tf_convert.py

import tensorflow as tf
from tensorflow import keras
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from training import load_pose_landmarks
def model_convert():
    model = tf.keras.models.load_model('F:\MoveNet1\weights.best.hdf5')
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    tflite_model = converter.convert()

    print('Model size: %dKB' % (len(tflite_model) / 1024))

    with open('pose_classifier.tflite', 'wb') as f:
        f.write(tflite_model)


model_convert()
def evaluate_model(interpreter, X, y_true):
  """Evaluates the given TFLite model and return its accuracy."""
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]

  # Run predictions on all given poses.
  y_pred = []
  for i in range(len(y_true)):
    # Pre-processing: add batch dimension and convert to float32 to match with
    # the model's input data format.
    test_image = X[i: i + 1].astype('float32')
    interpreter.set_tensor(input_index, test_image)

    # Run inference.
    interpreter.invoke()

    # Post-processing: remove batch dimension and find the class with highest
    # probability.
    output = interpreter.tensor(output_index)
    predicted_label = np.argmax(output()[0])
    y_pred.append(predicted_label)

  # Compare prediction results with ground truth labels to calculate accuracy.
  y_pred = keras.utils.to_categorical(y_pred)
  return accuracy_score(y_true, y_pred)
X_test, y_test= load_pose_landmarks('F:/MoveNet1/test_5_4.csv')
# Evaluate the accuracy of the converted TFLite model
tflite_model = open('pose_classifier.tflite', 'rb').read()
classifier_interpreter = tf.lite.Interpreter(model_content=tflite_model)
classifier_interpreter.allocate_tensors()
print('Accuracy of TFLite model: %s' %
      evaluate_model(classifier_interpreter, X_test, y_test))