changes

fcd2_inpainting/DDPM_Pseudo3D/guided_diffusion/.ipynb_checkpoints/fcd2loader-checkpoint.py

@@ -0,0 +1,212 @@
+import os
+import numpy as np
+import nibabel as nib
+import torch
+from torch.utils.data import Dataset, random_split
+
+class FCD2Dataset(Dataset):
+    """
+    Dataset for FCD2 pathological volumes.
+    Expects directory structure:
+        <root_dir>/
+            images/
+                sub-XXX.nii.gz  # Images WITH lesions (ground truth)
+            labels/
+                sub-XXX_roi.nii.gz  # Masks of healthy regions to void (where lesions should be added)
+    Returns for training (test_flag=False): (input, label, path, slice_indices)
+    Returns for sampling (test_flag=True): (input, path, slice_indices)
+    Input channels are [voided_image, mask]; label is original image with lesions.
+    
+    For lesion addition: void healthy regions marked by ROI, reconstruct lesions in those regions
+    """
+    def __init__(self, root_dir, test_flag=False, mode="add_lesions", validation_split=0.2, seed=42):
+        super().__init__()
+        self.root_dir = os.path.expanduser(root_dir)
+        self.images_dir = os.path.join(self.root_dir, 'images')
+        self.labels_dir = os.path.join(self.root_dir, 'labels')
+        self.test_flag = test_flag
+        self.mode = mode  # "add_lesions" or "remove_lesions"
+        self.validation_split = validation_split
+        self.seed = seed
+        
+        # List image files
+        self.image_files = sorted([f for f in os.listdir(self.images_dir) if f.endswith('.nii.gz')])
+        # Full paths
+        self.image_paths = [os.path.join(self.images_dir, f) for f in self.image_files]
+        self.label_paths = [os.path.join(self.labels_dir, f.replace('.nii.gz', '_roi.nii.gz')) for f in self.image_files]
+        
+        # Validate files exist
+        missing_files = []
+        for label_path in self.label_paths:
+            if not os.path.exists(label_path):
+                missing_files.append(label_path)
+        if missing_files:
+            raise FileNotFoundError(f"Missing label files: {missing_files}")
+        
+        # Split into train/validation if not test mode
+        if not test_flag and validation_split > 0:
+            total_size = len(self.image_paths)
+            val_size = int(total_size * validation_split)
+            train_size = total_size - val_size
+            
+            # Set seed for reproducible splits
+            torch.manual_seed(seed)
+            train_indices, val_indices = random_split(
+                range(total_size), [train_size, val_size]
+            )
+            
+            # Convert to lists
+            train_indices = list(train_indices.indices)
+            val_indices = list(val_indices.indices)
+            
+            # Store indices for train/val split
+            self.train_indices = train_indices
+            self.val_indices = val_indices
+            self.is_validation = False  # Will be set by get_train_val_datasets
+        
+        # Compute slice ranges where mask is non-zero
+        self.slice_ranges = []
+        self.mask_stats = []  # Track mask statistics for monitoring
+        for label_path in self.label_paths:
+            # Load and reorient mask to canonical orientation
+            mask_nii = nib.as_closest_canonical(nib.load(label_path))
+            mask = mask_nii.get_fdata()
+            if mask.ndim != 3:
+                raise ValueError(f'Expected 3D mask at {label_path}, got shape {mask.shape}')
+            # Assume slices along last axis
+            idxs = [i for i in range(mask.shape[2]) if mask[..., i].sum() > 0]
+            self.slice_ranges.append(idxs)
+            
+            # Track mask statistics
+            mask_volume = (mask > 0).sum()
+            total_volume = mask.size
+            mask_percentage = (mask_volume / total_volume) * 100
+            self.mask_stats.append({
+                'mask_volume': mask_volume,
+                'total_volume': total_volume,
+                'mask_percentage': mask_percentage,
+                'num_slices': len(idxs)
+            })
+        
+        # Print dataset statistics
+        if not self.test_flag:
+            total_masks = sum(stat['mask_volume'] for stat in self.mask_stats)
+            total_voxels = sum(stat['total_volume'] for stat in self.mask_stats)
+            avg_mask_percentage = (total_masks / total_voxels) * 100
+            print(f"Dataset loaded: {len(self.image_files)} volumes")
+            print(f"Mode: {self.mode}")
+            print(f"Average mask percentage: {avg_mask_percentage:.2f}%")
+            print(f"Total training slices: {sum(len(ranges) for ranges in self.slice_ranges)}")
+            
+            if validation_split > 0:
+                print(f"Train/Validation split: {len(self.train_indices)}/{len(self.val_indices)}")
+
+    def __len__(self):
+        return len(self.image_paths)
+
+    def __getitem__(self, idx):
+        # Load image and mask
+        image_path = self.image_paths[idx]
+        label_path = self.label_paths[idx]
+        
+        # Load and reorient image and mask to canonical orientation
+        img_nii = nib.as_closest_canonical(nib.load(image_path))
+        image = img_nii.get_fdata().astype(np.float32)
+        msk_nii = nib.as_closest_canonical(nib.load(label_path))
+        mask = msk_nii.get_fdata().astype(np.float32)
+        
+        # Create voided image based on mode
+        voided = image.copy()
+        if self.mode == "add_lesions":
+            # For adding lesions: void healthy regions (marked by ROI)
+            # The model will learn to reconstruct lesions in these voided regions
+            voided[mask > 0] = 0.0
+        else:  # "remove_lesions"
+            # For removing lesions: void lesion regions
+            # The model will learn to reconstruct healthy tissue in these voided regions
+            voided[mask > 0] = 0.0
+        
+        # Intensity clipping and normalization between 0 and 1
+        min_val = np.quantile(image, 0.001)
+        max_val = np.quantile(image, 0.999)
+        image = np.clip(image, min_val, max_val)
+        voided = np.clip(voided, min_val, max_val)
+        if max_val > min_val:
+            image = (image - min_val) / (max_val - min_val)
+            voided = (voided - min_val) / (max_val - min_val)
+        else:
+            image = np.zeros_like(image)
+            voided = np.zeros_like(voided)
+        
+        mask = (mask > 0).astype(np.float32)
+        
+        # Stack channels: voided, mask, original (with lesions)
+        volume = np.stack([voided, mask, image], axis=0)
+        volume = torch.from_numpy(volume)
+        slice_range = self.slice_ranges[idx]
+        
+        # Return inputs and label for training, or inputs only for sampling
+        if not self.test_flag:
+            inp = volume[:2]       # (2, H, W, D) - voided image + mask
+            label = volume[2:3]    # (1, H, W, D) - original image with lesions
+            return inp, label, image_path, slice_range
+        # Sampling
+        inp = volume[:2]
+        return inp, image_path, slice_range
+    
+    def get_dataset_stats(self):
+        """Return dataset statistics for monitoring"""
+        # Convert NumPy types to native Python types for JSON serialization
+        mask_stats_serializable = []
+        for stat in self.mask_stats:
+            mask_stats_serializable.append({
+                'mask_volume': int(stat['mask_volume']),
+                'total_volume': int(stat['total_volume']),
+                'mask_percentage': float(stat['mask_percentage']),
+                'num_slices': int(stat['num_slices'])
+            })
+        
+        return {
+            'num_volumes': len(self.image_files),
+            'mask_stats': mask_stats_serializable,
+            'mode': self.mode,
+            'total_slices': sum(len(ranges) for ranges in self.slice_ranges),
+            'validation_split': self.validation_split if hasattr(self, 'validation_split') else 0
+        }
+    
+    @classmethod
+    def get_train_val_datasets(cls, root_dir, mode="add_lesions", validation_split=0.2, seed=42):
+        """
+        Create separate training and validation datasets.
+        
+        Args:
+            root_dir: Path to data directory
+            mode: "add_lesions" or "remove_lesions"
+            validation_split: Fraction of data to use for validation
+            seed: Random seed for reproducible splits
+            
+        Returns:
+            train_dataset, val_dataset: Two FCD2Dataset instances
+        """
+        # Create full dataset
+        full_dataset = cls(root_dir, test_flag=False, mode=mode, validation_split=validation_split, seed=seed)
+        
+        # Create training dataset
+        train_dataset = cls(root_dir, test_flag=False, mode=mode, validation_split=0, seed=seed)
+        train_dataset.image_paths = [full_dataset.image_paths[i] for i in full_dataset.train_indices]
+        train_dataset.label_paths = [full_dataset.label_paths[i] for i in full_dataset.train_indices]
+        train_dataset.slice_ranges = [full_dataset.slice_ranges[i] for i in full_dataset.train_indices]
+        train_dataset.mask_stats = [full_dataset.mask_stats[i] for i in full_dataset.train_indices]
+        train_dataset.is_validation = False
+        
+        # Create validation dataset
+        val_dataset = cls(root_dir, test_flag=False, mode=mode, validation_split=0, seed=seed)
+        val_dataset.image_paths = [full_dataset.image_paths[i] for i in full_dataset.val_indices]
+        val_dataset.label_paths = [full_dataset.label_paths[i] for i in full_dataset.val_indices]
+        val_dataset.slice_ranges = [full_dataset.slice_ranges[i] for i in full_dataset.val_indices]
+        val_dataset.mask_stats = [full_dataset.mask_stats[i] for i in full_dataset.val_indices]
+        val_dataset.is_validation = True
+        
+        print(f"Created train/validation split: {len(train_dataset)}/{len(val_dataset)} volumes")
+        
+        return train_dataset, val_dataset

fcd2_inpainting/DDPM_Pseudo3D/guided_diffusion/.ipynb_checkpoints/gaussian_diffusion-checkpoint.py

@@ -0,0 +1,1060 @@
+"""
+This code started out as a PyTorch port of Ho et al's diffusion models:
+https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
+Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
+"""
+from torch.autograd import Variable
+import enum
+import torch.nn.functional as F
+from torchvision.utils import save_image
+import torch
+import math
+import numpy as np
+import torch as th
+from .train_util import visualize
+from .nn import mean_flat
+from .losses import normal_kl, discretized_gaussian_log_likelihood
+from scipy import ndimage
+from torchvision import transforms
+
+
+def standardize(img):
+    mean = th.mean(img)
+    std = th.std(img)
+    img = (img - mean) / std
+    return img
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        beta_start = scale * 0.0001
+        beta_end = scale * 0.02
+        return np.linspace(
+            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
+        )
+    elif schedule_name == "cosine":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+class ModelMeanType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
+    START_X = enum.auto()  # the model predicts x_0
+    EPSILON = enum.auto()  # the model predicts epsilon
+
+
+class ModelVarType(enum.Enum):
+    """
+    What is used as the model's output variance.
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    LEARNED = enum.auto()
+    FIXED_SMALL = enum.auto()
+    FIXED_LARGE = enum.auto()
+    LEARNED_RANGE = enum.auto()
+
+
+class LossType(enum.Enum):
+    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
+    RESCALED_MSE = (
+        enum.auto()
+    )  # use raw MSE loss (with RESCALED_KL when learning variances)
+    KL = enum.auto()  # use the variational lower-bound
+    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
+
+    def is_vb(self):
+        return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+class GaussianDiffusion:
+    """
+    Utilities for training and sampling diffusion models.
+    Ported directly from here, and then adapted over time to further experimentation.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    :param model_mean_type: a ModelMeanType determining what the model outputs.
+    :param model_var_type: a ModelVarType determining how variance is output.
+    :param loss_type: a LossType determining the loss function to use.
+    :param rescale_timesteps: if True, pass floating point timesteps into the
+                              model so that they are always scaled like in the
+                              original paper (0 to 1000).
+    """
+
+    def __init__(
+        self,
+        *,
+        betas,
+        model_mean_type,
+        model_var_type,
+        loss_type,
+        rescale_timesteps=False,
+    ):
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        self.loss_type = loss_type
+        self.rescale_timesteps = rescale_timesteps
+
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (
+            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        # log calculation clipped because the posterior variance is 0 at the
+        # beginning of the diffusion chain.
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:])
+        )
+        self.posterior_mean_coef1 = (
+            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        self.posterior_mean_coef2 = (
+            (1.0 - self.alphas_cumprod_prev)
+            * np.sqrt(alphas)
+            / (1.0 - self.alphas_cumprod)
+        )
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        )
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = _extract_into_tensor(
+            self.log_one_minus_alphas_cumprod, t, x_start.shape
+        )
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+        In other words, sample from q(x_t | x_0).
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
+            * noise
+        )
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+            q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        assert (
+            posterior_mean.shape[0]
+            == posterior_variance.shape[0]
+            == posterior_log_variance_clipped.shape[0]
+            == x_start.shape[0]
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(
+        self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
+    ):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+        B, C = x.shape[:2]
+        C = 1
+        assert t.shape == (B,)
+        model_output = model(x, self._scale_timesteps(t), **model_kwargs)
+        # Keep only the dynamic channel for diffusion (last channel)
+        x = x[:, -1:, ...]
+        # If model outputs multiple channels (e.g., static+dynamic), select dynamic channel(s)
+        if model_output.shape[1] != x.shape[1]:
+            model_output = model_output[:, -x.shape[1]:, ...]
+        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
+            assert model_output.shape == (B, C * 2, *x.shape[2:])
+            model_output, model_var_values = th.split(model_output, C, dim=1)
+            if self.model_var_type == ModelVarType.LEARNED:
+                model_log_variance = model_var_values
+                model_variance = th.exp(model_log_variance)
+            else:
+                min_log = _extract_into_tensor(
+                    self.posterior_log_variance_clipped, t, x.shape
+                )
+                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+                # The model_var_values is [-1, 1] for [min_var, max_var].
+                frac = (model_var_values + 1) / 2
+                model_log_variance = frac * max_log + (1 - frac) * min_log
+                model_variance = th.exp(model_log_variance)
+        else:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                ModelVarType.FIXED_LARGE: (
+                    np.append(self.posterior_variance[1], self.betas[1:]),
+                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
+                ),
+                ModelVarType.FIXED_SMALL: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+            pred_xstart = process_xstart(
+                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
+            )
+            model_mean = model_output
+        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
+            if self.model_mean_type == ModelMeanType.START_X:
+                pred_xstart = process_xstart(model_output)
+            else:
+                pred_xstart = process_xstart(
+                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+                )
+            model_mean, _, _ = self.q_posterior_mean_variance(
+                x_start=pred_xstart, x_t=x, t=t
+            )
+        else:
+            raise NotImplementedError(self.model_mean_type)
+
+        assert (
+            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+        )
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def _predict_xstart_from_xprev(self, x_t, t, xprev):
+        assert x_t.shape == xprev.shape
+        return (  # (xprev - coef2*x_t) / coef1
+            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
+            - _extract_into_tensor(
+                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
+            )
+            * x_t
+        )
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - pred_xstart
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _scale_timesteps(self, t):
+        if self.rescale_timesteps:
+            return t.float() * (1000.0 / self.num_timesteps)
+        return t
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, org, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        a, gradient = cond_fn(x, self._scale_timesteps(t), org, **model_kwargs)
+
+        new_mean = (
+            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+        )
+        return a, new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+        See condition_mean() for details on cond_fn.
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+
+        eps = eps.detach() - (1 - alpha_bar).sqrt() * p_mean_var["update"] * 0
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x.detach(), t.detach(), eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(
+            x_start=out["pred_xstart"], x_t=x, t=t
+        )
+        return out, eps
+
+    def sample_known(self, img, batch_size=1):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop_known(
+            model, (batch_size, channels, image_size, image_size), img
+        )
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x[:, -1:, ...])
+        nonzero_mask = (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
+
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model.
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        print("apparently we use this function")
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_known(
+        self,
+        model,
+        shape,
+        img,
+        org=None,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        conditioner=None,
+        classifier=None,
+    ):
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        img = img.to(device)
+        noise = th.randn_like(img[:, :1, ...]).to(device)
+        x_noisy = torch.cat(
+            (img[:, :-1, ...], noise), dim=1
+        )  # add noise as the last channel
+
+        img = img.to(device)
+
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=x_noisy,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            org=org,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+
+        return final["sample"], x_noisy, img
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        time=1000,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        org=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(time))[::-1]
+
+        org_MRI = img[:, :-1, ...]  # original brain MR image
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        # Always iterate through all timesteps, handling the first few specially
+        i_exceptions = [0, 1, 2]
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+
+            imarr = np.asarray(img.cpu().detach())
+            with th.no_grad():
+                if img.shape[1] == 1:
+                    img = torch.cat((org_MRI, img), dim=1)
+
+                out = self.p_sample(
+                    model,
+                    img.float(),
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+
+                if i in i_exceptions:
+                    img = out["pred_xstart"]
+                else:
+                    img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+        sigma = (
+            eta
+            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+        )
+        # Equation 12.
+        noise = th.randn_like(x[:, -1:, ...])
+
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+            + th.sqrt(1 - alpha_bar_prev - sigma**2) * eps
+        )
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+            - out["pred_xstart"]
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+        # Equation 12. reversed
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_next)
+            + th.sqrt(1 - alpha_bar_next) * eps
+        )
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop_interpolation(
+        self,
+        model,
+        shape,
+        img1,
+        img2,
+        lambdaint,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        b = shape[0]
+        t = th.randint(499, 500, (b,), device=device).long().to(device)
+
+        img1 = torch.tensor(img1).to(device)
+        img2 = torch.tensor(img2).to(device)
+
+        noise = th.randn_like(img1).to(device)
+        x_noisy1 = self.q_sample(x_start=img1, t=t, noise=noise).to(device)
+        x_noisy2 = self.q_sample(x_start=img2, t=t, noise=noise).to(device)
+        interpol = lambdaint * x_noisy1 + (1 - lambdaint) * x_noisy2
+
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            time=t,
+            noise=interpol,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+        return final["sample"], interpol, img1, img2
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+        Same usage as p_sample_loop().
+        """
+        final = None
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        b = shape[0]
+        t = th.randint(99, 100, (b,), device=device).long().to(device)
+
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            time=t,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+
+        return final["sample"]
+
+    def ddim_sample_loop_known(
+        self,
+        model,
+        shape,
+        img,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        b = shape[0]
+
+        img = img.to(device)
+
+        t = th.randint(499, 500, (b,), device=device).long().to(device)
+        noise = th.randn_like(img[:, :1, ...]).to(device)
+
+        x_noisy = torch.cat((img[:, :-1, ...], noise), dim=1).float()
+        img = img.to(device)
+
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            time=t,
+            noise=x_noisy,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+
+        return final["sample"], x_noisy, img
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        time=1000,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(time - 1))[::-1]
+        orghigh = img[:, :-1, ...]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                if img.shape != (1, 5, 224, 224):
+                    img = torch.cat((orghigh, img), dim=1).float()
+
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                )
+            yield out
+            img = out["sample"]
+
+    def _vb_terms_bpd(
+        self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
+    ):
+        """
+        Get a term for the variational lower-bound.
+        The resulting units are bits (rather than nats, as one might expect).
+        This allows for comparison to other papers.
+        :return: a dict with the following keys:
+                 - 'output': a shape [N] tensor of NLLs or KLs.
+                 - 'pred_xstart': the x_0 predictions.
+        """
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+            x_start=x_start, x_t=x_t, t=t
+        )
+        out = self.p_mean_variance(
+            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
+        )
+        kl = normal_kl(
+            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
+        )
+        kl = mean_flat(kl) / np.log(2.0)
+
+        decoder_nll = -discretized_gaussian_log_likelihood(
+            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
+        )
+        assert decoder_nll.shape == x_start.shape
+        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+        # At the first timestep return the decoder NLL,
+        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+        output = th.where((t == 0), decoder_nll, kl)
+        return {"output": output, "pred_xstart": out["pred_xstart"]}
+
+    def training_losses_segmentation(
+        self, model, classifier, x_start, t, model_kwargs=None, noise=None
+    ):
+        """
+        Compute training losses for a single timestep.
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+
+        if model_kwargs is None:
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start[:, -1:, ...])
+
+        goal = x_start[:, -1:, ...]
+
+        res_t = self.q_sample(
+            goal, t, noise=noise
+        )  # during q, noise is only added to the ground truth!
+
+        x_t = x_start.float()
+
+        x_t[:, -1:, ...] = res_t.float()  # replace last channel by noisy GT
+
+        terms = {}
+
+        if self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
+
+            if self.model_var_type in [
+                ModelVarType.LEARNED,
+                ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                C = 1
+                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
+                model_output, model_var_values = th.split(model_output, C, dim=1)
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=goal,
+                    x_t=res_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            target = {
+                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
+                    x_start=goal, x_t=res_t, t=t
+                )[0],
+                ModelMeanType.START_X: goal,
+                ModelMeanType.EPSILON: noise,
+            }[self.model_mean_type]
+            terms["mse"] = mean_flat((target - model_output) ** 2)
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return (terms, model_output)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(
+            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
+        )
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
+        """
+        Compute the entire variational lower-bound, measured in bits-per-dim,
+        as well as other related quantities.
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param clip_denoised: if True, clip denoised samples.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - total_bpd: the total variational lower-bound, per batch element.
+                 - prior_bpd: the prior term in the lower-bound.
+                 - vb: an [N x T] tensor of terms in the lower-bound.
+                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+        """
+        device = x_start.device
+        batch_size = x_start.shape[0]
+
+        vb = []
+        xstart_mse = []
+        mse = []
+        for t in list(range(self.num_timesteps))[::-1]:
+            t_batch = th.tensor([t] * batch_size, device=device)
+            noise = th.randn_like(x_start)
+            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+
+            # Calculate VLB term at the current timestep
+            with th.no_grad():
+                out = self._vb_terms_bptimestepsd(
+                    model,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t_batch,
+                    clip_denoised=clip_denoised,
+                    model_kwargs=model_kwargs,
+                )
+            vb.append(out["output"])
+            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
+            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
+            mse.append(mean_flat((eps - noise) ** 2))
+
+        vb = th.stack(vb, dim=1)
+        xstart_mse = th.stack(xstart_mse, dim=1)
+        mse = th.stack(mse, dim=1)
+
+        prior_bpd = self._prior_bpd(x_start)
+        total_bpd = vb.sum(dim=1) + prior_bpd
+        return {
+            "total_bpd": total_bpd,
+            "prior_bpd": prior_bpd,
+            "vb": vb,
+            "xstart_mse": xstart_mse,
+            "mse": mse,
+        }
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res.expand(broadcast_shape)

fcd2_inpainting/DDPM_Pseudo3D/guided_diffusion/.ipynb_checkpoints/train_util-checkpoint.py

