data/resampling_test.py

from typing import Union, Tuple, List
import numpy as np
import torch
import torch.nn.functional as F
from einops import rearrange
import time
from copy import deepcopy
from default_resampling import determine_do_sep_z_and_axis
import psutil
import nibabel as nib
import os
from pathlib import Path

ANISO_THRESHOLD = 3

def compute_new_shape(current_shape: Union[Tuple[int, ...], List[int], np.ndarray],

                     current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],

                     target_spacing: Union[Tuple[float, ...], List[float], np.ndarray]) -> List[int]:
    """Compute new shape based on spacing ratios."""
    current_shape = np.array(current_shape)
    current_spacing = np.array(current_spacing)
    target_spacing = np.array(target_spacing)
    return [int(round(s * (cs / ts))) for s, cs, ts in zip(current_shape, current_spacing, target_spacing)]

def optimized_3d_resample(

    data: Union[torch.Tensor, np.ndarray],

    current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],

    target_spacing: Union[Tuple[float, ...], List[float], np.ndarray],

    is_seg: bool = False,

    device: torch.device = torch.device('cpu'),

    num_threads: int = 8,

    chunk_size: int = 64,

    force_separate_z: Union[bool, None] = None,

    separate_z_anisotropy_threshold: float = ANISO_THRESHOLD,

    preserve_range: bool = True

) -> Union[torch.Tensor, np.ndarray]:
    """

    Optimized 3D image resampling with adaptive interpolation and chunked processing.



    Args:

        data: Input 3D volume [C, D, H, W] or [D, H, W]

        current_spacing: Current voxel spacing (z, y, x)

        target_spacing: Target voxel spacing (z, y, x)

        is_seg: Whether the input is a segmentation mask

        device: Torch device for computation

        num_threads: Number of threads for CPU operations

        chunk_size: Size of chunks for large volume processing

        force_separate_z: Force separate z resampling

        separate_z_anisotropy_threshold: Threshold for anisotropic resampling

        preserve_range: Preserve original value range for non-segmentation data



    Returns:

        Resampled 3D volume

    """
    print(f"\nStarting optimized_3d_resample with input shape: {data.shape}, is_seg: {is_seg}")
    input_was_numpy = isinstance(data, np.ndarray)
    if input_was_numpy:
        data = torch.from_numpy(data).to(device)
    else:
        data = data.to(device)
    print(f"Input converted to tensor on {device}, shape: {data.shape}")

    if data.ndim == 3:
        data = data.unsqueeze(0)
    assert data.ndim == 4, "Data must be 3D or 4D (C, D, H, W)"

    new_shape = compute_new_shape(data.shape[1:], current_spacing, target_spacing)
    print(f"Computed new shape: {new_shape} from current_spacing: {current_spacing}, target_spacing: {target_spacing}")

    if all(i == j for i, j in zip(new_shape, data.shape[1:])):
        print("No resampling needed, shapes identical.")
        return data.cpu().numpy() if input_was_numpy else data

    mode = 'nearest' if is_seg else 'trilinear'
    aniso_axis_mode = 'nearest-exact' if is_seg else 'linear'
    print(f"Interpolation mode: {mode}, Anisotropic axis mode: {aniso_axis_mode}")

    do_separate_z, axis = determine_do_sep_z_and_axis(force_separate_z, current_spacing, 
                                                     target_spacing, separate_z_anisotropy_threshold)
    print(f"Do separate Z: {do_separate_z}, Axis: {axis}")

    if preserve_range and not is_seg:
        v_min, v_max = data.min(), data.max()
        print(f"Preserving range for non-segmentation data: min={v_min.item():.4f}, max={v_max.item():.4f}")

    torch.set_num_threads(num_threads)
    print(f"Set number of threads to {num_threads}")

    start_time = time.time()
    if do_separate_z:
        tmp = "xyz"
        axis_letter = tmp[axis]
        others_int = [i for i in range(3) if i != axis]
        others = [tmp[i] for i in others_int]
        print(f"Separate Z resampling along axis {axis_letter}, others: {others}")

        tmp_new_shape = [new_shape[i] for i in others_int]
        print(f"First pass: Resampling to shape {tmp_new_shape} for axes {others}")
        data = rearrange(data, f"c x y z -> (c {axis_letter}) {others[0]} {others[1]}")
        print(f"Rearranged data shape: {data.shape}")
        data = _chunked_resample(data, tmp_new_shape, mode, chunk_size, device, is_seg)
        print(f"After first pass resampling, shape: {data.shape}")

