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)
