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tensorpredictionhandler

tensorpredictionhandler

Module for TensorPredictionHandler

Classes

TensorPredictionHandler 

TensorPredictionHandler(
    immediately_write=False, round_decimals=None
)

Bases: PredictionHandler

Gets tensors and stores them numpy compressed files

Source code in niceml/mlcomponents/predictionhandlers/tensorpredictionhandler.py 
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def __init__(
    self,
    immediately_write: bool = False,
    round_decimals: int = None,
):
    super().__init__()
    self.immediately_write = immediately_write
    self.should_round = round_decimals is not None
    self.round_decimals = round_decimals
    self.data = None
Functions
add_prediction 
add_prediction(data_info_list, prediction_batch)

adds a prediction after processed by the net

Source code in niceml/mlcomponents/predictionhandlers/tensorpredictionhandler.py 
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def add_prediction(self, data_info_list: List[DataInfo], prediction_batch):
    """adds a prediction after processed by the net"""
    if self.should_round:
        prediction_batch = np.round(prediction_batch, self.round_decimals)
    for idx, data_info in enumerate(data_info_list):
        cur_pred = prediction_batch[idx, :]
        if self.immediately_write:
            with open_location(self.exp_context.fs) as (exp_fs, exp_path):
                with exp_fs.open(
                    join(
                        exp_path,
                        ExperimentFilenames.PREDICTION_FOLDER,
                        self.filename + ".npy",
                    ),
                    "wb",
                ) as file:
                    np.save(
                        file,
                        cur_pred,
                    )
        else:
            self.data[data_info.get_identifier()] = cur_pred

TensorPredictionHandlerZarr 

TensorPredictionHandlerZarr(
    key_seperator="", scale_to_int=False, save_as_int=False
)

Bases: TensorPredictionHandler

Saves tensors in da compressed Zarr directory. Each tensor is compressed with gzip.

Parameters

key_seperator: str default "" string which seperates image_location and output type scale_to_int: bool, default False If true data is multiplied by 255 save_as_int: bool, default False If true dtype of data is converted to uint8 to save space

Source code in niceml/mlcomponents/predictionhandlers/tensorpredictionhandler.py 
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def __init__(
    self,
    key_seperator: str = "",
    scale_to_int: bool = False,
    save_as_int: bool = False,
):
    super().__init__()
    self.save_as_int = save_as_int
    self.scale_to_int = scale_to_int
    self.key_seperator = key_seperator
    self.data = None
Functions
add_prediction 
add_prediction(data_info_list, prediction_batch)

adds a prediction after processed by the net

Source code in niceml/mlcomponents/predictionhandlers/tensorpredictionhandler.py 
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def add_prediction(self, data_info_list: List[DataInfo], prediction_batch):
    """adds a prediction after processed by the net"""
    if isinstance(prediction_batch, list):
        pred_list = zip(prediction_batch, self.data_description.get_output_names())
    else:
        pred_list = [(prediction_batch, "")]

    for prediction, suffix in pred_list:
        if self.scale_to_int:
            prediction = prediction * 255
        if self.save_as_int:
            prediction = prediction.astype(np.uint8)
        for idx, data_info in enumerate(data_info_list):
            cur_pred: np.ndarray = prediction[idx, :]
            # self.data[data_info.file_id] = cur_instance
            dataset = self.data.create(
                data_info.get_identifier() + self.key_seperator + suffix,
                shape=cur_pred.shape,
                dtype=cur_pred.dtype,
                compressor=GZip(level=7),
                chunks=False,
            )
            dataset[:] = cur_pred

Functions