def forward(self, inp):
if self.is_hd:
x = tf.concat([ nn.tf_flatten(self.down1(inp)),
nn.tf_flatten(self.down2(inp)),
nn.tf_flatten(self.down3(inp)),
nn.tf_flatten(self.down4(inp)) ], -1 )
else:
x = nn.tf_flatten(self.down1(inp))
return x
def forward(self, inp):
return nn.tf_flatten(self.down1(inp))