diff --git a/.github/workflows/check-pr-main-version.yml b/.github/workflows/check-pr-main-version.yml index 8e545f0d..809e3158 100644 --- a/.github/workflows/check-pr-main-version.yml +++ b/.github/workflows/check-pr-main-version.yml @@ -17,16 +17,24 @@ jobs: steps: - name: Checkout PR branch (head) - uses: actions/checkout@v4 + uses: actions/checkout@v6 with: ref: ${{ github.head_ref }} fetch-depth: 0 - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 + + - name: Ensure uv.lock is up to date + run: | + if ! uv lock --check; then + echo "❌ uv.lock is out of date." + echo "Please update uv.lock before versioning can proceed." + exit 1 + fi - name: Install the project - run: uv sync --all-extras --dev --locked + run: uv sync --locked --group test - name: Get PR branch version id: pr_version @@ -86,7 +94,7 @@ jobs: echo "Version matches — bumping version and creating new release branch" uv version --bump patch - uv sync --locked + uv sync --locked --group test NEXT_VERSION=$(uv version --short) if [[ "$NEXT_VERSION" == "$PR_VERSION" ]]; then diff --git a/.github/workflows/check-uv-lock.yml b/.github/workflows/check-uv-lock.yml new file mode 100644 index 00000000..a899e571 --- /dev/null +++ b/.github/workflows/check-uv-lock.yml @@ -0,0 +1,83 @@ +name: Check uv.lock is up to date + +on: + pull_request: + branches: + - main + +permissions: + contents: write + pull-requests: write + +jobs: + check-uv-lock: + runs-on: ubuntu-latest + + steps: + - name: Checkout PR branch + uses: actions/checkout@v6 + with: + ref: ${{ github.head_ref }} + fetch-depth: 0 + + - name: Install uv + uses: astral-sh/setup-uv@v7 + + - name: Check lock file + id: lockcheck + run: | + if uv lock --check; then + echo "up_to_date=true" >> $GITHUB_OUTPUT + else + echo "up_to_date=false" >> $GITHUB_OUTPUT + fi + + - name: Exit early if lock is up to date + if: steps.lockcheck.outputs.up_to_date == 'true' + run: echo "uv.lock is up to date ✔" + + - name: Regenerate lock file + if: steps.lockcheck.outputs.up_to_date == 'false' + run: | + if [[ "${GITHUB_HEAD_REF}" == release/* ]]; then + echo "❌ Release branch — uv.lock must already be up to date." + exit 1 + fi + + uv lock + + - name: Commit updated lock file + if: steps.lockcheck.outputs.up_to_date == 'false' + run: | + SAFE_REF=$(echo "${GITHUB_HEAD_REF}" | tr '/' '-') + + git config user.name "quantem-bot" + git config user.email "quantembot@gmail.com" + + git checkout -b lockfix/${SAFE_REF} + git commit -am "Update uv.lock" + git push origin lockfix/${SAFE_REF} + + - name: Create PR for updated lock file + if: steps.lockcheck.outputs.up_to_date == 'false' + env: + GH_TOKEN: ${{ secrets.QUANTEM_BOT_PAT }} + run: | + SAFE_REF=$(echo "${GITHUB_HEAD_REF}" | tr '/' '-') + + if gh pr list --head "lockfix/${SAFE_REF}" --json number --jq 'length > 0'; then + echo "Lockfix PR already exists — skipping." + exit 1 + fi + + gh pr create \ + --base "${{ github.base_ref }}" \ + --head "lockfix/${SAFE_REF}" \ + --title "Update uv.lock" \ + --body "This PR updates \`uv.lock\` to match the current dependency specification." + + - name: Fail original PR + if: steps.lockcheck.outputs.up_to_date == 'false' + run: | + echo "❌ uv.lock was out of date — a fix PR has been opened." + exit 1 diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 4b4aeb05..f4eab293 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -13,10 +13,10 @@ jobs: UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 - name: Verify lock file is up to date run: uv lock --check diff --git a/.github/workflows/sync-dev-main.yml b/.github/workflows/sync-dev-main.yml index 7f5ec439..555d6a7b 100644 --- a/.github/workflows/sync-dev-main.yml +++ b/.github/workflows/sync-dev-main.yml @@ -14,7 +14,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout repo - uses: actions/checkout@v4 + uses: actions/checkout@v6 - name: Open PR from main to dev env: diff --git a/.github/workflows/update-lock-file-cron.yml b/.github/workflows/update-lock-file-cron.yml index da3647a0..5cf62249 100644 --- a/.github/workflows/update-lock-file-cron.yml +++ b/.github/workflows/update-lock-file-cron.yml @@ -13,12 +13,12 @@ jobs: update-deps: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: ref: dev - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 - name: Update dependencies env: diff --git a/.github/workflows/uv-pytests.yml b/.github/workflows/uv-pytests.yml index 84d3f89b..474ad347 100644 --- a/.github/workflows/uv-pytests.yml +++ b/.github/workflows/uv-pytests.yml @@ -13,7 +13,7 @@ jobs: strategy: matrix: # Test other OSs main branch PRs - os: ${{ github.base_ref == 'main' && fromJSON('["ubuntu-latest", "windows-latest", "macos-latest", "macos-15-intel"]') || fromJSON('["ubuntu-latest"]') }} + os: ${{ github.base_ref == 'main' && fromJSON('["ubuntu-latest", "windows-latest", "macos-latest"]') || fromJSON('["ubuntu-latest"]') }} python-version: - "3.11" - "3.12" @@ -21,15 +21,15 @@ jobs: - "3.14" steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Install uv and set python version - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v7 with: python-version: ${{ matrix.python-version }} - name: Install the project - run: uv sync --locked --all-extras --group test + run: uv sync --locked --group test - name: Run pytests run: uv run pytest tests diff --git a/pyproject.toml b/pyproject.toml index bbbb8c1b..4f08e141 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ members = ["widget"] [project] name = "quantem" -version = "0.1.7" +version = "0.1.8" description = "quantitative electron microscopy analysis toolkit." keywords = ["EM","TEM","STEM","4DSTEM"] readme = "README.md" @@ -53,6 +53,7 @@ dependencies = [ "torchmetrics>=1.7.3", "optuna>=4.5.0", "hdf5plugin>=6.0.0", + "torchinfo>=1.8.0", ] [project.optional-dependencies] @@ -72,6 +73,7 @@ only-include = [ [dependency-groups] test = [ "pytest>=8.3.5", + "pytest-cov>=7.0.0", ] dev = [ { include-group = "test" }, diff --git a/src/quantem/core/config.py b/src/quantem/core/config.py index 5a35cb43..c2eda10b 100644 --- a/src/quantem/core/config.py +++ b/src/quantem/core/config.py @@ -270,6 +270,7 @@ def update( """ for k, v in new.items(): + k, v = check_key_val(k, v) k = canonical_name(k, old) if isinstance(v, Mapping): @@ -480,11 +481,10 @@ def check_key_val(key: str, val: Any, deprecations: dict = deprecations) -> tupl new_val = "cpu" else: new_val, gpu_id = validate_device(new_val) - if config["has_torch"]: - if torch.cuda.is_available(): - torch.cuda.set_device(f"cuda:{gpu_id}") - if config["has_cupy"]: - cp.cuda.runtime.setDevice(gpu_id) + if "cuda" in new_val: + torch.cuda.set_device(gpu_id) + if config["has_cupy"]: + cp.cuda.runtime.setDevice(gpu_id) return key, new_val @@ -503,33 +503,42 @@ def validate_device(dev: str | int | torch.device | None = None) -> tuple[str, i dev = torch.device( "cuda" if torch.cuda.is_available() else "mps" if torch.mps.is_available() else "cpu" ) - elif isinstance(dev, str) and dev.lower() == "gpu": - if torch.cuda.is_available(): - dev = torch.device("cuda") - elif torch.mps.is_available(): - dev = torch.device("mps") - else: - raise RuntimeError("Requested 'gpu' device, but neither CUDA nor MPS is available.") - - # Convert to torch.device early - if isinstance(dev, (str, int)): - if isinstance(dev, int): + elif isinstance(dev, str): + if "cuda" in dev.lower(): + dev = torch.device(dev) + elif "gpu" in dev.lower(): if torch.cuda.is_available(): - dev = torch.device(f"cuda:{dev}") + dev = torch.device("cuda") elif torch.mps.is_available(): dev = torch.device("mps") else: - dev = torch.device("cpu") + raise RuntimeError("gpu requested but cuda and mps are not available.") + elif dev.lower() == "mps": + dev = torch.device("mps") + elif dev.lower() == "cpu": + dev = torch.device("cpu") else: - dev = torch.device(dev) + raise ValueError( + f"Requested unknown device type: {dev} (must be 'cuda', 'mps', or 'cpu')" + ) + elif isinstance(dev, int): + if dev < 0: + raise ValueError(f"Requested negative GPU index: {dev} (must be >= 0)") + if torch.cuda.is_available(): + dev = torch.device(f"cuda:{dev}") + else: + raise RuntimeError(f"Requested GPU index '{dev}' device, but cuda is not available.") elif not isinstance(dev, torch.device): raise TypeError(f"Unsupported device type: {type(dev)} ({dev})") - # Normalize to supported device types if dev.type == "cuda": if not torch.cuda.is_available(): raise RuntimeError("CUDA device requested but not available.") index = dev.index if dev.index is not None else torch.cuda.current_device() + if index >= NUM_DEVICES: + raise RuntimeError( + f"CUDA device index {index} is out of range for {NUM_DEVICES} available devices." + ) return f"cuda:{index}", index elif dev.type == "mps": @@ -560,7 +569,18 @@ def write(path: Path | str = PATH / "config.yaml") -> None: yaml.dump(config, f) -def set_device(dev: str | int) -> None: +def set_device(dev: str | int | "torch.device") -> None: + """Set the current device. Accepts a torch-style string, an integer index, or a + torch.device object. + Examples + -------- + >>> set_device("cuda:0") + >>> set_device(0) + >>> set_device(torch.device("cuda:0")) + >>> set_device("mps") + >>> set_device("cpu") + >>> set_device("gpu") + """ set({"device": dev}) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 1005e834..90f02f50 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -1,5 +1,5 @@ -import os import numbers +import os from pathlib import Path from typing import Any, Literal, Optional, Self, Union, overload @@ -52,7 +52,9 @@ def __init__( super().__init__() arr = ensure_valid_array(array) if not isinstance(arr, np.ndarray): - raise TypeError("Dataset requires a NumPy array (CuPy is not supported on this branch).") + raise TypeError( + "Dataset requires a NumPy array (CuPy is not supported on this branch)." + ) self._array = arr self.name = name self.origin = origin @@ -97,7 +99,9 @@ def from_array( """ validated_array = ensure_valid_array(array) if not isinstance(validated_array, np.ndarray): - raise TypeError("Dataset requires a NumPy array (CuPy is not supported on this branch).") + raise TypeError( + "Dataset requires a NumPy array (CuPy is not supported on this branch)." + ) _ndim = validated_array.ndim # Set defaults if None @@ -126,7 +130,9 @@ def array(self) -> NDArray: def array(self, value: NDArray) -> None: arr = ensure_valid_array(value, ndim=self.ndim) # want to allow changing dtype if not isinstance(arr, np.ndarray): - raise TypeError("Dataset requires a NumPy array (CuPy is not supported on this branch).") + raise TypeError( + "Dataset requires a NumPy array (CuPy is not supported on this branch)." + ) self._array = arr # self._array = ensure_valid_array(value, dtype=self.dtype, ndim=self.ndim) @@ -384,8 +390,9 @@ def min(self, axes: int | tuple[int, ...] | None = None) -> Any: @overload def pad( self, - pad_width: int | tuple[int, int] | tuple[tuple[int, int], ...] | None, - output_shape: tuple[int, ...] | None, + pad_width: int | tuple[int, int] | tuple[tuple[int, int], ...] | None = None, + output_shape: tuple[int, ...] | None = None, + *, modify_in_place: Literal[True], **kwargs: Any, ) -> None: ... @@ -397,7 +404,7 @@ def pad( output_shape: tuple[int, ...] | None = None, modify_in_place: Literal[False] = False, **kwargs: Any, - ) -> "Dataset": ... + ) -> Self: ... def pad( self, @@ -405,7 +412,7 @@ def pad( output_shape: tuple[int, ...] | None = None, modify_in_place: bool = False, **kwargs: Any, - ) -> "Dataset | None": + ) -> Self | None: """ Pads Dataset data array using numpy.pad. Metadata (origin, sampling) is not modified. @@ -460,7 +467,8 @@ def pad( def crop( self, crop_widths: tuple[tuple[int, int], ...], - axes: tuple | None, + axes: tuple | None = None, + *, modify_in_place: Literal[True], ) -> None: ... @@ -498,7 +506,7 @@ def crop( if len(crop_widths) != self.ndim: raise ValueError("crop_widths must match number of dimensions when axes is None.") axes = tuple(range(self.ndim)) - elif np.isscalar(axes): + elif isinstance(axes, int | float): axes = (int(axes),) crop_widths = (crop_widths[0],) # Take first crop_width for single axis else: @@ -530,7 +538,8 @@ def crop( def bin( self, bin_factors, - axes, + axes=None, + *, modify_in_place: Literal[True], reducer: str = "sum", ) -> None: ... @@ -579,7 +588,7 @@ def bin( if axes is None: axes = tuple(range(self.ndim)) - elif np.isscalar(axes): + elif isinstance(axes, int | float): axes = (int(axes),) else: axes = tuple(int(ax) for ax in axes) @@ -694,7 +703,7 @@ def fourier_resample( """ if axes is None: axes = tuple(range(self.ndim)) - elif np.isscalar(axes): + elif isinstance(axes, int | float): axes = (int(axes),) else: axes = tuple(int(a0) for a0 in axes) @@ -704,14 +713,17 @@ def fourier_resample( # Resolve out_shape & factors if factors is not None: - if np.isscalar(factors): + if isinstance(factors, int | float): factors = (float(factors),) * len(axes) else: factors = tuple(float(f) for f in factors) if len(factors) != len(axes): raise ValueError("factors length must match number of axes.") - out_shape = tuple(max(1, int(round(self.shape[a1] * f))) for a1, f in zip(axes, factors)) + out_shape = tuple( + max(1, int(round(self.shape[a1] * f))) for a1, f in zip(axes, factors) + ) else: + assert out_shape is not None # Guaranteed by check above if len(out_shape) != len(axes): raise ValueError("out_shape length must match number of axes.") out_shape = tuple(int(nl) for nl in out_shape) @@ -840,7 +852,9 @@ def __getitem__(self, index) -> Self: kept_axes = [i for i, idx in enumerate(index) if not isinstance(idx, (int, np.integer))] # Slice/reduce metadata accordingly - new_origin = np.asarray(self.origin)[kept_axes] if np.ndim(self.origin) > 0 else self.origin + new_origin = ( + np.asarray(self.origin)[kept_axes] if np.ndim(self.origin) > 0 else self.origin + ) new_sampling = ( np.asarray(self.sampling)[kept_axes] if np.ndim(self.sampling) > 0 else self.sampling ) diff --git a/src/quantem/core/datastructures/dataset3d.py b/src/quantem/core/datastructures/dataset3d.py index 53d6d02c..118ec66f 100644 --- a/src/quantem/core/datastructures/dataset3d.py +++ b/src/quantem/core/datastructures/dataset3d.py @@ -1,10 +1,13 @@ -from typing import Any, Self, Union +from typing import Self import numpy as np +from matplotlib.axes import Axes +from matplotlib.figure import Figure from numpy.typing import NDArray from