@@ -0,0 +1,430 @@
+import copy
+import functools
+import os
+
+import math
+import numpy as np
+import nibabel as nib
+import blobfile as bf
+import torch as th
+import torch.distributed as dist
+from torch.nn.parallel.distributed import DistributedDataParallel as DDP
+from torch.optim import AdamW
+
+from . import dist_util, logger
+from .fp16_util import MixedPrecisionTrainer
+from .nn import update_ema
+from .resample import LossAwareSampler, UniformSampler
+
+# For ImageNet experiments, this was a good default value.
+# We found that the lg_loss_scale quickly climbed to
+# 20-21 within the first ~1K steps of training.
+INITIAL_LOG_LOSS_SCALE = 20.0
+
+
+def visualize(img):
+    _min = img.min()
+    _max = img.max()
+    normalized_img = (img - _min) / (_max - _min)
+    return normalized_img
+
+
+class TrainLoop:
+    def __init__(
+        self,
+        *,
+        model,
+        classifier,
+        diffusion,
+        data,
+        dataloader,
+        batch_size,
+        microbatch,
+        lr,
+        ema_rate,
+        log_interval,
+        save_interval,
+        resume_checkpoint,
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        schedule_sampler=None,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+    ):
+        self.model = model
+        self.dataloader = dataloader
+        self.classifier = classifier
+        self.diffusion = diffusion
+        self.data = data
+        self.batch_size = batch_size
+        self.microbatch = microbatch if microbatch > 0 else batch_size
+        self.lr = lr
+        self.ema_rate = (
+            [ema_rate]
+            if isinstance(ema_rate, float)
+            else [float(x) for x in ema_rate.split(",")]
+        )
+        self.log_interval = log_interval
+        self.save_interval = save_interval
+        self.resume_checkpoint = resume_checkpoint
+        self.use_fp16 = use_fp16
+        self.fp16_scale_growth = fp16_scale_growth
+        self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
+        self.weight_decay = weight_decay
+        self.lr_anneal_steps = lr_anneal_steps
+
+        self.step = 0
+        self.resume_step = 0
+        self.global_batch = self.batch_size * dist.get_world_size()
+
+        self.sync_cuda = th.cuda.is_available()
+
+        self._load_and_sync_parameters()
+        self.mp_trainer = MixedPrecisionTrainer(
+            model=self.model,
+            use_fp16=self.use_fp16,
+            fp16_scale_growth=fp16_scale_growth,
+        )
+
+        self.opt = AdamW(
+            self.mp_trainer.master_params, lr=self.lr, weight_decay=self.weight_decay
+        )
+        if self.resume_step:
+            self._load_optimizer_state()
+            # Model was resumed, either due to a restart or a checkpoint
+            # being specified at the command line.
+            self.ema_params = [
+                self._load_ema_parameters(rate) for rate in self.ema_rate
+            ]
+        else:
+            self.ema_params = [
+                copy.deepcopy(self.mp_trainer.master_params)
+                for _ in range(len(self.ema_rate))
+            ]
+
+        if th.cuda.is_available():
+            self.use_ddp = True
+            self.ddp_model = DDP(
+                self.model,
+                device_ids=[dist_util.dev()],
+                output_device=dist_util.dev(),
+                broadcast_buffers=False,
+                bucket_cap_mb=128,
+                find_unused_parameters=False,
+            )
+        else:
+            if dist.get_world_size() > 1:
+                logger.warn(
+                    "Distributed training requires CUDA. "
+                    "Gradients will not be synchronized properly!"
+                )
+            self.use_ddp = False
+            self.ddp_model = self.model
+
+    def _load_and_sync_parameters(self):
+        resume_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
+
+        if resume_checkpoint:
+            print("resume model")
+            self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
+            if dist.get_rank() == 0:
+                logger.log(f"loading model from checkpoint: {resume_checkpoint}...")
+                self.model.load_state_dict(
+                    dist_util.load_state_dict(
+                        resume_checkpoint, map_location=dist_util.dev()
+                    )
+                )
+
+        dist_util.sync_params(self.model.parameters())
+
+    def _load_ema_parameters(self, rate):
+        ema_params = copy.deepcopy(self.mp_trainer.master_params)
+
+        main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
+        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
+        if ema_checkpoint:
+            if dist.get_rank() == 0:
+                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
+                state_dict = dist_util.load_state_dict(
+                    ema_checkpoint, map_location=dist_util.dev()
+                )
+                ema_params = self.mp_trainer.state_dict_to_master_params(state_dict)
+
+        dist_util.sync_params(ema_params)
+        return ema_params
+
+    def _load_optimizer_state(self):
+        main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
+        opt_checkpoint = bf.join(
+            bf.dirname(main_checkpoint), f"opt{self.resume_step:06}.pt"
+        )
+        if bf.exists(opt_checkpoint):
+            logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}")
+            state_dict = dist_util.load_state_dict(
+                opt_checkpoint, map_location=dist_util.dev()
+            )
+            self.opt.load_state_dict(state_dict)
+
+    def run_loop(self):
+        i = 0
+        data_iter = iter(self.dataloader)
+        while (
+            not self.lr_anneal_steps
+            or self.step + self.resume_step < self.lr_anneal_steps
+        ):
+            try:
+                batch, cond, path, slicedict = next(data_iter)
+
+                # Number of slices per sub-batch (adjust for memory usage)
+                # Increase to 12 for more temporal context
+                batch_size_vol = 12
+                nr_batches = len(slicedict) / batch_size_vol
+
+                nr_batches = math.ceil(nr_batches)
+
+                for b in range(0, nr_batches):
+                    out_batch = []
+                    out_cond = []
+
+                    if len(slicedict) > b * batch_size_vol + batch_size_vol:
+                        for s in slicedict[b * batch_size_vol : (b * batch_size_vol + batch_size_vol)]:
+                            out_batch.append(th.tensor(batch[..., s]))
+                            out_cond.append(th.tensor(cond[..., s]))
+
+                        out_batch = th.stack(out_batch)
+                        out_cond = th.stack(out_cond)
+
+                        out_batch = out_batch.squeeze(1)
+                        out_cond = out_cond.squeeze(1)
+
+                        out_batch = out_batch.squeeze(4)
+                        out_cond = out_cond.squeeze(4)
+
+                        p_s = path[0].split("/")[3]
+
+                        self.run_step(out_batch, out_cond)
+
+                        i += 1
+
+                    else:
+                        for s in slicedict[b * batch_size_vol :]:
+                            out_batch.append(th.tensor(batch[..., s]))
+                            out_cond.append(th.tensor(cond[..., s]))
+
+                        out_batch = th.stack(out_batch)
+                        out_cond = th.stack(out_cond)
+
+                        out_batch = out_batch.squeeze(1)
+                        out_cond = out_cond.squeeze(1)
+                        out_batch = out_batch.squeeze(4)
+                        out_cond = out_cond.squeeze(4)
+
+                        p_s = path[0].split("/")[3]
+
+                        self.run_step(out_batch, out_cond)
+
+                        i += 1
+
+            except StopIteration:
+                # StopIteration is thrown if dataset ends
+                # reinitialize data loader
+                data_iter = iter(self.dataloader)
+
+                batch, cond, path, slicedict = next(data_iter)
+
+                # Number of slices per sub-batch (adjust for memory usage)
+                # Increase to 12 for more temporal context
+                batch_size_vol = 12
+                nr_batches = len(slicedict) / batch_size_vol
+
+                nr_batches = math.ceil(nr_batches)
+
+                for b in range(0, nr_batches):
+                    out_batch = []
+                    out_cond = []
+
+                    if len(slicedict) > b * batch_size_vol + batch_size_vol:
+                        for s in slicedict[b * batch_size_vol : (b * batch_size_vol + batch_size_vol)]:
+                            out_batch.append(th.tensor(batch[..., s]))
+                            out_cond.append(th.tensor(cond[..., s]))
+
+                        out_batch = th.stack(out_batch)
+                        out_cond = th.stack(out_cond)
+
+                        out_batch = out_batch.squeeze(1)
+                        out_cond = out_cond.squeeze(1)
+                        out_batch = out_batch.squeeze(4)
+                        out_cond = out_cond.squeeze(4)
+
+                        p_s = path[0].split("/")[3]
+
+                        self.run_step(out_batch, out_cond)
+
+                        i += 1
+
+                    else:
+                        for s in slicedict[b * batch_size_vol :]:
+                            out_batch.append(th.tensor(batch[..., s]))
+                            out_cond.append(th.tensor(cond[..., s]))
+
+                        out_batch = th.stack(out_batch)
+                        out_cond = th.stack(out_cond)
+
+                        out_batch = out_batch.squeeze(1)
+                        out_cond = out_cond.squeeze(1)
+                        out_batch = out_batch.squeeze(4)
+                        out_cond = out_cond.squeeze(4)
+
+                        p_s = path[0].split("/")[3]
+
+                        self.run_step(out_batch, out_cond)
+
+                        i += 1
+
+            if self.step % self.log_interval == 0:
+                logger.dumpkvs()
+            if self.step % self.save_interval == 0:
+                self.save()
+                # Run for a finite amount of time in integration tests.
+                if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0:
+                    return
+            self.step += 1
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+
+    def run_step(self, batch, cond):
+        batch = th.cat((batch, cond), dim=1)
+        cond = {}
+        sample = self.forward_backward(batch, cond)
+        print("sample shape", sample.shape)
+        took_step = self.mp_trainer.optimize(self.opt)
+        if took_step:
+            self._update_ema()
+        self._anneal_lr()
+        self.log_step()
+        return sample
+
+    def forward_backward(self, batch, cond):
+        self.mp_trainer.zero_grad()
+
+        micro = batch.to(dist_util.dev())
+        t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
+
+        compute_losses = functools.partial(
+            self.diffusion.training_losses_segmentation,
+            self.ddp_model,
+            self.classifier,
+            micro,
+            t,
+        )
+
+        #    if last_batch or not self.use_ddp:
+        losses1 = compute_losses()
+
+        # else:
+        #       with self.ddp_model.no_sync():
+        #                    losses1 = compute_losses()
+
+        if isinstance(self.schedule_sampler, LossAwareSampler):
+            self.schedule_sampler.update_with_local_losses(t, losses["loss"].detach())
+        losses = losses1[0]
+        sample = losses1[1]
+
+        loss = (losses["loss"] * weights).mean()
+
+        log_loss_dict(self.diffusion, t, {k: v * weights for k, v in losses.items()})
+        self.mp_trainer.backward(loss)
+        return sample
+
+    def _update_ema(self):
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            update_ema(params, self.mp_trainer.master_params, rate=rate)
+
+    def _anneal_lr(self):
+        if not self.lr_anneal_steps:
+            return
+        frac_done = (self.step + self.resume_step) / self.lr_anneal_steps
+        lr = self.lr * (1 - frac_done)
+        for param_group in self.opt.param_groups:
+            param_group["lr"] = lr
+
+    def log_step(self):
+        logger.logkv("step", self.step + self.resume_step)
+        logger.logkv("samples", (self.step + self.resume_step + 1) * self.global_batch)
+
+    def save(self):
+        def save_checkpoint(rate, params):
+            state_dict = self.mp_trainer.master_params_to_state_dict(params)
+            if dist.get_rank() == 0:
+                logger.log(f"saving model {rate}...")
+                if not rate:
+                    filename = f"savedmodel{(self.step+self.resume_step):06d}.pt"
+                else:
+                    filename = (
+                        f"emasavedmodel_{rate}_{(self.step+self.resume_step):06d}.pt"
+                    )
+                with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f:
+                    th.save(state_dict, f)
+
+        save_checkpoint(0, self.mp_trainer.master_params)
+        for rate, params in zip(self.ema_rate, self.ema_params):
+            save_checkpoint(rate, params)
+
+        if dist.get_rank() == 0:
+            with bf.BlobFile(
+                bf.join(
+                    get_blob_logdir(),
+                    f"optsavedmodel{(self.step+self.resume_step):06d}.pt",
+                ),
+                "wb",
+            ) as f:
+                th.save(self.opt.state_dict(), f)
+
+        dist.barrier()
+
+
+def parse_resume_step_from_filename(filename):
+    """
+    Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
+    checkpoint's number of steps.
+    """
+    split = filename.split("model")
+    if len(split) < 2:
+        return 0
+    split1 = split[-1].split(".")[0]
+    try:
+        return int(split1)
+    except ValueError:
+        return 0
+
+
+def get_blob_logdir():
+    # You can change this to be a separate path to save checkpoints to
+    # a blobstore or some external drive.
+    return logger.get_dir()
+
+
+def find_resume_checkpoint():
+    # On your infrastructure, you may want to override this to automatically
+    # discover the latest checkpoint on your blob storage, etc.
+    return None
+
+
+def find_ema_checkpoint(main_checkpoint, step, rate):
+    if main_checkpoint is None:
+        return None
+    filename = f"ema_{rate}_{(step):06d}.pt"
+    path = bf.join(bf.dirname(main_checkpoint), filename)
+    if bf.exists(path):
+        return path
+    return None
+
+
+def log_loss_dict(diffusion, ts, losses):
+    for key, values in losses.items():
+        logger.logkv_mean(key, values.mean().item())
+        # Log the quantiles (four quartiles, in particular).
+        for sub_t, sub_loss in zip(ts.cpu().numpy(), values.detach().cpu().numpy()):
+            quartile = int(4 * sub_t / diffusion.num_timesteps)
+            logger.logkv_mean(f"{key}_q{quartile}", sub_loss)

fcd2_inpainting/DDPM_Pseudo3D/guided_diffusion/.ipynb_checkpoints/unet-checkpoint.py

@@ -0,0 +1,904 @@
+from abc import abstractmethod
+import math
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+from .fp16_util import convert_module_to_f16, convert_module_to_f32
+from .nn import (
+    checkpoint,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    zero_module,
+    normalization,
+    timestep_embedding,
+)
+
+
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(
+            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
+        )
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, (x, emb), self.parameters(), self.use_checkpoint
+        )
+
+    def _forward(self, x, emb):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return checkpoint(self._forward, (x,), self.parameters(), True)
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial**2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps, y=None):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        if self.num_classes is not None:
+            assert y.shape == (x.shape[0],)
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            hs.append(h)
+        h = self.middle_block(h, emb)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        return self.out(h)
+
+
+class SuperResModel(UNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(self, image_size, in_channels, *args, **kwargs):
+        super().__init__(image_size, in_channels * 2, *args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
+        x = th.cat([x, upsampled], dim=1)
+        return super().forward(x, timesteps, **kwargs)
+
+
+class EncoderUNetModel(nn.Module):
+    """
+    The half UNet model with attention and timestep embedding.
+
+    For usage, see UNet.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        pool="adaptive",
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+        self.pool = pool
+        self.gap = nn.AvgPool2d((8, 8))  # global average pooling
+        self.cam_feature_maps = None
+        print("pool", pool)
+        if pool == "adaptive":
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                nn.AdaptiveAvgPool2d((1, 1)),
+                zero_module(conv_nd(dims, ch, out_channels, 1)),
+                nn.Flatten(),
+            )
+        elif pool == "attention":
+            assert num_head_channels != -1
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                AttentionPool2d(
+                    (image_size // ds), ch, num_head_channels, out_channels
+                ),
+            )
+        elif pool == "spatial":
+            self.out = nn.Linear(256, self.out_channels)
+
+        elif pool == "spatial_v2":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                normalization(2048),
+                nn.SiLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        else:
+            raise NotImplementedError(f"Unexpected {pool} pooling")
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :return: an [N x K] Tensor of outputs.
+        """
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        results = []
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            if self.pool.startswith("spatial"):
+                results.append(h.type(x.dtype).mean(dim=(2, 3)))
+        h = self.middle_block(h, emb)
+
+        if self.pool.startswith("spatial"):
+            self.cam_feature_maps = h
+            h = self.gap(h)
+            N = h.shape[0]
+            h = h.reshape(N, -1)
+            print("h1", h.shape)
+            return self.out(h)
+        else:
+            h = h.type(x.dtype)
+            self.cam_feature_maps = h
+            return self.out(h)

fcd2_inpainting/DDPM_Pseudo3D/guided_diffusion/fcd2loader.py

@@ -2,7 +2,7 @@ import os
 import numpy as np
 import nibabel as nib
 import torch
-from torch.utils.data import Dataset
+from torch.utils.data import Dataset, random_split
 
 class FCD2Dataset(Dataset):
     """
@@ -10,26 +10,63 @@ class FCD2Dataset(Dataset):
     Expects directory structure:
         <root_dir>/
             images/
-                sub-XXX.nii.gz
+                sub-XXX.nii.gz  # Images WITH lesions (ground truth)
             labels/
-                sub-XXX_roi.nii.gz
+                sub-XXX_roi.nii.gz  # Masks of healthy regions to void (where lesions should be added)
     Returns for training (test_flag=False): (input, label, path, slice_indices)
     Returns for sampling (test_flag=True): (input, path, slice_indices)
-    Input channels are [voided_image, mask]; label is original image.
+    Input channels are [voided_image, mask]; label is original image with lesions.
+    
+    For lesion addition: void healthy regions marked by ROI, reconstruct lesions in those regions
     """
-    def __init__(self, root_dir, test_flag=False):
+    def __init__(self, root_dir, test_flag=False, mode="add_lesions", validation_split=0.2, seed=42):
         super().__init__()
         self.root_dir = os.path.expanduser(root_dir)
         self.images_dir = os.path.join(self.root_dir, 'images')
         self.labels_dir = os.path.join(self.root_dir, 'labels')
         self.test_flag = test_flag
+        self.mode = mode  # "add_lesions" or "remove_lesions"
+        self.validation_split = validation_split
+        self.seed = seed
+        
         # List image files
         self.image_files = sorted([f for f in os.listdir(self.images_dir) if f.endswith('.nii.gz')])
         # Full paths
         self.image_paths = [os.path.join(self.images_dir, f) for f in self.image_files]
         self.label_paths = [os.path.join(self.labels_dir, f.replace('.nii.gz', '_roi.nii.gz')) for f in self.image_files]
+        
+        # Validate files exist
+        missing_files = []
+        for label_path in self.label_paths:
+            if not os.path.exists(label_path):
+                missing_files.append(label_path)
+        if missing_files:
+            raise FileNotFoundError(f"Missing label files: {missing_files}")
+        
+        # Split into train/validation if not test mode
+        if not test_flag and validation_split > 0:
+            total_size = len(self.image_paths)
+            val_size = int(total_size * validation_split)
+            train_size = total_size - val_size
+            
+            # Set seed for reproducible splits
+            torch.manual_seed(seed)
+            train_indices, val_indices = random_split(
+                range(total_size), [train_size, val_size]
+            )
+            
+            # Convert to lists
+            train_indices = list(train_indices.indices)
+            val_indices = list(val_indices.indices)
+            
+            # Store indices for train/val split
+            self.train_indices = train_indices
+            self.val_indices = val_indices
+            self.is_validation = False  # Will be set by get_train_val_datasets
+        
         # Compute slice ranges where mask is non-zero
         self.slice_ranges = []
+        self.mask_stats = []  # Track mask statistics for monitoring
         for label_path in self.label_paths:
             # Load and reorient mask to canonical orientation
             mask_nii = nib.as_closest_canonical(nib.load(label_path))
@@ -39,6 +76,30 @@ class FCD2Dataset(Dataset):
             # Assume slices along last axis
             idxs = [i for i in range(mask.shape[2]) if mask[..., i].sum() > 0]
             self.slice_ranges.append(idxs)
+            
+            # Track mask statistics
+            mask_volume = (mask > 0).sum()
+            total_volume = mask.size
+            mask_percentage = (mask_volume / total_volume) * 100
+            self.mask_stats.append({
+                'mask_volume': mask_volume,
+                'total_volume': total_volume,
+                'mask_percentage': mask_percentage,
+                'num_slices': len(idxs)
+            })
+        
+        # Print dataset statistics
+        if not self.test_flag:
+            total_masks = sum(stat['mask_volume'] for stat in self.mask_stats)
+            total_voxels = sum(stat['total_volume'] for stat in self.mask_stats)
+            avg_mask_percentage = (total_masks / total_voxels) * 100
+            print(f"Dataset loaded: {len(self.image_files)} volumes")
+            print(f"Mode: {self.mode}")
+            print(f"Average mask percentage: {avg_mask_percentage:.2f}%")
+            print(f"Total training slices: {sum(len(ranges) for ranges in self.slice_ranges)}")
+            
+            if validation_split > 0:
+                print(f"Train/Validation split: {len(self.train_indices)}/{len(self.val_indices)}")
 
