def test_dynamic_create_unary(self) -> None:
"""Test code for unary operators."""
unary_ops = [
'Identity', 'BinaryMeanScalingQuantizer', 'Transpose',
'LinearMidTreadHalfQuantizer', 'MaxPool', 'AveragePool', 'Reshape',
'Softmax'
]
# unary input
shape = [1, 3, 3, 3]
x = Constant('const', Float32(), np.zeros(shape))
name = 'test'
dtype = Float32()
for op in unary_ops:
shape = [1, 3, 3, 3]
module = importlib.import_module(
'blueoil.converter.core.operators')
try:
op_def = getattr(module, op)
input_ops = {n: x for n in op_def.input_names}
shape = self.reverse_shape(shape) if op == 'Transpose' \
else [1, 2, 2, 3] if op == 'MaxPool' or op == 'AveragePool' \
else shape
args = [name, shape, dtype, input_ops]
obj = op_def(*args)
self.assertEqual(obj.name, name)
except Exception as e:
print(f'failed in testing {op}.')
raise e
print("Dynamic unary operator load test passed!")
def make_simple_model(self) -> Graph:
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)
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', Float32(), np.array(1))
s2 = Constant('aq_const2', Float32(), np.array(2))
aq = LinearMidTreadHalfQuantizer('aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2})
# Conv2
w2 = Constant('weight2', Float32(), data2)
kq = BinaryMeanScalingQuantizer('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 test_dynamic_create_batchnorm(self) -> None:
"""Test code for n-ary operators (BatchNormalization)."""
x = Constant(
'const',
Float32(),
np.zeros([1, 3, 3, 3])
)
nary_ops = [
'BatchNormalization'
]
name = 'test'
shape = [1, 3, 3, 3]
dtype = Float32()
for op in nary_ops:
module = importlib.import_module('blueoil.converter.core.operators')
try:
op_def = getattr(module, op)
input_ops = {n: x for n in op_def.input_names}
args = [name, shape, dtype, input_ops]
obj = op_def(*args)
self.assertEqual(obj.name, name)
except Exception as e:
print(f'failed in testing {op}.')
raise e
print("Dynamic batchnorm operator load test passed!")
def test_add_consistency2(self) -> None:
"""Test code for 'Add', which fails."""
a = Constant(
'const1',
Float32(),
np.zeros([1, 3, 3])
)
b = Constant(
'const2',
Float32(),
np.zeros([2])
)
input_ops = {'A': cast(Operator, a), 'B': cast(Operator, b)}
try:
Add(
'add1',
[1, 3, 3],
Float32(),
input_ops
)
except AssertionError:
print("Consistency test for 'Add' #2 passed!")
else:
self.assertTrue(False, "Consistency test for 'Add' #2 failed.")
def test_dynamic_create_binary(self) -> None:
"""Test code for binary operators."""
x = Constant('const1', Float32(), np.zeros([1, 3, 3, 3]))
w = Constant('const2', Float32(), np.zeros([1, 2, 2, 3]))
binary_ops = ['Conv', 'Add']
name = 'test'
dtype = Float32()
for op in binary_ops:
shape = [1, 3, 3, 3]
module = importlib.import_module(
'blueoil.converter.core.operators')
try:
op_def = getattr(module, op)
shape = [1, 2, 2, 3] if op == 'Conv' else shape
input_ops = {n: opw for n, opw in zip(op_def.input_names, [x, w])} \
if op == 'Conv' else {n: x for n in op_def.input_names}
args = [name, shape, dtype, input_ops]
obj = op_def(*args)
self.assertEqual(obj.name, name)
except Exception as e:
print(f'failed in testing {op}.')
raise e
print("Dynamic binary operator load test passed!")
def test_add_consistency1(self) -> None:
"""Test code for 'Add', which succeeds."""
a = Constant('const1', Float32(), np.zeros([1, 3, 3]))
b = Constant('const2', Float32(), np.zeros([3]))
input_ops = {'A': cast(Operator, a), 'B': cast(Operator, b)}
add = Add('add1', [1, 3, 3], Float32(), input_ops)
print("Consistency test for 'Add' #1 passed!")
def test_pool_consistency(self) -> None:
"""Test code for Pool."""
x = Constant('const1', Float32(), np.zeros([1, 3, 3, 3]))
input_ops = {'X': cast(Operator, x)}
add = MaxPool('max_pool1', [1, 2, 2, 3],
Float32(),
input_ops,
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2])
print("Consistency test for pooling operator passed!")
