def test_q_average_pooling(pooling, input_size, pool_size, strides, padding,
data_format, average_quantizer,
activation_quantizer, y):
"""q_average_pooling test utility."""
np.random.seed(33)
x = Input(input_size)
xin = x
if pooling == 'QAveragePooling2D':
x = QAveragePooling2D(pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
average_quantizer=average_quantizer,
activation=activation_quantizer,
name='qpooling')(x)
else:
x = QGlobalAveragePooling2D(data_format=data_format,
average_quantizer=average_quantizer,
activation=activation_quantizer,
name='qpooling')(x)
model = Model(inputs=xin, outputs=x)
# Prints qstats to make sure it works with Conv1D layer
print_qstats(model)
size = (2, ) + input_size
inputs = np.random.rand(size[0], size[1], size[2], size[3])
if data_format == 'channels_first':
assert_raises(tf.errors.InvalidArgumentError, model.predict, inputs)
else:
p = model.predict(inputs).astype(np.float16)
assert_allclose(p, y, rtol=1e-4)
# Reloads the model to ensure saving/loading works
json_string = model.to_json()
clear_session()
reload_model = quantized_model_from_json(json_string)
p = reload_model.predict(inputs).astype(np.float16)
assert_allclose(p, y, rtol=1e-4)
# Saves the model as an h5 file using Keras's model.save()
fd, fname = tempfile.mkstemp(".h5")
model.save(fname)
del model # Delete the existing model
# Returns a compiled model identical to the previous one
loaded_model = load_qmodel(fname)
# Cleans the created h5 file after loading the model
os.close(fd)
os.remove(fname)
# Applys quantizer to weights
model_save_quantized_weights(loaded_model)
p = loaded_model.predict(inputs).astype(np.float16)
assert_allclose(p, y, rtol=1e-4)
def load_model_quantized(model_name):
json_file = open(model_name + '.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = quantized_model_from_json(loaded_model_json)
model.load_weights(model_name + '.h5')
return model
def test_qbidirectional(rnn, all_weights_signature, expected_output):
K.set_learning_phase(0)
np.random.seed(22)
tf.random.set_seed(22)
x = x_in = Input((2, 4), name='input')
x = QBidirectional(
rnn(16,
activation="quantized_po2(8)",
kernel_quantizer="quantized_po2(8)",
recurrent_quantizer="quantized_po2(8)",
bias_quantizer="quantized_po2(8)",
name='qbirnn_0'))(x)
x = QDense(4,
kernel_quantizer=quantized_bits(8, 2, 1, alpha=1.0),
bias_quantizer=quantized_bits(8, 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)
# Save the model as an h5 file using Keras's model.save()
fd, fname = tempfile.mkstemp('.h5')
model.save(fname)
del model # Delete the existing model
# Return a compiled model identical to the previous one
model = load_qmodel(fname)
# Clean the created h5 file after loading the model
os.close(fd)
os.remove(fname)
# 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)
assert all_weights.size == all_weights_signature.size
assert np.all(all_weights == all_weights_signature)
# test forward:
inputs = 2 * np.random.rand(10, 2, 4)
actual_output = model.predict(inputs).astype(np.float16)
assert_allclose(actual_output, expected_output, rtol=1e-4)
def test_loading():
"""Test to load model using different approahches."""
loss_fn = tf.keras.losses.MeanSquaredError()
loss_metric = metrics.Mean()
optimizer = tf.keras.optimizers.SGD(learning_rate=1e-3)
x_shape = (2, 2, 1)
custom_objects = {}
qkeras_utils._add_supported_quantized_objects(custom_objects)
train_ds = generate_dataset(train_size=1,
batch_size=1,
input_shape=x_shape,
num_class=2)
model_fold = get_qconv2d_batchnorm_model(input_shape=x_shape,
kernel_size=(2, 2),
folding_mode="ema_stats_folding")
model_fold.compile(loss=loss_fn, optimizer=optimizer, metrics="acc")
run_training(model_fold,
10,
loss_fn,
loss_metric,
optimizer,
train_ds,
do_print=False)
# test load model from json to ensure saving/loading model architecture works
json_string = model_fold.to_json()
clear_session()
model_from_json = qkeras_utils.quantized_model_from_json(json_string)
assert json_string == model_from_json.to_json()
# test reload model from hdf5 files to ensure saving/loading works
_, fname = tempfile.mkstemp(".h5")
model_fold.save(fname)
model_loaded = qkeras_utils.load_qmodel(fname)
weight1 = model_fold.layers[1].get_folded_quantized_weight()
weight2 = model_loaded.layers[1].get_folded_quantized_weight()
assert_equal(weight1[0], weight2[0])
assert_equal(weight1[1], weight2[1])
# test convert a folded model to a normal model for zpm
# the kernel/bias weight in the normal model should be the same as the folded
# kernel/bias in the folded model
normal_model = bn_folding_utils.convert_folded_model_to_normal(model_fold)
weight2 = normal_model.layers[1].get_weights()
assert_equal(weight1[0], weight2[0])
assert_equal(weight1[1], weight2[1])
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_qconv1d():
np.random.seed(33)
x = Input((4, 4,))
y = QConv1D(
2, 1,
kernel_quantizer=quantized_bits(6, 2, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='qconv1d')(
x)
model = Model(inputs=x, outputs=y)
#Extract model operations
model_ops = extract_model_operations(model)
# Assertion about the number of operations for this Conv1D layer
assert model_ops['qconv1d']["number_of_operations"] == 32
# Print qstats to make sure it works with Conv1D layer
print_qstats(model)
# reload the model to ensure saving/loading works
json_string = model.to_json()
clear_session()
model = quantized_model_from_json(json_string)
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 = 10 * 10
shape = weights.shape
assert input_size > 0, 'input size for {} {}'.format(layer.name, i)
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)
# Save the model as an h5 file using Keras's model.save()
fd, fname = tempfile.mkstemp('.h5')
model.save(fname)
del model # Delete the existing model
# Returns a compiled model identical to the previous one
model = load_qmodel(fname)
#Clean the created h5 file after loading the model
os.close(fd)
os.remove(fname)
# apply quantizer to weights
model_save_quantized_weights(model)
inputs = np.random.rand(2, 4, 4)
p = model.predict(inputs).astype(np.float16)
'''
y = np.array([[[0.1309, -1.229], [-0.4165, -2.639], [-0.08105, -2.299],
[1.981, -2.195]],
[[-0.3174, -3.94], [-0.3352, -2.316], [0.105, -0.833],
[0.2115, -2.89]]]).astype(np.float16)
'''
y = np.array([[[-2.441, 3.816], [-3.807, -1.426], [-2.684, -1.317],
[-1.659, 0.9834]],
[[-4.99, 1.139], [-2.559, -1.216], [-2.285, 1.905],
[-2.652, -0.467]]]).astype(np.float16)
assert np.all(p == y)