     def __len__(self):
         return len(self.image_paths)
@@ -47,14 +108,24 @@ class FCD2Dataset(Dataset):
         # Load image and mask
         image_path = self.image_paths[idx]
         label_path = self.label_paths[idx]
+        
         # Load and reorient image and mask to canonical orientation
         img_nii = nib.as_closest_canonical(nib.load(image_path))
         image = img_nii.get_fdata().astype(np.float32)
         msk_nii = nib.as_closest_canonical(nib.load(label_path))
         mask = msk_nii.get_fdata().astype(np.float32)
-        # Create voided image (zero out lesion region)
+        
+        # Create voided image based on mode
         voided = image.copy()
-        voided[mask > 0] = 0.0
+        if self.mode == "add_lesions":
+            # For adding lesions: void healthy regions (marked by ROI)
+            # The model will learn to reconstruct lesions in these voided regions
+            voided[mask > 0] = 0.0
+        else:  # "remove_lesions"
+            # For removing lesions: void lesion regions
+            # The model will learn to reconstruct healthy tissue in these voided regions
+            voided[mask > 0] = 0.0
+        
         # Intensity clipping and normalization between 0 and 1
         min_val = np.quantile(image, 0.001)
         max_val = np.quantile(image, 0.999)
@@ -66,16 +137,76 @@ class FCD2Dataset(Dataset):
         else:
             image = np.zeros_like(image)
             voided = np.zeros_like(voided)
+        
         mask = (mask > 0).astype(np.float32)
-        # Stack channels: voided, mask, original
+        
+        # Stack channels: voided, mask, original (with lesions)
         volume = np.stack([voided, mask, image], axis=0)
         volume = torch.from_numpy(volume)
         slice_range = self.slice_ranges[idx]
+        
         # Return inputs and label for training, or inputs only for sampling
         if not self.test_flag:
-            inp = volume[:2]       # (2, H, W, D)
-            label = volume[2:3]    # (1, H, W, D)
+            inp = volume[:2]       # (2, H, W, D) - voided image + mask
+            label = volume[2:3]    # (1, H, W, D) - original image with lesions
             return inp, label, image_path, slice_range
         # Sampling
         inp = volume[:2]
-        return inp, image_path, slice_range
+        return inp, image_path, slice_range
+    
+    def get_dataset_stats(self):
+        """Return dataset statistics for monitoring"""
+        # Convert NumPy types to native Python types for JSON serialization
+        mask_stats_serializable = []
+        for stat in self.mask_stats:
+            mask_stats_serializable.append({
+                'mask_volume': int(stat['mask_volume']),
+                'total_volume': int(stat['total_volume']),
+                'mask_percentage': float(stat['mask_percentage']),
+                'num_slices': int(stat['num_slices'])
+            })
+        
+        return {
+            'num_volumes': len(self.image_files),
+            'mask_stats': mask_stats_serializable,
+            'mode': self.mode,
+            'total_slices': sum(len(ranges) for ranges in self.slice_ranges),
+            'validation_split': self.validation_split if hasattr(self, 'validation_split') else 0
+        }
+    
+    @classmethod
+    def get_train_val_datasets(cls, root_dir, mode="add_lesions", validation_split=0.2, seed=42):
+        """
+        Create separate training and validation datasets.
+        
+        Args:
+            root_dir: Path to data directory
+            mode: "add_lesions" or "remove_lesions"
+            validation_split: Fraction of data to use for validation
+            seed: Random seed for reproducible splits
+            
+        Returns:
+            train_dataset, val_dataset: Two FCD2Dataset instances
+        """
+        # Create full dataset
+        full_dataset = cls(root_dir, test_flag=False, mode=mode, validation_split=validation_split, seed=seed)
+        
+        # Create training dataset
+        train_dataset = cls(root_dir, test_flag=False, mode=mode, validation_split=0, seed=seed)
+        train_dataset.image_paths = [full_dataset.image_paths[i] for i in full_dataset.train_indices]
+        train_dataset.label_paths = [full_dataset.label_paths[i] for i in full_dataset.train_indices]
+        train_dataset.slice_ranges = [full_dataset.slice_ranges[i] for i in full_dataset.train_indices]
+        train_dataset.mask_stats = [full_dataset.mask_stats[i] for i in full_dataset.train_indices]
+        train_dataset.is_validation = False
+        
+        # Create validation dataset
+        val_dataset = cls(root_dir, test_flag=False, mode=mode, validation_split=0, seed=seed)
+        val_dataset.image_paths = [full_dataset.image_paths[i] for i in full_dataset.val_indices]
+        val_dataset.label_paths = [full_dataset.label_paths[i] for i in full_dataset.val_indices]
+        val_dataset.slice_ranges = [full_dataset.slice_ranges[i] for i in full_dataset.val_indices]
+        val_dataset.mask_stats = [full_dataset.mask_stats[i] for i in full_dataset.val_indices]
+        val_dataset.is_validation = True
+        
+        print(f"Created train/validation split: {len(train_dataset)}/{len(val_dataset)} volumes")
+        
+        return train_dataset, val_dataset

fcd2_inpainting/DDPM_Pseudo3D/scripts/.ipynb_checkpoints/TRAINING-checkpoint.md

@@ -0,0 +1,277 @@
+# Enhanced FCD2 Lesion Addition Training System
+
+This enhanced training system allows you to train a diffusion model to **add lesions to healthy brain tissue** by learning to reconstruct lesions in masked regions.
+
+## Overview
+
+### What This System Does
+- **Input**: Healthy brain images + ROI masks (healthy regions where lesions should be added)
+- **Training**: Model learns to reconstruct lesions in the masked regions
+- **Output**: Model that can add realistic lesions to healthy brain tissue
+
+### Key Features
+- ✅ **Corrected Logic**: Now properly supports lesion addition (not removal)
+- ✅ **Real-time Monitoring**: Live training progress visualization
+- ✅ **Comprehensive Metrics**: Loss, MSE, learning rate tracking
+- ✅ **Validation**: Sample predictions during training
+- ✅ **Progress Tracking**: JSON metrics and PNG plots
+- ✅ **Dataset Statistics**: Automatic dataset analysis
+
+## Data Format
+
+### Directory Structure
+```
+<data_dir>/
+├── images/
+│   ├── sub-001.nii.gz  # Images WITH lesions (ground truth)
+│   ├── sub-002.nii.gz
+│   └── ...
+└── labels/
+    ├── sub-001_roi.nii.gz  # Masks of healthy regions to void
+    ├── sub-002_roi.nii.gz
+    └── ...
+```
+
+### File Requirements
+- **Images**: 3D NIfTI files (.nii.gz) containing brain MRIs with lesions
+- **Labels**: Binary masks where `>0` = healthy region to be replaced with lesion
+- **Naming**: Labels must match images with `_roi` suffix
+- **Dimensions**: Labels must match corresponding image dimensions
+
+## Installation
+
+### Install Monitoring Dependencies
+```bash
+pip install -r requirements_monitoring.txt
+```
+
+### Optional: Install Visdom for Real-time Visualization
+```bash
+pip install visdom
+python -m visdom.server -port 8097
+```
+
+## Usage
+
+### 1. Training
+
+#### Basic Training
+```bash
+python scripts/inpainting_train.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --mode add_lesions \
+    --batch_size 1 \
+    --lr 1e-4 \
+    --save_interval 5000
+```
+
+#### Advanced Training with Custom Parameters
+```bash
+python scripts/inpainting_train.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --mode add_lesions \
+    --batch_size 1 \
+    --lr 1e-4 \
+    --save_interval 5000 \
+    --log_interval 100 \
+    --ema_rate 0.9999 \
+    --use_fp16 \
+    --image_size 64 \
+    --num_channels 128
+```
+
+### 2. Real-time Monitoring
+
+#### Start Monitoring in Separate Terminal
+```bash
+python scripts/monitor_training.py --log_dir log --update_interval 30
+```
+
+#### Check Training Status
+```bash
+python scripts/monitor_training.py --log_dir log --check_only
+```
+
+### 3. Validation
+
+#### Run Validation on Trained Model
+```bash
+python scripts/validate_training.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --model_path log/emasavedmodel_0.9999_050000.pt \
+    --mode add_lesions \
+    --num_samples 5
+```
+
+#### Plot Training Progress Only
+```bash
+python scripts/validate_training.py \
+    --log_dir log \
+    --plot_progress \
+    --validate False
+```
+
+## Output Files
+
+### Training Output
+```
+log/
+├── log.txt                    # Training logs
+├── training_metrics.json      # Detailed metrics history
+├── training_metrics.png       # Training plots
+├── training_progress.png      # Progress visualization
+├── savedmodel000000.pt       # Model checkpoints
+├── emasavedmodel_0.9999_000000.pt  # EMA model checkpoints
+└── optsavedmodel000000.pt    # Optimizer state
+```
+
+### Validation Output
+```
+log/
+├── validation_results.json    # Validation metrics
+└── validation_samples/
+    ├── validation_sample_0.png
+    ├── validation_sample_1.png
+    └── ...
+```
+
+## Monitoring Features
+
+### Real-time Metrics
+- **Training Loss**: Overall model loss
+- **MSE Loss**: Mean squared error
+- **Learning Rate**: Current learning rate
+- **Variational Bound**: VB loss component
+- **Best Loss**: Best loss achieved so far
+
+### Visualization
+- **Live Plots**: Real-time updating plots
+- **Log Scale**: Automatic log scaling for better visualization
+- **Grid Lines**: Easy-to-read grid lines
+- **Auto-scaling**: Automatic axis scaling
+
+### Console Output
+```
+============================================================
+Training Progress - 2025-01-14 15:30:45
+============================================================
+Current Step: 15000
+Current Loss: 0.023456 (+0.001234)
+Learning Rate: 1.00e-04
+Best Loss: 0.020123
+Current MSE: 0.018765
+Best MSE: 0.015432
+Total Steps: 15000
+ETA: 2h 30m
+============================================================
+```
+
+## Configuration Options
+
+### Training Parameters
+- `--mode`: `add_lesions` or `remove_lesions`
+- `--batch_size`: Batch size (default: 1)
+- `--lr`: Learning rate (default: 1e-4)
+- `--save_interval`: Save checkpoint every N steps
+- `--log_interval`: Log metrics every N steps
+- `--ema_rate`: EMA rate for model averaging
+
+### Model Parameters
+- `--image_size`: Input image size (default: 64)
+- `--num_channels`: Model channels (default: 128)
+- `--num_res_blocks`: Number of residual blocks
+- `--attention_resolutions`: Attention resolution levels
+
+### Monitoring Parameters
+- `--update_interval`: Monitor update frequency (seconds)
+- `--num_samples`: Number of validation samples
+
+## Troubleshooting
+
+### Common Issues
+
+#### 1. "No model checkpoint found"
+- **Solution**: Train the model first or specify correct `--model_path`
+
+#### 2. "Missing label files"
+- **Solution**: Ensure ROI files exist and match image naming convention
+
+#### 3. "Visdom not available"
+- **Solution**: Install visdom or use console-only monitoring
+
+#### 4. "CUDA out of memory"
+- **Solution**: Reduce batch size or image size
+
+### Performance Tips
+
+1. **Use FP16**: Add `--use_fp16` for faster training
+2. **Adjust Batch Size**: Start with batch_size=1, increase if memory allows
+3. **Monitor GPU**: Use `nvidia-smi` to monitor GPU usage
+4. **Save Regularly**: Use appropriate `--save_interval`
+
+## Example Workflow
+
+### Complete Training Session
+```bash
+# Terminal 1: Start training
+python scripts/inpainting_train.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --mode add_lesions
+
+# Terminal 2: Monitor progress
+python scripts/monitor_training.py --log_dir log
+
+# Terminal 3: Run validation (after some training)
+python scripts/validate_training.py \
+    --log_dir log \
+    --model_path log/emasavedmodel_0.9999_010000.pt
+```
+
+### Check Training Progress
+```bash
+# Plot training progress
+python scripts/validate_training.py --log_dir log --plot_progress
+
+# Check if training is running
+python scripts/monitor_training.py --log_dir log --check_only
+```
+
+## Data Preparation
+
+### Creating ROI Masks
+1. Load healthy brain image
+2. Mark regions where lesions should be added
+3. Save as binary mask (0 = keep, >0 = replace with lesion)
+4. Ensure mask dimensions match image dimensions
+
+### Validation
+- Use images with known lesions as ground truth
+- Create ROI masks for healthy regions
+- Model learns to reconstruct lesions in masked regions
+
+## Advanced Usage
+
+### Custom Loss Functions
+Modify `training_losses_segmentation` in `gaussian_diffusion.py` for custom loss functions.
+
+### Custom Metrics
+Add new metrics in `EnhancedTrainLoop.log_metrics()` method.
+
+### Custom Visualization
+Modify `TrainingMonitor.setup_plots()` for custom plot layouts.
+
+## Support
+
+For issues or questions:
+1. Check the troubleshooting section
+2. Review log files in the log directory
+3. Verify data format and file paths
+4. Check GPU memory usage
+
+## License
+
+This enhanced training system is part of the FCD2 inpainting project. 

fcd2_inpainting/DDPM_Pseudo3D/scripts/.ipynb_checkpoints/inpainting_sample-checkpoint.py

@@ -403,7 +403,7 @@ def create_argparser():
         clip_denoised=True,
         batch_size=1,
         use_ddim=False,
-        model_path="log/xemasavedmodel_0.9999_050000.pt",
+        model_path="log/emasavedmodel_0.9999_050000.pt",
         range_padding=2,  # slices of context added on each side
         save_intermediate_dir="",
         save_intermediate_interval=0,

fcd2_inpainting/DDPM_Pseudo3D/scripts/.ipynb_checkpoints/inpainting_train-checkpoint.py