def create_sample_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)
i1 = Identity('identity1', [1, 2, 2, 3], Float32(), {'input': w})
q = BinaryMeanScalingQuantizer('qtz1', [1, 2, 2, 3], Float32(),
{'input': i1})
# Conv
conv = Conv('conv', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2])
# One output
i2 = Identity('identity2', [1, 4, 4, 3], Float32(), {'input': conv})
rs = Reshape('reshape', [1, 48], Float32(), {'data': i2})
y = Output(
'output',
[1, 48],
Float32(),
{'input': rs},
)
# add ops to the graph
graph.add_op_and_inputs(y)
return graph
def test_maxpool(self) -> None:
"""Test code for MaxPool."""
# get MaxPool's input names
i_names = MaxPool.input_names
self.assertEqual(i_names, ['X'])
# set x to MaxPool m's input
x = Constant('const', Float32(), np.zeros([1, 3, 3, 3]))
inputs: Dict[str, Operator] = {i_names[0]: x}
m = MaxPool("MaxPool", [1, 2, 2, 3],
Float32(),
inputs,
kernel_shape=[2, 2])
print("MaxPool test passed!")
def test_conv_consistency(self) -> None:
"""Test code for Conv."""
x = Input(
'const1',
[1, 3, 3, 3],
Float32(),
)
w = Constant('weight', Float32(), np.zeros([1, 2, 2, 3]))
input_ops = {'X': cast(Operator, x), 'W': cast(Operator, w)}
add = Conv('conv_under_test', [1, 3, 3, 3],
Float32(),
input_ops,
pads=[1, 1, 2, 2],
strides=[2, 2])
print("Consistency test for conv operator passed!")
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])
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 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 test_pass_propagate_output_type_backward(self) -> None:
"""Test pass."""
data1 = np.float32(np.random.rand(1, 2, 2, 3))
graph1 = self.create_sample_graph(data1)
pass_propagate_output_type_backward(graph1)
self.assertEqual(graph1.get_op('conv1').dtype, Float32(),
'[Failed] Found dtype of SpaceToDepth not propagate correctly')
print("Test pass #7 propagate output type backward passed!")
def pass_insert_cast(graph: Graph) -> None:
"""Insert Cast Operator if needed
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
cast_idx = 0
exec_list = sort_graph(graph)
for m in exec_list:
if m.dtype != QUANTIZED_PACKED():
continue
to_be_updated = {}
to_be_updated_names = []
for out_name, out_nodes in m.output_ops.items():
cast_needed = []
new_out_nodes = []
channels = m.channels
for out_node in out_nodes:
if out_node.preserve_quantization:
if out_node.op_type != 'Conv' or out_node.is_quantized:
new_out_nodes.append(out_node)
continue
if isinstance(
out_node, Output
): # need to shrink the number of output channels
channels = out_node.channels
cast_needed.append(out_node)
if not cast_needed:
continue
shape = [1, m.height, m.width, channels]
cast_op = Cast(
f'automatic_cast_{cast_idx}',
shape, # TODO(primenumber): fix shape
Float32(),
{'x': m},
'NHWC')
cast_idx += 1
for out_node in cast_needed:
cast_op.add_output('y', out_node)
in_names = []
for in_name, in_node in out_node.input_ops.items():
if m == in_node:
in_names.append(in_name)
for in_name in in_names:
out_node.add_input(in_name, cast_op)
new_out_nodes.append(cast_op)
graph.add_op(cast_op)
to_be_updated_names.append(out_name)
to_be_updated.update({out_name: new_out_nodes})
for out_name in to_be_updated_names:
m.remove_output(out_name)
m.add_outputs(to_be_updated)
def test_conv(self) -> None:
"""Test code for Conv."""
# get Conv's input names
i_names = Conv.input_names
self.assertTrue({'X', 'W'}.issubset(set(i_names)))
# set x to MaxPool m's input
x = Input(
'input',
[1, 3, 3, 3],
Float32(),
)
w = Constant('weight', Float32(), np.zeros([1, 2, 2, 5]))
inputs: Dict[str, Operator] = {i_names[0]: x, i_names[1]: w}
c = Conv("conv1", [1, 2, 2, 3], Float32(), inputs, kernel_shape=[2, 2])
self.assertEqual(c.batchsize, 1)
self.assertEqual(c.height, 2)
self.assertEqual(c.width, 2)
self.assertEqual(c.channel, 3)
self.assertEqual(c.kernel_height, 2)
self.assertEqual(c.kernel_width, 2)
print("Conv test passed!")
