def qbn_model_inference():
x = x_in = keras.layers.Input((23, 23, 1), name="input")
x = QConv2D(4,
2,
23,
kernel_quantizer=quantizers.quantized_ulaw(4, 1, 1),
bias_quantizer=quantizers.stochastic_ternary(),
use_bias=False,
name="qconv2d_1")(x)
x = QBatchNormalization(gamma_quantizer=quantizers.quantized_relu_po2(
3, 2),
variance_quantizer=quantizers.quantized_po2(
3, 2, quadratic_approximation=False),
beta_quantizer=quantizers.quantized_bits(6, 0, 1),
scale=False,
center=False,
gamma_range=8,
beta_range=4,
name="qbn_2")(x)
x = QConv2D(2,
1,
1,
kernel_quantizer=quantizers.quantized_po2(3, 0),
bias_quantizer=quantizers.quantized_po2(3, 2),
name="qconv2d_3")(x)
model = keras.Model(inputs=[x_in], outputs=[x])
layer = model.get_layer("qbn_2")
weight_arr = [
np.array([3, 4, 1, 7]),
np.array([6, 4, 1, -7]),
np.array([2, 7, -8, 2]),
np.array([-1, -7, 4, 9])
]
# quantize the weights
quantizer_list = layer.get_quantizers()
for (i, quantizer) in enumerate(quantizer_list):
if quantizer is not None:
weight_arr[i] = keras.backend.eval(
quantizer(keras.backend.constant(weight_arr[i])))
num_weights = 4
if not layer.scale:
num_weights -= 1
if not layer.center:
num_weights -= 1
layer.set_weights(weight_arr[:num_weights])
return model
def test_StochasticTernary():
qkeras_quantizer = quantizers.stochastic_ternary()
qtools_quantizer = quantizer_impl.StochasticTernary()
qtools_quantizer.convert_qkeras_quantizer(qkeras_quantizer)
new_quantizer = qtools_quantizer.convert_to_qkeras_quantizer(
alpha=qkeras_quantizer.alpha, threshold=qkeras_quantizer.threshold,
temperature=qkeras_quantizer.temperature,
use_real_sigmoid=qkeras_quantizer.use_real_sigmoid,
number_of_unrolls=qkeras_quantizer.number_of_unrolls)
result = new_quantizer.__dict__
for (key, val) in result.items():
assert_equal(val, qkeras_quantizer.__dict__[key])
def convert_to_qkeras_quantizer(self,
alpha=None,
threshold=None,
temperature=8.0,
use_real_sigmoid=True,
number_of_unrolls=5):
"""convert qtools quantizer to qkeras quantizer."""
return quantizers.stochastic_ternary(
alpha=alpha,
threshold=threshold,
temperature=temperature,
use_real_sigmoid=use_real_sigmoid,
number_of_unrolls=number_of_unrolls)