wehub-resource-sync
167 lines
5.2 KiB
ReStructuredText
167 lines
5.2 KiB
ReStructuredText
.. _qspec_api:
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**tensorflow_quantization.QuantizationSpec**
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=============================================
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.. autoclass:: tensorflow_quantization.QuantizationSpec
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:members:
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Examples
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Let's write a simple network to use in all examples.
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.. code-block:: python
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import tensorflow as tf
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# Import necessary methods from the Quantization Toolkit
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from tensorflow_quantization.quantize import quantize_model, QuantizationSpec
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# 1. Create a small network
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input_img = tf.keras.layers.Input(shape=(28, 28))
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x = tf.keras.layers.Reshape(target_shape=(28, 28, 1))(input_img)
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x = tf.keras.layers.Conv2D(filters=126, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=8, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = tf.keras.layers.Flatten()(x)
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x = tf.keras.layers.Dense(100)(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Dense(10)(x)
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model = tf.keras.Model(input_img, x)
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#. **Select layers based on layer names**
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**Goal**: Quantize the 2nd Conv2D, 4th Conv2D and 1st Dense layer in the following network.
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.. code-block:: python
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# 1. Find out layer names
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print(model.summary())
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# 2. Create quantization spec and add layer names
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q_spec = QuantizationSpec()
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layer_name = ['conv2d_1', 'conv2d_3', 'dense']
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"""
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# Alternatively, each layer configuration can be added one at a time:
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q_spec.add('conv2d_1')
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q_spec.add('conv2d_3')
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q_spec.add('dense')
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"""
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q_spec.add(name=layer_name)
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# 3. Quantize model
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q_model = quantize_model(model, quantization_mode='partial', quantization_spec=q_spec)
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print(q_model.summary())
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tf.keras.backend.clear_session()
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#. **Select layers based on layer class**
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**Goal**: Quantize all `Conv2D` layers.
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.. code-block:: python
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# 1. Create QuantizationSpec object and add layer class
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q_spec = QuantizationSpec()
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q_spec.add(name='Conv2D', is_keras_class=True)
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# 2. Quantize model
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q_model = quantize_model(model, quantization_mode='partial', quantization_spec=q_spec)
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q_model.summary()
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tf.keras.backend.clear_session()
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#. **Select layers based both layer name and layer class**
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**Goal**: Quantize all `Dense` layers and the 3rd `Conv2D` layer.
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.. code-block:: python
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# 1. Create QuantizationSpec object and add layer information
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q_spec = QuantizationSpec()
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layer_name = ['Dense', 'conv2d_2']
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layer_is_keras_class = [True, False]
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"""
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# Alternatively, each layer configuration can be added one at a time:
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q_spec.add(name='Dense', is_keras_class=True)
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q_spec.add(name='conv2d_2')
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"""
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q_spec.add(name=layer_name, is_keras_class=layer_is_keras_class)
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# 2. Quantize model
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q_model = quantize_model(model, quantization_mode='partial', quantization_spec=q_spec)
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q_model.summary()
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tf.keras.backend.clear_session()
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#. **Select inputs at specific index for multi-input layers**
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For layers with multiple inputs, the user can choose which ones need to be quantized. Assume a network that has two layers of class `Add`.
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**Goal**: Quantize index 1 of `add` layer, index 0 of `add_1` layer and the 3rd `Conv2D` layer.
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.. code-block:: python
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# 1. Create QuantizationSpec object and add layer information
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q_spec = QuantizationSpec()
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layer_name = ['add', 'add_1', 'conv2d_2']
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layer_q_indices = [[1], [0], None]
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"""
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# Alternatively, each layer configuration can be added one at a time:
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q_spec.add(name='add', quantization_index=[1])
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q_spec.add(name='add', quantization_index=[0])
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q_spec.add(name='conv2d_2')
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"""
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q_spec.add(name=layer_name, quantization_index=layer_q_indices)
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# 2. Quantize model
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q_model = quantize_model(model, quantization_mode='partial', quantization_spec=q_spec)
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q_model.summary()
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tf.keras.backend.clear_session()
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#. **Quantize only weight and NOT input**
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**Goal**: Quantize the 2nd Conv2D, 4th Conv2D and 1st Dense layer in the following network. In addition to that, quantize only the weights of the 2nd Conv2D.
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.. code-block:: python
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# 1. Find out layer names
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print(model.summary())
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# 2. Create quantization spec and add layer names
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q_spec = QuantizationSpec()
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layer_name = ['conv2d_1', 'conv2d_3', 'dense']
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layer_q_input = [False, True, True]
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"""
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# Alternatively, each layer configuration can be added one at a time:
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q_spec.add('conv2d_1', quantize_input=False)
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q_spec.add('conv2d_3')
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q_spec.add('dense')
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"""
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q_spec.add(name=layer_name, quantize_input=layer_q_input)
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# 3. Quantize model
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q_model = quantize_model(model, quantization_mode='partial', quantization_spec=q_spec)
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print(q_model.summary())
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tf.keras.backend.clear_session()
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