def __init__(self,
in_channels,
channels,
groups,
stride=1,
se_reduction=4,
drop_path=0.2):
super().__init__()
branch1 = [
Norm(in_channels),
Conv2d(in_channels,
channels,
kernel_size=1,
norm='default',
act='default'),
*([Pool2d(3, 2)] if stride != 1 else []),
Conv2d(channels,
channels,
kernel_size=3,
groups=groups,
norm='default',
act='default'),
*([SELayer(channels, se_reduction, groups)]
if se_reduction else []),
Conv2d(channels, channels, kernel_size=1, norm='default'),
*([DropPath(drop_path)] if drop_path and stride == 1 else []),
]
self.branch1 = Sequential(branch1)
self.branch2 = Shortcut(in_channels, channels, stride)
def __init__(self,
in_channels,
channels,
stride,
zero_init_residual=True,
reduction=16):
super().__init__()
out_channels = channels * self.expansion
self.conv1 = Conv2d(in_channels,
channels,
kernel_size=1,
norm='def',
act='def')
self.conv2 = Conv2d(channels,
channels,
kernel_size=3,
stride=stride,
norm='def',
act='def')
self.conv3 = Conv2d(channels, out_channels, kernel_size=1)
self.bn3 = Norm(out_channels,
gamma_init='zeros' if zero_init_residual else 'ones')
self.se = SELayer(out_channels, reduction=reduction)
if stride != 1 or in_channels != out_channels:
shortcut = []
if stride != 1:
shortcut.append(Pool2d(2, 2, type='avg'))
shortcut.append(
Conv2d(in_channels, out_channels, kernel_size=1, norm='def'))
self.shortcut = Sequential(shortcut)
else:
self.shortcut = Identity()
self.act = Act()
def __init__(self,
in_channels,
out_channels,
stride,
dropout,
use_se=False):
super().__init__()
self.use_se = use_se
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()
self.dropout = Dropout(dropout) if dropout else Identity()
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3)
if self.use_se:
self.se = SELayer(out_channels, reduction=8)
if stride != 1:
assert in_channels != out_channels
self.shortcut = Sequential([
Pool2d(2, 2, type='avg'),
Conv2d(in_channels, out_channels, kernel_size=1, norm='def'),
])
else:
self.shortcut = Identity()
def __init__(self, in_channels, out_channels, stride, groups, use_se):
super().__init__()
self.use_se = use_se
self.conv1 = Conv2d(in_channels,
out_channels,
kernel_size=1,
norm='def',
act='def')
self.conv2 = Conv2d(out_channels,
out_channels,
kernel_size=3,
stride=stride,
groups=groups,
norm='def',
act='def')
if self.use_se:
self.se = SELayer(out_channels, 4)
self.conv3 = Sequential(
Conv2d(out_channels, out_channels, kernel_size=1, bias=False),
Norm(out_channels, gamma_init='zeros'))
if stride != 1 or in_channels != out_channels:
self.shortcut = Conv2d(in_channels,
out_channels,
kernel_size=1,
norm='def')
else:
self.shortcut = Identity()
self.act = Act()
def __init__(self, in_channels, out_channels, kernel_size, stride,
expand_ratio, se_ratio, drop_connect):
super().__init__()
self._has_se = se_ratio is not None and 0 < se_ratio <= 1
channels = in_channels * expand_ratio
self.expand = Conv2d(in_channels, channels, 1, norm='def',
act='def') if expand_ratio != 1 else Identity()
self.depthwise = Conv2d(channels,
channels,
kernel_size,
stride,
groups=channels,
padding='SAME',
norm='def',
act='def')
if self._has_se:
self.se = SELayer(channels,
se_channels=int(in_channels * se_ratio),
min_se_channels=1)
self.project = Conv2d(channels, out_channels, 1, norm='def')
self._use_residual = in_channels == out_channels and stride == 1
if self._use_residual:
self.drop_connect = DropPath(
drop_connect) if drop_connect else Identity()
def __init__(self, in_channels, use_se):
super().__init__()
c = in_channels // 2
branch2 = [
Conv2d(c, c, kernel_size=1, norm='def', act='def'),
Conv2d(c, c, kernel_size=3, groups=c, norm='def'),
Conv2d(c, c, kernel_size=1, norm='def', act='def')
]
if use_se:
branch2.append(SELayer(c, reduction=2))
self.branch2 = Sequential(branch2)
def __init__(self, in_channels, out_channels, dropout, reduction):
layers = [
Norm(in_channels),
Act(),
Conv2d(in_channels, out_channels, kernel_size=3),
Norm(out_channels),
Act(),
Conv2d(out_channels, out_channels, kernel_size=3),
]
if dropout:
layers.insert(5, Dropout(dropout))
layers.append(SELayer(out_channels, reduction=reduction))
super().__init__(layers)
def __init__(self, in_channels, out_channels, dropout, use_se, drop_path):
layers = [
Norm(in_channels),
Act(),
Conv2d(in_channels, out_channels, kernel_size=3),
Norm(out_channels),
Act(),
Conv2d(out_channels, out_channels, kernel_size=3),
]
if dropout:
layers.insert(5, Dropout(dropout))
if use_se:
layers.append(SELayer(out_channels, reduction=8))
if drop_path:
layers.append(DropPath(drop_path))
super().__init__(layers)
def __init__(self, in_channels, channels, stride, cardinality, base_width, reduction):
super().__init__()
out_channels = channels * 4
D = math.floor(channels * (base_width / 64))
C = cardinality
self.conv1 = Conv2d(in_channels, D * C, kernel_size=1,
norm='def', act='def')
self.conv2 = Conv2d(D * C, D * C, kernel_size=3, stride=stride, groups=cardinality,
norm='def', act='def')
self.conv3 = Conv2d(D * C, out_channels, kernel_size=1,
norm='def')
self.se = SELayer(out_channels, reduction=reduction)
self.shortcut = Conv2d(in_channels, out_channels, kernel_size=1, stride=stride,
norm='def') if in_channels != out_channels else Identity()
self.act = Act()
def __init__(self,
in_channels,
out_channels,
stride,
dropout,
reduction=8):
super().__init__()
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()
self.dropout = Dropout(dropout) if dropout else Identity()
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3)
self.se = SELayer(out_channels, reduction=reduction)
self.shortcut = Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=stride)