2021-11-30 15:00:37 +08:00

70 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = '.'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (128, 128)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 128),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
val=dict(
type=dataset_type,
data_root=data_root,
img_dir='',
ann_dir='',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
data_root=data_root,
img_dir='',
ann_dir='',
pipeline=test_pipeline))
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='FastSCNN',
downsample_dw_channels=(32, 48),
global_in_channels=64,
global_block_channels=(64, 96, 128),
global_block_strides=(2, 2, 1),
global_out_channels=128,
higher_in_channels=64,
lower_in_channels=128,
fusion_out_channels=128,
out_indices=(0, 1, 2),
norm_cfg=norm_cfg,
align_corners=False),
decode_head=dict(
type='DepthwiseSeparableFCNHead',
in_channels=128,
channels=128,
concat_input=False,
num_classes=19,
in_index=-1,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))