quantem.core.datastructures.dataset import Dataset from quantem.core.utils.validators import ensure_valid_array +from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -21,7 +24,7 @@ class Dataset3d(Dataset): def __init__( self, - array: NDArray | Any, + array: NDArray, name: str, origin: NDArray | tuple | list | float | int, sampling: NDArray | tuple | list | float | int, @@ -33,7 +36,7 @@ def __init__( Parameters ---------- - array : NDArray | Any + array : NDArray The underlying 3D array data name : str A descriptive name for the dataset @@ -61,13 +64,57 @@ def __init__( @classmethod def from_array( cls, - array: NDArray | Any, + array: NDArray, 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: + """Create a Dataset3d from a 3D array. + + Parameters + ---------- + array : NDArray + 3D array with shape (n_frames, height, width) + name : str | None + Dataset name. Default: "3D dataset" + origin : NDArray | tuple | list | float | int | None + Origin for each dimension. Default: [0, 0, 0] + sampling : NDArray | tuple | list | float | int | None + Sampling for each dimension. Default: [1, 1, 1] + units : list[str] | tuple | list | None + Units for each dimension. Default: ["index", "pixels", "pixels"] + signal_units : str + Units for array values. Default: "arb. units" + + Returns + ------- + Dataset3d + + Examples + -------- + >>> import numpy as np + >>> from quantem.core.datastructures import Dataset3d + >>> arr = np.random.rand(10, 64, 64) + >>> data = Dataset3d.from_array(arr) + >>> data.shape + (10, 64, 64) + + With calibration: + + >>> data = Dataset3d.from_array( + ... arr, + ... sampling=[1, 0.1, 0.1], + ... units=["frame", "nm", "nm"], + ... ) + + Visualize: + + >>> data.show() # all frames in grid + >>> data.show(index=0) # single frame + >>> data.show(ncols=2) # 2 columns + """ array = ensure_valid_array(array, ndim=3) return cls( array=array, @@ -85,12 +132,42 @@ def from_shape( shape: tuple[int, int, int], name: str = "constant 3D dataset", fill_value: float = 0.0, - origin: Union[NDArray, tuple, list, float, int] | None = None, - sampling: Union[NDArray, tuple, list, float, int] | 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: - """Create a new Dataset3d filled with a constant value.""" + """Create a Dataset3d filled with a constant value. + + Parameters + ---------- + shape : tuple[int, int, int] + Shape (n_frames, height, width) + name : str + Dataset name. Default: "constant 3D dataset" + fill_value : float + Value to fill array with. Default: 0.0 + origin : NDArray | tuple | list | float | int | None + Origin for each dimension + sampling : NDArray | tuple | list | float | int | None + Sampling for each dimension + units : list[str] | tuple | list | None + Units for each dimension + signal_units : str + Units for array values + + Returns + ------- + Dataset3d + + Examples + -------- + >>> data = Dataset3d.from_shape((10, 64, 64)) + >>> data.shape + (10, 64, 64) + >>> data.array.max() + 0.0 + """ array = np.full(shape, fill_value, dtype=np.float32) return cls.from_array( array=array, @@ -107,24 +184,160 @@ def to_dataset2d(self): def show( self, - index: int = 0, - scalebar: ScalebarConfig | bool = True, - title: str | None = None, + start: int = 0, + end: int | None = None, + step: int = 1, + max: int | None = 20, + ncols: int = 4, + scalebar: ScalebarConfig | bool = False, + title_prefix: str | None = None, + suptitle: str | None = None, + returnfig: bool = False, **kwargs, - ): + ) -> tuple[Figure, Axes] | None: """ - Display a 2D slice of the 3D dataset. + Display 2D slices of the 3D dataset. Parameters ---------- - index : int - Index of the 2D slice to display (along axis 0). - scalebar: ScalebarConfig or bool - If True, displays scalebar - title: str - Title of Dataset - **kwargs: dict - Keyword arguments for show_2d + start : int, default 0 + First frame index. Supports negative indexing. + end : int or None, optional + End frame index (exclusive). If None, determined by max. + step : int, default 1 + Step between frames. Negative step shows frames in reverse order. + max : int or None, default 20 + Maximum number of frames to show. Prevents memory issues. + Set to None to show all frames. + ncols : int, default 4 + Number of columns in grid. + scalebar : ScalebarConfig or bool, default False + If True, displays scalebar on each frame. + title_prefix : str or None, optional + Prefix for frame titles. If None, uses "Frame 0", "Frame 1", etc. + If set to "DP", generates "DP 0", "DP 1", etc. + suptitle : str or None, optional + Figure super title displayed above all subplots. + returnfig : bool, default False + If True, returns (fig, axes). + **kwargs : dict + Keyword arguments for show_2d (cmap, cbar, vmin, vmax, norm, etc.). + + Returns + ------- + tuple[Figure, Axes] or None + If returnfig=True, returns (fig, axes) tuple for further customization. + If returnfig=False, returns None. + + Raises + ------ + ValueError + If step is zero, ncols < 1, max < 1, start is out of bounds, + or the specified range has no frames to display. + + Examples + -------- + Basic usage: + + >>> data.show() # first 20 frames + >>> data.show(max=None) # all frames (use with caution) + + Single frame: + + >>> data.show(start=5, max=1) # frame 5 + >>> data.show(start=-1, max=1) # last frame + + Frame range: + + >>> data.show(start=10, end=50) # frames 10-49 + >>> data.show(step=5) # frames 0,5,10,15,... + >>> data.show(start=0, end=100, step=10) # every 10th frame + >>> data.show(start=9, step=-1) # reverse order: 9,8,7,...,0 + + Grid layout: + + >>> data.show(ncols=2) # 2 columns + >>> data.show(ncols=5, max=10) # 5x2 grid + + Titles: + + >>> data.show(title_prefix="DP") # "DP 0", "DP 1", ... + >>> data.show(suptitle="Time Series") # figure title + >>> data.show(title_prefix="T", suptitle="Tomography Slices") + + Customization: + + >>> data.show(cmap="viridis") # colormap + >>> data.show(scalebar=True) # show scalebar + >>> fig, axes = data.show(returnfig=True) # get figure for further customization """ + total_frames = self.shape[0] + + # Validate inputs + if step == 0: + raise ValueError("Step cannot be zero.") + if ncols < 1: + raise ValueError(f"ncols must be >= 1, got {ncols}.") + if max is not None and max < 1: + raise ValueError(f"max must be >= 1 or None, got {max}.") + if start < 0: + start = total_frames + start + if start < 0 or start >= total_frames: + raise ValueError( + f"Start {start} is out of bounds for dataset with {total_frames} frames. " + f"Please use start in range [0, {total_frames - 1}]." + ) + + # Compute frame indices + # Default end: go forward to total_frames, or backward to -1 (before index 0) + if end is not None: + end_idx = end + else: + end_idx = total_frames if step > 0 else -1 + + # Clamp to valid range (only needed for positive step) + if step > 0: + end_idx = min(end_idx, total_frames) + + # Apply max limit to avoid creating huge list + if max is not None: + max_end = start + max * step + if step > 0: + end_idx = min(end_idx, max_end) + elif max_end > end_idx: + end_idx = max_end - return self[index].show(scalebar=scalebar, title=title, **kwargs) + frame_idx = list(range(start, end_idx, step)) + if len(frame_idx) == 0: + raise ValueError( + f"No frames to display with start={start}, end={end}, step={step}. " + "Please check your range parameters." + ) + # Build grid + n_frames = len(frame_idx) + ncols = min(ncols, n_frames) # Don't create more columns than frames + images = [self.array[i] for i in frame_idx] + labels = [ + f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" + for i in frame_idx + ] + # Pad last row to complete the grid (show_2d requires rectangular input) + remainder = n_frames % ncols + pad_count = 0 if remainder == 0 else ncols - remainder + if pad_count > 0: + images.extend([np.zeros_like(self.array[0])] * pad_count) + labels.extend([""] * pad_count) + image_grid = [images[i : i + ncols] for i in range(0, len(images), ncols)] + label_grid = [labels[i : i + ncols] for i in range(0, len(labels), ncols)] + fig, axes = show_2d(image_grid, scalebar=scalebar, title=label_grid, **kwargs) + if pad_count > 0: + for ax in np.array(axes).flat[-pad_count:]: + ax.clear() + ax.axis("off") + if suptitle: + # Reserve fixed space for suptitle (absolute, not proportional) + suptitle_margin = 0.7 + fig_height = fig.get_figheight() + fig.subplots_adjust(top=1 - suptitle_margin / fig_height) + fig.suptitle(suptitle, fontsize=14, y=1 - 0.2 / fig_height) + return (fig, axes) if returnfig else None diff --git a/src/quantem/core/ml/__init__.py b/src/quantem/core/ml/__init__.py index ca591803..ce1f6de0 100644 --- a/src/quantem/core/ml/__init__.py +++ b/src/quantem/core/ml/__init__.py @@ -1 +1,5 @@ from quantem.core.ml.cnn import CNN2d as CNN2d, CNN3d as CNN3d +from quantem.core.ml.inr import HSiren as HSiren +from quantem.core.ml.dense_nn import DenseNN as DenseNN +from quantem.core.ml.cnn_dense import CNNDense as CNNDense +from quantem.core.ml.conv_autoencoder import ConvAutoencoder2d as ConvAutoencoder2d diff --git a/src/quantem/core/ml/activation_functions.py b/src/quantem/core/ml/activation_functions.py index c9c27896..25db3edb 100644 --- a/src/quantem/core/ml/activation_functions.py +++ b/src/quantem/core/ml/activation_functions.py @@ -1,70 +1,86 @@ -from typing import TYPE_CHECKING, Callable +from typing import Callable -from quantem.core import config - -if TYPE_CHECKING: - import torch - import torch.nn as nn - import torch.nn.functional as F -else: - if config.get("has_torch"): - import torch - import torch.nn as nn - import torch.nn.functional as F +import torch +import torch.nn as nn +import torch.nn.functional as F class ModReLU(nn.Module): - def __init__(self): + """Modulated ReLU activation for complex-valued inputs. + + Applies ReLU to the absolute value plus a learnable bias, then multiplies + by the complex exponential of the phase. + """ + + def __init__(self) -> None: super().__init__() self.b = nn.Parameter(torch.tensor(0.0)) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: return F.relu(torch.abs(x) + self.b) * torch.exp(1.0j * torch.angle(x)) class Complex_ReLU(nn.Module): + """Complex ReLU activation that applies ReLU separately to real and imaginary parts. + + For a complex input z = a + bi, returns ReLU(a) + ReLU(b)i. + """ + def __init__(self) -> None: super().__init__() - def _complex_relu(self, z: "torch.Tensor"): + def _complex_relu(self, z: torch.Tensor) -> torch.Tensor: return F.relu(z.real) + 1.0j * F.relu(z.imag) - def forward(self, z): + def forward(self, z: torch.Tensor) -> torch.Tensor: return self._complex_relu(z) class Complex_Phase_ReLU(nn.Module): - def __init__(self, phase_frac=0.5, sigmoid=True) -> None: + """Complex Phase ReLU activation that modulates activation based on complex phase. + + Applies activation based on the magnitude and phase of complex inputs, + with configurable phase fraction and activation function type. + """ + + def __init__(self, phase_frac: float = 0.5, sigmoid: bool = True) -> None: + """Initialize Complex_Phase_ReLU. + + Parameters + ---------- + phase_frac : float, optional + Fraction of the complex phase range that gets activated (0-1), by default 0.5 + sigmoid : bool, optional + Whether to use sigmoid-like activation (True) or linear (False), by default True + """ super().__init__() self.phase_frac = phase_frac self.sigmoid = sigmoid - def forward(self, z): + def forward(self, z: torch.Tensor) -> torch.Tensor: return self.PhaseReLU(z, self.phase_frac, self.sigmoid) def PhaseReLU( self, - z, - phase_frac=0.5, - sigmoid=True, - ): - """ - This function returns the activation for an array of complex numbers z. + z: torch.Tensor, + phase_frac: float = 0.5, + sigmoid: bool = True, + ) -> torch.Tensor: + """Apply phase-based ReLU activation to complex tensor. Parameters ---------- - z: np.array - array-like inputs - phase_frac: float - Fraction of complex number phase range which are activated. - sigmoid: bool - Set to True to use a sigmoid function along phase, or False to use a linear function. + z : torch.Tensor + Complex input tensor. + phase_frac : float, optional + Fraction of complex phase range that gets activated, by default 0.5 + sigmoid : bool, optional + If True, use sigmoid function along phase; if False, use linear, by default True Returns ------- - f: np.array - output array with the same dimensions as z. - + torch.Tensor + Activated complex tensor with same shape as input. """ # complex inputs @@ -83,37 +99,6 @@ def PhaseReLU( return f.type(torch.complex64) -class FinerActivation(nn.Module): - """ - Finer Activation function for complex and real inputs. - - - Similar to sine activation function. - """ - def __init__( - self, - omega: float = 1, - ): - super().__init__() - self.omega = omega - - def generate_alpha(self, x): - - with torch.no_grad(): - return torch.abs(x) + 1 - - def forward(self, x): - if x.is_complex(): # Complex Input Check - with torch.no_grad(): - alpha_real = torch.abs(x.real) + 1 - alpha_imag = torch.abs(x.imag) + 1 - x.real = x.real * alpha_real - x.imag = x.imag * alpha_imag - return torch.sin(self.omega * self.generate_alpha(x) * x) - else: - return torch.sin(self.omega * self.generate_alpha(x) * x) - - - def get_activation_function( activation_type: str | Callable, @@ -121,19 +106,29 @@ def get_activation_function( activation_phase_frac: float = 0.5, activation_sigmoid: bool = True, ) -> nn.Module: - """ - Get an activation function module. - - Args: - activation_type: String name of activation or a callable activation function - dtype: Data type (used for complex vs real activations) - activation_phase_frac: Fraction for phase relu (complex only) - activation_sigmoid: Whether to use sigmoid for phase relu (complex only) - - Returns: - Activation function module - - Allowed activation_types: relu, phase_relu, identity, etc. + """Get an activation function module. + + Parameters + ---------- + activation_type : str or Callable + String name of activation (e.g., 'relu', 'phase_relu', 'identity') or + a callable activation function. + dtype : torch.dtype + Data type (used to determine complex vs real activations). + activation_phase_frac : float, optional + Fraction for phase relu (complex only), by default 0.5 + activation_sigmoid : bool, optional + Whether to use sigmoid for phase relu (complex only), by default True + + Returns + ------- + nn.Module + Activation function module. + + Raises + ------ + ValueError + If activation type is unknown or not supported for the given dtype. """ # If it's already a callable/module, check if it's a module if callable(activation_type): @@ -142,11 +137,11 @@ def get_activation_function( else: # Wrap callable in a lambda module class CallableWrapper(nn.Module): - def __init__(self, func): + def __init__(self, func: Callable) -> None: super().