def create_sample_graph(data1: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [1, 5, 5, 3], Float32())
# Conv1
w1 = Constant('weight1', Float32(), data1)
conv1 = Conv('conv1', [1, 4, 4, 3],
QUANTIZED_PACKED(), {
'X': x,
'W': w1
},
kernel_shape=[2, 2])
conv1.is_quantized = True
pool1 = SpaceToDepth('s2d', [1, 2, 2, 12], Float32(), {'input': conv1})
# One output
y = Output('output', [1, 2, 2, 12], Float32(), {'input': pool1})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_expected_graph(data: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [1, 5, 5, 3], Float32())
# constant and internal nodes
w = Constant('weight', Float32(), data)
q = QTZ_binary_mean_scaling('qtz1', [1, 2, 2, 3], Float32(),
{'input': w})
# Conv
conv = Conv('conv', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2])
# One output
rs = Reshape('reshape', [1, 48], Float32(), {'data': conv})
y = Output(
'output',
[1, 48],
Float32(),
{'input': rs},
)
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def make_simple_model(self) -> Model:
graph = Graph()
# two inputs
x = Input(
'input',
[1, 5, 5, 3],
Float32(),
)
w = Constant(
'weight',
Float32(),
np.zeros([1, 2, 2, 3]),
dimension_format='NHWC',
)
# Conv
conv = Conv('conv', [1, 4, 4, 1],
Float32(), {
'X': x,
'W': w
},
kernel_shape=[2, 2])
# One output
y = Output('output', [1, 4, 4, 1], Float32(), {'input': conv})
# add ops to the graph
graph.add_op_and_inputs(y)
model = Model()
model.graph = graph
return model
def create_sample_graph(data1: np.ndarray, data2: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [1, 5, 5, 3], Float32())
# Conv1
w1 = Constant('weight1', Float32(), data1)
conv1 = Conv('conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w1}, kernel_shape=[2, 2])
# activation quantizer
s1 = Constant('aq_const1', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2})
# Conv2
w2 = Constant('weight2', Float32(), data2)
kq = QTZ_binary_mean_scaling('kqtz1', [1, 2, 2, 3], Float32(), {'input': w2})
conv2 = Conv('conv2', [1, 3, 3, 3], Float32(), {'X': aq, 'W': kq}, kernel_shape=[2, 2])
conv2.a_quantizer = [aq]
conv2.quantizer = kq
# One output
y = Output('output', [1, 3, 3, 3], Float32(), {'input': conv2})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_sample_graph() -> Graph:
graph = Graph()
x = Input('placeholder', [2], Float32())
s1 = Constant('potato_1', Float32(), np.array([1, 2]))
s2 = Constant('potato_2', Float32(), np.array([1, 3]))
add1 = Add('potatoes', [2], Float32(), {'A': s1, 'B': s2})
add2 = Add('more_potatoes', [2], Float32(), {'A': x, 'B': add1})
# One output
y = Output('output', [2], Float32(), {'input': add2})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_sample_graph(data1: np.ndarray, data2: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [1, 5, 5, 3], Float32())
# Conv1
w1 = Constant('weight1', Float32(), data1)
conv1 = Conv('conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w1}, kernel_shape=[2, 2])
# activation quantizer
s1 = Constant('aq_const1', Int32(), np.array([2], dtype=np.int32))
s2 = Constant('aq_const2', Float32(), np.array([2.0], dtype=np.float32))
aq1 = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2})
# Conv2
w2 = Constant('weight2', Float32(), data2)
kq = QTZ_binary_mean_scaling('kqtz1', [1, 2, 2, 3], Float32(), {'input': w2})
conv2 = Conv('conv2', [1, 3, 3, 3], Float32(), {'X': aq1, 'W': kq}, kernel_shape=[2, 2])
conv2.a_quantizer = [aq1]
conv2.quantizer = kq
conv2.is_quantized = True
sc = Constant('bn_scale', Float32(), np.random.rand(3))
be = Constant('bn_b', Float32(), np.random.rand(3))
mu = Constant('bn_mu', Float32(), np.random.rand(3))
va = Constant('bn_var', Float32(), np.random.rand(3))
bn = BatchNormalization('bn', [1, 3, 3, 3], Float32(), {'X': conv2,
'scale': sc,
'B': be,
'mean': mu,
'var': va})
# activation quantizer
s3 = Constant('aq_const3', Int32(), np.array([2], dtype=np.int32))
s4 = Constant('aq_const4', Float32(), np.array([2.0], dtype=np.float32))
aq2 = QTZ_linear_mid_tread_half('aqtz2', [1, 3, 3, 3], Float32(), {'X': bn, 'Y': s3, 'Z': s4})
# One output
y = Output('output', [1, 3, 3, 3], Float32(), {'input': aq2})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_transposed_graph(self, data: np.ndarray) -> Graph:
graph = Graph()
data = data.transpose([3, 2, 1, 0])
# input
x = Input('placeholder', [1, 5, 5, 3],
Float32(),
dimension_format='NHWC')
# constant and internal nodes
w = Constant('weight', Float32(), data, dimension_format='NHWC')
i = Identity('identity1', [1, 2, 2, 3],
Float32(), {'input': w},
dimension_format='NHWC')
q = QTZ_binary_mean_scaling('qtz1', [1, 2, 2, 3],
Float32(), {'input': i},
dimension_format='NHWC')
# Conv
conv = Conv('conv', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2],
dimension_format='NHWC')
rs = Reshape('reshape', [1, 48], Float32(), {'data': conv})
# One output
y = Output(
'output',
[1, 48],
Float32(),
{'input': rs},
)
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_sample_graph_2(data1: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [1, 5, 5, 3], Float32())
# Conv1
w1 = Constant('weight1', Float32(), data1)
conv1 = Conv('conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w1}, kernel_shape=[2, 2])
s1 = Constant('const1', Float32(), np.zeros([1, 4, 4, 3]))
add1 = Add('add', [1, 4, 4, 3], Float32(), {'A': conv1, 'B': s1})
y = Output('output', [1, 4, 4, 3], Float32(), {'input': add1})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def test_graph_conv(self) -> None:
"""Test code for making a simple graph with Conv."""
