diff --git a/pyproject.toml b/pyproject.toml index 5ca6787b..19882bf9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,4 +82,4 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", -] +] \ No newline at end of file diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py new file mode 100644 index 00000000..553b0611 --- /dev/null +++ b/src/quantem/core/ml/constraints.py @@ -0,0 +1,100 @@ +from abc import ABC, abstractmethod +from copy import deepcopy +from dataclasses import dataclass +from typing import Any, Self + +import numpy as np +import torch +from numpy.typing import NDArray + + +@dataclass(slots=False) +class Constraints(ABC): + """ + Any model that inherits from BaseConstraints will contain a Constraints instance that contains soft and hard constraints. + """ + + soft_constraint_keys = [] + hard_constraint_keys = [] + + @property + def allowed_keys(self) -> list[str]: + """ + List of all allowed keys. + """ + return self.hard_constraint_keys + self.soft_constraint_keys + + def copy(self) -> Self: + """ + Copy the constraints. + """ + return deepcopy(self) + + def __str__(self) -> str: + hard = "\n".join(f"{key}: {getattr(self, key)}" for key in self.hard_constraint_keys) + soft = "\n".join(f"{key}: {getattr(self, key)}" for key in self.soft_constraint_keys) + + # Fix: Move the replace operations outside the f-string or assign to variables + hard_indented = hard.replace("\n", "\n ") + soft_indented = soft.replace("\n", "\n ") + + return ( + "Constraints:\n" + " Hard constraints:\n" + f" {hard_indented}\n" + " Soft constraints:\n" + f" {soft_indented}" + ) + + +class BaseConstraints(ABC): + """ + Base class for constraints. + """ + + # Default constraints are the dataclasses themselves. + DEFAULT_CONSTRAINTS = Constraints() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._soft_constraint_losses = [] + self.constraints = self.DEFAULT_CONSTRAINTS.copy() + + @property + def soft_constraint_losses(self) -> NDArray[np.float32]: + return np.array(self._soft_constraint_losses, dtype=np.float32) + + @property + def constraints(self) -> Constraints: + """ + Constraints for the model. + """ + return self._constraints + + @constraints.setter + def constraints(self, constraints: Constraints | dict[str, Any]): + """ + Setter for constraints class, can be a Constraints instance or a dictionary. + """ + if isinstance(constraints, Constraints): + self._constraints = constraints + elif isinstance(constraints, dict): + for key, value in constraints.items(): + setattr(self._constraints, key, value) + else: + raise ValueError(f"Invalid constraints type: {type(constraints)}") + + # --- Required methods tha tneeds to implemented in subclasses --- + @abstractmethod + def apply_hard_constraints(self, *args, **kwargs) -> torch.Tensor: + """ + Apply hard constraints to the model. + """ + raise NotImplementedError + + @abstractmethod + def apply_soft_constraints(self, *args, **kwargs) -> torch.Tensor: + """ + Apply soft constraints to the model. + """ + raise NotImplementedError diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py new file mode 100644 index 00000000..dedd90dd --- /dev/null +++ b/src/quantem/core/ml/ddp.py @@ -0,0 +1,181 @@ +import os + +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.utils.data import DataLoader, Dataset, DistributedSampler, random_split + +from quantem.tomography.dataset_models import DatasetModelType + + +def worker_init_fn(worker_id): + os.environ["CUDA_VISIBLE_DEVICES"] = "" + + +class DDPMixin: + """ + Class for setting up all distributed training. + + - + """ + + def setup_distributed(self, device: str | torch.device | None = None): + """ + Initializes parameters depending if multiple-GPU training, single-GPU training, or CPU training. + """ + if "RANK" in os.environ: + if not dist.is_initialized(): + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" + ) + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + self.local_rank = int(os.environ["LOCAL_RANK"]) + torch.cuda.set_device(self.local_rank) + device = torch.device("cuda", self.local_rank) + else: + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + device = torch.device("cuda:0" if device is None else device) + torch.cuda.set_device(device.index) + else: + device = torch.device("cpu") + + if device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + self.device = device + + def setup_dataloader( + self, + dataset: Dataset | DatasetModelType, + batch_size: int, + num_workers: int = 0, + val_fraction: float = 0.0, + ): + pin_mem = self.device.type == "cuda" + persist = num_workers > 0 + + if val_fraction > 0.0: + train_dataset, val_dataset = random_split(dataset, [1 - val_fraction, val_fraction]) # type: ignore[reportArgumentType] --> dataset inherits from torch Dataset so this is fine. + else: + train_dataset = dataset + val_dataset = None + + if self.world_size > 1: + shuffle = True + train_sampler = DistributedSampler( + train_dataset, # type: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=shuffle, + ) + + if val_dataset: + val_sampler = DistributedSampler( + val_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False, + ) + else: + val_sampler = None + shuffle = False + + else: + train_sampler = None + val_sampler = None + shuffle = True + + train_dataloader = DataLoader( + train_dataset, # type: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. + batch_size=batch_size, + num_workers=num_workers, + sampler=train_sampler, + shuffle=shuffle, + pin_memory=pin_mem, + drop_last=True, + persistent_workers=persist, + multiprocessing_context="spawn", + worker_init_fn=worker_init_fn, + ) + + if val_dataset: + val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size * 4, + num_workers=num_workers, + sampler=val_sampler, + shuffle=False, + pin_memory=pin_mem, + drop_last=False, + persistent_workers=persist, + multiprocessing_context="spawn", + worker_init_fn=worker_init_fn, + ) + val_dataloader = val_dataloader + else: + val_dataloader = None + + if self.global_rank == 0: + print("Dataloader setup complete:") + print(f" Total train samples: {len(train_dataset)}") # pyright: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. + print(f" Local batch size: {batch_size}") + print(f" Global batch size: {batch_size * self.world_size}") + print(f" Train batches per GPU per epoch: {len(train_dataloader)}") + + if val_dataset: + print(f" Total val samples: {len(val_dataset)}") + print(f" Val batches per GPU per epoch: {len(val_dataloader)}") # pyright: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. + + return train_dataloader, train_sampler, val_dataloader, val_sampler + + def distribute_model( + self, + model: nn.Module, + ) -> nn.Module | nn.parallel.DistributedDataParallel: + """ + Wraps the model with DistributedDataParallel if mulitple GPUs are available. + + Returns the model. + """ + model = model.to(self.device) + + if self.world_size > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, + bucket_cap_mb=100, + gradient_as_bucket_view=True, + ) + + if self.global_rank == 0: + print("Model wrapped with DDP and compiled") + + if self.world_size > 1: + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully") + + else: + print("Model built, compiled successfully") + + return model + + @property + def device(self) -> torch.device: + return self._device + + @device.setter + def device(self, device: torch.device | str): + if isinstance(device, str): + device = torch.device(device) + self._device = device diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 276e5680..08b1732d 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -23,6 +23,7 @@ def __init__( hsiren: bool = False, dtype: torch.dtype = torch.float32, final_activation: str | Callable = "identity", + winner_initialization: bool | int = False, ) -> None: """Initialize Siren. @@ -59,7 +60,7 @@ def __init__( self.alpha = alpha self.hsiren = hsiren self.dtype = dtype - + self.winner_initialization = winner_initialization self.final_activation = final_activation self._build() @@ -109,6 +110,21 @@ def _build(self) -> None: net_list.append(self._final_activation) self.net = nn.Sequential(*net_list) + if self.winner_initialization: + if type(self.winner_initialization) is int: + rng = torch.Generator() + rng.manual_seed(self.winner_initialization) + else: + rng = torch.Generator() + rng.manual_seed(42) + with torch.no_grad(): + self.net[0].linear.weight += ( # type: ignore[reportAttributeAccessIssue] + torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 # type:ignore + ) + self.net[1].linear.weight += ( # type: ignore[reportAttributeAccessIssue] + torch.randn_like(self.net[1].linear.weight) * 0.1 / self.hidden_omega_0 # type:ignore + ) + def forward(self, coords: torch.Tensor) -> torch.Tensor: output = self.net(coords) return output @@ -201,6 +217,7 @@ def __init__( alpha: float = 1.0, dtype: torch.dtype = torch.float32, final_activation: str | Callable = "identity", + winner_initialization: bool | int = False, ) -> None: """Initialize HSiren. @@ -236,4 +253,5 @@ def __init__( hsiren=True, dtype=dtype, final_activation=final_activation, + winner_initialization=winner_initialization, ) diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index 272115ea..a81b4fb0 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -1,5 +1,7 @@ from typing import TYPE_CHECKING, Callable +import torch.nn as nn + from quantem.core import config if TYPE_CHECKING: @@ -9,133 +11,165 @@ import torch -def get_loss_function(name: str | Callable, dtype: torch.dtype) -> Callable: - """Get a loss function by name or return callable if provided. +def get_loss_module(name: str | nn.Module | Callable, dtype: torch.dtype, **kwargs) -> nn.Module: + """Return a loss *module* by name, or wrap/return what was provided.""" + if isinstance(name, nn.Module): + return name - Parameters - ---------- - name : str or Callable - Loss function name or callable function. - dtype : torch.dtype - Data type (used to determine complex vs real loss functions). + if callable(name) and not isinstance(name, str): + # Wrap a bare callable into an nn.Module + class _CallableLoss(nn.Module): + def __init__(self, fn: Callable): + super().__init__() + self.fn = fn - Returns - ------- - Callable - Loss function. + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + return self.fn(pred, target) + + return _CallableLoss(name) + + loss_name = str(name).lower() - Raises - ------ - ValueError - If loss function name is unknown for the given dtype. - """ - if isinstance(name, Callable): - return name - else: - name = name.lower() if dtype.is_complex: - if name in ["l2", "complex_l2"]: - return complex_l2 - elif name in ["complex_cartesian_l2"]: - return complex_cartesian_l2 - elif name in ["amp_phase_l2"]: - return amp_phase_l2 - elif name in ["combined_l2"]: - return combined_l2 - else: - raise ValueError(f"Unknown loss function for complex dtype: {name}") - else: - if name in ["l2"]: - return torch.nn.functional.mse_loss - elif name in ["l1"]: - return torch.nn.functional.l1_loss - else: - raise ValueError(f"Unknown loss function for real dtype: {name}") - - -def complex_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute L2 loss for complex tensors (separate real and imaginary parts). - - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. - - Returns - ------- - torch.Tensor - L2 loss value. - """ - real_l2 = torch.mean((pred.real - target.real) ** 2) - imag_l2 = torch.mean((pred.imag - target.imag) ** 2) - return (real_l2 + imag_l2) / 2 + if loss_name in {"l2", "complex_l2"}: + return ComplexL2Loss(**kwargs) + if loss_name in {"complex_cartesian_l2"}: + return ComplexCartesianL2Loss(**kwargs) + if loss_name in {"amp_phase_l2"}: + return AmpPhaseL2Loss(**kwargs) + if loss_name in {"combined_l2"}: + return CombinedL2Loss(**kwargs) + raise ValueError(f"Unknown loss module for complex dtype: {loss_name}") + + # real dtype + if loss_name in {"l2"}: + return nn.MSELoss(**kwargs) + if loss_name in {"l1"}: + return nn.L1Loss(**kwargs) + if loss_name in {"smooth_l1"}: + return nn.SmoothL1Loss(**kwargs) + if loss_name in {"charbonnier"}: + return CharbonnierLoss(**kwargs) + if loss_name in {"llmse"}: + return LLMSELoss(**kwargs) + if loss_name in {"mse_log_mse"}: + return MSELogMSELoss(**kwargs) + + raise ValueError(f"Unknown loss module for real dtype: {loss_name}") + + +class ComplexL2Loss(nn.Module): + """L2 loss for complex tensors (separate real/imag, then average).""" + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + real_l2 = torch.mean((pred.real - target.real) ** 2) + imag_l2 = torch.mean((pred.imag - target.imag) ** 2) + return (real_l2 + imag_l2) / 2 -def complex_cartesian_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute L2 loss for complex tensors in Cartesian coordinates. - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. +class ComplexCartesianL2Loss(nn.Module): + """L2 loss for complex tensors in Cartesian form: E[(Δre^2 + Δim^2)].""" - Returns - ------- - torch.Tensor - L2 loss value. + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + real_dif = pred.real - target.real + imag_dif = pred.imag - target.imag + return torch.mean(real_dif**2 + imag_dif**2) + + +class AmpPhaseL2Loss(nn.Module): + """L2 loss on amplitude + wrapped phase.""" + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + amp_l2 = ((target.abs() - pred.abs()) ** 2).mean() + + phase_dif = torch.abs(target.angle() - pred.angle()) + phase_dif = torch.min(phase_dif, 2 * torch.pi - phase_dif) # wrap to [0, pi] + phase_l2 = torch.mean(phase_dif**2) + + return amp_l2 + phase_l2 + + +class CombinedL2Loss(nn.Module): + """Weighted sum of AmpPhaseL2 and ComplexL2. + + loss = alpha * amp_phase + (1 - alpha) * complex_l2 """ - real_dif = pred.real - target.real - imag_dif = pred.imag - target.imag - loss = torch.mean(real_dif**2 + imag_dif**2) - return loss - - -def amp_phase_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute L2 loss for complex tensors in amplitude-phase representation. - - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. - - Returns - ------- - torch.Tensor - L2 loss value (amplitude + phase). + + def __init__(self, alpha: float = 0.7): + super().__init__() + self.alpha = float(alpha) + self.complex_l2 = ComplexL2Loss() + self.amp_phase_l2 = AmpPhaseL2Loss() + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + comp_l2 = self.complex_l2(pred, target) + amp_ph_l2 = self.amp_phase_l2(pred, target) + return self.alpha * amp_ph_l2 + (1 - self.alpha) * comp_l2 + + +class MSELogMSELoss(nn.Module): + def __init__( + self, + eps: float = 1e-8, + reduction: str = "mean", + ): + super(MSELogMSELoss, self).__init__() + self.eps = eps + self.reduction = reduction + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + mse = (pred - target) ** 2 + log_mse = -mse * torch.log(mse + self.eps) + if self.reduction == "mean": + return log_mse.mean() + elif self.reduction == "sum": + return log_mse.sum() + return log_mse + + +class LLMSELoss(nn.Module): """ - amp_l2 = ((target.abs() - pred.abs()) ** 2).mean() - phase_dif = torch.abs(target.angle() - pred.angle()) - phase_dif = torch.min(phase_dif, 2 * torch.pi - phase_dif) # phase wrapping - phase_l2 = torch.mean(phase_dif**2) - return amp_l2 + phase_l2 - - -def combined_l2(pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.7) -> torch.Tensor: - """Combined L2 loss: weighted sum of amplitude-phase and complex L2 losses. - - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. - alpha : float, optional - Weight for amplitude-phase loss. Larger alpha gives more weight to - amp/phase, smaller alpha gives more weight to real/imag, by default 0.7 - - Returns - ------- - torch.Tensor - Combined L2 loss value. - - different alpha values can affect stability of training. + Logarithmic Linear Mean Squared Error (LLMSE) loss: + L = -log(1 - |y - y_hat| / max(|y - y_hat|)) """ - comp_l2 = complex_l2(pred, target) - amp_ph_l2 = amp_phase_l2(pred, target) - return alpha * amp_ph_l2 + (1 - alpha) * comp_l2 + + def __init__(self, eps: float = 1e-8, reduction: str = "mean"): + super().__init__() + self.eps = eps + self.reduction = reduction + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + # Absolute residual + abs_diff = torch.abs(pred - target) + + # Normalization by max error in batch (avoid div-by-zero) + max_diff = torch.max(abs_diff.detach()) + self.eps + norm_diff = abs_diff / max_diff + + # Apply -log(1 - normalized_error) + loss = -torch.log(1.0 - norm_diff + self.eps) + + # Reduce + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + return loss + + +class CharbonnierLoss(nn.Module): + def __init__(self, epsilon=1e-12, reduction="mean"): + super(CharbonnierLoss, self).__init__() + self.epsilon = epsilon + self.reduction = reduction + + def forward(self, prediction, target): + diff = prediction - target + loss = torch.sqrt(diff * diff + self.epsilon**2) + + if self.reduction == "mean": + return torch.mean(loss) + elif self.reduction == "sum": + return torch.sum(loss) + else: # 'none' + return loss diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index d30229da..4ea263c0 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,3 +1,4 @@ +import textwrap from abc import abstractmethod from dataclasses import dataclass from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence @@ -68,6 +69,16 @@ def params(self) -> dict: "weight_decay": self.weight_decay, } + def __str__(self) -> str: + return textwrap.dedent(f""" + OptimizerParams.Adam( + lr = {self.lr}, + betas = {self.betas}, + eps = {self.eps}, + weight_decay = {self.weight_decay}, + ) + """).strip() + @dataclass class AdamW: """ @@ -103,6 +114,16 @@ def params(self) -> dict: "weight_decay": self.weight_decay, } + def __str__(self) -> str: + return textwrap.dedent(f""" + OptimizerParams.AdamW( + lr = {self.lr}, + betas = {self.betas}, + eps = {self.eps}, + weight_decay = {self.weight_decay}, + ) + """).strip() + @dataclass class SGD: """ @@ -138,6 +159,17 @@ def params(self) -> dict: "nesterov": self.nesterov, } + def __str__(self) -> str: + return textwrap.dedent(f""" + OptimizerParams.SGD( + lr = {self.lr}, + momentum = {self.momentum}, + dampening = {self.dampening}, + weight_decay = {self.weight_decay}, + nesterov = {self.nesterov}, + ) + """).strip() + @dataclass class NoneOptimizer: """ @@ -152,6 +184,11 @@ class NoneOptimizer: def params(self) -> dict: return {} + def __str__(self) -> str: + return textwrap.dedent(""" + OptimizerParams.NoneOptimizer() + """).strip() + @classmethod def parse_dict(cls, d: dict): """ @@ -236,7 +273,7 @@ class Plateau: min_lr : float or None Absolute lower bound on the learning rate. Overrides ``min_lr_factor`` when set. Default: None. - factor : float + factor : float Factor by which the LR is reduced: ``new_lr = lr * factor``. Default: 0.5. patience : int Number of epochs with no improvement before reducing LR. Default: 10. @@ -454,7 +491,7 @@ def parse_dict(cls, d: dict): type_ = d.pop("type", None) name = name or type_ if name is None: - raise ValueError("Must provide either 'name' or 'type' key") + name = "none" if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): diff --git a/src/quantem/core/ml/profiling.py b/src/quantem/core/ml/profiling.py new file mode 100644 index 00000000..aec6e5ed --- /dev/null +++ b/src/quantem/core/ml/profiling.py @@ -0,0 +1,14 @@ +from contextlib import contextmanager + +import torch.cuda.nvtx as nvtx + + +@contextmanager +def nvtx_range(enabled: bool, name: str): + if enabled: + nvtx.range_push(name) + try: + yield + finally: + if enabled: + nvtx.range_pop() diff --git a/src/quantem/core/utils/filter.py b/src/quantem/core/utils/filter.py index 589d84ca..3f9d13b7 100644 --- a/src/quantem/core/utils/filter.py +++ b/src/quantem/core/utils/filter.py @@ -1,5 +1,6 @@ import numpy as np import scipy.ndimage as ndi +import torch from quantem.core.utils.utils import extract_patches @@ -109,3 +110,53 @@ def otsu_threshold(img: np.ndarray, bins: int = 256) -> float: current_max = between_var threshold = bin_edges[i] return threshold + + +# --- Gaussian filters --- + + +def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: + radius = np.ceil(num_sigmas * sigma) + support = torch.arange(-radius, radius + 1, dtype=torch.float) + kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() + # Ensure kernel weights sum to 1, so that image brightness is not altered + return kernel.mul_(1 / kernel.sum()) + + +def gaussian_filter_2d( + img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor +) -> torch.Tensor: # Add kernel_1d as an argument + # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function + padding = len(kernel_1d) // 2 # Ensure that image size does not change + img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` + # Convolve along columns and rows + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + return img.squeeze_(0).squeeze_(0) # Make 2D again + + +def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: + """ + Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. + + Args: + stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms + kernel_1d (torch.Tensor): 1D Gaussian kernel + + Returns: + torch.Tensor: Blurred stack of same shape (H, N, W) + """ + H, N, W = stack.shape + padding = len(kernel_1d) // 2 + + # Reshape to (N, 1, H, W) for conv2d + stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) + + # Apply separable conv2d: vertical then horizontal + out = torch.nn.functional.conv2d( + stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) + ) + out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + + # Restore shape to (H, N, W) + return out.squeeze(1).permute(1, 0, 2) diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py new file mode 100644 index 00000000..9f5df102 --- /dev/null +++ b/src/quantem/core/utils/tomography_utils.py @@ -0,0 +1,307 @@ +from typing import Any, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from matplotlib import cm # TODO: Temporary +from