def __init__(self, in_channels, out_channels, use_se):
super().__init__()
assert out_channels % 2 == 0
channels = out_channels // 2
self.branch1 = DWConv2d(in_channels,
channels,
kernel_size=3,
stride=2,
norm='default',
act='default')
branch2 = [
Conv2d(in_channels,
channels,
kernel_size=1,
act='default',
norm='default'),
DWConv2d(channels,
channels,
kernel_size=3,
stride=2,
norm='default',
act='default'),
]
if use_se:
branch2.append(SELayer(channels, reduction=2))
self.branch2 = nn.Sequential(*branch2)
def __init__(self, in_channels, out_channels, kernel_size, activation,
use_se):
super().__init__()
assert kernel_size in [3, 5, 7]
mid_channels = out_channels // 2
output = out_channels - in_channels
branch_main = [
Conv2d(in_channels,
mid_channels,
kernel_size=1,
norm='default',
act=activation),
DWConv2d(mid_channels,
output,
kernel_size=kernel_size,
stride=2,
norm='default',
act=activation),
]
if use_se:
branch_main.append(SELayer(output, reduction=2))
self.branch_main = nn.Sequential(*branch_main)
self.branch_proj = DWConv2d(in_channels,
in_channels,
kernel_size=kernel_size,
stride=2,
norm='default',
act=activation)
def __init__(self, in_channels, activation, use_se):
super().__init__()
channels = in_channels // 2
branch = [
DWConv2d(channels, channels, kernel_size=3,
norm_layer='default', activation=activation),
DWConv2d(channels, channels, kernel_size=3,
norm_layer='default', activation=activation),
DWConv2d(channels, channels, kernel_size=3,
norm_layer='default', activation=activation),
]
if use_se:
branch.append(SELayer(channels, reduction=2))
self.branch = nn.Sequential(*branch)
def __init__(self, num_anchors, num_classes=2, in_channels=245, f_channels=256):
super().__init__()
self.num_classes = num_classes
self.conv = DWConv2d(
in_channels, f_channels, kernel_size=5,
norm='default', act='default')
self.loc_conv = Conv2d(
f_channels, num_anchors * 4, kernel_size=1)
self.cls_conv = Conv2d(
f_channels, num_anchors * num_classes, kernel_size=1)
bias_init_constant(self.cls_conv, inverse_sigmoid(0.01))
def __init__(self, in_channels, use_se):
super().__init__()
assert in_channels % 2 == 0
channels = in_channels // 2
branch = [
Conv2d(channels,
channels,
kernel_size=1,
norm='default',
act='default'),
DWConv2d(channels,
channels,
kernel_size=3,
norm='default',
act='default'),
]
if use_se:
branch.append(SELayer(channels, reduction=2))
self.branch = nn.Sequential(*branch)
def __init__(self, in_channels, kernel_size, activation, use_se):
super().__init__()
assert kernel_size in [3, 5, 7]
channels = in_channels // 2
branch = [
Conv2d(channels,
channels,
kernel_size=1,
norm='default',
act=activation),
DWConv2d(channels,
channels,
kernel_size=kernel_size,
norm='default',
act=activation),
]
if use_se:
branch.append(SELayer(channels, reduction=2))
self.branch = nn.Sequential(*branch)