graph = Graph()
# two inputs
x = Input(
'input',
[1, 5, 5, 3],
Float32(),
)
w = Constant('weight', Float32(), np.zeros([1, 2, 2, 3]))
# Conv
conv = Conv(
'conv',
[1, 4, 4, 3],
Float32(),
{
'X': x,
'W': w
}, # you can get these keys by 'Conv.input_names'
kernel_shape=[2, 2])
# One output
y = Output(
'output',
[1, 4, 4, 3],
Float32(),
{'input': conv} # you can get this key by 'Output.input_names'
)
# add ops to the graph
graph.add_op(x)
graph.add_op(w)
graph.add_op(conv)
graph.add_op(y)
self.assertTrue(graph.check_nodes(),
"All inputs of operators must match their outputs.")
print("Graph test passed!")
def create_sample_graph(data: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input('placeholder', [3, 5, 5, 1],
Float32(),
dimension_format='CWHN')
# constant and internal nodes
w = Constant('weight', Float32(), data, dimension_format='CWHN')
i1 = Identity('identity1', [3, 2, 2, 1],
Float32(), {'input': w},
dimension_format='CWHN')
q = QTZ_binary_mean_scaling('qtz1', [3, 2, 2, 1],
Float32(), {'input': i1},
dimension_format='CWHN')
# Conv
conv = Conv('conv', [3, 4, 4, 1],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2],
dimension_format='CWHN')
# One output
rs = Reshape('reshape', [1, 48], Float32(), {'data': conv})
y = Output(
'output',
[1, 48],
Float32(),
{'input': rs},
)
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_quantized_graph2(self, data1: np.ndarray, data2: np.ndarray,
data3: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input(
'placeholder',
[1, 5, 5, 3],
Float32(),
)
# constant and internal nodes
scaling1, qdata1 = self.binary_mean_scaling(data1)
w = Constant('weight', Float32(), qdata1 * scaling1)
q = QTZ_binary_mean_scaling('qtz1', [3, 2, 2, 3], Float32(),
{'input': w})
# Conv
conv1 = Conv('conv1', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': w
},
kernel_shape=[2, 2])
s1 = Constant('aq_const1', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3],
QUANTIZED_NOT_PACKED(), {
'X': conv1,
'Y': s1,
'Z': s2
})
from modules.packer import Packer
packer = Packer(1, 32)
scaling2, qdata2 = self.binary_mean_scaling(data2)
w2 = Constant('weight2',
Uint32(),
packer.run(qdata2),
packed=True,
actual_shape=[3, 2, 2, 3])
q2 = QTZ_binary_mean_scaling('qtz2', [3, 2, 2, 3], Float32(),
{'input': w2})
q2.scaling_factor = scaling2
conv2 = Conv(
'conv2',
[1, 3, 3, 3],
Float32(),
{
'X': aq,
'W': w2
},
kernel_shape=[2, 2],
quantized=True,
)
conv2.quantizer = q2
scaling3, qdata3 = self.binary_mean_scaling(data3)
w3 = Constant('weight2',
Uint32(),
packer.run(qdata3),
packed=True,
actual_shape=[3, 2, 2, 3])
q3 = QTZ_binary_mean_scaling('qtz3', [3, 2, 2, 3], Float32(),
{'input': w3})
q3.scaling_factor = scaling3
conv3 = Conv('conv3', [1, 3, 3, 3],
Float32(), {
'X': aq,
'W': w3
},
kernel_shape=[2, 2],
quantized=True)
conv3.quantizer = q3
y1 = Output('output1', [1, 3, 3, 3], Float32(), {'input': conv2})
y2 = Output('output2', [1, 3, 3, 3], Float32(), {'input': conv3})
# add ops to the graph
graph.add_op_and_inputs(y1)
graph.add_op_and_inputs(y2)
return graph, scaling2, scaling3
def create_sample_graph3(self, data1: np.ndarray, data2: np.ndarray,
data3: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input(
'placeholder',
[1, 5, 5, 3],
Float32(),
)
# constant and internal nodes
w = Constant('weight', Float32(), data1)
