OM_reg_flexres.py

import torch
import torchvision
from torch import nn
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.optim import Adam
from torchvision.utils import make_grid
from Diffusion.diffuser import DeformDDPM
from Diffusion.networks import get_net, STN
from torchvision.transforms import Lambda
import random
import os
import utils
from Dataloader.dataloader0 import get_dataloader
from Dataloader.dataLoader import *

from torchvision.utils import save_image
from einops import rearrange, reduce, repeat
import numpy as np
import nibabel as nib
from tqdm import tqdm
import yaml
import argparse
import torch.nn.functional as F
import SimpleITK as sitk
from skimage.transform import resize

EPS = 10e-8

parser = argparse.ArgumentParser()

parser.add_argument(
        "--config",
        "-C",
        help="Path for the config file",
        type=str,
        default="Config/config_om.yaml",
        required=False,
    )
args = parser.parse_args()
#=======================================================================================================================

# Load the YAML file into a dictionary
with open(args.config, 'r') as file:
    hyp_parameters = yaml.safe_load(file)
    print(hyp_parameters)

if not os.path.exists(hyp_parameters["aug_img_savepath"]):
    os.makedirs(hyp_parameters["aug_img_savepath"])
if not os.path.exists(hyp_parameters["aug_msk_savepath"]):
    os.makedirs(hyp_parameters["aug_msk_savepath"])
if not os.path.exists(hyp_parameters["aug_ddf_savepath"]):
    os.makedirs(hyp_parameters["aug_ddf_savepath"])
print(hyp_parameters["aug_img_savepath"])

hyp_parameters['batchsize'] = 1
model_img_sz = hyp_parameters['img_size']  # e.g. 128

# =======================================================================================================================
# Dataset is used only for its filtering logic (to get the right set of keys + metadata).
# We bypass the DataLoader and load volumes directly to ensure deterministic center-padding
# that is identical between the 128^3 model input and the full-res volume.
label_keys = ['brain']
database = ['Brats2019']

dataset = OminiDataset_inference_w_all(
    transform=None, min_crop_ratio=1.0, label_key=label_keys, database=database)
# =======================================================================================================================


epoch=f'{hyp_parameters["model_id_str"]}_{hyp_parameters["data_name"]}_{hyp_parameters["net_name"]}'
model_save_path = f'Models/{hyp_parameters["data_name"]}_{hyp_parameters["net_name"]}/'
model_save_path = os.path.join(model_save_path, str(epoch)+'.pth')


Net = get_net(hyp_parameters["net_name"])

Deformddpm = DeformDDPM(
    network=Net(n_steps = hyp_parameters["timesteps"],
                ndims = hyp_parameters["ndims"],
                num_input_chn = hyp_parameters["num_input_chn"],
                res = model_img_sz
                ),
    n_steps = hyp_parameters["timesteps"],
    image_chw = [hyp_parameters["num_input_chn"]] + [model_img_sz]*hyp_parameters["ndims"],
    device = hyp_parameters["device"],
    batch_size = hyp_parameters["batchsize"],
    img_pad_mode = hyp_parameters["img_pad_mode"],
    ddf_pad_mode = hyp_parameters["ddf_pad_mode"],
    padding_mode = hyp_parameters["padding_mode"],
    v_scale = hyp_parameters["v_scale"],
    resample_mode = hyp_parameters["resample_mode"],
    inf_mode = True,
)
Deformddpm.to(hyp_parameters["device"])

ddf_stn = STN(
    img_sz = model_img_sz,
    ndims = hyp_parameters["ndims"],
    padding_mode = hyp_parameters['padding_mode'],
    device = hyp_parameters["device"],
)
ddf_stn.to(hyp_parameters["device"])

print("Loading model from:", model_save_path)
checkpoint = torch.load(model_save_path, map_location='cpu')
Deformddpm.load_state_dict(checkpoint['model_state_dict'])
Deformddpm.eval()

# Full-res output directories (append _fullres to the standard paths)
reg_img_savepath_fullres = hyp_parameters['reg_img_savepath'].rstrip('/') + '_fullres/'
reg_msk_savepath_fullres = hyp_parameters['reg_msk_savepath'].rstrip('/') + '_fullres/'
reg_ddf_savepath_fullres = hyp_parameters['reg_ddf_savepath'].rstrip('/') + '_fullres/'

os.makedirs(hyp_parameters['reg_img_savepath'], exist_ok=True)
os.makedirs(hyp_parameters['reg_msk_savepath'], exist_ok=True)
os.makedirs(hyp_parameters['reg_ddf_savepath'], exist_ok=True)
os.makedirs(reg_img_savepath_fullres, exist_ok=True)
os.makedirs(reg_msk_savepath_fullres, exist_ok=True)
os.makedirs(reg_ddf_savepath_fullres, exist_ok=True)