@@ -0,0 +1,722 @@
+"""
+Train a diffusion model on images with enhanced progress tracking, validation, and overfitting prevention.
+"""
+
+import argparse
+import sys
+import os
+import json
+from datetime import datetime
+import matplotlib.pyplot as plt
+import numpy as np
+import math
+import nibabel as nib
+import gc
+import torch
+
+sys.path.append("..")
+sys.path.append(".")
+
+import torch as th
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fcd2loader import FCD2Dataset
+from guided_diffusion.resample import create_named_schedule_sampler
+from guided_diffusion.script_util import (
+    add_dict_to_argparser,
+    args_to_dict,
+    create_model_and_diffusion,
+    model_and_diffusion_defaults,
+)
+from guided_diffusion.train_util import TrainLoop
+
+
+def clear_gpu_memory():
+    """Clear GPU memory and garbage collect."""
+    if th.cuda.is_available():
+        th.cuda.empty_cache()
+        th.cuda.synchronize()
+    gc.collect()
+
+
+def get_gpu_memory_info():
+    """Get current GPU memory usage information."""
+    if th.cuda.is_available():
+        allocated = th.cuda.memory_allocated() / 1024**3  # GB
+        reserved = th.cuda.memory_reserved() / 1024**3   # GB
+        total = th.cuda.get_device_properties(0).total_memory / 1024**3  # GB
+        free = total - reserved
+        return {
+            'allocated_gb': allocated,
+            'reserved_gb': reserved,
+            'total_gb': total,
+            'free_gb': free
+        }
+    return None
+
+
+def safe_json_dumps(obj, indent=2):
+    """Safely serialize object to JSON, handling NumPy types."""
+    def convert_numpy(obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, dict):
+            return {key: convert_numpy(value) for key, value in obj.items()}
+        elif isinstance(obj, list):
+            return [convert_numpy(item) for item in obj]
+        else:
+            return obj
+    
+    converted_obj = convert_numpy(obj)
+    return json.dumps(converted_obj, indent=indent)
+
+
+class EnhancedTrainLoop(TrainLoop):
+    """Enhanced training loop with validation, early stopping, and better metrics."""
+    
+    def __init__(self, *args, validation_data=None, validation_interval=1000, 
+                 early_stopping_patience=10, min_delta=1e-4, log_dir=None, 
+                 enable_memory_management=True, min_batch_size=1, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.validation_data = validation_data
+        self.validation_interval = validation_interval
+        self.early_stopping_patience = early_stopping_patience
+        self.min_delta = min_delta
+        self.log_dir = log_dir
+        self.enable_memory_management = enable_memory_management
+        self.min_batch_size = min_batch_size
+        self.original_batch_size = self.batch_size
+        
+        self.metrics_history = {
+            'train_loss': [],
+            'train_mse': [],
+            'train_vb': [],
+            'val_loss': [],
+            'val_mse': [],
+            'learning_rate': [],
+            'step': [],
+            'gpu_memory_gb': []
+        }
+        self.validation_metrics = []
+        self.best_val_loss = float('inf')
+        self.patience_counter = 0
+        self.early_stopped = False
+        self.memory_errors = 0
+        self.max_memory_errors = 5
+        
+        # Create validation output directory
+        if self.log_dir:
+            self.val_output_dir = os.path.join(self.log_dir, 'validation_samples')
+            os.makedirs(self.val_output_dir, exist_ok=True)
+        else:
+            self.val_output_dir = None
+        
+        # Enable gradient checkpointing for memory efficiency
+        if self.enable_memory_management:
+            self.enable_gradient_checkpointing()
+    
+    def enable_gradient_checkpointing(self):
+        """Enable gradient checkpointing on the model to save memory."""
+        try:
+            if hasattr(self.model, 'enable_gradient_checkpointing'):
+                self.model.enable_gradient_checkpointing()
+                logger.log("Gradient checkpointing enabled")
+            else:
+                logger.log("Model does not support gradient checkpointing")
+        except Exception as e:
+            logger.log(f"Failed to enable gradient checkpointing: {e}")
+    
+    def reduce_batch_size(self):
+        """Reduce batch size to prevent memory issues."""
+        if self.batch_size > self.min_batch_size:
+            old_batch_size = self.batch_size
+            self.batch_size = max(self.min_batch_size, self.batch_size // 2)
+            logger.log(f"Reduced batch size from {old_batch_size} to {self.batch_size} due to memory issues")
+            return True
+        return False
+    
+    def check_memory_and_clear(self):
+        """Check GPU memory and clear if necessary."""
+        if not self.enable_memory_management or not th.cuda.is_available():
+            return
+        
+        memory_info = get_gpu_memory_info()
+        if memory_info:
+            free_gb = memory_info['free_gb']
+            allocated_gb = memory_info['allocated_gb']
+            
+            # Log memory usage periodically
+            if hasattr(self, 'step') and self.step % 100 == 0:
+                logger.log(f"GPU Memory: {allocated_gb:.2f}GB allocated, {free_gb:.2f}GB free")
+                self.metrics_history['gpu_memory_gb'].append(allocated_gb)
+            
+            # Clear memory if free space is low
+            if free_gb < 2.0:  # Less than 2GB free
+                logger.log(f"Low GPU memory detected ({free_gb:.2f}GB free), clearing cache...")
+                clear_gpu_memory()
+                th.cuda.synchronize()
+                
+                # Check memory after clearing
+                memory_info_after = get_gpu_memory_info()
+                if memory_info_after:
+                    logger.log(f"After clearing: {memory_info_after['allocated_gb']:.2f}GB allocated, {memory_info_after['free_gb']:.2f}GB free")
+    
+    def handle_memory_error(self):
+        """Handle CUDA out of memory error."""
+        self.memory_errors += 1
+        logger.log(f"CUDA out of memory error #{self.memory_errors}")
+        
+        # Clear memory
+        clear_gpu_memory()
+        
+        # Reduce batch size if possible
+        if self.reduce_batch_size():
+            logger.log("Batch size reduced, retrying...")
+            return True
+        
+        # If we've had too many memory errors, stop training
+        if self.memory_errors >= self.max_memory_errors:
+            logger.log(f"Too many memory errors ({self.memory_errors}), stopping training")
+            return False
+        
+        logger.log("Memory cleared, retrying...")
+        return True
+    
+    def validate_model(self):
+        """Run validation on the current model."""
+        if self.validation_data is None:
+            return None
+        
+        self.model.eval()
+        val_losses = []
+        val_mses = []
+        
+        with th.no_grad():
+            for i, (batch, cond, path, slicedict) in enumerate(self.validation_data):
+                if i >= 5:  # Limit validation to 5 batches for speed
+                    break
+                
+                # Process validation batch similar to training
+                batch_size_vol = 12
+                nr_batches = math.ceil(len(slicedict) / batch_size_vol)
+                
+                for b in range(nr_batches):
+                    out_batch = []
+                    out_cond = []
+                    
+                    if len(slicedict) > b * batch_size_vol + batch_size_vol:
+                        for s in slicedict[b * batch_size_vol : (b * batch_size_vol + batch_size_vol)]:
+                            out_batch.append(batch[..., s].clone().detach())
+                            out_cond.append(cond[..., s].clone().detach())
+                    else:
+                        for s in slicedict[b * batch_size_vol :]:
+                            out_batch.append(batch[..., s].clone().detach())
+                            out_cond.append(cond[..., s].clone().detach())
+                    
+                    out_batch = th.stack(out_batch).squeeze(1).squeeze(4)
+                    out_cond = th.stack(out_cond).squeeze(1).squeeze(4)
+                    
+                    # Compute validation loss
+                    batch_combined = th.cat((out_batch, out_cond), dim=1)
+                    micro = batch_combined.to(dist_util.dev())
+                    t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
+                    
+                    losses = self.diffusion.training_losses_segmentation(
+                        self.ddp_model, self.classifier, micro, t
+                    )[0]
+                    
+                    val_losses.append(losses['loss'].mean().item())
+                    val_mses.append(losses.get('mse', th.tensor(0.0)).mean().item())
+                    
+                    # Save validation visualization for first batch
+                    if i == 0 and b == 0:
+                        self.save_validation_visualization(out_batch, out_cond, path[0], slicedict)
+        
+        self.model.train()
+        
+        avg_val_loss = np.mean(val_losses)
+        avg_val_mse = np.mean(val_mses)
+        
+        return {
+            'val_loss': avg_val_loss,
+            'val_mse': avg_val_mse
+        }
+    
+    def save_validation_visualization(self, voided_batch, mask_batch, image_path, slicedict):
+        """Save validation data as NIfTI files for easy viewing."""
+        if self.val_output_dir is None:
+            logger.log("Validation output directory not set, skipping visualization")
+            return
+        
+        try:
+            # Extract the first sample from the batch
+            voided_sample = voided_batch[0]  # (2, H, W, D) - voided image + mask
+            mask_sample = mask_batch[0]      # (1, H, W, D) - original image with lesions
+            
+            # Convert to numpy and save as NIfTI files
+            # Save voided image (channel 0)
+            voided_img = voided_sample[0].cpu().numpy()  # (H, W, D)
+            voided_nii = nib.Nifti1Image(voided_img, np.eye(4))
+            
+            # Save ROI mask (channel 1)
+            roi_mask = voided_sample[1].cpu().numpy()  # (H, W, D)
+            roi_nii = nib.Nifti1Image(roi_mask, np.eye(4))
+            
+            # Save ground truth (original image with lesions)
+            gt_img = mask_sample[0].cpu().numpy()  # (H, W, D)
+            gt_nii = nib.Nifti1Image(gt_img, np.eye(4))
+            
+            # Save with step number
+            step_str = f"{self.step:06d}" if hasattr(self, 'step') else "000000"
+            
+            # Save files
+            voided_filename = f"validation_step_{step_str}_voided.nii.gz"
+            roi_filename = f"validation_step_{step_str}_roi_mask.nii.gz"
+            gt_filename = f"validation_step_{step_str}_ground_truth.nii.gz"
+            
+            nib.save(voided_nii, os.path.join(self.val_output_dir, voided_filename))
+            nib.save(roi_nii, os.path.join(self.val_output_dir, roi_filename))
+            nib.save(gt_nii, os.path.join(self.val_output_dir, gt_filename))
+            
+            logger.log(f"Validation NIfTI files saved: {voided_filename}, {roi_filename}, {gt_filename}")
+            
+        except Exception as e:
+            logger.log(f"Failed to save validation NIfTI files: {e}")
+            import traceback
+            logger.log(f"Traceback: {traceback.format_exc()}")
+    
+    def check_early_stopping(self, val_loss):
+        """Check if training should stop early."""
+        if val_loss < self.best_val_loss - self.min_delta:
+            self.best_val_loss = val_loss
+            self.patience_counter = 0
+            return False
+        else:
+            self.patience_counter += 1
+            if self.patience_counter >= self.early_stopping_patience:
+                return True
+            return False
+    
+    def log_metrics(self, losses, step, val_metrics=None):
+        """Log metrics to history and console."""
+        # Handle case where losses might not be a dictionary
+        if isinstance(losses, dict):
+            train_loss = losses['loss'].mean().item() if 'loss' in losses else 0.0
+            train_mse = losses.get('mse', th.tensor(0.0)).mean().item()
+            train_vb = losses.get('vb', th.tensor(0.0)).mean().item()
+        else:
+            # If losses is not a dict, assume it's a tensor or scalar
+            train_loss = float(losses) if hasattr(losses, 'item') else losses
+            train_mse = 0.0
+            train_vb = 0.0
+        
+        # Store training metrics
+        self.metrics_history['step'].append(step)
+        self.metrics_history['train_loss'].append(train_loss)
+        self.metrics_history['train_mse'].append(train_mse)
+        self.metrics_history['train_vb'].append(train_vb)
+        self.metrics_history['learning_rate'].append(self.opt.param_groups[0]['lr'])
+        
+        # Store validation metrics
+        if val_metrics:
+            self.metrics_history['val_loss'].append(val_metrics['val_loss'])
+            self.metrics_history['val_mse'].append(val_metrics['val_mse'])
+        else:
+            self.metrics_history['val_loss'].append(None)
+            self.metrics_history['val_mse'].append(None)
+        
+        # Log to progress.csv using the logger system
+        logger.logkv('step', step)
+        logger.logkv('train_loss', train_loss)
+        logger.logkv('train_mse', train_mse)
+        logger.logkv('train_vb', train_vb)
+        logger.logkv('learning_rate', self.opt.param_groups[0]['lr'])
+        
+        if val_metrics:
+            logger.logkv('val_loss', val_metrics['val_loss'])
+            logger.logkv('val_mse', val_metrics['val_mse'])
+        
+        # Console logging
+        log_str = f"Step {step}: Train Loss={train_loss:.6f}"
+        if val_metrics:
+            log_str += f", Val Loss={val_metrics['val_loss']:.6f}"
+        log_str += f", LR={self.opt.param_groups[0]['lr']:.2e}"
+        logger.log(log_str)
+    
+    def save_metrics(self, log_dir):
+        """Save metrics to JSON file."""
+        metrics_file = os.path.join(log_dir, 'training_metrics.json')
+        with open(metrics_file, 'w') as f:
+            json.dump(self.metrics_history, f, indent=2)
+        
+        # Save plots
+        self.save_plots(log_dir)
+    
+    def save_plots(self, log_dir):
+        """Save metric plots as PNG files."""
+        if not self.metrics_history['step']:
+            return
+        
+        # Determine number of subplots based on available data
+        plot_count = 2  # train/val loss and MSE
+        if any(x is not None and x > 0 for x in self.metrics_history['learning_rate']):
+            plot_count += 1  # learning rate
+        if any(x is not None and x > 0 for x in self.metrics_history['train_vb']):
+            plot_count += 1  # variational bound
+        if self.enable_memory_management and self.metrics_history['gpu_memory_gb']:
+            plot_count += 1  # GPU memory
+        
+        # Calculate subplot layout
+        cols = min(3, plot_count)
+        rows = (plot_count + cols - 1) // cols
+        
+        fig, axes = plt.subplots(rows, cols, figsize=(5*cols, 4*rows))
+        if plot_count == 1:
+            axes = [axes]
+        elif rows == 1:
+            axes = axes.reshape(1, -1)
+        elif cols == 1:
+            axes = axes.reshape(-1, 1)
+        
+        plot_idx = 0
+        
+        # Training loss plot
+        train_loss = [x for x in self.metrics_history['train_loss'] if x is not None and x > 0]
+        train_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['train_loss']) if x is not None and x > 0]
+        
+        if train_loss:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            ax.plot(train_steps, train_loss, 'b-', label='Train')
+            if any(x is not None and x > 0 for x in self.metrics_history['val_loss']):
+                val_loss = [x for x in self.metrics_history['val_loss'] if x is not None and x > 0]
+                val_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['val_loss']) if x is not None and x > 0]
+                ax.plot(val_steps, val_loss, 'r-', label='Validation')
+            ax.set_title('Training vs Validation Loss')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('Loss')
+            ax.grid(True)
+            ax.legend()
+            if min(train_loss) > 0:
+                ax.set_yscale('log')
+            plot_idx += 1
+        
+        # MSE plot
+        train_mse = [x for x in self.metrics_history['train_mse'] if x is not None and x > 0]
+        if train_mse:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            train_mse_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['train_mse']) if x is not None and x > 0]
+            ax.plot(train_mse_steps, train_mse, 'b-', label='Train')
+            if any(x is not None and x > 0 for x in self.metrics_history['val_mse']):
+                val_mse = [x for x in self.metrics_history['val_mse'] if x is not None and x > 0]
+                val_mse_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['val_mse']) if x is not None and x > 0]
+                ax.plot(val_mse_steps, val_mse, 'r-', label='Validation')
+            ax.set_title('MSE Loss')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('MSE')
+            ax.grid(True)
+            ax.legend()
+            if min(train_mse) > 0:
+                ax.set_yscale('log')
+            plot_idx += 1
+        
+        # Learning rate plot
+        lr_values = [x for x in self.metrics_history['learning_rate'] if x is not None and x > 0]
+        if lr_values:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            lr_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['learning_rate']) if x is not None and x > 0]
+            ax.plot(lr_steps, lr_values)
+            ax.set_title('Learning Rate')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('LR')
+            if min(lr_values) > 0:
+                ax.set_yscale('log')
+            ax.grid(True)
+            plot_idx += 1
+        
+        # VB plot (if available)
+        train_vb = [x for x in self.metrics_history['train_vb'] if x is not None and x > 0]
+        if train_vb:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            vb_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['train_vb']) if x is not None and x > 0]
+            ax.plot(vb_steps, train_vb)
+            ax.set_title('Variational Bound')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('VB')
+            ax.grid(True)
+            if min(train_vb) > 0:
+                ax.set_yscale('log')
+            plot_idx += 1
+        
+        # GPU Memory plot (if available)
+        if self.enable_memory_management and self.metrics_history['gpu_memory_gb']:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            memory_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['gpu_memory_gb']) if x is not None]
+            memory_values = [x for x in self.metrics_history['gpu_memory_gb'] if x is not None]
+            ax.plot(memory_steps, memory_values, 'g-')
+            ax.set_title('GPU Memory Usage')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('Memory (GB)')
+            ax.grid(True)
+            plot_idx += 1
+        
+        # Hide unused subplots
+        for i in range(plot_idx, rows * cols):
+            if plot_count > 1:
+                axes[i // cols, i % cols].set_visible(False)
+        
+        plt.tight_layout()
+        plt.savefig(os.path.join(log_dir, 'training_metrics.png'), dpi=300, bbox_inches='tight')
+        plt.close()
+    
+    def run_loop(self):
+        """Enhanced run loop with validation and early stopping."""
+        logger.log("Starting enhanced training loop with validation...")
+        
+        # Run validation at the beginning
+        if self.validation_data is not None:
+            val_metrics = self.validate_model()
+            if val_metrics:
+                logger.log(f"Step 0: Val Loss={val_metrics['val_loss']:.6f}, LR={self.opt.param_groups[0]['lr']:.2e}")
+                # Log initial validation metrics to progress.csv
+                logger.logkv('step', 0)
+                logger.logkv('val_loss', val_metrics['val_loss'])
+                logger.logkv('val_mse', val_metrics['val_mse'])
+                logger.dumpkvs()
+        
+        # Use the same termination condition as parent class
+        i = 0
+        while (
+            not self.lr_anneal_steps
+            or self.step + self.resume_step < self.lr_anneal_steps
+        ):
+            try:
+                # Check memory before processing batch
+                self.check_memory_and_clear()
+                
+                # Original training step
+                batch, cond, path, slicedict = next(self.data)
+                
+                # Process batch (same as original)
+                batch_size_vol = 12
+                nr_batches = math.ceil(len(slicedict) / batch_size_vol)
+                
+                for b in range(nr_batches):
+                    try:
+                        out_batch = []
+                        out_cond = []
+                        
+                        if len(slicedict) > b * batch_size_vol + batch_size_vol:
+                            for s in slicedict[b * batch_size_vol : (b * batch_size_vol + batch_size_vol)]:
+                                out_batch.append(batch[..., s].clone().detach())
+                                out_cond.append(cond[..., s].clone().detach())
+                        else:
+                            for s in slicedict[b * batch_size_vol :]:
+                                out_batch.append(batch[..., s].clone().detach())
+                                out_cond.append(cond[..., s].clone().detach())
+                        
+                        out_batch = th.stack(out_batch).squeeze(1).squeeze(4)
+                        out_cond = th.stack(out_cond).squeeze(1).squeeze(4)
+                        
+                        # Call parent class run_step method
+                        losses = self.run_step(out_batch, out_cond)
+                        i += 1
+                        
+                        # Log training metrics
+                        if i % 100 == 0:  # Log every 100 steps
+                            self.log_metrics(losses, i)
+                            logger.dumpkvs()  # Write training metrics to progress.csv
+                        
+                        # Run validation periodically
+                        if self.validation_data is not None and i % self.validation_interval == 0:
+                            val_metrics = self.validate_model()
+                            self.log_metrics(losses, i, val_metrics)
+                            logger.dumpkvs()  # Write validation metrics to progress.csv
+                            
+                            # Check early stopping
+                            if self.check_early_stopping(val_metrics['val_loss']):
+                                logger.log(f"Early stopping triggered after {i} steps")
+                                self.early_stopped = True
+                                break
+                        
+                        # Clear memory after each batch
+                        if self.enable_memory_management and i % 50 == 0:
+                            clear_gpu_memory()
+                        
+                    except th.cuda.OutOfMemoryError as e:
+                        logger.log(f"CUDA out of memory error in batch {b}: {e}")
+                        if not self.handle_memory_error():
+                            logger.log("Too many memory errors, stopping training")
+                            return
+                        # Skip this batch and continue
+                        continue
+                
+                if self.early_stopped:
+                    break
+                    
+            except StopIteration:
+                # StopIteration is thrown if dataset ends
+                # reinitialize data loader
+                self.data = iter(self.dataloader)
+                continue
+            except th.cuda.OutOfMemoryError as e:
+                logger.log(f"CUDA out of memory error in main loop: {e}")
+                if not self.handle_memory_error():
+                    logger.log("Too many memory errors, stopping training")
+                    break
+                # Clear memory and continue
+                clear_gpu_memory()
+                continue
+            except Exception as e:
+                logger.log(f"Unexpected error: {e}")
+                import traceback
+                logger.log(f"Traceback: {traceback.format_exc()}")
+                # Clear memory and continue
+                clear_gpu_memory()
+                continue
+            
+            # Original logging and saving logic
+            if self.step % self.log_interval == 0:
+                logger.dumpkvs()
+            if self.step % self.save_interval == 0:
+                self.save()
+            
+            self.step += 1
+        
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+        
+        # Save final metrics
+        self.save_metrics(self.log_dir)
+        logger.log("Enhanced training loop completed.")
+
+
+def main():
+    args = create_argparser().parse_args()
+
+    dist_util.setup_dist()
+    logger.configure(dir=args.log_dir)
+    today = datetime.now()
+    logger.log("TRAINING START " + str(today))
+    logger.log("args: " + str(args))
+    
+    # Create log directory for metrics
+    os.makedirs(args.log_dir, exist_ok=True)
+    
+    logger.log("creating model and diffusion...")
+    model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args, model_and_diffusion_defaults().keys())
+    )
+
+    model.to(dist_util.dev())
+    schedule_sampler = create_named_schedule_sampler(
+        args.schedule_sampler, diffusion, maxt=1000
+    )
+
+    logger.log("creating data loaders...")
+    
+    # Create train/validation datasets
+    if args.validation_split > 0:
+        train_ds, val_ds = FCD2Dataset.get_train_val_datasets(
+            args.data_dir, 
+            mode=args.mode, 
+            validation_split=args.validation_split, 
+            seed=args.seed
+        )
+        
+        # Log dataset statistics
+        train_stats = train_ds.get_dataset_stats()
+        val_stats = val_ds.get_dataset_stats()
+        logger.log(f"Training dataset: {safe_json_dumps(train_stats, indent=2)}")
+        logger.log(f"Validation dataset: {safe_json_dumps(val_stats, indent=2)}")
+        
+        train_datal = th.utils.data.DataLoader(train_ds, batch_size=args.batch_size, shuffle=True)
+        val_datal = th.utils.data.DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)
+        
+        train_data = iter(train_datal)
+        validation_data = iter(val_datal)
+    else:
+        # Use single dataset without validation
+        ds = FCD2Dataset(args.data_dir, test_flag=False, mode=args.mode)
+        dataset_stats = ds.get_dataset_stats()
+        logger.log(f"Dataset statistics: {safe_json_dumps(dataset_stats, indent=2)}")
+        
+        train_datal = th.utils.data.DataLoader(ds, batch_size=args.batch_size, shuffle=True)
+        train_data = iter(train_datal)
+        validation_data = None
+
+    logger.log("training...")
+    train_loop = EnhancedTrainLoop(
+        model=model,
+        diffusion=diffusion,
+        classifier=None,
+        data=train_data,
+        dataloader=train_datal,
+        batch_size=args.batch_size,
+        microbatch=args.microbatch,
+        lr=args.lr,
+        ema_rate=args.ema_rate,
+        log_interval=args.log_interval,
+        save_interval=args.save_interval,
+        resume_checkpoint=args.resume_checkpoint,
+        use_fp16=args.use_fp16,
+        fp16_scale_growth=args.fp16_scale_growth,
+        schedule_sampler=schedule_sampler,
+        weight_decay=args.weight_decay,
+        lr_anneal_steps=args.lr_anneal_steps,
+        validation_data=validation_data,
+        validation_interval=args.validation_interval,
+        early_stopping_patience=args.early_stopping_patience,
+        min_delta=args.min_delta,
+        log_dir=args.log_dir,
+        enable_memory_management=args.enable_memory_management,
+        min_batch_size=args.min_batch_size,
+    )
+    
+    try:
+        train_loop.run_loop()
+    except KeyboardInterrupt:
+        logger.log("Training interrupted by user")
+    finally:
+        # Save final metrics
+        train_loop.save_metrics(args.log_dir)
+        logger.log("Training metrics saved")
+
+
+def create_argparser():
+    defaults = dict(
+        data_dir="../FCD2/Pathological",
+        log_dir="log",
+        mode="add_lesions",  # "add_lesions" or "remove_lesions"
+        validation_split=0.2,  # Fraction of data for validation
+        seed=42,  # Random seed for reproducible splits
+        dropout=0.1,  # Dropout rate for regularization
+        schedule_sampler="uniform",
+        lr=1e-4,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=1000,
+        save_interval=5000,
+        validation_interval=2000,  # Run validation every N steps
+        early_stopping_patience=10,  # Stop if no improvement for N validation runs
+        min_delta=1e-4,  # Minimum improvement for early stopping
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+        enable_memory_management=True,
+        min_batch_size=1,
+    )
+    defaults.update(model_and_diffusion_defaults())
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+    return parser
+
+
+if __name__ == "__main__":
+    main()

fcd2_inpainting/DDPM_Pseudo3D/scripts/.ipynb_checkpoints/memory-checkpoint.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Environment variables for better PyTorch memory management
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export CUDA_LAUNCH_BLOCKING=1
+export TORCH_CUDNN_V8_API_ENABLED=1
+
+# Optional: Set memory fraction (uncomment if needed)
+# export CUDA_VISIBLE_DEVICES=0
+
+echo "Memory management environment variables set:"
+echo "PYTORCH_CUDA_ALLOC_CONF=$PYTORCH_CUDA_ALLOC_CONF"
+echo "CUDA_LAUNCH_BLOCKING=$CUDA_LAUNCH_BLOCKING"
+echo "TORCH_CUDNN_V8_API_ENABLED=$TORCH_CUDNN_V8_API_ENABLED"
+
+# Show current GPU memory
+if command -v nvidia-smi &> /dev/null; then
+    echo ""
+    echo "Current GPU memory usage:"
+    nvidia-smi --query-gpu=memory.used,memory.total --format=csv,noheader,nounits
+fi

fcd2_inpainting/DDPM_Pseudo3D/scripts/.ipynb_checkpoints/monitor_training-checkpoint.py

@@ -0,0 +1,230 @@
+"""
+Real-time training monitoring script.
+Run this in a separate terminal during training to monitor progress.
+"""
+
+import argparse
+import os
+import json
+import time
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+from datetime import datetime
+import numpy as np
+
+
+class TrainingMonitor:
+    """Real-time training monitor with live plotting."""
+    
+    def __init__(self, log_dir, update_interval=30):
+        self.log_dir = log_dir
+        self.update_interval = update_interval
+        self.metrics_file = os.path.join(log_dir, 'training_metrics.json')
+        
+        # Setup live plotting
+        plt.ion()
+        self.fig, self.axes = plt.subplots(2, 2, figsize=(15, 10))
+        self.fig.suptitle('Training Progress Monitor', fontsize=16)
+        
+        # Initialize plots
+        self.lines = {}
+        self.setup_plots()
+        
+    def setup_plots(self):
+        """Setup the monitoring plots."""
+        # Loss plot
+        self.axes[0, 0].set_title('Training Loss')
+        self.axes[0, 0].set_xlabel('Step')
+        self.axes[0, 0].set_ylabel('Loss')
+        self.axes[0, 0].grid(True)
+        self.axes[0, 0].set_yscale('log')
+        self.lines['loss'], = self.axes[0, 0].plot([], [], 'b-', label='Loss')
+        
+        # MSE plot
+        self.axes[0, 1].set_title('MSE Loss')
+        self.axes[0, 1].set_xlabel('Step')
+        self.axes[0, 1].set_ylabel('MSE')
+        self.axes[0, 1].grid(True)
+        self.axes[0, 1].set_yscale('log')
+        self.lines['mse'], = self.axes[0, 1].plot([], [], 'r-', label='MSE')
+        
+        # Learning rate plot
+        self.axes[1, 0].set_title('Learning Rate')
+        self.axes[1, 0].set_xlabel('Step')
+        self.axes[1, 0].set_ylabel('LR')
+        self.axes[1, 0].grid(True)
+        self.axes[1, 0].set_yscale('log')
+        self.lines['lr'], = self.axes[1, 0].plot([], [], 'g-', label='LR')
+        
+        # VB plot
+        self.axes[1, 1].set_title('Variational Bound')
+        self.axes[1, 1].set_xlabel('Step')
+        self.axes[1, 1].set_ylabel('VB')
+        self.axes[1, 1].grid(True)
+        self.axes[1, 1].set_yscale('log')
+        self.lines['vb'], = self.axes[1, 1].plot([], [], 'm-', label='VB')
+        
+        plt.tight_layout()
+        
+    def load_metrics(self):
+        """Load metrics from JSON file."""
+        if not os.path.exists(self.metrics_file):
+            return None
+        
+        try:
+            with open(self.metrics_file, 'r') as f:
+                return json.load(f)
+        except (json.JSONDecodeError, FileNotFoundError):
+            return None
+    
+    def update_plots(self, metrics):
+        """Update the live plots with new data."""
+        if not metrics or not metrics['step']:
+            return
+        
+        # Update loss plot
+        self.lines['loss'].set_data(metrics['step'], metrics['loss'])
+        self.axes[0, 0].relim()
+        self.axes[0, 0].autoscale_view()
+        
+        # Update MSE plot
+        if metrics['mse']:
+            self.lines['mse'].set_data(metrics['step'], metrics['mse'])
+            self.axes[0, 1].relim()
+            self.axes[0, 1].autoscale_view()
+        
+        # Update learning rate plot
+        self.lines['lr'].set_data(metrics['step'], metrics['learning_rate'])
+        self.axes[1, 0].relim()
+        self.axes[1, 0].autoscale_view()
+        
+        # Update VB plot
+        if metrics['vb']:
+            self.lines['vb'].set_data(metrics['step'], metrics['vb'])
+            self.axes[1, 1].relim()
+            self.axes[1, 1].autoscale_view()
+        
+        # Update display
+        self.fig.canvas.draw()
+        self.fig.canvas.flush_events()
+        
+    def print_summary(self, metrics):
+        """Print training summary."""
+        if not metrics or not metrics['step']:
+            return
+        
+        current_step = metrics['step'][-1]
+        current_loss = metrics['loss'][-1]
+        current_lr = metrics['learning_rate'][-1]
+        
+        # Calculate improvement
+        if len(metrics['loss']) > 1:
+            loss_improvement = metrics['loss'][-2] - current_loss
+            improvement_str = f"({loss_improvement:+.6f})" if loss_improvement != 0 else "(no change)"
+        else:
+            improvement_str = "(first step)"
+        
+        # Calculate training time estimate
+        if len(metrics['step']) > 1:
+            steps_per_second = 1.0  # This would need to be calculated from timestamps
+            eta_str = "N/A"
+        else:
+            eta_str = "N/A"
+        
+        print(f"\n{'='*60}")
+        print(f"Training Progress - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        print(f"{'='*60}")
+        print(f"Current Step: {current_step}")
+        print(f"Current Loss: {current_loss:.6f} {improvement_str}")
+        print(f"Learning Rate: {current_lr:.2e}")
+        print(f"Best Loss: {min(metrics['loss']):.6f}")
+        
+        if metrics['mse']:
+            current_mse = metrics['mse'][-1]
+            print(f"Current MSE: {current_mse:.6f}")
+            print(f"Best MSE: {min(metrics['mse']):.6f}")
+        
+        print(f"Total Steps: {len(metrics['step'])}")
+        print(f"ETA: {eta_str}")
+        print(f"{'='*60}")
+    
+    def monitor(self):
+        """Main monitoring loop."""
+        print(f"Starting training monitor for {self.log_dir}")
+        print(f"Update interval: {self.update_interval} seconds")
+        print("Press Ctrl+C to stop monitoring")
+        
+        last_metrics = None
+        
+        try:
+            while True:
+                metrics = self.load_metrics()
+                
+                if metrics and metrics != last_metrics:
+                    self.update_plots(metrics)
+                    self.print_summary(metrics)
+                    last_metrics = metrics
+                
+                time.sleep(self.update_interval)
+                
+        except KeyboardInterrupt:
+            print("\nMonitoring stopped by user")
+        finally:
+            plt.ioff()
+            plt.close()
+
+
+def check_training_status(log_dir):
+    """Check if training is currently running."""
+    log_file = os.path.join(log_dir, 'log.txt')
+    
+    if not os.path.exists(log_file):
+        return False, "No log file found"
+    
+    # Check if log file was modified recently (within last 5 minutes)
+    mod_time = os.path.getmtime(log_file)
+    if time.time() - mod_time > 300:  # 5 minutes
+        return False, "Log file not updated recently"
+    
+    # Check for training indicators in log
+    try:
+        with open(log_file, 'r') as f:
+            lines = f.readlines()
+            if lines:
+                last_line = lines[-1].strip()
+                if "training" in last_line.lower() or "step" in last_line.lower():
+                    return True, f"Training active - Last log: {last_line}"
+    except:
+        pass
+    
+    return False, "No clear training indicators"
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Monitor training progress in real-time")
+    parser.add_argument("--log_dir", default="log", help="Training log directory")
+    parser.add_argument("--update_interval", type=int, default=30, 
+                       help="Update interval in seconds")
+    parser.add_argument("--check_only", action="store_true", 
+                       help="Only check if training is running")
+    
+    args = parser.parse_args()
+    
+    if args.check_only:
+        is_running, status = check_training_status(args.log_dir)
+        print(f"Training status: {status}")
+        return
+    
+    # Check if training is running
+    is_running, status = check_training_status(args.log_dir)
+    if not is_running:
+        print(f"Warning: {status}")
+        print("Starting monitor anyway...")
+    
+    # Start monitoring
+    monitor = TrainingMonitor(args.log_dir, args.update_interval)
+    monitor.monitor()
+
+
+if __name__ == "__main__":
+    main() 

fcd2_inpainting/DDPM_Pseudo3D/scripts/.ipynb_checkpoints/validate_training-checkpoint.py

