def __init__(self,
in_features: int,
out_features: int,
activation: nn.Module = ReLUInPlace,
expansion: int = 4,
**kwargs):
super().__init__(in_features, out_features, activation, **kwargs)
self.expansion = expansion
self.expanded_features = out_features * self.expansion
self.block = nn.Sequential(
OrderedDict({
"bn1":
nn.BatchNorm2d(in_features),
"act1":
activation(),
"conv1":
Conv2dPad(in_features,
self.expanded_features,
kernel_size=1,
bias=False,
**kwargs),
"bn2":
nn.BatchNorm2d(self.expanded_features),
"act2":
activation(),
"conv2":
Conv2dPad(self.expanded_features,
out_features,
kernel_size=3,
bias=False,
**kwargs),
}))
def test_Conv2dPad():
x = torch.rand((1, 1, 5, 5))
block = Conv2dPad(1, 5, kernel_size=3)
res = block(x)
assert x.shape[-1] == res.shape[-1]
assert x.shape[-2] == res.shape[-2]
# no padding
block = Conv2dPad(1, 5, kernel_size=3, mode = None)
res = block(x)
assert x.shape[-1] != res.shape[-1]
assert x.shape[-2] != res.shape[-2]
assert res.shape[-1] == 3
assert res.shape[-2] == 3
def __init__(self, in_features: int, out_features: int, stride: int = 2):
super().__init__(OrderedDict({
'pool': nn.AvgPool2d((2, 2)) if stride == 2 else nn.Identity(),
'conv': Conv2dPad(in_features, out_features,
kernel_size=1, bias=False),
'bn': nn.BatchNorm2d(out_features)
}))
def __init__(self, in_features: int, out_features: int, activation: nn.Module = ReLUInPlace, stride: int = 1, shortcut: nn.Module = ResNetShorcut, **kwargs):
super().__init__(in_features, out_features, activation,
stride=stride, shortcut=shortcut, **kwargs)
self.block = nn.Sequential(
OrderedDict(
{
'bn1': nn.BatchNorm2d(in_features),
'act1': activation(),
'conv1': Conv2dPad(in_features, out_features, kernel_size=3, bias=False, stride=stride, **kwargs),
'bn2': nn.BatchNorm2d(out_features),
'act2': activation(),
'conv2': Conv2dPad(out_features, out_features, kernel_size=3, bias=False),
}
))
self.act = nn.Identity()
def __init__(self, in_features: int, out_features: int, stride: int = 2):
super().__init__()
self.conv = Conv2dPad(in_features,
out_features,
kernel_size=1,
stride=stride,
bias=False)
self.bn = nn.BatchNorm2d(out_features)
def __init__(self, in_features: int, out_features: int, stride: int = 2):
super().__init__(
OrderedDict({
"pool":
nn.AvgPool2d(
(2, 2), ceil_mode=True) if stride == 2 else nn.Identity(),
"conv":
Conv2dPad(in_features, out_features, kernel_size=1,
bias=False),
"bn":
nn.BatchNorm2d(out_features),
}))
def __init__(
self,
in_features: int,
out_features: int,
activation: nn.Module = ReLUInPlace,
stride: int = 1,
shortcut: nn.Module = ResNetShorcut,
**kwargs,
):
super().__init__()
self.should_apply_shortcut = in_features != out_features or stride != 1
self.block = nn.Sequential(
OrderedDict({
"conv1":
Conv2dPad(
in_features,
out_features,
kernel_size=3,
stride=stride,
bias=False,
**kwargs,
),
"bn1":
nn.BatchNorm2d(out_features),
"act1":
activation(),
"conv2":
Conv2dPad(out_features,
out_features,
kernel_size=3,
bias=False),
"bn2":
nn.BatchNorm2d(out_features),
}))
self.shortcut = (shortcut(in_features, out_features, stride=stride)
if self.should_apply_shortcut else nn.Identity())
self.act = activation()
def __init__(self,
in_features: int,
factor: int = 2,
activation: nn.Module = ReLUInPlace):
super().__init__(
OrderedDict({
"bn":
nn.BatchNorm2d(in_features),
"act":
activation(),
"conv":
Conv2dPad(in_features,
in_features // factor,
kernel_size=1,
bias=False),
"pool":
nn.AvgPool2d(kernel_size=2, stride=2),
}))
def __init__(self,
in_features: int,
factor: int = 2,
activation: nn.Module = ReLUInPlace):
super().__init__()
self.block = nn.Sequential(
OrderedDict({
'bn':
nn.BatchNorm2d(in_features),
'act':
activation(),
'conv':
Conv2dPad(in_features,
in_features // factor,
kernel_size=1,
bias=False),
'pool':
nn.AvgPool2d(kernel_size=2, stride=2)
}))
def __init__(self,
in_features: int,
out_features: int,
activation: nn.Module = ReLUInPlace,
*args,
**kwargs):
super().__init__()
self.block = nn.Sequential(
OrderedDict({
"bn":
nn.BatchNorm2d(in_features),
"act":
activation(),
"conv":
Conv2dPad(in_features,
out_features,
kernel_size=3,
*args,
**kwargs),
}))