def testSeparableConvQuantize_(self, kwargs):
kwargs['filters'] = 2
kwargs['kernel_size'] = 3
num_samples = 2
stack_size = 3
num_row = 7
num_col = 6
sepconv_model = tf.keras.Sequential([
tf.keras.Input(shape=(num_row, num_col, stack_size),
batch_size=num_samples),
tf.keras.layers.SeparableConv2D(**kwargs)
])
transformed_model, updated_metadata = ModelTransformer(
sepconv_model,
[default_8bit_transforms.SeparableConvQuantize()],
).transform()
self.assertContainsSubset(updated_metadata.keys(),
{'depthwise_conv2d', 'conv2d'})
# Transformed model should have the same output shape
self.assertEqual(sepconv_model.output_shape,
transformed_model.output_shape)
x = np.random.rand(*sepconv_model.input_shape)
y = np.random.rand(*sepconv_model.output_shape)
# Ensure model is equivalent, and forward pass results are the same.
self.assertAllClose(sepconv_model.predict(x),
transformed_model.predict(x))
# Ensure model is equivalent, and training results are the same.
sepconv_model.compile(loss='categorical_crossentropy', optimizer='sgd')
transformed_model.compile(loss='categorical_crossentropy',
optimizer='sgd')
epochs = 100
for _ in range(epochs):
sepconv_model.fit(x, y, epochs=1, verbose=2)
transformed_model.fit(x, y, epochs=1, verbose=2)
self.assertAllClose(sepconv_model.get_weights(),
transformed_model.get_weights())
# To prevent accumulated numerical errors.
transformed_model.set_weights(sepconv_model.get_weights())
self.assertAllClose(sepconv_model.predict(x),
transformed_model.predict(x))
def testSeparableConvQuantize_(self, kwargs):
kwargs['filters'] = 2
kwargs['kernel_size'] = 3
num_samples = 2
stack_size = 3
num_row = 7
num_col = 6
sepconv_model = tf.keras.Sequential([
tf.keras.Input(shape=(num_row, num_col, stack_size),
batch_size=num_samples),
tf.keras.layers.SeparableConv2D(**kwargs)
])
transformed_model, updated_metadata = ModelTransformer(
sepconv_model,
[default_8bit_transforms.SeparableConvQuantize()],
).transform()
self.assertContainsSubset(updated_metadata.keys(),
{'depthwise_conv2d', 'conv2d'})
# Transformed model should have the same output shape
self.assertEqual(sepconv_model.output_shape,
transformed_model.output_shape)
x = np.random.rand(*sepconv_model.input_shape)
y = np.random.rand(*sepconv_model.output_shape)
# Ensure model is equivalent, and forward pass results are the same.
self.assertAllClose(sepconv_model.predict(x),
transformed_model.predict(x))
# Ensure model is equivalent, and training results are the same.
sepconv_model.compile(loss='categorical_crossentropy', optimizer='sgd')
sepconv_model.fit(x, y, epochs=100)
transformed_model.compile(loss='categorical_crossentropy',
optimizer='sgd')
transformed_model.fit(x, y, epochs=100)
# Over a long training cycle with constraints and regularizers, the model
# can build very minute differences. Hence reducing tol to 1e-5.
self.assertAllClose(sepconv_model.predict(x),
transformed_model.predict(x),
atol=1e-5,
rtol=1e-5)
def apply(self, model, layer_quantize_map):
"""Implement default 8-bit 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 = [
default_8bit_transforms.InputLayerQuantize(),
default_8bit_transforms.SeparableConv1DQuantize(),
default_8bit_transforms.SeparableConvQuantize(),
default_8bit_transforms.Conv2DReshapeBatchNormReLUQuantize(),
default_8bit_transforms.Conv2DReshapeBatchNormActivationQuantize(),
default_8bit_transforms.Conv2DBatchNormReLUQuantize(),
default_8bit_transforms.Conv2DBatchNormActivationQuantize(),
default_8bit_transforms.Conv2DReshapeBatchNormQuantize(),
default_8bit_transforms.Conv2DBatchNormQuantize(),
default_8bit_transforms.ConcatTransform6Inputs(),
default_8bit_transforms.ConcatTransform5Inputs(),
default_8bit_transforms.ConcatTransform4Inputs(),
default_8bit_transforms.ConcatTransform3Inputs(),
default_8bit_transforms.ConcatTransform(),
default_8bit_transforms.AddReLUQuantize(),
default_8bit_transforms.AddActivationQuantize(),
OptFlowQuantize(),
FlowQuantize()
]
return model_transformer.ModelTransformer(
model, transforms, set(layer_quantize_map.keys()),
layer_quantize_map).transform()