def test_pass_quantize_convolutions(self) -> None:
"""Test pass."""
data1 = np.float32(np.random.rand(1, 2, 2, 3))
data2 = np.float32(np.random.rand(1, 2, 2, 3))
graph1 = self.create_sample_graph(data1, data2)
pass_quantize_convolutions(graph1)
self.assertEqual(graph1.get_op('aqtz1').dtype, QUANTIZED_PACKED(),
'[Failed] Found output dtype of activation quantizer not proper')
self.assertEqual(graph1.get_op('kqtz1').dtype, PackedUint32(),
'[Failed] Found output dtype of kernel quantizer not proper')
self.assertEqual(graph1.get_op('conv2').dtype, Float32(),
'[Failed] Found output dtype of conv not proper')
print("Test pass #5 quantize_convolutions passed!")
def create_expected_graph(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')
i1 = Identity('identity1', [1, 2, 2, 3],
Float32(), {'input': w},
dimension_format='NHWC')
q = BinaryMeanScalingQuantizer('qtz1', [1, 2, 2, 3],
Float32(), {'input': i1},
dimension_format='NHWC')
# Conv
conv = Conv('conv', [1, 4, 4, 3],
Float32(), {
'X': x,
'W': q
},
kernel_shape=[2, 2],
dimension_format='NHWC')
# 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_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_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 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 = LinearMidTreadHalfQuantizer('aqtz1', [1, 4, 4, 3], Float32(), {
'X': conv1,
'Y': s1,
'Z': s2
})
# Conv2
w2 = Constant('weight2', Float32(), data2)
kq = BinaryMeanScalingQuantizer('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 = LinearMidTreadHalfQuantizer('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
import blueoil.converter.core.operators as dlk_op
from blueoil.converter.core.data_types import DataType, Float32, Float64, Int8, Int16, Int32, \
Int64, Uint8, Uint16, Uint32, Uint64, Bool, String
from blueoil.converter.core.exceptions import UnsupportedNode, UnsupportedDataType
from blueoil.converter.core.graph import Graph
from blueoil.converter.core.operators import Operator, Conv, \
Identity, BinaryMeanScalingQuantizer, \
BatchNormalization, LinearMidTreadHalfQuantizer, Add, Sub, \
MaxPool, AveragePool, Reshape, Softmax, Transpose, Relu, SpaceToDepth, \
Mul, BinaryChannelWiseMeanScalingQuantizer, ConcatOnDepth, Maximum, \
DepthToSpace, ResizeNearestNeighbor, \
Split, Pad, MatMul, Gather, Unique, Cast, Minimum, StridedSlice, Prod, Shape, LeakyRelu
DLK_DTYPE_MAP: Dict[str, Optional[DataType]] = {
# any
'DT_INVALID': Float32(),
# primitives
'DT_FLOAT': Float32(),
'DT_INT32': Int32(),
'DT_UINT8': Uint8(),
'DT_INT8': Int8(),
'DT_UINT16': Uint16(),
'DT_INT16': Int16(),
'DT_INT64': Int64(),
'f': Float32(),
'i': Int32(),
# primitive vector
'FLOATS': None,
'INTS': None,
Int64, Uint8, Uint16, Uint32, Uint64, Bool, String
from blueoil.converter.core.exceptions import UnsupportedNode, UnsupportedDataType
from blueoil.converter.core.graph import Graph
from blueoil.converter.core.operators import Operator, Conv, \
Identity, BinaryMeanScalingQuantizer, \
BatchNormalization, QTZ_linear_mid_tread_half, Add, \
MaxPool, AveragePool, Reshape, Softmax, Transpose, Relu, SpaceToDepth, \
Mul, BinaryChannelWiseMeanScalingQuantizer, ConcatOnDepth, Maximum, \
DepthToSpace, ResizeNearestNeighbor, \
Split, Pad, MatMul, Gather, Unique, Cast, Minimum, StridedSlice, Prod, Shape, LeakyRelu
DLK_DTYPE_MAP: Dict[str, Optional[DataType]] = {
# any
'DT_INVALID': Float32,
# primitives
'DT_FLOAT': Float32(),
'DT_INT32': Int32(),
'DT_UINT8': Uint8(),
'DT_INT8': Int8(),
'DT_UINT16': Uint16(),
'DT_INT16': Int16(),
'DT_INT64': Int64(),
'f': Float32(),
'i': Int32(),
# primitive vector
'FLOATS': None,
'INTS': None,
# custom
'DT_BOOL': Bool(),
