def forward(self, inputs): if self.activation: out = F.linear(inputs, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul) else: out = F.linear(inputs, self.weight * self.scale, bias=self.bias * self.lr_mul) return out
def forward(self, input): if self.activation: out = F.linear(input, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul) else: bias = None if self.bias is None else self.bias * self.lr_mul out = F.linear(input, self.weight * self.scale, bias=bias) return out
def forward(self, input, labels): out = F.linear(input, self.weight * self.scale_w, bias=None) if self.bias is not None: bias = F.linear(labels, self.bias * self.scale_b, bias=None) out = out + bias * self.lr_mul if self.activation: out = fused_leaky_relu(out, bias=None) return out
def forward(self, input): if 'fused_lrelu' == self.activation: negative_slope = 0.2 scale = 2 ** 0.5 out = F.linear(input, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul,negative_slope,scale) elif 'tanh' == self.activation: out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) out = torch.tanh(out) else: out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) return out
def forward(self, input): if self.activation: # print('weight', self.weight.size()) # print('input', input.size()) out = F.linear(input, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul) else: out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) return out
def forward(self, input): if self.activation == 'fused_lrelu': out = F.linear(input, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul) else: out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) if self.activation == 'relu': out = F.relu(out) elif self.activation == 'lrelu': out = F.leaky_relu(out, negative_slope=0.2) elif self.activation == 'selu': out = F.selu(out) elif self.activation == 'tanh': out = F.tanh(out) return out
def forward(self, input): """ Return, the transformed x. Parameters ---------- x: pytorch tensor, used for the input of linear. Returns ------- the transformed x. """ if self.activation: out = F.linear(input, self.weight * self.scale) out = fused_leaky_relu(out, self.bias * self.lr_mul) else: out = F.linear( input, self.weight * self.scale, bias=self.bias * self.lr_mul ) return out