def _onnx_zero(target_opset=None, dtype=numpy.float32):
"""
Returns the ONNX graph for function
:math:`Y = X * 0`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('zero')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import OnnxMul
res = OnnxMul('X',
numpy.array([0], dtype=dtype),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X', var_type())]
onx = res.to_onnx(varsx,
outputs=[('Y', var_type())],
target_opset=target_opset)
return onx
def _onnx_axpyw2(target_opset=None, dtype=numpy.float32):
"""
Returns the ONNX graph for function
:math:`Y, Z = f(X1, X2, G, \\alpha, \\beta) = (Y, Z)`
where :math:`Z = \\beta G + \\alpha X1` and
:math:`Y = \\beta * Z + \\alpha X1 + X2`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('axpy')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import OnnxAdd, OnnxMul
s1 = OnnxMul('X1', 'alpha', op_version=target_opset)
s2 = OnnxMul('G', 'beta', op_version=target_opset)
Z = OnnxAdd(s1, s2, op_version=target_opset, output_names=['Z'])
s2_2 = OnnxMul(Z, 'beta', op_version=target_opset)
s2_3 = OnnxAdd(s1, s2_2, op_version=target_opset)
Y = OnnxAdd(s2_3, 'X2', op_version=target_opset, output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type()), ('X2', var_type()), ('G', var_type()),
('alpha', var_type([1])), ('beta', var_type([1]))]
onx = Y.to_onnx(varsx,
outputs=[('Y', var_type()), ('Z', var_type())],
target_opset=target_opset,
other_outputs=[Z])
return onx
def test_grad_helper_mul(self):
opv = opset
xi = OnnxIdentity('X', op_version=opv)
node = OnnxMul(xi, xi, op_version=opv, output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
new_onx = onnx_derivative(onx)
self.check_runtime(new_onx, 'test_grad_helper_mul')
def pyod_iforest_converter(scope, operator, container):
op = operator.raw_operator
opv = container.target_opset
out = operator.outputs
# We retrieve the unique input.
X = operator.inputs[0]
# In most case, computation happen in floats.
# But it might be with double. ONNX is very strict
# about types, every constant should have the same
# type as the input.
dtype = guess_numpy_type(X.type)
detector = op.detector_ # Should be IForest from scikit-learn.
lab_pred = OnnxSubEstimator(detector, X, op_version=opv)
scores = OnnxIdentity(lab_pred[1], op_version=opv)
# labels
threshold = op.threshold_
above = OnnxLess(scores,
np.array([threshold], dtype=dtype),
op_version=opv)
labels = OnnxCast(above,
op_version=opv,
to=onnx_proto.TensorProto.INT64,
output_names=out[:1])
# probabilities
train_scores = op.decision_scores_
scaler = MinMaxScaler().fit(train_scores.reshape(-1, 1))
scores_ = OnnxMul(scores, np.array([-1], dtype=dtype), op_version=opv)
print(scaler.min_)
print(scaler.scale_)
scaled = OnnxMul(scores_, scaler.scale_.astype(dtype), op_version=opv)
scaled_centered = OnnxAdd(scaled,
scaler.min_.astype(dtype),
op_version=opv)
clipped = OnnxClip(scaled_centered,
np.array([0], dtype=dtype),
np.array([1], dtype=dtype),
op_version=opv)
clipped_ = OnnxAdd(OnnxMul(clipped,
np.array([-1], dtype=dtype),
op_version=opv),
np.array([1], dtype=dtype),
op_version=opv)
scores_2d = OnnxConcat(clipped_,
clipped,
axis=1,
op_version=opv,
output_names=out[1:])
labels.add_to(scope, container)
scores_2d.add_to(scope, container)
def _onnx_grad_loss_absolute_error(target_opset=None,
dtype=numpy.float32,
weight_name=None):
"""
Returns the ONNX graph for function
:math:`Y = f(X1, X2) = \\lVert X1 - X2 \\rVert` or
:math:`Y = f(X1, X2) = \\lVert (X1 - X2)w \\rVert` if
*weight_name* is not None and its gradient.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_loss_absolute_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxReduceSum,
