def __init__(self, input_channels, block_channels, stride,
projection_shortcut, use_dropout, builder: ConvBuilder):
super(WRNCifarBlock, self).__init__()
assert len(block_channels) == 2
if projection_shortcut:
self.proj = builder.BNReLUConv2d(in_channels=input_channels,
out_channels=block_channels[1],
kernel_size=1,
stride=stride,
padding=0)
else:
self.proj = builder.ResIdentity(num_channels=block_channels[1])
self.conv1 = builder.BNReLUConv2d(in_channels=input_channels,
out_channels=block_channels[0],
kernel_size=3,
stride=stride,
padding=1)
if use_dropout:
self.dropout = builder.Dropout(keep_prob=0.7)
print('use dropout for WRN')
else:
self.dropout = builder.Identity()
self.conv2 = builder.BNReLUConv2d(in_channels=block_channels[0],
out_channels=block_channels[1],
kernel_size=3,
stride=1,
padding=1)
def __init__(self, builder:ConvBuilder, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = builder.Conv2dBNReLU(in_channels=in_planes, out_channels=planes, kernel_size=3, stride=stride, padding=1)
self.conv2 = builder.Conv2dBN(in_channels=planes, out_channels=self.expansion * planes, kernel_size=3, stride=1, padding=1)
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = builder.Conv2dBN(in_channels=in_planes, out_channels=self.expansion * planes, kernel_size=1, stride=stride)
else:
self.shortcut = builder.ResIdentity(num_channels=in_planes)