numpy.typing import NDArray +from scipy.ndimage import center_of_mass, gaussian_filter, shift +from scipy.special import comb +from tqdm.auto import tqdm + +from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.utils.utils import to_numpy +from quantem.core.visualization import show_2d + +ImageType = NDArray[Any] +BoolArray = NDArray[np.bool_] + + +def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: + k = np.arange(n + 1, dtype=int) + return ( + comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) + ) + + +def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray[Any]: + H, W = im_shape + ou, ov = int(order[0]), int(order[1]) + u = np.linspace(0.0, 1.0, H) + v = np.linspace(0.0, 1.0, W) + Bu = _bernstein_basis_1d(ou, u) + Bv = _bernstein_basis_1d(ov, v) + basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) + return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) + + +def background_subtract( + image: ImageType, + mask: Optional[BoolArray] = None, + thresh_bg: Optional[float] = None, + order: Tuple[int, int] = (1, 1), + sigma: Optional[float] = None, + num_iter: int = 10, + plot_result: bool = True, + axsize: Tuple[int, int] = (3, 3), + cmap: str = "turbo", + return_background_and_mask: bool = False, + **show_kwargs, +) -> ImageType | Tuple[ImageType, NDArray[Any], BoolArray]: + """ + Background subtraction via bivariate Bernstein polynomial fitting. + + Returns + ------- + - If `return_background_and_mask=False`: ImageType (same as input) + - If `True`: (ImageType, numpy.ndarray, numpy.ndarray[bool]) + where background and mask are always NumPy. + """ + im = to_numpy(image).astype(float, copy=True) + if im.ndim != 2: + raise ValueError("`image` must be 2D") + + mask_arr: BoolArray = ( + np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) + ) + if mask_arr.shape != im.shape: + raise ValueError("`mask` must match `image` shape") + + order = (int(order[0]), int(order[1])) + A_full = _build_basis_matrix(im.shape, order) + H, W = im.shape + im_flat = im.ravel() + + im_bg = np.zeros_like(im) + thresh_val = np.median(im[mask_arr]) if thresh_bg is None else float(thresh_bg) + + resid = im - im_bg + if sigma and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + mask_bg: BoolArray = (resid < thresh_val) & mask_arr + + for _ in range(int(num_iter)): + idx = mask_bg.ravel() + if not np.any(idx): + idx = mask_arr.ravel() + coefs, *_ = np.linalg.lstsq(A_full[idx, :], im_flat[idx], rcond=None) + im_bg = (A_full @ coefs).reshape(H, W) + + resid = im - im_bg + if sigma and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + + thr = thresh_val if thresh_bg is None else float(thresh_bg) + mask_bg = (resid < thr) & mask_arr + + im_sub_np = im - im_bg + + if plot_result: + vals = im_sub_np[mask_arr] + vals = vals[np.isfinite(vals)] + if vals.size == 0: + vals = np.array([0.0]) + vmin_sub = float(np.min(vals)) + vmax_sub = float(np.max(vals)) + vrange = float(max(abs(vmin_sub), abs(vmax_sub))) or 1e-12 + + bg_disp = (im_bg - np.mean(im_bg)).copy() + bg_disp[~mask_bg] = np.nan + + cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") + cmap_div = "RdBu_r" + + disp = [im - np.mean(im_bg), bg_disp, im_sub_np] + norm = [ + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "centered", + "stretch_type": "linear", + "vcenter": 0.0, + "half_range": vrange, + }, + ] + + show_2d( + disp, + cmap=[cmap_base, cmap_base, cmap_div], + norm=norm, + cbar=[False, False, True], + title=["Input Image", "Background (fit region)", "Background Subtracted"], + axsize=axsize, + **show_kwargs, + ) + + # # preserve if needed + # if isinstance( + # image, + # ): + # meta = dict(origin=image.origin, sampling=image.sampling, units=image.units) + # name_base = getattr(image, "name", "image") + # im_sub: ImageType = .from_array(im_sub_np, name=f"{name_base} (bg-sub)", **meta) # type: ignore[assignment] + # else: + im_sub = im_sub_np # type: ignore[assignment] + + if return_background_and_mask: + return im_sub, im_bg, mask_bg + return im_sub + + +# --- Tilt Series Processing Utility Functions --- + + +def fourier_binning(img, crop_size): + """ + Crop the img in Fourier space to the specified size. + """ + center = np.array(img.shape) // 2 + + fft_img = np.fft.fftshift(np.fft.fft2(img)) + + cropped_fft = fft_img[ + center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, + center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, + ] + cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real + return cropped_img + + +def cross_correlation_align_stack(ref_img, stack, print_pred=False): + """ + Aligns a stack of images to a reference image using cross-correlation. + + This function assumes the stack does not contain the reference image itself. + + Stack shape should be (N, H, W) where N is the number of images. + """ + + new_images = [] + pred_shifts = [] + + prev_img = ref_img + for img in tqdm(stack): + shift_pred = cross_correlation_shift(prev_img, img) + if print_pred: + print(f"Shift prediction: {shift_pred}") + shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) + + pred_shifts.append(shift_pred) + new_images.append(shifted_image) + + prev_img = shifted_image + + return new_images, pred_shifts + + +def centering_com_alignment(image_stack): + """ + Aligns the image stack to the center of mass of the whole image_stack to the + image center. This is useful for aligning the tilt series to the invariant line. + """ + + aligned_stack = np.zeros_like(image_stack) + h, w = image_stack.shape[1:] + image_center = np.array([h // 2, w // 2]) + + com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) + + for i, img in enumerate(image_stack): + com_img = np.array(center_of_mass(img)) + shift_vec = com_reference - com_img + aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) + + final_shift = image_center - com_reference + for i in range(aligned_stack.shape[0]): + aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) + + return aligned_stack + + +def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): + """ + Shifts a 2D image using grid_sample in a differentiable manner with zero-pad boundary conditions applied. + + Args: + image: Tensor of shape [H, W] + shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) + shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) + sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts + + Returns: + Shifted image of shape [H, W] + """ + H, W = image.shape + + # Convert physical shift to pixel shift + shift_x_pixel = shift_x + shift_y_pixel = shift_y + + # Normalize shift for grid_sample (assuming align_corners=True) + normalized_shift_x = shift_x_pixel * 2 / (W - 1) + normalized_shift_y = shift_y_pixel * 2 / (H - 1) + + # Create normalized grid + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=image.device), + torch.linspace(-1, 1, W, device=image.device), + indexing="ij", + ) + + grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] + + # Apply shift (ensure it's differentiable) + grid[:, :, :, 0] -= normalized_shift_x + grid[:, :, :, 1] -= normalized_shift_y + + # Add batch and channel dimensions + image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] + + # Sample using grid_sample (fully differentiable) + shifted_image = F.grid_sample( + image, grid, mode="bicubic", padding_mode="zeros", align_corners=True + ) + + return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] + + +# --- TV loss --- + + +def get_TV_loss(tensor, factor=1e-3): + tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + + +# Circular mask + + +def torch_phase_cross_correlation(im1, im2): + f1 = torch.fft.fft2(im1) + f2 = torch.fft.fft2(im2) + cc = torch.fft.ifft2(f1 * torch.conj(f2)) + cc_abs = torch.abs(cc) + + max_idx = torch.argmax(cc_abs) + shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() + + for i, dim in enumerate(im1.shape): + if shifts[i] > dim // 2: + shifts[i] -= dim + + # return shifts.flip(0) # (dx, dy) + return shifts diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 0c66629e..9623e7b3 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -13,7 +13,7 @@ from quantem.core import config from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights -from quantem.core.ml.loss_functions import get_loss_function +from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.core.utils.validators import ( @@ -1085,7 +1085,7 @@ def pretrain( "No pretrain target set. Provide pretrain_target or set it beforehand." ) - loss_fn = get_loss_function(loss_fn, self.dtype) + loss_fn = get_loss_module(loss_fn, self.dtype) self._pretrain( num_iters=num_iters, loss_fn=loss_fn, diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index 77dbb1c1..e9027620 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -14,7 +14,7 @@ from quantem.core.datastructures import Dataset2d, Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights -from quantem.core.ml.loss_functions import get_loss_function +from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.core.utils.utils import electron_wavelength_angstrom, to_numpy @@ -1391,7 +1391,7 @@ def pretrain( elif self.pretrain_target is None: self.pretrain_target = self._initial_probe.clone().detach() - loss_fn = get_loss_function(loss_fn, self.dtype) + loss_fn = get_loss_module(loss_fn, self.dtype) self._pretrain( num_iters=num_iters, loss_fn=loss_fn, diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py new file mode 100644 index 00000000..f6343e4e --- /dev/null +++ b/src/quantem/tomography/dataset_models.py @@ -0,0 +1,688 @@ +from abc import abstractmethod +from dataclasses import dataclass +from typing import Any + +import torch +import torch.nn as nn +from numpy.typing import NDArray +from torch.utils.data import Dataset + +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml.constraints import BaseConstraints, Constraints +from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.tomography.utils import tv_loss_1d + +# --- Constraints --- + + +class DatasetConstraintParams: + """ + Namespace class for dataset constraint parameter dataclasses and parsing utilities. + + Contains constraint definitions for different tomography dataset types and a + factory method for instantiating the appropriate constraint class from a dict. + + Supported constraint types + -------------------------- + BaseTomographyDatasetConstraints + Base soft constraints for z-position and lateral shift regularization. + ThroughFocalDatasetConstraints + Inherits base constraints; not yet implemented. + + Examples + -------- + >>> DatasetConstraintParams.parse_dict({"name": "base_tomography_dataset", "tv_zs": 0.1}) + BaseTomographyDatasetConstraints(tv_zs=0.1, tv_shifts=0.0) + >>> DatasetConstraintParams.parse_dict({"type": "base_tomography_dataset"}) + BaseTomographyDatasetConstraints(tv_zs=0.0, tv_shifts=0.0) + """ + + @dataclass + class BaseTomographyDatasetConstraints(Constraints): + """ + Soft constraints for a base tomography dataset. + + Attributes + ---------- + tv_zs : float + Total variation regularization weight for Z1 and Z3 Euler angles. + tv_shifts : float + Total variation regularization weight for X and Y shifts. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly (none for this class). + """ + + tv_zs: float = 0.0 + tv_shifts: float = 0.0 + _name: str = "base_tomography_dataset" + + soft_constraint_keys = ["tv_zs", "tv_shifts"] + hard_constraint_keys = [] + + @dataclass + class ThroughFocalDatasetConstraints(BaseTomographyDatasetConstraints): + """ + Constraints for a through-focal tomography dataset. + + Inherits all constraints from ``BaseTomographyDatasetConstraints``. + Currently not implemented — instantiation will raise ``NotImplementedError``. + """ + + pass + + @classmethod + def parse_dict( + cls, d: dict + ) -> "DatasetConstraintParams.BaseTomographyDatasetConstraints | DatasetConstraintParams.ThroughFocalDatasetConstraints": + """ + Instantiate a dataset constraint dataclass from a configuration dictionary. + + The dictionary must contain a ``'name'`` or ``'type'`` key identifying + which constraint class to construct. All remaining keys are forwarded as + keyword arguments to the selected dataclass. + + Parameters + ---------- + d : dict + Configuration dictionary. Must include ``'name'`` or ``'type'`` + with one of the following values (case-insensitive): + + - ``'base_tomography_dataset'`` → :class:`BaseTomographyDatasetConstraints` + - ``'through_focal_dataset'`` → :class:`ThroughFocalDatasetConstraints` + *(not yet implemented)* + + The value may also be a class ``type`` object, in which case its + ``__name__`` is used after lower-casing. + + Returns + ------- + BaseTomographyDatasetConstraints or ThroughFocalDatasetConstraints + An instance of the appropriate constraint dataclass. + + Raises + ------ + ValueError + If neither ``'name'`` nor ``'type'`` is present, if the value is not + a string or type, or if the name does not match any known dataset + constraint type. + NotImplementedError + If ``'through_focal_dataset'`` is requested, as it is not yet implemented. + """ + d = dict(d) + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown dataset constraint type: {name}") + if name == "base_tomography_dataset": + return DatasetConstraintParams.BaseTomographyDatasetConstraints(**d) + elif name == "through_focal_dataset": + raise NotImplementedError("Through focal dataset constraints are not implemented yet.") + else: + raise ValueError(f"Unknown dataset constraint type: {name.lower()}") + + +DatasetConstraintsType = ( + DatasetConstraintParams.BaseTomographyDatasetConstraints + | DatasetConstraintParams.ThroughFocalDatasetConstraints +) + + +@dataclass +class DatasetValue: + """ + Class for storing the forward call for both PixDataset and INRDataset. + """ + + target: torch.Tensor + tilt_angle: int | float + pixel_loc: tuple[int, int] | None = None # Only for INRDataset + projection_idx: int | None = None # Only for INRDataset + pose: tuple[torch.nn.Parameter, torch.nn.Parameter, torch.nn.Parameter] | None = ( + None # If there is pose optimization. # Pose is tuple (shifts, z1, z3) + ) + + +class TomographyDatasetBase(AutoSerialize, OptimizerMixin, nn.Module): + """ + Base tomography dataset class for all tomography datasets to inherit from. + """ + + _token = object() + + DEFAULT_LRS = { + "pose_lr": 5e-2, + } + + def __init__( + self, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_shift: bool = True, + learn_tilt_axis: bool = True, + _token: object | None = None, + ): + AutoSerialize.__init__(self) + OptimizerMixin.__init__(self) + nn.Module.__init__(self) + if _token is not self._token: + raise RuntimeError("Use TomographyPixDataset.from_* to instantiate this class.") + + if not ( + tilt_stack.shape[0] < tilt_stack.shape[1] or tilt_stack.shape[0] < tilt_stack.shape[2] + ): + raise ValueError( + "The number of tilt projections should be in the first dimension of the dataset." + ) + + if type(tilt_stack) is not torch.Tensor: + tilt_stack = torch.from_numpy(tilt_stack) + if type(tilt_angles) is not torch.Tensor: + tilt_angles = torch.from_numpy(tilt_angles) + max_val = torch.quantile(tilt_stack, 0.95) + + # Tilt stack normalization + tilt_stack = tilt_stack / max_val + + self.tilt_stack = tilt_stack + self.tilt_angles = tilt_angles + self.learn_shift = learn_shift + self.learn_tilt_axis = learn_tilt_axis + + # The reference tilt angle is the one with the smallest absolute tilt angle. + # I.e, the pose will not be optimized for the reference tilt angle. + self._reference_tilt_angle_idx = torch.argmin(torch.abs(self.tilt_angles)) + # TODO: Implement AuxParams from old tomography_dataset.py here. + + # TODO: The parameters won't be initialized unless .to(device) is called. + self._z1_angles = torch.zeros(self.learnable_tilts) + self._z3_angles = torch.zeros(self.learnable_tilts) + self._shifts = torch.zeros(self.learnable_tilts, 2) + + # Fixed zeros for reference tilt + self._z1_ref = torch.zeros(1) + self._z3_ref = torch.zeros(1) + self._shifts_ref = torch.zeros(1, 2) + + # --- Class methods --- + @classmethod + def from_data( + cls, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_shift: bool = True, + learn_tilt_axis: bool = True, + ): + return cls( + tilt_stack=tilt_stack, + tilt_angles=tilt_angles, + learn_shift=learn_shift, + learn_tilt_axis=learn_tilt_axis, + _token=cls._token, + ) + + # --- Optimization Parameters --- + + def get_optimization_parameters(self) -> list[nn.Parameter]: + """ + Get the parameters that should be optimized for this model. + """ + return list(self.parameters()) + + # --- Forward pass --- + @abstractmethod + def forward( + self, + dummy_input: Any = None, # Note all nn.Modules require some input. + ): + """ + Forward pass should be implemented in subclasses. + """ + raise NotImplementedError("This method should be implemented in subclasses.") + + # --- Properties --- + @property + def tilt_stack(self) -> torch.Tensor: + return self._tilt_stack + + @tilt_stack.setter + def tilt_stack(self, tilt_stack: torch.Tensor): + if type(tilt_stack) is not torch.Tensor: + print("Converting tilt stack to torch.Tensor") + tilt_stack = torch.from_numpy(tilt_stack) + + self._tilt_stack = tilt_stack + + @property + def tilt_angles(self) -> torch.Tensor: + return self._tilt_angles + + @tilt_angles.setter + def tilt_angles(self, tilt_angles: torch.Tensor): + if type(tilt_angles) is not torch.Tensor: + print("Converting tilt angles to torch.Tensor") + tilt_angles = torch.from_numpy(tilt_angles) + + self._tilt_angles = tilt_angles + + @property + def learn_shift(self) -> bool: + return self._learn_shift + + @learn_shift.setter + def learn_shift(self, learn_shift: bool): + self._learn_shift = learn_shift + + @property + def learn_tilt_axis(self) -> bool: + return self._learn_tilt_axis + + @learn_tilt_axis.setter + def learn_tilt_axis(self, learn_tilt_axis: bool): + self._learn_tilt_axis = learn_tilt_axis + + @property + def reference_tilt_idx(self) -> int: + return int(self._reference_tilt_angle_idx) + + @reference_tilt_idx.setter + def reference_tilt_idx(self, reference_tilt_idx: int): + self._reference_tilt_angle_idx = reference_tilt_idx + + @property + def learnable_tilts(self) -> int: + return self.tilt_angles.shape[0] - 1 + + @learnable_tilts.setter + def learnable_tilts(self, learnable_tilts: int): + self._learnable_tilts = learnable_tilts + + @property + def z1_params(self) -> torch.nn.Parameter: + return self._z1_params + + @z1_params.setter + def z1_params(self, z1_angles: torch.Tensor, device: str): + self._z1_params = nn.Parameter(z1_angles.to(device)) + + @property + def z3_params(self) -> torch.nn.Parameter: + return self._z3_params + + @z3_params.setter + def z3_params(self, z3_angles: torch.Tensor, device: str): + self._z3_params = nn.Parameter(z3_angles.to(device)) + + @property + def shifts_params(self) -> torch.nn.Parameter: + return self._shifts_params + + @shifts_params.setter + def shifts_params(self, shifts: torch.Tensor, device: str): + self._shifts_params = nn.Parameter(shifts.to(device)) + + @property + def device(self) -> torch.device: + return self._device + + @device.setter + def device(self, device: torch.device | str): + if isinstance(device, str): + device = torch.device(device) + self._device = device + + # --- Helper Functions --- + @abstractmethod + def to(self, device: torch.device | str): # type: ignore + """ + Moves the dataset to the device, and also insantiates the aux params to the device. + """ + + raise NotImplementedError("This method should be implemented in subclasses.") + + +class TomographyDatasetConstraints(BaseConstraints, TomographyDatasetBase): + DEFAULT_CONSTRAINTS = DatasetConstraintParams.BaseTomographyDatasetConstraints() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.constraints: DatasetConstraintParams.BaseTomographyDatasetConstraints = ( + self.DEFAULT_CONSTRAINTS.copy() + ) + + def apply_soft_constraints(self) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=self.z1_params.device) + if self.constraints.tv_zs > 0: + tv_loss_zs = tv_loss_1d(self.z1_params) + tv_loss_zs += tv_loss_1d(self.z3_params) + tv_loss_zs = self.constraints.tv_zs * tv_loss_zs + soft_loss += tv_loss_zs + + if self.constraints.tv_shifts > 0: + # Shift params is of shape (N, 2) + tv_loss_shifts = tv_loss_1d(self.shifts_params[:, 0]) + tv_loss_shifts += tv_loss_1d(self.shifts_params[:, 1]) + tv_loss_shifts = self.constraints.tv_shifts * tv_loss_shifts + soft_loss += tv_loss_shifts + return soft_loss + + def apply_hard_constraints(self) -> torch.Tensor: + """ + No hard constraints have been implemented yet. + """ + return torch.tensor(0.0) + + +class TomographyPixDataset(TomographyDatasetConstraints): + """ + Dataset class for pixel-based tomography, i.e AD, SIRT, WBP, etc... + + These algorithms only require the tilt image in the forward call. + """ + + def __init__( + self, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_shift: bool = True, + learn_tilt_axis: bool = True, + _token: object | None = None, + ): + super().__init__( + tilt_stack=tilt_stack, + tilt_angles=-tilt_angles, # TODO: Flip the tilt angles to be negative to match the convention of INR. + learn_shift=learn_shift, + learn_tilt_axis=learn_tilt_axis, + _token=_token, + ) + + def forward( # type:ignore + self, + proj_idx: int, + ) -> DatasetValue: + """ + Forward pass for pixel-based tomography. + Returns the full tilt image for the given projection index, and the tilt angle. + """ + + return DatasetValue( + target=self.tilt_stack[proj_idx], + tilt_angle=self.tilt_angles[proj_idx].item(), + pixel_loc=None, + ) + + def to(self, device: str | torch.device): + """ + Moves the tilt stack and tilt_angles to the device, along with other nn.Parameters to the device. + """ + self.tilt_stack = self.tilt_stack.to(device) + self.tilt_angles = self.tilt_angles.to(device) + + self._z1_params = nn.Parameter(self._z1_angles.to(device)) + self._z3_params = nn.Parameter(self._z3_angles.to(device)) + self._shifts_params = nn.Parameter(self._shifts.to(device)) + + self._z1_ref = self._z1_ref.to(device) + self._z3_ref = self._z3_ref.to(device) + self._shifts_ref = self._shifts_ref.to(device) + + self.device = device + + +class TomographyINRDataset(TomographyDatasetConstraints, Dataset): + """ + Dataset class for INR-based tomography. + + The two main methods here are that the `forward` call will return the relative pose parameters, + while `__getitem__` will actually return the pixel values of the tilt stack. + + TODO: I think TomographyINRDataset shouldn't handle the train/val split and will be handled later? Yea this is handled in setup_dataloader in DDP + """ + + def __init__( + self, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_shift: bool = True, + learn_tilt_axis: bool = True, + seed: int = 42, + _token: object | None = None, + ): + super().