        data = rearrange(data, f"(c {axis_letter}) {others[0]} {others[1]} -> c x y z",
                        **{axis_letter: data.shape[1], others[0]: tmp_new_shape[0], others[1]: tmp_new_shape[1]})
        print(f"Rearranged back to shape: {data.shape}")
        data = _chunked_resample(data, new_shape, aniso_axis_mode, chunk_size, device, is_seg)
        print(f"After second pass resampling, final shape: {data.shape}")
    else:
        print(f"Direct resampling to shape: {new_shape}")
        data = _chunked_resample(data, new_shape, mode, chunk_size, device, is_seg)
        print(f"After direct resampling, final shape: {data.shape}")
    resample_time = time.time() - start_time
    print(f"Resampling completed in {resample_time:.3f}s")

    if is_seg:
        unique_values = torch.unique(data)
        result_dtype = torch.int8 if max(unique_values) < 127 else torch.int16
        data = data.round().to(result_dtype)
        print(f"Segmentation data rounded and converted to {result_dtype}, unique values: {unique_values.tolist()}")

    if preserve_range and not is_seg:
        data = torch.clamp(data, v_min, v_max)
        print(f"Clamped data to original range: min={v_min.item():.4f}, max={v_max.item():.4f}")

    output = data.cpu().numpy() if input_was_numpy else data
    print(f"Output shape: {output.shape}, type: {type(output)}")
    return output

def _chunked_resample(

    volume: torch.Tensor,

    target_shape: Tuple[int, ...],

    mode: str,

    chunk_size: int,

    device: torch.device,

    is_seg: bool

) -> torch.Tensor:
    """Chunked resampling for large volumes with adaptive chunk sizing."""
    print(f"\nStarting _chunked_resample with input shape: {volume.shape}, target shape: {target_shape}")
    C, D, H, W = volume.shape
    tD, tH, tW = target_shape

    # Adaptive chunk size based on available memory
    if device.type == 'cpu':
        available_memory = psutil.virtual_memory().available / 1024**2  # in MB
    else:
        total_memory = torch.cuda.get_device_properties(device).total_memory / 1024**2  # in MB
        allocated_memory = torch.cuda.memory_allocated(device) / 1024**2
        available_memory = total_memory - allocated_memory

    mem_per_voxel = volume.element_size() * volume.nelement() / volume.numel()
    target_voxel_count = C * tD * tH * tW
    chunk_mem_ratio = 0.5 if device.type == 'cpu' else 0.3
    adaptive_chunk_size = max(
        32, 
        min(chunk_size, int((available_memory * chunk_mem_ratio / mem_per_voxel / C) ** (1/3)))
    )

    # Early return for small volumes
    if D * H * W <= 128**3:
        with torch.cuda.amp.autocast(enabled=not is_seg):
            start_time = time.time()
            # Cast to float for interpolation if is_seg and mode is nearest
            input_tensor = volume.float() if is_seg and mode == 'nearest' else volume
            result = F.interpolate(
                input_tensor.unsqueeze(0), 
                size=target_shape, 
                mode=mode, 
                align_corners=False if mode != 'nearest' else None
            ).squeeze(0)
            # Convert back to original dtype for segmentation
            if is_seg:
                result = result.round().to(volume.dtype)
            # print(f"Direct interpolation completed in {time.time() - start_time:.3f}s, output shape: {result.shape}")
            return result

    result = torch.zeros((C, tD, tH, tW), device=device, dtype=volume.dtype)

    out_chunk_size = max(1, int(adaptive_chunk_size * min(tD/D, tH/H, tW/W)))

    for c in range(C):
        for z in range(0, tD, out_chunk_size):
            z_end = min(z + out_chunk_size, tD)
            for y in range(0, tH, out_chunk_size):
                y_end = min(y + out_chunk_size, tH)
                for x in range(0, tW, out_chunk_size):
                    x_end = min(x + out_chunk_size, tW)

                    in_z = max(0, int(z * D / tD) - 1)
                    in_z_end = min(D, int(z_end * D / tD) + 2)
                    in_y = max(0, int(y * H / tH) - 1)
                    in_y_end = min(H, int(y_end * H / tH) + 2)
                    in_x = max(0, int(x * W / tW) - 1)
                    in_x_end = min(W, int(x_end * W / tW) + 2)

                    chunk = volume[c:c+1, in_z:in_z_end, in_y:in_y_end, in_x:in_x_end]
                    chunk_target = (z_end - z, y_end - y, x_end - x)

