code/OCT_RAE_main/src/eval/__init__.py

from .ref_iqa import calculate_psnr, calculate_lpips, calculate_ssim
from .fid import calculate_rfid, calculate_gfid
import numpy as np
import torch
import numpy as np
import torch
import torch.distributed as dist
from PIL import Image
from torch.cuda.amp import autocast
from torch.utils.data import DataLoader, Subset
from tqdm import tqdm
from typing import Dict, Optional
import os
import sys
def compute_reconstruction_metrics(
    ref_arr: np.ndarray,
    rec_arr: np.ndarray,
    device: torch.device,
    batch_size: int = 128,
    metrics_to_compute = ("psnr", "ssim", "rfid"),
    disable_bar: bool = True
) -> Dict[str, float]:
    """
    Compute reconstruction metrics between reference and reconstructed images.

    Args:
        ref_arr: Reference images [N, H, W, C] uint8
        rec_arr: Reconstructed images [N, H, W, C] uint8
        device: Device for computation
        batch_size: Batch size for metric computation

    Returns:
        Dictionary with metrics: eval/psnr, eval/ssim, eval/rfid
        Note: LPIPS is not computed here since it's already tracked during training
    """
    device_str = "cuda" if device.type == "cuda" else "cpu"
    results_dict = {}
    if 'psnr' in metrics_to_compute:
        psnr = calculate_psnr(ref_arr, rec_arr, batch_size, device_str, disable_bar=disable_bar)
        results_dict["psnr"] = psnr
    if 'ssim' in metrics_to_compute:
        ssim = calculate_ssim(ref_arr, rec_arr, batch_size, device_str, disable_bar=disable_bar)
        results_dict["ssim"] = ssim
    if 'rfid' in metrics_to_compute:
        rfid = calculate_rfid(ref_arr, rec_arr, batch_size, device_str)
        results_dict["rfid"] = rfid
    assert len(results_dict) > 0, "No metrics were computed."
    return results_dict
def compute_generation_metrics(
    ref_arr: np.ndarray,
    rec_arr: np.ndarray,
    device: torch.device,
    batch_size: int = 128,
):
    device_str = "cuda" if device.type == "cuda" else "cpu"
    # only eval FID
    fid = calculate_gfid(rec_arr, ref_arr, batch_size, device_str) 
    return {
        'fid': fid
    }
@torch.no_grad()
def evaluate_generation_distributed(
    model_fn,
    sample_fn,
    latent_size, # for noise 
    additional_model_kwargs,
    use_guidance: bool,
    rae, 
    val_dataset,
    num_samples: int,
    batch_size: int,
    rank: int,
    world_size: int,
    device: torch.device,
    experiment_dir: str,
    global_step: int,
    autocast_kwargs: dict,
    metric_batch_size: int = 128,
    reference_npz_path: Optional[str] = None,
) -> Optional[Dict[str, float]]:
    """
    Evaluate reconstruction metrics using all GPUs in a distributed manner.

    Args:
        val_dataset: Validation dataset
        batch_size: Batch size per GPU for reconstruction
        rank: Current GPU rank
        world_size: Total number of GPUs
        device: Device to use
        experiment_dir: Experiment directory
        global_step: Current training step
        autocast_kwargs: Autocast configuration
        metric_batch_size: Batch size for metric computation (on rank 0)
        reference_npz_path: Optional path to existing reference NPZ file

    Returns:
        Dictionary of metrics (only on rank 0, None on other ranks)
    """
    # model.eval()
    # Save shard NPZ
    temp_dir = os.path.join(experiment_dir, "eval_npzs")
    if rank == 0:
        print(f"\n[Eval] Starting distributed sampling evaluation at step {global_step}")
        os.makedirs(temp_dir, exist_ok=True)

    # Wait for rank 0 to create the directory before other ranks try to save
    dist.barrier()
    # print(f"[Rank {rank}] Starting sampling...")
    # Each rank processes its shard
    N = min(len(val_dataset), num_samples)
    chunk = N // world_size

    if rank < world_size - 1:
        start = rank * chunk
        end   = (rank + 1) * chunk
    else:
        # Last rank takes the remainder (and handles N < world_size gracefully)
        start = rank * chunk
        end   = N

    rank_indices = list(range(start, end))
    subset = Subset(val_dataset, rank_indices)
    loader = DataLoader(
        subset,
        batch_size=batch_size,
        shuffle=False,
        num_workers=4,
        pin_memory=True,
        drop_last=False,
    )

