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, dropout):
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))
super().__init__(layers)
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, out_channels, stride, dropout):
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.shortcut = Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=stride)