| from iSparrow.sparrow_model_base import ModelBase |
|
|
| try: |
| import tflite_runtime.interpreter as tflite |
| except ImportError: |
| import tensorflow.lite as tflite |
|
|
| from iSparrow import utils |
| from iSparrow import ModelBase |
|
|
| import numpy as np |
| from pathlib import Path |
| from scipy.special import softmax |
|
|
|
|
| class Model(ModelBase): |
| """ |
| Model Implementation of a iSparrow model that uses the google perch tflite model. |
| |
| Args: |
| ModelBase (iSparrow.ModelBase): Model base class that provides the interface through which to interact with iSparrow. |
| """ |
|
|
| def __init__(self, model_path: str, num_threads: int = 1, **kwargs): |
| """ |
| __init__ Create a new model instance that uses the google perch tflite converted model. |
| |
| Args: |
| model_path (str): path to where the google perch tflite model is stored |
| num_threads (int, optional): number of threads to use. Defaults to 1. |
| """ |
| labels_path = str(Path(model_path) / "labels.txt") |
| model_path = str(Path(model_path) / "model.tflite") |
|
|
| |
| super().__init__( |
| "google_perch_lite", |
| model_path, |
| labels_path, |
| num_threads=num_threads, |
| **kwargs |
| ) |
|
|
| |
| input_details = self.model.get_input_details() |
|
|
| output_details = self.model.get_output_details() |
|
|
| self.input_layer_index = input_details[0]["index"] |
|
|
| self.output_layer_index = output_details[1]["index"] |
|
|
| def predict(self, sample: np.array) -> np.array: |
| """ |
| predict Make inference about the bird species for the preprocessed data passed to this function as arguments. |
| |
| Args: |
| data (np.array): list of preprocessed data chunks |
| Returns: |
| numpy array: array of probabilities per class |
| """ |
| data = np.array([sample], dtype="float32") |
|
|
| self.model.resize_tensor_input( |
| self.input_layer_index, [len(data), *data[0].shape] |
| ) |
| self.model.allocate_tensors() |
|
|
| |
| self.model.set_tensor(self.input_layer_index, data) |
| self.model.invoke() |
|
|
| logits = self.model.get_tensor(self.output_layer_index) |
|
|
| confidence = softmax(logits) |
|
|
| return confidence |
|
|
| @classmethod |
| def from_cfg(cls, sparrow_folder: str, cfg: dict): |
| """ |
| from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file. |
| |
| Args: |
| sparrow_dir (str): Installation directory of the Sparrow package |
| cfg (dict): Dictionary containing the keyword arguments |
| |
| Returns: |
| Model: New model instance created with the supplied kwargs. |
| """ |
| cfg["model_name"] = str( |
| Path(sparrow_folder) / Path("models") / cfg["model_name"] |
| ) |
|
|
| return cls(**cfg) |
|
|
