fnet package¶
Submodules¶
fnet.fnet_ensemble module¶
-
class
fnet.fnet_ensemble.FnetEnsemble(paths_model: Union[str, List[str]])[source]¶ Bases:
fnet.fnet_model.ModelEnsemble of FnetModels.
- Parameters
paths_model – Path to a directory of saved models or a list of paths to saved models.
-
paths_model¶ Paths to saved models in the ensemble.
- Type
Union[str, List[str]]
-
gpu_ids¶ GPU(s) used for prediction tasks.
- Type
List[int]
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predict(x: Union[torch.Tensor, numpy.ndarray], tta: bool = False) → torch.Tensor[source]¶ Performs model prediction.
- Parameters
x – Batched input.
tta – Set to to use test-time augmentation.
- Returns
Model prediction.
- Return type
torch.Tensor
fnet.fnet_model module¶
Module to define main fnet model wrapper class.
-
class
fnet.fnet_model.Model(betas=(0.5, 0.999), criterion_class='fnet.losses.WeightedMSE', init_weights=True, lr=0.001, nn_class='fnet.nn_modules.fnet_nn_3d.Net', nn_kwargs={}, scheduler=None, weight_decay=0, gpu_ids=-1)[source]¶ Bases:
objectClass that encompasses a pytorch network and its optimizer.
-
apply_on_single_zstack(input_img: Optional[numpy.ndarray] = None, filename: Union[pathlib.Path, str, None] = None, inputCh: Optional[int] = None, normalization: Optional[Callable] = None, already_normalized: bool = False, ResizeRatio: Optional[Sequence[float]] = None, cutoff: Optional[float] = None) → numpy.ndarray[source]¶ Applies model to a single z-stack input.
This assumes the loaded network architecture can receive 3d grayscale images as input.
- Parameters
input_img – 3d or 4d image with shape (Z, Y, X) or (C, Z, Y, X) respectively.
filename – Path to input image. Ignored if input_img is supplied.
inputCh – Selected channel if filename is a path to a 4d image.
normalization – Input image normalization function.
already_normalized – Set to skip input normalization.
ResizeRatio – Resizes each dimension of the the input image by the specified factor if specified.
cutoff – If specified, converts the output to a binary image with cutoff as threshold value.
- Returns
Predicted image with shape (Z, Y, X). If cutoff is set, dtype will be numpy.uint8. Otherwise, dtype will be numpy.float.
- Return type
np.ndarray
- Raises
ValueError – If parameters are invalid.
FileNotFoundError – If specified file does not exist.
IndexError – If inputCh is invalid.
-
evaluate(x: torch.Tensor, y: torch.Tensor, metric: Optional = None, piecewise: bool = False, **kwargs) → Tuple[float, torch.Tensor][source]¶ Evaluates model output using a metric function.
- Parameters
x – Input data.
y – Target data.
metric – Metric function. If None, uses fnet.metrics.corr_coef.
piecewise – Set to perform predictions piecewise.
**kwargs – Additional kwargs to be passed to predict() method.
- Returns
float – Evaluation as determined by metric function.
torch.Tensor – Model prediction.
-
predict(x: Union[torch.Tensor, numpy.ndarray], tta: bool = False) → torch.Tensor[source]¶ Performs model prediction on a single example.
- Parameters
x – Input data.
piecewise – Set to perform piecewise predictions. i.e., predict on patches of the input and stitch together the predictions.
tta – Set to use test-time augmentation.
- Returns
Model prediction.
- Return type
torch.Tensor
-
predict_on_batch(x_batch: torch.Tensor) → torch.Tensor[source]¶ Performs model prediction on a batch of data.
- Parameters
x_batch – Batch of input data.
- Returns
Batch of model predictions.
- Return type
torch.Tensor
-
predict_piecewise(x: Union[torch.Tensor, numpy.ndarray], **predict_kwargs) → torch.Tensor[source]¶ Performs model prediction piecewise on a single example.
Predicts on patches of the input and stitchs together the predictions.
- Parameters
x – Input data.
**predict_kwargs – Kwargs to pass to predict method.
- Returns
Model prediction.
- Return type
torch.Tensor
-
save(path_save: str)[source]¶ Saves model to disk.
- Parameters
path_save – Filename to which model is saved.
-
test_on_batch(x_batch: torch.Tensor, y_batch: torch.Tensor, weight_map_batch: Optional[torch.Tensor] = None) → float[source]¶ Test model on a batch of inputs and targets.
- Parameters
x_batch – Batched input.
y_batch – Batched target.
weight_map_batch – Optional batched weight map.
- Returns
Loss as evaluated by self.criterion.
- Return type
float
-
test_on_iterator(iterator: Iterator, **kwargs: dict) → float[source]¶ Test model on iterator which has items to be passed to test_on_batch.
- Parameters
iterator – Iterator that generates items to be passed to test_on_batch.
kwargs – Additional keyword arguments to be passed to test_on_batch.
- Returns
Mean loss for items in iterable.