__init__(tilt_stack, tilt_angles, learn_shift, learn_tilt_axis, _token=_token) + + # --- Forward Pass w/ Params Method for OptimizerMixin --- + def forward(self, dummy_input: Any = None): + """ + Forward pass for INR-based tomography. In the forward pass, the only parameters that + are passed will be the shifts, z1 and z3 Euler angles. + """ + + first_half_shifts = self.shifts_params[: self.reference_tilt_idx] + second_half_shifts = self.shifts_params[self.reference_tilt_idx :] + shifts = torch.cat([first_half_shifts, self._shifts_ref, second_half_shifts], dim=0) + + first_half_z1 = self.z1_params[: self.reference_tilt_idx] + second_half_z1 = self.z1_params[self.reference_tilt_idx :] + z1 = torch.cat([first_half_z1, self._z1_ref, second_half_z1], dim=0) + + first_half_z3 = self.z3_params[: self.reference_tilt_idx] + second_half_z3 = self.z3_params[self.reference_tilt_idx :] + z3 = torch.cat([first_half_z3, self._z3_ref, second_half_z3], dim=0) + + if self.learn_shift and self.learn_tilt_axis: + return shifts, z1, z3 + elif self.learn_shift: + return shifts, torch.zeros_like(z1), torch.zeros_like(z3) + elif self.learn_tilt_axis: + return torch.zeros_like(shifts), z1, z3 + elif self.learn_shift and self.learn_tilt_axis: + return shifts, z1, z3 + else: + return torch.zeros_like(shifts), torch.zeros_like(z1), torch.zeros_like(z3) + + def get_coords( + self, batch: dict[str, torch.Tensor], N: int, num_samples_per_ray: int + ) -> torch.Tensor: + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + # target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + with torch.no_grad(): + batch_ray_coords = self.create_batch_rays(pixel_i, pixel_j, N, num_samples_per_ray) + + shifts, z1_params, z3_params = self.forward(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + transformed_rays = self.transform_batch_rays( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + all_coords = transformed_rays.view(-1, 3) + + all_coords = all_coords.to(self.device, dtype=torch.float32, non_blocking=True) + return all_coords + + @staticmethod + @torch.compile(mode="reduce-overhead") + def create_batch_rays( + pixel_i: torch.Tensor, pixel_j: torch.Tensor, N: int, num_samples_per_ray: int + ) -> torch.Tensor: + batch_size = len(pixel_i) + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=pixel_i.device) + + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=pixel_i.device) + + rays[:, :, 0] = x_coords.unsqueeze(1) + rays[:, :, 1] = y_coords.unsqueeze(1) + rays[:, :, 2] = z_coords.unsqueeze(0) + + return rays + + @staticmethod + def transform_batch_rays( + rays: torch.Tensor, + z1: torch.Tensor, + x: torch.Tensor, + z3: torch.Tensor, + shifts: torch.Tensor, + N: int, + sampling_rate: float, + ) -> torch.Tensor: + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + @staticmethod + @torch.compile(mode="reduce-overhead") + def integrate_rays( + rays: torch.Tensor, num_samples_per_ray: int, target_values_len: int + ) -> torch.Tensor: + ray_densities = rays.view( + target_values_len, + num_samples_per_ray, + ) + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + return predicted_values + + # --- Torch Dataset Methods --- + def __getitem__( + self, + idx: int, + ) -> dict: + """ + Gets the item for INR i.e, the project index, pixel value at (i, j), and the tilt angle. + """ + + actual_idx = idx + + projection_idx = actual_idx // (self.tilt_stack.shape[1] * self.tilt_stack.shape[2]) + remaining = actual_idx % (self.tilt_stack.shape[1] * self.tilt_stack.shape[2]) + + pixel_i = remaining // self.tilt_stack.shape[1] + pixel_j = remaining % self.tilt_stack.shape[1] + + return { + "projection_idx": torch.tensor(projection_idx), + "pixel_i": torch.tensor(pixel_i), + "pixel_j": torch.tensor(pixel_j), + "phi": self.tilt_angles[projection_idx], # tensor + "target_value": self.tilt_stack[projection_idx, pixel_i, pixel_j], # tensor + } + + def __len__( + self, + ): + """ + Returns the number of pixels in the tilt stack. + """ + N = max(self.tilt_stack.shape) + return self.tilt_stack.shape[0] * N * N + + def to(self, device: torch.device | str): + self._z1_params = nn.Parameter(self._z1_angles.to(device)) + self._z3_params = nn.Parameter(self._z3_angles.to(device)) + self._shifts_params = nn.Parameter(self._shifts.to(device)) + + self._z1_ref = self._z1_ref.to(device) + self._z3_ref = self._z3_ref.to(device) + self._shifts_ref = self._shifts_ref.to(device) + + self.device = device + self.reconnect_optimizer_to_parameters() + + +class TomographyINRPretrainDataset(Dataset): + """ + Dataset class for pretraining INR models. + """ + + def __init__( + self, + pretrain_target: torch.Tensor, + ): + data = pretrain_target.float() + + total_elements = data.numel() + if total_elements > 1e6: + sample_size = min(int(1e6), total_elements) + flat_data = data.flatten() + indices = torch.randperm(total_elements)[:sample_size] + sampled_data = flat_data[indices] + data_quantile = torch.quantile(sampled_data, 0.95) + else: + data_quantile = torch.quantile(data, 0.95) + + data = data / data_quantile + data = torch.permute(data, (0, 3, 2, 1)) + # data = torch.flip(data, dims=(2,)) + + self.volume = data.cpu() + self.N = pretrain_target.shape[1] # Assumes cubic volume. + self.total_samples = pretrain_target.shape[1] ** 3 + + coords_1d = torch.linspace(-1, 1, self.N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + self.coords = torch.stack([x, y, z], dim=-1).reshape(-1, 3).cpu() + self.targets = self.volume.reshape(-1).cpu() + + def __len__(self): + return self.total_samples + + def __getitem__(self, idx): + return {"coords": self.coords[idx], "target": self.targets[idx]} + + +DatasetModelType = TomographyINRDataset | TomographyPixDataset diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py new file mode 100644 index 00000000..e07072f7 --- /dev/null +++ b/src/quantem/tomography/logger_tomography.py @@ -0,0 +1,91 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch + +from quantem.core.ml.logger import LoggerBase +from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.object_models import ObjectModelType + + +class LoggerTomography(LoggerBase): + """ + Logger for ML-based tomography reconstructions. + """ + + def __init__( + self, + log_dir: str, + run_prefix: str, + run_suffix: str = "", + log_images_every: int = 10, + ): + super().__init__(log_dir, run_prefix, run_suffix, log_images_every) + + def log_epoch(self, epoch: int, loss: float, tilt_series_loss: float, soft_loss: float): + self.log_scalar("loss/total", loss, epoch) + self.log_scalar("loss/tilt_series", tilt_series_loss, epoch) + self.log_scalar("loss/soft", soft_loss, epoch) + + def log_iter( + self, + object_model: ObjectModelType, + iter: int, + consistency_loss: float, + total_loss: float, + learning_rates: dict[str, float], + num_samples_per_ray: int, + val_loss: float | None = None, + ): + self.log_scalar("loss/consistency", consistency_loss, iter) + self.log_scalar("loss/total", total_loss, iter) + self.log_scalar("loss/soft", object_model._soft_constraint_losses[-1], iter) + self.log_scalar("num_samples_per_ray", num_samples_per_ray, iter) + for param_name, lr_value in learning_rates.items(): + self.log_scalar(f"learning_rate/{param_name}", float(lr_value), iter) + if val_loss is not None: + self.log_scalar("loss/val", val_loss, iter) + + def log_iter_images( + self, + pred_volume: np.ndarray, + dataset_model: DatasetModelType, + iter: int, + logger_cmap: str = "turbo", + ): + with torch.no_grad(): + z1_vals = dataset_model.z1_params.detach().cpu().numpy() + z3_vals = dataset_model.z3_params.detach().cpu().numpy() + shifts_vals = dataset_model.shifts_params.detach().cpu().numpy() + + for channel in range(pred_volume.shape[0]): + self.log_image( + f"volume/sum_z_{channel}", pred_volume[channel].sum(axis=0), iter, logger_cmap + ) + self.log_image( + f"volume/sum_y_{channel}", pred_volume[channel].sum(axis=1), iter, logger_cmap + ) + self.log_image( + f"volume/sum_x_{channel}", pred_volume[channel].sum(axis=2), iter, logger_cmap + ) + + # Plotting z1 and z3 vals + fig, ax = plt.subplots() + ax.plot(z1_vals, label="Z1") + ax.plot(z3_vals, label="Z3") + ax.legend() + ax.set_title("Z1 and Z3 Angles") + ax.set_xlabel("Tilt Image") + ax.set_ylabel("Degree") + self.log_figure("z1_z3_angles", fig, iter) + plt.close(fig) + + # Plotting shifts + fig, ax = plt.subplots() + ax.plot(shifts_vals[:, 0], label="Shifts X") + ax.plot(shifts_vals[:, 1], label="Shifts Y") + ax.legend() + ax.set_title("Shifts") + ax.set_xlabel("Tilt Image") + ax.set_ylabel("Pixel") + self.log_figure("shifts", fig, iter) + plt.close(fig) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d103087d..c5eb2406 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,540 +1,850 @@ from abc import abstractmethod from copy import deepcopy -from typing import Any, Callable, Union +from dataclasses import dataclass +from typing import Any, Callable, Generator import numpy as np import torch +import torch.distributed as dist +import torch.nn as nn from tqdm.auto import tqdm from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml.blocks import reset_weights -from quantem.core.utils.validators import validate_gt, validate_tensor -from quantem.tomography.utils import get_TV_loss +from quantem.core.ml.constraints import BaseConstraints, Constraints +from quantem.core.ml.ddp import DDPMixin +from quantem.core.ml.loss_functions import get_loss_module +from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.utils.rng import RNGMixin +from quantem.tomography.dataset_models import TomographyINRPretrainDataset -class ObjectBase(AutoSerialize): +class ObjConstraintParams: + """ + Namespace class for object reconstruction constraint dataclasses and parsing utilities. + + Contains constraint definitions for pixelated and implicit neural representation + (INR) object types, along with a factory method for instantiating the appropriate + class from a configuration dictionary. + + Supported constraint types + -------------------------- + ObjPixelatedConstraints + Constraints for a voxel-grid (pixelated) object representation. + ObjINRConstraints + Constraints for a network-parameterized (INR) object representation; + adds a sparsity term not present in the pixelated variant. + + Examples + -------- + >>> ObjConstraintParams.parse_dict({"name": "obj_pixelated", "tv_vol": 0.01}) + ObjPixelatedConstraints(positivity=True, shrinkage=0.0, tv_vol=0.01) + >>> ObjConstraintParams.parse_dict({"type": "obj_inr", "sparsity": 0.05}) + ObjINRConstraints(positivity=True, shrinkage=0.0, tv_vol=0.0, sparsity=0.05) + """ + + @dataclass + class ObjPixelatedConstraints(Constraints): + """ + Constraints for a pixelated (voxel-grid) object representation. + + Attributes + ---------- + positivity : bool + If ``True``, enforces non-negative values in the reconstruction. + shrinkage : float + Shrinkage regularization strength; pushes values toward zero. + tv_vol : float + Total variation regularization weight for the 3-D volume. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly during optimization. + """ + + positivity: bool = True + shrinkage: float = 0.0 + tv_vol: float = 0.0 + _name: str = "obj_pixelated" + + soft_constraint_keys = ["tv_vol"] + hard_constraint_keys = ["positivity", "shrinkage"] + + @dataclass + class ObjINRConstraints(Constraints): + """ + Constraints for an implicit neural representation (INR) object. + + Extends pixelated constraints with an additional sparsity term suited + to the continuous, network-parameterized object representation. + + Attributes + ---------- + positivity : bool + If ``True``, enforces non-negative values in the reconstruction. + shrinkage : float + Shrinkage regularization strength; pushes values toward zero. + tv_vol : float + Total variation regularization weight for the 3-D volume. + sparsity : float + Sparsity regularization weight; encourages near-zero activations. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly during optimization. + """ + + positivity: bool = True + shrinkage: float = 0.0 + tv_vol: float = 0.0 + sparsity: float = 0.0 + _name: str = "obj_inr" + + soft_constraint_keys = ["tv_vol", "sparsity"] + hard_constraint_keys = ["positivity", "shrinkage"] + + @classmethod + def parse_dict( + cls, d: dict + ) -> "ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints": + """ + Instantiate an object constraint dataclass from a configuration dictionary. + + The dictionary must contain a ``'name'`` or ``'type'`` key identifying + which constraint class to construct. All remaining keys are forwarded as + keyword arguments to the selected dataclass. + + Parameters + ---------- + d : dict + Configuration dictionary. Must include ``'name'`` or ``'type'`` + with one of the following values (case-insensitive): + + - ``'obj_pixelated'`` → :class:`ObjPixelatedConstraints` + - ``'obj_inr'`` → :class:`ObjINRConstraints` + + The value may also be a class ``type`` object, in which case its + ``__name__`` is used after lower-casing. + + Returns + ------- + ObjPixelatedConstraints or ObjINRConstraints + An instance of the appropriate object constraint dataclass. + + Raises + ------ + ValueError + If neither ``'name'`` nor ``'type'`` is present, if the value is not + a string or type, or if the name does not match any known object + constraint type. + """ + d = dict(d) + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown object constraint type: {name}") + if name == "obj_pixelated": + return ObjConstraintParams.ObjPixelatedConstraints(**d) + elif name == "obj_inr": + return ObjConstraintParams.ObjINRConstraints(**d) + else: + raise ValueError(f"Unknown object constraint type: {name.lower()}") + + +ObjConstraintsType = ( + ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints +) + + +class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): + DEFAULT_LRS = { + "object": 8e-6, + } + _token = object() """ Base class for all ObjectModels to inherit from. """ def __init__( self, - volume_shape: tuple[int, int, int], - device: str, - offset_obj: float = 1e-5, + shape: tuple[int, int, int], # pyright: ignore[reportRedeclaration] + device: str = "cpu", + rng: np.random.Generator | int | None = None, + _token: object | None = None, ): - self._shape = volume_shape + if _token is not self._token: + raise RuntimeError("Use a factory method to instantiate this class.") - self._obj = torch.zeros(self._shape, device=device, dtype=torch.float32) + offset_obj - self._offset_obj = offset_obj - self._device = device - self._hard_constraints = {} - self._soft_constraints = {} # One big dicitonary + self._shape = shape - @property - def shape(self) -> tuple[int, int, int]: - return self._shape + # Initialize dependencies + nn.Module.__init__(self) + RNGMixin.__init__(self, rng=rng, device=device) + OptimizerMixin.__init__(self) + + # --- Instantiation ---- + + # --- Properties --- + @property + def shape(self) -> tuple[int, int, int]: + """ + Shape of the object (x, y, z). + """ + return self._shape + + @shape.setter + def shape(self, new_shape: tuple[int, int, int]): + self._shape = new_shape + + @property + def obj(self) -> torch.Tensor: + """ + Returns the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @property + def model(self) -> nn.Module: + """ + Returns the model, should be implemented in subclasses. + """ + raise NotImplementedError + + @abstractmethod + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + raise NotImplementedError + + @abstractmethod + def forward(self, *args, **kwargs) -> torch.Tensor: + """ + Forward pass, should be implemented in subclasses. Note for any nn.Module this is + a required method. + """ + raise NotImplementedError + + @abstractmethod + def reset(self) -> None: + """ + Reset the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @property + def params(self) -> Generator[torch.nn.Parameter, None, None]: + """ + Get the parameters that should be optimized for this model. + + Should be implemented in subclasses. + """ + raise NotImplementedError + + # --- Helper Functions --- + def get_optimization_parameters(self) -> list[nn.Parameter]: + """ + Get the parameters that should be optimized for this model. + """ + return list(self.params) + + @abstractmethod # Each subclass should implement this. + def to(self, *args, **kwargs): + """ + Move the object to a device + """ + + raise NotImplementedError + + +class ObjectConstraints(BaseConstraints, ObjectBase): # TODO: Ask Arthur why we still need this + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape + @abstractmethod + def get_tv_loss(self, **kwargs) -> torch.Tensor: + """ + Get the TV loss for the object model. Must be implemented in each subclass. + """ + raise NotImplementedError - @property - def offset_obj(self) -> float: - return self._offset_obj - @offset_obj.setter - def offset_obj(self, offset_obj: float): - self._offset_obj = offset_obj +class ObjectPixelated(ObjectConstraints): + """ + Object model for pixelated objects. + + Supports: Conventional algorithms (SIRT, FBP), and AD-based reconstructions. + """ + + DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjPixelatedConstraints() + def __init__( + self, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + super().__init__( + shape=shape, + device=device, + rng=rng, + _token=self._token, + ) + self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + + # --- Instantiation ---- + @classmethod + def from_uniform( + cls, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + # Initialize a torch.zeros volume with the given shape + obj = torch.zeros(shape, device=device, dtype=torch.float32) + obj_model = cls(shape=shape, device=device, rng=rng) + obj_model._obj = obj + return obj_model + + @classmethod + def from_array( + cls, + initial_obj: torch.Tensor | np.ndarray, + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + obj_model = cls(shape=initial_obj.shape, device=device, rng=rng) + if isinstance(initial_obj, np.ndarray): + initial_obj = torch.tensor(initial_obj, dtype=torch.float32) + else: + initial_obj = initial_obj.clone() + obj_model._obj = initial_obj.to(device) + return obj_model + + # --- Properties ---- @property def obj(self) -> torch.Tensor: - pass + return self.apply_hard_constraints( + self._obj + ) # TODO: Normalization factor to ensure object agrees with INR. @obj.setter def obj(self, obj: torch.Tensor): self._obj = obj @property - def device(self) -> str: - return self._device - - @device.setter - def device(self, device: str): - self._device = device + def obj_view(self) -> np.ndarray: + return self.obj.cpu().unsqueeze(0).numpy() - @abstractmethod - def forward( - self, z1: torch.Tensor, z3: torch.Tensor, shift_x: torch.Tensor, shift_y: torch.Tensor - ): - pass - - @abstractmethod - def obj(self): - pass + @property + def shape(self) -> tuple[int, int, int]: + return self._shape - @abstractmethod - def reset(self): - pass + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape - @abstractmethod - def to(self, device: str): - pass + @property + def soft_loss(self) -> torch.Tensor: + return self.apply_soft_constraints(self._obj) - @abstractmethod + @property def name(self) -> str: - pass - - @abstractmethod - def params(self) -> torch.Tensor: - pass - - -class ObjectConstraints(ObjectBase): - DEFAULT_HARD_CONSTRAINTS = { - "fourier_filter": False, - "positivity": False, - "shrinkage": False, - "circular_mask": False, - } - - DEFAULT_SOFT_CONSTRAINTS = { - "tv_vol": 0, - } + return "obj_pixelated" @property - def hard_constraints(self) -> dict[str, Any]: - return self._hard_constraints - - @hard_constraints.setter - def hard_constraints(self, hard_constraints: dict[str, Any]): - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - for key, value in hard_constraints.items(): - if key not in gkeys: # This might be redundant since add_constraint is checking. - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._hard_constraints[key] = value + def obj_type(self) -> str: + return "pixelated" @property - def soft_constraints(self) -> dict[str, Any]: - return self._soft_constraints - - @soft_constraints.setter - def soft_constraints(self, soft_constraints: dict[str, Any]): - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - for key, value in soft_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._soft_constraints[key] = value - - def add_hard_constraint(self, constraint: str, value: Any): - """Add constraints to the object model.""" - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - if constraint not in gkeys: - raise KeyError( - f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" - ) - self._hard_constraints[constraint] = value - - def add_soft_constraint(self, constraint: str, value: Any): - """Add constraints to the object model.""" - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - if constraint not in gkeys: - raise KeyError( - f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" - ) - self._soft_constraints[constraint] = value + def dtype(self) -> torch.dtype: + return self._obj.dtype def apply_hard_constraints( self, - obj: torch.Tensor, + pred: torch.Tensor, ) -> torch.Tensor: """ - Apply constraints to the object model. + Apply hard constraints to the object model. + + Only hard constraint here is the positivity and shrinkage. TODO: Add the other hard constraints. """ - obj2 = obj.clone() - if self.hard_constraints["positivity"]: - obj2 = torch.clamp(obj, min=0.0, max=None) - if self.hard_constraints["shrinkage"]: - obj2 = torch.max(obj2 - self.hard_constraints["shrinkage"], torch.zeros_like(obj2)) + obj2 = pred.clone() + if self.constraints.positivity: + obj2 = torch.clamp(obj2, min=0.0, max=None) + if self.constraints.shrinkage: + obj2 = torch.max(obj2 - self.constraints.shrinkage, torch.zeros_like(obj2)) + # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. return obj2 - def apply_soft_constraints( - self, - obj: torch.Tensor, - ) -> torch.Tensor: - """ - 'Applies' soft constraints to the object model. This will return additional loss terms. - """ + def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) - if self.soft_constraints["tv_vol"] > 0: - tv_loss = get_TV_loss( - obj.unsqueeze(0).unsqueeze(0), factor=self.soft_constraints["tv_vol"] + if self.constraints.tv_vol > 0: + tv_loss = self.get_tv_loss( + obj.unsqueeze(0).unsqueeze(0), tv_weight=self.constraints.tv_vol ) - soft_loss += tv_loss - return soft_loss + # --- Forward method --- + def forward(self, dummy_input=None) -> torch.Tensor: + return self.obj -class ObjectVoxelwise(ObjectConstraints): - """ - Object model for voxelwise objects. - """ + # --- Defining the TV loss --- + def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tensor: # pyright: ignore[reportIncompatibleMethodOverride] -> get_tv_loss has different arguments depending on the object. + tv_d = torch.pow(obj[:, :, 1:, :, :] - obj[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(obj[:, :, :, 1:, :] - obj[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(obj[:, :, :, :, 1:] - obj[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * tv_weight / (torch.prod(torch.tensor(obj.shape))) + + # --- Helper Functions --- + def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + if isinstance(device, str): + device = torch.device(device) + self._device = device + self._obj = self._obj.to(device) + self.reconnect_optimizer_to_parameters() + return self + + +class ObjectINR(ObjectConstraints, DDPMixin): + DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() def __init__( self, - volume_shape: tuple[int, int, int], - device: str, - initial_volume: torch.Tensor | None = None, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + model: nn.Module | None = None, + _token: object | None = None, ): super().