API Reference¶
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class spotiflow.model.spotiflow.Spotiflow(config: SpotiflowModelConfig | None =
None
)¶ Supervised spot detector using a multi-stage neural network as a backbone for feature extraction followed by resolution-dependent post-processing modules to allow loss computation and optimization at different resolution levels.
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fit(train_images: Sequence[ndarray], train_spots: Sequence[ndarray], val_images: Sequence[ndarray], val_spots: Sequence[ndarray], augment_train: bool | Pipeline =
True
, save_dir: str | None =None
, train_config: dict | SpotiflowTrainingConfig | None =None
, device: 'auto' | 'cpu' | 'cuda' | 'mps' ='auto'
, logger: 'none' | 'tensorboard' | 'wandb' ='tensorboard'
, number_of_devices: int | None =1
, num_workers: int | None =0
, callbacks: Sequence[Callback] | None =None
, deterministic: bool | None =True
, benchmark: bool | None =False
, **dataset_kwargs)¶ Train a Spotiflow model.
- Parameters:¶
- train_images : Sequence[np.ndarray]¶
training images
- train_spots : Sequence[np.ndarray]¶
training spots
- val_images : Sequence[np.ndarray]¶
validation images
- val_spots : Sequence[np.ndarray]¶
validation spots
- augment_train : Union[bool, AugmentationPipeline]¶
whether to augment the training data. Defaults to True.
- save_dir : Optional[str], optional¶
directory to save the model to. Must be given if no checkpoint logger is given as a callback. Defaults to None.
- train_config : Optional[SpotiflowTrainingConfig], optional¶
training config. If not given, will use the default config. Defaults to None.
- device : Literal["cpu", "cuda", "mps"], optional¶
computing device to use. Can be “cpu”, “cuda”, “mps”. Defaults to “cpu”.
- logger : Optional[pl.loggers.Logger], optional¶
logger to use. Defaults to “tensorboard”.
- number_of_devices : Optional[int], optional¶
number of accelerating devices to use. Only applicable to “cuda” acceleration. Defaults to 1.
- num_workers : Optional[int], optional¶
number of workers to use for data loading. Defaults to 0 (main process only).
- callbacks : Optional[Sequence[pl.callbacks.Callback]], optional¶
callbacks to use during training. Defaults to no callbacks.
- deterministic : Optional[bool], optional¶
whether to use deterministic training. Set to True for deterministic behaviour at a cost of performance. Defaults to True.
- benchmark : Optional[bool], optional¶
whether to use benchmarking. Set to False for deterministic behaviour at a cost of performance. Defaults to False.
- **dataset_kwargs¶
additional arguments to pass to the SpotsDataset class. Defaults to no additional arguments.
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classmethod from_folder(pretrained_path: str, inference_mode=
True
, which: str ='best'
, map_location: 'auto' | 'cpu' | 'cuda' | 'mps' ='auto'
, verbose: bool =False
) Self ¶ Load a pretrained model.
- Parameters:¶
- pretrained_path : str¶
path to the model folder
- inference_mode : bool, optional¶
whether to set the model in eval mode. Defaults to True.
- which : str, optional¶
which checkpoint to load. Defaults to “best”.
- map_location : str, optional¶
device string to load the model to. Defaults to ‘auto’ (hardware-based).
- Returns:¶
loaded model
- Return type:¶
Self
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classmethod from_pretrained(pretrained_name: str, inference_mode: bool =
True
, which: str ='best'
, map_location: 'auto' | 'cpu' | 'cuda' | 'mps' ='auto'
, cache_dir: Path | str | None =None
, verbose: bool =True
, **kwargs) Self ¶ Load a pretrained model with given name
- Parameters:¶
- pretrained_name : str¶
name of the pretrained model to be loaded
- inference_mode : bool, optional¶
whether to set the model in eval mode. Defaults to True.
- which : str, optional¶
which checkpoint to load. Defaults to “best”.
- map_location : str, optional¶
device string to load the model to. Defaults to ‘auto’ (hardware-based).
