def testPostTrainingQuantize16x8(self):
"""Test for post-training quantization mode: activations/weights - int16/int8."""
saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
input_size = [5, 5, 3]
kernel_size = [3, 3, 1]
layer_name = 'test_conv2d'
input_0 = keras.layers.Input(shape=input_size)
layer_0 = keras.layers.Conv2D(
filters=kernel_size[-1],
kernel_size=kernel_size[0:2],
use_bias=False,
name=layer_name)(
input_0)
model = keras.models.Model(inputs=[input_0], outputs=[layer_0])
keras_layer = [layer for layer in model.layers if layer.name == layer_name
][0]
keras_layer.set_weights([
np.random.rand(
input_size[-1],
kernel_size[0],
kernel_size[1],
kernel_size[2],
).astype(np.float32)
])
saved_model.save(model, saved_model_dir)
model_coverage.test_saved_model(
saved_model_dir,
post_training_quantize_16x8=True,
model_input_size=input_size)
def testFloat(self):
saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
with session.Session().as_default() as sess:
in_tensor_1 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
in_tensor_2 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
out_tensor = in_tensor_1 + in_tensor_2
inputs = {'x': in_tensor_1, 'y': in_tensor_2}
outputs = {'z': out_tensor}
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
model_coverage.test_saved_model(saved_model_dir)
def testFloat(self):
saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
with ops.Graph().as_default():
with session.Session().as_default() as sess:
in_tensor_1 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
in_tensor_2 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
out_tensor = in_tensor_1 + in_tensor_2
inputs = {'x': in_tensor_1, 'y': in_tensor_2}
outputs = {'z': out_tensor}
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
model_coverage.test_saved_model(saved_model_dir)