OnnxReshape, OnnxSign, OnnxAbs)
diff = OnnxSub('X1', 'X2', op_version=target_opset)
abs_diff = OnnxAbs(diff, op_version=target_opset)
if weight_name is None:
res = OnnxReduceSum(abs_diff, op_version=target_opset)
res2 = OnnxSign(diff, op_version=target_opset, output_names=['Y_grad'])
else:
resh = OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset)
mul = OnnxMul(abs_diff, resh, op_version=target_opset)
res = OnnxReduceSum(mul, op_version=target_opset)
res2 = OnnxMul(OnnxSign(diff, op_version=target_opset),
resh,
op_version=target_opset,
output_names=['Y_grad'])
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type([None, None])), ('X2', var_type([None, None]))]
if weight_name is not None:
varsx.append((weight_name, var_type([None])))
onx = res.to_onnx(varsx,
outputs=[('Y', var_type()), ('Y_grad', var_type())],
target_opset=target_opset,
other_outputs=[res2])
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx
def build_leaky_relu_decomposed_greater(alpha=0.5, target_opset=15):
signo = OnnxGreater('X',
numpy.array([0], dtype=numpy.float32),
op_version=target_opset)
sign = OnnxCast(signo, to=TensorProto.FLOAT, op_version=target_opset)
fact = OnnxAdd(OnnxMul(sign,
numpy.array([1 - alpha], dtype=numpy.float32),
op_version=target_opset),
numpy.array([alpha], dtype=numpy.float32),
op_version=target_opset)
x = OnnxMul('X', fact, op_version=target_opset, output_names=['Y'])
return x.to_onnx({'X': FloatTensorType()},
outputs={'Y': FloatTensorType()},
target_opset=target_opset)
def build_ort_where_add(op_version=12):
node = OnnxSub(
OnnxMul('x', 'cond', op_version=op_version),
OnnxMul('y',
OnnxSub('cond', numpy.array([1], dtype=numpy.float32),
op_version=op_version),
op_version=op_version),
op_version=op_version, output_names=['z'])
onx = node.to_onnx(inputs=[('cond', FloatTensorType()),
('x', FloatTensorType()),
('y', FloatTensorType())],
target_opset=op_version)
sess = InferenceSession(onx.SerializeToString())
return lambda cond, x, y: sess.run(None, {'cond': cond, 'x': x, 'y': y})
def test_onnxruntime_bug(self):
rnd = numpy.random.randn(3, 20, 20).astype(numpy.float32)
bni = (numpy.random.random((20, 20)).astype( # pylint: disable=E1101
numpy.float32) >= 0.7).astype(numpy.float32)
mul = rnd * bni
isn = any(numpy.isnan(mul.ravel()))
self.assertFalse(isn)
node = OnnxMul('X', bni, output_names=['Y4'],
op_version=TARGET_OPSET)
onx = node.to_onnx({'X': rnd})
for rt in ['python', 'onnxruntime1']:
with self.subTest(runtime=rt):
oinf = OnnxInference(onx, runtime=rt)
y = oinf.run({'X': rnd})['Y4']
self.assertEqualArray(mul, y)
def _onnx_axpy(target_opset=None, dtype=numpy.float32):
"""
Returns the ONNX graph for function
:math:`Y = f(X1, X2, \\alpha) = \\alpha X1 + X2`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('axpy')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import OnnxAdd, OnnxMul
res = OnnxAdd(OnnxMul('X1', 'alpha', op_version=target_opset),
'X2',
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type()), ('X2', var_type()), ('alpha', var_type([1]))]
onx = res.to_onnx(varsx,
outputs=[('Y', var_type())],
target_opset=target_opset)
return onx
def test_onnx_if_algebra_indirect_unnamed_clear_input(self):
opv = TARGET_OPSET
x1 = np.array([[0, 3], [7, 0]], dtype=np.float32)
x2 = np.array([[1, 0], [2, 0]], dtype=np.float32)
node_xy = OnnxMul('x1', 'x2', op_version=opv)
node_then = OnnxAdd(
'x1', 'xy', output_names=['absxythen'], op_version=opv)
then_body = node_then.to_onnx(
{'x1': x1, 'xy': x2}, target_opset=opv,
outputs=[('absxythen', FloatTensorType())])
node_else = OnnxSub(
'x1', 'x2', output_names=['absxyelse'], op_version=opv)