__init__( - volume_shape=volume_shape, + shape=shape, device=device, + rng=rng, + _token=self._token, + ) + self._pretrain_losses = [] + self._pretrain_lrs = [] + self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + # Register the network submodule (important: real nn.Module attribute) + if model is not None: + self.setup_distributed(device=device) + self._model = self.distribute_model(model) + + @classmethod + def from_model( + cls, + model: nn.Module, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + obj_model = cls( + shape=shape, + device=device, + rng=rng, + model=model, # ✅ build/register in __init__ ) - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - if initial_volume is not None: - self._initial_obj = initial_volume - else: - self.initial_obj = ( - torch.zeros(self._shape, device=self._device, dtype=torch.float32) - + self.offset_obj - ) - @property - def obj(self): - return self.apply_hard_constraints(self._obj) + obj_model.setup_distributed(device=device) + obj_model.to(device) + return obj_model - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj + # --- Properties --- @property - def initial_obj(self): - return self._initial_obj - - @initial_obj.setter - def initial_obj(self, initial_obj: torch.Tensor): - if not isinstance(initial_obj, torch.Tensor): - raise ValueError("initial_obj must be a torch.Tensor") - - self._initial_obj = initial_obj - - def forward(self): - return self.obj - - def reset(self): - self._obj = ( - torch.zeros(self._shape, device=self._device, dtype=torch.float32) + self.offset_obj - ) + def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: + """ + Returns the INR model. + """ + return self._model - def to(self, device: str): - self._device = device - self._obj = self._obj.to(self._device) + # @model.setter + # def model(self, model: "nn.Module"): + # """ + # This doesn't work -- can't have setters for torch sub modules + # https://github.com/pytorch/pytorch/issues/52664 - @property - def name(self) -> str: - return "ObjectVoxelwise" + # For now, upon initialization private variable `._model` is set to the built model. + # """ + # raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") @property - def params(self) -> torch.Tensor: + def obj(self) -> torch.Tensor: return self._obj - @property - def soft_loss(self) -> torch.Tensor: - return self.apply_soft_constraints(self._obj) + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + @property + def obj_view(self) -> np.ndarray: + """ + Returns the object as a view of the x, y, z axes. -class ObjectDIP(ObjectConstraints): - """ - Object model for DIP objects. - """ + Matches the axes of conventionally reconstructed objects, this is the object that will be saved. + """ + self.create_volume() + return self._obj.cpu().numpy().transpose(0, 1, 3, 2) - def __init__( + def apply_soft_constraints( self, - model: torch.nn.Module, - volume_shape: tuple[int, int, int], - model_input: torch.Tensor - | None = None, # Determines output size, model input pretraining target - input_noise_std: float = 0.0, - device: str = "cpu", - ): - super().__init__( - volume_shape=volume_shape, - device=device, - ) - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() + coords: torch.Tensor, + pred: torch.Tensor, + ) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=pred.device) + if self.constraints.tv_vol > 0: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + soft_loss += self.constraints.tv_vol * grad_norm.mean() + + if ( + isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) + and self.constraints.sparsity > 0 + ): # NOTE: For the linter, I must make this :) + sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + soft_loss += sparsity_loss - if model_input is None: - self.model_input = torch.randn(1, 1, volume_shape[0], volume_shape[1], volume_shape[2]) - else: - self.model_input = model_input.clone().detach() + return soft_loss - self.pretrain_target = model_input.clone().detach() + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: + """ + Apply hard constraints to the predicted values of the INR model. + """ - self._model = model - self._optimizer = None - self._scheduler = None - self._pretrain_losses = [] - self._pretrain_lrs = [] - self._model_input_noise_std = input_noise_std + if self.constraints.positivity: + pred = torch.clamp(pred, min=0.0, max=None) + if self.constraints.shrinkage: + pred = torch.max(pred - self.constraints.shrinkage, torch.zeros_like(pred)) - @property - def name(self) -> str: - return "ObjectDIP" + return pred + # --- Optimization Parameters --- @property - def model(self) -> torch.nn.Module: - return self._model + def params(self) -> Generator[torch.nn.Parameter, None, None]: + return self.model.parameters() # type: ignore[attr-defined] - @model.setter - def model(self, model: torch.nn.Module): - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Model must be a torch.nn.Module, got {type(model)}") - self._model = model.to(self._device) - self.set_pretrained_weights(self._model) + def get_optimization_parameters(self) -> list[nn.Parameter]: + return list(self.params) + # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: + """get the pretrained weights of the INR model""" return self._pretrained_weights - def set_pretrained_weights(self, model: torch.nn.Module): + def _set_pretrained_weights(self, model: "torch.nn.Module"): + """set the pretrained weights of the INR model""" if not isinstance(model, torch.nn.Module): raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") self._pretrained_weights = deepcopy(model.state_dict()) @property - def model_input(self) -> torch.Tensor: - return self._model_input - - @model_input.setter - def model_input(self, input_tensor: torch.Tensor): - inp = validate_tensor( - input_tensor, - name="model_input", - dtype=torch.float32, - ndim=5, - expand_dims=True, - ) - self._model_input = inp.to(self._device) - - @property - def pretrain_target(self) -> torch.Tensor: + def pretrain_target(self) -> TomographyINRPretrainDataset: + """get the pretrain target""" return self._pretrain_target @pretrain_target.setter - def pretrain_target(self, target: torch.Tensor): - if target.ndim == 5: - target = target.squeeze(0).squeeze(0) - - target = validate_tensor( - target, - name="pretrain_target", - ndim=3, - dtype=torch.float32, - expand_dims=True, - ) - if target.shape[-3:] != self.model_input.shape[-3:]: - raise ValueError( - f"Pretrain target shape {target.shape} does not match model input shape {self.model_input.shape}" - ) - self._pretrain_target = target.to(self._device) + def pretrain_target(self, target: TomographyINRPretrainDataset): + """set the pretrain target""" + self._pretrain_target = target @property - def _model_input_noise_std(self) -> float: - """standard deviation of the gaussian noise added to the model input each forward call""" - return self._input_noise_std - - @_model_input_noise_std.setter - def _model_input_noise_std(self, std: float): - validate_gt(std, 0.0, "input_noise_std", geq=True) - self._input_noise_std = std + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + # TODO: This is a temporary solution to get the dtype of the object. + return torch.float32 @property - def optimizer(self) -> torch.optim.Optimizer: - """get the optimizer for the DIP model""" - if self._optimizer is None: - raise ValueError("Optimizer is not set. Use set_optimizer() to set it.") - return self._optimizer - - def set_optimizer(self, opt_params: dict): - opt_type = opt_params.pop("type") - if isinstance(opt_type, torch.optim.Optimizer): - self._optimizer = opt_type - elif isinstance(opt_type, type): - self._optimizer = opt_type(self.model.parameters(), **opt_params) - elif opt_type == "adam": - self._optimizer = torch.optim.Adam(self.model.parameters(), **opt_params) - elif opt_type == "adamw": - self._optimizer = torch.optim.AdamW(self.model.parameters(), **opt_params) - elif opt_type == "sgd": - self._optimizer = torch.optim.SGD(self.model.parameters(), **opt_params) - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") + def shape(self) -> tuple[int, int, int]: + return self._shape - @property - def scheduler( - self, - ) -> ( - torch.optim.lr_scheduler._LRScheduler - | torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ): - return self._scheduler + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape - def set_scheduler(self, params: dict, num_iter: int | None = None) -> None: - sched_type: str = params["type"].lower() - optimizer = self.optimizer - base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - scheduler = None - elif sched_type == "cyclic": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 20), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params.keys(): - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.999 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") - self._scheduler = scheduler + # --- Helper Functions --- + def rebuild_model(self): + self._model = self.distribute_model(self._model) - @property - def pretrain_losses(self) -> np.ndarray: - return np.array(self._pretrain_losses) + # Reset method that goes back to the pretrained weights. + def reset(self): + """reset the model to the pretrained weights""" + self.model.load_state_dict(self._pretrained_weights.copy()) + self._model = self.distribute_model( + self.model + ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. - @property - def pretrain_lrs(self) -> np.ndarray: - return np.array(self._pretrain_lrs) + # --- Forward Method --- - @property - def obj(self): - obj = self.model(self._model_input)[0] - return self.apply_hard_constraints(obj) + def forward(self, coords: torch.Tensor) -> torch.Tensor: + """forward pass for the INR model""" + all_densities = self.model(coords) - def forward(self): - return self.model(self._model_input) + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) + valid_mask = ( + (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) + ).float() - def to(self, device: str): - self.device = device - self._model = self._model.to(self.device) - self._model_input = self._model_input.to(self.device) - self._pretrain_target = self._pretrain_target.to(self.device) + if all_densities.dim() > 1: + valid_mask = valid_mask.unsqueeze(-1) + # Multi-dimensional mask + all_densities = all_densities * valid_mask - @property - def params(self): - return self._model.parameters() + all_densities = self.apply_hard_constraints(all_densities) - def reset(self): - self.model.load_state_dict(self.pretrained_weights.copy()) + return all_densities + + # Pretrain Loop def pretrain( self, - model_input: torch.Tensor, - pretrain_target: torch.Tensor, - reset: bool = True, - num_epochs: int = 100, + pretrain_dataset: TomographyINRPretrainDataset, + batch_size: int, + reset: bool = False, + num_iters: int = 10, + num_workers: int = 0, optimizer_params: dict | None = None, scheduler_params: dict | None = None, - loss_fn: Callable | str = "l2", - apply_constraints: bool = False, - show: bool = True, + loss_fn: Callable | str = "l1", + verbose: bool = True, ): - model_input.to(self.device) - pretrain_target.to(self.device) + """ + Pretrain the INR model to fit target volume. + """ + + if ( + pretrain_dataset is not None + ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. + self.pretrain_dataset = pretrain_dataset + ( + self.pretraining_dataloader, + self.pretraining_sampler, + self.pretraining_val_dataloader, + self.pretraining_val_sampler, + ) = self.setup_dataloader(pretrain_dataset, batch_size, num_workers=num_workers) if optimizer_params is not None: self.set_optimizer(optimizer_params) - if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_epochs) + self.set_scheduler(scheduler_params, num_iters) if reset: - self._model.apply(reset_weights) - self._pretrain_losses = [] - self._pretrain_lrs = [] - - if model_input is not None: - self.model_input = model_input - - if pretrain_target.shape[-3:] != self.model_input.shape[-3:]: - raise ValueError( - f"Pretrain target shape {pretrain_target.shape} does not match model input shape {self.model_input.shape}" - ) - self.pretrain_target = pretrain_target.clone().detach().to(self.device) + self.reset() - loss_fn = torch.nn.functional.mse_loss + loss_fn = get_loss_module(loss_fn, self.dtype) self._pretrain( - num_epochs=num_epochs, + num_iters=num_iters, loss_fn=loss_fn, - apply_constraints=apply_constraints, - show=show, + verbose=verbose, ) - self.set_pretrained_weights(self.model) def _pretrain( self, - num_epochs: int, + num_iters: int, loss_fn: Callable, - apply_constraints: bool = False, - show: bool = False, + verbose: bool, ): - if not hasattr(self, "pretrain_target"): - raise ValueError("Pretrain target is not set. Use pretrain_target to set it.") + if self.optimizer is None: + raise RuntimeError("Optimizer not set. Call set_optimizer() first.") + if self.scheduler is None: + raise RuntimeError("Scheduler not set. Call set_scheduler() first.") self.model.train() optimizer = self.optimizer - sch = self.scheduler - pbar = tqdm(range(num_epochs)) - output = self.obj + scheduler = self.scheduler + pbar = tqdm(range(num_iters), desc="Pretraining", disable=not verbose) for a0 in pbar: - if apply_constraints: - output = self.obj - else: - output = self.model(self.model_input).squeeze(0).squeeze(0) + epoch_loss = 0 + for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) + + with torch.autocast( + device_type=self.device.type, dtype=torch.bfloat16, enabled=True + ): + outputs = self.forward(coords) + loss = loss_fn(outputs, target) + + loss.backward() + epoch_loss += loss.item() - loss = loss_fn(output, self.pretrain_target) - loss.backward() - optimizer.step() - optimizer.zero_grad() + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) - if sch is not None: - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(loss.item()) + optimizer.step() + optimizer.zero_grad() + + if scheduler is not None: + if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + scheduler.step(epoch_loss) else: - sch.step() + scheduler.step() - self._pretrain_losses.append(loss.item()) + self._pretrain_losses.append(epoch_loss / len(self.pretraining_dataloader)) + print( + f"Epoch {a0 + 1}/{num_iters}, Pretrain Loss: {epoch_loss / len(self.pretraining_dataloader):.4f}" + ) self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) - pbar.set_description(f"Epoch {a0 + 1}/{num_epochs}, Loss: {loss.item():.4f}, ") + + def create_volume(self, return_vol: bool = False): + N = max(self._shape) + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + model = self.model.module if isinstance(self.model, nn.DataParallel) else self.model + + inference_batch_size = 5 * N * N + total_samples = N**3 + samples_per_gpu = total_samples // self.world_size + remainder = total_samples % self.world_size + + if self.global_rank < remainder: + start_idx = self.global_rank * (samples_per_gpu + 1) + end_idx = start_idx + samples_per_gpu + 1 + else: + start_idx = self.global_rank * samples_per_gpu + remainder + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx] + num_samples = inputs_subset.shape[0] + + outputs_list = [] + for batch_start in range(0, num_samples, inference_batch_size): + batch_end = min(batch_start + inference_batch_size, num_samples) + batch_coords = inputs_subset[batch_start:batch_end].to( + self.device, non_blocking=True + ) + + batch_outputs = model(batch_coords) # (B, C) or (B,) etc. + + if isinstance(batch_outputs, tuple): + batch_outputs = batch_outputs[0] + batch_outputs = self.apply_hard_constraints(batch_outputs) + + # Ensure shape is (B, C) + if batch_outputs.dim() == 1: + batch_outputs = batch_outputs.unsqueeze(-1) # (B, 1) + + outputs_list.append(batch_outputs.cpu()) + + outputs = torch.cat(outputs_list, dim=0) # (local_B, C) + C = outputs.shape[-1] # e.g. 5 + + if self.world_size > 1: + # gather variable-sized first dimension (local_B) while keeping channels + local_B = outputs.shape[0] + output_size = torch.tensor(local_B, device=self.device, dtype=torch.long) + all_sizes = [ + torch.zeros(1, device=self.device, dtype=torch.long) + for _ in range(self.world_size) + ] + dist.all_gather(all_sizes, output_size) + max_size = max(size.item() for size in all_sizes) + + outputs_dev = outputs.to(self.device) # (local_B, C) + if local_B < max_size: + pad = torch.zeros( + (max_size - local_B, C), # type: ignore + device=self.device, + dtype=outputs_dev.dtype, + ) + outputs_padded = torch.cat([outputs_dev, pad], dim=0) # (max_size, C) + else: + outputs_padded = outputs_dev + + gathered_outputs = [ + torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) # type: ignore + for _ in range(self.world_size) + ] + dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + + trimmed_outputs = [] + for rank, size in enumerate(all_sizes): + trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) + + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(C, N, N, N).float() + else: + pred_full = outputs.reshape(C, N, N, N).float() + + if return_vol: + return pred_full.detach().cpu() + + self._obj = pred_full.detach().cpu() + + def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] + self, + coords: torch.Tensor, + ) -> torch.Tensor: + tv_loss = torch.tensor(0.0, device=coords.device) + + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.forward(tv_coords) + + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + + tv_loss += self.constraints.tv_vol * grad_norm.mean() + return tv_loss + + def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + if isinstance(device, str): + device = torch.device(device) + self._device = device + if self.world_size == 1: + self._model = self._model.to(device) + elif not isinstance(self._model, torch.nn.parallel.DistributedDataParallel): + self.distribute_model(self._model) + self.reconnect_optimizer_to_parameters() -ObjectModelType = Union[ObjectVoxelwise] # | ObjectDIP | ObjectImplicit (ObjectFFN?) +ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/preprocess/__init__.py b/src/quantem/tomography/preprocess/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography/preprocess/drift.py b/src/quantem/tomography/preprocess/drift.