@@ -0,0 +1,279 @@
+"""
+Validation script to monitor training progress and generate sample predictions.
+"""
+
+import argparse
+import os
+import sys
+import json
+import numpy as np
+import nibabel as nib
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+sys.path.append("..")
+sys.path.append(".")
+
+import torch as th
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fcd2loader import FCD2Dataset
+from guided_diffusion.script_util import (
+    add_dict_to_argparser,
+    args_to_dict,
+    create_model_and_diffusion,
+    model_and_diffusion_defaults,
+)
+
+
+def validate_model(model, diffusion, dataset, device, num_samples=3, save_dir="validation_samples"):
+    """Validate model by generating sample predictions."""
+    os.makedirs(save_dir, exist_ok=True)
+    
+    model.eval()
+    validation_metrics = []
+    
+    # Sample a few validation cases
+    for i in range(min(num_samples, len(dataset))):
+        inp, label, path, slice_range = dataset[i]
+        
+        if not slice_range:  # Skip if no lesions
+            continue
+            
+        # Get a representative slice
+        slice_idx = slice_range[len(slice_range) // 2]
+        
+        # Prepare input for the slice
+        voided_slice = inp[0, :, :, slice_idx].unsqueeze(0).unsqueeze(0)  # (1, 1, H, W)
+        mask_slice = inp[1, :, :, slice_idx].unsqueeze(0).unsqueeze(0)    # (1, 1, H, W)
+        gt_slice = label[0, :, :, slice_idx].unsqueeze(0).unsqueeze(0)    # (1, 1, H, W)
+        
+        # Stack input channels
+        input_tensor = th.cat([voided_slice, mask_slice], dim=1).to(device)  # (1, 2, H, W)
+        
+        with th.no_grad():
+            # Generate prediction using DDIM for faster sampling
+            sample, _, _ = diffusion.ddim_sample_loop_known(
+                model,
+                (1, 2, input_tensor.shape[2], input_tensor.shape[3]),
+                input_tensor,
+                clip_denoised=True,
+                progress=False,
+            )
+            
+            # Extract predicted intensity
+            pred_slice = sample[:, 0:1, :, :]  # (1, 1, H, W)
+            
+            # Compute metrics
+            mse = th.mean((pred_slice - gt_slice) ** 2).item()
+            mae = th.mean(th.abs(pred_slice - gt_slice)).item()
+            
+            # Compute metrics only in masked region
+            mask_bool = mask_slice > 0
+            if mask_bool.sum() > 0:
+                masked_mse = th.mean((pred_slice[mask_bool] - gt_slice[mask_bool]) ** 2).item()
+                masked_mae = th.mean(th.abs(pred_slice[mask_bool] - gt_slice[mask_bool])).item()
+            else:
+                masked_mse = masked_mae = 0.0
+            
+            validation_metrics.append({
+                'sample_id': i,
+                'mse': mse,
+                'mae': mae,
+                'masked_mse': masked_mse,
+                'masked_mae': masked_mae,
+                'mask_percentage': (mask_bool.sum() / mask_bool.numel()).item() * 100
+            })
+            
+            # Save visualization
+            save_validation_sample(
+                voided_slice.cpu().numpy(),
+                mask_slice.cpu().numpy(),
+                pred_slice.cpu().numpy(),
+                gt_slice.cpu().numpy(),
+                save_dir,
+                i,
+                mse,
+                masked_mse
+            )
+    
+    return validation_metrics
+
+
+def save_validation_sample(voided, mask, pred, gt, save_dir, sample_id, mse, masked_mse):
+    """Save validation sample as visualization."""
+    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+    
+    # Input (voided image)
+    axes[0, 0].imshow(voided[0, 0], cmap='gray')
+    axes[0, 0].set_title('Input (Voided)')
+    axes[0, 0].axis('off')
+    
+    # Mask
+    axes[0, 1].imshow(mask[0, 0], cmap='Reds', alpha=0.7)
+    axes[0, 1].set_title('Mask (ROI)')
+    axes[0, 1].axis('off')
+    
+    # Prediction
+    axes[1, 0].imshow(pred[0, 0], cmap='gray')
+    axes[1, 0].set_title(f'Prediction\nMSE: {mse:.4f}')
+    axes[1, 0].axis('off')
+    
+    # Ground Truth
+    axes[1, 1].imshow(gt[0, 0], cmap='gray')
+    axes[1, 1].set_title(f'Ground Truth\nMasked MSE: {masked_mse:.4f}')
+    axes[1, 1].axis('off')
+    
+    plt.tight_layout()
+    plt.savefig(os.path.join(save_dir, f'validation_sample_{sample_id}.png'), 
+                dpi=150, bbox_inches='tight')
+    plt.close()
+
+
+def plot_training_progress(log_dir):
+    """Plot training progress from saved metrics."""
+    metrics_file = os.path.join(log_dir, 'training_metrics.json')
+    
+    if not os.path.exists(metrics_file):
+        print(f"No metrics file found at {metrics_file}")
+        return
+    
+    with open(metrics_file, 'r') as f:
+        metrics = json.load(f)
+    
+    if not metrics['step']:
+        print("No training metrics available")
+        return
+    
+    fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+    
+    # Loss plot
+    axes[0, 0].plot(metrics['step'], metrics['loss'])
+    axes[0, 0].set_title('Training Loss')
+    axes[0, 0].set_xlabel('Step')
+    axes[0, 0].set_ylabel('Loss')
+    axes[0, 0].grid(True)
+    axes[0, 0].set_yscale('log')
+    
+    # MSE plot
+    if metrics['mse']:
+        axes[0, 1].plot(metrics['step'], metrics['mse'])
+        axes[0, 1].set_title('MSE Loss')
+        axes[0, 1].set_xlabel('Step')
+        axes[0, 1].set_ylabel('MSE')
+        axes[0, 1].grid(True)
+        axes[0, 1].set_yscale('log')
+    
+    # Learning rate plot
+    axes[1, 0].plot(metrics['step'], metrics['learning_rate'])
+    axes[1, 0].set_title('Learning Rate')
+    axes[1, 0].set_xlabel('Step')
+    axes[1, 0].set_ylabel('LR')
+    axes[1, 0].set_yscale('log')
+    axes[1, 0].grid(True)
+    
+    # VB plot (if available)
+    if metrics['vb']:
+        axes[1, 1].plot(metrics['step'], metrics['vb'])
+        axes[1, 1].set_title('Variational Bound')
+        axes[1, 1].set_xlabel('Step')
+        axes[1, 1].set_ylabel('VB')
+        axes[1, 1].grid(True)
+        axes[1, 1].set_yscale('log')
+    
+    plt.tight_layout()
+    plt.savefig(os.path.join(log_dir, 'training_progress.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    # Print summary statistics
+    print(f"Training Progress Summary:")
+    print(f"Total steps: {len(metrics['step'])}")
+    print(f"Final loss: {metrics['loss'][-1]:.6f}")
+    print(f"Best loss: {min(metrics['loss']):.6f}")
+    if metrics['mse']:
+        print(f"Final MSE: {metrics['mse'][-1]:.6f}")
+        print(f"Best MSE: {min(metrics['mse']):.6f}")
+
+
+def main():
+    args = create_argparser().parse_args()
+    
+    dist_util.setup_dist()
+    logger.configure(dir=args.log_dir)
+    
+    logger.log("VALIDATION START " + str(datetime.now()))
+    logger.log("args: " + str(args))
+    
+    # Plot training progress if available
+    if args.plot_progress:
+        plot_training_progress(args.log_dir)
+    
+    if not args.validate:
+        return
+    
+    logger.log("creating model and diffusion...")
+    model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args, model_and_diffusion_defaults().keys())
+    )
+    
+    # Load model checkpoint
+    if args.model_path and os.path.exists(args.model_path):
+        model.load_state_dict(dist_util.load_state_dict(args.model_path, map_location="cpu"))
+        logger.log(f"Loaded model from {args.model_path}")
+    else:
+        logger.log("No model checkpoint found, using random weights")
+    
+    model.to(dist_util.dev())
+    model.eval()
+    
+    logger.log("creating validation dataset...")
+    dataset = FCD2Dataset(args.data_dir, test_flag=False, mode=args.mode)
+    
+    logger.log("running validation...")
+    validation_metrics = validate_model(
+        model, 
+        diffusion, 
+        dataset, 
+        dist_util.dev(),
+        num_samples=args.num_samples,
+        save_dir=os.path.join(args.log_dir, 'validation_samples')
+    )
+    
+    # Save validation results
+    validation_file = os.path.join(args.log_dir, 'validation_results.json')
+    with open(validation_file, 'w') as f:
+        json.dump(validation_metrics, f, indent=2)
+    
+    # Print summary
+    if validation_metrics:
+        avg_mse = np.mean([m['mse'] for m in validation_metrics])
+        avg_masked_mse = np.mean([m['masked_mse'] for m in validation_metrics])
+        avg_mae = np.mean([m['mae'] for m in validation_metrics])
+        avg_masked_mae = np.mean([m['masked_mae'] for m in validation_metrics])
+        
+        logger.log(f"Validation Summary:")
+        logger.log(f"Average MSE: {avg_mse:.6f}")
+        logger.log(f"Average Masked MSE: {avg_masked_mse:.6f}")
+        logger.log(f"Average MAE: {avg_mae:.6f}")
+        logger.log(f"Average Masked MAE: {avg_masked_mae:.6f}")
+    
+    logger.log("Validation complete.")
+
+
+def create_argparser():
+    defaults = dict(
+        data_dir="../FCD2/Pathological",
+        log_dir="log",
+        model_path="",  # Path to model checkpoint
+        mode="add_lesions",
+        num_samples=5,
+        validate=True,
+        plot_progress=True,
+    )
+    defaults.update(model_and_diffusion_defaults())
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+    return parser
+
+
+if __name__ == "__main__":
+    main() 

fcd2_inpainting/DDPM_Pseudo3D/scripts/06_28_2025_sanity_test_log/inputs/sub-00099_acq-T2sel_FLAIR_mask.nii.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 188553

fcd2_inpainting/DDPM_Pseudo3D/scripts/06_28_2025_sanity_test_log/inputs/sub-00099_acq-T2sel_FLAIR_voided_input.nii.gz

@@ -1,3 +0,0 @@
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-oid sha256:2ecdcad579ad5ce40bd709f5385c18a9a93c6b30f6c55fcea08760c943ec9d5e
-size 8792996

fcd2_inpainting/DDPM_Pseudo3D/scripts/06_28_2025_sanity_test_log/log.txt

@@ -1,7 +0,0 @@
-Logging to 06_28_2025_sanity_test_log
-SAMPLING START 2025-06-29 03:49:22.605757
-args: Namespace(data_dir='06_28_2025_sanity_test', log_dir='06_28_2025_sanity_test_log', adapted_samples='', subbatch=16, clip_denoised=True, batch_size=1, use_ddim=False, model_path='log/xemasavedmodel_0.9999_050000.pt', range_padding=2, save_intermediate_dir='', save_intermediate_interval=0, image_size=64, num_channels=128, num_res_blocks=2, num_heads=4, num_heads_upsample=-1, num_head_channels=-1, attention_resolutions='16,8', channel_mult='', dropout=0.0, class_cond=False, use_checkpoint=False, use_scale_shift_norm=True, resblock_updown=False, use_fp16=False, use_new_attention_order=False, learn_sigma=False, diffusion_steps=1000, noise_schedule='linear', timestep_respacing='', use_kl=False, predict_xstart=False, rescale_timesteps=False, rescale_learned_sigmas=False)
-creating model and diffusion …
-Generating slices 142:185 (orig 144:183, pad=2)
-Generating inference for sub-00099_acq-T2sel_FLAIR …
-Inpainting coverage before fallback: 13323/13323 voxels predicted, 0 missing; missing slices: []

fcd2_inpainting/DDPM_Pseudo3D/scripts/06_28_2025_sanity_test_log/sub-00099_acq-T2sel_FLAIR_inference.nii.gz

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fcd2_inpainting/DDPM_Pseudo3D/scripts/TRAINING.md

@@ -0,0 +1,277 @@
+# Enhanced FCD2 Lesion Addition Training System
+
+This enhanced training system allows you to train a diffusion model to **add lesions to healthy brain tissue** by learning to reconstruct lesions in masked regions.
+
+## Overview
+
+### What This System Does
+- **Input**: Healthy brain images + ROI masks (healthy regions where lesions should be added)
+- **Training**: Model learns to reconstruct lesions in the masked regions
+- **Output**: Model that can add realistic lesions to healthy brain tissue
+
+### Key Features
+- ✅ **Corrected Logic**: Now properly supports lesion addition (not removal)
+- ✅ **Real-time Monitoring**: Live training progress visualization
+- ✅ **Comprehensive Metrics**: Loss, MSE, learning rate tracking
+- ✅ **Validation**: Sample predictions during training
+- ✅ **Progress Tracking**: JSON metrics and PNG plots
+- ✅ **Dataset Statistics**: Automatic dataset analysis
+
+## Data Format
+
+### Directory Structure
+```
+<data_dir>/
+├── images/
+│   ├── sub-001.nii.gz  # Images WITH lesions (ground truth)
+│   ├── sub-002.nii.gz
+│   └── ...
+└── labels/
+    ├── sub-001_roi.nii.gz  # Masks of healthy regions to void
+    ├── sub-002_roi.nii.gz
+    └── ...
+```
+
+### File Requirements
+- **Images**: 3D NIfTI files (.nii.gz) containing brain MRIs with lesions
+- **Labels**: Binary masks where `>0` = healthy region to be replaced with lesion
+- **Naming**: Labels must match images with `_roi` suffix
+- **Dimensions**: Labels must match corresponding image dimensions
+
+## Installation
+
+### Install Monitoring Dependencies
+```bash
+pip install -r requirements_monitoring.txt
+```
+
+### Optional: Install Visdom for Real-time Visualization
+```bash
+pip install visdom
+python -m visdom.server -port 8097
+```
+
+## Usage
+
+### 1. Training
+
+#### Basic Training
+```bash
+python scripts/inpainting_train.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --mode add_lesions \
+    --batch_size 1 \
+    --lr 1e-4 \
+    --save_interval 5000
+```
+
+#### Advanced Training with Custom Parameters
+```bash
+python scripts/inpainting_train.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --mode add_lesions \
+    --batch_size 1 \
+    --lr 1e-4 \
+    --save_interval 5000 \
+    --log_interval 100 \
+    --ema_rate 0.9999 \
+    --use_fp16 \
+    --image_size 64 \
+    --num_channels 128
+```
+
+### 2. Real-time Monitoring
+
+#### Start Monitoring in Separate Terminal
+```bash
+python scripts/monitor_training.py --log_dir log --update_interval 30
+```
+
+#### Check Training Status
+```bash
+python scripts/monitor_training.py --log_dir log --check_only
+```
+
+### 3. Validation
+
+#### Run Validation on Trained Model
+```bash
+python scripts/validate_training.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --model_path log/emasavedmodel_0.9999_050000.pt \
+    --mode add_lesions \
+    --num_samples 5
+```
+
+#### Plot Training Progress Only
+```bash
+python scripts/validate_training.py \
+    --log_dir log \
+    --plot_progress \
+    --validate False
+```
+
+## Output Files
+
+### Training Output
+```
+log/
+├── log.txt                    # Training logs
+├── training_metrics.json      # Detailed metrics history
+├── training_metrics.png       # Training plots
+├── training_progress.png      # Progress visualization
+├── savedmodel000000.pt       # Model checkpoints
+├── emasavedmodel_0.9999_000000.pt  # EMA model checkpoints
+└── optsavedmodel000000.pt    # Optimizer state
+```
+
+### Validation Output
+```
+log/
+├── validation_results.json    # Validation metrics
+└── validation_samples/
+    ├── validation_sample_0.png
+    ├── validation_sample_1.png
+    └── ...
+```
+
+## Monitoring Features
+
+### Real-time Metrics
+- **Training Loss**: Overall model loss
+- **MSE Loss**: Mean squared error
+- **Learning Rate**: Current learning rate
+- **Variational Bound**: VB loss component
+- **Best Loss**: Best loss achieved so far
+
+### Visualization
+- **Live Plots**: Real-time updating plots
+- **Log Scale**: Automatic log scaling for better visualization
+- **Grid Lines**: Easy-to-read grid lines
+- **Auto-scaling**: Automatic axis scaling
+
+### Console Output
+```
+============================================================
+Training Progress - 2025-01-14 15:30:45
+============================================================
+Current Step: 15000
+Current Loss: 0.023456 (+0.001234)
+Learning Rate: 1.00e-04
+Best Loss: 0.020123
+Current MSE: 0.018765
+Best MSE: 0.015432
+Total Steps: 15000
+ETA: 2h 30m
+============================================================
+```
+
+## Configuration Options
+
+### Training Parameters
+- `--mode`: `add_lesions` or `remove_lesions`
+- `--batch_size`: Batch size (default: 1)
+- `--lr`: Learning rate (default: 1e-4)
+- `--save_interval`: Save checkpoint every N steps
+- `--log_interval`: Log metrics every N steps
+- `--ema_rate`: EMA rate for model averaging
+
+### Model Parameters
+- `--image_size`: Input image size (default: 64)
+- `--num_channels`: Model channels (default: 128)
+- `--num_res_blocks`: Number of residual blocks
+- `--attention_resolutions`: Attention resolution levels
+
+### Monitoring Parameters
+- `--update_interval`: Monitor update frequency (seconds)
+- `--num_samples`: Number of validation samples
+
+## Troubleshooting
+
+### Common Issues
+
+#### 1. "No model checkpoint found"
+- **Solution**: Train the model first or specify correct `--model_path`
+
+#### 2. "Missing label files"
+- **Solution**: Ensure ROI files exist and match image naming convention
+
+#### 3. "Visdom not available"
+- **Solution**: Install visdom or use console-only monitoring
+
+#### 4. "CUDA out of memory"
+- **Solution**: Reduce batch size or image size
+
+### Performance Tips
+
+1. **Use FP16**: Add `--use_fp16` for faster training
+2. **Adjust Batch Size**: Start with batch_size=1, increase if memory allows
+3. **Monitor GPU**: Use `nvidia-smi` to monitor GPU usage
+4. **Save Regularly**: Use appropriate `--save_interval`
+
+## Example Workflow
+
+### Complete Training Session
+```bash
+# Terminal 1: Start training
+python scripts/inpainting_train.py \
+    --data_dir ../FCD2/Pathological \
+    --log_dir log \
+    --mode add_lesions
+
+# Terminal 2: Monitor progress
+python scripts/monitor_training.py --log_dir log
+
+# Terminal 3: Run validation (after some training)
+python scripts/validate_training.py \
+    --log_dir log \
+    --model_path log/emasavedmodel_0.9999_010000.pt
+```
+
+### Check Training Progress
+```bash
+# Plot training progress
+python scripts/validate_training.py --log_dir log --plot_progress
+
+# Check if training is running
+python scripts/monitor_training.py --log_dir log --check_only
+```
+
+## Data Preparation
+
+### Creating ROI Masks
+1. Load healthy brain image
+2. Mark regions where lesions should be added
+3. Save as binary mask (0 = keep, >0 = replace with lesion)
+4. Ensure mask dimensions match image dimensions
+
+### Validation
+- Use images with known lesions as ground truth
+- Create ROI masks for healthy regions
+- Model learns to reconstruct lesions in masked regions
+
+## Advanced Usage
+
+### Custom Loss Functions
+Modify `training_losses_segmentation` in `gaussian_diffusion.py` for custom loss functions.
+
+### Custom Metrics
+Add new metrics in `EnhancedTrainLoop.log_metrics()` method.
+
+### Custom Visualization
+Modify `TrainingMonitor.setup_plots()` for custom plot layouts.
+
+## Support
+
+For issues or questions:
+1. Check the troubleshooting section
+2. Review log files in the log directory
+3. Verify data format and file paths
+4. Check GPU memory usage
+
+## License
+
+This enhanced training system is part of the FCD2 inpainting project. 