                    with torch.cuda.amp.autocast(enabled=not is_seg):
                        start_time = time.time()
                        # Cast to float for interpolation if is_seg and mode is nearest
                        input_chunk = chunk.float() if is_seg and mode == 'nearest' else chunk
                        resampled_chunk = F.interpolate(
                            input_chunk.unsqueeze(0),
                            size=chunk_target,
                            mode=mode,
                            align_corners=False if mode != 'nearest' else None
                        ).squeeze(0)
                        # Convert back to original dtype for segmentation
                        if is_seg:
                            resampled_chunk = resampled_chunk.round().to(volume.dtype)
                        # print(f"Chunk interpolation completed in {time.time() - start_time:.3f}s, shape: {resampled_chunk.shape}")

                    result[c, z:z_end, y:y_end, x:x_end] = resampled_chunk
                    del chunk, resampled_chunk
                    if device.type == 'cuda':
                        torch.cuda.empty_cache()

    return result

def resample_torch_simple(

    data: Union[torch.Tensor, np.ndarray],

    new_shape: Union[Tuple[int, ...], List[int], np.ndarray],

    is_seg: bool = False,

    num_threads: int = 4,

    device: torch.device = torch.device('cpu'),

    memefficient_seg_resampling: bool = False,

    mode: str = 'linear'

) -> Union[torch.Tensor, np.ndarray]:
    if mode == 'linear':
        torch_mode = 'trilinear' if data.ndim == 4 else 'bilinear'
    else:
        torch_mode = mode

    if isinstance(new_shape, np.ndarray):
        new_shape = [int(i) for i in new_shape]

    if all([i == j for i, j in zip(new_shape, data.shape[1:])]):
        return data

    n_threads = torch.get_num_threads()
    torch.set_num_threads(num_threads)
    new_shape = tuple(new_shape)
    with torch.no_grad():
        input_was_numpy = isinstance(data, np.ndarray)
        if input_was_numpy:
            data = torch.from_numpy(data).to(device)
        else:
            orig_device = deepcopy(data.device)
            data = data.to(device)

        if is_seg:
            unique_values = torch.unique(data)
            result_dtype = torch.int8 if max(unique_values) < 127 else torch.int16
            result = torch.zeros((data.shape[0], *new_shape), dtype=result_dtype, device=device)
            if not memefficient_seg_resampling:
                result_tmp = torch.zeros((len(unique_values), data.shape[0], *new_shape), dtype=torch.float16,
                                       device=device)
                scale_factor = 1000
                done_mask = torch.zeros_like(result, dtype=torch.bool, device=device)
                for i, u in enumerate(unique_values):
                    result_tmp[i] = F.interpolate((data[None] == u).float() * scale_factor, new_shape, mode=torch_mode,
                                                antialias=False)[0]
                    mask = result_tmp[i] > (0.7 * scale_factor)
                    result[mask] = u.item()
                    done_mask |= mask
                if not torch.all(done_mask):
                    result[~done_mask] = unique_values[result_tmp[:, ~done_mask].argmax(0)].to(result_dtype)
            else:
                for i, u in enumerate(unique_values):
                    if u == 0:
                        continue
                    result[F.interpolate((data[None] == u).float(), new_shape, mode=torch_mode, antialias=False)[0] > 0.5] = u
        else:
            result = F.interpolate(data[None].float(), new_shape, mode=torch_mode, antialias=False)[0]

        if input_was_numpy:
            result = result.cpu().numpy()
        else:
            result = result.to(orig_device)

    torch.set_num_threads(n_threads)
    return result

def resample_torch_fornnunet(

    data: Union[torch.Tensor, np.ndarray],

    new_shape: Union[Tuple[int, ...], List[int], np.ndarray],

    current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],

    new_spacing: Union[Tuple[float, ...], List[float], np.ndarray],

    is_seg: bool = False,

    num_threads: int = 4,

    device: torch.device = torch.device('cpu'),

    memefficient_seg_resampling: bool = False,

    force_separate_z: Union[bool, None] = None,

    separate_z_anisotropy_threshold: float = ANISO_THRESHOLD,

    mode: str = 'linear',

    aniso_axis_mode: str = 'nearest-exact'