__init__() self.func = func - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: return self.func(x) return CallableWrapper(activation_type) @@ -182,8 +177,11 @@ def forward(self, x): activation = nn.Sigmoid() elif activation_type in ["softplus"]: activation = nn.Softplus() - elif activation_type in ["finer"]: - activation = FinerActivation() + elif activation_type in ["finer", "siren", "hsiren"]: + raise ValueError( + "Siren type layers are not supported for activation functions. " + + "Use the Siren class instead." + ) else: raise ValueError(f"Unknown activation type {activation_type}") diff --git a/src/quantem/core/ml/blocks.py b/src/quantem/core/ml/blocks.py index f9fa81ad..c2e67833 100644 --- a/src/quantem/core/ml/blocks.py +++ b/src/quantem/core/ml/blocks.py @@ -1,41 +1,49 @@ -from typing import TYPE_CHECKING, Callable +from typing import Callable import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F -import math +from .activation_functions import get_activation_function -from quantem.core import config +# region ---- Utility Functions ---- -from .activation_functions import Complex_ReLU, FinerActivation -if TYPE_CHECKING: - import torch - import torch.nn as nn - import torch.nn.functional as F -else: - if config.get("has_torch"): - import torch - import torch.nn as nn - import torch.nn.functional as F +def passfunc(z: torch.Tensor, m: Callable) -> torch.Tensor: + return m(z) +# endregion ---- Utility Functions ---- -# ---- Convolutional Layers ---- +# region ---- Convolutional Layers ---- -def complex_pool(z, m, **kwargs): - return m(z.real) + 1.0j * m(z.imag) +def complex_pool(z: torch.Tensor, pooler: Callable) -> torch.Tensor: + """Apply pooling operation to complex tensor by applying pooler separately to real and imaginary parts. -def passfunc(z, m): - return m(z) + Parameters + ---------- + z : torch.Tensor + Complex input tensor. + pooler : callable + Pooling function to apply. + + Returns + ------- + torch.Tensor + Complex tensor with pooled real and imaginary parts. + """ + return pooler(z.real) + 1.0j * pooler(z.imag) def reset_weights(m: nn.Module) -> None: - """ - Reset the weights of a given module. + """Reset the weights of a given module. - Args: - m: The neural network module to reset. + Parameters + ---------- + m : nn.Module + The neural network module to reset. """ reset_parameters = getattr(m, "reset_parameters", None) if callable(reset_parameters): @@ -43,26 +51,46 @@ def reset_weights(m: nn.Module) -> None: class Conv2dBlock(nn.Module): - """ - Creates block(s) consisting of convolutional - layer, leaky relu and (optionally) dropout and - batch normalization - """ + """Block of convolutional layers with activation, optional batch normalization and dropout.""" def __init__( self, - nb_layers, + nb_layers: int, input_channels: int | list | tuple, output_channels: int | list | tuple, - kernel_size=3, - stride=1, - padding=1, - use_batchnorm=False, + kernel_size: int = 3, + stride: int = 1, + padding: int = 1, + use_batchnorm: bool = False, dropout: float = 0.0, - dtype=torch.float32, - activation: Callable | None = None, - ): - """Initializes module nn.Parameters""" + dtype: torch.dtype = torch.float32, + activation: str | Callable | None = None, + ) -> None: + """Initialize Conv2dBlock. + + Parameters + ---------- + nb_layers : int + Number of convolutional layers in the block. + input_channels : int or list or tuple + Number of input channels. If list/tuple, must match nb_layers. + output_channels : int or list or tuple + Number of output channels. If list/tuple, must match nb_layers. + kernel_size : int, optional + Size of the convolutional kernel, by default 3 + stride : int, optional + Stride of the convolution, by default 1 + padding : int, optional + Padding added to input, by default 1 + use_batchnorm : bool, optional + Whether to include batch normalization, by default False + dropout : float, optional + Dropout probability, by default 0.0 + dtype : torch.dtype, optional + Data type for the layers, by default torch.float32 + activation : str or Callable or None, optional + Activation function to use (string or callable). If None, defaults to "relu", by default None + """ super().__init__() if not isinstance(input_channels, (int, float)): @@ -84,11 +112,7 @@ def __init__( else: self.bn = nn.BatchNorm2d - if activation is None: # defaults to ReLU - if dtype == torch.complex64: - activation = Complex_ReLU() - else: - activation = nn.ReLU() + self._activation = activation if activation is not None else "relu" block = [] for idx in range(nb_layers): @@ -103,30 +127,21 @@ def __init__( padding_mode="circular", ) ) - if dropout > 0: - block.append(nn.Dropout(dropout)) - block.append(activation) + # Get new activation instance for each conv layer + block.append(get_activation_function(self._activation, dtype)) if use_batchnorm: block.append(self.bn(output_channels_list[idx])) + if dropout > 0: + block.append(nn.Dropout(dropout)) self.block = nn.Sequential(*block) - def forward(self, x): - """Forward path""" + def forward(self, x: torch.Tensor) -> torch.Tensor: output = self.block(x) return output - class Upsample2dBlock(nn.Module): - """ - Upsampling block using interpolation followed by a convolution. - - Args: - input_channels: int, number of input channels. - output_channels: int, number of output channels. - scale_factor: int, factor by which to scale the input. - mode: str, interpolation mode, either "bilinear" or "nearest". - """ + """Upsampling block using transposed convolution or interpolation followed by convolution.""" def __init__( self, @@ -137,6 +152,23 @@ def __init__( scale_factor: int = 2, mode: str = "bilinear", ): + """Initialize Upsample2dBlock. + + Parameters + ---------- + input_channels : int + Number of input channels. + output_channels : int + Number of output channels. + use_batchnorm : bool, optional + Whether to include batch normalization, by default False + dtype : torch.dtype, optional + Data type for the layers, by default torch.float32 + scale_factor : int, optional + Factor by which to scale the input, by default 2 + mode : str, optional + Interpolation mode, either "bilinear" or "nearest", by default "bilinear" + """ super().__init__() assert mode in ["bilinear", "nearest"], "Mode must be 'bilinear' or 'nearest'." self.scale_factor = scale_factor @@ -167,15 +199,6 @@ def __init__( self.bn = nn.BatchNorm2d(output_channels) def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Forward pass for the upsample_block. - - Args: - x: Input tensor. - - Returns: - Output tensor after upsampling and convolution. - """ if self.scale_factor == 2: x = self.upsample2x(x) else: @@ -187,29 +210,77 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x +class ComplexBatchNorm1D(nn.Module): + """Batch normalization for complex inputs (real and imaginary parts separately).""" + + def __init__(self, num_features: int) -> None: + """Initialize ComplexBatchNorm1D. + + Parameters + ---------- + num_features : int + Number of features/channels. + """ + super(ComplexBatchNorm1D, self).__init__() + self.real_bn = nn.BatchNorm1d(num_features) + self.imag_bn = nn.BatchNorm1d(num_features) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.complex(self.real_bn(x.real), self.imag_bn(x.imag)) + + class ComplexBatchNorm2D(nn.Module): - """ - Batch normalization for complex inputs (real and imaginary parts separately). - """ + """Batch normalization for complex inputs (real and imaginary parts separately).""" + + def __init__(self, num_features: int) -> None: + """Initialize ComplexBatchNorm2D. - def __init__(self, num_features): + Parameters + ---------- + num_features : int + Number of features/channels. + """ super(ComplexBatchNorm2D, self).__init__() self.real_bn = nn.BatchNorm2d(num_features) self.imag_bn = nn.BatchNorm2d(num_features) - def forward(self, x): - real = x.real - imag = x.imag - real_out = self.real_bn(real) - imag_out = self.imag_bn(imag) - return torch.complex(real_out, imag_out) + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.complex(self.real_bn(x.real), self.imag_bn(x.imag)) + +class ComplexBatchNorm3D(nn.Module): + """Batch normalization for complex inputs (real and imaginary parts separately).""" -class ComplexNormalize(nn.Module): - def __init__(self, mean, std, inplace=True): + def __init__(self, num_features: int) -> None: + """Initialize ComplexBatchNorm3D. + + Parameters + ---------- + num_features : int + Number of features/channels. """ - Mean shape (C, 2) (real, complex) - std shape (C, 2) (real, complex) + super(ComplexBatchNorm3D, self).__init__() + self.real_bn = nn.BatchNorm3d(num_features) + self.imag_bn = nn.BatchNorm3d(num_features) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.complex(self.real_bn(x.real), self.imag_bn(x.imag)) + + +class ComplexNormalize(nn.Module): + """Normalization layer for complex tensors using separate mean and std for real and imaginary parts.""" + + def __init__(self, mean: torch.Tensor, std: torch.Tensor, inplace: bool = True) -> None: + """Initialize ComplexNormalize. + + Parameters + ---------- + mean : torch.Tensor + Mean values with shape (C, 2) for (real, imaginary) parts. + std : torch.Tensor + Standard deviation values with shape (C, 2) for (real, imaginary) parts. + inplace : bool, optional + Whether to perform normalization in-place, by default True """ super().__init__() self.mean = mean @@ -232,7 +303,19 @@ def forward(self, tensor: torch.Tensor) -> torch.Tensor: return torch.view_as_complex(view) - def to_device(self, device): + def to_device(self, device: torch.device | str) -> "ComplexNormalize": + """Move mean and std tensors to the specified device. + + Parameters + ---------- + device : torch.device or str + Target device. + + Returns + ------- + ComplexNormalize + Self for method chaining. + """ self.mean = self.mean.to(device) self.std = self.std.to(device) return self @@ -242,23 +325,50 @@ def __repr__(self): class Conv3dBlock(nn.Module): + """3D convolutional block with optional batch normalization, dropout, and activation.""" + def __init__( self, - nb_layers, - input_channels, - output_channels, - kernel_size=3, - stride=1, - padding=1, - use_batchnorm=False, - dropout=0.0, - dtype=torch.float32, - activation: None | Callable = None, - ): + nb_layers: int, + input_channels: int, + output_channels: int, + kernel_size: int = 3, + stride: int = 1, + padding: int = 1, + use_batchnorm: bool = False, + dropout: float = 0.0, + dtype: torch.dtype = torch.float32, + activation: str | Callable | None = None, + ) -> None: + """Initialize Conv3dBlock. + + Parameters + ---------- + nb_layers : int + Number of convolutional layers in the block. + input_channels : int + Number of input channels. + output_channels : int + Number of output channels. + kernel_size : int, optional + Size of the convolutional kernel, by default 3 + stride : int, optional + Stride of the convolution, by default 1 + padding : int, optional + Padding added to input, by default 1 + use_batchnorm : bool, optional + Whether to include batch normalization, by default False + dropout : float, optional + Dropout probability, by default 0.0 + dtype : torch.dtype, optional + Data type for the layers, by default torch.float32 + activation : str or Callable or None, optional + Activation function to use (string or callable). If None, defaults to "relu", by default None + """ super().__init__() self.dtype = dtype self.bn = ComplexBatchNorm3D if dtype.is_complex else nn.BatchNorm3d - activation_func = activation + self._activation = activation if activation is not None else "relu" layers = [] for _ in range(nb_layers): @@ -275,26 +385,48 @@ def __init__( ) if dropout > 0: layers.append(nn.Dropout(dropout)) - layers.append(activation_func) + # Get new activation instance for each conv layer + from .activation_functions import get_activation_function + + layers.append(get_activation_function(self._activation, dtype)) if use_batchnorm: layers.append(self.bn(output_channels)) input_channels = output_channels self.block = nn.Sequential(*layers) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: return self.block(x) class Upsample3dBlock(nn.Module): + """3D upsampling block using transposed convolution followed by 1x1x1 convolution.""" + def __init__( self, - input_channels, - output_channels, - use_batchnorm=False, - dtype=torch.float32, - scale_factor=2, - mode="trilinear", - ): + input_channels: int, + output_channels: int, + use_batchnorm: bool = False, + dtype: torch.dtype = torch.float32, + scale_factor: int = 2, + mode: str = "trilinear", + ) -> None: + """Initialize Upsample3dBlock. + + Parameters + ---------- + input_channels : int + Number of input channels. + output_channels : int + Number of output channels. + use_batchnorm : bool, optional + Whether to include batch normalization, by default False + dtype : torch.dtype, optional + Data type for the layers, by default torch.float32 + scale_factor : int, optional + Factor by which to scale the input, by default 2 + mode : str, optional + Interpolation mode, by default "trilinear" + """ super().__init__() self.dtype = dtype self.use_batchnorm = use_batchnorm @@ -314,7 +446,7 @@ def __init__( else nn.BatchNorm3d(output_channels) ) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.upsample(x) x = self.conv(x) if self.use_batchnorm: @@ -322,53 +454,20 @@ def forward(self, x): return x -class ComplexBatchNorm3D(nn.Module): - def __init__(self, num_features): - super().__init__() - self.real_bn = nn.BatchNorm3d(num_features) - self.imag_bn = nn.BatchNorm3d(num_features) - - def forward(self, x): - return torch.complex(self.real_bn(x.real), self.imag_bn(x.imag)) +# endregion ---- Convolutional Layers ---- -# ---- Linear Layers ---- -class ComplexLinear(nn.Module): - def __init__(self, in_features: int, out_features: int): - super().