def apply(self, model, layer_quantize_map):
"""Implement TFLite transforms.
Currently this means the following.
1. Pull activations into layers, and apply fuse activations. (TODO)
2. Modify range in incoming layers for Concat. (TODO)
3. Fuse Conv2D/DepthwiseConv2D + BN into single layer.
Args:
model: Keras model to be quantized.
layer_quantize_map: Map with keys as layer names, and values as dicts
containing custom `QuantizeConfig`s which may have been passed with
layers.
Returns:
(Transformed Keras model to better match TensorFlow Lite backend, updated
layer quantize map.)
"""
transforms = [
tflite_transforms.InputLayerQuantize(),
tflite_transforms.Conv2DBatchNormReLUQuantize(),
tflite_transforms.Conv2DBatchNormActivationQuantize(),
tflite_transforms.Conv2DBatchNormQuantize(),
tflite_transforms.ConcatTransform6Inputs(),
tflite_transforms.ConcatTransform5Inputs(),
tflite_transforms.ConcatTransform4Inputs(),
tflite_transforms.ConcatTransform3Inputs(),
tflite_transforms.ConcatTransform(),
]
return model_transformer.ModelTransformer(
model, transforms, layer_quantize_map.keys(),
layer_quantize_map).transform()
def apply(self, model, layer_quantize_map):
"""Implement TFLite transforms.
Currently this means the following.
1. Pull activations into layers, and apply fuse activations. (TODO)
2. Modify range in incoming layers for Concat. (TODO)
3. Fuse Conv2D/DepthwiseConv2D + BN into single layer.
Args:
model: Keras model to be quantized.
layer_quantize_map: Map with keys as layer names, and values as dicts
containing custom `QuantizeProvider`s which may have been passed with
layers.
Returns:
(Transformed Keras model to better match TensorFlow Lite backend, updated
layer quantize map.)
"""
# TODO(pulkitb): Sequential models not supported yet. Remove once support is
# added.
if isinstance(model, keras.Sequential):
return model, layer_quantize_map
transforms = [
tflite_transforms.InputLayerQuantize(),
tflite_transforms.Conv2DBatchNormReLUQuantize(),
tflite_transforms.Conv2DBatchNormQuantize(),
]
return model_transformer.ModelTransformer(
model, transforms,
layer_quantize_map.keys(), layer_quantize_map).transform()
def testConv2DBatchNormQuantize(self, layer_type):
model = self._get_model(layer_type, False)
input_shape = self._get_input_shape(layer_type)
transformed_model, updated_metadata = ModelTransformer(
model,
[tflite_transforms.Conv2DBatchNormQuantize()],
).transform()
conv_layer = transformed_model.layers[1]
bn_layer = transformed_model.layers[2]
self.assertIsInstance(conv_layer.activation,
quantize_aware_activation.NoOpActivation)
self.assertIsInstance(
updated_metadata.get(bn_layer.name).get('quantize_provider'),
tflite_quantize_providers.OutputQuantizeProvider)
inputs = np.random.standard_normal(input_shape)
self.assertAllClose(transformed_model.predict(inputs),
model.predict(inputs))