fcd2_inpainting/DDPM_Pseudo3D/scripts/inpainting_sample.py

@@ -403,7 +403,7 @@ def create_argparser():
         clip_denoised=True,
         batch_size=1,
         use_ddim=False,
-        model_path="log/xemasavedmodel_0.9999_050000.pt",
+        model_path="log/emasavedmodel_0.9999_050000.pt",
         range_padding=2,  # slices of context added on each side
         save_intermediate_dir="",
         save_intermediate_interval=0,

fcd2_inpainting/DDPM_Pseudo3D/scripts/inpainting_train.py

@@ -1,13 +1,21 @@
 """
-Train a diffusion model on images.
+Train a diffusion model on images with enhanced progress tracking, validation, and overfitting prevention.
 """
 
 import argparse
 import sys
+import os
+import json
+from datetime import datetime
+import matplotlib.pyplot as plt
+import numpy as np
+import math
+import nibabel as nib
+import gc
+import torch
 
 sys.path.append("..")
 sys.path.append(".")
-from datetime import datetime
 
 import torch as th
 from guided_diffusion import dist_util, logger
@@ -20,7 +28,570 @@ from guided_diffusion.script_util import (
     model_and_diffusion_defaults,
 )
 from guided_diffusion.train_util import TrainLoop
-from visdom import Visdom
+
+
+def clear_gpu_memory():
+    """Clear GPU memory and garbage collect."""
+    if th.cuda.is_available():
+        th.cuda.empty_cache()
+        th.cuda.synchronize()
+    gc.collect()
+
+
+def get_gpu_memory_info():
+    """Get current GPU memory usage information."""
+    if th.cuda.is_available():
+        allocated = th.cuda.memory_allocated() / 1024**3  # GB
+        reserved = th.cuda.memory_reserved() / 1024**3   # GB
+        total = th.cuda.get_device_properties(0).total_memory / 1024**3  # GB
+        free = total - reserved
+        return {
+            'allocated_gb': allocated,
+            'reserved_gb': reserved,
+            'total_gb': total,
+            'free_gb': free
+        }
+    return None
+
+
+def safe_json_dumps(obj, indent=2):
+    """Safely serialize object to JSON, handling NumPy types."""
+    def convert_numpy(obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, dict):
+            return {key: convert_numpy(value) for key, value in obj.items()}
+        elif isinstance(obj, list):
+            return [convert_numpy(item) for item in obj]
+        else:
+            return obj
+    
+    converted_obj = convert_numpy(obj)
+    return json.dumps(converted_obj, indent=indent)
+
+
+class EnhancedTrainLoop(TrainLoop):
+    """Enhanced training loop with validation, early stopping, and better metrics."""
+    
+    def __init__(self, *args, validation_data=None, validation_interval=1000, 
+                 early_stopping_patience=10, min_delta=1e-4, log_dir=None, 
+                 enable_memory_management=True, min_batch_size=1, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.validation_data = validation_data
+        self.validation_interval = validation_interval
+        self.early_stopping_patience = early_stopping_patience
+        self.min_delta = min_delta
+        self.log_dir = log_dir
+        self.enable_memory_management = enable_memory_management
+        self.min_batch_size = min_batch_size
+        self.original_batch_size = self.batch_size
+        
+        self.metrics_history = {
+            'train_loss': [],
+            'train_mse': [],
+            'train_vb': [],
+            'val_loss': [],
+            'val_mse': [],
+            'learning_rate': [],
+            'step': [],
+            'gpu_memory_gb': []
+        }
+        self.validation_metrics = []
+        self.best_val_loss = float('inf')
+        self.patience_counter = 0
+        self.early_stopped = False
+        self.memory_errors = 0
+        self.max_memory_errors = 5
+        
+        # Create validation output directory
+        if self.log_dir:
+            self.val_output_dir = os.path.join(self.log_dir, 'validation_samples')
+            os.makedirs(self.val_output_dir, exist_ok=True)
+        else:
+            self.val_output_dir = None
+        
+        # Enable gradient checkpointing for memory efficiency
+        if self.enable_memory_management:
+            self.enable_gradient_checkpointing()
+    
+    def enable_gradient_checkpointing(self):
+        """Enable gradient checkpointing on the model to save memory."""
+        try:
+            if hasattr(self.model, 'enable_gradient_checkpointing'):
+                self.model.enable_gradient_checkpointing()
+                logger.log("Gradient checkpointing enabled")
+            else:
+                logger.log("Model does not support gradient checkpointing")
+        except Exception as e:
+            logger.log(f"Failed to enable gradient checkpointing: {e}")
+    
+    def reduce_batch_size(self):
+        """Reduce batch size to prevent memory issues."""
+        if self.batch_size > self.min_batch_size:
+            old_batch_size = self.batch_size
+            self.batch_size = max(self.min_batch_size, self.batch_size // 2)
+            logger.log(f"Reduced batch size from {old_batch_size} to {self.batch_size} due to memory issues")
+            return True
+        return False
+    
+    def check_memory_and_clear(self):
+        """Check GPU memory and clear if necessary."""
+        if not self.enable_memory_management or not th.cuda.is_available():
+            return
+        
+        memory_info = get_gpu_memory_info()
+        if memory_info:
+            free_gb = memory_info['free_gb']
+            allocated_gb = memory_info['allocated_gb']
+            
+            # Log memory usage periodically
+            if hasattr(self, 'step') and self.step % 100 == 0:
+                logger.log(f"GPU Memory: {allocated_gb:.2f}GB allocated, {free_gb:.2f}GB free")
+                self.metrics_history['gpu_memory_gb'].append(allocated_gb)
+            
+            # Clear memory if free space is low
+            if free_gb < 2.0:  # Less than 2GB free
+                logger.log(f"Low GPU memory detected ({free_gb:.2f}GB free), clearing cache...")
+                clear_gpu_memory()
+                th.cuda.synchronize()
+                
+                # Check memory after clearing
+                memory_info_after = get_gpu_memory_info()
+                if memory_info_after:
+                    logger.log(f"After clearing: {memory_info_after['allocated_gb']:.2f}GB allocated, {memory_info_after['free_gb']:.2f}GB free")
+    
+    def handle_memory_error(self):
+        """Handle CUDA out of memory error."""
+        self.memory_errors += 1
+        logger.log(f"CUDA out of memory error #{self.memory_errors}")
+        
+        # Clear memory
+        clear_gpu_memory()
+        
+        # Reduce batch size if possible
+        if self.reduce_batch_size():
+            logger.log("Batch size reduced, retrying...")
+            return True
+        
+        # If we've had too many memory errors, stop training
+        if self.memory_errors >= self.max_memory_errors:
+            logger.log(f"Too many memory errors ({self.memory_errors}), stopping training")
+            return False
+        
+        logger.log("Memory cleared, retrying...")
+        return True
+    
+    def validate_model(self):
+        """Run validation on the current model."""
+        if self.validation_data is None:
+            return None
+        
+        self.model.eval()
+        val_losses = []
+        val_mses = []
+        
+        with th.no_grad():
+            for i, (batch, cond, path, slicedict) in enumerate(self.validation_data):
+                if i >= 5:  # Limit validation to 5 batches for speed
+                    break
+                
+                # Process validation batch similar to training
+                batch_size_vol = 12
+                nr_batches = math.ceil(len(slicedict) / batch_size_vol)
+                
+                for b in range(nr_batches):
+                    out_batch = []
+                    out_cond = []
+                    
+                    if len(slicedict) > b * batch_size_vol + batch_size_vol:
+                        for s in slicedict[b * batch_size_vol : (b * batch_size_vol + batch_size_vol)]:
+                            out_batch.append(batch[..., s].clone().detach())
+                            out_cond.append(cond[..., s].clone().detach())
+                    else:
+                        for s in slicedict[b * batch_size_vol :]:
+                            out_batch.append(batch[..., s].clone().detach())
+                            out_cond.append(cond[..., s].clone().detach())
+                    
+                    out_batch = th.stack(out_batch).squeeze(1).squeeze(4)
+                    out_cond = th.stack(out_cond).squeeze(1).squeeze(4)
+                    
+                    # Compute validation loss
+                    batch_combined = th.cat((out_batch, out_cond), dim=1)
+                    micro = batch_combined.to(dist_util.dev())
+                    t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
+                    
+                    losses = self.diffusion.training_losses_segmentation(
+                        self.ddp_model, self.classifier, micro, t
+                    )[0]
+                    
+                    val_losses.append(losses['loss'].mean().item())
+                    val_mses.append(losses.get('mse', th.tensor(0.0)).mean().item())
+                    
+                    # Save validation visualization for first batch
+                    if i == 0 and b == 0:
+                        self.save_validation_visualization(out_batch, out_cond, path[0], slicedict)
+        
+        self.model.train()
+        
+        avg_val_loss = np.mean(val_losses)
+        avg_val_mse = np.mean(val_mses)
+        
+        return {
+            'val_loss': avg_val_loss,
+            'val_mse': avg_val_mse
+        }
+    
+    def save_validation_visualization(self, voided_batch, mask_batch, image_path, slicedict):
+        """Save validation data as NIfTI files for easy viewing."""
+        if self.val_output_dir is None:
+            logger.log("Validation output directory not set, skipping visualization")
+            return
+        
+        try:
+            # Extract the first sample from the batch
+            voided_sample = voided_batch[0]  # (2, H, W, D) - voided image + mask
+            mask_sample = mask_batch[0]      # (1, H, W, D) - original image with lesions
+            
+            # Convert to numpy and save as NIfTI files
+            # Save voided image (channel 0)
+            voided_img = voided_sample[0].cpu().numpy()  # (H, W, D)
+            voided_nii = nib.Nifti1Image(voided_img, np.eye(4))
+            
+            # Save ROI mask (channel 1)
+            roi_mask = voided_sample[1].cpu().numpy()  # (H, W, D)
+            roi_nii = nib.Nifti1Image(roi_mask, np.eye(4))
+            
+            # Save ground truth (original image with lesions)
+            gt_img = mask_sample[0].cpu().numpy()  # (H, W, D)
+            gt_nii = nib.Nifti1Image(gt_img, np.eye(4))
+            
+            # Save with step number
+            step_str = f"{self.step:06d}" if hasattr(self, 'step') else "000000"
+            
+            # Save files
+            voided_filename = f"validation_step_{step_str}_voided.nii.gz"
+            roi_filename = f"validation_step_{step_str}_roi_mask.nii.gz"
+            gt_filename = f"validation_step_{step_str}_ground_truth.nii.gz"
+            
+            nib.save(voided_nii, os.path.join(self.val_output_dir, voided_filename))
+            nib.save(roi_nii, os.path.join(self.val_output_dir, roi_filename))
+            nib.save(gt_nii, os.path.join(self.val_output_dir, gt_filename))
+            
+            logger.log(f"Validation NIfTI files saved: {voided_filename}, {roi_filename}, {gt_filename}")
+            
+        except Exception as e:
+            logger.log(f"Failed to save validation NIfTI files: {e}")
+            import traceback
+            logger.log(f"Traceback: {traceback.format_exc()}")
+    
+    def check_early_stopping(self, val_loss):
+        """Check if training should stop early."""
+        if val_loss < self.best_val_loss - self.min_delta:
+            self.best_val_loss = val_loss
+            self.patience_counter = 0
+            return False
+        else:
+            self.patience_counter += 1
+            if self.patience_counter >= self.early_stopping_patience:
+                return True
+            return False
+    
+    def log_metrics(self, losses, step, val_metrics=None):
+        """Log metrics to history and console."""
+        # Handle case where losses might not be a dictionary
+        if isinstance(losses, dict):
+            train_loss = losses['loss'].mean().item() if 'loss' in losses else 0.0
+            train_mse = losses.get('mse', th.tensor(0.0)).mean().item()
+            train_vb = losses.get('vb', th.tensor(0.0)).mean().item()
+        else:
+            # If losses is not a dict, assume it's a tensor or scalar
+            train_loss = float(losses) if hasattr(losses, 'item') else losses
+            train_mse = 0.0
+            train_vb = 0.0
+        
+        # Store training metrics
+        self.metrics_history['step'].append(step)
+        self.metrics_history['train_loss'].append(train_loss)
+        self.metrics_history['train_mse'].append(train_mse)
+        self.metrics_history['train_vb'].append(train_vb)
+        self.metrics_history['learning_rate'].append(self.opt.param_groups[0]['lr'])
+        
+        # Store validation metrics
+        if val_metrics:
+            self.metrics_history['val_loss'].append(val_metrics['val_loss'])
+            self.metrics_history['val_mse'].append(val_metrics['val_mse'])
+        else:
+            self.metrics_history['val_loss'].append(None)
+            self.metrics_history['val_mse'].append(None)
+        
+        # Log to progress.csv using the logger system
+        logger.logkv('step', step)
+        logger.logkv('train_loss', train_loss)
+        logger.logkv('train_mse', train_mse)
+        logger.logkv('train_vb', train_vb)
+        logger.logkv('learning_rate', self.opt.param_groups[0]['lr'])
+        
+        if val_metrics:
+            logger.logkv('val_loss', val_metrics['val_loss'])
+            logger.logkv('val_mse', val_metrics['val_mse'])
+        
+        # Console logging
+        log_str = f"Step {step}: Train Loss={train_loss:.6f}"
+        if val_metrics:
+            log_str += f", Val Loss={val_metrics['val_loss']:.6f}"
+        log_str += f", LR={self.opt.param_groups[0]['lr']:.2e}"
+        logger.log(log_str)
+    
+    def save_metrics(self, log_dir):
+        """Save metrics to JSON file."""
+        metrics_file = os.path.join(log_dir, 'training_metrics.json')
+        with open(metrics_file, 'w') as f:
+            json.dump(self.metrics_history, f, indent=2)
+        
+        # Save plots
+        self.save_plots(log_dir)
+    
+    def save_plots(self, log_dir):
+        """Save metric plots as PNG files."""
+        if not self.metrics_history['step']:
+            return
+        
+        # Determine number of subplots based on available data
+        plot_count = 2  # train/val loss and MSE
+        if any(x is not None and x > 0 for x in self.metrics_history['learning_rate']):
+            plot_count += 1  # learning rate
+        if any(x is not None and x > 0 for x in self.metrics_history['train_vb']):
+            plot_count += 1  # variational bound
+        if self.enable_memory_management and self.metrics_history['gpu_memory_gb']:
+            plot_count += 1  # GPU memory
+        
+        # Calculate subplot layout
+        cols = min(3, plot_count)
+        rows = (plot_count + cols - 1) // cols
+        
+        fig, axes = plt.subplots(rows, cols, figsize=(5*cols, 4*rows))
+        if plot_count == 1:
+            axes = [axes]
+        elif rows == 1:
+            axes = axes.reshape(1, -1)
+        elif cols == 1:
+            axes = axes.reshape(-1, 1)
+        
+        plot_idx = 0
+        
+        # Training loss plot
+        train_loss = [x for x in self.metrics_history['train_loss'] if x is not None and x > 0]
+        train_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['train_loss']) if x is not None and x > 0]
+        
+        if train_loss:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            ax.plot(train_steps, train_loss, 'b-', label='Train')
+            if any(x is not None and x > 0 for x in self.metrics_history['val_loss']):
+                val_loss = [x for x in self.metrics_history['val_loss'] if x is not None and x > 0]
+                val_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['val_loss']) if x is not None and x > 0]
+                ax.plot(val_steps, val_loss, 'r-', label='Validation')
+            ax.set_title('Training vs Validation Loss')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('Loss')
+            ax.grid(True)
+            ax.legend()
+            if min(train_loss) > 0:
+                ax.set_yscale('log')
+            plot_idx += 1
+        
+        # MSE plot
+        train_mse = [x for x in self.metrics_history['train_mse'] if x is not None and x > 0]
+        if train_mse:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            train_mse_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['train_mse']) if x is not None and x > 0]
+            ax.plot(train_mse_steps, train_mse, 'b-', label='Train')
+            if any(x is not None and x > 0 for x in self.metrics_history['val_mse']):
+                val_mse = [x for x in self.metrics_history['val_mse'] if x is not None and x > 0]
+                val_mse_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['val_mse']) if x is not None and x > 0]
+                ax.plot(val_mse_steps, val_mse, 'r-', label='Validation')
+            ax.set_title('MSE Loss')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('MSE')
+            ax.grid(True)
+            ax.legend()
+            if min(train_mse) > 0:
+                ax.set_yscale('log')
+            plot_idx += 1
+        
+        # Learning rate plot
+        lr_values = [x for x in self.metrics_history['learning_rate'] if x is not None and x > 0]
+        if lr_values:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            lr_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['learning_rate']) if x is not None and x > 0]
+            ax.plot(lr_steps, lr_values)
+            ax.set_title('Learning Rate')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('LR')
+            if min(lr_values) > 0:
+                ax.set_yscale('log')
+            ax.grid(True)
+            plot_idx += 1
+        
+        # VB plot (if available)
+        train_vb = [x for x in self.metrics_history['train_vb'] if x is not None and x > 0]
+        if train_vb:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            vb_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['train_vb']) if x is not None and x > 0]
+            ax.plot(vb_steps, train_vb)
+            ax.set_title('Variational Bound')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('VB')
+            ax.grid(True)
+            if min(train_vb) > 0:
+                ax.set_yscale('log')
+            plot_idx += 1
+        
+        # GPU Memory plot (if available)
+        if self.enable_memory_management and self.metrics_history['gpu_memory_gb']:
+            ax = axes[plot_idx // cols, plot_idx % cols] if plot_count > 1 else axes[0]
+            memory_steps = [self.metrics_history['step'][i] for i, x in enumerate(self.metrics_history['gpu_memory_gb']) if x is not None]
+            memory_values = [x for x in self.metrics_history['gpu_memory_gb'] if x is not None]
+            ax.plot(memory_steps, memory_values, 'g-')
+            ax.set_title('GPU Memory Usage')
+            ax.set_xlabel('Step')
+            ax.set_ylabel('Memory (GB)')
+            ax.grid(True)
+            plot_idx += 1
+        
+        # Hide unused subplots
+        for i in range(plot_idx, rows * cols):
+            if plot_count > 1:
+                axes[i // cols, i % cols].set_visible(False)
+        
+        plt.tight_layout()
+        plt.savefig(os.path.join(log_dir, 'training_metrics.png'), dpi=300, bbox_inches='tight')
+        plt.close()
+    
+    def run_loop(self):
+        """Enhanced run loop with validation and early stopping."""
+        logger.log("Starting enhanced training loop with validation...")
+        
+        # Run validation at the beginning
+        if self.validation_data is not None:
+            val_metrics = self.validate_model()
+            if val_metrics:
+                logger.log(f"Step 0: Val Loss={val_metrics['val_loss']:.6f}, LR={self.opt.param_groups[0]['lr']:.2e}")
+                # Log initial validation metrics to progress.csv
+                logger.logkv('step', 0)
+                logger.logkv('val_loss', val_metrics['val_loss'])
+                logger.logkv('val_mse', val_metrics['val_mse'])
+                logger.dumpkvs()
+        
+        # Use the same termination condition as parent class
+        i = 0
+        while (
+            not self.lr_anneal_steps
+            or self.step + self.resume_step < self.lr_anneal_steps
+        ):
+            try:
+                # Check memory before processing batch
+                self.check_memory_and_clear()
+                
+                # Original training step
+                batch, cond, path, slicedict = next(self.data)
+                
+                # Process batch (same as original)
+                batch_size_vol = 12
+                nr_batches = math.ceil(len(slicedict) / batch_size_vol)
+                
+                for b in range(nr_batches):
+                    try:
+                        out_batch = []
+                        out_cond = []
+                        
+                        if len(slicedict) > b * batch_size_vol + batch_size_vol:
+                            for s in slicedict[b * batch_size_vol : (b * batch_size_vol + batch_size_vol)]:
+                                out_batch.append(batch[..., s].clone().detach())
+                                out_cond.append(cond[..., s].clone().detach())
+                        else:
+                            for s in slicedict[b * batch_size_vol :]:
+                                out_batch.append(batch[..., s].clone().detach())
+                                out_cond.append(cond[..., s].clone().detach())
+                        
+                        out_batch = th.stack(out_batch).squeeze(1).squeeze(4)
+                        out_cond = th.stack(out_cond).squeeze(1).squeeze(4)
+                        
+                        # Call parent class run_step method
+                        losses = self.run_step(out_batch, out_cond)
+                        i += 1
+                        
+                        # Log training metrics
+                        if i % 100 == 0:  # Log every 100 steps
+                            self.log_metrics(losses, i)
+                            logger.dumpkvs()  # Write training metrics to progress.csv
+                        
+                        # Run validation periodically
+                        if self.validation_data is not None and i % self.validation_interval == 0:
+                            val_metrics = self.validate_model()
+                            self.log_metrics(losses, i, val_metrics)
+                            logger.dumpkvs()  # Write validation metrics to progress.csv
+                            
+                            # Check early stopping
+                            if self.check_early_stopping(val_metrics['val_loss']):
+                                logger.log(f"Early stopping triggered after {i} steps")
+                                self.early_stopped = True
+                                break
+                        
+                        # Clear memory after each batch
+                        if self.enable_memory_management and i % 50 == 0:
+                            clear_gpu_memory()
+                        
+                    except th.cuda.OutOfMemoryError as e:
+                        logger.log(f"CUDA out of memory error in batch {b}: {e}")
+                        if not self.handle_memory_error():
+                            logger.log("Too many memory errors, stopping training")
+                            return
+                        # Skip this batch and continue
+                        continue
+                
+                if self.early_stopped:
+                    break
+                    
+            except StopIteration:
+                # StopIteration is thrown if dataset ends
+                # reinitialize data loader
+                self.data = iter(self.dataloader)
+                continue
+            except th.cuda.OutOfMemoryError as e:
+                logger.log(f"CUDA out of memory error in main loop: {e}")
+                if not self.handle_memory_error():
+                    logger.log("Too many memory errors, stopping training")
+                    break
+                # Clear memory and continue
+                clear_gpu_memory()
+                continue
+            except Exception as e:
+                logger.log(f"Unexpected error: {e}")
+                import traceback
+                logger.log(f"Traceback: {traceback.format_exc()}")
+                # Clear memory and continue
+                clear_gpu_memory()
+                continue
+            
+            # Original logging and saving logic
+            if self.step % self.log_interval == 0:
+                logger.dumpkvs()
+            if self.step % self.save_interval == 0:
+                self.save()
+            
+            self.step += 1
+        
+        # Save the last checkpoint if it wasn't already saved.
+        if (self.step - 1) % self.save_interval != 0:
+            self.save()
+        
+        # Save final metrics
+        self.save_metrics(self.log_dir)
+        logger.log("Enhanced training loop completed.")
 
 
 def main():
@@ -31,6 +602,10 @@ def main():
     today = datetime.now()
     logger.log("TRAINING START " + str(today))
     logger.log("args: " + str(args))
+    
+    # Create log directory for metrics
+    os.makedirs(args.log_dir, exist_ok=True)
+    
     logger.log("creating model and diffusion...")
     model, diffusion = create_model_and_diffusion(
         **args_to_dict(args, model_and_diffusion_defaults().keys())
@@ -41,19 +616,45 @@ def main():
         args.schedule_sampler, diffusion, maxt=1000
     )
 
