def forward(self, x):
x = self._conv(x)
x = self._batch_norm(x)
if self.act == "relu":
x = F.relu(x)
elif self.act == "relu6":
x = F.relu6(x)
return x
def forward(self, x):
x = self._conv(x)
x = self._batch_norm(x)
if self.act == "relu":
x = F.relu(x)
elif self.act == "relu6":
x = F.relu6(x)
elif self.act == 'leaky':
x = F.leaky_relu(x)
elif self.act == 'hard_swish':
x = hard_swish(x)
return x
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.act is not None:
if self.act == "relu":
x = F.relu(x)
elif self.act == "relu6":
x = F.relu6(x)
elif self.act == "hard_swish":
x = F.hardswish(x)
else:
raise NotImplementedError(
"The activation function is selected incorrectly.")
return x
def forward(self, inputs, if_act=True):
y = self._conv(inputs)
y = self._batch_norm(y)
if if_act:
y = F.relu6(y)
return y
def hard_swish(x):
return x * F.relu6(x + 3) / 6.
def forward(self, x):
x = self.dw_conv(x)
x = F.relu6(self.bn(x))
x = self.pw_conv(x)
return x
def forward(self, inputs: paddle.Tensor, if_act: bool = True):
y = self._conv(inputs)
y = self._batch_norm(y)
if if_act:
y = F.relu6(y)
return y
