def weight_op(self):
padding = get_same_padding(self.kernel_size)
if isinstance(padding, int):
padding *= self.dilation
else:
padding[0] *= self.dilation
padding[1] *= self.dilation
weight_dict = OrderedDict()
weight_dict['depth_conv'] = nn.Conv2d(self.in_channels,
self.in_channels,
kernel_size=self.kernel_size,
stride=self.stride,
padding=padding,
dilation=self.dilation,
groups=self.in_channels,
bias=False)
weight_dict['point_conv'] = nn.Conv2d(self.in_channels,
self.out_channels,
kernel_size=1,
groups=self.groups,
bias=self.bias)
if self.has_shuffle and self.groups > 1:
weight_dict['shuffle'] = ShuffleLayer(self.groups)
return weight_dict
def weight_op(self):
padding = get_same_padding(self.kernel_size)
if isinstance(padding, int):
padding *= self.dilation
else:
padding[0] *= self.dilation
padding[1] *= self.dilation
weight_dict = OrderedDict()
# weight_dict['conv'] = nn.Conv2d(
# self.in_channels, self.out_channels, kernel_size=self.kernel_size, stride=self.stride, padding=padding,
# dilation=self.dilation, groups=self.groups, bias=self.bias
# )
weight_dict['conv'] = LsqConv(self.in_channels,
self.out_channels,
kernel_size=self.kernel_size,
quan_name_w='lsq',
quan_name_a='lsq',
nbit_w=self.nbit_w,
nbit_a=self.nbit_a,
stride=self.stride,
padding=padding,
dilation=self.dilation,
groups=self.groups,
bias=self.bias)
if self.has_shuffle and self.groups > 1:
weight_dict['shuffle'] = ShuffleLayer(self.groups)
return weight_dict