def encode_input()

in generation/models/pix2pixHD_model.py [0:0]

• 21 lines of code
• 10 McCabe index (conditional complexity)

    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):
        if self.opt.label_nc == 0:
            input_label = label_map.data.cuda()
        else:
            # create one-hot vector for label map
            size = label_map.size()
            oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])
            input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
            input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)
            if self.opt.data_type == 16:
                input_label = input_label.half()

        # get edges from instance map
        if not self.opt.no_instance:
            inst_map = inst_map.data.cuda()
            edge_map = self.get_edges(inst_map)
            input_label = torch.cat((input_label, edge_map), dim=1)
        input_label = Variable(input_label, volatile=infer)

        # real images for training
        if real_image is not None:
            real_image = Variable(real_image.data.cuda())

        # instance map for feature encoding
        if self.use_features:
            # get precomputed feature maps
            if self.opt.load_features:
                feat_map = Variable(feat_map.data.cuda())

        return input_label, inst_map, real_image, feat_map