- cache_dir : Optional[Union[Path, str]], optional¶
directory to cache the model. Defaults to None. If None, will use the default cache directory (given by the env var SPOTIFLOW_CACHE_DIR if set, otherwise ~/.spotiflow).
- Returns:¶
loaded model
- Return type:¶
Self
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load(path: str, which: 'best' | 'last' =
'best'
, inference_mode: bool =True
, map_location: str ='cuda'
) None ¶ Load a model from disk.
- Parameters:¶
- path : str¶
folder to load the model from
- which : Literal['best', 'last'], optional¶
which checkpoint to load. Defaults to “best”.
- inference_mode : bool, optional¶
whether to set the model in eval mode. Defaults to True.
- map_location : str, optional¶
device string to load the model to. Defaults to ‘cuda’.
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optimize_threshold(val_ds: Dataset, cutoff_distance: int =
3
, min_distance: int =1
, exclude_border: bool =False
, threshold_range: tuple[float, float] =(0.3, 0.7)
, niter: int =11
, batch_size: int =1
, device: device | 'auto' | 'cpu' | 'cuda' | 'mps' | None =None
, subpix: bool | None =None
) None ¶ - Optimize the probability threshold on an annotated dataset.
The metric used to optimize is the F1 score.
- Parameters:¶
- val_ds : torch.utils.data.Dataset¶
dataset to optimize on
- cutoff_distance : int, optional¶
distance tolerance considered for points matching. Defaults to 3.
- min_distance : int, optional¶
Minimum distance between spots for NMS. Defaults to 1.. Defaults to 2.
- exclude_border : bool, optional¶
Whether to exclude spots at the border. Defaults to False.
- threshold_range : Tuple[float, float], optional¶
Range of thresholds to consider. Defaults to (.3, .7).
- niter : int, optional¶
number of iterations for both coarse- and fine-grained search. Defaults to 11.
- batch_size : int, optional¶
batch size to use. Defaults to 2.
- device : Optional[Union[torch.device, Literal["auto", "cpu", "cuda", "mps"]]], optional¶
computing device to use. If None, will infer from model location. If “auto”, will infer from available hardware. Defaults to None.
- subpix : Optional[bool], optional¶
whether to use the stereographic flow to compute subpixel localization. If None, will deduce from the model configuration. Defaults to None.
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predict(img: ndarray | Array, prob_thresh: float | None =
None
, n_tiles: tuple[int] =None
, max_tile_size: int =None
, min_distance: int =1
, exclude_border: bool =False
, scale: int | None =None
, subpix: bool | int | None =None
, peak_mode: 'skimage' | 'fast' ='fast'
, normalizer: Callable | 'auto' | None ='auto'
, verbose: bool =True
, progress_bar_wrapper: Callable | None =None
, device: device | 'auto' | 'cpu' | 'cuda' | 'mps' | None =None
, distributed_params: dict | None =None
) tuple[ndarray, SimpleNamespace] ¶ Predict spots in an image.
- Parameters:¶
- img : Union[np.ndarray, da.Array]¶
input image
- prob_thresh : Optional[float], optional¶
Probability threshold for peak detection. If None, will load the optimal one. Defaults to None.
- n_tiles : Tuple[int, int], optional¶
Number of tiles to split the image into. Defaults to (1,1).
- min_distance : int, optional¶
Minimum distance between spots for NMS. Defaults to 1.
- exclude_border : bool, optional¶
Whether to exclude spots at the border. Defaults to False.
- scale : Optional[int], optional¶
Scale factor to apply to the image. Defaults to None.
- subpix : bool, optional¶
Whether to use the stereographic flow to compute subpixel localization. If None, will deduce from the model configuration. Defaults to None.
- peak_mode : str, optional¶
Peak detection mode (can be either “skimage” or “fast”, which is a faster custom C++ implementation). Defaults to “fast”.
- normalizer : Optional[Union[Literal["auto"], callable]], optional¶
Normalizer to use. If None, will use the default normalizer. Defaults to “auto” (percentile-based normalization with p_min=1, p_max=99.8).
- verbose : bool, optional¶
Whether to print logs and progress. Defaults to True.
- progress_bar_wrapper : Optional[callable], optional¶
Progress bar wrapper to use. Defaults to None.