__init__() - linear = nn.Linear(in_features, out_features, dtype=torch.cfloat) - self.weight = nn.Parameter(torch.view_as_real(linear.weight)) - self.bias = nn.Parameter(torch.view_as_real(linear.bias)) - - def forward(self, x): - weight = torch.view_as_complex(self.weight) - bias = torch.view_as_complex(self.bias) - return F.linear(x, weight, bias) - -## ---- Siren Family of Layers ---- - -def init_weights(m: nn.Module, omega: float = 1., c: float = 1., is_first: bool = False): - if hasattr(m, 'weight'): - fan_in = m.weight.size(-1) - if is_first: - bound = 1 / fan_in # SIREN - else: - bound = math.sqrt(c / fan_in) / omega - nn.init.uniform_(m.weight, -bound, bound) +# region ---- Siren Family of Layers ---- -def init_bias(m: nn.Module, k: float): - if hasattr(m, 'bias'): - nn.init.uniform_(m.bias, -k, k) class SineLayer(nn.Module): - """ - - Sine layer for H-Siren, and SIREN implementations. - + """Sine layer for H-Siren and SIREN implementations. + Note: H-Siren uses the hyperbolic sine function only for the first layer. """ + def __init__( - self, + self, in_features: int, out_features: int, bias: bool = True, @@ -376,66 +475,59 @@ def __init__( omega_0: float = 30, hsiren: bool = False, alpha: float = 1.0, + dtype: torch.dtype = torch.float32, ): + """Initialize SineLayer. + + Parameters + ---------- + in_features : int + Dimensionality of input coordinates (e.g., 3 for 3D: z, y, x). + out_features : int + Dimensionality of output (e.g., 1 for scalar field). + bias : bool, optional + Whether to use a bias term in the linear layer, by default True + is_first : bool, optional + Whether this is the first layer (affects weight initialization and activation), by default False + omega_0 : float, optional + Scaling factor for the activation function, by default 30 + hsiren : bool, optional + Whether this is an H-Siren layer (uses hyperbolic sine for first layer if True), by default False + alpha : float, optional + Scaling factor for weight initialization, by default 1.0 + dtype : torch.dtype, optional + Data type for the layer, by default torch.float32 + """ super().__init__() self.omega_0 = omega_0 self.is_first = is_first self.hsiren = hsiren self.in_features = in_features self.alpha = alpha - self.linear = nn.Linear(in_features, out_features, bias=bias) + self.dtype = dtype + self.linear = nn.Linear(in_features, out_features, bias=bias, dtype=dtype) self.init_weights() - def init_weights(self): + def init_weights(self) -> None: with torch.no_grad(): if self.is_first: # Scale the first layer initialization by alpha - self.linear.weight.uniform_(-self.alpha / self.in_features, - self.alpha / self.in_features) + self.linear.weight.uniform_( + -self.alpha / self.in_features, self.alpha / self.in_features + ) else: # Scale the hidden layer initialization by alpha - self.linear.weight.uniform_(-self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, - self.alpha * np.sqrt(6 / self.in_features) / self.omega_0) + self.linear.weight.uniform_( + -self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, + self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, + ) - def forward(self, input): + def forward(self, input: torch.Tensor) -> torch.Tensor: if self.is_first and self.hsiren: - out = torch.sin(self.omega_0 * torch.sinh(2*self.linear(input))) + out = torch.sin(self.omega_0 * torch.sinh(2 * self.linear(input))) else: out = torch.sin(self.omega_0 * self.linear(input)) return out -class FinerLayer(nn.Module): - - def __init__( - self, - in_features: int, - out_features: int, - bias: bool = True, - omega: float = 30, - is_first: bool = False, - is_last: bool = False, - init_method: str = 'sine', - init_gain: float = 1, - fbs: bool = None, - hbs = None, - alphaType = None, - alphaReqGrad = False, - ): - - super().__init__() - self.omega = omega - self.is_last = is_last - self.alphaType = alphaType - self.alphaReqGrad = alphaReqGrad - self.linear = nn.Linear(in_features, out_features, bias=bias) - - # init weights - init_weights(self.linear, omega, init_gain, is_first) - # init bias - init_bias(self.linear, fbs, is_first) - - def forward(self, input): - wx_b = self.linear(input) - if not self.is_last: - return FinerActivation(wx_b, self.omega) - return wx_b # is_last==True \ No newline at end of file + +# endregion ---- Siren Family of Layers ---- diff --git a/src/quantem/core/ml/cnn.py b/src/quantem/core/ml/cnn.py index b2f6d56b..5b4f93e4 100644 --- a/src/quantem/core/ml/cnn.py +++ b/src/quantem/core/ml/cnn.py @@ -1,29 +1,29 @@ -from typing import TYPE_CHECKING, Callable +from typing import Callable -from quantem.core import config +import torch +import torch.nn as nn from .activation_functions import get_activation_function -from .blocks import Conv2dBlock, Upsample2dBlock, Conv3dBlock, Upsample3dBlock, complex_pool, passfunc - -if TYPE_CHECKING: - import torch - import torch.nn as nn -else: - if config.get("has_torch"): - import torch - import torch.nn as nn +from .blocks import ( + Conv2dBlock, + Conv3dBlock, + Upsample2dBlock, + Upsample3dBlock, + complex_pool, + passfunc, +) class CNN2d(nn.Module): - """ """ + """UNet-like CNN for 2D images. Can be used for classification, regression, or segmentation.""" def __init__( self, - in_channels: int, # input channels (C_in, H, W) - out_channels: int | None = None, # output channels (C_out, H, W) + in_channels: int, + out_channels: int | None = None, start_filters: int = 16, - num_layers: int = 3, # num_layers - num_per_layer: int = 2, # number conv per layer + num_layers: int = 3, + num_per_layer: int = 2, use_skip_connections: bool = True, dtype: torch.dtype = torch.float32, dropout: float = 0, @@ -31,6 +31,38 @@ def __init__( final_activation: str | Callable = nn.Identity(), use_batchnorm: bool = True, ): + """Initialize CNN2d. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int or None, optional + Number of output channels. If None, defaults to in_channels, by default None + start_filters : int, optional + Starting number of filters for CNN, by default 16 + num_layers : int, optional + Number of CNN encoder layers, by default 3 + num_per_layer : int, optional + Number of conv blocks per CNN layer, by default 2 + use_skip_connections : bool, optional + Whether to use skip connections, by default True + dtype : torch.dtype, optional + Data type for the network, by default torch.float32 + dropout : float, optional + Dropout probability, by default 0 + activation : str or Callable, optional + Activation function for hidden layers, by default "relu" + final_activation : str or Callable, optional + Activation function for output layer, by default nn.Identity() + use_batchnorm : bool, optional + Whether to use batch normalization, by default True + + Raises + ------ + ValueError + If use_skip_connections is True and num_per_layer < 2. + """ super().__init__() self.in_channels = int(in_channels) self.out_channels = int(out_channels) if out_channels is not None else int(in_channels) @@ -63,14 +95,12 @@ def __init__( @property def activation(self) -> Callable: - return self._activation + """Create a new activation instance each time this is accessed.""" + return get_activation_function(self._activation, self.dtype) @activation.setter def activation(self, act: str | Callable): - if callable(act): - self._activation = act - else: - self._activation = get_activation_function(act, self.dtype) + self._activation = act @property def final_activation(self) -> Callable: @@ -78,12 +108,9 @@ def final_activation(self) -> Callable: @final_activation.setter def final_activation(self, act: str | Callable): - if callable(act): - self._final_activation = act - else: - self._final_activation = get_activation_function(act, self.dtype) + self._final_activation = get_activation_function(act, self.dtype) - def _build(self): + def _build(self) -> None: self.down_conv_blocks = nn.ModuleList() self.up_conv_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() @@ -101,7 +128,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, # Pass activation config, not instance ) ) in_channels = out_channels @@ -114,7 +141,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, ) in_channels = out_channels @@ -137,7 +164,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, ) ) @@ -175,9 +202,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return y def reset_weights(self): - """ - Reset all weights. - """ + """Reset all weights in the network.""" def _reset(m: nn.Module) -> None: reset_parameters = getattr(m, "reset_parameters", None) @@ -188,11 +213,12 @@ def _reset(m: nn.Module) -> None: class CNN3d(nn.Module): - """Complex 3D CNN for processing volumetric data.""" + """UNet-like 3D CNN for processing volumetric data.""" def __init__( self, - num_channels: int, # (C, D, H, W) input shape same as output shape + in_channels: int, + out_channels: int | None = None, start_filters: int = 16, num_layers: int = 3, num_per_layer: int = 2, @@ -204,8 +230,43 @@ def __init__( use_batchnorm: bool = True, mode: str = "complex", ): + """Initialize CNN3d. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int or None, optional + Number of output channels. If None, defaults to in_channels, by default None + start_filters : int, optional + Starting number of filters for CNN, by default 16 + num_layers : int, optional + Number of CNN encoder layers, by default 3 + num_per_layer : int, optional + Number of conv blocks per CNN layer, by default 2 + use_skip_connections : bool, optional + Whether to use skip connections, by default True + dtype : torch.dtype, optional + Data type for the network, by default torch.complex64 + dropout : float, optional + Dropout probability, by default 0 + activation : str or Callable, optional + Activation function for hidden layers, by default "relu" + final_activation : str or Callable, optional + Activation function for output layer, by default nn.Identity() + use_batchnorm : bool, optional + Whether to use batch normalization, by default True + mode : str, optional + Mode for the network, by default "complex" + + Raises + ------ + ValueError + If use_skip_connections is True and num_per_layer < 2. + """ super().__init__() - self.in_channels = self.out_channels = int(num_channels) + self.in_channels = int(in_channels) + self.out_channels = int(out_channels) if out_channels is not None else int(in_channels) self.start_filters = start_filters self.num_layers = num_layers self._num_per_layer = num_per_layer @@ -232,14 +293,12 @@ def __init__( @property def activation(self) -> Callable: - return self._activation + """Create a new activation instance each time this is accessed.""" + return get_activation_function(self._activation, self.dtype) @activation.setter def activation(self, act: str | Callable): - if isinstance(act, Callable): - self._activation = act - else: - self._activation = get_activation_function(act, self.dtype) + self._activation = act @property def final_activation(self) -> Callable: @@ -247,12 +306,9 @@ def final_activation(self) -> Callable: @final_activation.setter def final_activation(self, act: str | Callable): - if isinstance(act, Callable): - self._final_activation = act - else: - self._final_activation = get_activation_function(act, self.dtype) + self._final_activation = get_activation_function(act, self.dtype) - def _build(self): + def _build(self) -> None: self.down_conv_blocks = nn.ModuleList() self.up_conv_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() @@ -270,7 +326,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, ) ) in_channels = out_channels @@ -283,7 +339,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, ) in_channels = out_channels @@ -312,7 +368,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, ) ) @@ -358,9 +414,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return y def reset_weights(self): - """ - Reset all weights. - """ + """Reset all weights in the network.""" def _reset(m: nn.Module) -> None: reset_parameters = getattr(m, "reset_parameters", None) diff --git a/src/quantem/core/ml/cnn_dense.py b/src/quantem/core/ml/cnn_dense.py new file mode 100644 index 00000000..923a3357 --- /dev/null +++ b/src/quantem/core/ml/cnn_dense.py @@ -0,0 +1,166 @@ +from typing import Callable + +import torch +import torch.nn as nn + +from .activation_functions import get_activation_function +from .blocks import Conv2dBlock, complex_pool, passfunc +from .dense_nn import DenseNN + + +class CNNDense(nn.Module): + """CNN encoder followed by dense layers for classification or regression.""" + + def __init__( + self, + in_channels: int, + output_dim: int, + image_shape: tuple[int, int], + start_filters: int = 16, + cnn_num_layers: int = 3, + cnn_num_per_layer: int = 2, + dense_num_layers: int = 2, + dense_hidden_size: int = 128, + dense_hidden_dims: list[int] | None = None, + dtype: torch.dtype = torch.float32, + dropout: float = 0, + activation: str | Callable = "relu", + final_activation: str | Callable = nn.Identity(), + use_batchnorm: bool = True, + ): + """Initialize CNNDense. + + Parameters + ---------- + in_channels : int + Number of input channels. + output_dim : int + Output dimension. + image_shape : tuple[int, int] + 2D input image shape (H, W). + start_filters : int, optional + Starting number of filters for CNN, by default 16 + cnn_num_layers : int, optional + Number of CNN encoder layers, by default 3 + cnn_num_per_layer : int, optional + Number of conv blocks per CNN layer, by default 2 + dense_num_layers : int, optional + Number of dense layers, by default 2 + dense_hidden_size : int, optional + Size of dense hidden layers, by default 128 + dense_hidden_dims : list[int] or None, optional + List of hidden layer dimensions for dense part (overrides dense_num_layers/dense_hidden_size), by default None + dtype : torch.dtype, optional + Data type for the network, by default torch.float32 + dropout : float, optional + Dropout probability, by default 0 + activation : str or Callable, optional + Activation function for hidden layers, by default "relu" + final_activation : str or Callable, optional + Activation function for output layer, by default nn.Identity() + use_batchnorm : bool, optional + Whether to use batch normalization, by default True + """ + super().