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# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import DepthwiseSeparableConvModule
from mmseg.registry import MODELS
from .fcn_head import FCNHead
@MODELS.register_module()
class DepthwiseSeparableFCNHead(FCNHead):
"""Depthwise-Separable Fully Convolutional Network for Semantic
Segmentation.
This head is implemented according to `Fast-SCNN: Fast Semantic
Segmentation Network <https://arxiv.org/abs/1902.04502>`_.
Args:
in_channels(int): Number of output channels of FFM.
channels(int): Number of middle-stage channels in the decode head.
concat_input(bool): Whether to concatenate original decode input into
the result of several consecutive convolution layers.
Default: True.
num_classes(int): Used to determine the dimension of
final prediction tensor.
in_index(int): Correspond with 'out_indices' in FastSCNN backbone.
norm_cfg (dict | None): Config of norm layers.
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
loss_decode(dict): Config of loss type and some
relevant additional options.
dw_act_cfg (dict):Activation config of depthwise ConvModule. If it is
'default', it will be the same as `act_cfg`. Default: None.
"""
def __init__(self, dw_act_cfg=None, **kwargs):
super().__init__(**kwargs)
self.convs[0] = DepthwiseSeparableConvModule(
self.in_channels,
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg,
dw_act_cfg=dw_act_cfg)
for i in range(1, self.num_convs):
self.convs[i] = DepthwiseSeparableConvModule(
self.channels,
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg,
dw_act_cfg=dw_act_cfg)
if self.concat_input:
self.conv_cat = DepthwiseSeparableConvModule(
self.in_channels + self.channels,
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg,
dw_act_cfg=dw_act_cfg)
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