def __init__(self, in_features, kernel_size, padding):
super(ResBlock2d, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_features,
out_channels=in_features,
kernel_size=kernel_size,
padding=padding)
self.conv2 = nn.Conv2d(in_channels=in_features,
out_channels=in_features,
kernel_size=kernel_size,
padding=padding)
self.norm1 = nn.BatchNorm2d(in_features)
self.norm2 = nn.BatchNorm2d(in_features)
def __init__(self,
ch_in,
ch_out,
filter_size=3,
stride=1,
groups=1,
padding=0,
act="leaky",
name=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2d(in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name +
'.conv.weights'),
bias_attr=False)
bn_name = name + '.bn'
self.batch_norm = nn.BatchNorm2d(
ch_out,
weight_attr=ParamAttr(name=bn_name + '.scale',
regularizer=L2Decay(0.)),
bias_attr=ParamAttr(name=bn_name + '.offset',
regularizer=L2Decay(0.)))
self.act = act
def __init__(
self,
in_channels,
out_channels,
**kwargs
):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def __init__(self,
in_features,
out_features,
groups=1,
kernel_size=3,
padding=1):
super(SameBlock2d, self).__init__()
self.conv = nn.Conv2d(in_channels=in_features,
out_channels=out_features,
kernel_size=kernel_size,
padding=padding,
groups=groups)
self.norm = nn.BatchNorm2d(out_features)
def __init__(self,
in_features,
out_features,
kernel_size=3,
padding=1,
groups=1):
super(DownBlock2d, self).__init__()
self.conv = nn.Conv2d(in_channels=in_features,
out_channels=out_features,
kernel_size=kernel_size,
padding=padding,
groups=groups)
self.norm = nn.BatchNorm2d(out_features)
self.pool = nn.AvgPool2d(kernel_size=(2, 2))
def __init__(self,
in_chan,
out_chan,
ks=3,
stride=1,
padding=1,
*args,
**kwargs):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(in_chan,
out_chan,
kernel_size=ks,
stride=stride,
padding=padding,
bias_attr=False)
self.bn = nn.BatchNorm2d(out_chan)
self.relu = nn.ReLU()
def __init__(self,
ch_in,
ch_out,
filter_size=3,
stride=1,
groups=1,
padding=0,
act="leaky"):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2d(in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
bias_attr=False)
self.batch_norm = nn.BatchNorm2d(
ch_out,
weight_attr=ParamAttr(regularizer=L2Decay(0.)),
bias_attr=ParamAttr(regularizer=L2Decay(0.)))
self.act = act