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tarun-ssharma/convert_tflite_rep_data_int8.md

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Last active April 22, 2022 02:44
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  1. tarun-ssharma revised this gist Apr 22, 2022. 2 changed files with 34 additions and 44 deletions.
    44 changes: 0 additions & 44 deletions convert.py
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    Original file line number Diff line number Diff line change
    @@ -1,44 +0,0 @@
    > ```
    > $ sudo edgetpu_compiler coral.tflite
    >
    > Edge TPU Compiler version 2.0.267685300
    > Invalid model: coral.tflite
    > Model not quantized
    > ```
    >
    > P.S. I found solution
    >
    > ```
    > model = tf.keras.models.Sequential([
    > tf.keras.layers.Flatten(input_shape=(28, 28)),
    > tf.keras.layers.Dense(128, activation='relu'),
    > tf.keras.layers.Dropout(0.2),
    > tf.keras.layers.Dense(10, activation='softmax')
    > ])
    >
    > model.compile(optimizer='adam',
    > loss='sparse_categorical_crossentropy',
    > metrics=['accuracy'])
    >
    > model.fit(x_train, y_train, epochs=1)
    > model.evaluate(x_test, y_test, verbose=2)
    >
    > image_shape = (28, 28)
    > def representative_dataset_gen():
    > num_calibration_images = 10
    > for i in range(num_calibration_images):
    > image = tf.random.normal([1] + list(image_shape))
    > yield [image]
    >
    > converter = tf.lite.TFLiteConverter.from_keras_model(model)
    >
    > converter.optimizations = [tf.lite.Optimize.DEFAULT]
    > converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    > converter.representative_dataset = representative_dataset_gen
    > converter.inference_input_type = tf.uint8
    > converter.inference_output_type = tf.uint8
    > tflite_quant_model = converter.convert()
    >
    > open("coral8.tflite", "wb").write(tflite_quant_model)
    > ```

    34 changes: 34 additions & 0 deletions convert_tflite_rep_data_int8.md
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    Original file line number Diff line number Diff line change
    @@ -0,0 +1,34 @@

    ```
    model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])
    model.fit(x_train, y_train, epochs=1)
    model.evaluate(x_test, y_test, verbose=2)
    image_shape = (28, 28)
    def representative_dataset_gen():
    num_calibration_images = 10
    for i in range(num_calibration_images):
    image = tf.random.normal([1] + list(image_shape))
    yield [image]
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.representative_dataset = representative_dataset_gen
    converter.inference_input_type = tf.uint8
    converter.inference_output_type = tf.uint8
    tflite_quant_model = converter.convert()
    open("coral8.tflite", "wb").write(tflite_quant_model)
    ```
  2. tarun-ssharma revised this gist Apr 22, 2022. 1 changed file with 43 additions and 9 deletions.
    52 changes: 43 additions & 9 deletions convert.py
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    Original file line number Diff line number Diff line change
    @@ -1,10 +1,44 @@
    # Load TensorFlow
    import tensorflow as tf
    > ```
    > $ sudo edgetpu_compiler coral.tflite
    >
    > Edge TPU Compiler version 2.0.267685300
    > Invalid model: coral.tflite
    > Model not quantized
    > ```
    >
    > P.S. I found solution
    >
    > ```
    > model = tf.keras.models.Sequential([
    > tf.keras.layers.Flatten(input_shape=(28, 28)),
    > tf.keras.layers.Dense(128, activation='relu'),
    > tf.keras.layers.Dropout(0.2),
    > tf.keras.layers.Dense(10, activation='softmax')
    > ])
    >
    > model.compile(optimizer='adam',
    > loss='sparse_categorical_crossentropy',
    > metrics=['accuracy'])
    >
    > model.fit(x_train, y_train, epochs=1)
    > model.evaluate(x_test, y_test, verbose=2)
    >
    > image_shape = (28, 28)
    > def representative_dataset_gen():
    > num_calibration_images = 10
    > for i in range(num_calibration_images):
    > image = tf.random.normal([1] + list(image_shape))
    > yield [image]
    >
    > converter = tf.lite.TFLiteConverter.from_keras_model(model)
    >
    > converter.optimizations = [tf.lite.Optimize.DEFAULT]
    > converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    > converter.representative_dataset = representative_dataset_gen
    > converter.inference_input_type = tf.uint8
    > converter.inference_output_type = tf.uint8
    > tflite_quant_model = converter.convert()
    >
    > open("coral8.tflite", "wb").write(tflite_quant_model)
    > ```

    # Set up the converter
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
    converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]

    # Perform conversion and output file
    tflite_quant_model = converter.convert()
    output_dir.write_bytes(tflite_quant_model)
  3. @dansitu dansitu created this gist Mar 14, 2019.
    10 changes: 10 additions & 0 deletions convert.py
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    # Load TensorFlow
    import tensorflow as tf

    # Set up the converter
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
    converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]

    # Perform conversion and output file
    tflite_quant_model = converter.convert()
    output_dir.write_bytes(tflite_quant_model)