) -> Union[torch.Tensor, np.ndarray]:
    assert data.ndim == 4, "data must be c, x, y, z"
    new_shape = [int(i) for i in new_shape]
    orig_shape = data.shape

    do_separate_z, axis = determine_do_sep_z_and_axis(force_separate_z, current_spacing, new_spacing,
                                                     separate_z_anisotropy_threshold)

    if do_separate_z:
        was_numpy = isinstance(data, np.ndarray)
        if was_numpy:
            data = torch.from_numpy(data)

        if isinstance(axis, list):
            axis = axis[0]

        tmp = "xyz"
        axis_letter = tmp[axis]
        others_int = [i for i in range(3) if i != axis]
        others = [tmp[i] for i in others_int]

        data = rearrange(data, f"c x y z -> (c {axis_letter}) {others[0]} {others[1]}")
        tmp_new_shape = [new_shape[i] for i in others_int]
        data = resample_torch_simple(data, tmp_new_shape, is_seg=is_seg, num_threads=num_threads, device=device,
                                   memefficient_seg_resampling=memefficient_seg_resampling, mode=mode)
        data = rearrange(data, f"(c {axis_letter}) {others[0]} {others[1]} -> c x y z",
                        **{axis_letter: orig_shape[axis + 1], others[0]: tmp_new_shape[0], others[1]: tmp_new_shape[1]})
        data = resample_torch_simple(data, new_shape, is_seg=is_seg, num_threads=num_threads, device=device,
                                   memefficient_seg_resampling=memefficient_seg_resampling, mode=aniso_axis_mode)
        if was_numpy:
            data = data.numpy()
        return data
    else:
        return resample_torch_simple(data, new_shape, is_seg, num_threads, device, memefficient_seg_resampling)

def dice_score(pred: np.ndarray, true: np.ndarray) -> float:
    """Compute Dice score for segmentation masks."""
    pred = pred.flatten()
    true = true.flatten()
    intersection = np.sum(pred * true)
    return (2. * intersection) / (np.sum(pred) + np.sum(true) + 1e-8)

# Placeholder for compute_new_shape if not provided
def compute_new_shape(original_shape, current_spacing, target_spacing):
    """

    Compute the new shape based on the spacing ratio.

    original_shape: (z, y, x)

    current_spacing: (z, y, x)

    target_spacing: (z, y, x)

    """
    zoom_factors = [c / t for c, t in zip(current_spacing, target_spacing)]
    new_shape = [int(round(s * z)) for s, z in zip(original_shape, zoom_factors)]
    return tuple(new_shape)

# Function to save as NIfTI
def save_nii(array, spacing, output_path, is_seg=False):
    """

    Save numpy array as NIfTI file with specified spacing.

    is_seg: If True, convert to int32 for segmentation masks.

    """
    # Convert torch tensor to numpy if necessary
    if isinstance(array, torch.Tensor):
        array = array.cpu().numpy()
    
    # Convert data type for NIfTI compatibility
    if is_seg:
        array = array.astype(np.int32)  # Convert segmentation to int32
    else:
        array = array.astype(np.float32)  # Ensure image is float32
    
    # Transpose to (X, Y, Z, C) for NIfTI
    if array.ndim == 4:
        array = array.transpose(2, 3, 1, 0)  # From (C, Z, Y, X) to (X, Y, Z, C)
    else:
        array = array.transpose(2, 3, 1)  # From (Z, Y, X) to (X, Y, Z)
    
    # Create NIfTI image with affine based on spacing
    affine = np.diag(list(spacing) + [1.0])
    nii_img = nib.Nifti1Image(array, affine=affine)
    nib.save(nii_img, output_path)
    print(f"Saved: {output_path}")

# Main resampling function
def main():
    torch.set_num_threads(4)
    device = torch.device('cuda') #torch.device('cpu')  # Force CPU as per provided code
    print(f"\nRunning tests on device: {device}")

    # Define paths
    npz_file_path = "/media/shipc/hhd_8T/spc/code/CVPR2025_Text_guided_seg_submission/inputs/Microscopy_cremi_000_sc.npz"
    gt_path = "/media/shipc/hhd_8T/spc/code/CVPR2025_Text_guided_seg_submission/gts/Microscopy_cremi_000_sc.npz"
    output_dir = "/media/shipc/hhd_8T/spc/code/CVPR2025_Text_guided_seg_submission/workspace_teamx/outputs_test_resample"

    # Ensure output directory exists
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # Load input data
    data = np.load(npz_file_path, allow_pickle=True)
    img_array = data['imgs']  # Shape: (C, Z, Y, X) or (Z, Y, X)
    img_spacing = data['spacing']  # (z, y, x)
    img_spacing = [1.0, 1.0, 1.0]  # Override as per provided code
    gt_data = np.load(gt_path, allow_pickle=True)
    gt_array = gt_data['gts']  # Shape: (C, Z, Y, X) or (Z, Y, X)

