def test_onnx_example_algebra(self):
initial = np.array([0, 0]).astype(np.float32).reshape((2,))
x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
opv = _TARGET_OPSET_
add_node = OnnxAdd(
'sum_in', 'next', output_names=['sum_out'],
op_version=opv)
id_node = OnnxIdentity(
add_node, output_names=['scan_out'],
op_version=opv)
scan_body = id_node.to_onnx(
{'sum_in': initial, 'next': initial},
outputs=[('sum_out', FloatTensorType()),
('scan_out', FloatTensorType())])
node = OnnxScan('initial', 'x', output_names=['y', 'z'],
num_scan_inputs=1, body=scan_body.graph,
op_version=opv)
model_def = node.to_onnx(
{'initial': initial, 'x': x},
outputs=[('y', FloatTensorType()),
('z', FloatTensorType())])
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'initial': initial, 'x': x})
y = np.array([9, 12]).astype(np.float32).reshape((2,))
z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
assert_almost_equal(y, res[0])
assert_almost_equal(z, res[1])
def test_onnx_example_pdist(self):
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
opv = _TARGET_OPSET_
diff = OnnxSub('next_in',
'next',
output_names=['diff'],
op_version=opv)
id_next = OnnxIdentity('next_in',
output_names=['next_out'],
op_version=opv)
norm = OnnxReduceSumSquare(diff,
output_names=['norm'],
axes=[1],
op_version=opv)
flat = OnnxSqueezeApi11(norm,
output_names=['scan_out'],
axes=[1],
op_version=opv)
scan_body = id_next.to_onnx(OrderedDict([('next_in', x),
('next', FloatTensorType())]),
outputs=[
('next_out', FloatTensorType([3, 2])),
('scan_out', FloatTensorType([3]))
],
other_outputs=[flat],
target_opset=opv)
sess = InferenceSession(scan_body.SerializeToString())
res = sess.run(None, {'next_in': x, 'next': x[:1]})
assert_almost_equal(x, res[0])
exp = np.array([0., 18., 20.], dtype=np.float32)
assert_almost_equal(exp, res[1])
node = OnnxScan('x',
'x',
output_names=['y', 'z'],
num_scan_inputs=1,
body=scan_body.graph,
op_version=opv)
model_def = node.to_onnx({'x': x},
outputs=[('y', FloatTensorType([3, 2])),
('z', FloatTensorType([3, 3]))])
try:
onnx.checker.check_model(model_def)
except ValidationError as e:
if StrictVersion(onnx__version__) <= StrictVersion("1.5.0"):
warnings.warn(e)
else:
raise e
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'x': x})
exp = squareform(pdist(x, metric="sqeuclidean"))
assert_almost_equal(x, res[0])
assert_almost_equal(exp, res[1])
def squareform_pdist(X, **kwargs):
opv = TARGET_OPSET
diff = OnnxSub('next_in',
'next',
output_names=['diff'],
op_version=opv)
id_next = OnnxIdentity('next_in',
output_names=['next_out'],
op_version=opv)
norm = OnnxReduceSumSquare(diff,
output_names=['norm'],
axes=[1],
op_version=opv)
flat = OnnxSqueezeApi11(norm,
output_names=['scan_out'],
axes=[1],
op_version=opv)
scan_body = id_next.to_onnx(
OrderedDict([('next_in', FloatTensorType()),
('next', FloatTensorType())]),
outputs=[('next_out', FloatTensorType([None, None])),
('scan_out', FloatTensorType([None]))],
other_outputs=[flat])
node = OnnxScan(X,
X,
output_names=['scan0_{idself}', 'scan1_{idself}'],
num_scan_inputs=1,
body=scan_body.graph,
op_version=opv,
**kwargs)
return node[1]
def test_onnx_example_pdist(self):
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
diff = OnnxSub('next_in', 'next', output_names=['diff'])
id_next = OnnxIdentity('next_in', output_names=['next_out'])
norm = OnnxReduceSumSquare(diff, output_names=['norm'], axes=[1])
flat = OnnxSqueeze(norm, output_names=['scan_out'], axes=[1])
scan_body = id_next.to_onnx(OrderedDict([('next_in', x),
('next', FloatTensorType())]),
outputs=[
('next_out', FloatTensorType([3, 2])),
('scan_out', FloatTensorType([3]))
],
other_outputs=[flat])
sess = InferenceSession(scan_body.SerializeToString())
res = sess.run(None, {'next_in': x, 'next': x[:1]})
assert_almost_equal(x, res[0])
exp = np.array([0., 18., 20.], dtype=np.float32)
assert_almost_equal(exp, res[1])
node = OnnxScan('x',
'x',
output_names=['y', 'z'],
num_scan_inputs=1,
body=scan_body.graph)
model_def = node.to_onnx({'x': x},
outputs=[('y', FloatTensorType([3, 2])),
('z', FloatTensorType([3, 3]))])
try:
onnx.checker.check_model(model_def)
except ValidationError as e:
if sys.platform.startswith("win"):
# schema information in onnx is incomplete on Windows
warnings.warn(e)
else:
raise e
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'x': x})
exp = squareform(pdist(x, metric="sqeuclidean"))
assert_almost_equal(x, res[0])
assert_almost_equal(exp, res[1])