- device : Optional[Union[torch.device, Literal["auto", "cpu", "cuda", "mps"]]], optional¶
computing device to use. If None, will infer from model location. If “auto”, will infer from available hardware. Defaults to None.
- Returns:¶
Tuple of (points, details). Points are the coordinates of the spots. Details is a namespace containing the spot-wise probabilities (prob), the heatmap (heatmap), the stereographic flow (flow), the 2D local offset vector field (subpix) and the spot intensities (intens).
- Return type:¶
Tuple[np.ndarray, SimpleNamespace]
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save(path: str, which: 'best' | 'last' =
'best'
, update_thresholds: bool =False
) None ¶ Save the model to disk.
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fit(train_images: Sequence[ndarray], train_spots: Sequence[ndarray], val_images: Sequence[ndarray], val_spots: Sequence[ndarray], augment_train: bool | Pipeline =
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class spotiflow.model.config.SpotiflowModelConfig(backbone: 'resnet' | 'unet' | 'unet_res' =
'unet'
, in_channels: int =1
, out_channels: int =1
, initial_fmaps: int =32
, fmap_inc_factor: Number =2
, n_convs_per_level: int =3
, levels: int =4
, downsample_factor: int =2
, kernel_size: int =3
, padding: int | str ='same'
, mode: 'direct' | 'fpn' | 'slim' ='slim'
, background_remover: bool =False
, compute_flow: bool =True
, batch_norm: bool =True
, downsample_factors: tuple[tuple[int, int]] | None =None
, kernel_sizes: tuple[tuple[int, int]] | None =None
, dropout: float =0.0
, sigma: Number =1.0
, is_3d: bool =False
, grid: int | tuple[int, int, int] =(1, 1, 1)
, **kwargs)¶
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class spotiflow.model.config.SpotiflowTrainingConfig(crop_size: int | tuple[int, int] | tuple[int, int, int] =
512
, smart_crop: bool =False
, heatmap_loss_f: str ='bce'
, flow_loss_f: str ='l1'
, loss_levels: int | None =None
, num_train_samples: int | None =None
, pos_weight: Number =10.0
, lr: float =0.0003
, optimizer: str ='adamw'
, batch_size: int =4
, lr_reduce_patience: int =10
, num_epochs: int =200
, finetuned_from: str | None =None
, early_stopping_patience: int =0
, crop_size_depth: int =32
, **kwargs)¶
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class spotiflow.data.spots.SpotsDataset(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None =
None
, downsample_factors: Sequence[int] =(1,)
, sigma: float =1.0
, mode: str ='max'
, compute_flow: bool =False
, image_files: Sequence[str] | None =None
, normalizer: 'auto' | Callable | None ='auto'
, add_class_label: bool =True
, grid: Sequence[int] | None =None
)¶ Base spot dataset class instantiated with loaded images and centers.
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__init__(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None =
None
, downsample_factors: Sequence[int] =(1,)
, sigma: float =1.0
, mode: str ='max'
, compute_flow: bool =False
, image_files: Sequence[str] | None =None
, normalizer: 'auto' | Callable | None ='auto'
, add_class_label: bool =True
, grid: Sequence[int] | None =None
)¶ Constructor
- Parameters:¶
- images : Sequence[np.ndarray]¶
Sequence of images.
- centers : Sequence[np.ndarray]¶
Sequence of center coordinates.
- augmenter : Optional[Callable], optional¶
Augmenter function. If given, function arguments should two (first image, second spots). Defaults to None.
- downsample_factors : Sequence[int], optional¶
Downsample factors. Defaults to (1,).
- sigma : float, optional¶
Sigma of Gaussian kernel to generate heatmap. Defaults to 1.
- mode : str, optional¶
Mode of heatmap generation. Defaults to “max”.
- compute_flow : bool, optional¶
Whether to compute flow from centers. Defaults to False.
- image_files : Optional[Sequence[str]], optional¶
Sequence of image filenames. If the dataset was not constructed from a folder, this will be None. Defaults to None.
