def __init__(self,
in_channels,
out_channels,
stride=1,
dropout=0,
use_se=False):
super().__init__()
self.use_se = use_se
self._dropout = dropout
self.norm1 = Norm(in_channels)
self.act1 = Act()
self.conv1 = Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=stride)
self.norm2 = Norm(out_channels)
self.act2 = Act()
if self._dropout:
self.dropout = nn.Dropout(dropout)
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3)
if self.use_se:
self.se = SEModule(out_channels, reduction=8)
self.shortcut = Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=stride)
def __init__(self,
in_channels,
channels,
out_channels,
kernel_size,
stride=1,
se_ratio=1 / 16):
super().__init__()
self.bn = get_norm_layer('default', in_channels)
if in_channels != channels:
self.expand = Conv2d(in_channels,
channels,
kernel_size=1,
norm_layer='default',
activation='default')
self.dwconv = Conv2d(channels,
channels,
kernel_size,
stride=stride,
groups=channels,
norm_layer='default',
activation='default')
if se_ratio:
assert 0 < se_ratio < 1
self.se = SEModule(channels, reduction=int(1 / se_ratio))
if out_channels is not None:
self.project = Conv2d(channels,
out_channels,
kernel_size=1,
norm_layer='default')
self.use_res_connect = stride == 1 and in_channels == out_channels
def __init__(self, in_channels, out_channels, shuffle_groups=2):
super().__init__()
channels = out_channels - in_channels // 2
self.conv1 = Conv2d(
in_channels // 2,
channels,
kernel_size=1,
norm='default',
act='default',
)
self.conv2 = Conv2d(
channels,
channels,
kernel_size=3,
groups=channels,
norm='default',
)
self.conv3 = Conv2d(
channels,
channels,
kernel_size=1,
norm='default',
)
self.shortcut = nn.Sequential()
self.se = SEModule(channels, reduction=2)
if in_channels != out_channels:
self.shortcut = Conv2d(in_channels // 2,
channels,
kernel_size=1,
norm='default')
self.relu = Act('default')
self.shuffle = ShuffleBlock(shuffle_groups)
def __init__(self, in_channels, shuffle_groups=2):
super().__init__()
channels = in_channels // 2
self.conv1 = Conv2d(
channels,
channels,
kernel_size=1,
norm='default',
act='default',
)
self.conv2 = Conv2d(
channels,
channels,
kernel_size=3,
groups=channels,
norm='default',
)
self.conv3 = Conv2d(
channels,
channels,
kernel_size=1,
norm='default',
act='default',
)
self.se = SEModule(channels, reduction=2)
self.shuffle = ShuffleBlock(shuffle_groups)
def __init__(self, in_channels, shuffle_groups=2, with_se=False):
super().__init__()
self.with_se = with_se
channels = in_channels // 2
self.conv1 = Conv2d(
channels,
channels,
kernel_size=1,
norm_layer='default',
activation='default',
)
self.conv2 = Conv2d(
channels,
channels,
kernel_size=5,
groups=channels,
norm_layer='default',
)
self.conv3 = Conv2d(
channels,
channels,
kernel_size=1,
norm_layer='default',
activation='default',
)
if with_se:
self.se = SEModule(channels, reduction=8)
self.shuffle = ShuffleBlock(shuffle_groups)
def __init__(self, in_channels, out_channels, use_se, drop_path):
super().__init__()
self.bn1 = Norm(in_channels)
self.nl1 = Act("default")
self.conv1 = Conv2d(in_channels, out_channels, kernel_size=3)
self.bn2 = Norm(out_channels)
self.nl2 = Act("default")
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3)
if use_se:
self.se = SEModule(out_channels, reduction=8)
if drop_path:
self.drop_path = DropPath(drop_path)
def __init__(self, in_channels, out_channels, dropout, use_se, drop_path):
super().__init__()
self.norm1 = Norm(in_channels)
self.act1 = Act()
self.conv1 = Conv2d(in_channels, out_channels, kernel_size=3)
self.norm2 = Norm(out_channels)
self.act2 = Act()
if dropout:
self.dropout = nn.Dropout(dropout)
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3)
if use_se:
self.se = SEModule(out_channels, reduction=8)
if drop_path:
self.drop_path = DropPath(drop_path)
def __init__(self, in_channels, out_channels, stride=1, use_se=False):
super().__init__()
self.use_se = use_se
self.bn1 = Norm(in_channels)
self.nl1 = Act("default")
self.conv1 = Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=stride)
self.bn2 = Norm(out_channels)
self.nl2 = Act("default")
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3)
if self.use_se:
self.se = SEModule(out_channels, reduction=8)
self.shortcut = Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=stride)
def __init__(self, in_channels_list, num_scales, num_levels,
f_channels=256, no_padding=0, se_reduction=16, lite=False):
super().__init__()
assert len(in_channels_list) == 2
assert num_levels >= 2
self.ffm1 = FFMv1(in_channels_list, f_channels, lite=lite)
self.tums = nn.ModuleList([
TUM(f_channels * 3, f_channels, num_scales, no_padding, lite)
])
for _ in range(num_levels - 1):
self.tums.append(
TUM(f_channels, f_channels, num_scales, no_padding, lite)
)
self.ffm2 = nn.ModuleList([
FFMv2(f_channels * 3, f_channels // 2)
for _ in range(num_levels - 1)
])
self.se = nn.ModuleList([
SEModule(f_channels // 2 * num_levels, se_reduction)
for _ in range(num_scales)
])
self.out_channels = [ f_channels // 2 * num_levels ] * num_scales
def __init__(self, in_channels_list, reduction=16):
super().__init__()
self.se = nn.ModuleList([
SEModule(c, reduction)
for c in in_channels_list
])
