Running Mapper on Batches about hairmapper HOT 4 CLOSED

         x = bias_act.bias_act(x, b, act=self.activation,dim=2) # x = bias_act.bias_act(x, b, act=self.activation)

        #x = x.squeeze(0)
        # Main layers.
        for idx in range(self.num_layers):
            layer = getattr(self, f'fc{idx}')
            x = layer(x)  # W
        #W_latent = torch.clone(x.unsqueeze(0))

        return x

    codes = []
    for code_path in code_list:
        code = np.load(code_path)
        codes.append(code)
    codes = np.concatenate(codes, axis=0) # shape: [batch size, 18, 512]

    mapper_input = codes.copy()
    mapper_input_tensor = torch.from_numpy(mapper_input).cuda().float()
    print('mapper_input_tensor shape: ', mapper_input_tensor.shape)
    edited_latent_codes_debug = codes.copy()
    mapper_out = mapper(mapper_input_tensor)
    print('mapper_out shape: ', mapper_out.shape, 'alpha', alpha)

    edited_latent_codes_debug[:, :8, :] += alpha * mapper_out.to('cpu').detach().numpy()