q = QTZ_binary_mean_scaling('qtz1', [3, 2, 2, 3], Float32(),
{'input': w})
# Conv
conv1 = Conv('conv1', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2])
i2 = Identity('identity2', [1, 4, 4, 3], Float32(), {'input': conv1})
s1 = Constant('aq_const1', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], Float32(), {
'X': i2,
'Y': s1,
'Z': s2
})
w2 = Constant('weight2', Float32(), data2)
q2 = QTZ_binary_mean_scaling('qtz2', [3, 2, 2, 3], Float32(),
{'input': w2})
conv2 = Conv('conv2', [1, 3, 3, 3],
Float32(), {
'X': aq,
'W': q2
},
kernel_shape=[2, 2])
w3 = Constant('weight3', Float32(), data3)
q3 = QTZ_binary_mean_scaling('qtz3', [3, 2, 2, 3], Float32(),
{'input': w3})
conv3 = Conv('conv3', [1, 3, 3, 3],
Float32(), {
'X': aq,
'W': q3
},
kernel_shape=[2, 2])
y1 = Output('output1', [1, 3, 3, 3], Float32(), {'input': conv2})
y2 = Output('output2', [1, 3, 3, 3], Float32(), {'input': conv3})
# add ops to the graph
graph.add_op_and_inputs(y1)
graph.add_op_and_inputs(y2)
return graph
def create_quantized_graph(self, data: np.ndarray, data2: np.ndarray, data3: np.ndarray) \
-> Tuple[Graph, np.float32, np.float32]:
graph = Graph()
# two inputs
x = Input(
'placeholder',
[1, 5, 5, 3],
Float32(),
)
from modules.packer import Packer
packer = Packer(1, 32)
data = data.transpose([3, 2, 1, 0])
scaling, qdata = self.binary_mean_scaling(data)
shape = list(data.shape)
w = Constant(
'weight',
Float32(),
qdata * scaling,
)
q = QTZ_binary_mean_scaling('qtz1', shape, Float32(), {'input': w})
q.scaling_factor = scaling
# Conv
conv1 = Conv(
'conv1',
[1, 4, 4, 3],
Float32(),
{
'X': x,
'W': w
},
kernel_shape=[2, 2],
)
s1 = Constant('aq_const1', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3],
QUANTIZED_NOT_PACKED(), {
'X': conv1,
'Y': s1,
'Z': s2
})
dummy = Transpose('dummy', [1, 4, 4, 3],
QUANTIZED_NOT_PACKED(), {'data': aq},
perm=[0, 1, 2, 3])
scaling2, qdata2 = self.binary_mean_scaling(data2)
w2 = Constant('weight2',
Uint32(),
packer.run(qdata2),
packed=True,
actual_shape=[3, 2, 2, 3])
# quantizer connected to conv2 as 'conv2.quantizer'
q2 = QTZ_binary_mean_scaling('qtz2', [3, 2, 2, 3], Uint32(),
{'input': w2})
q2.scaling_factor = scaling2
conv2 = Conv('conv2', [1, 3, 3, 3],
Float32(), {
'X': dummy,
'W': w2
},
kernel_shape=[2, 2],
quantized=True)
conv2.quantizer = q2
s3 = Constant('aq_const1', Float32(), np.array(1))
s4 = Constant('aq_const2', Float32(), np.array(2))
aq2 = QTZ_linear_mid_tread_half('aqtz2', [1, 3, 3, 3], Float32(), {
'X': conv2,
'Y': s3,
'Z': s4
})
w3 = Constant('weight3', Float32(), data3)
conv3 = Conv('conv3', [1, 2, 2, 3],
Float32(), {
'X': aq2,
'W': w3
},
kernel_shape=[2, 2])
# One output
y = Output('output', [1, 2, 2, 3], Float32(), {'input': conv3})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph, scaling, scaling2
def create_precompute_graph(self, data1: np.ndarray, data2: np.ndarray,
data3: np.ndarray) -> Graph:
graph = Graph()
# two inputs
x = Input(
'placeholder',
[1, 5, 5, 3],
Float32(),
)
scaling1, qdata = self.binary_mean_scaling(
data1.transpose([3, 2, 1, 0]))
w = Constant('weight', Float32(), qdata * scaling1)
# Conv
conv1 = Conv('conv1', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': w
},
kernel_shape=[2, 2])
