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File size: 2,753 Bytes
e2a99cd | 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 69 70 71 72 73 74 75 76 77 78 79 | import tensorflow as tf
import numpy as np
import time
import os
from keras.utils import image_dataset_from_directory
val_dir="/media/data/plants_diseases_dataset/valid"
path="/media/data/plants_diseases_dataset/"
img_size=(224,224)
batch_size=32
INPUT_SHAPE = (224, 224, 3)
keras_model = tf.keras.models.load_model("models/mobileNet_10.keras")
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
with open("models/mobilenet_int8.tflite", "wb") as f:
f.write(tflite_model)
def evaluate_models(keras_model, tflite_model_path, dataset):
"""Compare size, speed, and accuracy of Keras vs TFLite model."""
keras_size = os.path.getsize("models/mobileNet_10.keras") / 1024**2
tflite_size = os.path.getsize(tflite_model_path) / 1024**2
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
correct = 0
total = 0
times = []
for batch_images, batch_labels in dataset.take(50):
batch_images = batch_images.numpy()
batch_labels = tf.argmax(batch_labels, axis=1).numpy()
for i in range(len(batch_images)):
input_data = np.expand_dims(batch_images[i], axis=0).astype(np.float32)
start = time.time()
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output = interpreter.get_tensor(output_details[0]['index'])
times.append(time.time() - start)
pred = np.argmax(output)
if pred == batch_labels[i]:
correct += 1
total += 1
tflite_acc = correct / total
avg_time = np.mean(times) * 1000
print(f"\nModel sizes:")
print(f" - Keras: {keras_size:.2f} MB")
print(f" - TFLite INT8: {tflite_size:.2f} MB")
print("---------------------------------------------")
_, keras_acc = keras_model.evaluate(dataset, verbose=0)
print(f"\n Keras Model Accuracy: {keras_acc*100:.2f}%")
print(f" TFLite Model Accuracy: {tflite_acc*100:.2f}%")
print(f" Avg Inference Time (1 image): {avg_time:.2f} ms")
print("\n Summary:")
print(f" - Size reduction: {(1 - tflite_size/keras_size)*100:.1f}%")
print(f" - Accuracy drop: {(keras_acc - tflite_acc)*100:.2f}%")
val_ds=image_dataset_from_directory(val_dir,
image_size=img_size,
batch_size=batch_size,
label_mode="categorical")
evaluate_models(keras_model, "models/mobilenet_int8.tflite", val_ds)
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