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.modality = { "inputs": modalities.ModalityType.VIDEO_L2_RAW, "targets": modalities.ModalityType.VIDEO_L2_RAW, } 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
def next_frame_sv2p(): """SV2P model hparams.""" hparams = basic_stochastic.next_frame_basic_stochastic() hparams.optimizer = "TrueAdam" 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.modality = { "inputs": modalities.VideoModalityL2Raw, "targets": modalities.VideoModalityL2Raw, } 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
def next_frame_sv2p(): """SV2P model hparams.""" hparams = basic_stochastic.next_frame_basic_stochastic() hparams.optimizer = "TrueAdam" 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.target_modality = "video:l2raw" hparams.input_modalities = "inputs:video:l2raw" hparams.video_modality_loss_cutoff = 0.0 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) # Scheduled sampling method. Choose between prob or count. hparams.add_hparam("scheduled_sampling_mode", "count") hparams.add_hparam("scheduled_sampling_decay_steps", 10000) hparams.add_hparam("scheduled_sampling_k", 900.0) hparams.add_hparam("upsample_method", "conv2d_transpose") hparams.add_hparam("internal_loss", True) hparams.add_hparam("reward_model", "basic") return hparams
def testBasicStochastic(self): self.TestOnVariousInputOutputSizes( basic_stochastic.next_frame_basic_stochastic(), basic_stochastic.NextFrameBasicStochastic, 256, False)
def testBasicStochastic(self): self.TestOnVariousInputOutputSizes( basic_stochastic.next_frame_basic_stochastic(), basic_stochastic.NextFrameBasicStochastic, 256, False)