__init__() + self.in_channels = int(in_channels) + self.output_dim = int(output_dim) + self.image_shape = image_shape + self.start_filters = start_filters + self.num_cnn_layers = cnn_num_layers + self._num_per_layer = cnn_num_per_layer + self.dtype = dtype + self.dropout = dropout + self._use_batchnorm = use_batchnorm + + if dense_hidden_dims is not None: + self.hidden_dims = [int(d) for d in dense_hidden_dims] + else: + self.hidden_dims = [int(dense_hidden_size)] * dense_num_layers + + if self.dtype.is_complex: + self.pool = complex_pool + else: + self.pool = passfunc + self._pooler = nn.MaxPool2d(kernel_size=2, stride=2) + + self.activation = activation + self.final_activation = final_activation + + self.flatten = nn.Flatten() + + self._build() + + @property + def activation(self) -> Callable: + """Create a new activation instance each time this is accessed.""" + return get_activation_function(self._activation, self.dtype) + + @activation.setter + def activation(self, act: str | Callable): + self._activation = act + + @property + def final_activation(self) -> Callable: + return self._final_activation + + @final_activation.setter + def final_activation(self, act: str | Callable): + self._final_activation = get_activation_function(act, self.dtype) + + def _build(self) -> None: + self.cnn_blocks = nn.ModuleList() + + in_channels = self.in_channels + out_channels = self.start_filters + for a0 in range(self.num_cnn_layers): + if a0 != 0: + out_channels = in_channels * 2 + self.cnn_blocks.append( + Conv2dBlock( + nb_layers=self._num_per_layer, + input_channels=in_channels, + output_channels=out_channels, + use_batchnorm=self._use_batchnorm, + dropout=self.dropout, + dtype=self.dtype, + activation=self._activation, + ) + ) + in_channels = out_channels + + h, w = self.image_shape + for _ in range(self.num_cnn_layers): + h = h // 2 + w = w // 2 + self.flattened_dim = out_channels * h * w + + self.dense_net = DenseNN( + input_dim=self.flattened_dim, + output_dim=self.output_dim, + hidden_dims=self.hidden_dims, + dtype=self.dtype, + dropout=self.dropout, + activation=self._activation, + final_activation=self.final_activation, + use_batchnorm=self._use_batchnorm, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + for cnn_block in self.cnn_blocks: + x = cnn_block(x) + x = self.pool(x, self._pooler) + + x = self.flatten(x) + y = self.dense_net(x) + + return y + + def reset_weights(self): + """Reset all weights in the network.""" + + def _reset(m: nn.Module) -> None: + reset_parameters = getattr(m, "reset_parameters", None) + if callable(reset_parameters): + reset_parameters() + + self.apply(_reset) diff --git a/src/quantem/core/ml/autoencoder.py b/src/quantem/core/ml/conv_autoencoder.py similarity index 78% rename from src/quantem/core/ml/autoencoder.py rename to src/quantem/core/ml/conv_autoencoder.py index 7c1e2131..f290e7d8 100644 --- a/src/quantem/core/ml/autoencoder.py +++ b/src/quantem/core/ml/conv_autoencoder.py @@ -9,6 +9,8 @@ class ConvAutoencoder2d(nn.Module): + """Convolutional autoencoder for 4DSTEM diffraction pattern analysis.""" + def __init__( self, input_size: int | tuple[int, int], @@ -23,30 +25,37 @@ def __init__( final_activation: str | Callable = "relu", use_batchnorm: bool = True, latent_normalization: Literal["none", "l2", "layer", "tanh"] = "none", - ): - """ - Convolutional autoencoder for 4DSTEM diffraction pattern analysis. + ) -> None: + """Initialize ConvAutoencoder2d. Parameters ---------- - input_size : int | tuple[int, int] + input_size : int or tuple[int, int] Input image size. If int, assumes square image. - input_channels : int, default=1 - Number of input channels (typically 1 for grayscale diffraction patterns) - latent_dim : int, default=128 - Dimensionality of the latent representation for clustering - final_activation : str | Callable, default="relu" - Output activation. Common choices: - - "relu": For positive-valued intensities [0,∞) - - "sigmoid": For normalized intensities [0,1] - - "softplus": Smooth positive activation - - "identity": For preprocessed data (mean=0, std=1) - latent_normalization : Literal["none", "l2", "layer", "tanh"], default="none" - Latent space normalization for clustering: - - "l2": For DBScan, K-means (unit hypersphere) - - "layer": For GMM (Gaussian-like distributions) - - "tanh": Bounded latent space [-1,1] - - "none": Raw learned representations + input_channels : int, optional + Number of input channels (typically 1 for grayscale diffraction patterns), by default 1 + latent_dim : int, optional + Dimensionality of the latent representation for clustering, by default 128 + start_filters : int, optional + Starting number of filters, by default 16 + num_layers : int, optional + Number of encoder/decoder layers, by default 4 + num_per_layer : int, optional + Number of conv blocks per layer, by default 2 + dtype : torch.dtype, optional + Data type for the network, by default torch.float32 + dropout : float, optional + Dropout probability, by default 0.0 + activation : str or Callable, optional + Activation function for hidden layers, by default "relu" + final_activation : str or Callable, optional + Output activation. Common choices: "relu" (positive intensities), + "sigmoid" (normalized [0,1]), "softplus" (smooth positive), "identity" (preprocessed), by default "relu" + use_batchnorm : bool, optional + Whether to use batch normalization, by default True + latent_normalization : Literal["none", "l2", "layer", "tanh"], optional + Latent space normalization: "l2" (unit hypersphere for DBScan/K-means), + "layer" (Gaussian-like for GMM), "tanh" (bounded [-1,1]), "none" (raw), by default "none" """ super().__init__() self.input_size = input_size @@ -86,23 +95,22 @@ def input_size(self, size: int | tuple[int, int]): @property def activation(self) -> Callable: - return self._activation + """Create a new activation instance each time this is accessed.""" + return get_activation_function(self._activation, self.dtype) @activation.setter def activation(self, act: str | Callable): - self._activation = get_activation_function(act, self.dtype) if not callable(act) else act + self._activation = act @property def final_activation(self) -> Callable: - return self._final_activation + return get_activation_function(self._final_activation, self.dtype) @final_activation.setter def final_activation(self, act: str | Callable): - self._final_activation = ( - get_activation_function(act, self.dtype) if not callable(act) else act - ) + self._final_activation = act - def _build(self): + def _build(self) -> None: self.encoder_blocks = nn.ModuleList() self.decoder_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() @@ -122,7 +130,7 @@ def _build(self): use_batchnorm=self._use_batchnorm, dropout=self.dropout, dtype=self.dtype, - activation=self.activation, + activation=self._activation, ) ) in_channels = out_channels @@ -167,9 +175,9 @@ def _build(self): ) ) - # Final layer uses different activation + # Final layer uses different activation (pass config string, not instance) layer_activation = ( - self.final_activation if layer_idx == self.num_layers - 1 else self.activation + self.final_activation if layer_idx == self.num_layers - 1 else self._activation ) self.decoder_blocks.append( @@ -229,7 +237,9 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: reconstructed = self.decode(latent) return reconstructed, latent - def reset_weights(self): + def reset_weights(self) -> None: + """Reset all weights in the network.""" + def _reset(m: nn.Module) -> None: reset_parameters = getattr(m, "reset_parameters", None) if callable(reset_parameters): diff --git a/src/quantem/core/ml/dense_nn.py b/src/quantem/core/ml/dense_nn.py index e69de29b..21ade285 100644 --- a/src/quantem/core/ml/dense_nn.py +++ b/src/quantem/core/ml/dense_nn.py @@ -0,0 +1,142 @@ +from typing import Callable + +import torch +import torch.nn as nn + +from quantem.core.ml.blocks import ComplexBatchNorm1D + +from .activation_functions import get_activation_function + + +class DenseNN(nn.Module): + """Fully connected neural network with configurable architecture.""" + + def __init__( + self, + input_dim: int, + output_dim: int | None = None, + hidden_dims: list[int] | None = None, + num_layers: int = 3, + hidden_size: int = 128, + dtype: torch.dtype = torch.float32, + dropout: float = 0, + activation: str | Callable = "relu", + final_activation: str | Callable = nn.Identity(), + use_batchnorm: bool = False, + ) -> None: + """Initialize DenseNN. + + Parameters + ---------- + input_dim : int + Input dimension. + output_dim : int or None, optional + Output dimension. If None, defaults to input_dim, by default None + hidden_dims : list[int] or None, optional + List of hidden layer dimensions (overrides num_layers/hidden_size), by default None + num_layers : int, optional + Number of hidden layers (used if hidden_dims not provided), by default 3 + hidden_size : int, optional + Size of hidden layers (used if hidden_dims not provided), by default 128 + dtype : torch.dtype, optional + Data type for the network, by default torch.float32 + dropout : float, optional + Dropout probability, by default 0 + activation : str or Callable, optional + Activation function for hidden layers, by default "relu" + final_activation : str or Callable, optional + Activation function for output layer, by default nn.Identity() + use_batchnorm : bool, optional + Whether to use batch normalization, by default False + """ + super().__init__() + self.input_dim = int(input_dim) + self.output_dim = int(output_dim) if output_dim is not None else int(input_dim) + self.dtype = dtype + self.dropout = dropout + self._use_batchnorm = use_batchnorm + + if hidden_dims is not None: + self.hidden_dims = [int(d) for d in hidden_dims] + else: + self.hidden_dims = [int(hidden_size)] * num_layers + + self.activation = activation + self.final_activation = final_activation + self.flatten = nn.Flatten() + + self._build() + + @property + def activation(self) -> Callable: + """Create a new activation instance each time this is accessed. + This avoids caching the activation function and helps with summary()""" + return get_activation_function(self._activation, self.dtype) + + @activation.setter + def activation(self, act: str | Callable): + self._activation = act + + @property + def final_activation(self) -> Callable: + return self._final_activation + + @final_activation.setter + def final_activation(self, act: str | Callable): + self._final_activation = get_activation_function(act, self.dtype) + + def _build(self) -> None: + self.layers = nn.ModuleList() + + dims = [self.input_dim] + self.hidden_dims + + for i in range(len(dims) - 1): + in_dim = dims[i] + out_dim = dims[i + 1] + + layer = [] + layer.append(nn.Linear(in_dim, out_dim, dtype=self.dtype)) + layer.append(self.activation) + + if self._use_batchnorm: + if self.dtype.is_complex: + layer.append(ComplexBatchNorm1D(out_dim)) + else: + layer.append(nn.BatchNorm1d(out_dim, dtype=self.dtype)) + + if self.dropout > 0: + layer.append(nn.Dropout(self.dropout)) + + self.layers.append(nn.Sequential(*layer)) + + self.layers.append( + nn.Linear( + self.hidden_dims[-1] if self.hidden_dims else self.input_dim, + self.output_dim, + dtype=self.dtype, + ) + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + original_shape = x.shape[:-1] + x = x.reshape(-1, self.input_dim) + + for layer in self.layers: + x = layer(x) + + y = self.final_activation(x) + + if len(original_shape) > 1: + y = y.reshape(*original_shape, self.output_dim) + + return y + + def reset_weights(self) -> None: + """Reset all weights in the network.""" + + def _reset(m: nn.Module) -> None: + reset_parameters = getattr(m, "reset_parameters", None) + if callable(reset_parameters): + reset_parameters() + + self.apply(_reset) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index f343ff99..24ee45e4 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -1,17 +1,16 @@ -from .blocks import SineLayer, FinerLayer -from torch import nn -import torch +from typing import Callable + import numpy as np +import torch +from torch import nn + +from .activation_functions import get_activation_function +from .blocks import SineLayer -"""" -All the INR implementations are used for coordinate inputs (x, y, z) to an intensity to that coordinate (I(x, y, z)). -Hence, we use 3 as the number of input features, and 1 output feature as a default. -""" class Siren(nn.Module): - """ - Original SIREN implementation. - """ + """Original SIREN implementation.""" + def __init__( self, in_features: int = 3, @@ -21,101 +20,201 @@ def __init__( first_omega_0: float = 30.0, hidden_omega_0: float = 30.0, alpha: float = 1.0, - ): + hsiren: bool = False, + dtype: torch.dtype = torch.float32, + final_activation: str | Callable = "identity", + ) -> None: + """Initialize Siren. + + Parameters + ---------- + in_features : int, optional + Dimensionality of input coordinates (3 for 3D: z, y, x), by default 3 + out_features : int, optional + Dimensionality of output (1 for scalar field), by default 1 + hidden_layers : int, optional + Number of hidden layers, by default 3 + hidden_features : int, optional + Number of features in each hidden layer, by default 256 + first_omega_0 : float, optional + Activation function scaling factor for the first layer, by default 30.0 + hidden_omega_0 : float, optional + Activation function scaling factor for the hidden layers, by default 30.0 + alpha : float, optional + Weight initialization scaling factor, by default 1.0 + hsiren : bool, optional + Whether to use the H-Siren activation function, by default False + dtype : torch.dtype, optional + Data type for the network, by default torch.float32 + final_activation : str or Callable, optional + Final activation function, by default "identity" + """ super().__init__() - self.net_list = [] - self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, - omega_0=first_omega_0, alpha=alpha)) + self.in_features = in_features + self.out_features = out_features + self.hidden_layers = hidden_layers + self.hidden_features = hidden_features + self.first_omega_0 = first_omega_0 + self.hidden_omega_0 = hidden_omega_0 + self.alpha = alpha + self.hsiren = hsiren + self.dtype = dtype - for i in range(hidden_layers): - self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, - omega_0=hidden_omega_0, alpha=alpha)) + self.final_activation = final_activation - final_linear = nn.Linear(hidden_features, out_features) - with torch.no_grad(): - # Final layer keeps original initialization (no alpha scaling) - final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, - np.sqrt(6 / hidden_features) / hidden_omega_0) - self.net_list.append(final_linear) - self.net_list.append(nn.Softplus()) - self.net = nn.Sequential(*self.net_list) + self._build() - def forward(self, coords): - output = self.net(coords) - return output + @property + def final_activation(self) -> Callable: + return self._final_activation -class HSiren(nn.Module): - """ - H-Siren implementation, the first layer is a sinh instead of a sine activation function. - """ - def __init__( - self, - in_features: int= 3, - out_features: int= 1, - hidden_layers: int= 3, - hidden_features: int= 256, - first_omega_0: float= 30, - hidden_omega_0: float= 30, - alpha: float= 1.0, - ): - super().