    # Reconstruct images on this rank
    generations = []
    iterator = tqdm(loader, desc=f"[Rank {rank}] Sampling", file=sys.stdout) if rank == 0 else loader

    with torch.inference_mode():
        for _, label in iterator: # don't actually need images at sampling time
            n = label.size(0)
            z = torch.randn(n, *latent_size, device = device)
            y = label.to(device)
            if use_guidance:
                z = torch.cat([z, z], dim=0)
                y_null = torch.full((n,), null_label, device=device)
                y = torch.cat([y, y_null], dim=0)
            model_kwargs = dict(y=y, **additional_model_kwargs)
            with autocast(**autocast_kwargs):
                samples = sample_fn(z, model_fn, **model_kwargs)[-1]
                if use_guidance:
                    samples = samples.chunk(2, dim = 0)
                samples = rae.decode(samples).clamp(0,1)
            gen_np = samples.mul(255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
            for img in gen_np:
                generations.append(img)

    generations = np.stack(generations)
    shard_path = os.path.join(temp_dir, f"gen_{global_step:07d}_{rank:02d}.npz")
    np.savez(shard_path, arr_0=generations)

    if rank == 0:
        print(f"[Rank {rank}] Saved {len(generations)} generation to {shard_path}")

    # Wait for all ranks to finish generation
    dist.barrier()

    # Rank 0 computes metrics
    metrics = None
    if rank == 0:
        # Combine all generation shards
        all_gens = []
        for r in range(world_size):
            shard_file = os.path.join(temp_dir, f"gen_{global_step:07d}_{r:02d}.npz")
            shard_data = np.load(shard_file)["arr_0"]
            all_gens.append(shard_data)

        combined_recons = np.concatenate(all_gens, axis=0)[:num_samples]
        print(f"[Eval] Combined generation NPZ shape: {combined_recons.shape}")

        # Load reference NPZ
        ref_npz_path = reference_npz_path

        if not os.path.exists(ref_npz_path):
            raise FileNotFoundError(f"Reference NPZ not found at {ref_npz_path}")

        ref_stats = np.load(ref_npz_path)
        print(f"[Eval] Loaded reference NPZ from {ref_npz_path}")

        # Compute metrics
        print("[Eval] Computing metrics...")
        metrics = compute_generation_metrics(
            ref_stats,
            combined_recons,
            device,
            metric_batch_size,
        )

        # Print results
        print(f"[Eval] Step {global_step} Metrics:")
        for key, value in metrics.items():
            print(f"  {key}: {value:.6f}")

        # Cleanup reconstruction shards
        for r in range(world_size):
            shard_file = os.path.join(temp_dir, f"gen_{global_step:07d}_{r:02d}.npz")
            if os.path.exists(shard_file):
                os.remove(shard_file)

    dist.barrier()
    return metrics
@torch.no_grad()
def evaluate_reconstruction_distributed(
    model,
    val_dataset,
    num_samples: int,
    batch_size: int,
    rank: int,
    world_size: int,
    device: torch.device,
    experiment_dir: str,
    global_step: int,
    autocast_kwargs: dict,
    metric_batch_size: int = 128,
    reference_npz_path: Optional[str] = None,
    metrics_to_compute: Optional[list] = ("psnr", "ssim", "rfid")
) -> Optional[Dict[str, float]]:
    """
    Evaluate reconstruction metrics using all GPUs in a distributed manner.

    Args:
        model: Model to evaluate (should be in eval mode)
        val_dataset: Validation dataset
        batch_size: Batch size per GPU for reconstruction
        rank: Current GPU rank
        world_size: Total number of GPUs
        device: Device to use
        experiment_dir: Experiment directory
        global_step: Current training step
        autocast_kwargs: Autocast configuration
        metric_batch_size: Batch size for metric computation (on rank 0)
        reference_npz_path: Optional path to existing reference NPZ file

    Returns:
        Dictionary of metrics (only on rank 0, None on other ranks)
    """
    # model.eval()
    # Save shard NPZ
    temp_dir = os.path.join(experiment_dir, "eval_npzs")
    if rank == 0:
        print(f"\n[Eval] Starting distributed reconstruction evaluation at step {global_step}")
        os.makedirs(temp_dir, exist_ok=True)