- normalizer : Union[Literal["auto"], Callable, None], optional¶
Normalizer function. Defaults to “auto” (percentile-based normalization with p_min=1 and p_max=99.8).
- property augmenter : Callable¶
Return augmenter function.
- property centers : Sequence[ndarray]¶
Return centers of spots in dataset.
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classmethod from_folder(path: Path | str, augmenter: Callable | None =
None
, downsample_factors: Sequence[int] =(1,)
, sigma: float =1.0
, image_extensions: Sequence[str] =('tif', 'tiff', 'png', 'jpg', 'jpeg')
, mode: str ='max'
, max_files: int | None =None
, compute_flow: bool =False
, normalizer: Callable | 'auto' | None ='auto'
, random_state: int | None =None
, add_class_label: bool =True
, grid: Sequence[int] | None =None
) Self ¶ Build dataset from folder. Images and centers are loaded from disk and normalized.
- Parameters:¶
- path : Union[Path, str]¶
Path to folder containing images (with given extensions) and centers.
- augmenter : Callable¶
Augmenter function.
- downsample_factors : Sequence[int], optional¶
Downsample factors. Defaults to (1,).
- sigma : float, optional¶
Sigma of Gaussian kernel to generate heatmap. Defaults to 1.
- image_extensions : Sequence[str], optional¶
Image extensions to look for in images. Defaults to (“tif”, “tiff”, “png”, “jpg”, “jpeg”).
- mode : str, optional¶
Mode of heatmap generation. Defaults to “max”.
- max_files : Optional[int], optional¶
Maximum number of files to load. Defaults to None (all of them).
- compute_flow : bool, optional¶
Whether to compute flow from centers. Defaults to False.
- normalizer : Optional[Union[Callable, Literal["auto"]]], optional¶
Normalizer function. Defaults to “auto” (percentile-based normalization with p_min=1 and p_max=99.8).
- random_state : Optional[int], optional¶
Random state used when shuffling file names when “max_files” is not None. Defaults to None.
- Returns:¶
Dataset instance.
- Return type:¶
Self
- property image_files : Sequence[str]¶
Return image filenames with the same order as in the dataset.
- property images : Sequence[ndarray]¶
Return images in dataset.
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__init__(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None =
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spotiflow.utils.get_data(path: Path | str, normalize: bool =
True
, include_test: bool =False
, is_3d: bool =False
) tuple[ndarray, ndarray, ndarray, ndarray] ¶ Get data from a given path. The path should contain a ‘train’ and ‘val’ folder.
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spotiflow.utils.normalize(x: ndarray, pmin: float =
1.0
, pmax: float =99.8
, subsample: int =1
, clip: bool =False
, ignore_val: int | float | None =None
) ndarray ¶ Normalizes (percentile-based) a 2d image with the additional option to ignore a value. The normalization is done as follows:
x = (x - I_{p_{min}}) / (I_{p_{max}} - I_{p_{min}})
where I_{p_{min}} and I_{p_{max}} are the pmin and pmax percentiles of the image intensity, respectively.
- Parameters:¶
- x : np.ndarray¶
Image to be normalized
- pmin : float, optional¶
Minimum percentile. Defaults to 1..
- pmax : float, optional¶
Maximum percentile. Defaults to 99.8.
- subsample : int, optional¶
Subsampling factor for percentile calculation. Defaults to 1.
- clip : bool, optional¶
Whether to clip the normalized image. Defaults to False.
- ignore_val : Optional[Union[int, float]], optional¶
Value to be ignored. Defaults to None.
- Returns:¶
Normalized image
- Return type:¶
np.ndarray
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spotiflow.utils.read_coords_csv(fname: str, add_class_column: bool =
False
) ndarray ¶ Parses a csv file and returns correctly ordered points array
- spotiflow.utils.write_coords_csv(pts: ndarray, fname: Path | str) None ¶
Writes points in a NumPy array to a CSV file
- spotiflow.sample_data.test_image_hybiss_2d()¶
Single test HybISS image from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)
- spotiflow.sample_data.test_image_synth_3d()¶
Single synthetic volumetric stack from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)
- spotiflow.sample_data.test_image_terra_2d()¶
Single test Terra frame from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)