    # Convert data types to PyTorch-compatible types
    img_array = img_array.astype(np.float32)  # Convert image to float32
    gt_array = gt_array.astype(np.int32)      # Convert segmentation mask to int32

    # Ensure img_array and gt_array have channel dimension
    if img_array.ndim == 3:
        img_array = img_array[np.newaxis, ...]  # Add channel dimension: (1, Z, Y, X)
    if gt_array.ndim == 3:
        gt_array = gt_array[np.newaxis, ...]  # Add channel dimension: (1, Z, Y, X)

    # Define target spacings to test
    target_spacings = [
        (1.2, 1.2, 1.2),
        (1.5, 1.5, 1.5),
        (2.0, 2.0, 2.0),
    ]

    # Original shape and spacing
    original_shape = img_array.shape[1:]  # (Z, Y, X)
    current_spacing = img_spacing
    print(f"\nOriginal image shape: {original_shape}, Current spacing (z,y,x): {current_spacing}")

    for target_spacing in target_spacings:
        print(f"\n=== Resampling to Target Spacing: {target_spacing} ===")
        
        # Compute new shape
        new_shape = compute_new_shape(original_shape, current_spacing, target_spacing)
        print(f"Computed target shape: {new_shape}")

        # === Image Resampling ===
        print("\nResampling image...")
        
        # Ground truth resampling
        print("Computing ground truth with resample_torch_simple...")
        start_time = time.time()
        if device.type == 'cuda':
            torch.cuda.synchronize()  # Ensure GPU operations are complete
        gt_img = resample_torch_simple(
            img_array,
            new_shape=new_shape,
            is_seg=False,
            num_threads=4,
            device=device
        )
        if device.type == 'cuda':
            torch.cuda.synchronize()  # Ensure GPU operations are complete
        gt_time = time.time() - start_time
        output_path = os.path.join(output_dir, f"img_gt_spacing_{target_spacing[0]}_{target_spacing[1]}_{target_spacing[2]}.nii.gz")
        print(f"Ground truth image shape: {gt_img.shape}, Time: {gt_time:.3f}s")
        save_nii(gt_img, target_spacing, output_path, is_seg=False)

        # Optimized resampling
        print("Running optimized_3d_resample...")
        start_time = time.time()
        if device.type == 'cuda':
            torch.cuda.synchronize()
        mem_before = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        resampled_img_opt = optimized_3d_resample(
            img_array,
            current_spacing,
            target_spacing,
            is_seg=False,
            device=device,
            num_threads=4,
            chunk_size=64
        )
        if device.type == 'cuda':
            torch.cuda.synchronize()

        opt_time = time.time() - start_time
        mem_after = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        opt_mae = np.mean(np.abs(resampled_img_opt - gt_img))
        output_path = os.path.join(output_dir, f"img_opt_spacing_{target_spacing[0]}_{target_spacing[1]}_{target_spacing[2]}.nii.gz")
        print(f"Optimized image shape: {resampled_img_opt.shape}, Time: {opt_time:.3f}s, "
              f"Memory used: {mem_after - mem_before:.2f} MB, MAE: {opt_mae:.6f}")
        save_nii(resampled_img_opt, target_spacing, output_path, is_seg=False)

        # Original resampling
        print("Running resample_torch_fornnunet...")
        start_time = time.time()
        if device.type == 'cuda':
            torch.cuda.synchronize()
        mem_before = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        resampled_img_orig = resample_torch_fornnunet(
            img_array,
            new_shape,
            current_spacing,
            target_spacing,
            is_seg=False,
            num_threads=4,
            device=device
        )
        if device.type == 'cuda':
            torch.cuda.synchronize()
        orig_time = time.time() - start_time
        mem_after = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        orig_mae = np.mean(np.abs(resampled_img_orig - gt_img))
        output_path = os.path.join(output_dir, f"img_orig_spacing_{target_spacing[0]}_{target_spacing[1]}_{target_spacing[2]}.nii.gz")
        print(f"Original image shape: {resampled_img_orig.shape}, Time: {orig_time:.3f}s, "
              f"Memory used: {mem_after - mem_before:.2f} MB, MAE: {orig_mae:.6f}")
        save_nii(resampled_img_orig, target_spacing, output_path, is_seg=False)

