File size: 2,082 Bytes
1582553 f16709f 1582553 f16709f 1582553 f16709f 1582553 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
from pathlib import Path
import numpy as np
import tensorflow as tf
from iSparrow.sparrow_model_base import ModelBase
import pandas as pd
class Model(ModelBase):
def __init__(self, model_path: str, num_threads: int = 1, **kwargs):
"""
__init__ Create a new Model instance using the google perch model.
Args:
model_path (str): Path to the model file to load from disk
num_threads (int): The number of threads used for inference. Currently not used for this model.
"""
labels_path = str(Path(model_path) / "labels.txt")
model_path = str(Path(model_path) / "saved_model.pb")
self.class_mask = None # used later
super().__init__(
"google_perch", model_path, labels_path, num_threads=num_threads, **kwargs
) # num_threads doesn't do anything here.
def predict(self, data: 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:
list: List of (label, inferred_probability)
"""
# README: this should be parallelized??
logits, _ = self.model.infer_tf(
np.array(
[
data,
]
)
)
results = tf.nn.softmax(logits).numpy()
return results
@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_path"] = str(
Path(sparrow_folder) / Path("models") / Path(cfg["model_path"])
)
return cls(**cfg)
|