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography/radon/radon.py index d6d408e3..8510e570 100644 --- a/src/quantem/tomography/radon/radon.py +++ b/src/quantem/tomography/radon/radon.py @@ -9,37 +9,25 @@ def radon_torch(images, theta=None, device=None): - """ - Batched Radon transform implemented in PyTorch. - images: torch.Tensor of shape [B, H, W] - Returns: torch.Tensor of shape [B, N_angles, N_pixels] - """ if images.ndim == 2: - images = images.unsqueeze(0) # [1, H, W] + images = images.unsqueeze(0) B, H, W = images.shape + device = device or images.device + theta = theta if theta is not None else torch.arange(180, device=device) + A = len(theta) - if device is None: - device = images.device - - if theta is None: - theta = torch.arange(180, device=device) - - N_angles = len(theta) shape_min = min(H, W) radius = shape_min // 2 - center = torch.tensor([H // 2, W // 2], device=device) + center_h, center_w = H // 2, W // 2 Y, X = torch.meshgrid( torch.arange(H, device=device), torch.arange(W, device=device), indexing="ij", ) - dist2 = (X - center[1]) ** 2 + (Y - center[0]) ** 2 - mask = dist2 <= radius**2 - images = images.clone() - images *= mask # broadcasting over batch + mask = (X - center_w) ** 2 + (Y - center_h) ** 2 <= radius**2 + images = images.clone() * mask - # Crop to square excess = torch.tensor([H, W], device=device) - shape_min slices = tuple( slice(int((e.item() + 1) // 2), int((e.item() + 1) // 2 + shape_min)) @@ -51,112 +39,104 @@ def radon_torch(images, theta=None, device=None): N = images.shape[-1] center = N // 2 - radon_images = torch.zeros((B, N_angles, N), dtype=images.dtype, device=device) + # Build all rotation matrices at once: [A, 2, 2] + angles_rad = torch.deg2rad(theta.float()) + cos_a = torch.cos(angles_rad) + sin_a = torch.sin(angles_rad) + rot = torch.stack( + [ + torch.stack([cos_a, -sin_a], dim=1), + torch.stack([-sin_a, -cos_a], dim=1), + ], + dim=1, + ) # [A, 2, 2] + # Pixel coords: [N*N, 2] grid_y, grid_x = torch.meshgrid( torch.arange(N, dtype=torch.float32, device=device), torch.arange(N, dtype=torch.float32, device=device), indexing="ij", ) - coords = torch.stack((grid_x - center, grid_y - center), dim=-1) # (N, N, 2) - coords = coords.view(1, N, N, 2).expand(B, -1, -1, -1) # [B, N, N, 2] - - for i, angle in enumerate(theta): - angle_rad = torch.deg2rad(angle) - rot = torch.tensor( - [ - [torch.cos(angle_rad), -torch.sin(angle_rad)], - [-torch.sin(angle_rad), -torch.cos(angle_rad)], - ], - device=device, - dtype=torch.float32, - ) + coords = torch.stack((grid_x - center, grid_y - center), dim=-1).view(-1, 2) # [N*N, 2] - rot = rot.unsqueeze(0).expand(B, -1, -1) # [B, 2, 2] - coords_rot = torch.matmul(coords.view(B, -1, 2), rot.transpose(1, 2)).view(B, N, N, 2) - coords_rot += center + # Rotate all angles at once: [A, N*N, 2] + coords_rot = (coords @ rot.transpose(1, 2)) + center # [A, N*N, 2] - # Normalize to [-1, 1] - grid = 2 * coords_rot / (N - 1) - 1 # [B, N, N, 2] + # Normalize to [-1, 1] for grid_sample + grid = (2 * coords_rot / (N - 1) - 1).view(A, N, N, 2) # [A, N, N, 2] - # grid = grid.unsqueeze(1) # [B, 1, N, N, 2] - imgs = images.unsqueeze(1) # [B, 1, N, N] + # Expand images: [B, 1, N, N] -> sample with [B*A, 1, N, N] style + # Tile images over angles, tile grid over batch + images_exp = images.unsqueeze(1).expand(-1, A, -1, -1).reshape(B * A, 1, N, N) + grid_exp = grid.unsqueeze(0).expand(B, -1, -1, -1, -1).reshape(B * A, N, N, 2) - sampled = F.grid_sample( - imgs, grid, mode="bilinear", padding_mode="zeros", align_corners=True - ) - projection = sampled.squeeze(1).sum(dim=1) # [B, N] - radon_images[:, i, :] = projection + sampled = F.grid_sample( + images_exp, grid_exp, mode="bilinear", padding_mode="zeros", align_corners=True + ) + # sampled: [B*A, 1, N, N] -> sum over rows -> [B, A, N] + radon_images = sampled.squeeze(1).sum(dim=1).view(B, A, N) - return radon_images.squeeze(0) if radon_images.shape[0] == 1 else radon_images + return radon_images.squeeze(0) if B == 1 else radon_images def iradon_torch( sinograms, theta=None, output_size=None, - filter_name="ramp", + filter_name: str | None = "ramp", circle=True, device=None, ): - """ - Batched inverse Radon transform (filtered backprojection). - sinograms: [B, N_angles, N_pixels] or [N_angles, N_pixels] (automatically batched) - Returns: [B, output_size, output_size] or [output_size, output_size] - """ if sinograms.ndim == 2: - sinograms = sinograms.unsqueeze(0) # [1, A, P] + sinograms = sinograms.unsqueeze(0) B, A, N = sinograms.shape - - device = sinograms.device if device is None else device + device = device or sinograms.device theta = theta if theta is not None else torch.linspace(0, 180, steps=A, device=device) - if theta.shape[0] != A: - raise ValueError("theta does not match number of projections") - if output_size is None: - output_size = N if circle else int(torch.floor(torch.sqrt(torch.tensor(N**2 / 2.0)))) + output_size = N if circle else int((N**2 / 2) ** 0.5) - # Padding for FFT - padded_size = max( - 64, int(2 ** torch.ceil(torch.log2(torch.tensor(2 * N, dtype=torch.float32)))) - ) + # Filter + padded_size = max(64, int(2 ** torch.ceil(torch.log2(torch.tensor(2.0 * N))))) pad_y = padded_size - N - sinograms_padded = F.pad(sinograms, (0, pad_y)) # [B, A, padded] - - f_filter = get_fourier_filter_torch(padded_size, filter_name, device=device) # [1, padded] - spectrum = torch.fft.fft(sinograms_padded, dim=2) - filtered = torch.real(torch.fft.ifft(spectrum * f_filter, dim=2))[:, :, :N] + sinograms_padded = F.pad(sinograms, (0, pad_y)) + f_filter = get_fourier_filter_torch(padded_size, filter_name, device=device) + filtered = torch.real( + torch.fft.ifft(torch.fft.fft(sinograms_padded, dim=2) * f_filter, dim=2) + )[:, :, :N] # [B, A, N] - # Backprojection - recon = torch.zeros((B, output_size, output_size), device=device) radius = output_size // 2 - y, x = torch.meshgrid( torch.arange(output_size, device=device) - radius, torch.arange(output_size, device=device) - radius, indexing="ij", ) - x = x.flatten() - y = y.flatten() + x = x.float().flatten() # [H*W] + y = y.float().flatten() - for i, angle in enumerate(torch.deg2rad(theta)): - t = (x * torch.cos(angle) - y * torch.sin(angle)).reshape(1, output_size, output_size) - t_idx = t + (N // 2) + # Compute t for all angles at once: [A, H*W] + angles_rad = torch.deg2rad(theta.float()) + t = x.unsqueeze(0) * torch.cos(angles_rad).unsqueeze(1) - y.unsqueeze(0) * torch.sin( + angles_rad + ).unsqueeze(1) # [A, H*W] + t_idx = t + (N // 2) # [A, H*W] - t0 = torch.floor(t_idx).long().clamp(0, N - 2) # [1, H, W] - t1 = t0 + 1 - w = t_idx - t0.float() + t0 = t_idx.long().clamp(0, N - 2) # [A, H*W] + t1 = t0 + 1 + w = t_idx - t0.float() # [A, H*W] - t0 = t0.expand(B, -1, -1) # [B, H, W] - t1 = t1.expand(B, -1, -1) + # Gather from filtered: [B, A, N] -> gather at [A, H*W] indices + # Expand: [B, A, H*W] + # HW = output_size * output_size + t0_exp = t0.unsqueeze(0).expand(B, -1, -1) # [B, A, H*W] + t1_exp = t1.unsqueeze(0).expand(B, -1, -1) + w_exp = w.unsqueeze(0).expand(B, -1, -1) - filtered_i = filtered[:, i, :] # [B, N] - val0 = torch.gather(filtered_i, 1, t0.view(B, -1)).view(B, output_size, output_size) - val1 = torch.gather(filtered_i, 1, t1.view(B, -1)).view(B, output_size, output_size) + val0 = torch.gather(filtered, 2, t0_exp) # [B, A, H*W] + val1 = torch.gather(filtered, 2, t1_exp) + proj = ((1 - w_exp) * val0 + w_exp * val1).sum(dim=1) # [B, H*W] - proj = (1 - w) * val0 + w * val1 - recon += proj + recon = proj.view(B, output_size, output_size) if circle: mask = ( @@ -166,10 +146,12 @@ def iradon_torch( recon[:, mask] = 0.0 recon *= torch.pi / (2 * A) - return recon.squeeze(0) if recon.shape[0] == 1 else recon + return recon.squeeze(0) if B == 1 else recon -def get_fourier_filter_torch(size, filter_name="ramp", device=None, dtype=torch.float32): +def get_fourier_filter_torch( + size, filter_name: str | None = "ramp", device=None, dtype=torch.float32 +): """ Construct the Fourier filter in PyTorch. """ diff --git a/src/quantem/tomography/tilt_series_dataset.py b/src/quantem/tomography/tilt_series_dataset.py deleted file mode 100644 index f6bff836..00000000 --- a/src/quantem/tomography/tilt_series_dataset.py +++ /dev/null @@ -1,153 +0,0 @@ -from typing import Any, List, Self - -import numpy as np -import torch -from numpy.typing import NDArray - -from quantem.core.datastructures.dataset3d import Dataset2d, Dataset3d -from quantem.core.utils.validators import ensure_valid_array - -# from quantem.tomography.alignment import tilt_series_cross_cor_align, compute_com_tilt_series - - -# DEPRECATED: Use TomographyDataset instead. -class TiltSeries(Dataset3d): - def __init__( - self, - array: NDArray | Any, # Assumes a input tilt series [phis, x, y] - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, - tilt_angles: list | NDArray, - z1_angles: list | NDArray, - z3_angles: list | NDArray, - shifts: list[tuple[float, float]] | NDArray, - signal_units: str = "arb. units", - _token: object | None = None, - ): - super().__init__( - array=array, - name=name, - origin=origin, - sampling=sampling, - units=units, - signal_units=signal_units, - _token=_token, - ) - self._tilt_angles = tilt_angles - self._z1_angles = z1_angles - self._z3_angles = z3_angles - self._shifts = shifts - - @classmethod - def from_array( - cls, - array: NDArray | List[Dataset2d] | Any, - tilt_angles: list | NDArray = None, - z1_angles: list | NDArray = None, - z3_angles: list | NDArray = None, - shifts: list[tuple[float, float]] | NDArray = None, - name: str | None = None, - origin: NDArray | tuple | list | float | int | None = None, - sampling: NDArray | tuple | list | float | int | None = None, - units: list[str] | tuple | list | None = None, - signal_units: str = "arb. units", - ) -> Self: - if tilt_angles is not None: - validated_tilt_angles = ensure_valid_array(tilt_angles, ndim=1) - else: - validated_tilt_angles = None - - # array = np.transpose(array, axes=(2, 0, 1)) - - if z1_angles is not None: - validated_z1_angles = ensure_valid_array(z1_angles, ndim=1) - else: - validated_z1_angles = torch.zeros(len(validated_tilt_angles)) - - if z3_angles is not None: - validated_z3_angles = ensure_valid_array(z3_angles, ndim=1) - else: - validated_z3_angles = torch.zeros(len(validated_tilt_angles)) - - if shifts is not None: - validated_shifts = ensure_valid_array(shifts, ndim=2) - else: - validated_shifts = torch.zeros((len(validated_tilt_angles), 2)) - - array = torch.from_numpy(array) - - return cls( - array=array, - tilt_angles=validated_tilt_angles - if validated_tilt_angles is not None - else ["duck" for _ in range(array.shape[0])], - z1_angles=validated_z1_angles, - z3_angles=validated_z3_angles, - shifts=validated_shifts, - name=name if name is not None else "Tilt Series Dataset", - origin=origin if origin is not None else np.zeros(3), - sampling=sampling if sampling is not None else np.ones(3), - units=units if units is not None else ["index", "pixels", "pixels"], - signal_units=signal_units, - _token=cls._token, - ) - - # --- Properties --- - - @property - def tilt_angles(self) -> NDArray: - """Get the tilt angles of the dataset.""" - return self._tilt_angles - - @property - def tilt_angles_rad(self) -> NDArray: - """Get the tilt angles of the dataset in radians.""" - return np.deg2rad(self._tilt_angles) - - @tilt_angles.setter - def tilt_angles(self, angles: NDArray | list) -> None: - """Set the tilt angles of the dataset.""" - if len(angles) != self.shape[0]: - raise ValueError("Tilt angles must match the number of projections.") - - # Convert to numpy array if not already - if isinstance(self._tilt_angles, NDArray): - self._tilt_angles = np.array(angles) - else: - self._tilt_angles = angles - - @property - def z1_angles(self) -> NDArray: - """Get the z1 angles of the dataset.""" - return self._z1_angles - - @z1_angles.setter - def z1_angles(self, angles: NDArray | list) -> None: - """Set the z1 angles of the dataset.""" - if len(angles) != self.shape[0]: - raise ValueError("Z1 angles must match the number of projections.") - self._z1_angles = angles - - @property - def z3_angles(self) -> NDArray: - """Get the z3 angles of the dataset.""" - return self._z3_angles - - @z3_angles.setter - def z3_angles(self, angles: NDArray | list) -> None: - """Set the z3 angles of the dataset.""" - if len(angles) != self.shape[0]: - raise ValueError("Z3 angles must match the number of projections.") - self._z3_angles = angles - - @property - def shifts(self) -> NDArray: - """Get the shifts of the dataset.""" - return self._shifts - - @shifts.setter - def shifts(self, shifts: list[tuple[float, float]] | NDArray) -> None: - """Set the shifts of the dataset.""" - self._shifts = shifts diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ef70d5f2..907ad994 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,237 +1,608 @@ -import torch +import os +from pathlib import Path +from typing import Literal, Self, Sequence -# from torch_radon.radon import ParallelBeam as Radon +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.distributed as dist from tqdm.auto import tqdm -from quantem.tomography.object_models import ObjectVoxelwise +from quantem.core.io.serialize import load as autoserialize_load +from quantem.core.ml.loss_functions import get_loss_module +from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d +from quantem.core.utils.tomography_utils import torch_phase_cross_correlation +from quantem.tomography.dataset_models import ( + DatasetConstraintParams, + DatasetConstraintsType, + DatasetModelType, + TomographyINRDataset, + TomographyPixDataset, +) +from quantem.tomography.logger_tomography import LoggerTomography +from quantem.tomography.object_models import ( + ObjConstraintParams, + ObjConstraintsType, + ObjectINR, + ObjectPixelated, +) +from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.tomography_conv import TomographyConv -from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ +from quantem.tomography.tomography_opt import TomographyOpt -class Tomography(TomographyConv, TomographyML, TomographyBase): +class Tomography(TomographyOpt, TomographyBase): """ - Top level class for either using conventional or ML-based reconstruction methods - for tomography. + Class for handling all ML tomography reconstruction methods. + Automatic handling between AD and INR-based tomography. """ - def __init__( + @classmethod + def from_models( + cls, + dset: DatasetModelType, + obj_model: ObjectINR, + logger: LoggerTomography | None = None, + device: str = "cuda", + verbose: int | bool = True, + rng: np.random.Generator | int | None = None, + ) -> Self: + return cls( + dset=dset, + obj_model=obj_model, + logger=logger, + device=device, + rng=rng, + verbose=verbose, + _token=cls._token, + ) + + def reconstruct( self, - dataset, - volume_obj, - device, - _token, + num_iter: int = 10, + batch_size: int = 1024, + num_workers: int = 32, + reset: bool = False, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + obj_constraints: dict | ObjConstraintsType | None = None, + dset_constraints: dict | DatasetConstraintsType | None = None, + num_samples_per_ray: int | list[tuple[int, int]] | None = None, + profiling_mode: bool = False, + val_fraction: float = 0.0, + loss_type: Literal[ + "l2", + "l1", + "smooth_l1", + "charbonnier", + "llmse", + "mse_log_mse", + ] = "l2", + loss_func_kwargs: dict = {}, + reset_dset: DatasetModelType | None = None, + show_metrics: bool = False, ): - super().__init__(dataset, volume_obj, device, _token) + """ + This function should be able to handle both AD and INR-based tomography reconstruction methods. + I.e, auto-detection through the obj model type, while both share the same pose optimization. + """ - # --- Reconstruction Method --- + # Check device consistency + self.obj_model.to(self.device) - def sirt_recon( - self, - num_iterations: int = 10, - inline_alignment: bool = False, - enforce_positivity: bool = True, - volume_shape: tuple = None, - reset: bool = True, - smoothing_sigma: float = None, - shrinkage: float = None, - filter_name: str = "hamming", - circle: bool = True, - plot_loss: bool = False, - ): - num_angles, num_rows, num_cols = self.dataset.tilt_series.shape - sirt_tilt_series = self.dataset.tilt_series.clone() - sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) - - hard_constraints = { - "positivity": enforce_positivity, - "shrinkage": shrinkage, - } - self.volume_obj.hard_constraints = hard_constraints - - if volume_shape is None: - volume_shape = (num_rows, num_rows, num_rows) - else: - D, H, W = volume_shape + # Saving batch size, num workers, and val fraction for reloading + self.batch_size = batch_size + self.num_workers = num_workers + self.val_fraction = val_fraction + + if profiling_mode: + if self.global_rank == 0: + print("Profiling mode enabled.") if reset: - self.volume_obj.reset() - self.loss = [] + raise NotImplementedError("Reset is not implemented yet.") - proj_forward = torch.zeros_like(self.dataset.tilt_series) + new_scheduler = reset + if optimizer_params is not None: + self.optimizer_params = optimizer_params + self.set_optimizers() + new_scheduler = True - pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") + if scheduler_params is not None: + self.scheduler_params = scheduler_params + new_scheduler = True - if smoothing_sigma is not None: - gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) - else: - gaussian_kernel = None + if new_scheduler: + self.set_schedulers(self.scheduler_params, num_iter=num_iter) - for iter in pbar: - proj_forward, loss = self._sirt_run_epoch( - tilt_series=sirt_tilt_series, - proj_forward=proj_forward, - angles=self.dataset.tilt_angles, - inline_alignment=iter > 0 and inline_alignment, - filter_name=filter_name, - gaussian_kernel=gaussian_kernel, - circle=circle, + if obj_constraints is not None: + if isinstance(obj_constraints, dict): + obj_constraints = ObjConstraintParams.parse_dict(obj_constraints) + + self.obj_model.constraints = obj_constraints + + if dset_constraints is not None: + if isinstance(dset_constraints, dict): + dset_constraints = DatasetConstraintParams.parse_dict(dset_constraints) + + self.dset.constraints = dset_constraints + # Setting up DDP + if not hasattr(self, "dataloader") or reset_dset is not None: + if reset_dset is not None: + print("Resetting Dataloader") + print("Putting in params from previous dataset.") + + self.dset = reset_dset + self.dset.to(self.device) + + if optimizer_params is not None: + self.optimizer_params = optimizer_params + self.set_optimizers() + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params) + + self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( + self.setup_dataloader( + self.dset, + batch_size, + num_workers=num_workers, + val_fraction=val_fraction, + ) ) - pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") + # Type check for INR-based reconstruction + if not isinstance(self.dset, TomographyINRDataset): + raise NotImplementedError( + "Only TomographyINRDataset is supported for this reconstruction method." + ) - self.loss.append(loss.item()) + N = max(self.obj_model.shape) - self.sirt_recon_vol = self.volume_obj + if num_samples_per_ray is None: + num_samples_per_ray = max(self.obj_model.shape) + else: + if isinstance(num_samples_per_ray, int): + num_samples_per_ray = num_samples_per_ray + else: + if len(num_samples_per_ray) != num_iter: + raise ValueError( + "num_samples_per_ray schedule must have the same length as num_iter" + ) + if self.global_rank == 0: + print("num_samples_per_ray schedule provided.") - # Permutation due to sinogram ordering. - self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) + loss_func = get_loss_module(name=loss_type, dtype=self.obj_model.dtype, **loss_func_kwargs) - if plot_loss: - self.plot_loss() + pbar = tqdm(range(num_iter), disable=not self.verbose) + for a0 in pbar: + consistency_loss = torch.tensor(0.0, device=self.device) + total_loss = torch.tensor(0.0, device=self.device) + epoch_soft_constraint_loss = torch.tensor(0.0, device=self.device) + if isinstance(self.obj_model, ObjectINR): + self.obj_model.model.train() + else: + raise NotImplementedError( + "AD Pixelated reconstruction is not yet implemented. Use ObjectINR instead." + ) + self.dset.train() + # self._reset_iter_constraints() + + if self.sampler is not None: + self.sampler.set_epoch(a0) + + if isinstance(num_samples_per_ray, list): + curr_num_samples_per_ray = num_samples_per_ray[a0][1] + else: + curr_num_samples_per_ray = num_samples_per_ray + + for batch_idx, batch in enumerate(self.dataloader): + self.zero_grad_all() + with torch.autocast( + device_type=self.device.type, + dtype=torch.bfloat16, + enabled=True, + ): + all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) + + all_densities = self.obj_model.forward(all_coords) + + integrated_densities = self.dset.integrate_rays( + all_densities, + curr_num_samples_per_ray, + len(batch["target_value"]), + ) - def ad_recon( - self, - optimizer_params: dict, - num_iter: int = 0, - reset: bool = False, - scheduler_params: dict | None = None, - hard_constraints: dict | None = None, - soft_constraints: dict | None = None, - # store_iterations: bool | None = None, - # store_iterations_every: int | None = None, - # autograd: bool = True, - ): - if reset: - self.reset_recon() + pred = integrated_densities.float() + soft_constraints_loss = 0.0 + if self.num_epochs > 0: + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) - self.hard_constraints = hard_constraints - self.soft_constraints = soft_constraints + target = batch["target_value"].to(self.device, non_blocking=True).float() - # Make sure everything is in the correct device, might be redundant/cleaner way to do this - self.dataset.to(self.device) - self.volume_obj.to(self.device) + batch_consistency_loss = loss_func(pred, target) - # Making optimizable parameters into leaf tensors. - self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) - self.dataset.z1_angles = ( - self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) - ) - self.dataset.z3_angles = ( - self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) - ) + soft_constraints_loss += self.dset.apply_soft_constraints() - if optimizer_params is not None: - self.optimizer_params = optimizer_params - self.set_optimizers() + epoch_soft_constraint_loss += soft_constraints_loss.detach() - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=num_iter) + batch_loss = batch_consistency_loss.float() + soft_constraints_loss.float() - if hard_constraints is not None: - self.volume_obj.hard_constraints = hard_constraints - if soft_constraints is not None: - self.volume_obj.soft_constraints = soft_constraints + batch_loss.backward() + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.obj_model.model.parameters(), max_norm=1.0) + self.step_optimizers() + total_loss += batch_loss.detach() + consistency_loss += batch_consistency_loss.detach() - pbar = tqdm(range(num_iter), desc="AD Reconstruction") + if self.world_size > 1: + dist.all_reduce(total_loss, dist.ReduceOp.AVG) + dist.all_reduce(consistency_loss, dist.ReduceOp.AVG) + dist.all_reduce(epoch_soft_constraint_loss, dist.ReduceOp.AVG) - for