# ========== Helper functions ==========

def center_pad_to_cube(volume):
    """Pad volume to a cube using the max dimension, with symmetric (center) padding."""
    max_dim = max(volume.shape[:3])
    pad_width = []
    for s in volume.shape[:3]:
        total_pad = max_dim - s
        pad_before = total_pad // 2
        pad_after = total_pad - pad_before
        pad_width.append((pad_before, pad_after))
    # Handle extra dims (e.g., multi-channel labels)
    for _ in range(volume.ndim - 3):
        pad_width.append((0, 0))
    return np.pad(volume, pad_width, mode='constant', constant_values=0)


def load_fullres_volume(key, ds):
    """Load original-resolution volume: axis reorder, clamp, normalize, center-pad to cube."""
    volume = sitk.ReadImage(key)
    volume = sitk.GetArrayFromImage(volume)
    volume = reverse_axis_order(volume)
    if volume.ndim == 4:
        channel_ids = ds.get_channel_ids(key)
        channel_id = channel_ids[0] if len(channel_ids) > 0 else 0
        volume = volume[:, :, :, channel_id]
    # CT clamping
    if ds.clamp_range is not None:
        modality = ds.ALLdata_filtered[key].get("Modality", None)
        if modality == "CT":
            volume = np.clip(volume, ds.clamp_range[0], ds.clamp_range[1])
    volume = ds.normalize(volume)
    volume = center_pad_to_cube(volume)
    return volume  # shape: [D, D, D] (cubic)


def load_fullres_label(key, ds, label_key):
    """Load original-resolution label: axis reorder, center-pad to cube (no resize)."""
    label_path_dict = ds.ALLdata_filtered[key].get('Label_path', {})
    task_labels = label_path_dict.get('segmentation', {})
    if label_key not in task_labels:
        return None
    label = sitk.ReadImage(task_labels[label_key])
    label = sitk.GetArrayFromImage(label)
    label = reverse_axis_order(label)
    if label.ndim > 3:
        channel_ids = ds.get_channel_ids(key)
        if len(channel_ids) != 0:
            label = label[..., channel_ids]
    label = center_pad_to_cube(label)
    return label


def apply_ddf(volume_tensor, ddf, padding_mode='border', resample_mode='bilinear'):
    """Apply DDF to volume tensor at any resolution.



    The DDF stores fractional displacements (value * max_sz = voxel displacement).

    When the DDF is spatially upscaled via trilinear interpolation from model resolution

    to full resolution, the fractional values remain correct — we use the new spatial

    size as max_sz, which correctly scales the voxel displacement proportionally.

    """
    device = ddf.device
    ndims = 3
    img_sz = list(volume_tensor.shape[2:])
    max_sz = torch.reshape(
        torch.tensor(img_sz, dtype=torch.float32, device=device),
        [1, ndims] + [1] * ndims)
    ref_grid = torch.reshape(
        torch.stack(torch.meshgrid(
            [torch.arange(s, device=device) for s in img_sz], indexing='ij'), 0),
        [1, ndims] + img_sz)
    img_shape = torch.reshape(
        torch.tensor([(s - 1) / 2. for s in img_sz], dtype=torch.float32, device=device),
        [1] + [1] * ndims + [ndims])
    grid = torch.flip(
        (ddf * max_sz + ref_grid).permute(
            [0] + list(range(2, 2 + ndims)) + [1]) / img_shape - 1,
        dims=[-1])
    return F.grid_sample(volume_tensor, grid.float(), mode=resample_mode,
                         padding_mode=padding_mode, align_corners=True)


# ========== Main inference loop ==========

keys = list(dataset.ALLdata_filtered.keys())
print("total num of images:", len(keys))

for e, key in enumerate(tqdm(keys)):
    pid = e
    print(f'Processing patient {pid}, image {e}, key: {key}')

    # --- Load full-resolution volume (center-padded to cube) ---
    fullres_vol = load_fullres_volume(key, dataset)
    orig_sz = list(fullres_vol.shape)  # e.g. [240, 240, 240]
    print(f"  Full-res padded shape: {orig_sz}")

