def forward(self, input_tensor: Tensor_Torch):
input_linked_tensor = input_tensor.get_linked_tensor()
output_linked_tensor = input_linked_tensor.view(
input_linked_tensor.shape[0], -1)
if self.inplace_forward:
input_tensor.set_linked_tensor(output_linked_tensor)
return output_linked_tensor
def forward(self, input_tensor: Tensor_Torch):
input_linked_tensor = input_tensor.get_linked_tensor()
# reference in Pytorch Style Transfer Tutorial
a, b, c, d = input_linked_tensor.size() # should be as four tuple
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
features = input_linked_tensor.view(a * b, c *
d) # resise F_XL into \hat F_XL
G = torch.mm(features, features.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
output_linked_tensor = G.div(a * b * c * d)
if self.inplace_forward:
input_tensor.set_linked_tensor(output_linked_tensor)
return output_linked_tensor
def forward(self, input_tensor: Tensor_Torch):
linked_tensor = self.linear(input_tensor.get_linked_tensor())
if self.inplace_forward:
input_tensor.set_linked_tensor(linked_tensor)
return linked_tensor