init
This commit is contained in:
esenke
408
finetune/mmseg/models/backbones/fast_scnn.py
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408
finetune/mmseg/models/backbones/fast_scnn.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
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from mmengine.model import BaseModule
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from mmseg.models.decode_heads.psp_head import PPM
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from mmseg.registry import MODELS
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from ..utils import InvertedResidual, resize
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class LearningToDownsample(nn.Module):
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"""Learning to downsample module.
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Args:
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in_channels (int): Number of input channels.
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dw_channels (tuple[int]): Number of output channels of the first and
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the second depthwise conv (dwconv) layers.
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out_channels (int): Number of output channels of the whole
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'learning to downsample' module.
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conv_cfg (dict | None): Config of conv layers. Default: None
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norm_cfg (dict | None): Config of norm layers. Default:
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dict(type='BN')
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act_cfg (dict): Config of activation layers. Default:
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dict(type='ReLU')
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dw_act_cfg (dict): In DepthwiseSeparableConvModule, activation config
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of depthwise ConvModule. If it is 'default', it will be the same
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as `act_cfg`. Default: None.
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"""
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def __init__(self,
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in_channels,
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dw_channels,
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out_channels,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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dw_act_cfg=None):
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super().__init__()
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.act_cfg = act_cfg
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self.dw_act_cfg = dw_act_cfg
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dw_channels1 = dw_channels[0]
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dw_channels2 = dw_channels[1]
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self.conv = ConvModule(
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in_channels,
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dw_channels1,
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3,
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stride=2,
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padding=1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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self.dsconv1 = DepthwiseSeparableConvModule(
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dw_channels1,
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dw_channels2,
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kernel_size=3,
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stride=2,
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padding=1,
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norm_cfg=self.norm_cfg,
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dw_act_cfg=self.dw_act_cfg)
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self.dsconv2 = DepthwiseSeparableConvModule(
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dw_channels2,
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out_channels,
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kernel_size=3,
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stride=2,
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padding=1,
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norm_cfg=self.norm_cfg,
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dw_act_cfg=self.dw_act_cfg)
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def forward(self, x):
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x = self.conv(x)
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x = self.dsconv1(x)
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x = self.dsconv2(x)
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return x
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class GlobalFeatureExtractor(nn.Module):
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"""Global feature extractor module.
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Args:
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in_channels (int): Number of input channels of the GFE module.
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Default: 64
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block_channels (tuple[int]): Tuple of ints. Each int specifies the
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number of output channels of each Inverted Residual module.
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Default: (64, 96, 128)
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out_channels(int): Number of output channels of the GFE module.
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Default: 128
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expand_ratio (int): Adjusts number of channels of the hidden layer
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in InvertedResidual by this amount.
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Default: 6
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num_blocks (tuple[int]): Tuple of ints. Each int specifies the
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number of times each Inverted Residual module is repeated.
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The repeated Inverted Residual modules are called a 'group'.
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Default: (3, 3, 3)
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strides (tuple[int]): Tuple of ints. Each int specifies
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the downsampling factor of each 'group'.
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Default: (2, 2, 1)
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pool_scales (tuple[int]): Tuple of ints. Each int specifies
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the parameter required in 'global average pooling' within PPM.
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Default: (1, 2, 3, 6)
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conv_cfg (dict | None): Config of conv layers. Default: None
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norm_cfg (dict | None): Config of norm layers. Default:
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dict(type='BN')
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act_cfg (dict): Config of activation layers. Default:
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dict(type='ReLU')
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align_corners (bool): align_corners argument of F.interpolate.