-    logger.log("creating data loader...")
-    # Use FCD2 dataset loader for pathological data
-    ds = FCD2Dataset(args.data_dir, test_flag=False)
-    datal = th.utils.data.DataLoader(ds, batch_size=args.batch_size, shuffle=True)
-    data = iter(datal)
+    logger.log("creating data loaders...")
+    
+    # Create train/validation datasets
+    if args.validation_split > 0:
+        train_ds, val_ds = FCD2Dataset.get_train_val_datasets(
+            args.data_dir, 
+            mode=args.mode, 
+            validation_split=args.validation_split, 
+            seed=args.seed
+        )
+        
+        # Log dataset statistics
+        train_stats = train_ds.get_dataset_stats()
+        val_stats = val_ds.get_dataset_stats()
+        logger.log(f"Training dataset: {safe_json_dumps(train_stats, indent=2)}")
+        logger.log(f"Validation dataset: {safe_json_dumps(val_stats, indent=2)}")
+        
+        train_datal = th.utils.data.DataLoader(train_ds, batch_size=args.batch_size, shuffle=True)
+        val_datal = th.utils.data.DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)
+        
+        train_data = iter(train_datal)
+        validation_data = iter(val_datal)
+    else:
+        # Use single dataset without validation
+        ds = FCD2Dataset(args.data_dir, test_flag=False, mode=args.mode)
+        dataset_stats = ds.get_dataset_stats()
+        logger.log(f"Dataset statistics: {safe_json_dumps(dataset_stats, indent=2)}")
+        
+        train_datal = th.utils.data.DataLoader(ds, batch_size=args.batch_size, shuffle=True)
+        train_data = iter(train_datal)
+        validation_data = None
 
     logger.log("training...")
-    TrainLoop(
+    train_loop = EnhancedTrainLoop(
         model=model,
         diffusion=diffusion,
         classifier=None,
-        data=data,
-        dataloader=datal,
+        data=train_data,
+        dataloader=train_datal,
         batch_size=args.batch_size,
         microbatch=args.microbatch,
         lr=args.lr,
@@ -66,13 +667,33 @@ def main():
         schedule_sampler=schedule_sampler,
         weight_decay=args.weight_decay,
         lr_anneal_steps=args.lr_anneal_steps,
-    ).run_loop()
+        validation_data=validation_data,
+        validation_interval=args.validation_interval,
+        early_stopping_patience=args.early_stopping_patience,
+        min_delta=args.min_delta,
+        log_dir=args.log_dir,
+        enable_memory_management=args.enable_memory_management,
+        min_batch_size=args.min_batch_size,
+    )
+    
+    try:
+        train_loop.run_loop()
+    except KeyboardInterrupt:
+        logger.log("Training interrupted by user")
+    finally:
+        # Save final metrics
+        train_loop.save_metrics(args.log_dir)
+        logger.log("Training metrics saved")
 
 
 def create_argparser():
     defaults = dict(
         data_dir="../FCD2/Pathological",
         log_dir="log",
+        mode="add_lesions",  # "add_lesions" or "remove_lesions"
+        validation_split=0.2,  # Fraction of data for validation
+        seed=42,  # Random seed for reproducible splits
+        dropout=0.1,  # Dropout rate for regularization
         schedule_sampler="uniform",
         lr=1e-4,
         weight_decay=0.0,
@@ -82,9 +703,14 @@ def create_argparser():
         ema_rate="0.9999",  # comma-separated list of EMA values
         log_interval=1000,
         save_interval=5000,
+        validation_interval=2000,  # Run validation every N steps
+        early_stopping_patience=10,  # Stop if no improvement for N validation runs
+        min_delta=1e-4,  # Minimum improvement for early stopping
         resume_checkpoint="",
         use_fp16=False,
         fp16_scale_growth=1e-3,
+        enable_memory_management=True,
+        min_batch_size=1,
     )
     defaults.update(model_and_diffusion_defaults())
     parser = argparse.ArgumentParser()

fcd2_inpainting/DDPM_Pseudo3D/scripts/log/emasavedmodel_0.9999_050000.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8a8ae071e21cb8a4eea14b771bb5f614d63311a1b5d97055ba52962ddcb8a73d
-size 439901150

fcd2_inpainting/DDPM_Pseudo3D/scripts/log/log.txt

@@ -1,284 +1,603 @@
 Logging to log
-TRAINING START 2025-04-17 03:29:18.143777
-args: Namespace(data_dir='../FCD2/Pathological', log_dir='log', schedule_sampler='uniform', lr=0.0001, weight_decay=0.0, lr_anneal_steps=0, batch_size=1, microbatch=-1, ema_rate='0.9999', log_interval=1000, save_interval=5000, resume_checkpoint='', use_fp16=False, fp16_scale_growth=0.001, image_size=64, num_channels=128, num_res_blocks=2, num_heads=4, num_heads_upsample=-1, num_head_channels=-1, attention_resolutions='16,8', channel_mult='', dropout=0.0, class_cond=False, use_checkpoint=False, use_scale_shift_norm=True, resblock_updown=False, use_new_attention_order=False, learn_sigma=False, diffusion_steps=1000, noise_schedule='linear', timestep_respacing='', use_kl=False, predict_xstart=False, rescale_timesteps=False, rescale_learned_sigmas=False)
+TRAINING START 2025-06-29 15:26:58.299329
+args: Namespace(data_dir='../FCD2/Pathological', log_dir='log', mode='add_lesions', validation_split=0.1, seed=42, dropout=0.15, schedule_sampler='uniform', lr=0.0001, weight_decay=0.0, lr_anneal_steps=0, batch_size=1, microbatch=-1, ema_rate='0.9999', log_interval=1000, save_interval=5000, validation_interval=1000, early_stopping_patience=15, min_delta=1e-05, resume_checkpoint='', use_fp16=False, fp16_scale_growth=0.001, enable_memory_management=True, min_batch_size=1, image_size=64, num_channels=128, num_res_blocks=2, num_heads=4, num_heads_upsample=-1, num_head_channels=-1, attention_resolutions='16,8', channel_mult='', class_cond=False, use_checkpoint=False, use_scale_shift_norm=True, resblock_updown=False, use_new_attention_order=False, learn_sigma=False, diffusion_steps=1000, noise_schedule='linear', timestep_respacing='', use_kl=False, predict_xstart=False, rescale_timesteps=False, rescale_learned_sigmas=False)
 creating model and diffusion...
-creating data loader...
+creating data loaders...
+Training dataset: {
+  "num_volumes": 84,
+  "mask_stats": [
+    {
+      "mask_volume": 1842,
+      "total_volume": 10485760,
+      "mask_percentage": 0.017566680908203125,
+      "num_slices": 22
+    },
+    {
+      "mask_volume": 15177,
+      "total_volume": 10485760,
+      "mask_percentage": 0.14473915100097656,
+      "num_slices": 55
+    },
+    {
+      "mask_volume": 2364,
+      "total_volume": 10485760,
+      "mask_percentage": 0.02254486083984375,
+      "num_slices": 14
+    },
+    {
+      "mask_volume": 2422,
+      "total_volume": 10485760,
+      "mask_percentage": 0.023097991943359375,
+      "num_slices": 22
+    },
+    {
+      "mask_volume": 9265,
+      "total_volume": 10485760,
+      "mask_percentage": 0.08835792541503906,
+      "num_slices": 40
+    },
+    {
+      "mask_volume": 5179,
+      "total_volume": 10485760,
+      "mask_percentage": 0.04939079284667969,
+      "num_slices": 30
+    },
+    {
+      "mask_volume": 1902,
+      "total_volume": 10485760,
+      "mask_percentage": 0.018138885498046875,
+      "num_slices": 16
+    },
+    {
+      "mask_volume": 889,
+      "total_volume": 10485760,
+      "mask_percentage": 0.008478164672851562,
+      "num_slices": 27
+    },
+    {
+      "mask_volume": 4581,
+      "total_volume": 3584000,
+      "mask_percentage": 0.12781808035714287,
+      "num_slices": 54
+    },
+    {
+      "mask_volume": 1769,
+      "total_volume": 10485760,
+      "mask_percentage": 0.016870498657226562,
+      "num_slices": 12
+    },
+    {
+      "mask_volume": 2598,
+      "total_volume": 10485760,
+      "mask_percentage": 0.024776458740234375,
+      "num_slices": 23
+    },
+    {
+      "mask_volume": 2753,
+      "total_volume": 10485760,
+      "mask_percentage": 0.026254653930664062,
+      "num_slices": 27
+    },
+    {
+      "mask_volume": 1697,
+      "total_volume": 10485760,
+      "mask_percentage": 0.016183853149414062,
+      "num_slices": 15
+    },
+    {
+      "mask_volume": 837,
+      "total_volume": 10485760,
+      "mask_percentage": 0.007982254028320312,
+      "num_slices": 12
+    },
+    {
+      "mask_volume": 2282,
+      "total_volume": 10485760,
+      "mask_percentage": 0.021762847900390625,
+      "num_slices": 26
+    },
+    {
+      "mask_volume": 51619,
+      "total_volume": 10485760,
+      "mask_percentage": 0.49227714538574213,
+      "num_slices": 49
+    },
+    {
+      "mask_volume": 4331,
+      "total_volume": 10485760,
+      "mask_percentage": 0.04130363464355469,
+      "num_slices": 28
+    },
+    {
+      "mask_volume": 2212,
+      "total_volume": 10485760,
+      "mask_percentage": 0.02109527587890625,
+      "num_slices": 33
+    },
+    {
+      "mask_volume": 1214,
+      "total_volume": 10485760,
+      "mask_percentage": 0.011577606201171875,
+      "num_slices": 27
+    },
+    {
+      "mask_volume": 3768,
+      "total_volume": 10485760,
+      "mask_percentage": 0.0359344482421875,
+      "num_slices": 29
+    },
+    {
+      "mask_volume": 645,
+      "total_volume": 10485760,
+      "mask_percentage": 0.0061511993408203125,
+      "num_slices": 18
+    },
+    {
+      "mask_volume": 795,
+      "total_volume": 10485760,
+      "mask_percentage": 0.0075817108154296875,
+      "num_slices": 17
+    },
+    {
+      "mask_volume": 1129,
+      "total_volume": 10485760,
+      "mask_percentage": 0.010766983032226562,
+      "num_slices": 26
+    },
+    {
+      "mask_volume": 3487,
+      "total_volume": 10485760,
+      "mask_percentage": 0.03325462341308594,
+      "num_slices": 32
+    },
+    {
+      "mask_volume": 1334,
+      "total_volume": 10485760,
+      "mask_percentage": 0.012722015380859375,
+      "num_slices": 13
+    },
+    {
+      "mask_volume": 8187,
+      "total_volume": 10485760,
+      "mask_percentage": 0.07807731628417969,
+      "num_slices": 28
+    },
+    {
+      "mask_volume": 3968,
+      "total_volume": 10485760,
+      "mask_percentage": 0.037841796875,
+      "num_slices": 27
+    },
+    {
+      "mask_volume": 2580,
+      "total_volume": 10485760,
+      "mask_percentage": 0.02460479736328125,
+      "num_slices": 16
+    },
+    {
+      "mask_volume": 1917,
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+    {
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+    {
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+    {
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+    {
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+    {
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+    {
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+    {
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+    {
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+      "mask_percentage": 0.024280548095703125,
+      "num_slices": 23
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+    {
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+    {
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+      "num_slices": 38
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+    {
+      "mask_volume": 2581,
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+      "mask_percentage": 0.024614334106445316,
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+    {
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+      "num_slices": 46
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+    {
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+      "mask_percentage": 0.022420883178710938,
+      "num_slices": 32
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+    {
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+    {
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+      "mask_percentage": 0.011682510375976562,
+      "num_slices": 12
+    }
+  ],
+  "mode": "add_lesions",
+  "total_slices": 1898,
+  "validation_split": 0
+}
+Validation dataset: {
+  "num_volumes": 84,
+  "mask_stats": [
+    {
+      "mask_volume": 9352,
+      "total_volume": 10485760,
+      "mask_percentage": 0.0891876220703125,
+      "num_slices": 42
+    },
+    {
+      "mask_volume": 1643,
+      "total_volume": 10485760,
+      "mask_percentage": 0.015668869018554688,
+      "num_slices": 16
+    },
+    {
+      "mask_volume": 1723,
+      "total_volume": 10485760,
+      "mask_percentage": 0.016431808471679688,
+      "num_slices": 29
+    },
+    {
+      "mask_volume": 2757,
+      "total_volume": 10485760,
+      "mask_percentage": 0.026292800903320312,
+      "num_slices": 24
+    },
+    {
+      "mask_volume": 1099,
+      "total_volume": 10485760,
+      "mask_percentage": 0.010480880737304688,
+      "num_slices": 10
+    },
+    {
+      "mask_volume": 2105,
+      "total_volume": 3072000,
+      "mask_percentage": 0.06852213541666667,
+      "num_slices": 3
+    },
+    {
+      "mask_volume": 4219,
+      "total_volume": 10485760,
+      "mask_percentage": 0.04023551940917969,
+      "num_slices": 28
+    },
+    {
+      "mask_volume": 1772,
+      "total_volume": 10485760,
+      "mask_percentage": 0.01689910888671875,
+      "num_slices": 21
+    }
+  ],
+  "mode": "add_lesions",
+  "total_slices": 173,
+  "validation_split": 0
+}
 training...
--------------------------
-| grad_norm  | 7.05     |
-| loss       | 0.98     |
-| loss_q1    | 0.981    |
-| loss_q2    | 0.997    |
-| loss_q3    | 0.975    |
-| mse        | 0.98     |
-| mse_q1     | 0.981    |
-| mse_q2     | 0.997    |
-| mse_q3     | 0.975    |
+Model does not support gradient checkpointing
+Starting enhanced training loop with validation...
+Validation NIfTI files saved: validation_step_000000_voided.nii.gz, validation_step_000000_roi_mask.nii.gz, validation_step_000000_ground_truth.nii.gz
+Step 0: Val Loss=0.999452, LR=1.00e-04
+-----------------------
+| step     | 0        |
+| val_loss | 0.999    |
+| val_mse  | 0.999    |
+-----------------------
+GPU Memory: 1.24GB allocated, 37.01GB free
+-------------------------
+| grad_norm  | 7.02     |
+| loss       | 0.974    |
+| loss_q0    | 0.985    |
+| loss_q1    | 0.98     |
+| loss_q2    | 0.976    |
+| loss_q3    | 0.972    |
+| mse        | 0.974    |
+| mse_q0     | 0.985    |
+| mse_q1     | 0.98     |
+| mse_q2     | 0.976    |
+| mse_q3     | 0.972    |
 | param_norm | 231      |
 | samples    | 1        |
 | step       | 0        |
 -------------------------
 saving model 0...
 saving model 0.9999...
--------------------------
-| grad_norm  | 0.17     |
-| loss       | 0.0068   |
-| loss_q0    | 0.0122   |
-| loss_q1    | 0.00547  |
-| loss_q2    | 0.00571  |
-| loss_q3    | 0.00516  |
-| mse        | 0.0068   |
-| mse_q0     | 0.0122   |
-| mse_q1     | 0.00547  |
-| mse_q2     | 0.00571  |
-| mse_q3     | 0.00516  |
-| param_norm | 231      |
-| samples    | 1e+03    |
-| step       | 1e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0867   |
-| loss       | 0.00111  |
-| loss_q0    | 0.00301  |
-| loss_q1    | 0.000476 |
-| loss_q2    | 0.000456 |
-| loss_q3    | 0.00043  |
-| mse        | 0.00111  |
-| mse_q0     | 0.00301  |
-| mse_q1     | 0.000476 |
-| mse_q2     | 0.000456 |
-| mse_q3     | 0.00043  |
-| param_norm | 233      |
-| samples    | 2e+03    |
-| step       | 2e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0709   |
-| loss       | 0.000596 |
-| loss_q0    | 0.00196  |
-| loss_q1    | 0.000198 |
-| loss_q2    | 0.000156 |
-| loss_q3    | 0.000173 |
-| mse        | 0.000596 |
-| mse_q0     | 0.00196  |
-| mse_q1     | 0.000198 |
-| mse_q2     | 0.000156 |
-| mse_q3     | 0.000173 |
-| param_norm | 235      |
-| samples    | 3e+03    |
-| step       | 3e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0605   |
-| loss       | 0.000375 |
-| loss_q0    | 0.00115  |
-| loss_q1    | 0.000149 |
-| loss_q2    | 0.000115 |
-| loss_q3    | 0.000123 |
-| mse        | 0.000375 |
-| mse_q0     | 0.00115  |
-| mse_q1     | 0.000149 |
-| mse_q2     | 0.000115 |
-| mse_q3     | 0.000123 |
-| param_norm | 235      |
-| samples    | 4e+03    |
-| step       | 4e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0578   |
-| loss       | 0.000384 |
-| loss_q0    | 0.00124  |
-| loss_q1    | 0.000143 |
-| loss_q2    | 0.000106 |
-| loss_q3    | 0.000117 |
-| mse        | 0.000384 |
-| mse_q0     | 0.00124  |
-| mse_q1     | 0.000143 |
-| mse_q2     | 0.000106 |
-| mse_q3     | 0.000117 |
-| param_norm | 235      |
-| samples    | 5e+03    |
-| step       | 5e+03    |
--------------------------
-saving model 0...
-saving model 0.9999...
--------------------------
-| grad_norm  | 0.0651   |
-| loss       | 0.000599 |
-| loss_q0    | 0.00161  |
-| loss_q1    | 0.000251 |
-| loss_q2    | 0.000269 |
-| loss_q3    | 0.000313 |
-| mse        | 0.000599 |
-| mse_q0     | 0.00161  |
-| mse_q1     | 0.000251 |
-| mse_q2     | 0.000269 |
-| mse_q3     | 0.000313 |
-| param_norm | 237      |
-| samples    | 6e+03    |
-| step       | 6e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0574   |
-| loss       | 0.000442 |
-| loss_q0    | 0.0013   |
-| loss_q1    | 0.000146 |
-| loss_q2    | 0.000121 |
-| loss_q3    | 0.000138 |
-| mse        | 0.000442 |
-| mse_q0     | 0.0013   |
-| mse_q1     | 0.000146 |
-| mse_q2     | 0.000121 |
-| mse_q3     | 0.000138 |
-| param_norm | 238      |
-| samples    | 7e+03    |
-| step       | 7e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0548   |
-| loss       | 0.000342 |
-| loss_q0    | 0.00106  |
-| loss_q1    | 0.000119 |
-| loss_q2    | 9.39e-05 |
-| loss_q3    | 0.000105 |
-| mse        | 0.000342 |
-| mse_q0     | 0.00106  |
-| mse_q1     | 0.000119 |
-| mse_q2     | 9.39e-05 |
-| mse_q3     | 0.000105 |
-| param_norm | 238      |
-| samples    | 8e+03    |
-| step       | 8e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0496   |
-| loss       | 0.000261 |
-| loss_q0    | 0.000793 |
-| loss_q1    | 0.000106 |
-| loss_q2    | 8.3e-05  |
-| loss_q3    | 8.84e-05 |
-| mse        | 0.000261 |
-| mse_q0     | 0.000793 |
-| mse_q1     | 0.000106 |
-| mse_q2     | 8.3e-05  |
-| mse_q3     | 8.84e-05 |
-| param_norm | 238      |
-| samples    | 9e+03    |
-| step       | 9e+03    |
--------------------------
--------------------------
-| grad_norm  | 0.0476   |
-| loss       | 0.000223 |
-| loss_q0    | 0.000681 |
-| loss_q1    | 9.2e-05  |
-| loss_q2    | 7.2e-05  |
-| loss_q3    | 7.66e-05 |
-| mse        | 0.000223 |
-| mse_q0     | 0.000681 |
-| mse_q1     | 9.2e-05  |
-| mse_q2     | 7.2e-05  |
-| mse_q3     | 7.66e-05 |
-| param_norm | 239      |
-| samples    | 1e+04    |
-| step       | 1e+04    |
--------------------------
-saving model 0...
-saving model 0.9999...
--------------------------
-| grad_norm  | 0.0481   |
-| loss       | 0.000244 |
-| loss_q0    | 0.000788 |
-| loss_q1    | 9.54e-05 |
-| loss_q2    | 7.58e-05 |
-| loss_q3    | 7.92e-05 |
-| mse        | 0.000244 |
-| mse_q0     | 0.000788 |
-| mse_q1     | 9.54e-05 |
-| mse_q2     | 7.58e-05 |
-| mse_q3     | 7.92e-05 |
-| param_norm | 239      |
-| samples    | 1.1e+04  |
-| step       | 1.1e+04  |
--------------------------
--------------------------
-| grad_norm  | 0.048    |
-| loss       | 0.000263 |
-| loss_q0    | 0.000823 |
-| loss_q1    | 0.000103 |
-| loss_q2    | 7.98e-05 |
-| loss_q3    | 8.69e-05 |
-| mse        | 0.000263 |
-| mse_q0     | 0.000823 |
-| mse_q1     | 0.000103 |
-| mse_q2     | 7.98e-05 |
-| mse_q3     | 8.69e-05 |
-| param_norm | 239      |
-| samples    | 1.2e+04  |
-| step       | 1.2e+04  |
--------------------------
--------------------------
-| grad_norm  | 0.0435   |
-| loss       | 0.000173 |
-| loss_q0    | 0.000532 |
-| loss_q1    | 7.64e-05 |
-| loss_q2    | 5.63e-05 |
-| loss_q3    | 5.97e-05 |
-| mse        | 0.000173 |
-| mse_q0     | 0.000532 |
-| mse_q1     | 7.64e-05 |
-| mse_q2     | 5.63e-05 |
-| mse_q3     | 5.97e-05 |
-| param_norm | 240      |
-| samples    | 1.3e+04  |
-| step       | 1.3e+04  |
--------------------------
--------------------------
-| grad_norm  | 0.0437   |
-| loss       | 0.000191 |
-| loss_q0    | 0.000615 |
-| loss_q1    | 7.82e-05 |
-| loss_q2    | 5.73e-05 |
-| loss_q3    | 6.1e-05  |
-| mse        | 0.000191 |
-| mse_q0     | 0.000615 |
-| mse_q1     | 7.82e-05 |
-| mse_q2     | 5.73e-05 |
-| mse_q3     | 6.1e-05  |
-| param_norm | 240      |
-| samples    | 1.4e+04  |
-| step       | 1.4e+04  |
--------------------------
--------------------------
-| grad_norm  | 0.0414   |
-| loss       | 0.00024  |
-| loss_q0    | 0.000731 |
-| loss_q1    | 0.000101 |
-| loss_q2    | 7.8e-05  |
-| loss_q3    | 8.8e-05  |
-| mse        | 0.00024  |
-| mse_q0     | 0.000731 |
-| mse_q1     | 0.000101 |
-| mse_q2     | 7.8e-05  |
-| mse_q3     | 8.8e-05  |
-| param_norm | 241      |
-| samples    | 1.5e+04  |
-| step       | 1.5e+04  |
--------------------------
-saving model 0...
-saving model 0.9999...
--------------------------
-| grad_norm  | 0.0404   |
-| loss       | 0.000183 |
-| loss_q0    | 0.000596 |
-| loss_q1    | 7.05e-05 |
-| loss_q2    | 5.14e-05 |
-| loss_q3    | 5.49e-05 |
-| mse        | 0.000183 |
-| mse_q0     | 0.000596 |
-| mse_q1     | 7.05e-05 |
-| mse_q2     | 5.14e-05 |
-| mse_q3     | 5.49e-05 |
-| param_norm | 241      |
-| samples    | 1.6e+04  |
-| step       | 1.6e+04  |
--------------------------
+CUDA out of memory error in batch 0: CUDA out of memory. Tried to allocate 10.66 GiB. GPU 0 has a total capacity of 47.27 GiB of which 8.49 GiB is free. Process 3888821 has 38.77 GiB memory in use. Of the allocated memory 34.02 GiB is allocated by PyTorch, and 4.22 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+CUDA out of memory error #1
+Memory cleared, retrying...
+Unexpected error: Sizes of tensors must match except in dimension 1. Expected size 10 but got size 9 for tensor number 1 in the list.
+Traceback: Traceback (most recent call last):
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/inpainting_train.py", line 524, in run_loop
+    losses = self.run_step(out_batch, out_cond)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/../guided_diffusion/train_util.py", line 299, in run_step
+    sample = self.forward_backward(batch, cond)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/../guided_diffusion/train_util.py", line 323, in forward_backward
+    losses1 = compute_losses()
+              ^^^^^^^^^^^^^^^^
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/../guided_diffusion/gaussian_diffusion.py", line 932, in training_losses_segmentation
+    model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
+                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.11/site-packages/torch/nn/parallel/distributed.py", line 1643, in forward
+    else self._run_ddp_forward(*inputs, **kwargs)
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.11/site-packages/torch/nn/parallel/distributed.py", line 1459, in _run_ddp_forward
+    return self.module(*inputs, **kwargs)  # type: ignore[index]
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/../guided_diffusion/openaimodel_pseudo3D.py", line 857, in forward
+    h = th.cat([h, hs.pop()], dim=1)
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 10 but got size 9 for tensor number 1 in the list.
+
+Unexpected error: only one element tensors can be converted to Python scalars
+Traceback: Traceback (most recent call last):
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/inpainting_train.py", line 529, in run_loop
+    self.log_metrics(losses, i)
+  File "/root/FCD2_inpainting/fcd2_inpainting/DDPM_Pseudo3D/scripts/inpainting_train.py", line 312, in log_metrics
+    train_loss = float(losses) if hasattr(losses, 'item') else losses
+                 ^^^^^^^^^^^^^
+ValueError: only one element tensors can be converted to Python scalars
+

