[tab] jasmine/sample.py (python)
from dataclasses import dataclass
import time
import os
import optax

import dm_pix as pix
import einops
import jax
import jax.numpy as jnp
import flax.linen as nn
import numpy as np
import orbax.checkpoint as ocp
from PIL import Image, ImageDraw
import tyro
from flax import nnx

from genie import Genie
from utils.dataloader import get_dataloader


@dataclass
class Args:
    # Experiment
    seed: int = 0
    seq_len: int = 16
    image_channels: int = 3
    image_height: int = 90
    image_width: int = 160
    data_dir: str = "data/coinrun_episodes"
    checkpoint: str = ""
    print_action_indices: bool = True
    output_dir: str = "gifs/"
    # Sampling
    batch_size: int = 1
    maskgit_steps: int = 25
    temperature: float = 1.0
    sample_argmax: bool = True
    start_frame: int = 1
    # Tokenizer checkpoint
    tokenizer_dim: int = 512
    tokenizer_ffn_dim: int = 2048
    latent_patch_dim: int = 32
    num_patch_latents: int = 1024
    patch_size: int = 4
    tokenizer_num_blocks: int = 4
    tokenizer_num_heads: int = 8
    # LAM checkpoint
    lam_dim: int = 512
    lam_ffn_dim: int = 2048
    latent_action_dim: int = 32
    num_actions: int = 6
    lam_patch_size: int = 16
    lam_num_blocks: int = 4
    lam_num_heads: int = 8
    use_gt_actions: bool = False
    # Dynamics checkpoint
    dyna_type: str = "maskgit"
    dyna_dim: int = 512
    dyna_ffn_dim: int = 2048
    dyna_num_blocks: int = 6
    dyna_num_heads: int = 8
    param_dtype = jnp.float32
    dtype = jnp.bfloat16
    use_flash_attention: bool = True


args = tyro.cli(Args)

if __name__ == "__main__":
    """
    Dimension keys:
        B: batch size
        T: number of input (conditioning) frames
        N: number of patches per frame
        S: sequence length
        H: height
        W: width
        E: B * (S - 1)
    """
    jax.distributed.initialize()

    rng = jax.random.key(args.seed)

    # --- Load Genie checkpoint ---
    rngs = nnx.Rngs(rng)
    genie = Genie(
        # Tokenizer
        in_dim=args.image_channels,
        tokenizer_dim=args.tokenizer_dim,
        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
        latent_patch_dim=args.latent_patch_dim,
        num_patch_latents=args.num_patch_latents,
        patch_size=args.patch_size,
        tokenizer_num_blocks=args.tokenizer_num_blocks,
        tokenizer_num_heads=args.tokenizer_num_heads,
        # LAM
        lam_dim=args.lam_dim,
        lam_ffn_dim=args.lam_ffn_dim,
        latent_action_dim=args.latent_action_dim,
        num_actions=args.num_actions,
        lam_patch_size=args.lam_patch_size,
        lam_num_blocks=args.lam_num_blocks,
        lam_num_heads=args.lam_num_heads,
        lam_co_train=False,
        use_gt_actions=args.use_gt_actions,
        # Dynamics
        dyna_type=args.dyna_type,
        dyna_dim=args.dyna_dim,
        dyna_ffn_dim=args.dyna_ffn_dim,
        dyna_num_blocks=args.dyna_num_blocks,
        dyna_num_heads=args.dyna_num_heads,
        param_dtype=args.param_dtype,
        dtype=args.dtype,
        use_flash_attention=args.use_flash_attention,
        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
        decode=False,
        rngs=rngs,
    )

    # Need to delete lam decoder for checkpoint loading
    if not args.use_gt_actions:
        assert genie.lam is not None
        del genie.lam.decoder

