def testRaisesErrorModelNotBuilt(self):
model = keras.Sequential(
[quantize_annotate(keras.layers.Dense(10), **self.quant_params1)])
self.assertFalse(model.built)
with self.assertRaises(ValueError):
quantize_emulate.quantize_apply(model)
def testRaisesErrorNoAnnotatedLayers_Sequential(self):
model = keras.Sequential(
[keras.layers.Dense(10),
keras.layers.Dropout(0.4)])
with self.assertRaises(ValueError):
quantize_emulate.quantize_apply(model)
def testRaisesErrorIfKerasSubclassedModel(self):
class MyModel(keras.Model):
def call(self, inputs, training=None, mask=None): # pylint: disable=g-wrong-blank-lines
return inputs
with self.assertRaises(ValueError):
quantize_emulate.quantize_apply(MyModel())
def testRaisesErrorNoAnnotatedLayers_Functional(self):
inputs = keras.Input(shape=(10, ))
x = keras.layers.Dense(32, activation='relu')(inputs)
results = keras.layers.Dense(5, activation='softmax')(x)
model = keras.Model(inputs=inputs, outputs=results)
with self.assertRaises(ValueError):
quantize_emulate.quantize_apply(model)
def testQuantizesActivationsWithinLayer_Sequential(self):
quant_params = {'num_bits': 8, 'symmetric': True}
model = keras.Sequential([
quantize_annotate(keras.layers.Conv2D(32, 5, activation='relu'),
input_shape=(28, 28, 1),
**quant_params)
])
quantized_model = quantize_emulate.quantize_apply(model)
# We expect activation to be modified.
self._assert_model_emulated(model, quantized_model, ['activation'])
conv_layer = quantized_model.layers[0].layer
self.assertIsInstance(conv_layer.activation, QuantizeAwareActivation)
self.assertEqual(keras.activations.get('relu'),
conv_layer.activation.activation)
self.assertEqual(keras.layers.Conv2D,
conv_layer.activation.parent_layer)
self.assertEqual(quant_params,
conv_layer.activation.get_quantize_params())
def testQuantizesActivationsWithinLayer_Functional(self):
quant_params = {'num_bits': 8, 'symmetric': True}
inputs = keras.Input(shape=(28, 28, 1))
results = quantize_annotate(
keras.layers.Conv2D(32, 5, activation='relu'),
**self.quant_params1)(inputs)
model = keras.Model(inputs=inputs, outputs=results)
quantized_model = quantize_emulate.quantize_apply(model)
# We expect activation to be modified.
self._assert_model_emulated(model, quantized_model, ['activation'])
conv_layer = quantized_model.layers[1].layer
self.assertIsInstance(conv_layer.activation, QuantizeAwareActivation)
self.assertEqual(keras.activations.get('relu'),
conv_layer.activation.activation)
self.assertEqual(keras.layers.Conv2D,
conv_layer.activation.parent_layer)
self.assertEqual(quant_params,
conv_layer.activation.get_quantize_params())
def testAppliesQuantizationToAnnotatedModel_Functional(self):
model = self._get_annotated_functional_model()
quantized_model = quantize_emulate.quantize_apply(model)
self._assert_model_emulated(model, quantized_model)
def testRaisesErrorIfNotKerasModel(self):
with self.assertRaises(ValueError):
quantize_emulate.quantize_apply(keras.layers.Dense(32))
