def initialize(self, outsize, batch_norm=False, affine=True, activation=-1, usebias=True, norm=False): self.fc = L.fclayer(outsize, usebias, norm) self.batch_norm = batch_norm self.activation = activation if self.activation == PARAM_PRELU: self.act = torch.nn.PReLU(num_parameters=outchn) elif self.activation == PARAM_PRELU1: self.act = torch.nn.PReLU(num_parameters=1) if batch_norm: self.bn = L.BatchNorm(affine=affine)
def initialize(self, size, outchn, stride=1, pad='SAME_LEFT', dilation_rate=1, activation=-1, batch_norm=False, affine=True, usebias=True, groups=1): self.conv = L.conv2D(size, outchn, stride, pad, dilation_rate, usebias, groups) if batch_norm: self.bn = L.BatchNorm(affine=affine) self.batch_norm = batch_norm self.activation = activation self.act = L.Activation(activation)
def initialize(self, size, outchn, stride=1, pad='SAME_LEFT', dilation_rate=1, activation=-1, batch_norm=False, affine=True, usebias=True, groups=1): self.conv = L.conv3D(size, outchn, stride, pad, dilation_rate, usebias, groups) if batch_norm: self.bn = L.BatchNorm(affine=affine) self.batch_norm = batch_norm self.activation = activation if self.activation == PARAM_PRELU: self.act = torch.nn.PReLU(num_parameters=outchn) elif self.activation == PARAM_PRELU1: self.act = torch.nn.PReLU(num_parameters=1)