    # --- Resize to model resolution for inference ---
    vol_model = resize(fullres_vol, [model_img_sz] * 3,
                       anti_aliasing=True, preserve_range=True)
    img = torch.tensor(vol_model[None, None, :, :, :],
                       dtype=torch.float32, device=hyp_parameters["device"])

    # --- Load full-res labels and resize to model resolution ---
    fullres_labels = {}
    for lk in label_keys:
        lab = load_fullres_label(key, dataset, lk)
        if lab is not None:
            fullres_labels[lk] = lab

    # Build mask at model resolution (128^3)
    label_arrays_model = []
    label_arrays_fullres = []
    for lk in label_keys:
        if lk in fullres_labels:
            lab = fullres_labels[lk]
            lab_model = resize(lab, [model_img_sz] * 3,
                               anti_aliasing=False, preserve_range=True, order=0)
            if lab_model.ndim == 3:
                lab_model = lab_model[None, :, :, :]
            elif lab_model.ndim > 3:
                lab_model = np.transpose(lab_model, (3, 0, 1, 2))
            label_arrays_model.append(lab_model)

            if lab.ndim == 3:
                lab = lab[None, :, :, :]
            elif lab.ndim > 3:
                lab = np.transpose(lab, (3, 0, 1, 2))
            label_arrays_fullres.append(lab)
        else:
            label_arrays_model.append(np.full([1] + [model_img_sz] * 3, -1))
            label_arrays_fullres.append(np.full([1] + orig_sz, -1))

    if len(label_arrays_model) > 0:
        mask_model_np = np.concatenate(label_arrays_model, axis=0)
        mask = torch.tensor(mask_model_np[None], dtype=torch.float32,
                            device=hyp_parameters["device"])
        fullres_msk_np = np.concatenate(label_arrays_fullres, axis=0)
        fullres_msk_tensor = torch.tensor(fullres_msk_np[None], dtype=torch.float32,
                                          device=hyp_parameters["device"])
    else:
        mask = None
        fullres_msk_np = None
        fullres_msk_tensor = None

    # Build full-res image tensor
    fullres_img_tensor = torch.tensor(fullres_vol[None, None, :, :, :],
                                      dtype=torch.float32,
                                      device=hyp_parameters["device"])

    # --- Save target conditioning image (first subject) ---
    if e <= 0:
        target_img = img.clone().detach()

    # --- Save original images at 128^3 ---
    image_original = img.cpu().numpy()
    nib.save(utils.converet_to_nibabel(image_original, ndims=hyp_parameters["ndims"]),
             os.path.join(hyp_parameters['reg_img_savepath'],
                          utils.get_barcode([pid, e]) + '.nii.gz'))
    if mask is not None:
        mask_original = mask.cpu().numpy()
        nib.save(utils.converet_to_nibabel(mask_original, ndims=hyp_parameters["ndims"]),
                 os.path.join(hyp_parameters['reg_msk_savepath'],
                              utils.get_barcode([pid, e]) + '_GT.nii.gz'))

    # --- Save original at full-res ---
    # fullres_vol is [D,D,D], wrap as [1,1,D,D,D] for converet_to_nibabel
    nib.save(utils.converet_to_nibabel(fullres_vol[None, None], ndims=hyp_parameters["ndims"]),
             os.path.join(reg_img_savepath_fullres,
                          utils.get_barcode([pid, e]) + '.nii.gz'))
    if fullres_msk_np is not None:
        # fullres_msk_np is [C,D,D,D], wrap as [1,C,D,D,D]
        nib.save(utils.converet_to_nibabel(fullres_msk_np[None], ndims=hyp_parameters["ndims"]),
                 os.path.join(reg_msk_savepath_fullres,
                              utils.get_barcode([pid, e]) + '_GT.nii.gz'))

    # --- Diffusion recovery at model resolution ---
    noise_step = hyp_parameters["start_noise_step"]
    with torch.no_grad():
        for im in range(1):
            print(f'  Generating -> Subject-{pid}, Scan-{e} ({im}/{hyp_parameters["aug_coe"]})', end='\r')

            [ddf_comp, ddf_rand], [img_rec, img_diff, img_save], [msk_rec, msk_diff, msk_save] = \
                Deformddpm.diff_recover(
                    img_org=img,
                    cond_imgs=target_img.clone().detach(),
                    msk_org=mask,
                    T=[None, hyp_parameters["timesteps"]],
                    v_scale=hyp_parameters["v_scale"],
                    t_save=None,
                    proc_type=hyp_parameters["condition_type"])

