def testTransformsDepthwiseConvBNReLUPattern(self):
model = DepthwiseConv2DModel.get_nonfolded_batchnorm_model(
post_bn_activation=keras.layers.ReLU(6.0), model_type='functional')
folded_model = DepthwiseConv2DModel.get_folded_batchnorm_model(
post_bn_activation=keras.layers.ReLU(6.0), is_quantized=True)
transformed_model, _ = ModelTransformer(
model, [default_8bit_transforms.DepthwiseConv2DBatchNormReLU6Fold()
]).transform()
inputs = np.random.standard_normal(
DepthwiseConv2DModel.get_batched_input_shape())
self.assertAllClose(
transformed_model.predict(inputs), folded_model.predict(inputs))
def testTransformsDepthwiseConvBNReLUPatternPreservesWeights(self):
# random_init to prevent non-random initialization in resulting
# in same weights between transformed and non-transformed models.
model = DepthwiseConv2DModel.get_nonfolded_batchnorm_model(
post_bn_activation=keras.layers.ReLU(6.0),
model_type='functional',
random_init=True)
transformed_model, _ = ModelTransformer(
model, [default_8bit_transforms.DepthwiseConv2DBatchNormReLU6Fold()
]).transform()
transformed_weights = transformed_model.get_weights()
# Remove quantization related weights.
del transformed_weights[3:8]
self.assertEqual(len(transformed_weights), len(model.get_weights()))
for i in range(len(transformed_weights)):
self.assertAllEqual(transformed_weights[i], model.get_weights()[i])
