforward_channels=1024,
                        num_fcs=2,
                        ffn_drop=0.0,
                        act_cfg=dict(type='ReLU', inplace=True)),
                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
                init_cfg=None),
            positional_encoding=dict(
                type='SinePositionalEncoding', num_feats=128, normalize=True),
            init_cfg=None),
        enforce_decoder_input_project=False,
        positional_encoding=dict(
            type='SinePositionalEncoding', num_feats=128, normalize=True),
        transformer_decoder=dict(
            type='DetrTransformerDecoder',
            return_intermediate=True,
            num_layers=9,
            transformerlayers=dict(
                type='DetrTransformerDecoderLayer',
                attn_cfgs=dict(
                    type='MultiheadAttention',
                    embed_dims=256,
                    num_heads=8,
                    attn_drop=0.0,
                    proj_drop=0.0,
                    dropout_layer=None,
                    batch_first=False),
                ffn_cfgs=dict(
                    embed_dims=256,
                    feedforward_channels=2048,
                    num_fcs=2,
                    act_cfg=dict(type='ReLU', inplace=True),
                    ffn_drop=0.0,
                    dropout_layer=None,
                    add_identity=True),
                feedforward_channels=2048,
                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
                                 'ffn', 'norm')),
            init_cfg=None),
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=False,
            loss_weight=2.0,
            reduction='mean',
            class_weight=[1.0] * num_classes + [0.1]),
        loss_mask=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='mean',
            loss_weight=5.0),
        loss_dice=dict(
            type='DiceLoss',
            use_sigmoid=True,
            activate=True,
            reduction='mean',
            naive_dice=True,
            eps=1.0,
            loss_weight=5.0)),
    panoptic_fusion_head=dict(
        type='MaskFormerFusionHead',
        num_things_classes=num_things_classes,
        num_stuff_classes=num_stuff_classes,
        loss_panoptic=None,
        init_cfg=None),
    train_cfg=dict(
        num_points=12544,
        oversample_ratio=3.0,
        importance_sample_ratio=0.75,
        assigner=dict(
            type='MaskHungarianAssigner',
            cls_cost=dict(type='ClassificationCost', weight=2.0),
            mask_cost=dict(
                type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
            dice_cost=dict(
                type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),
        sampler=dict(type='MaskPseudoSampler')),
    test_cfg=dict(
        panoptic_on=True,
        # For now, the dataset does not support
        # evaluating semantic segmentation metric.
        semantic_on=False,
        instance_on=True,
        # max_per_image is for instance segmentation.
        max_per_image=100,
        iou_thr=0.8,
        # In Mask2Former's panoptic postprocessing,
        # it will filter mask area where score is less than 0.5 .
        filter_low_score=True),
    init_cfg=None)

# dataset settings
image_size = (1024, 1024)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile', to_float32=True),
    dict(
        type='LoadPanopticAnnotations',
        with_bbox=True,
        with_mask=True,
        with_seg=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    # large scale jittering
    dict(
        type='Resize',
        img_scale=image_size,
        ratio_range=(0.1, 2.0),
        multiscale_mode='range',
        keep_ratio=True),
    dict(
        type='RandomCrop',
        crop_size=image_size,
        crop_type='absolute',
        recompute_bbox=True,
        allow_negative_crop=True),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=image_size),
    dict(type='DefaultFormatBundle', img_to_float=True),
    dict(
        type='Collect',
        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data_root = 'data/coco/'
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(pipeline=train_pipeline),
    val=dict(
        pipeline=test_pipeline,
        ins_ann_file=data_root + 'annotations/instances_val2017.json',
    ),
    test=dict(
        pipeline=test_pipeline,
        ins_ann_file=data_root + 'annotations/instances_val2017.json',
    ))

embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
# optimizer
optimizer = dict(
    type='AdamW',
    lr=0.0001,
    weight_decay=0.05,
    eps=1e-8,
    betas=(0.9, 0.999),
    paramwise_cfg=dict(
        custom_keys={
            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
            'query_embed': embed_multi,
            'query_feat': embed_multi,
            'level_embed': embed_multi,
        },
        norm_decay_mult=0.0))
optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))

# learning policy
lr_config = dict(
    policy='step',
    gamma=0.1,
    by_epoch=False,
    step=[327778, 355092],
    warmup='linear',
    warmup_by_epoch=False,
    warmup_ratio=1.0,  # no warmup
    warmup_iters=10)

max_iters = 368750
runner = dict(type='IterBasedRunner', max_iters=max_iters)

log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),
        dict(type='TensorboardLoggerHook', by_epoch=False)
    ])
interval = 5000
workflow = [('train', interval)]
checkpoint_config = dict(
    by_epoch=False, interval=interval, save_last=True, max_keep_ckpts=3)

# Before 365001th iteration, we do evaluation every 5000 iterations.
# After 365000th iteration, we do evaluation every 368750 iterations,
# which means that we do evaluation at the end of training.
dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
evaluation = dict(
    interval=interval,
    dynamic_intervals=dynamic_intervals,
    metric=['PQ', 'bbox', 'segm'])