def forward(self, inputs):
x1 = self._conv_dw_1(inputs)
x1 = self._conv_linear_1(x1)
x2 = self._conv_pw_2(inputs)
x2 = self._conv_dw_2(x2)
x2 = self._conv_linear_2(x2)
out = paddle.concat([x1, x2], axis=1)
return channel_shuffle(out, 2)
def forward(self, inputs):
x1, x2 = paddle.split(
inputs,
num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
axis=1)
x2 = self._conv_pw(x2)
x2 = self._conv_dw(x2)
x2 = self._conv_linear(x2)
out = paddle.concat([x1, x2], axis=1)
return channel_shuffle(out, 2)
def forward(self, x):
if self.stride > 1:
x1 = self.branch1(x)
x2 = self.branch2(x)
else:
x1, x2 = x.chunk(2, axis=1)
x2 = self.branch2(x2)
out = paddle.concat([x1, x2], axis=1)
out = channel_shuffle(out, groups=2)
return out
def forward(self, x):
x = self.conv1(x)
x1, x2 = x.chunk(2, axis=1)
x1 = self.branch1(x1)
x2 = self.expand_conv(x2)
x2 = self.depthwise_conv(x2)
x2 = self.linear_conv(x2)
out = paddle.concat([x1, x2], axis=1)
out = channel_shuffle(out, groups=2)
return out
def forward(self, x):
x = [s.chunk(2, axis=1) for s in x]
x1 = [s[0] for s in x]
x2 = [s[1] for s in x]
x2 = self.cross_resolution_weighting(x2)
x2 = [dw(s) for s, dw in zip(x2, self.depthwise_convs)]
x2 = [sw(s) for s, sw in zip(x2, self.spatial_weighting)]
out = [paddle.concat([s1, s2], axis=1) for s1, s2 in zip(x1, x2)]
out = [channel_shuffle(s, groups=2) for s in out]
return out