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Default: False
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"""
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def __init__(self,
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in_channels=64,
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block_channels=(64, 96, 128),
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out_channels=128,
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expand_ratio=6,
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num_blocks=(3, 3, 3),
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strides=(2, 2, 1),
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pool_scales=(1, 2, 3, 6),
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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align_corners=False):
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super().__init__()
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.act_cfg = act_cfg
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assert len(block_channels) == len(num_blocks) == 3
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self.bottleneck1 = self._make_layer(in_channels, block_channels[0],
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num_blocks[0], strides[0],
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expand_ratio)
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self.bottleneck2 = self._make_layer(block_channels[0],
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block_channels[1], num_blocks[1],
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strides[1], expand_ratio)
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self.bottleneck3 = self._make_layer(block_channels[1],
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block_channels[2], num_blocks[2],
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strides[2], expand_ratio)
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self.ppm = PPM(
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pool_scales,
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block_channels[2],
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block_channels[2] // 4,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg,
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align_corners=align_corners)
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self.out = ConvModule(
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block_channels[2] * 2,
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out_channels,
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3,
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padding=1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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def _make_layer(self,
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in_channels,
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out_channels,
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blocks,
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stride=1,
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expand_ratio=6):
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layers = [
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InvertedResidual(
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in_channels,
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out_channels,
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stride,
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expand_ratio,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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]
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for i in range(1, blocks):
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layers.append(
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InvertedResidual(
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out_channels,
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out_channels,
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1,
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expand_ratio,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.bottleneck1(x)
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x = self.bottleneck2(x)
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x = self.bottleneck3(x)
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x = torch.cat([x, *self.ppm(x)], dim=1)
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x = self.out(x)
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return x
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class FeatureFusionModule(nn.Module):
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"""Feature fusion module.
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Args:
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higher_in_channels (int): Number of input channels of the
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higher-resolution branch.
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lower_in_channels (int): Number of input channels of the
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lower-resolution branch.
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out_channels (int): Number of output channels.
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conv_cfg (dict | None): Config of conv layers. Default: None
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norm_cfg (dict | None): Config of norm layers. Default:
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dict(type='BN')
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dwconv_act_cfg (dict): Config of activation layers in 3x3 conv.
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Default: dict(type='ReLU').
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conv_act_cfg (dict): Config of activation layers in the two 1x1 conv.
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Default: None.
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align_corners (bool): align_corners argument of F.interpolate.
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Default: False.
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"""
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def __init__(self,
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higher_in_channels,
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lower_in_channels,
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out_channels,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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dwconv_act_cfg=dict(type='ReLU'),
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conv_act_cfg=None,
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align_corners=False):
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super().__init__()
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.dwconv_act_cfg = dwconv_act_cfg
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self.conv_act_cfg = conv_act_cfg
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self.align_corners = align_corners
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self.dwconv = ConvModule(
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lower_in_channels,
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out_channels,
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3,
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padding=1,
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groups=out_channels,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.dwconv_act_cfg)
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self.conv_lower_res = ConvModule(
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out_channels,
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out_channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.conv_act_cfg)
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self.conv_higher_res = ConvModule(
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higher_in_channels,
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out_channels,
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1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.conv_act_cfg)
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self.relu = nn.ReLU(True)
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def forward(self, higher_res_feature, lower_res_feature):
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lower_res_feature = resize(
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lower_res_feature,
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size=higher_res_feature.size()[2:],
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mode='bilinear',
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align_corners=self.align_corners)
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lower_res_feature = self.dwconv(lower_res_feature)
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lower_res_feature = self.conv_lower_res(lower_res_feature)
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higher_res_feature = self.conv_higher_res(higher_res_feature)
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out = higher_res_feature + lower_res_feature
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return self.relu(out)
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@MODELS.register_module()
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class FastSCNN(BaseModule):
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"""Fast-SCNN Backbone.
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This backbone is the implementation of `Fast-SCNN: Fast Semantic
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Segmentation Network <https://arxiv.org/abs/1902.04502>`_.
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Args:
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in_channels (int): Number of input image channels. Default: 3.
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downsample_dw_channels (tuple[int]): Number of output channels after
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the first conv layer & the second conv layer in
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Learning-To-Downsample (LTD) module.
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Default: (32, 48).
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global_in_channels (int): Number of input channels of
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Global Feature Extractor(GFE).
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Equal to number of output channels of LTD.
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Default: 64.
