def conv3x3(in_planes, out_planes, bitW, stride=1):
"3x3 convolution with padding"
return self_conv(in_planes,
out_planes,
bitW,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
def __init__(self,
inplanes,
planes,
bitW,
kernel_size=1,
stride=1,
bias=False):
super(downsample_layer, self).__init__()
self.conv = self_conv(inplanes,
planes,
bitW,
kernel_size=kernel_size,
stride=stride,
bias=False)
self.batch_norm = nn.BatchNorm2d(planes)