CVE-2022-36027: Skip reordering dq-q patterns when the new quantization dimension is … · tensorflow/tensorflow@aa0b852

@@ -2311,6 +2311,44 @@ def testKerasFullyConnectedOutputShape3D(self):

list(output_details[0][‘shape_signature’]),

list(model.layers[-1].output_shape))

@test_util.run_v2_only

def testKerasConv2DTransposedWithMismatchQuantizedAxes(self):

class QuantConv2DTransposed(tf.keras.layers.Layer):

def build(self, input_shape):

self.kernel = self.add_weight('kernel’, [3, 3, input_shape[-1], 24])

def call(self, inputs):

filters = tf.quantization.fake_quant_with_min_max_vars_per_channel(

self.kernel,

-3.0 * tf.ones([24]),

3.0 * tf.ones([24]),

narrow_range=True)

filters = tf.transpose(filters, (0, 1, 3, 2))

return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24],

inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1)

x = tf.quantization.fake_quant_with_min_max_vars(

inp, -3.0, 3.0, narrow_range=True)

x = QuantConv2DTransposed()(x)

x = tf.quantization.fake_quant_with_min_max_vars(

x, -3.0, 3.0, narrow_range=True)

model = tf.keras.Model(inp, x)

saved_model_dir = os.path.join(self.get_temp_dir(),

‘keras_conv2d_transpose’)

model.save(saved_model_dir)

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)

converter.optimizations = [tf.lite.Optimize.DEFAULT]

with self.assertRaises(convert.ConverterError) as error:

_ = converter.convert()

self.assertIn('mismatched quantized axes of input and output’,

str(error.exception))

def _createModelWithInputShape(self, shape):

“""Create a simple SavedModel with a certain shape.""”

saved_model_dir = os.path.join(self.get_temp_dir(), ‘input_shape_model’)