s1 = Constant('aq_const1', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], Float32(), {
'X': conv1,
'Y': s1,
'Z': s2
})
dummy = Transpose('dummy', [1, 4, 4, 3],
Float32(), {'data': aq},
perm=[0, 1, 2, 3])
scaling2, qdata2 = self.binary_mean_scaling(data2)
w2 = Constant('weight2', Float32(), qdata2 * scaling2)
conv2 = Conv('conv2', [1, 3, 3, 3],
Float32(), {
'X': dummy,
'W': w2
},
kernel_shape=[2, 2])
s3 = Constant('aq_const1', Float32(), np.array(1))
s4 = Constant('aq_const2', Float32(), np.array(2))
aq2 = QTZ_linear_mid_tread_half('aqtz2', [1, 3, 3, 3], Float32(), {
'X': conv2,
'Y': s3,
'Z': s4
})
w3 = Constant('weight3', Float32(), data3)
conv3 = Conv('conv3', [1, 2, 2, 3],
Float32(), {
'X': aq2,
'W': w3
},
kernel_shape=[2, 2])
# One output
y = Output('output', [1, 2, 2, 3], Float32(), {'input': conv3})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_sample_graph(self, data1: np.ndarray, data2: np.ndarray,
data3: np.ndarray) -> Graph:
graph = Graph()
# input
x = Input(
'placeholder',
[1, 5, 5, 3],
Float32(),
)
# constant and internal nodes
w = Constant('weight', Float32(), data1)
i = Identity('identity1', [3, 2, 2, 3], Float32(), {'input': w})
t = Transpose('transpose1', [3, 2, 2, 3],
Float32(), {'data': i},
perm=[3, 2, 1, 0])
q = QTZ_binary_mean_scaling('qtz1', [3, 2, 2, 3], Float32(),
{'input': t})
# Conv
conv1 = Conv('conv1', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2])
i2 = Identity('identity2', [1, 4, 4, 3], Float32(), {'input': conv1})
s1 = Constant('aq_const1', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = QTZ_linear_mid_tread_half('aqtz1', [1, 4, 4, 3], Float32(), {
'X': i2,
'Y': s1,
'Z': s2
})
dummy = Transpose('dummy', [1, 4, 4, 3],
Float32(), {'data': aq},
perm=[0, 1, 2, 3])
w2 = Constant('weight2', Float32(), data2)
q2 = QTZ_binary_mean_scaling('qtz2', [3, 2, 2, 3], Float32(),
{'input': w2})
conv2 = Conv('conv2', [1, 3, 3, 3],
Float32(), {
'X': dummy,
'W': q2
},
kernel_shape=[2, 2])
s3 = Constant('aq_const1', Float32(), np.array(1))
s4 = Constant('aq_const2', Float32(), np.array(2))
aq2 = QTZ_linear_mid_tread_half('aqtz2', [1, 3, 3, 3], Float32(), {
'X': conv2,
'Y': s3,
'Z': s4
})
w3 = Constant('weight3', Float32(), data3)
i3 = Identity('identity3', [1, 3, 3, 3], Float32(), {'input': aq2})
conv3 = Conv('conv3', [1, 2, 2, 3],
Float32(), {
'X': i3,
'W': w3
},
kernel_shape=[2, 2])
# One output
y = Output('output', [1, 2, 2, 3], Float32(), {'input': conv3})
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def create_graph(self, graph):
x1 = Input(
'input1',
[1, 4, 4, 3],
Float32(),
)
w1 = Constant(
'weight1',
Float32(),
np.zeros([1, 2, 2, 3])
)
conv1 = Conv(
'conv1',
[1, 3, 3, 3],
Float32(),
{'X': x1, 'W': w1},
kernel_shape=[2, 2]
)
w2 = Constant(
'weight2',
Float32(),
np.zeros([3, 2, 2, 3])
)
conv2 = Conv(
'conv2',
[1, 2, 2, 3],
Float32(),
{'X': conv1, 'W': w2},
kernel_shape=[2, 2]
)
x2 = Input(
'input2',
[3, 3, 3, 3],
Float32(),
)
x3 = Input(
'input3',
[3, 3, 3, 3],
Float32(),
)
conv3 = Conv(
'conv3',
[3, 2, 2, 3],
Float32(),
{'X': x2, 'W': conv2},
kernel_shape=[2, 2]
)
conv4 = Conv(
'conv4',
[1, 2, 2, 3],
Float32(),
{'X': x3, 'W': conv3},
kernel_shape=[2, 2]
)
y = Output(
'output',
[1, 2, 2, 3],
Float32(),
{'input': conv4}
)
# add ops to the graph
graph.add_op_and_inputs(y)