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# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth' # noqa
# TODO: delete custom_imports after mmpretrain supports auto import
# please install mmpretrain >= 1.0.0rc7
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.models'], allow_failed_imports=False)
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,
seg_pad_val=255)
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
backbone=dict(
type='mmpretrain.PoolFormer',
arch='s12',
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.'),
in_patch_size=7,
in_stride=4,
in_pad=2,
down_patch_size=3,
down_stride=2,
down_pad=1,
drop_rate=0.,
drop_path_rate=0.,
out_indices=(0, 2, 4, 6),
frozen_stages=0,
),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=4),
decode_head=dict(
type='FPNHead',
in_channels=[256, 256, 256, 256],
in_index=[0, 1, 2, 3],
feature_strides=[4, 8, 16, 32],
channels=128,
dropout_ratio=0.1,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))
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