    # Wait for rank 0 to create the directory before other ranks try to save
    dist.barrier()
    # print(f"[Rank {rank}] Starting reconstruction...")
    # Each rank processes its shard
    N = min(len(val_dataset), num_samples)
    chunk = N // world_size

    if rank < world_size - 1:
        start = rank * chunk
        end   = (rank + 1) * chunk
    else:
        # Last rank takes the remainder (and handles N < world_size gracefully)
        start = rank * chunk
        end   = N

    rank_indices = list(range(start, end))
    subset = Subset(val_dataset, rank_indices)
    loader = DataLoader(
        subset,
        batch_size=batch_size,
        shuffle=False,
        num_workers=4,
        pin_memory=True,
        drop_last=False,
    )

    # Reconstruct images on this rank
    reconstructions = []
    iterator = tqdm(loader, desc=f"[Rank {rank}] Reconstructing", file=sys.stdout) if rank == 0 else loader

    with torch.inference_mode():
        for images, _ in iterator:
            images = images.to(device, non_blocking=True)
            with autocast(**autocast_kwargs):
                recon = model(images)

            # Convert to numpy uint8 [H, W, C]
            recon = recon.clamp(0, 1)
            recon_np = recon.mul(255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()

            for img in recon_np:
                reconstructions.append(img)

    reconstructions = np.stack(reconstructions)
    shard_path = os.path.join(temp_dir, f"recon_{global_step:07d}_{rank:02d}.npz")
    np.savez(shard_path, arr_0=reconstructions)

    if rank == 0:
        print(f"[Rank {rank}] Saved {len(reconstructions)} reconstructions to {shard_path}")

    # Wait for all ranks to finish reconstruction
    dist.barrier()

    # Rank 0 computes metrics
    metrics = None
    if rank == 0:
        # Combine all reconstruction shards
        all_recons = []
        for r in range(world_size):
            shard_file = os.path.join(temp_dir, f"recon_{global_step:07d}_{r:02d}.npz")
            shard_data = np.load(shard_file)["arr_0"]
            all_recons.append(shard_data)

        combined_recons = np.concatenate(all_recons, axis=0)[:num_samples]
        print(f"[Eval] Combined reconstruction NPZ shape: {combined_recons.shape}")

        # Load reference NPZ
        ref_npz_path = reference_npz_path

        if not os.path.exists(ref_npz_path):
            raise FileNotFoundError(f"Reference NPZ not found at {ref_npz_path}")

        ref_images = np.load(ref_npz_path)["arr_0"]
        print(f"[Eval] Loaded reference NPZ from {ref_npz_path}, shape: {ref_images.shape}")

        # Compute metrics
        print("[Eval] Computing metrics...")
        metrics = compute_reconstruction_metrics(
            ref_images,
            combined_recons,
            device,
            metric_batch_size,
            metrics_to_compute=metrics_to_compute,
            disable_bar= True, # by default no bar
        )

        # Print results
        print(f"[Eval] Step {global_step} Metrics:")
        for key, value in metrics.items():
            print(f"  {key}: {value:.6f}")

        # Cleanup reconstruction shards
        for r in range(world_size):
            shard_file = os.path.join(temp_dir, f"recon_{global_step:07d}_{r:02d}.npz")
            if os.path.exists(shard_file):
                os.remove(shard_file)

    dist.barrier()
    # model.train()

    return metrics
if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--ref-img", type=str, default="samples/imagenet-256-val.npz")
    parser.add_argument("--rec-img", type=str, default="samples/sdvae-ft-mse-f8d4.npz")
    parser.add_argument("--bs", type=int, default=128)
    args = parser.parse_args()

    # Load images
    device = "cuda"
    ref_img = np.load(args.ref_img)["arr_0"]
    rec_img = np.load(args.rec_img)["arr_0"]
    print(f"Loaded images: ref: {ref_img.shape}, rec: {rec_img.shape}")
    
    psnr = calculate_psnr(ref_img, rec_img, args.bs, device)
    print(f"PSNR: {psnr:.6f}")
    lpips = calculate_lpips(ref_img, rec_img, args.bs, device)
    print(f"LPIPS: {lpips:.6f}")
    ssim_val = calculate_ssim(ref_img, rec_img, args.bs, device)
    print(f"SSIM: {ssim_val:.6f}")
    rfid = calculate_rfid(ref_img, rec_img, args.bs, device)
    print(f"rFID: {rfid:.6f}")