def test_qnetwork():
x = x_in = Input((28, 28, 1), name='input')
x = QSeparableConv2D(
32, (2, 2),
strides=(2, 2),
depthwise_quantizer="binary",
pointwise_quantizer=quantized_bits(4, 0, 1),
depthwise_activation=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_0_m')(
x)
x = QActivation('quantized_relu(6,2,1)', name='act0_m')(x)
x = QConv2D(
64, (3, 3),
strides=(2, 2),
kernel_quantizer="ternary",
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_1_m',
activation=quantized_relu(6, 3, 1))(
x)
x = QConv2D(
64, (2, 2),
strides=(2, 2),
kernel_quantizer=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_2_m')(
x)
x = QActivation('quantized_relu(6,4,1)', name='act2_m')(x)
x = Flatten(name='flatten')(x)
x = QDense(
10,
kernel_quantizer=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='dense')(
x)
x = Activation('softmax', name='softmax')(x)
model = Model(inputs=[x_in], outputs=[x])
# reload the model to ensure saving/loading works
json_string = model.to_json()
clear_session()
model = quantized_model_from_json(json_string)
# generate same output for weights
np.random.seed(42)
for layer in model.layers:
all_weights = []
for i, weights in enumerate(layer.get_weights()):
input_size = np.prod(layer.input.shape.as_list()[1:])
if input_size is None:
input_size = 576 * 10 # to avoid learning sizes
shape = weights.shape
assert input_size > 0, 'input size for {} {}'.format(layer.name, i)
# he normal initialization with a scale factor of 2.0
all_weights.append(
10.0 * np.random.normal(0.0, np.sqrt(2.0 / input_size), shape))
if all_weights:
layer.set_weights(all_weights)
# apply quantizer to weights
model_save_quantized_weights(model)
all_weights = []
for layer in model.layers:
for i, weights in enumerate(layer.get_weights()):
w = np.sum(weights)
all_weights.append(w)
all_weights = np.array(all_weights)
# test_qnetwork_weight_quantization
all_weights_signature = np.array(
[2., -6.75, -0.625, -2., -0.25, -56., 1.125, -1.625, -1.125])
assert all_weights.size == all_weights_signature.size
assert np.all(all_weights == all_weights_signature)
# test_qnetwork_forward:
expected_output = np.array([[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00, 1.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00, 1.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00, 0.e+00, 0.e+00, 6.e-08, 1.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00, 1.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00 ,0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00, 1.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00, 0.e+00, 0.e+00, 5.e-07, 1.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00 ,1.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00, 1.e+00, 0.e+00, 0.e+00, 0.e+00,
0.e+00 ,0.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 1.e+00,
0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00],
[0.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00,
1.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00]]).astype(np.float16)
inputs = 2 * np.random.rand(10, 28, 28, 1)
actual_output = model.predict(inputs).astype(np.float16)
assert_allclose(actual_output, expected_output, rtol=1e-4)
def test_qnetwork():
x = x_in = Input((28, 28, 1), name='input')
x = QSeparableConv2D(32, (2, 2),
strides=(2, 2),
depthwise_quantizer=binary(),
pointwise_quantizer=quantized_bits(4, 0, 1),
depthwise_activation=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_0_m')(x)
x = QActivation('quantized_relu(6,2,1)', name='act0_m')(x)
x = QConv2D(64, (3, 3),
strides=(2, 2),
kernel_quantizer=ternary(),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_1_m')(x)
x = QActivation('quantized_relu(6, 3, 1)', name='act1_m')(x)
x = QConv2D(64, (2, 2),
strides=(2, 2),
kernel_quantizer=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_2_m')(x)
x = QActivation('quantized_relu(6,4,1)', name='act2_m')(x)
x = Flatten(name='flatten')(x)
x = QDense(10,
kernel_quantizer=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='dense')(x)
x = Activation('softmax', name='softmax')(x)
model = Model(inputs=[x_in], outputs=[x])
# generate same output for weights
np.random.seed(42)
for layer in model.layers:
all_weights = []
for i, weights in enumerate(layer.get_weights()):
input_size = np.prod(layer.input.shape.as_list()[1:])
if input_size is None:
input_size = 576 * 10 # hack to avoid learning sizes
shape = weights.shape
assert input_size > 0, 'input size for {} {}'.format(layer.name, i)
# he normal initialization with a scale factor of 2.0
all_weights.append(
10.0 * np.random.normal(0.0, np.sqrt(2.0 / input_size), shape))
if all_weights:
layer.set_weights(all_weights)
# apply quantizer to weights
model_save_quantized_weights(model)
all_weights = []
for layer in model.layers:
for i, weights in enumerate(layer.get_weights()):
w = np.sum(weights)
all_weights.append(w)
all_weights = np.array(all_weights)
# test_qnetwork_weight_quantization
all_weights_signature = np.array(
[2.0, -6.75, -0.625, -2.0, -0.25, -56.0, 1.125, -2.625, -0.75])
assert all_weights.size == all_weights_signature.size
assert np.all(all_weights == all_weights_signature)
# test_qnetwork_forward:
y = np.array([[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 5.341e-02,
9.468e-01, 0.000e+00, 0.000e+00, 0.000e+00
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 5.960e-08,
0.000e+00, 1.919e-01, 0.000e+00, 0.000e+00, 8.081e-01
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.378e-04,
0.000e+00, 0.000e+00, 0.000e+00, 2.843e-05, 9.995e-01
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,
0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,
0.000e+00, 1.000e+00, 0.000e+00, 2.623e-06, 0.000e+00
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,
7.749e-07, 0.000e+00, 0.000e+00, 1.634e-04, 1.000e+00
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,
0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00
],
[
0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,
0.000e+00, 6.557e-07, 0.000e+00, 0.000e+00, 0.000e+00
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00,
0.000e+00, 5.960e-08, 0.000e+00, 0.000e+00, 0.000e+00
],
[
0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 9.125e-03,
9.907e-01, 9.418e-06, 0.000e+00, 5.597e-05, 0.000e+00
]]).astype(np.float16)
inputs = 2 * np.random.rand(10, 28, 28, 1)
p = model.predict(inputs).astype(np.float16)
assert np.all(p == y)