knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py

_base_ = [
    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(
    type='SegDataPreProcessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    bgr_to_rgb=True,
    pad_val=0,
    size=crop_size,
    seg_pad_val=255)
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
num_stages = 3
conv_kernel_size = 1

model = dict(
    type='EncoderDecoder',
    data_preprocessor=data_preprocessor,
    pretrained='open-mmlab://resnet50_v1c',
    backbone=dict(
        type='ResNetV1c',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 1, 1),
        strides=(1, 2, 2, 2),
        norm_cfg=norm_cfg,
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),
    decode_head=dict(
        type='IterativeDecodeHead',
        num_stages=num_stages,
        kernel_update_head=[
            dict(
                type='KernelUpdateHead',
                num_classes=150,
                num_ffn_fcs=2,
                num_heads=8,
                num_mask_fcs=1,
                feedforward_channels=2048,
                in_channels=512,
                out_channels=512,
                dropout=0.0,
                conv_kernel_size=conv_kernel_size,
                ffn_act_cfg=dict(type='ReLU', inplace=True),
                with_ffn=True,
                feat_transform_cfg=dict(
                    conv_cfg=dict(type='Conv2d'), act_cfg=None),
                kernel_updator_cfg=dict(
                    type='KernelUpdator',
                    in_channels=256,
                    feat_channels=256,
                    out_channels=256,
                    act_cfg=dict(type='ReLU', inplace=True),
                    norm_cfg=dict(type='LN'))) for _ in range(num_stages)
        ],
        kernel_generate_head=dict(
            type='UPerHead',
            in_channels=[256, 512, 1024, 2048],
            in_index=[0, 1, 2, 3],
            pool_scales=(1, 2, 3, 6),
            channels=512,
            dropout_ratio=0.1,
            num_classes=150,
            norm_cfg=norm_cfg,
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=1024,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
# optimizer
optim_wrapper = dict(
    _delete_=True,
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0005),
    clip_grad=dict(max_norm=1, norm_type=2))
# learning policy
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='MultiStepLR',
        begin=1000,
        end=80000,
        milestones=[60000, 72000],
        by_epoch=False,
    )
]
# In K-Net implementation we use batch size 2 per GPU as default
train_dataloader = dict(batch_size=2, num_workers=2)
val_dataloader = dict(batch_size=1, num_workers=4)
test_dataloader = val_dataloader