(self, inp)
return outp
Get Inception feature maps

        Parameters
        ----------
        inp : torch.autograd.Variable
            Input tensor of shape Bx3xHxW. Values are expected to be in
            range (0, 1)

        Returns
        -------
        List of torch.autograd.Variable, corresponding to the selected output
        block, sorted ascending by index
Get Inception feature maps
Get Inception feature maps
def forward(self, inp):
        """Get Inception feature maps

        Parameters
        ----------
        inp : torch.autograd.Variable
            Input tensor of shape Bx3xHxW. Values are expected to be in
            range (0, 1)

        Returns
        -------
        List of torch.autograd.Variable, corresponding to the selected output
        block, sorted ascending by index
        """
        outp = []
        x = inp

        #if self.resize_input:
        #    x = F.upsample(x, size=(299, 299), mode='bilinear')

        # if self.normalize_input:
        #     x = x.clone()
        #     x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
        #     x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
        #     x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
        #print(x)

        for idx, block in enumerate(self.blocks):
            #print(idx, block)
            x = block(x)
            #print(x.size(), block)
            if idx in self.output_blocks:
                outp.append(x)

            if idx == self.last_needed_block:
                break
        #print(x.size())
        return outp
def forward ( self , inp ) : outp = [ ] x = inp #if self.resize_input: #    x = F.upsample(x, size=(299, 299), mode='bilinear') # if self.normalize_input: #     x = x.clone() #     x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 #     x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 #     x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 #print(x) for idx , block in enumerate ( self . blocks ) : #print(idx, block) x = block ( x ) #print(x.size(), block) if idx in self . output_blocks : outp . append ( x ) if idx == self . last_needed_block : break #print(x.size()) return outp