SV2P model hparams.

def next_frame_sv2p():
  """SV2P model hparams."""
  hparams = basic_stochastic.next_frame_basic_stochastic()
  hparams.optimizer = "true_adam"
  hparams.learning_rate_schedule = "constant"
  hparams.learning_rate_constant = 1e-3
  hparams.video_num_input_frames = 1
  hparams.video_num_target_frames = 3
  hparams.batch_size = 16
  hparams.bottom = {
      "inputs": modalities.video_raw_bottom,
      "targets": modalities.video_raw_targets_bottom,
  }
  hparams.loss = {
      "targets": modalities.video_l2_raw_loss,
  }
  hparams.top = {
      "targets": modalities.video_raw_top,
  }
  hparams.video_modality_loss_cutoff = 0.0
  hparams.scheduled_sampling_mode = "count"
  hparams.scheduled_sampling_k = 900.0
  hparams.add_hparam("reward_prediction", True)
  hparams.add_hparam("reward_prediction_stop_gradient", False)
  hparams.add_hparam("reward_prediction_buffer_size", 0)
  hparams.add_hparam("model_options", "CDNA")
  hparams.add_hparam("num_masks", 10)
  hparams.add_hparam("multi_latent", False)
  hparams.add_hparam("relu_shift", 1e-12)
  hparams.add_hparam("dna_kernel_size", 5)
  hparams.add_hparam("upsample_method", "conv2d_transpose")
  hparams.add_hparam("reward_model", "basic")
  hparams.add_hparam("visualize_logits_histogram", True)
  return hparams