84c0cd80f13df87c1b8e5f547a9b77a490a874b1,hypergan/samplers/factorization_batch_walk_sampler.py,FactorizationBatchWalkSampler,__init__,#FactorizationBatchWalkSampler#Any#Any#Any#,11

Before Change

        self.hardtanh = nn.Hardtanh()
        //self.mask = 1 - self.mask
        g_params = list(self.gan.g_parameters())
        params = [g_params[8]]
        params += [g_params[0]]
        print([p.shape for p in params])
        cat_params = torch.cat(params,1)

After Change

        g_params = self.gan.latent_parameters()
        if self.latent1.shape[1] // 2 == g_params[0].shape[1]:
            //recombine a split
            g_params = [torch.cat([p1, p2], 1) for p1, p2 in zip(g_params[:len(g_params)//2], g_params[len(g_params)//2:])]
            
        self.eigvec = torch.svd(torch.cat(g_params, 0)).V
        //self.eigvec = torch.svd(list(self.gan.g_parameters())[0]).V
        self.index = 0

Instances