fcd2_inpainting/DDPM_Pseudo3D/scripts/log/progress.csv

@@ -1,18 +1,3 @@
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+step,val_loss,val_mse,grad_norm,loss,loss_q0,loss_q1,loss_q2,loss_q3,mse,mse_q0,mse_q1,mse_q2,mse_q3,param_norm,samples
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fcd2_inpainting/DDPM_Pseudo3D/scripts/memory.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Environment variables for better PyTorch memory management
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export CUDA_LAUNCH_BLOCKING=1
+export TORCH_CUDNN_V8_API_ENABLED=1
+
+# Optional: Set memory fraction (uncomment if needed)
+# export CUDA_VISIBLE_DEVICES=0
+
+echo "Memory management environment variables set:"
+echo "PYTORCH_CUDA_ALLOC_CONF=$PYTORCH_CUDA_ALLOC_CONF"
+echo "CUDA_LAUNCH_BLOCKING=$CUDA_LAUNCH_BLOCKING"
+echo "TORCH_CUDNN_V8_API_ENABLED=$TORCH_CUDNN_V8_API_ENABLED"
+
+# Show current GPU memory
+if command -v nvidia-smi &> /dev/null; then
+    echo ""
+    echo "Current GPU memory usage:"
+    nvidia-smi --query-gpu=memory.used,memory.total --format=csv,noheader,nounits
+fi

fcd2_inpainting/DDPM_Pseudo3D/scripts/monitor_training.py

@@ -0,0 +1,230 @@
+"""
+Real-time training monitoring script.
+Run this in a separate terminal during training to monitor progress.
+"""
+
+import argparse
+import os
+import json
+import time
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+from datetime import datetime
+import numpy as np
+
+
+class TrainingMonitor:
+    """Real-time training monitor with live plotting."""
+    
+    def __init__(self, log_dir, update_interval=30):
+        self.log_dir = log_dir
+        self.update_interval = update_interval
+        self.metrics_file = os.path.join(log_dir, 'training_metrics.json')
+        
+        # Setup live plotting
+        plt.ion()
+        self.fig, self.axes = plt.subplots(2, 2, figsize=(15, 10))
+        self.fig.suptitle('Training Progress Monitor', fontsize=16)
+        
+        # Initialize plots
+        self.lines = {}
+        self.setup_plots()
+        
+    def setup_plots(self):
+        """Setup the monitoring plots."""
+        # Loss plot
+        self.axes[0, 0].set_title('Training Loss')
+        self.axes[0, 0].set_xlabel('Step')
+        self.axes[0, 0].set_ylabel('Loss')
+        self.axes[0, 0].grid(True)
+        self.axes[0, 0].set_yscale('log')
+        self.lines['loss'], = self.axes[0, 0].plot([], [], 'b-', label='Loss')
+        
+        # MSE plot
+        self.axes[0, 1].set_title('MSE Loss')
+        self.axes[0, 1].set_xlabel('Step')
+        self.axes[0, 1].set_ylabel('MSE')
+        self.axes[0, 1].grid(True)
+        self.axes[0, 1].set_yscale('log')
+        self.lines['mse'], = self.axes[0, 1].plot([], [], 'r-', label='MSE')
+        
+        # Learning rate plot
+        self.axes[1, 0].set_title('Learning Rate')
+        self.axes[1, 0].set_xlabel('Step')
+        self.axes[1, 0].set_ylabel('LR')
+        self.axes[1, 0].grid(True)
+        self.axes[1, 0].set_yscale('log')
+        self.lines['lr'], = self.axes[1, 0].plot([], [], 'g-', label='LR')
+        
+        # VB plot
+        self.axes[1, 1].set_title('Variational Bound')
+        self.axes[1, 1].set_xlabel('Step')
+        self.axes[1, 1].set_ylabel('VB')
+        self.axes[1, 1].grid(True)
+        self.axes[1, 1].set_yscale('log')
+        self.lines['vb'], = self.axes[1, 1].plot([], [], 'm-', label='VB')
+        
+        plt.tight_layout()
+        
+    def load_metrics(self):
+        """Load metrics from JSON file."""
+        if not os.path.exists(self.metrics_file):
+            return None
+        
+        try:
+            with open(self.metrics_file, 'r') as f:
+                return json.load(f)
+        except (json.JSONDecodeError, FileNotFoundError):
+            return None
+    
+    def update_plots(self, metrics):
+        """Update the live plots with new data."""
+        if not metrics or not metrics['step']:
+            return
+        
+        # Update loss plot
+        self.lines['loss'].set_data(metrics['step'], metrics['loss'])
+        self.axes[0, 0].relim()
+        self.axes[0, 0].autoscale_view()
+        
+        # Update MSE plot
+        if metrics['mse']:
+            self.lines['mse'].set_data(metrics['step'], metrics['mse'])
+            self.axes[0, 1].relim()
+            self.axes[0, 1].autoscale_view()
+        
+        # Update learning rate plot
+        self.lines['lr'].set_data(metrics['step'], metrics['learning_rate'])
+        self.axes[1, 0].relim()
+        self.axes[1, 0].autoscale_view()
+        
+        # Update VB plot
+        if metrics['vb']:
+            self.lines['vb'].set_data(metrics['step'], metrics['vb'])
+            self.axes[1, 1].relim()
+            self.axes[1, 1].autoscale_view()
+        
+        # Update display
+        self.fig.canvas.draw()
+        self.fig.canvas.flush_events()
+        
+    def print_summary(self, metrics):
+        """Print training summary."""
+        if not metrics or not metrics['step']:
+            return
+        
+        current_step = metrics['step'][-1]
+        current_loss = metrics['loss'][-1]
+        current_lr = metrics['learning_rate'][-1]
+        
+        # Calculate improvement
+        if len(metrics['loss']) > 1:
+            loss_improvement = metrics['loss'][-2] - current_loss
+            improvement_str = f"({loss_improvement:+.6f})" if loss_improvement != 0 else "(no change)"
+        else:
+            improvement_str = "(first step)"
+        
+        # Calculate training time estimate
+        if len(metrics['step']) > 1:
+            steps_per_second = 1.0  # This would need to be calculated from timestamps
+            eta_str = "N/A"
+        else:
+            eta_str = "N/A"
+        
+        print(f"\n{'='*60}")
+        print(f"Training Progress - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        print(f"{'='*60}")
+        print(f"Current Step: {current_step}")
+        print(f"Current Loss: {current_loss:.6f} {improvement_str}")
+        print(f"Learning Rate: {current_lr:.2e}")
+        print(f"Best Loss: {min(metrics['loss']):.6f}")
+        
+        if metrics['mse']:
+            current_mse = metrics['mse'][-1]
+            print(f"Current MSE: {current_mse:.6f}")
+            print(f"Best MSE: {min(metrics['mse']):.6f}")
+        
+        print(f"Total Steps: {len(metrics['step'])}")
+        print(f"ETA: {eta_str}")
+        print(f"{'='*60}")
+    
+    def monitor(self):
+        """Main monitoring loop."""
+        print(f"Starting training monitor for {self.log_dir}")
+        print(f"Update interval: {self.update_interval} seconds")
+        print("Press Ctrl+C to stop monitoring")
+        
+        last_metrics = None
+        
+        try:
+            while True:
+                metrics = self.load_metrics()
+                
+                if metrics and metrics != last_metrics:
+                    self.update_plots(metrics)
+                    self.print_summary(metrics)
+                    last_metrics = metrics
+                
+                time.sleep(self.update_interval)
+                
+        except KeyboardInterrupt:
+            print("\nMonitoring stopped by user")
+        finally:
+            plt.ioff()
+            plt.close()
+
+
+def check_training_status(log_dir):
+    """Check if training is currently running."""
+    log_file = os.path.join(log_dir, 'log.txt')
+    
+    if not os.path.exists(log_file):
+        return False, "No log file found"
+    
+    # Check if log file was modified recently (within last 5 minutes)
+    mod_time = os.path.getmtime(log_file)
+    if time.time() - mod_time > 300:  # 5 minutes
+        return False, "Log file not updated recently"
+    
+    # Check for training indicators in log
+    try:
+        with open(log_file, 'r') as f:
+            lines = f.readlines()
+            if lines:
+                last_line = lines[-1].strip()
+                if "training" in last_line.lower() or "step" in last_line.lower():
+                    return True, f"Training active - Last log: {last_line}"
+    except:
+        pass
+    
+    return False, "No clear training indicators"
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Monitor training progress in real-time")
+    parser.add_argument("--log_dir", default="log", help="Training log directory")
+    parser.add_argument("--update_interval", type=int, default=30, 
+                       help="Update interval in seconds")
+    parser.add_argument("--check_only", action="store_true", 
+                       help="Only check if training is running")
+    
+    args = parser.parse_args()
+    
+    if args.check_only:
+        is_running, status = check_training_status(args.log_dir)
+        print(f"Training status: {status}")
+        return
+    
+    # Check if training is running
+    is_running, status = check_training_status(args.log_dir)
+    if not is_running:
+        print(f"Warning: {status}")
+        print("Starting monitor anyway...")
+    
+    # Start monitoring
+    monitor = TrainingMonitor(args.log_dir, args.update_interval)
+    monitor.monitor()
+
+
+if __name__ == "__main__":
+    main() 

fcd2_inpainting/DDPM_Pseudo3D/scripts/validate_training.py

@@ -0,0 +1,279 @@
+"""
+Validation script to monitor training progress and generate sample predictions.
+"""
+
+import argparse
+import os
+import sys
+import json
+import numpy as np
+import nibabel as nib
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+sys.path.append("..")
+sys.path.append(".")
+
+import torch as th
+from guided_diffusion import dist_util, logger
+from guided_diffusion.fcd2loader import FCD2Dataset
+from guided_diffusion.script_util import (
+    add_dict_to_argparser,
+    args_to_dict,
+    create_model_and_diffusion,
+    model_and_diffusion_defaults,
+)
+
+
+def validate_model(model, diffusion, dataset, device, num_samples=3, save_dir="validation_samples"):
+    """Validate model by generating sample predictions."""
+    os.makedirs(save_dir, exist_ok=True)
+    
+    model.eval()
+    validation_metrics = []
+    
+    # Sample a few validation cases
+    for i in range(min(num_samples, len(dataset))):
+        inp, label, path, slice_range = dataset[i]
+        
+        if not slice_range:  # Skip if no lesions
+            continue
+            
+        # Get a representative slice
+        slice_idx = slice_range[len(slice_range) // 2]
+        
+        # Prepare input for the slice
+        voided_slice = inp[0, :, :, slice_idx].unsqueeze(0).unsqueeze(0)  # (1, 1, H, W)
+        mask_slice = inp[1, :, :, slice_idx].unsqueeze(0).unsqueeze(0)    # (1, 1, H, W)
+        gt_slice = label[0, :, :, slice_idx].unsqueeze(0).unsqueeze(0)    # (1, 1, H, W)
+        
+        # Stack input channels
+        input_tensor = th.cat([voided_slice, mask_slice], dim=1).to(device)  # (1, 2, H, W)
+        
+        with th.no_grad():
+            # Generate prediction using DDIM for faster sampling
+            sample, _, _ = diffusion.ddim_sample_loop_known(
+                model,
+                (1, 2, input_tensor.shape[2], input_tensor.shape[3]),
+                input_tensor,
+                clip_denoised=True,
+                progress=False,
+            )
+            
+            # Extract predicted intensity
+            pred_slice = sample[:, 0:1, :, :]  # (1, 1, H, W)
+            
+            # Compute metrics
+            mse = th.mean((pred_slice - gt_slice) ** 2).item()
+            mae = th.mean(th.abs(pred_slice - gt_slice)).item()
+            
+            # Compute metrics only in masked region
+            mask_bool = mask_slice > 0
+            if mask_bool.sum() > 0:
+                masked_mse = th.mean((pred_slice[mask_bool] - gt_slice[mask_bool]) ** 2).item()
+                masked_mae = th.mean(th.abs(pred_slice[mask_bool] - gt_slice[mask_bool])).item()
+            else:
+                masked_mse = masked_mae = 0.0
+            
+            validation_metrics.append({
+                'sample_id': i,
+                'mse': mse,
+                'mae': mae,
+                'masked_mse': masked_mse,
+                'masked_mae': masked_mae,
+                'mask_percentage': (mask_bool.sum() / mask_bool.numel()).item() * 100
+            })
+            
+            # Save visualization
+            save_validation_sample(
+                voided_slice.cpu().numpy(),
+                mask_slice.cpu().numpy(),
+                pred_slice.cpu().numpy(),
+                gt_slice.cpu().numpy(),
+                save_dir,
+                i,
+                mse,
+                masked_mse
+            )
+    
+    return validation_metrics
+
+
+def save_validation_sample(voided, mask, pred, gt, save_dir, sample_id, mse, masked_mse):
+    """Save validation sample as visualization."""
+    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+    
+    # Input (voided image)
+    axes[0, 0].imshow(voided[0, 0], cmap='gray')
+    axes[0, 0].set_title('Input (Voided)')
+    axes[0, 0].axis('off')
+    
+    # Mask
+    axes[0, 1].imshow(mask[0, 0], cmap='Reds', alpha=0.7)
+    axes[0, 1].set_title('Mask (ROI)')
+    axes[0, 1].axis('off')
+    
+    # Prediction
+    axes[1, 0].imshow(pred[0, 0], cmap='gray')
+    axes[1, 0].set_title(f'Prediction\nMSE: {mse:.4f}')
+    axes[1, 0].axis('off')
+    
+    # Ground Truth
+    axes[1, 1].imshow(gt[0, 0], cmap='gray')
+    axes[1, 1].set_title(f'Ground Truth\nMasked MSE: {masked_mse:.4f}')
+    axes[1, 1].axis('off')
+    
+    plt.tight_layout()
+    plt.savefig(os.path.join(save_dir, f'validation_sample_{sample_id}.png'), 
+                dpi=150, bbox_inches='tight')
+    plt.close()
+
+
+def plot_training_progress(log_dir):
+    """Plot training progress from saved metrics."""
+    metrics_file = os.path.join(log_dir, 'training_metrics.json')
+    
+    if not os.path.exists(metrics_file):
+        print(f"No metrics file found at {metrics_file}")
+        return
+    
+    with open(metrics_file, 'r') as f:
+        metrics = json.load(f)
+    
+    if not metrics['step']:
+        print("No training metrics available")
+        return
+    
+    fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+    
+    # Loss plot
+    axes[0, 0].plot(metrics['step'], metrics['loss'])
+    axes[0, 0].set_title('Training Loss')
+    axes[0, 0].set_xlabel('Step')
+    axes[0, 0].set_ylabel('Loss')
+    axes[0, 0].grid(True)
+    axes[0, 0].set_yscale('log')
+    
+    # MSE plot
+    if metrics['mse']:
+        axes[0, 1].plot(metrics['step'], metrics['mse'])
+        axes[0, 1].set_title('MSE Loss')
+        axes[0, 1].set_xlabel('Step')
+        axes[0, 1].set_ylabel('MSE')
+        axes[0, 1].grid(True)
+        axes[0, 1].set_yscale('log')
+    
+    # Learning rate plot
+    axes[1, 0].plot(metrics['step'], metrics['learning_rate'])
+    axes[1, 0].set_title('Learning Rate')
+    axes[1, 0].set_xlabel('Step')
+    axes[1, 0].set_ylabel('LR')
+    axes[1, 0].set_yscale('log')
+    axes[1, 0].grid(True)
+    
+    # VB plot (if available)
+    if metrics['vb']:
+        axes[1, 1].plot(metrics['step'], metrics['vb'])
+        axes[1, 1].set_title('Variational Bound')
+        axes[1, 1].set_xlabel('Step')
+        axes[1, 1].set_ylabel('VB')
+        axes[1, 1].grid(True)
+        axes[1, 1].set_yscale('log')
+    
+    plt.tight_layout()
+    plt.savefig(os.path.join(log_dir, 'training_progress.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    # Print summary statistics
+    print(f"Training Progress Summary:")
+    print(f"Total steps: {len(metrics['step'])}")
+    print(f"Final loss: {metrics['loss'][-1]:.6f}")
+    print(f"Best loss: {min(metrics['loss']):.6f}")
+    if metrics['mse']:
+        print(f"Final MSE: {metrics['mse'][-1]:.6f}")
+        print(f"Best MSE: {min(metrics['mse']):.6f}")
+
+
+def main():
+    args = create_argparser().parse_args()
+    
+    dist_util.setup_dist()
+    logger.configure(dir=args.log_dir)
+    
+    logger.log("VALIDATION START " + str(datetime.now()))
+    logger.log("args: " + str(args))
+    
+    # Plot training progress if available
+    if args.plot_progress:
+        plot_training_progress(args.log_dir)
+    
+    if not args.validate:
+        return
+    
+    logger.log("creating model and diffusion...")
+    model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args, model_and_diffusion_defaults().keys())
+    )
+    
+    # Load model checkpoint
+    if args.model_path and os.path.exists(args.model_path):
+        model.load_state_dict(dist_util.load_state_dict(args.model_path, map_location="cpu"))
+        logger.log(f"Loaded model from {args.model_path}")
+    else:
+        logger.log("No model checkpoint found, using random weights")
+    
+    model.to(dist_util.dev())
+    model.eval()
+    
+    logger.log("creating validation dataset...")
+    dataset = FCD2Dataset(args.data_dir, test_flag=False, mode=args.mode)
+    
+    logger.log("running validation...")
+    validation_metrics = validate_model(
+        model, 
+        diffusion, 
+        dataset, 
+        dist_util.dev(),
+        num_samples=args.num_samples,
+        save_dir=os.path.join(args.log_dir, 'validation_samples')
+    )
+    
+    # Save validation results
+    validation_file = os.path.join(args.log_dir, 'validation_results.json')
+    with open(validation_file, 'w') as f:
+        json.dump(validation_metrics, f, indent=2)
+    
+    # Print summary
+    if validation_metrics:
+        avg_mse = np.mean([m['mse'] for m in validation_metrics])
+        avg_masked_mse = np.mean([m['masked_mse'] for m in validation_metrics])
+        avg_mae = np.mean([m['mae'] for m in validation_metrics])
+        avg_masked_mae = np.mean([m['masked_mae'] for m in validation_metrics])
+        
+        logger.log(f"Validation Summary:")
+        logger.log(f"Average MSE: {avg_mse:.6f}")
+        logger.log(f"Average Masked MSE: {avg_masked_mse:.6f}")
+        logger.log(f"Average MAE: {avg_mae:.6f}")
+        logger.log(f"Average Masked MAE: {avg_masked_mae:.6f}")
+    
+    logger.log("Validation complete.")
+
+
+def create_argparser():
+    defaults = dict(
+        data_dir="../FCD2/Pathological",
+        log_dir="log",
+        model_path="",  # Path to model checkpoint
+        mode="add_lesions",
+        num_samples=5,
+        validate=True,
+        plot_progress=True,
+    )
+    defaults.update(model_and_diffusion_defaults())
+    parser = argparse.ArgumentParser()
+    add_dict_to_argparser(parser, defaults)
+    return parser
+
+
+if __name__ == "__main__":
+    main()