Lucas Oskorep
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import os
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from glob import glob
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from pathlib import Path
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import pandas as pd
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import tensorflow as tf
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from keras.preprocessing.image import ImageDataGenerator
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import tensorflow as tf
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# TODO: Move these to a config for the project
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input_shape = (224, 224, 3)
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batch_size = 32
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single_gen = ImageDataGenerator().flow_from_directory(
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'./single_image_test_set',
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target_size=(input_shape[0], input_shape[1]),
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batch_size=batch_size,
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shuffle=False
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)
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pd.DataFrame(sorted([f.name for f in os.scandir("./data/train") if f.is_dir()])).to_csv("./models/tflite/labels.txt",
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index=False, header=False)
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for file in glob("./models/keras/*.hdf5"):
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path = Path(file)
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tflite_file = f'./models/tflite/models/{path.name[:-5] + ".tflite"}'
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if not Path(tflite_file).exists():
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print(tflite_file)
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keras_model = tf.keras.models.load_model(file)
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keras_model.summary()
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print(keras_model.input)
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print(keras_model.layers)
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converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
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tflite_model = converter.convert()
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with open(tflite_file, 'wb') as f:
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f.write(tflite_model)
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# TODO: Verify the model performance after converting to TFLITE
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# interpreter = tf.lite.Interpreter(model_path=tflite_file)
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# single_acc, single_ll = get_metrics(single_gen, keras_model)
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# tf_single_acc, tf_single_ll = get_metrics(single_gen, tflite_model)
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#
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# print(single_acc, tf_single_acc)
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# print(single_ll, tf_single_ll)
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