    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
    handler_registry.add(
        "model_state", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
    )
    handler_registry.add(
        "model_state", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
    )
    checkpoint_options = ocp.CheckpointManagerOptions(
        step_format_fixed_length=6,
    )
    checkpoint_manager = ocp.CheckpointManager(
        args.checkpoint,
        options=checkpoint_options,
        handler_registry=handler_registry,
    )

    dummy_tx = optax.adamw(
        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
        b1=0.9,
        b2=0.9,
        weight_decay=1e-4,
        mu_dtype=args.dtype,
    )
    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)

    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
    abstract_optimizer_state = nnx.state(abstract_optimizer)
    restored = checkpoint_manager.restore(
        checkpoint_manager.latest_step(),
        args=ocp.args.Composite(
            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
        ),
    )
    restored_optimizer_state = restored["model_state"]
    nnx.update(dummy_optimizer, restored_optimizer_state)

    # --- Define sampling function ---
    def _sampling_fn(model: Genie, batch: dict) -> jax.Array:
        """Runs Genie.sample with pre-defined generation hyper-parameters."""
        assert args.dyna_type in [
            "maskgit",
            "causal",
        ], f"Invalid dynamics type: {args.dyna_type}"
        frames, _ = model.sample(
            batch,
            args.seq_len,
            args.temperature,
            args.sample_argmax,
            args.maskgit_steps,
        )
        return frames

    # --- Define autoregressive sampling loop ---
    def _autoreg_sample(genie, rng, batch):
        batch["videos"] = batch["videos"][:, : args.start_frame]
        batch["rng"] = rng
        generated_vid_BSHWC = _sampling_fn(genie, batch)
        return generated_vid_BSHWC

    # --- Get video + latent actions ---
    array_record_files = [
        os.path.join(args.data_dir, x)
        for x in os.listdir(args.data_dir)
        if x.endswith(".array_record")
    ]
    dataloader = get_dataloader(
        array_record_files,
        args.seq_len,
        args.batch_size,
        args.image_height,
        args.image_width,
        args.image_channels,
        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
        num_workers=0,
        prefetch_buffer_size=1,
        seed=args.seed,
    )
    dataloader = iter(dataloader)
    batch = next(dataloader)
    gt_video = jnp.asarray(batch["videos"], dtype=jnp.float32) / 255.0
    batch["videos"] = gt_video.astype(args.dtype)
    # Get latent actions for all videos in the batch
    action_batch_E = None
    if not args.use_gt_actions:
        action_batch_E = genie.vq_encode(batch, training=False)
        batch["latent_actions"] = action_batch_E

    # --- Sample + evaluate video ---
    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)

    gt = gt_video.clip(0, 1)[:, args.start_frame :]
    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]

    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
    ssim = ssim_vmap(gt, recon)
    psnr = psnr_vmap(gt, recon)
    per_frame_ssim = jnp.mean(ssim, 0)
    per_frame_psnr = jnp.mean(psnr, 0)
    avg_ssim = jnp.mean(ssim)
    avg_psnr = jnp.mean(psnr)

    print("Per-frame SSIM:
", per_frame_ssim)
    print("Per-frame PSNR:
", per_frame_psnr)

    print(f"SSIM: {avg_ssim}")
    print(f"PSNR: {avg_psnr}")

    # --- Construct horizontally concatenated PNG for selected sequence (ground-truth only) ---
    true_videos = (gt_video * 255).astype(np.uint8)
    # Move from JAX device arrays to NumPy for PIL compatibility
    true_videos = jax.device_get(true_videos)

    # Select sequence index in the batch (user-specified)
    seq_idx = 31
    gt_seq_Thwc = true_videos[seq_idx, : args.seq_len]

    T, H, W, C = gt_seq_Thwc.shape

    # Create canvas with one row (Ground-truth only) and T frames concatenated horizontally
    canvas = Image.new("RGB", (T * W, H))
    for t in range(T):
        gt_img = Image.fromarray(gt_seq_Thwc[t])
        canvas.paste(gt_img, (t * W, 0))

    # Do not draw action indices on the ground-truth-only PNG

    # Save PNG
    os.makedirs(args.output_dir, exist_ok=True)
    canvas.save(os.path.join(args.output_dir, f"sequence_{time.time()}.png"))