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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_base_p16_384_20220308-96dfe169.pth' # noqa
# model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255)
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
pretrained=checkpoint,
backbone=dict(
type='VisionTransformer',
img_size=(512, 512),
patch_size=16,
in_channels=3,
embed_dims=768,
num_layers=12,
num_heads=12,
drop_path_rate=0.1,
attn_drop_rate=0.0,
drop_rate=0.0,
final_norm=True,
norm_cfg=backbone_norm_cfg,
with_cls_token=True,
interpolate_mode='bicubic',
),
decode_head=dict(
type='SegmenterMaskTransformerHead',
in_channels=768,
channels=768,
num_classes=150,
num_layers=2,
num_heads=12,
embed_dims=768,
dropout_ratio=0.0,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
),
test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(480, 480)),
)
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