            # --- Save 128^3 results (same as OM_reg.py) ---
            denoise_imgs = img_rec.cpu().numpy()
            noisy_imgs_np = img_diff.cpu().numpy()

            nib.save(utils.converet_to_nibabel(denoise_imgs, ndims=hyp_parameters["ndims"]),
                     os.path.join(hyp_parameters['reg_img_savepath'],
                                  utils.get_barcode([pid, e, im, noise_step]) + '.nii.gz'))
            nib.save(utils.converet_to_nibabel(noisy_imgs_np, ndims=hyp_parameters["ndims"]),
                     os.path.join(hyp_parameters['reg_img_savepath'],
                                  utils.get_barcode([pid, e, im, noise_step],
                                                    header=['Patient', 'Slice', 'NoiseImg', 'NoiseStep']) + '.nii.gz'))

            if msk_rec is not None:
                denoise_msks = msk_rec.cpu().numpy()
                noisy_msks_np = msk_diff.cpu().numpy()
                nib.save(utils.converet_to_nibabel(denoise_msks, ndims=hyp_parameters["ndims"]),
                         os.path.join(hyp_parameters['reg_msk_savepath'],
                                      utils.get_barcode([pid, e, im, noise_step]) + '_GT.nii.gz'))
                nib.save(utils.converet_to_nibabel(noisy_msks_np, ndims=hyp_parameters["ndims"]),
                         os.path.join(hyp_parameters['reg_msk_savepath'],
                                      utils.get_barcode([pid, e, im, noise_step],
                                                        header=['Patient', 'Slice', 'NoiseImg', 'NoiseStep']) + '_GT.nii.gz'))

            # --- Upscale DDFs to original resolution ---
            ddf_fullres = F.interpolate(ddf_comp, size=orig_sz,
                                        mode='trilinear', align_corners=False)
            ddf_rand_fullres = F.interpolate(ddf_rand, size=orig_sz,
                                             mode='trilinear', align_corners=False)

            # --- Apply DDFs at original resolution ---
            img_rec_fullres = apply_ddf(fullres_img_tensor, ddf_fullres,
                                        padding_mode='border')
            img_noisy_fullres = apply_ddf(fullres_img_tensor, ddf_rand_fullres,
                                          padding_mode='border')

            if fullres_msk_tensor is not None:
                msk_rec_fullres = apply_ddf(fullres_msk_tensor, ddf_fullres,
                                            padding_mode='zeros', resample_mode='nearest')
                msk_noisy_fullres = apply_ddf(fullres_msk_tensor, ddf_rand_fullres,
                                              padding_mode='zeros', resample_mode='nearest')

            # --- Save full-res results ---
            nib.save(utils.converet_to_nibabel(img_rec_fullres, ndims=hyp_parameters["ndims"]),
                     os.path.join(reg_img_savepath_fullres,
                                  utils.get_barcode([pid, e, im, noise_step]) + '.nii.gz'))
            nib.save(utils.converet_to_nibabel(img_noisy_fullres, ndims=hyp_parameters["ndims"]),
                     os.path.join(reg_img_savepath_fullres,
                                  utils.get_barcode([pid, e, im, noise_step],
                                                    header=['Patient', 'Slice', 'NoiseImg', 'NoiseStep']) + '.nii.gz'))

            if fullres_msk_tensor is not None:
                nib.save(utils.converet_to_nibabel(msk_rec_fullres, ndims=hyp_parameters["ndims"]),
                         os.path.join(reg_msk_savepath_fullres,
                                      utils.get_barcode([pid, e, im, noise_step]) + '_GT.nii.gz'))
                nib.save(utils.converet_to_nibabel(msk_noisy_fullres, ndims=hyp_parameters["ndims"]),
                         os.path.join(reg_msk_savepath_fullres,
                                      utils.get_barcode([pid, e, im, noise_step],
                                                        header=['Patient', 'Slice', 'NoiseImg', 'NoiseStep']) + '_GT.nii.gz'))

            # Save full-res DDF (converet_to_nibabel handles multi-channel → channel-last)
            nib.save(utils.converet_to_nibabel(ddf_fullres, ndims=hyp_parameters["ndims"]),
                     os.path.join(reg_ddf_savepath_fullres,
                                  utils.get_barcode([pid, e, im, noise_step]) + '.nii.gz'))

            if (im - hyp_parameters["start_noise_step"]) % 2 == 0:
                noise_step = noise_step + hyp_parameters["noise_step"]

    if e > 5:
        break