__init__() - self.net_list = [] - self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, - omega_0=first_omega_0, hsiren=True, alpha=alpha)) + @final_activation.setter + def final_activation(self, act: str | Callable): + self._final_activation = get_activation_function(act, dtype=self.dtype) + + def _build(self) -> None: + net_list = [] + net_list.append( + SineLayer( + self.in_features, + self.hidden_features, + is_first=True, + omega_0=self.first_omega_0, + hsiren=self.hsiren, + alpha=self.alpha, + dtype=self.dtype, + ) + ) - for i in range(hidden_layers): - self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, - omega_0=hidden_omega_0, alpha=alpha)) + for i in range(self.hidden_layers): + net_list.append( + SineLayer( + self.hidden_features, + self.hidden_features, + is_first=False, + omega_0=self.hidden_omega_0, + alpha=self.alpha, + dtype=self.dtype, + ) + ) - final_linear = nn.Linear(hidden_features, out_features) + final_linear = nn.Linear(self.hidden_features, self.out_features, dtype=self.dtype) with torch.no_grad(): # Final layer keeps original initialization (no alpha scaling) - final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, - np.sqrt(6 / hidden_features) / hidden_omega_0) - self.net_list.append(final_linear) - self.net_list.append(nn.Softplus()) - self.net = nn.Sequential(*self.net_list) + final_linear.weight.uniform_( + -np.sqrt(6 / self.hidden_features) / self.hidden_omega_0, + np.sqrt(6 / self.hidden_features) / self.hidden_omega_0, + ) + net_list.append(final_linear) + net_list.append(self._final_activation) + self.net = nn.Sequential(*net_list) - def forward(self, coords): + def forward(self, coords: torch.Tensor) -> torch.Tensor: output = self.net(coords) return output - -class Finer(nn.Module): - """ - Finer implementation. - """ + + def reset_weights(self) -> None: + """Reset all weights in the network.""" + self._build() + + def make_equispaced_grid( + self, bounds: tuple[tuple[float, float], ...], sampling: tuple[float, ...] + ) -> torch.Tensor: + """Create an equispaced coordinate grid for the implicit neural representation. + + Parameters + ---------- + bounds : tuple of tuples + Bounds for each dimension as ((min_0, max_0), (min_1, max_1), ...). + Length must match in_features. + sampling : tuple of float + Sampling interval for each dimension (spacing_0, spacing_1, ...). + Length must match in_features. + + Returns + ------- + torch.Tensor + Flattened coordinate grid of shape (N, in_features), where N is the + total number of grid points. + + Raises + ------ + ValueError + If bounds or sampling length does not match in_features. + + Examples + -------- + For a model with in_features=2: + >>> bounds = ((0, 1), (0, 1)) + >>> sampling = (0.1, 0.1) + >>> coords = siren.make_equispaced_grid(bounds, sampling) + """ + if len(bounds) != self.in_features: + raise ValueError( + f"Bounds length ({len(bounds)}) must match in_features ({self.in_features})" + ) + if len(sampling) != self.in_features: + raise ValueError( + f"Sampling length ({len(sampling)}) must match in_features ({self.in_features})" + ) + + grids = [] + for (bound_min, bound_max), sample in zip(bounds, sampling): + num_points = int((bound_max - bound_min) / sample) + 1 + grids.append(torch.linspace(bound_min, bound_max, num_points)) + + coords = torch.meshgrid(*grids, indexing="ij") + coords = torch.stack(coords, dim=-1).to(self.dtype) + return coords.reshape(-1, self.in_features) + + +class HSiren(Siren): + """H-Siren implementation, the first layer uses sinh instead of sine activation function.""" + def __init__( - self, - in_features: int=3, - out_features: int=1, - hidden_layers: int=3, - hidden_features: int=256, - first_omega: float=30, - hidden_omega: float=30, - init_method: str='sine', - init_gain: float=1, - fbs=None, # Need to check what FBS/HBS/alphaType/alphaReqGrad are - hbs=None, - alphaType=None, - alphaReqGrad=False - ): - super().__init__() - self.net_list = [] - self.net_list.append(FinerLayer(in_features, hidden_features, is_first=True, - omega=first_omega, - init_method=init_method, init_gain=init_gain, fbs=fbs, - alphaType=alphaType, alphaReqGrad=alphaReqGrad)) - - for i in range(hidden_layers): - self.net_list.append(FinerLayer(hidden_features, hidden_features, - omega=hidden_omega, - init_method=init_method, init_gain=init_gain, hbs=hbs, - alphaType=alphaType, alphaReqGrad=alphaReqGrad)) - - self.net_list.append(FinerLayer(hidden_features, out_features, is_last=True, - omega=hidden_omega, - init_method=init_method, init_gain=init_gain, hbs=hbs)) # omega: For weight init - self.net = nn.Sequential(*self.net_list) - - def forward(self, coords): - return self.net(coords) \ No newline at end of file + self, + in_features: int = 3, + out_features: int = 1, + hidden_layers: int = 3, + hidden_features: int = 256, + first_omega_0: float = 30, + hidden_omega_0: float = 30, + alpha: float = 1.0, + dtype: torch.dtype = torch.float32, + final_activation: str | Callable = "identity", + ) -> None: + """Initialize HSiren. + + Parameters + ---------- + in_features : int, optional + Dimensionality of input coordinates (3 for 3D: z, y, x), by default 3 + out_features : int, optional + Dimensionality of output (1 for scalar field), by default 1 + hidden_layers : int, optional + Number of hidden layers, by default 3 + hidden_features : int, optional + Number of features in each hidden layer, by default 256 + first_omega_0 : float, optional + Activation function scaling factor for the first layer, by default 30 + hidden_omega_0 : float, optional + Activation function scaling factor for the hidden layers, by default 30 + alpha : float, optional + Weight initialization scaling factor, by default 1.0 + dtype : torch.dtype, optional + Data type for the network, by default torch.float32 + final_activation : str or Callable, optional + Final activation function, by default "identity" + """ + super().__init__( + in_features, + out_features, + hidden_layers, + hidden_features, + first_omega_0, + hidden_omega_0, + alpha, + hsiren=True, + dtype=dtype, + final_activation=final_activation, + ) diff --git a/src/quantem/core/ml/logger.py b/src/quantem/core/ml/logger.py index 4535e5ab..13f3beb7 100644 --- a/src/quantem/core/ml/logger.py +++ b/src/quantem/core/ml/logger.py @@ -16,12 +16,12 @@ from quantem.core.io.serialize import AutoSerialize, load -""" -Tensorboard logger class for AD/ML reconstruction methods -""" +"""Tensorboard logger class for AD/ML reconstruction methods.""" class LoggerBase(AutoSerialize): + """Tensorboard logger for AD/ML reconstruction methods.""" + def __init__( self, base_log_dir: os.PathLike | str, @@ -29,6 +29,19 @@ def __init__( run_suffix: str = "", log_images_every: int = 10, ) -> None: + """Initialize LoggerBase. + + Parameters + ---------- + base_log_dir : os.PathLike or str + Base directory for log files. + run_prefix : str + Prefix for run directory name. + run_suffix : str, optional + Suffix for run directory name, by default "" + log_images_every : int, optional + Frequency for logging images, by default 10 + """ self._timestamp = datetime.datetime.now().strftime( "%Y%m%d_%H%M%S" ) # This should never be reinstantiated. @@ -49,13 +62,33 @@ def log_figure(self, tag: str, fig: Figure, step: int) -> None: self.writer.add_figure(tag, fig, step) def log_histogram(self, tag: str, values: NDArray | Tensor, step: int) -> None: - """Log histogram of values for monitoring distributions.""" + """Log histogram of values for monitoring distributions. + + Parameters + ---------- + tag : str + Tag for the histogram. + values : NDArray or Tensor + Values to create histogram from. + step : int + Step number. + """ if isinstance(values, Tensor): values = values.detach().cpu().numpy() self.writer.add_histogram(tag, values, step) def log_text(self, tag: str, text: str, step: int) -> None: - """Log text for configuration, hyperparameters, or notes.""" + """Log text for configuration, hyperparameters, or notes. + + Parameters + ---------- + tag : str + Tag for the text. + text : str + Text to log. + step : int + Step number. + """ self.writer.add_text(tag, text, step) def flush(self) -> None: @@ -66,6 +99,7 @@ def close(self) -> None: self.writer.close() def new_timestamp(self) -> None: + """Create new timestamp and reinitialize writer with new log directory.""" self.close() self._timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") name = self.run_prefix + "_" + self._timestamp @@ -77,6 +111,13 @@ def new_timestamp(self) -> None: self.writer = SummaryWriter(str(self.log_dir)) def clone(self) -> Self: + """Create a cloned logger with new timestamp. + + Returns + ------- + Self + Cloned logger instance. + """ try: cloned: Self = copy.deepcopy(self) except Exception: @@ -155,8 +196,19 @@ def log_images_every(self, value: int) -> None: @staticmethod def apply_colormap(tensor_2d: Tensor | NDArray, cmap_name: str = "turbo") -> NDArray: - """ - Apply colormap to a 2D tensor and return a [3, H, W] NumPy float32 array in [0, 1]. + """Apply colormap to a 2D tensor and return a [3, H, W] NumPy float32 array in [0, 1]. + + Parameters + ---------- + tensor_2d : Tensor or NDArray + 2D tensor to apply colormap to. + cmap_name : str, optional + Colormap name, by default "turbo" + + Returns + ------- + NDArray + Colored array with shape [3, H, W] and dtype float32 in range [0, 1]. """ if isinstance(tensor_2d, Tensor): tensor_2d = tensor_2d.detach().cpu().numpy() diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index daf96919..272115ea 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -10,6 +10,25 @@ def get_loss_function(name: str | Callable, dtype: torch.dtype) -> Callable: + """Get a loss function by name or return callable if provided. + + Parameters + ---------- + name : str or Callable + Loss function name or callable function. + dtype : torch.dtype + Data type (used to determine complex vs real loss functions). + + Returns + ------- + Callable + Loss function. + + Raises + ------ + ValueError + If loss function name is unknown for the given dtype. + """ if isinstance(name, Callable): return name else: @@ -35,12 +54,40 @@ def get_loss_function(name: str | Callable, dtype: torch.dtype) -> Callable: 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 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. + + Returns + ------- + torch.Tensor + L2 loss value. + """ real_dif = pred.real - target.real imag_dif = pred.imag - target.imag loss = torch.mean(real_dif**2 + imag_dif**2) @@ -48,6 +95,20 @@ def complex_cartesian_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tens 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). + """ 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 @@ -56,11 +117,24 @@ def amp_phase_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: def combined_l2(pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.7) -> torch.Tensor: - """ - alpha * amp_phase_l2 + (1 - alpha) * complex_l2 - so larger alpha -> more weight on amp/phase and smaller alpha -> more weight on real/imag + """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. - funnily enough alpha = 0.7 is stable, 0.8 isnt and 0.6 + different alpha values can affect stability of training. """ comp_l2 = complex_l2(pred, target) amp_ph_l2 = amp_phase_l2(pred, target) diff --git a/src/quantem/core/ml/siren.py b/src/quantem/core/ml/siren.py deleted file mode 100644 index 65548e9d..00000000 --- a/src/quantem/core/ml/siren.py +++ /dev/null @@ -1,232 +0,0 @@ -from collections import OrderedDict -from typing import TYPE_CHECKING - -import numpy as np -import torch -import torch.nn as nn - -if TYPE_CHECKING: - import torch - - -class SineLayer(nn.Module): - # See paper sec. 3.2, final paragraph, and supplement Sec. 1.5 for discussion of omega_0. - - # If is_first=True, omega_0 is a frequency factor which simply multiplies the activations before the - # nonlinearity. Different signals may require different omega_0 in the first layer - this is a - # hyperparameter. - - # If is_first=False, then the weights will be divided by omega_0 so as to keep the magnitude of - # activations constant, but boost gradients to the weight matrix (see supplement Sec. 1.5) - - def __init__(self, in_features, out_features, bias=True, is_first=False, omega_0=30.0): - super().__init__() - self.omega_0 = omega_0 - self.is_first = is_first - - self.in_features = in_features - self.linear = nn.Linear(in_features, out_features, bias=bias) - - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - if self.is_first: - self.linear.weight.uniform_(-1 / self.in_features, 1 / self.in_features) - else: - self.linear.weight.uniform_( - -np.sqrt(6 / self.in_features) / self.omega_0, - np.sqrt(6 / self.in_features) / self.omega_0, - ) - - def forward(self, input): - return torch.sin(self.omega_0 * self.linear(input)) - - def forward_with_intermediate(self, input): - # For visualization of activation distributions - intermediate = self.omega_0 * self.linear(input) - return torch.sin(intermediate), intermediate - - -class Siren(nn.Module): - def __init__( - self, - shape: tuple[int, int, int], # (C, H, W) shape of output image - in_features: int = 2, # number of spatial dimensions of input - hidden_features: int | None = None, - num_layers: int = 3, - outermost_linear: bool = False, - hidden_omega_0: float = 30.0, - first_omega_0: float | None = None, - ): - super().