a0 in pbar: - total_loss = 0.0 - tilt_series_loss = 0.0 - - pred_volume = self.volume_obj.forward() - - for i in range(len(self.dataset.tilt_series)): - forward_projection = self.projection_operator( - vol=pred_volume, - z1=self.dataset.z1_angles[i], - x=self.dataset.tilt_angles[i], - z3=self.dataset.z3_angles[i], - shift_x=self.dataset.shifts[i, 0], - shift_y=self.dataset.shifts[i, 1], - device=self.device, - ) + total_loss = total_loss.item() / len(self.dataloader) + consistency_loss = consistency_loss.item() / len(self.dataloader) + epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len(self.dataloader) - tilt_series_loss += torch.nn.functional.mse_loss( - forward_projection, self.dataset.tilt_series[i] - ) - tilt_series_loss /= len(self.dataset.tilt_series) + self.step_schedulers(loss=total_loss) + # TODO: Maybe reorganize the losses so that the order makes sense lol. + + avg_val_loss = None + if self.val_dataloader is not None: + print("Validating...") + self.obj_model.model.eval() + self.dset.eval() + with torch.no_grad(): + val_loss = torch.tensor(0.0, device=self.device) + + for batch in self.val_dataloader: + with torch.autocast( + device_type=self.device.type, + dtype=torch.bfloat16, + enabled=True, + ): + all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) + + all_densities = self.obj_model.forward(all_coords) + + integrated_densities = self.dset.integrate_rays( + all_densities, + curr_num_samples_per_ray, + len(batch["target_value"]), + ) + + target = ( + batch["target_value"].to(self.device, non_blocking=True).float() + ) + + batch_val_loss = torch.nn.functional.mse_loss( + integrated_densities, target + ) + + val_loss += batch_val_loss.detach() + + avg_val_loss = val_loss.item() / len(self.val_dataloader) - total_loss = tilt_series_loss + self.volume_obj.soft_loss - self.loss.append(total_loss.item()) + metrics = torch.tensor( + [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device + ) - total_loss.backward() + if self.world_size > 1: + dist.all_reduce(metrics, dist.ReduceOp.AVG) - for opt in self.optimizers.values(): - opt.step() - opt.zero_grad() + total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() - if self.schedulers is not None: - for sch in self.schedulers.values(): - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(total_loss) - elif sch is not None: - sch.step() + pbar.set_description( + f"Reconstruction | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}" + ) - pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") + self._epoch_losses.append(total_loss) + self._consistency_losses.append(consistency_loss) + self.append_learning_rates(self.get_current_lrs()) + self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) + if avg_val_loss is not None: + self._val_losses.append(avg_val_loss) if self.logger is not None: - self.logger.log_scalar("loss/total", total_loss.item(), a0) - self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) - self.logger.log_scalar( - "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 - ) + if ( + self.logger.log_images_every > 0 + and self.num_epochs % self.logger.log_images_every == 0 + ): + pred_full = self.obj_model.obj_view + + if self.global_rank == 0: + self.logger.log_iter_images( + pred_volume=pred_full, + dataset_model=self.dset, + iter=self.num_epochs, + ) + pbar.set_description( + f"Reconstruction | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e} | Images Logged" + ) - if a0 % self.logger.log_images_every == 0: - self.logger.projection_images( - volume_obj=self.volume_obj, - epoch=a0, + if self.global_rank == 0: + self.logger.log_iter( + object_model=self.obj_model, + iter=self.num_epochs, + consistency_loss=consistency_loss, + total_loss=total_loss, + learning_rates=self.get_current_lrs(), + num_samples_per_ray=curr_num_samples_per_ray, + val_loss=avg_val_loss if self.val_dataloader is not None else None, ) - self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) self.logger.flush() + if not self.verbose: + if self.global_rank == 0: + print( + f"Reconstruction Epoch {self.num_epochs} | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}" + ) + if show_metrics and self.world_size == 1: + self.plot_losses() + + # --- Helper Functions --- + + def save_volume(self, path: str = "recon_volume.npz", overwrite: bool = False): + """ + Saves volume to a numpy array file. Does not save the full Tomography object. + """ + if self.global_rank == 0: + if not overwrite and os.path.exists(path): + raise FileExistsError( + f"File {path} already exists. Use overwrite=True to overwrite." + ) + print(f"Saving volume to {path}") + np.savez(path, volume=self.obj_model.obj_view) + + if torch.distributed.is_initialized(): + print("Barrier") + torch.distributed.barrier() + + # Loading and Saving + @classmethod + def _recursive_load_from_path(cls, path: str): + return autoserialize_load(path) + + @classmethod + def from_file( + cls, + path: str, + device: str = "cpu", + ) -> Self: + tomography = cls._recursive_load_from_path(path) + tomography.to(device) + tomography._rebuild_dataloader( + batch_size=tomography.batch_size, + num_workers=tomography.num_workers, + val_fraction=tomography.val_fraction, + ) + return tomography + + def _rebuild_dataloader(self, batch_size: int, num_workers: int, val_fraction: float): + """ + Rebuilds the dataloader due to persistent workers error when reloading the object. + """ + self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( + self.setup_dataloader( + self.dset, + batch_size, + num_workers=num_workers, + val_fraction=val_fraction, + ) + ) + + def save( + self, + path: str | Path, + mode: Literal["w", "o"] = "w", + store: Literal["auto", "zip", "dir"] = "auto", + skip: str | type | Sequence[str | type] = ["dataloader"], + compression_level: int | None = 4, + ) -> None: + super(Tomography, self).save( + path=path, + mode=mode, + store=store, + skip=skip, + compression_level=compression_level, + ) + + def plot_losses(self): + fig, ax = plt.subplots(figsize=(10, 4), ncols=2) + + ax[0].plot(self._epoch_losses, label="Total Training Loss") + if len(self._val_losses) > 0: + ax[0].plot(self._val_losses, label="Validation Loss") + ax[0].legend() - self.ad_recon_vol = self.volume_obj.forward() + for key, value in self._lrs.items(): + ax[1].plot(value, label=key) - return self + ax[1].legend() + ax[0].legend() + ax[0].set_yscale("log") + ax[1].set_yscale("log") + ax[0].set_xlabel("Epoch") + ax[1].set_xlabel("Epoch") + ax[0].set_ylabel("Loss") + ax[1].set_ylabel("Learning Rate") - def reset_recon(self) -> None: - if isinstance(self.volume_obj, ObjectVoxelwise): - self.volume_obj.reset() - self.ad_recon_vol = None +class TomographyConventional(TomographyBase): + """ + Class for handling all conventional tomography reconstruction methods. + Will also handle choosing the appropriate dataset model to use. + """ + + @classmethod + def from_models( + cls, + dset: TomographyPixDataset, + obj_model: ObjectPixelated, + logger: LoggerTomography | None = None, + device: str = "cuda", + verbose: int | bool = True, + rng: np.random.Generator | int | None = None, + ) -> Self: + return cls( + dset=dset, + obj_model=obj_model, + logger=logger, + device=device, + rng=rng, + verbose=verbose, + _token=cls._token, + ) - # --- Projection Operators ---- - def projection_operator( + def reconstruct( self, - vol, - z1, - x, - z3, - shift_x, - shift_y, - device, + num_iter: int = 10, + obj_constraints: dict | ObjConstraintsType | None = None, + mode: Literal["sirt", "fbp"] = "sirt", + relaxation: float = 0.25, + reset: bool = False, + inline_alignment: bool = False, + smoothing_sigma: float | None = None, + show_metrics: bool = False, ): - projection = ( - rot_ZXZ( - mags=vol.unsqueeze(0), # Add batch dimension - z1=z1, - x=-x, - z3=z3, - device=device, - mode="bilinear", - ) - .squeeze() - .sum(axis=0) + if obj_constraints is not None: + if isinstance(obj_constraints, dict): + obj_constraints = ObjConstraintParams.parse_dict(obj_constraints) + + self.obj_model.constraints = obj_constraints + + pbar = tqdm( + range(num_iter), + desc=f"{mode} Reconstruction | Loss: {0:.4f}", + disable=not self.verbose, ) + if mode == "sirt" or mode == "fbp": + proj_forward = torch.zeros_like(self.dset.tilt_stack).permute(2, 0, 1) + else: + proj_forward = torch.zeros_like(self.dset.tilt_stack) - shifted_projection = differentiable_shift_2d( - image=projection, - shift_x=shift_x, - shift_y=shift_y, - sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit + if smoothing_sigma is not None: + gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) + else: + gaussian_kernel = None + + patience = self.dset.tilt_angles.max() // 10 + for iter in pbar: + proj_forward, loss = self._reconstruction_epoch( + inline_alignment=inline_alignment, + mode=mode, + proj_forward=proj_forward, + gaussian_kernel=gaussian_kernel, + relaxation=relaxation, + ) + + pbar.set_description(f"{mode} Reconstruction | Loss: {loss.item():.4f}") + + self._epoch_losses.append(loss.item()) + + # Change relaxation parameter if loss greater than last epoch + if len(self._epoch_losses) > 1 and self._epoch_losses[-1] > self._epoch_losses[-2]: + if patience == 0: + relaxation *= 0.85 + print(f"Relaxation parameter changed to: {relaxation}") + patience = 10 + else: + patience -= 1 + + if mode == "fbp": + break + + if show_metrics: + self.plot_losses() + + # --- Conventional reconstruction method --- + def _adaptive_relaxation(self, n_power_iter: int = 10) -> float: + raise NotImplementedError( + "Adaptive relaxation hasn't been implemented, please input a valid relaxation parameter." ) - return shifted_projection + def _reconstruction_epoch( + self, + inline_alignment: bool, + mode: Literal["sirt", "fbp"], + proj_forward: torch.Tensor, + relaxation: float, + gaussian_kernel: torch.Tensor | None = None, + ): + loss = 0 + + if relaxation == 0.0: + relaxation = self._adaptive_relaxation() + print(f"Adaptive relaxation: {relaxation}") + if inline_alignment: + for ind in range(len(self.dset.tilt_angles)): + im_proj = proj_forward[:, ind, :] + im_meas = self.dset.forward(ind).target # type: ignore + shift = torch_phase_cross_correlation(im_proj, im_meas) + if torch.linalg.norm(shift) <= 32: + shifted = torch.fft.ifft2( + torch.fft.fft2(im_meas) + * torch.exp( + -2j + * np.pi + * ( + shift[0] + * torch.fft.fftfreq( + im_meas.shape[0], device=im_meas.device + ).unsqueeze(1) + + shift[1] + * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) + ) + ) + ).real + + proj_forward[:, ind, :] = shifted + + if mode == "sirt" or mode == "fbp": + proj_forward = radon_torch( + self.obj_model.obj, + theta=self.dset.tilt_angles, + device=self.device, + ) + + error = self.dset.tilt_stack.permute(2, 0, 1) - proj_forward + + correction = iradon_torch( + error, + theta=self.dset.tilt_angles, + device=self.device, + filter_name="ramp", + circle=True, + ) + + normalization = iradon_torch( + torch.ones_like(error), + theta=self.dset.tilt_angles, + device=self.device, + circle=True, + filter_name=None, + ) + + normalization[normalization == 0] = 1e-6 + + correction /= normalization + + self.obj_model.obj += correction * relaxation + + if gaussian_kernel is not None: + self.obj_model.obj = gaussian_filter_2d_stack(self.obj_model.obj, gaussian_kernel) + + loss = torch.mean(torch.abs(error)) + + return proj_forward, loss + + # --- Helper Functions --- + + def plot_losses(self): + fig, ax = plt.subplots() + ax.plot(self._epoch_losses) + ax.set_xlabel("Iteration") + ax.set_ylabel("Loss") + ax.set_title("Reconstruction Loss") + ax.set_yscale("log") + plt.show() diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 0d937020..58909bdf 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1,499 +1,155 @@ -import matplotlib.pyplot as plt import numpy as np -import torch from numpy.typing import NDArray -from torch._tensor import Tensor -from tqdm.auto import tqdm -from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.io.serialize import AutoSerialize -from quantem.core.visualization.visualization import show_2d -from quantem.imaging.drift import cross_correlation_shift -from quantem.tomography.object_models import ObjectModelType, ObjectVoxelwise -from quantem.tomography.tomography_dataset import TomographyDataset -from quantem.tomography.tomography_logger import LoggerTomography +from quantem.core.ml.ddp import DDPMixin +from quantem.core.utils.rng import RNGMixin +from quantem.tomography.dataset_models import DatasetModelType, TomographyDatasetBase +from quantem.tomography.logger_tomography import LoggerTomography +from quantem.tomography.object_models import ( + ObjConstraintParams, + ObjConstraintsType, + ObjectINR, + ObjectModelType, +) + + +class TomographyBase(AutoSerialize, RNGMixin, DDPMixin): + """ + A base class for performing electron tomography reconstructions. + Should have all the default attributes needed for tomography reconstructions. + """ -class TomographyBase(AutoSerialize): _token = object() - DEFAULT_HARD_CONSTRAINTS = { - "positivity": False, - "shrinkage": False, - "circular_mask": False, - "fourier_filter": False, - } - - DEFAULT_SOFT_CONSTRAINTS = { - "tv_vol": 0.0, - } - def __init__( self, - dataset: TomographyDataset, - volume_obj: ObjectModelType, # ObjectDIP? - device: str = "cuda", - # ABF/HAADF property + dset: DatasetModelType, + obj_model: ObjectModelType, logger: LoggerTomography | None = None, + device: str = "cuda", + rng: np.random.Generator | int | None = None, + verbose: int | bool = True, _token: object | None = None, ): - """Initialize a Tomography object. - - Parameters - ---------- - array : NDArray | Any - The underlying 3D array data - name : str - A descriptive name for the dataset - """ - - # if _token is not self._token: - # raise RuntimeError( - # "This class is not meant to be instantiated directly. Use the from_data method." - # ) - - self._device = device - self._dataset = dataset - self._volume_obj = volume_obj - self._loss = [] - self._mode = [] - - self._hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self._soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - self._logger = None - - @classmethod - def from_data( - cls, - tilt_series: Dataset3d | NDArray | Tensor, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - volume_obj: NDArray | Dataset3d | ObjectModelType | None = None, - device: str = "cpu", - ): - device = device.lower() - - dataset = TomographyDataset.from_data( - tilt_series=tilt_series, - tilt_angles=tilt_angles, - z1_angles=z1_angles, - z3_angles=z3_angles, - shifts=shifts, - ) - - dataset.to(device) - - if volume_obj is None: - max_shape = max(dataset.tilt_series.shape) - volume_obj = ObjectVoxelwise( - volume_shape=(max_shape, max_shape, max_shape), - device=device, - ) - elif isinstance(volume_obj, Dataset3d): - volume = torch.from_numpy(volume_obj.array) - volume_obj = ObjectVoxelwise( - volume_shape=volume_obj.shape, device=device, initial_volume=volume - ) - volume_obj.obj = volume - elif isinstance(volume_obj, np.ndarray): - volume = torch.from_numpy(volume_obj) - volume_obj = ObjectVoxelwise( - volume_shape=volume_obj.shape, device=device, initial_volume=volume - ) - volume_obj.obj = volume - else: - raise ValueError("volume_obj must be a Dataset3d, NDArray ObjectModelType") - - return cls( - dataset=dataset, - volume_obj=volume_obj, - device=device, - _token=cls._token, - ) + if _token is not self._token: + raise RuntimeError("Use .from_* to instantiate this class.") + + super().__init__() + self.obj_model = obj_model + + self.dset = dset + self.verbose = verbose + self.rng = rng + self.device = device + self.logger = logger + + # Loss + self._epoch_losses: list[float] = [] + self._consistency_losses: list[float] = [] + self._val_losses: list[float] = [] + self._lrs: dict[str, list] = {} + # DDP Initialization + if isinstance(obj_model, ObjectINR): + self.setup_distributed(device=device) + if self.global_rank == 0: + print("Setting up DDP for obj_model") + + self.dset = dset + self.dset.to(device) # --- Properties --- @property - def dataset(self) -> TomographyDataset: - """Tomography dataset.""" + def dset(self) -> DatasetModelType: + return self._dset - return self._dataset - - @dataset.setter - def dataset( - self, - tilt_series: Dataset3d | NDArray | TomographyDataset, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - # name: str | None = None, - # origin: NDArray | tuple | list | float | int | None = None, - # sampling: NDArray | tuple | list | float | int | None = None, - # units: list[str] | tuple | list | None = None, - # signal_units: str = "arb. units", - ): - """Set the tilt series dataset.""" - - if not isinstance(tilt_series, TomographyDataset): - dataset = TomographyDataset.from_array( - array=tilt_series, - tilt_angles=tilt_angles, - z1_angles=z1_angles, - z3_angles=z3_angles, - shifts=shifts, - ) - - self._dataset = dataset + @dset.setter + def dset(self, new_dset: DatasetModelType): + if not isinstance(new_dset, TomographyDatasetBase) and "TomographyDataset" not in str( + type(new_dset) + ): + raise TypeError(f"dset should be a TomographyDataset, got {type(new_dset)}") + self._dset = new_dset @property - def volume_obj(self) -> Dataset3d | ObjectModelType | None: - """Reconstruction volume dataset.""" + def verbose(self) -> int | bool: + return self._verbose - return self._volume_obj - - @volume_obj.setter - # TODO: add support for ObjectModelType - def volume_obj(self, volume_obj: Dataset3d | NDArray): - """Set the reconstruction volume dataset.""" - if isinstance(volume_obj, ObjectModelType): - self._volume_obj = volume_obj - elif not isinstance(volume_obj, Dataset3d): - volume_obj = Dataset3d.from_array( - array=volume_obj, - # name=self._tilt_series.name, - # origin=self._tilt_series.origin, - # sampling=self._tilt_series.sampling, - # units=self._tilt_series.units, - # signal_units=self._tilt_series.signal_units, - ) - elif isinstance(volume_obj, Dataset3d): - self._volume_obj = volume_obj - else: - raise ValueError("volume_obj must be a Dataset3d or ObjectModelType") - - @property - def device(self) -> str: - """Computation device.""" - - return self._device - - @device.setter - def device(self, device: str): - """Set the computation device.""" - - # if "cuda" not in device or "gpu" not in device: - # raise NotImplementedError("Tomography not currently supported on CPU.") - - self._device = device - - @property - def loss(self) -> list: - """List of loss values during reconstruction.""" - - return self._loss - - @loss.setter - def loss(self, loss: list): - """Set the loss values during reconstruction.""" - - if not isinstance(loss, list): - raise TypeError("Loss must be a list.") - - self._loss = loss + @verbose.setter + def verbose(self, verbose: int | bool): + self._verbose = verbose @property - def mode(self) -> list: - """List of modes used during reconstruction.""" + def obj_model(self) -> ObjectModelType: + return self._obj_model - return self._mode + @obj_model.setter + def obj_model(self, obj_model: ObjectModelType): + self._obj_model = obj_model @property - def epochs(self) -> int: - """Number of epochs used during reconstruction.""" - return len(self.loss) + def constraints(self) -> ObjConstraintsType: # TODO: Also looks at Dataset constraints + return self.obj_model.constraints + + @constraints.setter + def constraints(self, constraints: ObjConstraintsType | dict | None): + if constraints is None: + return + elif isinstance(constraints, dict): + self.obj_model.constraints = ObjConstraintParams.parse_dict(constraints) + elif isinstance(constraints, ObjConstraintsType): + self.obj_model.constraints = constraints + else: + raise ValueError(f"Invalid constraints type: {type(constraints)}") @property - def logger(self) -> LoggerTomography: + def logger(self) -> LoggerTomography | None: return self._logger @logger.setter - def logger(self, logger: LoggerTomography): - if not isinstance(logger, LoggerTomography): - raise TypeError("Logger must be a LoggerTomography") - + def logger(self, logger: LoggerTomography | None): + if not isinstance(logger, LoggerTomography) and logger is not None: + raise TypeError(f"logger should be a LoggerTomography, got {type(logger)}") self._logger = logger - # --- Constraints --- - @property - def hard_constraints(self) -> dict: - """Hard constraints for the reconstruction.""" - return self._hard_constraints - - @hard_constraints.setter - def hard_constraints(self, hard_constraints: dict): - """Set the hard constraints for the reconstruction.""" - - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - for key, value in hard_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._hard_constraints[key] = value - - self._hard_constraints = hard_constraints + def epoch_losses(self) -> NDArray: + """ + Returns the fidelity loss for each epoch ran. + """ + return np.array(self._epoch_losses) @property - def soft_constraints(self) -> dict: - """Soft constraints for the reconstruction.""" - return self._soft_constraints - - @soft_constraints.setter - def soft_constraints(self, soft_constraints: dict): - """Set the soft constraints for the reconstruction.""" - - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - for key, value in soft_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._soft_constraints[key] = value - - self._soft_constraints = soft_constraints - - # --- RESET --- - - def reset_recon(self) -> None: - self.volume_obj.reset() - self.dataset.reset() - self.loss = [] - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - self._optimizers = {} - self._schedulers = {} - - # --- Preprocessing --- - - """ - TODO - 1. Implement tilt series cross-correlation alignment - 2. Background subtraction (for ABF) - 3. COM Alignment - 4. Masking - 5. Drift Correction - """ - - def cross_corr_alignment( - self, - upsample_factor: int = 1, - overwrite: bool = False, - ): - # TODO: This needs to be able to work with torch tensors. - - placeholder_tilt_series = self.dataset.tilt_series.clone().detach().cpu().numpy() - - aligned_tilt_series = np.zeros_like(placeholder_tilt_series) - aligned_tilt_series[0] = placeholder_tilt_series[0] - shifts = [] - num_imgs = placeholder_tilt_series.shape[1] - - pbar = tqdm(range(num_imgs - 1), desc="Cross-correlation alignment") - - for i in pbar: - shift, aligned_img = cross_correlation_shift( - placeholder_tilt_series[i], - placeholder_tilt_series[i + 1], - upsample_factor=upsample_factor, - return_shifted_image=True, - ) - - aligned_tilt_series[i + 1] = aligned_img - shifts.append(shift) - - if overwrite: - # TODO: Check this overwrite idea, maybe also need to save the relative shifts? - self.dataset.tilt_series = np.array(aligned_tilt_series) - - return np.array(aligned_tilt_series), np.array(shifts) - - # --- Postprocessing --- - - """ - TODO - 1. Apply circular mask - """ - - def circular_mask(self, shape, radius, center=None, dtype=torch.float32, device="cpu"): - """Generate a 2D circular mask of given shape and radius.""" - H, W = shape - - if center is None: - center = (H // 2, W // 2) - y = torch.arange(H, dtype=dtype, device=device).view(-1, 1) - x = torch.arange(W, dtype=dtype, device=device).view(1, -1) - dist_sq = (x - center[1]) ** 2 + (y - center[0]) ** 2 - return (dist_sq <= radius**2).to(dtype) - - def recon_vol_circular_mask(self, radii): + def consistency_losses(self) -> NDArray: """ - Apply 2D circular masks along all three axes of a 3D volume. - - Args: - volume (torch.Tensor): 3D tensor of shape (H, W, D) - radii (tuple): (r0, r1, r2) for axes 0, 1, 2 - Returns: - masked_volume: tensor with all masks applied + Returns the consistency loss for each epoch ran. """ - H, W, D = self.volume_obj.array.shape - device = self.device - dtype = torch.float32 - volume_obj = torch.tensor( - self.volume_obj.array, - device=self.device, - dtype=dtype, - ) - # Masks for each axis - mask0 = self.circular_mask((W, D), radii[0], dtype=dtype, device=device).unsqueeze( - 0 - ) # shape (1, W, D) - mask1 = self.circular_mask((H, D), radii[1], dtype=dtype, device=device).unsqueeze( - 1 - ) # shape (H, 1, D) - mask2 = self.circular_mask((H, W), radii[2], dtype=dtype, device=device).unsqueeze( - 2 - ) # shape (H, W, 1) - - # Broadcast and multiply all masks together - total_mask = mask0 * mask1 * mask2 # shape (H, W, D) - - volume_obj = volume_obj * total_mask - volume_obj = volume_obj.detach().cpu().numpy() - self.volume_obj = Dataset3d.from_array( - array=volume_obj, - # name=self.volume_obj.name, - # origin=self.volume_obj.origin, - # sampling=self.volume_obj.sampling, - # units=self.volume_obj.units, - # signal_units=self.volume_obj.signal_units, - ) + return np.array(self._consistency_losses) - # --- Visualizations --- - - def plot_projections( - self, - cmap: str = "turbo", - fft: bool = False, - norm: str = "log_auto", - figax: tuple[plt.Figure, plt.Axes] | None = None, - **kwargs, - ): + @property + def learning_rates(self) -> dict[str, list]: """ - Plots the projections of the volume object. - Note that the volume object is in the order of (z, y, x). - Parameters - ---------- - cmap : str - The colormap to use for the projections. - fft : bool + Returns the learning rates for each epoch ran. """ + return self._lrs - volume_obj_np = self.volume_obj.obj.detach().cpu().numpy() - - if figax is None: - fig, ax = plt.subplots(ncols=3, figsize=(20, 8)) - else: - fig, ax = figax - - show_2d( - volume_obj_np.sum(axis=0), - figax=(fig, ax[0]), - cmap=cmap, - title="Y-X Projection", - ) - show_2d( - volume_obj_np.sum(axis=1), - figax=(fig, ax[1]), - cmap=cmap, - title="Z-X Projection", - ) - show_2d( - volume_obj_np.sum(axis=2), - figax=(fig, ax[2]), - cmap=cmap, - title="Z-Y Projection", - ) - - if fft: - fig, ax = plt.subplots(ncols=3, figsize=(25, 8)) - - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=0)))), - figax=(fig, ax[0]), - cmap=cmap, - title="Y-X Projection FFT", - norm=norm, - ) - - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=1)))), - figax=(fig, ax[1]), - cmap=cmap, - title="Z-X Projection FFT", - norm=norm, - ) - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=2)))), - figax=(fig, ax[2]), - cmap=cmap, - title="Z-Y Projection FFT", - norm=norm, - ) - - def plot_slice( - self, - cmap="turbo", - slice_index: int = 0, - vmin: float = 0, - figax: tuple[plt.Figure, plt.Axes] | None = None, - ): - if figax is None: - fig, ax = plt.subplots(figsize=(15, 8), ncols=3) - else: - fig, ax = figax - - show_2d( - self.volume_obj.obj[slice_index, :, :], - figax=(fig, ax[0]), - cmap=cmap, - vmin=vmin, - ) - show_2d( - self.volume_obj.obj[:, slice_index, :], - figax=(fig, ax[1]), - cmap=cmap, - vmin=vmin, - ) + def append_learning_rates(self, learning_rates: dict[str, float]): + """ + Appends the learning rates for each epoch ran. + """ + for key, value in learning_rates.items(): + if key not in self._lrs: + self._lrs[key] = [] + self._lrs[key].append(float(value)) - show_2d( - self.volume_obj.obj[:, :, slice_index], - figax=(fig, ax[2]), - cmap=cmap, - vmin=vmin, - ) + @property + def num_epochs(self) -> int: + return len(self._epoch_losses) - def plot_loss( - self, - figsize: tuple = (8, 4), - figax: tuple[plt.Figure, plt.Axes] | None = None, - ): - if figax is None: - fig, ax = plt.subplots(figsize=figsize) - else: - fig, ax = figax + # --- Helper Functions --- - ax.semilogy( - self.loss, - label="Loss", - ) + def to(self, device: str): + self.obj_model.to(device) + self.dset.to(device) + self.device = device diff --git a/src/quantem/tomography/tomography_conv.py b/src/quantem/tomography/tomography_conv.py deleted file mode 100644 index 1056da17..00000000 --- a/src/quantem/tomography/tomography_conv.py +++ /dev/null @@ -1,235 +0,0 @@ -import numpy as np -import torch - -# from torch_radon.radon import ParallelBeam as Radon -from quantem.tomography.radon.radon import iradon_torch, radon_torch -from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.utils import gaussian_filter_2d_stack, torch_phase_cross_correlation - - -class TomographyConv(TomographyBase): - """ - Class for handling conventional reconstruction methods of tomography data. - """ - - # --- Reconstruction Methods --- - - def _sirt_run_epoch( - self, - tilt_series: torch.Tensor, - proj_forward: torch.Tensor, - angles: torch.Tensor, - inline_alignment: bool, - filter_name: str, - circle: bool, - gaussian_kernel: torch.Tensor | None, - ): - loss = 0 - - if inline_alignment: - for ind in range(len(self.dataset.tilt_angles)): - im_proj = proj_forward[ind] - im_meas = tilt_series[ind] - - shift = torch_phase_cross_correlation(im_proj, im_meas) - if torch.linalg.norm(shift) <= 32: - shifted = torch.fft.ifft2( - torch.fft.fft2(im_meas) - * torch.exp( - -2j - * np.pi - * ( - shift[0] - * torch.fft.fftfreq( - im_meas.shape[0], device=im_meas.device - ).unsqueeze(1) - + shift[1] - * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) - ) - ) - ).real - - proj_forward[ind] = shifted - - # Forward projection - - sinogram_est = radon_torch(self.volume_obj.obj, theta=angles, device=self.device) - # proj_forward = sinogram_est.permute(1, 2, 0) - # error = (tilt_series - proj_forward).permute(2, 0, 1) - proj_forward = sinogram_est - error = tilt_series - proj_forward - - correction = iradon_torch( - error, theta=angles, device=self.device, filter_name=filter_name, circle=circle - ) - - normalization = iradon_torch( - torch.ones_like(error), - theta=angles, - device=self.device, - filter_name=None, - circle=circle, - ) - - normalization[normalization == 0] = 1e-6 - - correction /= normalization - - self.volume_obj._obj += correction - - loss = torch.mean(torch.abs(error)) - - # for z in tqdm(range(self.volume_obj.obj.shape[0]), desc="SIRT Reconstruction"): - # slice_estimate = self.volume_obj.obj[z] - # sinogram_est = radon_torch(slice_estimate, theta=angles, device=self.device) - - # sinogram_true = tilt_series[:, :, z] - - # error = sinogram_true - sinogram_est - - # correction = iradon_torch( - # error, theta=angles, device=self.device, filter_name=filter_name, circle=circle - # ) - - # # I'm pretty sure this implementation of normalization is wrong - # normalization = iradon_torch( - # torch.ones_like(error), - # theta=angles, - # device=self.device, - # filter_name=None, - # circle=circle, - # ) - # normalization[normalization == 0] = 1e-6 - - # correction /= normalization - - # self.volume_obj._obj[z] += correction - - # proj_forward[:, :, z] = sinogram_est - - # loss += torch.mean(torch.abs(error)) - - # loss /= self.volume_obj._obj.shape[0] - - if gaussian_kernel is not None: - self.volume_obj.obj = gaussian_filter_2d_stack(self.volume_obj.obj, gaussian_kernel) - - return proj_forward, loss - - # Deprecated torch_radon implementations - # def _sirt_run_epoch( - # self, - # radon: Radon, - # stack_recon: torch.Tensor, - # stack_torch: torch.Tensor, - # proj_forward: torch.Tensor, - # step_size: float = 0.25, - # gaussian_kernel: torch.Tensor = None, - # inline_alignment=True, - # enforce_positivity=True, - # shrinkage: float = None, - # ): - # loss = 0 - - # if inline_alignment: - # for ind in range(len(self.tilt_series.tilt_angles)): - # im_proj = proj_forward[:, ind, :] - # im_meas = stack_torch[:, ind, :] - - # shift = torch_phase_cross_correlation(im_proj, im_meas) - # if torch.linalg.norm(shift) <= 32: - # shifted = torch.fft.ifft2( - # torch.fft.fft2(im_meas) - # * torch.exp( - # -2j - # * np.pi - # * ( - # shift[0] - # * torch.fft.fftfreq( - # im_meas.shape[0], device=im_meas.device - # ).unsqueeze(1) - # + shift[1] - # * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) - # ) - # ) - # ).real - - # stack_torch[:, ind, :] = shifted - - # proj_forward = radon.forward(stack_recon) - - # proj_diff = stack_torch - proj_forward - - # loss = torch.mean(torch.abs(proj_diff)) - - # recon_slice_update = radon.backward( - # radon.filter_sinogram( - # proj_diff, - # ) - # ) - - # stack_recon += step_size * recon_slice_update - # if enforce_positivity: - # stack_recon = torch.clamp(stack_recon, min=0) - - # if gaussian_kernel is not None: - # stack_recon = gaussian_filter_2d_stack( - # stack_recon, - # gaussian_kernel, - # ) - - # if shrinkage is not None: - # stack_recon = torch.max( - # stack_recon - shrinkage, - # torch.zeros_like(stack_recon), - # ) - - # return stack_recon, loss - - # def _sirt_serial_run_epoch( - # self, - # radon: Radon, - # stack_recon: torch.Tensor, - # stack_torch: torch.Tensor, - # proj_forward: torch.Tensor, - # step_size: float = 0.25, - # gaussian_kernel: torch.Tensor = None, - # inline_alignment=True, - # enforce_positivity=True, - # ): - # recon_slice_update = torch.zeros_like(stack_recon).to(self.device) - - # loss = 0 - - # for i in range(stack_recon.shape[0]): - # proj_forward[i] = radon.forward(stack_recon[i]) - - # proj_diff = stack_torch - proj_forward - - # loss = torch.mean(torch.abs(proj_diff)) - - # for i in range(stack_recon.shape[0]): - # recon_slice_update[i] = radon.backward( - # radon.filter_sinogram( - # proj_diff[i], - # ) - # ) - - # stack_recon += step_size * recon_slice_update - - # if enforce_positivity: - # stack_recon = torch.clamp(stack_recon, min=0) - - # return stack_recon, loss - - # --- Properties --- - # @property - # def reconstruction_method(self) -> str: - # """Get the reconstruction method.""" - # return self._reconstruction_method - # @reconstruction_method.setter - # def reconstruction_method(self, value: str): - # """Set the reconstruction method.""" - # if value not in ["SIRT", "FBP"]: - # raise ValueError("Invalid reconstruction method. Choose 'SIRT' or 'FBP'.") - # self._reconstruction_method = value diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py deleted file mode 100644 index 816259b6..00000000 --- a/src/quantem/tomography/tomography_dataset.py +++ /dev/null @@ -1,226 +0,0 @@ -import torch -from numpy.typing import NDArray -from torch._tensor import Tensor - -from quantem.core.datastructures.dataset3d import Dataset3d -from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.validators import ( - validate_array, - validate_tensor, -) - - -class TomographyDataset(AutoSerialize): - _token = object() - - """ - A tomography dataset which contains the tilt series, and also instatiates the - z1, z3, and shifts of the tilt series. - - Idea for this dataset is so that we can avoid moving things around as a torch tensor, - since the SIRT reconstruction algorthim, and AD reconstruction we have are all torch based. - """ - - def __init__( - self, - tilt_series: Tensor, - tilt_angles: Tensor, - z1_angles: Tensor, - z3_angles: Tensor, - shifts: Tensor, - ): - self._tilt_series = tilt_series - self._tilt_angles = tilt_angles - self._z1_angles = z1_angles - self._z3_angles = z3_angles - self._shifts = shifts - - self._initial_tilt_angles = tilt_angles.clone() - self._initial_z1_angles = z1_angles.clone() - self._initial_z3_angles = z3_angles.clone() - self._initial_shifts = shifts.clone() - - # --- Class Methods --- - @classmethod - def from_data( - cls, - tilt_series: Dataset3d | NDArray | Tensor, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - # name: str | None = None, - # origin: NDArray | tuple | list | float | int | None = None, - # sampling: NDArray | tuple | list | float | int | None = None, - # units: list[str] | tuple | list | None = None, - # signal_units: str = "arb. units", - # _token: object | None = None, - ): - """ - tilt_series: (N, H, W) - tilt_angles: (N,) - In units of degrees, the alpha tilt angle. - z1_angles: (N,) - In units of degrees, beta tilt angle. - z3_angles: (N,) - In units of degrees, negative beta tilt angle. - shifts: (N, 2) - - - The convention we use for projecting down is ZXZ Euler Angles. - - In theory, Z1 and Z3 should be the same value, except Z3 would be the - negative value of Z1. However, in some cases this is not the case and - there could be some merit in also optimizing both angles. However, the - downside is the rotation becomes less interpretable. - - The tilt angle can also be optimized. - """ - validated_tilt_series = torch.tensor(validate_array(tilt_series, "tilt_series")) - validated_tilt_angles = torch.tensor(validate_array(tilt_angles, "tilt_angles")) - - if z1_angles is not None: - validated_z1_angles = torch.tensor(validate_array(z1_angles, "z1_angles")) - else: - validated_z1_angles = torch.zeros(len(validated_tilt_angles)) - - if z3_angles is not None: - validated_z3_angles = torch.tensor(validate_array(z3_angles, "z3_angles")) - else: - validated_z3_angles = torch.zeros(len(validated_tilt_angles)) - - if shifts is not None: - validated_shifts = torch.tensor(validate_array(shifts, "shifts")) - else: - validated_shifts = torch.zeros(len(validated_tilt_angles), 2) - - return cls( - tilt_series=validated_tilt_series, - tilt_angles=validated_tilt_angles, - z1_angles=validated_z1_angles, - z3_angles=validated_z3_angles, - shifts=validated_shifts, - # name=name, - # origin=origin, - # sampling=sampling, - # units=units, - # signal_units=signal_units, - ) - - def to(self, device: str): - self._tilt_series = self._tilt_series.to(device) - self._tilt_angles = self._tilt_angles.to(device) - self._z1_angles = self._z1_angles.to(device) - self._z3_angles = self._z3_angles.to(device) - self._shifts = self._shifts.to(device) - - # --- Properties --- - - @property - def tilt_series(self) -> Tensor: - return self._tilt_series - - @property - def tilt_angles(self) -> Tensor: - return self._tilt_angles - - @property - def z1_angles(self) -> Tensor: - return self._z1_angles - - @property - def z3_angles(self) -> Tensor: - return self._z3_angles - - @property - def shifts(self) -> Tensor: - return self._shifts - - @property - def initial_tilt_angles(self) -> Tensor: - return self._initial_tilt_angles - - @property - def initial_z1_angles(self) -> Tensor: - return self._initial_z1_angles - - @property - def initial_z3_angles(self) -> Tensor: - return self._initial_z3_angles - - @property - def initial_shifts(self) -> Tensor: - return self._initial_shifts - - # --- Setters --- - - @tilt_series.setter - def tilt_series(self, tilt_series: NDArray | Tensor | Dataset3d) -> None: - if isinstance(tilt_series, Dataset3d): - validated_tilt_series = torch.tensor(tilt_series.array) - elif isinstance(tilt_series, Tensor): - validated_tilt_series = validate_tensor(tilt_series, "tilt_series") - else: - validated_tilt_series = torch.tensor(validate_array(tilt_series, "tilt_series")) - - self._tilt_series = validated_tilt_series - - @tilt_angles.setter - def tilt_angles(self, tilt_angles: NDArray | Tensor) -> None: - if tilt_angles.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Tilt angles must match the number of projections.") - - validated_tilt_angles = torch.tensor(validate_array(tilt_angles, "tilt_angles")) - self._tilt_angles = validated_tilt_angles - - @z1_angles.setter - def z1_angles(self, z1_angles: NDArray | Tensor) -> None: - if z1_angles.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Z1 angles must match the number of projections.") - - if isinstance(z1_angles, Tensor): - validated_z1_angles = validate_tensor(z1_angles, "z1_angles") - else: - validated_z1_angles = torch.tensor(validate_array(z1_angles, "z1_angles")) - self._z1_angles = validated_z1_angles - - @z3_angles.setter - def z3_angles(self, z3_angles: NDArray | Tensor) -> None: - if z3_angles.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Z3 angles must match the number of projections.") - - if isinstance(z3_angles, Tensor): - validated_z3_angles = validate_tensor(z3_angles, "z3_angles") - else: - validated_z3_angles = torch.tensor(validate_array(z3_angles, "z3_angles")) - self._z3_angles = validated_z3_angles - - @shifts.setter - def shifts(self, shifts: NDArray | Tensor) -> None: - if shifts.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Shifts must match the number of projections.") - - if isinstance(shifts, Tensor): - validated_shifts = validate_tensor(shifts, "shifts") - else: - validated_shifts = torch.tensor(validate_array(shifts, "shifts")) - - self._shifts = validated_shifts - - @initial_tilt_angles.setter - def initial_tilt_angles(self, tilt_angles: NDArray | Tensor) -> None: - self._initial_tilt_angles = tilt_angles - - @initial_z1_angles.setter - def initial_z1_angles(self, z1_angles: NDArray | Tensor) -> None: - self._initial_z1_angles = z1_angles - - @initial_z3_angles.setter - def initial_z3_angles(self, z3_angles: NDArray | Tensor) -> None: - self._initial_z3_angles = z3_angles - - @initial_shifts.setter - def initial_shifts(self, shifts: NDArray | Tensor) -> None: - self._initial_shifts = shifts - - # --- RESET --- - - def reset(self) -> None: - self._tilt_angles = self._initial_tilt_angles.clone() - self._z1_angles = self._initial_z1_angles.clone() - self._z3_angles = self._initial_z3_angles.clone() - self._shifts = self._initial_shifts.clone() diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py new file mode 100644 index 00000000..7bd9ad67 --- /dev/null +++ b/src/quantem/tomography/tomography_lite.py @@ -0,0 +1,172 @@ +import os +from typing import Literal, Self + +import numpy as np +import torch +from numpy.typing import NDArray + +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.ml.inr import HSiren +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams, + SchedulerParams, +) +from quantem.tomography.dataset_models import ( + DatasetConstraintsType, + TomographyINRDataset, + TomographyPixDataset, +) +from quantem.tomography.logger_tomography import LoggerTomography +from quantem.tomography.object_models import ObjConstraintsType, ObjectINR, ObjectPixelated +from quantem.tomography.tomography import Tomography, TomographyConventional + + +class TomographyLiteINR(Tomography): + """ + A lite version of the Tomography class. + """ + + @classmethod + def from_dataset( + cls, + tilt_series: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + device: str = "cuda", + log_dir: os.PathLike | str | None = None, + log_images_every: int = 10, + rng: np.random.Generator | int | None = None, + ) -> Self: + dset_model = TomographyINRDataset.from_data( + tilt_stack=tilt_series, + tilt_angles=tilt_angles, + ) + + # Define the object model + model = HSiren(alpha=1, winner_initialization=72) + obj_model = ObjectINR.from_model( + model=model, + shape=( + max(dset_model.tilt_stack.shape), + max(dset_model.tilt_stack.shape), + max(dset_model.tilt_stack.shape), + ), + device=device, + rng=rng, + ) + + # TODO: Implement pretrain + + if log_dir is not None: + logger = LoggerTomography( + log_dir=str(log_dir), + run_prefix="tomography_lite_inr", + run_suffix="", + log_images_every=log_images_every, + ) + else: + logger = None + + tomography = cls.from_models( + dset=dset_model, + obj_model=obj_model, + device=device, + rng=rng, + logger=logger, + ) + + return tomography + + def reconstruct( # type:ignore[reportIncompatibleMethodOverride] ## easier than overloads + self, + num_iter: int = 10, + reset: bool = False, + obj_lr: float = 1e-4, + pose_lr: float = 1e-2, + batch_size: int = 1024, + num_workers: int = 32, + learn_pose: bool = True, + warmup_routine: bool = True, + scheduler_type: Literal[ + "exp", "cyclic", "plateau", "cosine_annealing", "linear", "full_warmup", "none" + ] = "none", + scheduler_params: dict = {}, + new_optimizers: bool = False, + obj_constraints: ObjConstraintsType | dict | None = None, + dset_constraints: DatasetConstraintsType | dict | None = None, + show_metrics: bool = False, + ): + if self.num_epochs == 0: + opt_params = { + "object": OptimizerParams.Adam(lr=obj_lr), + } + + all_scheduler_params = { + "object": SchedulerParams.parse_dict( + { + "name": scheduler_type, + **scheduler_params, + } + ), + } + + if learn_pose: + opt_params["pose"] = OptimizerParams.Adam(lr=pose_lr) + all_scheduler_params["pose"] = SchedulerParams.parse_dict( + { + "name": scheduler_type, + **scheduler_params, + } + ) + else: + opt_params = None + all_scheduler_params = None + obj_constraints = None + dset_constraints = None + + num_samples_per_ray = int(max(self.dset.tilt_stack.shape)) + return super().reconstruct( + num_iter=num_iter, + batch_size=batch_size, + num_workers=num_workers, + reset=reset, + num_samples_per_ray=num_samples_per_ray, + optimizer_params=opt_params, + scheduler_params=all_scheduler_params, + obj_constraints=obj_constraints, + dset_constraints=dset_constraints, + show_metrics=show_metrics, + ) + + +class TomographyLiteConv(TomographyConventional): + @classmethod + def from_dataset( + cls, + tilt_series: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + device: str = "cuda", + rng: np.random.Generator | int | None = None, + ) -> Self: + dset_model = TomographyPixDataset.from_data( + tilt_stack=tilt_series, + tilt_angles=tilt_angles, + ) + + obj_model = ObjectPixelated.from_uniform( + shape=( + max(tilt_series.shape), + max(tilt_series.shape), + max(tilt_series.shape), + ), + device=device, + rng=rng, + ) + + tomography = cls.from_models( + dset=dset_model, + obj_model=obj_model, + device=device, + rng=rng, + ) + + return tomography diff --git a/src/quantem/tomography/tomography_logger.py b/src/quantem/tomography/tomography_logger.py deleted file mode 100644 index 8156837b..00000000 --- a/src/quantem/tomography/tomography_logger.py +++ /dev/null @@ -1,68 +0,0 @@ -import matplotlib.pyplot as plt - -from quantem.core.ml.logger import LoggerBase -from quantem.tomography.object_models import ObjectModelType -from quantem.tomography.tomography_dataset import TomographyDataset - - -class LoggerTomography(LoggerBase): - def __init__(self, log_dir: str, run_prefix: str, run_suffix: str = None): - super().