- Return type
float
-
to_gpu(gpu_ids: Union[int, List[int]]) → None[source]¶ Move network to specified GPU(s).
- Parameters
gpu_ids – GPU(s) on which to perform training or prediction.
-
train_on_batch(x_batch: torch.Tensor, y_batch: torch.Tensor, weight_map_batch: Optional[torch.Tensor] = None) → float[source]¶ Update model using a batch of inputs and targets.
- Parameters
x_batch – Batched input.
y_batch – Batched target.
weight_map_batch – Optional batched weight map.
- Returns
Loss as determined by self.criterion.
- Return type
float
-
fnet.fnetlogger module¶
fnet.losses module¶
Loss functions for fnet models.
-
class
fnet.losses.HeteroscedasticLoss[source]¶ Bases:
torch.nn.modules.module.ModuleLoss function to capture heteroscedastic aleatoric uncertainty.
fnet.metrics module¶
Model evaluation metrics.
-
fnet.metrics.corr_coef(a: Union[numpy.ndarray, torch.Tensor], b: Union[numpy.ndarray, torch.Tensor]) → float[source]¶ Calculates the Pearson correlation coefficient between the inputs.
- Parameters
a – First input.
b – Second input.
- Returns
Pearson correlation coefficient between the inputs.
- Return type
float
-
fnet.metrics.corr_coef_chan0(a: Union[numpy.ndarray, torch.Tensor], b: Union[numpy.ndarray, torch.Tensor]) → float[source]¶ Calculates the Pearson correlation coefficient between channel 0 of the inputs.
Assumes the first dimension of the inputs is the channel dimension.
- Parameters
a – First input.
b – Second input.
- Returns
Pearson correlation coefficient between channel 0 of the inputs.
- Return type
float
fnet.models module¶
-
fnet.models.create_ensemble(paths_model: Union[str, List[str]], path_save_dir: str) → None[source]¶ Create and save an ensemble model.
- Parameters
paths_model – Paths to models or model directories. Paths can be specified as items in list or as a string with paths separated by spaces. Any model specified as a directory assumed to be at ‘directory/model.p’.
path_save_dir – Model save path directory. Model will be saved at in path_save_dir as ‘model.p’.
-
fnet.models.load_model(path_model: str, no_optim: bool = False, checkpoint: Optional[str] = None, path_options: Optional[str] = None) → fnet.fnet_model.Model[source]¶ Loaded saved FnetModel.
- Parameters
path_model – Path to model as a directory or .p file.
no_optim – Set to not the model optimizer.
checkpoint – Optional string that identifies a model checkpoint
path_options – Path to training options json. For legacy saved models where the FnetModel class/kwargs are not not included in the model save file.
- Returns
Loaded model.
- Return type
-
fnet.models.load_or_init_model(path_model: str, path_options: str)[source]¶ Loaded saved model if it exists otherwise inititialize new model.
- Parameters
path_model – Path to saved model.
path_options – Path to json where model training options are saved.
- Returns
Loaded or new FnetModel instance.
- Return type
FnetModel
fnet.predict_piecewise module¶
-
fnet.predict_piecewise.predict_piecewise(predictor, tensor_in: torch.Tensor, dims_max: Union[int, List[int]] = 64, overlaps: Union[int, List[int]] = 0, **predict_kwargs) → torch.Tensor[source]¶ Performs piecewise prediction and combines results.
- Parameters
predictor – An object with a predict() method.
tensor_in – Tensor to be input into predictor piecewise. Should be 3d or 4d with with the first dimension channel.
dims_max – Specifies dimensions of each sub prediction.
overlaps – Specifies overlap along each dimension for sub predictions.
**predict_kwargs – Kwargs to pass to predict method.
- Returns
Prediction with size tensor_in.size().
- Return type
torch.Tensor
fnet.transforms module¶
-
class
fnet.transforms.Cropper(cropping='-', by=16, offset='mid', n_max_pixels=9732096, dims_no_crop=None)[source]¶ Bases:
object
-
fnet.transforms.flip_x(ar: numpy.ndarray) → numpy.ndarray[source]¶ Flip array along x axis.
Array dimensions should end in YX.
- Parameters
ar – Input array to be flipped.
- Returns
Flipped array.
- Return type
np.ndarray
-
fnet.transforms.flip_y(ar: numpy.ndarray) → numpy.ndarray[source]¶ Flip array along y axis.
Array dimensions should end in YX.
- Parameters
ar – Input array to be flipped.
- Returns
Flipped array.
- Return type
np.ndarray
-
fnet.transforms.norm_around_center(ar: numpy.ndarray, z_center: Optional[int] = None)[source]¶ Returns normalized version of input array.
The array will be normalized with respect to the mean, std pixel intensity of the sub-array of length 32 in the z-dimension centered around the array’s “z_center”.
- Parameters
ar – Input 3d array to be normalized.
z_center – Z-index of cell centers.
- Returns
Nomralized array, dtype = float32
- Return type
np.ndarray