def qconv_model():
x = x_in = keras.layers.Input((23, 23, 1), name="input")
x = QActivation("quantized_relu(4)", name="QA_0")(x)
x = QConv2D(16,
2,
2,
kernel_quantizer=quantizers.binary(),
bias_quantizer=quantizers.ternary(),
name="qconv2d_1")(x)
x = QConv2D(8,
2,
2,
kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
activation=quantizers.quantized_relu(6, 2),
name="qconv2D_2")(x)
x = QConv2D(2,
2,
2,
kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
activation=quantizers.quantized_relu(6, 2),
name="qconv2d_3")(x)
x = QActivation("quantized_bits(6, 0, 1)", name="QA_4")(x)
model = keras.Model(inputs=[x_in], outputs=[x])
return model
def test_invalid_denominator_qbn():
input_quantizers = None
act = "binary(use_01=0)"
gamma = quantizers.ternary()
variance = gamma
model = qbn_model(act=act,
gamma=gamma,
variance=variance,
beta=None,
mean=None)
with pytest.raises(divider_factory.UnacceptedQuantizerError):
run(model, input_quantizers)
def convert_to_qkeras_quantizer(self,
alpha=None,
threshold=None,
use_stochastic_rounding=False,
number_of_unrolls=5):
"""convert qtools quantizer to qkeras quantizer."""
return quantizers.ternary(
alpha=alpha,
threshold=threshold,
use_stochastic_rounding=use_stochastic_rounding,
number_of_unrolls=number_of_unrolls)
def test_Ternary():
qkeras_quantizer = quantizers.ternary()
qtools_quantizer = quantizer_impl.Ternary()
qtools_quantizer.convert_qkeras_quantizer(qkeras_quantizer)
new_quantizer = qtools_quantizer.convert_to_qkeras_quantizer(
alpha=qkeras_quantizer.alpha, threshold=qkeras_quantizer.threshold,
use_stochastic_rounding=qkeras_quantizer.use_stochastic_rounding,
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])
name='dense'))
if use_batchnorm:
model.add(BatchNormalization(name='bn'))
model.add(QActivation(activation=activation_quantizer))
model.compile()
return model, is_xnor, test_no
@pytest.fixture(scope='module')
def randX_100_10():
return randX(100, 10)
@pytest.mark.parametrize(
'test_no,N,kernel_quantizer,bias_quantizer,activation_quantizer,use_batchnorm,is_xnor',
[(1, 10, ternary(alpha=1), quantized_bits(5,
2), 'binary_tanh', False, False),
(2, 10, binary(), quantized_bits(5, 2), 'binary_tanh', False, True),
(3, 10, ternary(alpha='auto'), quantized_bits(5,
2), binary(), True, True),
(4, 10, ternary(alpha='auto'), quantized_bits(5,
2), 'ternary', True, False),
(5, 10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.2), True, False),
(6, 10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.8), True, False),
(7, 10, binary(), quantized_bits(5, 2), binary(), False, True)])
def test_btnn(make_btnn, randX_100_10):
model, is_xnor, test_no = make_btnn
X = randX_100_10
cfg = hls4ml.utils.config_from_keras_model(model, granularity='name')
name='dense'))
if use_batchnorm:
model.add(BatchNormalization(name='bn'))
model.add(QActivation(activation=activation_quantizer))
model.compile()
return model, is_xnor
@pytest.fixture(scope='module')
def randX_100_10():
return randX(100, 10)
@pytest.mark.parametrize(
'N,kernel_quantizer,bias_quantizer,activation_quantizer,use_batchnorm,is_xnor',
[(10, ternary(alpha=1), quantized_bits(5, 2), 'binary_tanh', False, False),
(10, binary(), quantized_bits(5, 2), 'binary_tanh', False, True),
(10, ternary(alpha='auto'), quantized_bits(5, 2), binary(), True, True),
(10, ternary(alpha='auto'), quantized_bits(5, 2), 'ternary', True, False),
(10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.2), True, False),
(10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.8), True, False),
(10, binary(), quantized_bits(5, 2), binary(), False, True)])
def test_btnn(make_btnn, randX_100_10):
model, is_xnor = make_btnn
X = randX_100_10
cfg = hls4ml.utils.config_from_keras_model(model, granularity='name')
hls_model = hls4ml.converters.convert_from_keras_model(model,
output_dir='btnn',
hls_config=cfg)
