def test_sequential_qnetwork():
model = tf.keras.Sequential()
model.add(Input((28, 28, 1), name='input'))
model.add(
QConv2D(32, (2, 2),
strides=(2, 2),
kernel_quantizer=quantized_bits(4, 0, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_0_m'))
model.add(QActivation(quantized_relu(4, 0), name='act0_m'))
model.add(
QConv2D(64, (3, 3),
strides=(2, 2),
kernel_quantizer=quantized_bits(4, 0, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_1_m'))
model.add(QActivation(quantized_relu(4, 0), name='act1_m'))
model.add(
QConv2D(64, (2, 2),
strides=(2, 2),
kernel_quantizer=quantized_bits(4, 0, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='conv2d_2_m'))
model.add(QActivation(quantized_relu(4, 0), name='act2_m'))
model.add(Flatten())
model.add(
QDense(10,
kernel_quantizer=quantized_bits(4, 0, 1),
bias_quantizer=quantized_bits(4, 0, 1),
name='dense'))
model.add(Activation('softmax', name='softmax'))
# Check that all model operation were found correctly
model_ops = extract_model_operations(model)
for layer in model_ops.keys():
assert model_ops[layer]['type'][0] != 'null'
return model
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)
def test_qconv1d(layer_cls):
np.random.seed(33)
if layer_cls == "QConv1D":
x = Input((
4,
4,
))
y = QConv1D(2,
1,
kernel_quantizer=quantized_bits(6, 2, 1, alpha=1.0),
bias_quantizer=quantized_bits(4, 0, 1),
name='qconv1d')(x)
model = Model(inputs=x, outputs=y)
else:
x = Input((
4,
4,
))
y = QSeparableConv1D(2,
2,
depthwise_quantizer=quantized_bits(6,
2,
1,
alpha=1.0),
pointwise_quantizer=quantized_bits(4,
0,
1,
alpha=1.0),
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)
# Check the input layer model operation was found correctly
assert model_ops['qconv1d']['type'][0] != 'null'
# Assertion about the number of operations for this (Separable)Conv1D layer
if layer_cls == "QConv1D":
assert model_ops['qconv1d']['number_of_operations'] == 32
else:
assert model_ops['qconv1d']['number_of_operations'] == 30
# 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
# 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)
inputs = np.random.rand(2, 4, 4)
p = model.predict(inputs).astype(np.float16)
if layer_cls == "QConv1D":
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)
else:
y = np.array([[[-2.275, -3.178], [-0.4358, -3.262], [1.987, 0.3987]],
[[-0.01251, -0.376], [0.3928, -1.328],
[-1.243, -2.43]]]).astype(np.float16)
assert_allclose(p, y, rtol=1e-4)
