p-doom/crowd-code-dataset-0.2 huggingface dataset:
[content] train_dynamics.py (python)
                val_results = {
                    "metrics": val_metrics,
                    "gt_batch": val_gt_batch,
                    "recon": val_recon,
                    "full_frame": val_full_frame,
                }
[content] train_dynamics.py (python)
                        if val_results:
                            val_gt_batch = val_results["gt_batch"]
                            val_recon = val_results["recon"]
                            val_full_frame = val_results["full_frame"]
                            
                            gt_seq_val = val_gt_batch["videos"][0].astype(jnp.float32) / 255.0
                            recon_seq_val = val_recon[0].clip(0, 1)
                            val_comparison_seq = jnp.concatenate((gt_seq_val, recon_seq_val), axis=1)
                            val_comparison_seq = einops.rearrange(
                                val_comparison_seq * 255, "t h w c -> h (t w) c"
                            )
                            full_frame_seq_val = val_full_frame[0].clip(0, 1)
                            val_full_frame_comparison_seq = jnp.concatenate((gt_seq_val, full_frame_seq_val), axis=1)
                            val_full_frame_comparison_seq = einops.rearrange(
                                val_full_frame_comparison_seq * 255, "t h w c -> h (t w) c"
                            )
                            
                            log_images.update({
                                "val_image": wandb.Image(np.asarray(gt_seq_val[args.seq_len - 1])),
                                "val_recon": wandb.Image(np.asarray(recon_seq_val[args.seq_len - 1])),
                                "val_true_vs_recon": wandb.Image(
                                    np.asarray(val_comparison_seq.astype(np.uint8))
                                ),
                                "val_full_frame": wandb.Image(np.asarray(full_frame_seq_val[args.seq_len - 1])),
                                "val_true_vs_full_frame": wandb.Image(
                                    np.asarray(val_full_frame_comparison_seq.astype(np.uint8))
                                ),
                            })
[content] train_lam.py (python)
                val_results = {
                    "metrics": val_metrics,
                    "gt_batch": val_gt_batch,
                    "recon": val_recon,
                    "full_frame": val_full_frame,
                }
[content] train_dynamics.py (python)
                        if args.val_data_dir and step % args.val_interval == 0:
                            log_images.update(
                                dict(
                                    val_image=wandb.Image(np.asarray(gt_seq_val[args.seq_len - 1])),
                                    val_recon=wandb.Image(np.asarray(recon_seq_val[args.seq_len - 1])),
                                    val_true_vs_recon=wandb.Image(
                                        np.asarray(val_comparison_seq.astype(np.uint8))
                                    ),
                                    val_full_frame=wandb.Image(np.asarray(full_frame_seq_val[args.seq_len - 1])),
                                    val_true_vs_full_frame=wandb.Image(
                                        np.asarray(val_full_frame_comparison_seq.astype(np.uint8))
                                    )
                                )
                            )
[content] train_tokenizer.py (python)

                print(f"Step {step}, validation loss: {val_metrics['val_loss']}")
                val_results = {
                    "metrics": val_metrics,
                    "gt_batch": val_gt_batch,
                    "recon": val_recon,
                }