__init__() - - if len(shape) != 3: - raise ValueError(f"shape should be of len 3, (C, H, W), got shape: {shape}") - elif shape[1] != shape[2]: - raise NotImplementedError - self.outshape = shape - - self.in_features = in_features - if hidden_features is None: - hidden_features = self.outshape[1] * self.outshape[2] - self.hidden_features = hidden_features - self.num_layers = num_layers - self.outermost_linear = outermost_linear - self.hidden_omega_0 = hidden_omega_0 - if first_omega_0 is None: - first_omega_0 = hidden_omega_0 - self.first_omega_0 = first_omega_0 - - self._build() - - def _build(self): - layers = [] - layers.append( - SineLayer( - self.in_features, self.hidden_features, is_first=True, omega_0=self.first_omega_0 - ) - ) - - for i in range(self.num_layers): - layers.append( - SineLayer( - self.hidden_features, - self.hidden_features, - is_first=False, - omega_0=self.hidden_omega_0, - ) - ) - - if self.outermost_linear: - final_linear = nn.Linear(self.hidden_features, self.outshape[0]) - - with torch.no_grad(): - final_linear.weight.uniform_( - -np.sqrt(6 / self.hidden_features) / self.hidden_omega_0, - np.sqrt(6 / self.hidden_features) / self.hidden_omega_0, - ) - - layers.append(final_linear) - else: - layers.append( - SineLayer( - self.hidden_features, - self.outshape[0], - is_first=False, - omega_0=self.hidden_omega_0, - ) - ) - - self.net = nn.Sequential(*layers) - - def forward(self, coords): - coords = ( - coords.clone().detach().requires_grad_(True) - ) # allows to take derivative w.r.t. input - output = self.net(coords) - return output, coords - - def forward_with_activations(self, coords, retain_grad=False): - """Returns not only model output, but also intermediate activations. - Only used for visualizing activations later!""" - activations = OrderedDict() - - activation_count = 0 - x = coords.clone().detach().requires_grad_(True) - activations["input"] = x - for i, layer in enumerate(self.net): - if isinstance(layer, SineLayer): - x, intermed = layer.forward_with_intermediate(x) - - if retain_grad: - x.retain_grad() - intermed.retain_grad() - - activations["_".join((str(layer.__class__), "%d" % activation_count))] = intermed - activation_count += 1 - else: - x = layer(x) - - if retain_grad: - x.retain_grad() - - activations["_".join((str(layer.__class__), "%d" % activation_count))] = x - activation_count += 1 - - return activations - - @staticmethod - def generate_coords(sidelen, dim=2): - """Generates a flattened grid of (x,y,...) coordinates in a range of -1 to 1. - sidelen: int - dim: int - - for now square only - """ - tensors = tuple(dim * [torch.linspace(-1, 1, steps=sidelen)]) - mgrid = torch.stack(torch.meshgrid(*tensors), dim=-1) - mgrid = mgrid.reshape(-1, dim) - return mgrid - - -# class Siren(nn.Module): -# def __init__(self, in_features, hidden_features, hidden_layers, out_features, outermost_linear=False, -# first_omega_0=30, hidden_omega_0=30.): -# super().__init__() - -# layers = [] -# layers.append(SineLayer(in_features, hidden_features, -# is_first=True, omega_0=first_omega_0)) - -# for i in range(hidden_layers): -# layers.append(SineLayer(hidden_features, hidden_features, -# is_first=False, omega_0=hidden_omega_0)) - -# if outermost_linear: -# final_linear = nn.Linear(hidden_features, out_features) - -# with torch.no_grad(): -# final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, -# np.sqrt(6 / hidden_features) / hidden_omega_0) - -# layers.append(final_linear) -# else: -# layers.append(SineLayer(hidden_features, out_features, -# is_first=False, omega_0=hidden_omega_0)) - -# self.net = nn.Sequential(*layers) - -# def forward(self, coords): -# coords = coords.clone().detach().requires_grad_(True) # allows to take derivative w.r.t. input -# output = self.net(coords) -# return output, coords - -# def forward_with_activations(self, coords, retain_grad=False): -# '''Returns not only model output, but also intermediate activations. -# Only used for visualizing activations later!''' -# activations = OrderedDict() - -# activation_count = 0 -# x = coords.clone().detach().requires_grad_(True) -# activations['input'] = x -# for i, layer in enumerate(self.net): -# if isinstance(layer, SineLayer): -# x, intermed = layer.forward_with_intermediate(x) - -# if retain_grad: -# x.retain_grad() -# intermed.retain_grad() - -# activations['_'.join((str(layer.__class__), "%d" % activation_count))] = intermed -# activation_count += 1 -# else: -# x = layer(x) - -# if retain_grad: -# x.retain_grad() - -# activations['_'.join((str(layer.__class__), "%d" % activation_count))] = x -# activation_count += 1 - -# return activations diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index ba5fc3e9..242f274d 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -432,14 +432,14 @@ def bilinear_kde( f_img = np.fft.fft2(image) fx = np.fft.fftfreq(rows) fy = np.fft.fftfreq(cols) - f_img /= np.sinc(fx)[:, None] # type: ignore - f_img /= np.sinc(fy)[None, :] # type: ignore + f_img /= np.sinc(fx)[:, None] + f_img /= np.sinc(fy)[None, :] image = np.real(np.fft.ifft2(f_img)) if return_pix_count: f_img = np.fft.fft2(pix_count) - f_img /= np.sinc(fx)[:, None] # type: ignore - f_img /= np.sinc(fy)[None, :] # type: ignore + f_img /= np.sinc(fx)[:, None] + f_img /= np.sinc(fy)[None, :] pix_count = np.real(np.fft.ifft2(f_img)) if return_pix_count: @@ -1114,3 +1114,120 @@ def rotate_image( for i in range(n): out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) return out_flat.reshape(*prefix, H, W) + + +def radially_project_fourier_tensor( + corner_centered_array: torch.Tensor, + sampling: Tuple[float, float], + q_bins: torch.Tensor | None = None, +): + """ + Radially project a corner-centered Fourier array onto radial bins. + + Supports: + - single array: (kx, ky) + - batched arrays: (n, kx, ky) + - implicit bins (from grid) or explicit external bins + + Parameters + ---------- + corner_centered_array : torch.Tensor + Shape (kx, ky) or (n, kx, ky) + sampling : (float, float) + Real-space sampling (sx, sy) + q_bins : torch.Tensor, optional + 1D tensor of radial bin centers + + Returns + ------- + q_bins_out : torch.Tensor + Radial bin centers + array_1d : torch.Tensor + Shape (n, nq) or (nq,) + """ + + if corner_centered_array.is_complex(): + q, real_part = radially_project_fourier_tensor( + corner_centered_array.real, sampling, q_bins + ) + # zero by symmetry + # _, imag_part = radially_project_fourier_tensor( + # corner_centered_array.imag, sampling, q_bins + # ) + return q, real_part # + 1j * imag_part + + device = corner_centered_array.device + sx, sy = sampling + + # --- normalize shape to (batch, kx, ky) + if corner_centered_array.ndim == 2: + array = corner_centered_array[None, ...] + squeeze_output = True + elif corner_centered_array.ndim == 3: + array = corner_centered_array + squeeze_output = False + else: + raise ValueError("Input must be 2D or 3D tensor") + + n_batch, nkx, nky = array.shape + + # --- build k-grid + kx = torch.fft.fftfreq(nkx, d=sx, device=device) + ky = torch.fft.fftfreq(nky, d=sy, device=device) + k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) + + # --- determine radial bins + k_max = min(0.5 / sx, 0.5 / sy) + + if q_bins is None: + dk = kx[1] - kx[0] + num_bins = int(torch.floor(k_max / dk).item()) + 1 + dq = dk + q_bins_out = torch.arange(num_bins, device=device, dtype=k.dtype) * dk + else: + q_bins = q_bins.to(device) + dq = q_bins[1] - q_bins[0] + q_bins_out = q_bins[q_bins <= k_max] + num_bins = q_bins_out.numel() + + # ---- INTERNAL padding (key change) + num_bins_internal = num_bins + 2 + + # --- map k -> bin indices (NO CLIPPING) + inds = k / dq + inds_f = torch.floor(inds).long() + inds_f = torch.clamp(inds_f, 0, num_bins_internal - 2) + + d_ind = inds - inds_f + w0 = 1.0 - d_ind + w1 = d_ind + + # --- flatten spatial dims + array_f = array.reshape(n_batch, -1) + + # --- accumulate per batch + out = [] + for b in range(n_batch): + a = array_f[b] + + num = torch.bincount(inds_f, weights=a * w0, minlength=num_bins_internal) + torch.bincount( + inds_f + 1, weights=a * w1, minlength=num_bins_internal + ) + + den = ( + torch.bincount(inds_f, weights=w0, minlength=num_bins_internal) + + torch.bincount(inds_f + 1, weights=w1, minlength=num_bins_internal) + ).clamp_min(1) + + out.append(num / den) + + array_1d = torch.stack(out, dim=0) + + # ---- truncate to physical bins (key change) + array_1d = array_1d[..., :num_bins] + q_bins_out = q_bins_out[:num_bins] + + if squeeze_output: + array_1d = array_1d[0] + + return q_bins_out, array_1d diff --git a/src/quantem/diffractive_imaging/__init__.py b/src/quantem/diffractive_imaging/__init__.py index 95b990ae..2c26de60 100644 --- a/src/quantem/diffractive_imaging/__init__.py +++ b/src/quantem/diffractive_imaging/__init__.py @@ -28,7 +28,3 @@ from quantem.diffractive_imaging.origin_models import ( CenterOfMassOriginModel as CenterOfMassOriginModel, ) - -from quantem.diffractive_imaging.streaming_parallax import ( - StreamingParallax as StreamingParallax, -) diff --git a/src/quantem/diffractive_imaging/complex_probe.py b/src/quantem/diffractive_imaging/complex_probe.py index 06c9967c..8bf742c3 100644 --- a/src/quantem/diffractive_imaging/complex_probe.py +++ b/src/quantem/diffractive_imaging/complex_probe.py @@ -383,9 +383,14 @@ def _passively_rotate_grid( rotation_angle: float, ): """ """ + cos_a = math.cos(-rotation_angle) sin_a = math.sin(-rotation_angle) - kxa, kya = kxa * cos_a + kya * sin_a, -kxa * sin_a + kya * cos_a + kxa, kya = ( + kxa * cos_a + kya * sin_a, + -kxa * sin_a + kya * cos_a, + ) + return kxa, kya diff --git a/src/quantem/diffractive_imaging/direct_ptychography.py b/src/quantem/diffractive_imaging/direct_ptychography.py index 59984e2e..ecbeece0 100644 --- a/src/quantem/diffractive_imaging/direct_ptychography.py +++ b/src/quantem/diffractive_imaging/direct_ptychography.py @@ -1114,7 +1114,7 @@ def _return_lateral_shifts( dx, dy = aberration_surface_cartesian_gradients( k * self.wavelength, phi, - aberration_coefs=aberration_coefs, # ty:ignore[invalid-argument-type] + aberration_coefs=aberration_coefs, ) grad_k = torch.stack((dx[bf_mask], dy[bf_mask]), -1) lateral_shifts = grad_k / 2 / np.pi diff --git a/src/quantem/diffractive_imaging/streaming_parallax.py b/src/quantem/diffractive_imaging/streaming_parallax.py deleted file mode 100644 index ff48dcb9..00000000 --- a/src/quantem/diffractive_imaging/streaming_parallax.py +++ /dev/null @@ -1,531 +0,0 @@ -import math -from typing import TYPE_CHECKING, Tuple - -from quantem.core import config -from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.utils import electron_wavelength_angstrom -from quantem.core.utils.validators import ( - validate_aberration_coefficients, - validate_tensor, -) -from quantem.diffractive_imaging.complex_probe import ( - aberration_surface, - aberration_surface_cartesian_gradients, - polar_coordinates, - spatial_frequencies, -) - -if TYPE_CHECKING: - import torch -else: - if config.get("has_torch"): - import torch - - -class StreamingParallax(AutoSerialize): - _token = object() - - def __init__( - self, - gpts: Tuple[int, int], - reciprocal_sampling: Tuple[float, float], - scan_gpts: Tuple[int, int], - scan_sampling: Tuple[float, float], - wavelength: float, - bf_mask: torch.Tensor, - aberration_coefs: dict | None = None, - rotation_angle: float | None = None, - upsampling_factor: int = 1, - enable_phase_flipping: bool = True, - device: str | None = None, - _token: object | None = None, - ): - """ """ - if _token is not self._token: - raise RuntimeError( - "Use StreamingParallax.from_parameters() to instantiate this class." - ) - - self.reciprocal_sampling = reciprocal_sampling - self.gpts = gpts - self.scan_gpts = scan_gpts - self.scan_sampling = scan_sampling - self.wavelength = wavelength - self.bf_mask = bf_mask - self.aberration_coefs = aberration_coefs or {} - self.rotation_angle = rotation_angle - self.upsampling_factor = upsampling_factor - self.enable_phase_flipping = enable_phase_flipping - self.device = device - - self._preprocess() - - @classmethod - def from_parameters( - cls, - gpts: Tuple[int, int], - scan_gpts: Tuple[int, int], - scan_sampling: Tuple[float, float], - energy: float, - bf_mask: torch.Tensor, - reciprocal_sampling: Tuple[float, float] | None = None, - angular_sampling: Tuple[float, float] | None = None, - aberration_coefs: dict | None = None, - rotation_angle: float | None = None, - upsampling_factor: int = 1, - enable_phase_flipping: bool = True, - device: str | None = None, - ): - wavelength = electron_wavelength_angstrom(energy) - if angular_sampling is not None: - if reciprocal_sampling is not None: - raise ValueError( - "Only one of reciprocal_sampling / angular_sampling can be specified" - ) - reciprocal_sampling = tuple(a / wavelength / 1e3 for a in angular_sampling) - - return cls( - gpts, - reciprocal_sampling, - scan_gpts, - scan_sampling, - wavelength, - bf_mask, - aberration_coefs=aberration_coefs, - rotation_angle=rotation_angle, - upsampling_factor=upsampling_factor, - enable_phase_flipping=enable_phase_flipping, - device=device, - _token=cls._token, - ) - - @property - def sampling(self): - return tuple(1 / s / n for n, s in zip(self.reciprocal_sampling, self.gpts)) - - @property - def upsampled_gpts(self): - return tuple(g * self.upsampling_factor for g in self.scan_gpts) - - @property - def upsampled_sampling(self): - return tuple(s / self.upsampling_factor for s in self.scan_sampling) - - @property - def aberration_coefs(self) -> dict: - return self._aberration_coefs - - @aberration_coefs.setter - def aberration_coefs(self, value: dict): - value = validate_aberration_coefficients(value) - self._aberration_coefs = value - - @property - def bf_mask(self) -> torch.Tensor: - return self._bf_mask - - @bf_mask.setter - def bf_mask(self, value: torch.Tensor): - self._bf_mask = validate_tensor(value, "bf_mask", dtype=torch.bool).to(device=self.device) - - @property - def device(self) -> str: - """This should be of form 'cuda:X' or 'cpu', as defined by quantem.config""" - if hasattr(self, "_device"): - return self._device - else: - return config.get("device") - - @device.setter - def device(self, device: str | int | None): - # allow setting gpu/cpu, but not changing the device from the config gpu device - if device is not None: - dev, _id = config.validate_device(device) - self._device = dev - - def _return_upsampled_qgrid( - self, - ): - qxa, qya = spatial_frequencies( - self.upsampled_gpts, - self.upsampled_sampling, - device=self.device, - ) - - return qxa, qya - - def _return_rotated_kgrid( - self, - ): - kxa, kya = spatial_frequencies( - self.gpts, - self.sampling, - rotation_angle=self.rotation_angle, - device=self.device, - ) - - return