        # === Segmentation Mask Resampling ===
        print("\nResampling segmentation mask...")
        
        # Ground truth resampling
        print("Computing ground truth with resample_torch_simple...")
        start_time = time.time()
        if device.type == 'cuda':
            torch.cuda.synchronize()
        gt_seg = resample_torch_simple(
            gt_array,
            new_shape=new_shape,
            is_seg=True,
            num_threads=4,
            device=device
        )
        if device.type == 'cuda':
            torch.cuda.synchronize()
        gt_seg_time = time.time() - start_time
        output_path = os.path.join(output_dir, f"seg_gt_spacing_{target_spacing[0]}_{target_spacing[1]}_{target_spacing[2]}.nii.gz")
        print(f"Ground truth segmentation shape: {gt_seg.shape}, Time: {gt_seg_time:.3f}s")
        save_nii(gt_seg, target_spacing, output_path, is_seg=True)

        # Optimized resampling
        print("Running optimized_3d_resample for segmentation...")
        start_time = time.time()
        if device.type == 'cuda':
            torch.cuda.synchronize()
        mem_before = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        resampled_seg_opt = optimized_3d_resample(
            gt_array,
            current_spacing,
            target_spacing,
            is_seg=True,
            device=device,
            num_threads=4,
            chunk_size=64
        )
        if device.type == 'cuda':
            torch.cuda.synchronize()

        opt_seg_time = time.time() - start_time
        mem_after = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        opt_dice = dice_score(resampled_seg_opt, gt_seg)
        output_path = os.path.join(output_dir, f"seg_opt_spacing_{target_spacing[0]}_{target_spacing[1]}_{target_spacing[2]}.nii.gz")
        print(f"Optimized segmentation shape: {resampled_seg_opt.shape}, Time: {opt_seg_time:.3f}s, "
              f"Memory used: {mem_after - mem_before:.2f} MB, Dice: {opt_dice:.6f}")
        save_nii(resampled_seg_opt, target_spacing, output_path, is_seg=True)

        # Original resampling
        print("Running resample_torch_fornnunet for segmentation...")
        start_time = time.time()
        if device.type == 'cuda':
            torch.cuda.synchronize()
        mem_before = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        resampled_seg_orig = resample_torch_fornnunet(
            gt_array,
            new_shape,
            current_spacing,
            target_spacing,
            is_seg=True,
            num_threads=4,
            device=device
        )
        if device.type == 'cuda':
            torch.cuda.synchronize()

        orig_seg_time = time.time() - start_time
        mem_after = psutil.virtual_memory().used / 1024**2 if device.type == 'cpu' else torch.cuda.memory_allocated(device) / 1024**2
        orig_dice = dice_score(resampled_seg_orig, gt_seg)
        output_path = os.path.join(output_dir, f"seg_orig_spacing_{target_spacing[0]}_{target_spacing[1]}_{target_spacing[2]}.nii.gz")
        print(f"Original segmentation shape: {resampled_seg_orig.shape}, Time: {orig_seg_time:.3f}s, "
              f"Memory used: {mem_after - mem_before:.2f} MB, Dice: {orig_dice:.6f}")
        save_nii(resampled_seg_orig, target_spacing, output_path, is_seg=True)

        # Summary
        print(f"\n=== Summary for Target Spacing: {target_spacing} ===")
        print("Image Resampling Metrics:")
        print(f"Optimized - Shape: {resampled_img_opt.shape}, Time: {opt_time:.3f}s, MAE: {opt_mae:.6f}")
        print(f"Original  - Shape: {resampled_img_orig.shape}, Time: {orig_time:.3f}s, MAE: {orig_mae:.6f}")
        print(f"Time Improvement: {(orig_time - opt_time) / orig_time * 100:.2f}%")
        print(f"MAE Improvement: {(orig_mae - opt_mae) / orig_mae * 100:.2f}%")
        print("Segmentation Mask Resampling Metrics:")
        print(f"Optimized - Shape: {resampled_seg_opt.shape}, Time: {opt_seg_time:.3f}s, Dice: {opt_dice:.6f}")
        print(f"Original  - Shape: {resampled_seg_orig.shape}, Time: {orig_seg_time:.3f}s, Dice: {orig_dice:.6f}")
        print(f"Time Improvement: {(orig_seg_time - opt_seg_time) / orig_seg_time * 100:.2f}%")
        print(f"Dice Improvement: {(opt_dice - orig_dice) / orig_dice * 100:.2f}%")

if __name__ == '__main__':
    main()