__init__(log_dir, run_prefix, run_suffix) - - # --- Tomography focused logging methods --- - - def tilt_angles_figure(self, dataset: TomographyDataset, step: int): - figs = [] - for angle_array, title in zip( - [dataset.z1_angles, dataset.tilt_angles, dataset.z3_angles], - ["Z1 Angles", "Tilt/ X Angles", "Z3 Angles"], - ): - fig, ax = plt.subplots(figsize=(5, 5)) - ax.plot(angle_array.detach().cpu().numpy()) - ax.set_title(title) - ax.set_xlabel("Index") - ax.set_ylabel("Angle") - figs.append(fig) - plt.close(fig) - - self.log_figure( - tag="tilt_angles/z1", - fig=figs[0], - step=step, - ) - self.log_figure( - tag="tilt_angles/x", - fig=figs[1], - step=step, - ) - self.log_figure( - tag="tilt_angles/z3", - fig=figs[2], - step=step, - ) - - def projection_images( - self, volume_obj: ObjectModelType, epoch: int, logger_cmap: str = "turbo" - ): - sum_0 = volume_obj.obj.sum(axis=0) - sum_1 = volume_obj.obj.sum(axis=1) - sum_2 = volume_obj.obj.sum(axis=2) - - self.log_image( - tag="projections/Y-X Projection", - image=sum_0, - step=epoch, - cmap=logger_cmap, - ) - self.log_image( - tag="projections/Z-X Projection", - image=sum_1, - step=epoch, - cmap=logger_cmap, - ) - self.log_image( - tag="projections/Z-Y Projection", - image=sum_2, - step=epoch, - cmap=logger_cmap, - ) diff --git a/src/quantem/tomography/tomography_ml.py b/src/quantem/tomography/tomography_ml.py deleted file mode 100644 index 4a902887..00000000 --- a/src/quantem/tomography/tomography_ml.py +++ /dev/null @@ -1,304 +0,0 @@ -from typing import Any, Generator, Iterator, Sequence - -import torch - -from quantem.tomography.tomography_base import TomographyBase - - -class TomographyML(TomographyBase): - """ - Class for handling conventional reconstruction methods of tomography data. - """ - - OPTIMIZABLE_VALS = ["volume", "z1", "x", "z3", "shifts"] - DEFAULT_LRS = { - "volume": 1e-2, - "z1": 1e-1, - "x": 1e-1, - "z3": 1e-1, - "shifts": 1e-1, - "tv_weight_vol": 0, - "tv_weight_z1": 0, - "tv_weight_x": 0, - "tv_weight_z3": 0, - } - DEFAULT_OPTIMIZER_TYPE = "adam" - - # --- Properties --- - - @property - def optimizer_params(self) -> dict[str, dict]: - """Returns the parameters used to set the optimizers.""" - return self._optimizer_params - - @optimizer_params.setter - def optimizer_params(self, d: dict) -> None: - """ - # Takes a dictionary {key: torch.optim.Adam(params=[blah], lr=[blah]), ...} - Takes a dictionary: - { - "key1": { - "type": "adam", - "lr": 0.001, - }, - "key2": { - ... - }, - ... - } - """ - # resets _optimizers as well - self._optimizers = {} - self._optimizer_params = {} - if isinstance(d, (tuple, list)): - d = {k: {} for k in d} - - for k, v in d.items(): - if k not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if "type" not in v.keys(): - v["type"] = self.DEFAULT_OPTIMIZER_TYPE - if "lr" not in v.keys(): - v["lr"] = self.DEFAULT_LRS[k] - self._optimizer_params[k] = v - - @property - def optimizers(self) -> dict[str, torch.optim.Adam | torch.optim.AdamW]: - return self._optimizers - - def set_optimizers(self): - """Reset all optimizers and set them according to the optimizer_params.""" - for key, _ in self._optimizer_params.items(): - if key == "volume": - self._add_optimizer(key, self.volume_obj.params, self._optimizer_params[key]) - elif key == "shifts": - self._add_optimizer(key, self.dataset.shifts, self._optimizer_params[key]) - elif key == "z1": - self._add_optimizer(key, self.dataset.z1_angles, self._optimizer_params[key]) - elif key == "x": - self._add_optimizer(key, self.dataset.tilt_angles, self._optimizer_params[key]) - elif key == "z3": - self._add_optimizer(key, self.dataset.z3_angles, self._optimizer_params[key]) - else: - raise ValueError( - f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - - def remove_optimizer(self, key: str) -> None: - self._optimizers.pop(key, None) - self._optimizer_params.pop(key, None) - return - - def _add_optimizer( - self, - key: str, - params: "torch.Tensor|Sequence[torch.Tensor]|Iterator[torch.Tensor]", - opt_params: dict, - ) -> None: - """Can be used to add an optimizer without resetting the other optimizers.""" - - if key not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if isinstance(params, torch.Tensor): - params = [params] - elif isinstance(params, Generator): - params = list(params) - [p.requires_grad_(True) for p in params] - self.optimizer_params[key] = opt_params - opt_params = opt_params.copy() - opt_type = opt_params.pop("type") - if isinstance(opt_type, type): - opt = opt_type(params, **opt_params) - elif opt_type == "adam": - opt = torch.optim.Adam(params, **opt_params) - elif opt_type == "adamw": - opt = torch.optim.AdamW(params, **opt_params) # TODO pass all other kwargs - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") - # if key in self.optimizers.keys(): - # self.vprint(f"Key {key} is already in optimizers, overwriting.") - self._optimizers[key] = opt - - @property - def scheduler_params(self) -> dict[str, dict]: - """Returns the parameters used to set the schedulers.""" - return self._scheduler_params - - @scheduler_params.setter - def scheduler_params(self, d: dict) -> None: - """ - Takes a dictionary: - { - "key1": { - "type": "cyclic", - "base_lr": 0.001, - }, - "key2": { - ... - }, - ... - } - """ - self._schedulers = {} - for k, v in d.items(): - if not any(v): - continue - if k not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if v["type"] not in ["cyclic", "plateau", "exp", "gamma", "none"]: - raise ValueError( - f"Unknown scheduler type: {v['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" - ) - self._scheduler_params = d - - @property - def schedulers( - self, - ) -> dict[ - str, - ( - torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ), - ]: - return self._schedulers - - def set_schedulers( - self, - params: dict[str, dict], - num_iter: int | None = None, - ): - """ - TODO allow for new schedulers to be passed in when adding new optimizers without - removing the old schedulers or overwrtiting them. Not entirely sure what usecases there - will be for this. - - Sets the schedulers for the optimizer from a dictionary. Expects a dictionary of the form: - { - "optimizable_key1": { - "type": "scheduler_type", - "scheduler_kwarg": scheduler_kwarg_value, - ... - }, - "optimizable_key2": { - "type": "scheduler_type", - "scheduler_kwarg": scheduler_kwarg_value, - ... - }, - ... - } - where the keys are the same as the keys in self.OPTIMIZABLE_VALS. - - The scheduler type can be one of the following: - - "cyclic" - - "plateau" or "reducelronplateau" - - "exponential" - - None - - The num_iter kwarg is only used for exponential schedulers and if a "factor" is given - as a scheduler_kwarg instead of gamma. In that case, the gamma is calculated from num_iter - and the factor. - - TODO could update this to allow passing key:optimizer directly, would likely need to - rewrite get_schedulers to check the tpye - """ - if not any(self.optimizers): - raise NameError("self.optimizers have not yet been set.") - self._schedulers = self._get_schedulers( - params=params, - optimizers=self.optimizers, - num_iter=num_iter, - ) - - def _get_schedulers( - self, - params: dict[str, dict], - optimizers: dict, - num_iter: int | None = None, - ) -> dict[ - str, - ( - torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ), - ]: - """ - return schedulers for a given set of optimizers. Kept seperate from schedulers.setter so - that it can be called for pre-training - """ - schedulers = {} - for opt_key, p in params.items(): - if not any(p): - continue - elif opt_key not in self.OPTIMIZABLE_VALS: - raise KeyError( - f"Scheduler got bad key {opt_key}, schedulers can only be attached to one of {self.OPTIMIZABLE_VALS}" - ) - elif opt_key not in optimizers.keys(): - raise KeyError(f"optimizers does not have an optimizer for: {opt_key}") - else: - schedulers[opt_key] = self._get_scheduler( - optimizer=optimizers[opt_key], params=p, num_iter=num_iter - ) - return schedulers - - def _get_scheduler( - self, - optimizer: torch.optim.Adam, - params: dict[str, Any] | torch.optim.lr_scheduler._LRScheduler, - num_iter: int | None = None, - ) -> ( - torch.optim.lr_scheduler._LRScheduler - | torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ): - if isinstance(params, torch.optim.lr_scheduler._LRScheduler): - return params - - sched_type: str = params["type"].lower() - base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - scheduler = None - elif sched_type == "cyclic": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 50), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params.keys(): - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.999 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") - return scheduler diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py new file mode 100644 index 00000000..16c846ab --- /dev/null +++ b/src/quantem/tomography/tomography_opt.py @@ -0,0 +1,174 @@ +from collections.abc import Mapping + +import torch + +from quantem.core.ml.optimizer_mixin import OptimizerParams, OptimizerType, SchedulerType +from quantem.tomography.tomography_base import TomographyBase + + +class TomographyOpt(TomographyBase): + """ + Class for handling all the optimizers and schedulers for the tomography reconstruction. + """ + + OPTIMIZABLE_VALS = ["object", "pose"] + DEFAULT_OPTIMIZER_TYPE = "adam" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def _get_default_lr(self, key: str) -> float: + """Get default learning rate for a given optimization key.""" + if key == "object": + return self.obj_model.DEFAULT_LRS.get("object", 1e-5) + elif key == "pose": + return self.dset.DEFAULT_LRS.get("pose", 5e-2) + else: + raise ValueError(f"Unknown optimization key: {key}") + + @property + def optimizer_params(self) -> dict[str, OptimizerType]: + return { + key: params + for key, params in [ + ("object", self.obj_model.optimizer_params), + ("pose", self.dset.optimizer_params), + ] + if params + } + + @optimizer_params.setter + def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): + """Set the optimizer parameters.""" + if isinstance(d, (tuple, list)): + d = {k: {} for k in d} + + targets = { + "object": self.obj_model, + "pose": self.dset, + } + + for k, v in d.items(): + if k not in targets: + raise ValueError(f"Unknown optimization key: {k}") + + if not isinstance(v, OptimizerType): + v = OptimizerParams.parse_dict(v) + + targets[k].optimizer_params = v + + @property + def optimizers(self) -> dict[str, torch.optim.Optimizer]: + optimizers = {} + + if self.obj_model.optimizer is not None: + optimizers["object"] = self.obj_model.optimizer + if self.dset.optimizer is not None: + optimizers["pose"] = self.dset.optimizer + + return optimizers + + def set_optimizers(self): + for key, params in self.optimizer_params.items(): + if key == "object": + self.obj_model.set_optimizer(params) + elif key == "pose": + self.dset.set_optimizer(params) + else: + raise ValueError(f"Unknown optimization key: {key}") + + def get_current_lrs(self) -> dict[str, float]: + if self.obj_model.has_optimizer(): + obj_lr = self.obj_model.get_current_lr() + else: + obj_lr = 0.0 + if self.dset.has_optimizer(): + pose_lr = self.dset.get_current_lr() + else: + pose_lr = 0.0 + return { + "object": obj_lr, + "pose": pose_lr, + } + + def remove_optimizer(self, key: str): + if key == "object": + self.obj_model.remove_optimizer() + elif key == "pose": + self.dset.remove_optimizer() + else: + raise ValueError(f"Unknown optimization key: {key}") + + @property + def scheduler_params(self) -> dict[str, SchedulerType]: + """Returns the parameters used to set the schedulers.""" + return { + "object": self.obj_model.scheduler_params, + "pose": self.dset.scheduler_params, + } + + @scheduler_params.setter + def scheduler_params(self, d: dict): + """Set the scheduler parameters.""" + self._scheduler_params = d.copy() if d else {} + + for key in self.OPTIMIZABLE_VALS: + if key not in d: + d[key] = {} + + for k, v in d.items(): + if k == "object": + self.obj_model.scheduler_params = v + elif k == "pose": + self.dset.scheduler_params = v + else: + raise ValueError(f"Unknown optimization key: {k}") + + @property + def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: + schedulers = {} + + if self.obj_model.scheduler is not None: + schedulers["object"] = self.obj_model.scheduler + if self.dset.scheduler is not None: + schedulers["pose"] = self.dset.scheduler + + return schedulers + + def set_schedulers( + self, params: Mapping[str, SchedulerType | dict], num_iter: int | None = None + ): + for key, scheduler_params in params.items(): + if key == "object": + self.obj_model.set_scheduler(scheduler_params, num_iter=num_iter) + elif key == "pose": + self.dset.set_scheduler(scheduler_params, num_iter=num_iter) + else: + raise ValueError(f"Unknown optimization key: {key}") + + def step_optimizers(self): + for key in self.optimizer_params.keys(): + if self.obj_model.has_optimizer(): + self.obj_model.step_optimizer() + if self.dset.has_optimizer(): + self.dset.step_optimizer() + if key not in self.OPTIMIZABLE_VALS: + raise ValueError(f"Unknown optimization key: {key}") + + def zero_grad_all(self): + for key in self.optimizer_params.keys(): + if self.obj_model.has_optimizer(): + self.obj_model.zero_optimizer_grad() + if self.dset.has_optimizer(): + self.dset.zero_optimizer_grad() + if key not in self.OPTIMIZABLE_VALS: + raise ValueError(f"Unknown optimization key: {key}") + + def step_schedulers(self, loss: float | None = None): + for key in self.scheduler_params.keys(): + if self.obj_model.scheduler is not None and key == "object": + self.obj_model.step_scheduler(loss) + if self.dset.scheduler is not None and key == "pose": + self.dset.step_scheduler(loss) + if key not in self.OPTIMIZABLE_VALS: + raise ValueError(f"Unknown optimization key: {key}") diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index d93f15e0..08093925 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -1,11 +1,5 @@ -import numpy as np import torch import torch.nn.functional as F -from scipy.ndimage import center_of_mass, gaussian_filter, shift -from scipy.stats import norm -from tqdm.auto import tqdm - -from quantem.core.utils.imaging_utils import cross_correlation_shift # --- Projection Operator Utils --- @@ -75,224 +69,29 @@ def transform_slice(mag_slice): return rotated_mags.permute(1, 2, 3, 0) -def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): - """ - Shifts a 2D image using grid_sample in a differentiable manner. - - Args: - image: Tensor of shape [H, W] - shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) - shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) - sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts - - Returns: - Shifted image of shape [H, W] +def tv_loss_1d(x: torch.Tensor, reduction: str = "mean") -> torch.Tensor: """ - H, W = image.shape - - # Convert physical shift to pixel shift - shift_x_pixel = shift_x - shift_y_pixel = shift_y - - # Normalize shift for grid_sample (assuming align_corners=True) - normalized_shift_x = shift_x_pixel * 2 / (W - 1) - normalized_shift_y = shift_y_pixel * 2 / (H - 1) - - # Create normalized grid - grid_y, grid_x = torch.meshgrid( - torch.linspace(-1, 1, H, device=image.device), - torch.linspace(-1, 1, W, device=image.device), - indexing="ij", - ) - - grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] - - # Apply shift (ensure it's differentiable) - grid[:, :, :, 0] -= normalized_shift_x - grid[:, :, :, 1] -= normalized_shift_y - - # Add batch and channel dimensions - image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] - - # Sample using grid_sample (fully differentiable) - shifted_image = F.grid_sample( - image, grid, mode="bicubic", padding_mode="zeros", align_corners=True - ) - - return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] - - -# --- TV loss --- - - -def get_TV_loss(tensor, factor=1e-3): - tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() - tv_loss = tv_d + tv_h + tv_w - - return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + 1D Total Variation Loss. - -# --- Gaussian filters --- - - -def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: - radius = np.ceil(num_sigmas * sigma) - support = torch.arange(-radius, radius + 1, dtype=torch.float) - kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() - # Ensure kernel weights sum to 1, so that image brightness is not altered - return kernel.mul_(1 / kernel.sum()) - - -def gaussian_filter_2d( - img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor -) -> torch.Tensor: # Add kernel_1d as an argument - # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function - padding = len(kernel_1d) // 2 # Ensure that image size does not change - img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` - # Convolve along columns and rows - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - return img.squeeze_(0).squeeze_(0) # Make 2D again - - -def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: - """ - Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. + Encourages piecewise smoothness by penalizing differences between + adjacent elements. Args: - stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms - kernel_1d (torch.Tensor): 1D Gaussian kernel + x: Input tensor of shape (N, C, L) or (N, L) or (L,) + reduction: 'mean' | 'sum' | 'none' Returns: - torch.Tensor: Blurred stack of same shape (H, N, W) + Scalar loss (or per-sample tensor if reduction='none') """ - H, N, W = stack.shape - padding = len(kernel_1d) // 2 - - # Reshape to (N, 1, H, W) for conv2d - stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) - - # Apply separable conv2d: vertical then horizontal - out = torch.nn.functional.conv2d( - stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) - ) - out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - - # Restore shape to (H, N, W) - return out.squeeze(1).permute(1, 0, 2) - - -# Circular mask - - -def torch_phase_cross_correlation(im1, im2): - f1 = torch.fft.fft2(im1) - f2 = torch.fft.fft2(im2) - cc = torch.fft.ifft2(f1 * torch.conj(f2)) - cc_abs = torch.abs(cc) - - max_idx = torch.argmax(cc_abs) - shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() - - for i, dim in enumerate(im1.shape): - if shifts[i] > dim // 2: - shifts[i] -= dim - - # return shifts.flip(0) # (dx, dy) - return shifts - - -# --- Tilt Series Processing Utility Functions --- - - -def fourier_cropping(img, crop_size): - """ - Crop the img in Fourier space to the specified size. - """ - center = np.array(img.shape) // 2 - - fft_img = np.fft.fftshift(np.fft.fft2(img)) - - cropped_fft = fft_img[ - center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, - center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, - ] - cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real - return cropped_img - - -def estimate_background( - img, - num_iterations=10, - cutoff=3, - smoothing_sigma=1.0, -): - """ - Estimate the background of the image using a Gaussian filter. - """ - if smoothing_sigma > 0: - img = gaussian_filter(img, sigma=smoothing_sigma) - pixel_vals = img.ravel() - - for i in range(num_iterations): - mu, std = norm.fit(pixel_vals) - - # Set cutoff threshold (e.g., 3 standard deviations) - lower = mu - cutoff * std - upper = mu + cutoff * std - - # Mask pixel values within ±3σ - pixel_vals = pixel_vals[(pixel_vals >= lower) & (pixel_vals <= upper)] - - return mu - - -def cross_correlation_align_stack(ref_img, stack): - """ - Aligns a stack of images to a reference image using cross-correlation. - - This function assumes the stack does not contain the reference image itself. - - Stack shape should be (N, H, W) where N is the number of images. - """ - - new_images = [] - pred_shifts = [] - - prev_img = ref_img - for img in tqdm(stack): - shift_pred = cross_correlation_shift(prev_img, img) - shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) - - pred_shifts.append(shift_pred) - new_images.append(shifted_image) - - prev_img = shifted_image - - return new_images, pred_shifts - - -def centering_com_alignment(image_stack): - """ - Aligns the image stack to the center of mass of the whole image_stack to the - image center. 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