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global_block_channels (tuple[int]): Tuple of integers that describe
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the output channels for each of the MobileNet-v2 bottleneck
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residual blocks in GFE.
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Default: (64, 96, 128).
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global_block_strides (tuple[int]): Tuple of integers
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that describe the strides (downsampling factors) for each of the
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MobileNet-v2 bottleneck residual blocks in GFE.
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Default: (2, 2, 1).
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global_out_channels (int): Number of output channels of GFE.
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Default: 128.
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higher_in_channels (int): Number of input channels of the higher
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resolution branch in FFM.
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Equal to global_in_channels.
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Default: 64.
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lower_in_channels (int): Number of input channels of the lower
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resolution branch in FFM.
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Equal to global_out_channels.
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Default: 128.
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fusion_out_channels (int): Number of output channels of FFM.
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Default: 128.
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out_indices (tuple): Tuple of indices of list
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[higher_res_features, lower_res_features, fusion_output].
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Often set to (0,1,2) to enable aux. heads.
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Default: (0, 1, 2).
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conv_cfg (dict | None): Config of conv layers. Default: None
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norm_cfg (dict | None): Config of norm layers. Default:
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dict(type='BN')
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act_cfg (dict): Config of activation layers. Default:
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dict(type='ReLU')
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align_corners (bool): align_corners argument of F.interpolate.
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Default: False
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dw_act_cfg (dict): In DepthwiseSeparableConvModule, activation config
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of depthwise ConvModule. If it is 'default', it will be the same
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as `act_cfg`. Default: None.
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Default: None
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"""
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def __init__(self,
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in_channels=3,
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downsample_dw_channels=(32, 48),
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global_in_channels=64,
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global_block_channels=(64, 96, 128),
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global_block_strides=(2, 2, 1),
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global_out_channels=128,
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higher_in_channels=64,
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lower_in_channels=128,
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fusion_out_channels=128,
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out_indices=(0, 1, 2),
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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align_corners=False,
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dw_act_cfg=None,
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init_cfg=None):
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super().__init__(init_cfg)
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if init_cfg is None:
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self.init_cfg = [
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dict(type='Kaiming', layer='Conv2d'),
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dict(
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type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
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]
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if global_in_channels != higher_in_channels:
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raise AssertionError('Global Input Channels must be the same \
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with Higher Input Channels!')
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elif global_out_channels != lower_in_channels:
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raise AssertionError('Global Output Channels must be the same \
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with Lower Input Channels!')
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self.in_channels = in_channels
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self.downsample_dw_channels1 = downsample_dw_channels[0]
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self.downsample_dw_channels2 = downsample_dw_channels[1]
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self.global_in_channels = global_in_channels
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self.global_block_channels = global_block_channels
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self.global_block_strides = global_block_strides
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self.global_out_channels = global_out_channels
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self.higher_in_channels = higher_in_channels
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self.lower_in_channels = lower_in_channels
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self.fusion_out_channels = fusion_out_channels
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self.out_indices = out_indices
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.act_cfg = act_cfg
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self.align_corners = align_corners
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self.learning_to_downsample = LearningToDownsample(
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in_channels,
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downsample_dw_channels,
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global_in_channels,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg,
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dw_act_cfg=dw_act_cfg)
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self.global_feature_extractor = GlobalFeatureExtractor(
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global_in_channels,
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global_block_channels,
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global_out_channels,
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strides=self.global_block_strides,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg,
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align_corners=self.align_corners)
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self.feature_fusion = FeatureFusionModule(
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higher_in_channels,
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lower_in_channels,
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fusion_out_channels,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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dwconv_act_cfg=self.act_cfg,
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align_corners=self.align_corners)
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def forward(self, x):
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higher_res_features = self.learning_to_downsample(x)
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lower_res_features = self.global_feature_extractor(higher_res_features)
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fusion_output = self.feature_fusion(higher_res_features,
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lower_res_features)
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outs = [higher_res_features, lower_res_features, fusion_output]
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outs = [outs[i] for i in self.out_indices]
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return tuple(outs)
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