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Add dilated RedNet support #46

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Add dilated RedNet support #46

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6551dc5
add dilated involution support
keke1u Jul 16, 2021
06d5842
add dilated RedNet
keke1u Jul 16, 2021
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5 changes: 4 additions & 1 deletion det/mmdet/models/backbones/rednet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def __init__(self,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = involution(self.mid_channels, 7, self.conv2_stride)
self.conv2 = involution(self.mid_channels, 7, self.conv2_stride, dilation=dilation)

self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
Expand Down Expand Up @@ -201,6 +201,7 @@ def __init__(self,
out_channels,
expansion=None,
stride=1,
dilation=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
Expand Down Expand Up @@ -239,6 +240,7 @@ def __init__(self,
out_channels=out_channels,
expansion=self.expansion,
stride=stride,
dilation=dilation,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
Expand All @@ -251,6 +253,7 @@ def __init__(self,
out_channels=out_channels,
expansion=self.expansion,
stride=1,
dilation=dilation,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
Expand Down
6 changes: 4 additions & 2 deletions det/mmdet/models/utils/involution_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -248,10 +248,12 @@ class involution(nn.Module):
def __init__(self,
channels,
kernel_size,
stride):
stride,
dilation=1):
super(involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.channels = channels
reduction_ratio = 4
self.group_channels = 16
Expand All @@ -278,5 +280,5 @@ def forward(self, x):
weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
b, c, h, w = weight.shape
weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, h, w)
out = _involution_cuda(x, weight, stride=self.stride, padding=(self.kernel_size-1)//2)
out = _involution_cuda(x, weight, stride=self.stride, padding=self.dilation * (self.kernel_size-1) // 2, dilation=self.dilation)
return out
6 changes: 4 additions & 2 deletions det/mmdet/models/utils/involution_naive.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,10 +7,12 @@ class involution(nn.Module):
def __init__(self,
channels,
kernel_size,
stride):
stride,
dilation=1):
super(involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.dilation=dilation
self.channels = channels
reduction_ratio = 4
self.group_channels = 16
Expand All @@ -32,7 +34,7 @@ def __init__(self,
act_cfg=None)
if stride > 1:
self.avgpool = nn.AvgPool2d(stride, stride)
self.unfold = nn.Unfold(kernel_size, 1, (kernel_size-1)//2, stride)
self.unfold = nn.Unfold(kernel_size, dilation, dilation * (kernel_size-1) // 2, stride)

def forward(self, x):
weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
Expand Down
5 changes: 4 additions & 1 deletion seg/mmseg/models/backbones/rednet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def __init__(self,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = involution(self.mid_channels, 7, self.conv2_stride)
self.conv2 = involution(self.mid_channels, 7, self.conv2_stride, dilation=dilation)

self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
Expand Down Expand Up @@ -201,6 +201,7 @@ def __init__(self,
out_channels,
expansion=None,
stride=1,
dilation=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
Expand Down Expand Up @@ -239,6 +240,7 @@ def __init__(self,
out_channels=out_channels,
expansion=self.expansion,
stride=stride,
dilation=dilation,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
Expand All @@ -251,6 +253,7 @@ def __init__(self,
out_channels=out_channels,
expansion=self.expansion,
stride=1,
dilation=dilation,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
Expand Down
6 changes: 4 additions & 2 deletions seg/mmseg/models/utils/involution_cuda.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -248,10 +248,12 @@ class involution(nn.Module):
def __init__(self,
channels,
kernel_size,
stride):
stride,
dilation=1):
super(involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.channels = channels
reduction_ratio = 4
self.group_channels = 16
Expand All @@ -278,5 +280,5 @@ def forward(self, x):
weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
b, c, h, w = weight.shape
weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, h, w)
out = _involution_cuda(x, weight, stride=self.stride, padding=(self.kernel_size-1)//2)
out = _involution_cuda(x, weight, stride=self.stride, padding=self.dilation * (self.kernel_size-1) // 2, dilation=self.dilation)
return out
6 changes: 4 additions & 2 deletions seg/mmseg/models/utils/involution_naive.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,10 +7,12 @@ class involution(nn.Module):
def __init__(self,
channels,
kernel_size,
stride):
stride,
dilation=1):
super(involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.dilation=dilation
self.channels = channels
reduction_ratio = 4
self.group_channels = 16
Expand All @@ -32,7 +34,7 @@ def __init__(self,
act_cfg=None)
if stride > 1:
self.avgpool = nn.AvgPool2d(stride, stride)
self.unfold = nn.Unfold(kernel_size, 1, (kernel_size-1)//2, stride)
self.unfold = nn.Unfold(kernel_size, dilation, dilation * (kernel_size-1) // 2, stride)

def forward(self, x):
weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
Expand Down