kxa, kya - - def _return_scan_positions( - self, - ): - upsampled_gpts = self.upsampled_gpts - upsampling_factor = self.upsampling_factor - r_up = torch.dstack( - torch.meshgrid( - torch.arange(0, upsampled_gpts[0], upsampling_factor), - torch.arange(0, upsampled_gpts[1], upsampling_factor), - indexing="ij", - ), - ).reshape((-1, 2)) - - return r_up - - def _prepare_phase_flipping(self, H, s_m_up): - device = self.device - Ny, Nx = self.upsampled_gpts - - h, w = H.shape - M = s_m_up.shape[0] - L0 = h * w - - # kernel grid - dy = torch.arange(h, device=device) - dx = torch.arange(w, device=device) - dy_grid = dy.repeat_interleave(w) - dx_grid = dx.repeat(h) - - dy_rep = dy_grid.repeat(M) - dx_rep = dx_grid.repeat(M) - - s_my = s_m_up[:, 0].repeat_interleave(L0) - s_mx = s_m_up[:, 1].repeat_interleave(L0) - - offsets = (dy_rep + s_my) * Nx + (dx_rep + s_mx) - offsets = offsets.long() - - unique_offsets, inv = torch.unique(offsets, sorted=True, return_inverse=True) - U = unique_offsets.numel() - - # build grouped kernel - H_flat = H.reshape(-1) - H_all = H_flat.repeat(M) - m_idx = torch.arange(M, device=device).repeat_interleave(L0) - - K = torch.zeros((U, M), dtype=H.dtype, device=device) - K.index_put_( - (inv, m_idx), - H_all, - accumulate=True, - ) - - return unique_offsets, K - - def _preprocess(self): - # ---- BF indices ---- - self.inds_i, self.inds_j = torch.where(self.bf_mask) - ny, nx = self.gpts - self.bf_flat_inds = self.inds_i * nx + self.inds_j - self._bf_lut = torch.full( - (ny * nx,), - -1, - dtype=torch.int64, - device=self.device, - ) - self._bf_lut[self.bf_flat_inds] = torch.arange(len(self.bf_flat_inds), device=self.device) - - # ---- parallax shifts ---- - kxa, kya = self._return_rotated_kgrid() - k, phi = polar_coordinates(kxa, kya) - dx, dy = aberration_surface_cartesian_gradients( - k * self.wavelength, - phi, - aberration_coefs=self.aberration_coefs, - ) - grad_k = torch.stack( - ( - dx[self.inds_i, self.inds_j], - dy[self.inds_i, self.inds_j], - ), - -1, - ) - - upsampled_sampling = torch.tensor(self.upsampled_sampling) - self.s_m_up = torch.round(grad_k / 2 / math.pi / upsampled_sampling).int() - - # ---- phase flipping prep ---- - if self.enable_phase_flipping: - qxa, qya = self._return_upsampled_qgrid() - q, theta = polar_coordinates(qxa, qya) - chi_q = aberration_surface( - q * self.wavelength, - theta, - self.wavelength, - aberration_coefs=self.aberration_coefs, - ) - sign_sin_chi_q = torch.sign(torch.sin(chi_q)) - - nx, ny = sign_sin_chi_q.shape - sign_sin_chi_q[nx // 2, :] = 0.0 - sign_sin_chi_q[:, ny // 2] = 0.0 - H = torch.fft.ifft2(sign_sin_chi_q).real.contiguous() - - self.unique_offsets, self.K = self._prepare_phase_flipping(H, self.s_m_up) - - self._preprocessed = True - return self - - def reconstruct_parallax(self, psi, I_batch, r_batch): - """ - psi : (Ny, Nx) accumulator - I_batch : (B, ny, nx) - r_batch : (B, 2) upsampled scan positions (y, x) - """ - - if not self._preprocessed: - raise RuntimeError("Call preprocess() first.") - - Ny, Nx = self.upsampled_gpts - I_bf = torch.fft.ifftshift(I_batch.to(torch.float32).to(self.device), dim=(-1, -2))[ - ..., self.inds_i, self.inds_j - ] # (B, M) - - I_bf -= I_bf.mean((-1, -2), keepdim=True) - - xs = (r_batch[:, None, 0] + self.s_m_up[None, :, 0]) % Ny - ys = (r_batch[:, None, 1] + self.s_m_up[None, :, 1]) % Nx - - idx = (xs * Nx + ys).reshape(-1) - vals = I_bf.reshape(-1) - - psi.view(-1).scatter_add_(0, idx, vals) - - return self - - def reconstruct_phase_flipping(self, psi_flipped, I_batch, r_batch): - """ - psi_flipped : (Ny, Nx) accumulator - I_batch : (B, ny, nx) - r_batch : (B, 2) - """ - if not self.enable_phase_flipping: - raise RuntimeError("Phase flipping not enabled.") - - if not self._preprocessed: - raise RuntimeError("Call preprocess() first.") - - Ny, Nx = self.upsampled_gpts - I_bf = torch.fft.ifftshift(I_batch.to(torch.float32).to(self.device), dim=(-1, -2))[ - ..., self.inds_i, self.inds_j - ] # (B, M) - - I_bf -= I_bf.mean((-1, -2), keepdim=True) - - # vals[b, u] = sum_m K[u, m] * I_bf[b, m] - vals = I_bf @ self.K.T # (B, U) - - r_off = r_batch[:, 0] * Nx + r_batch[:, 1] - idx = (r_off[:, None] + self.unique_offsets[None, :]) % (Ny * Nx) - - psi_flipped.view(-1).scatter_add_( - 0, - idx.reshape(-1), - vals.reshape(-1).to(psi_flipped.dtype), - ) - - return self - - def reconstruct(self, psi, psi_flipped, I_batch, r_batch): - """ - psi : (Ny, Nx) prlx accumulator - psi_flipped : (Ny, Nx) phase-flipped accumulator - I_batch : (B, ny, nx) - r_batch : (B, 2) - """ - self.reconstruct_parallax(psi, I_batch, r_batch) - if self.enable_phase_flipping: - self.reconstruct_phase_flipping(psi_flipped, I_batch, r_batch) - return self - - def reconstruct_parallax_sparse( - self, - psi, - streamed_hits, # list[list[int]] or list[Tensor] - r_batch, # (B, 2) - ): - if not self._preprocessed: - raise RuntimeError("Call preprocess() first.") - - device = self.device - Ny, Nx = self.upsampled_gpts - ny, nx = self.bf_mask.shape - M = self.s_m_up.shape[0] - - # ---- concatenate hits ---- - lengths = torch.tensor( - [len(h) for h in streamed_hits], - device=device, - dtype=torch.float32, - ) - if lengths.sum() == 0: - return self - - hits = torch.cat([torch.as_tensor(h, device=device) for h in streamed_hits]) - - frame_idx = torch.repeat_interleave( - torch.arange(len(streamed_hits), device=device), - lengths.to(torch.int64), - ) - - # ---- ifftshift detector coordinates ---- - y = hits // nx - x = hits % nx - y = (y + ny // 2) % ny - x = (x + nx // 2) % nx - hits_shifted = y * nx + x - - # ---- BF lookup ---- - m = self._bf_lut[hits_shifted] - valid = m >= 0 - if not valid.any(): - return self - - m = m[valid] - frame_idx = frame_idx[valid] - - r = r_batch[frame_idx] - s = self.s_m_up[m] - - ys = (r[:, 0] + s[:, 0]) % Ny - xs = (r[:, 1] + s[:, 1]) % Nx - idx_pos = ys * Nx + xs - - psi.view(-1).scatter_add_( - 0, - idx_pos, - torch.ones_like(idx_pos, dtype=psi.dtype), - ) - - # bg_weight_j = -N_j / M - bg_weights = -lengths / M # (B,) - - # Expand over BF pixels - r_bg = r_batch[:, None, :] # (B, 1, 2) - s_bg = self.s_m_up[None, :, :] # (1, M, 2) - - ys_bg = (r_bg[..., 0] + s_bg[..., 0]) % Ny - xs_bg = (r_bg[..., 1] + s_bg[..., 1]) % Nx - idx_bg = (ys_bg * Nx + xs_bg).reshape(-1) - - vals_bg = bg_weights[:, None].expand(-1, M).reshape(-1) - - psi.view(-1).scatter_add_( - 0, - idx_bg, - vals_bg.to(psi.dtype), - ) - - return self - - def reconstruct_phase_flipping_sparse( - self, - psi_flipped, - streamed_hits, # list[list[int]] or list[Tensor] - r_batch, # (B, 2) - ): - if not self.enable_phase_flipping: - raise RuntimeError("Phase flipping not enabled.") - - if not self._preprocessed: - raise RuntimeError("Call preprocess() first.") - - device = self.device - Ny, Nx = self.upsampled_gpts - ny, nx = self.bf_mask.shape - M = self.s_m_up.shape[0] - - # ---- gather hits ---- - lengths = torch.tensor( - [len(h) for h in streamed_hits], - device=device, - dtype=torch.float32, - ) - if lengths.sum() == 0: - return self - - hits = torch.cat([torch.as_tensor(h, device=device) for h in streamed_hits]) - - frame_idx = torch.repeat_interleave( - torch.arange(len(streamed_hits), device=device), - lengths.to(torch.int64), - ) - - # ---- ifftshift detector coords ---- - y = hits // nx - x = hits % nx - y = (y + ny // 2) % ny - x = (x + nx // 2) % nx - hits_shifted = y * nx + x - - # ---- BF pixel lookup ---- - m = self._bf_lut[hits_shifted] - valid = m >= 0 - if not valid.any(): - return self - - m = m[valid] - frame_idx = frame_idx[valid] - - # Build sparse BF intensity matrix: I_pos[b, m] = hit count - I_pos = torch.zeros( - (len(streamed_hits), M), - device=device, - dtype=self.K.dtype, - ) - I_pos.index_put_( - (frame_idx, m), - torch.ones_like(m, dtype=I_pos.dtype), - accumulate=True, - ) - - # vals_pos[b, u] = sum_m K[u, m] * I_pos[b, m] - vals_pos = I_pos @ self.K.T # (B, U) - - r_off = r_batch[:, 0] * Nx + r_batch[:, 1] - idx_pos = (r_off[:, None] + self.unique_offsets[None, :]) % (Ny * Nx) - - psi_flipped.view(-1).scatter_add_( - 0, - idx_pos.reshape(-1), - vals_pos.reshape(-1).to(psi_flipped.dtype), - ) - - # bg_weight_j = -N_j / M - bg_weights = -lengths / M # (B,) - - # Uniform BF vector per frame - I_bg = bg_weights[:, None].expand(-1, M) - - vals_bg = I_bg @ self.K.T # (B, U) - - idx_bg = idx_pos # same indices - - psi_flipped.view(-1).scatter_add_( - 0, - idx_bg.reshape(-1), - vals_bg.reshape(-1).to(psi_flipped.dtype), - ) - - return self - - def reconstruct_sparse(self, psi, psi_flipped, hits_batch, r_batch): - """ - psi : (Ny, Nx) prlx accumulator - psi_flipped : (Ny, Nx) phase-flipped accumulator - hits_batch : list[list[int]] or list[Tensor] - r_batch : (B, 2) - """ - self.reconstruct_parallax_sparse(psi, hits_batch, r_batch) - if self.enable_phase_flipping: - self.reconstruct_phase_flipping_sparse(psi_flipped, hits_batch, r_batch) - return self diff --git a/tests/conftest.py b/tests/conftest.py index 3178a8fd..fa046f3d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,8 @@ +import matplotlib import pytest +matplotlib.use("Agg") + def pytest_addoption(parser): parser.addoption("--runslow", action="store_true", default=False, help="run slow tests") diff --git a/tests/datastructures/test_autoserialize.py b/tests/datastructures/test_autoserialize.py index 5dbc600b..877f72c9 100644 --- a/tests/datastructures/test_autoserialize.py +++ b/tests/datastructures/test_autoserialize.py @@ -85,6 +85,8 @@ def __init__(self): class TestingClass(AutoSerialize): + __test__ = False # tell pytest this is not a test class despite name starting with Test + def __init__(self): self.data = np.random.rand(104, 100) @@ -245,6 +247,8 @@ def test_torch_tensor_and_optimizer(tmp_path, store): class TestModule(torch.nn.Module, AutoSerialize): + __test__ = False + def __init__(self): super().__init__() self.linear = torch.nn.Linear(10, 5) @@ -270,6 +274,8 @@ def test_torch_nn_module_roundtrip(tmp_path, store): class TestHybrid(AutoSerialize, nn.Module): + __test__ = False + def __init__(self): nn.Module.__init__(self) super().__init__() diff --git a/tests/datastructures/test_dataset3d_show.py b/tests/datastructures/test_dataset3d_show.py new file mode 100644 index 00000000..27c18f41 --- /dev/null +++ b/tests/datastructures/test_dataset3d_show.py @@ -0,0 +1,87 @@ +"""Tests for Dataset3d.show() frame selection logic.""" + +import matplotlib.pyplot as plt +import numpy as np +import pytest + +from quantem.core.datastructures import Dataset3d + + +@pytest.fixture +def dataset_with_10_frames(): + """Create a Dataset3d with 10 frames for testing.""" + return Dataset3d.from_array(np.random.rand(10, 8, 8)) + + +@pytest.fixture +def dataset_with_100_frames(): + """Create a Dataset3d with 100 frames for testing default max behavior.""" + return Dataset3d.from_array(np.random.rand(100, 8, 8)) + + +def extract_frame_indices_from_figure(fig): + """Extract frame indices from subplot titles like 'Frame 0', 'Frame 1', etc.""" + return [ + int(ax.get_title().split()[-1]) + for ax in fig.get_axes() + if ax.get_title().startswith("Frame ") + ] + + +class TestShowInputValidation: + """Test that invalid inputs raise clear errors.""" + + @pytest.mark.parametrize("kwargs,match", [ + ({"step": 0}, "cannot be zero"), + ({"start": 100}, "out of bounds"), + ({"start": -100}, "out of bounds"), + ({"start": 5, "end": 5}, "No frames to display"), + ({"ncols": 0}, "ncols must be >= 1"), + ({"ncols": -1}, "ncols must be >= 1"), + ({"max": 0}, "max must be >= 1"), + ({"max": -1}, "max must be >= 1"), + ]) + def test_raises_value_error(self, dataset_with_10_frames, kwargs, match): + with pytest.raises(ValueError, match=match): + dataset_with_10_frames.show(**kwargs) + + +class TestShowFrameSelection: + """Test frame selection with start, end, step, max combinations.""" + + @pytest.mark.parametrize("kwargs,expected_indices", [ + ({}, list(range(20))), + ({"max": 5}, [0, 1, 2, 3, 4]), + ({"max": None}, list(range(100))), + ({"start": 90}, list(range(90, 100))), + ({"start": 95, "max": 3}, [95, 96, 97]), + ]) + def test_large_dataset(self, dataset_with_100_frames, kwargs, expected_indices): + fig, _ = dataset_with_100_frames.show(returnfig=True, **kwargs) + assert extract_frame_indices_from_figure(fig) == expected_indices + plt.close(fig) + + @pytest.mark.parametrize("kwargs,expected_indices", [ + # Default shows all frames (< max) + ({}, list(range(10))), + # Start and end + ({"start": 5}, [5, 6, 7, 8, 9]), + ({"end": 5}, [0, 1, 2, 3, 4]), + # Step + ({"step": 2}, [0, 2, 4, 6, 8]), + ({"step": 3}, [0, 3, 6, 9]), + ({"start": 2, "end": 8, "step": 2}, [2, 4, 6]), + # Negative start index + ({"start": -1, "max": 1}, [9]), + ({"start": -3, "max": 2}, [7, 8]), + # Negative step (reverse order) + ({"start": 9, "step": -1}, [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]), + ({"start": 9, "end": 4, "step": -1}, [9, 8, 7, 6, 5]), + ({"start": 9, "step": -2}, [9, 7, 5, 3, 1]), + ({"start": 9, "step": -1, "max": 3}, [9, 8, 7]), + ]) + def test_small_dataset(self, dataset_with_10_frames, kwargs, expected_indices): + fig, _ = dataset_with_10_frames.show(returnfig=True, **kwargs) + assert extract_frame_indices_from_figure(fig) == expected_indices + plt.close(fig) + diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 00000000..975a0613 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,49 @@ +import pytest +import torch + +from quantem.core import config + + +def test_config_set_device(): + config.set_device("cpu") + assert config.get_device() == "cpu" + if torch.cuda.is_available(): + config.set_device("gpu") + assert config.get_device() == "cuda:0" + config.set_device("GPU") + assert config.get_device() == "cuda:0" + config.set_device(0) + assert config.get_device() == "cuda:0" + NUM_DEVICES = torch.cuda.device_count() + if NUM_DEVICES > 0: + config.set_device(NUM_DEVICES - 1) + assert config.get_device() == f"cuda:{NUM_DEVICES - 1}" + config.refresh() + assert config.get_device() == "cpu" + else: + with pytest.raises(RuntimeError): + config.set_device("cuda:0") + + if torch.mps.is_available(): + config.set_device("mps") + assert config.get_device() == "mps" + config.set_device("gpu") + assert config.get_device() == "mps" + config.set_device("GPU") + assert config.get_device() == "mps" + else: + 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