else_body = node_else.to_onnx(
{'x1': x1, 'x2': x2}, target_opset=opv,
outputs=[('absxyelse', FloatTensorType())])
cond = OnnxGreater(
OnnxReduceSum('x1', op_version=opv),
OnnxReduceSum('x2', op_version=opv),
op_version=opv)
ifnode = OnnxIf(cond, then_branch=then_body.graph,
else_branch=else_body.graph,
op_version=opv, output_names=['y'],
global_context={'xy': node_xy},
clear_subgraph_inputs=True)
model_def = ifnode.to_onnx(
{'x1': x1, 'x2': x2}, target_opset=opv,
outputs=[('y', FloatTensorType())])
sess = InferenceSession(model_def.SerializeToString())
res = sess.run(None, {'x1': x1, 'x2': x2})
assert_almost_equal(x1 + x1 * x2, res[0])
def test_onnx_rename_names_type(self):
rows = []
def flog(*s):
rows.append(" ".join(map(str, s)))
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def = cop4.to_onnx({'X': x})
oinf1 = OnnxInference(model_def)
new_model = onnx_rename_names(model_def,
verbose=1,
fLOG=flog,
strategy='type')
total = "\n".join(rows)
self.assertIn("'Ad_Addcst' -> 'i_05'", total)
oinf2 = OnnxInference(new_model)
y1 = oinf1.run({'X': x})
y2 = oinf2.run({'X': x})
self.assertEqualArray(y1['final'], y2['final'])
def build_leaky_relu_decomposed(alpha=0.5, target_opset=15):
signo = OnnxSign('X', op_version=target_opset)
sign = OnnxDiv(OnnxAdd(signo,
numpy.array([1], dtype=numpy.float32),
op_version=target_opset),
numpy.array([2], dtype=numpy.float32),
op_version=target_opset)
fact = OnnxAdd(OnnxMul(sign,
numpy.array([1 - alpha], dtype=numpy.float32),
op_version=target_opset),
numpy.array([alpha], dtype=numpy.float32),
op_version=target_opset)
x = OnnxMul('X', fact, op_version=target_opset, output_names=['Y'])
return x.to_onnx({'X': FloatTensorType()},
outputs={'Y': FloatTensorType()},
target_opset=target_opset)
def _onnx_update_penalty_elastic_error(target_opset=None,
dtype=numpy.float32,
l1=1e-4,
l2=1e-4):
"""
Returns the ONNX graph for function
:math:`Y = f(W) = W - 2 \\beta W - \\alpha sign(W)`
*l1* is :math:`\\beta` and
*l2* is :math:`\\alpha`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph(
'update_penalty_elastic_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxSign)
res = OnnxSub(OnnxMul('X',
numpy.array([1 - 2 * l2], dtype=dtype),
op_version=target_opset),
OnnxMul(OnnxSign('X', op_version=target_opset),
numpy.array([l1], dtype=dtype),
op_version=target_opset),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X', var_type())]
onx = res.to_onnx(varsx,
outputs=[('Y', var_type())],
target_opset=target_opset)
return onx
def build_ort_op(op_version=14, save=None, slices=None): # opset=13, 14, ...
if slices is None:
starts = numpy.array([1, 1], dtype=numpy.int64)
ends = numpy.array([-1, -1], dtype=numpy.int64)
axes = None
else:
starts, ends = slices
if starts[0] is None:
indexes = [i for i in range(len(starts)) if starts[i] is not None]
starts = numpy.array([n for n in starts if n is not None],
dtype=numpy.int64)
ends = numpy.array([n for n in ends if n is not None],
dtype=numpy.int64)
axes = numpy.array(indexes, dtype=numpy.int64)
else:
starts = numpy.array(starts, dtype=numpy.int64)
ends = numpy.array(ends, dtype=numpy.int64)
axes = None
if axes is None:
node1 = OnnxSlice('X', starts, ends, op_version=op_version)
else:
node1 = OnnxSlice('X', starts, ends, axes, op_version=op_version)
node2 = OnnxAdd(node1,
numpy.array([1], dtype=numpy.float32),
op_version=op_version)
if axes is None:
node3 = OnnxSlice(node2, starts, ends, op_version=op_version)
else:
node3 = OnnxSlice(node2, starts, ends, axes, op_version=op_version)
node4 = OnnxMul(node3,
numpy.array([2], dtype=numpy.float32),
op_version=op_version,
output_names=['Y'])
onx = node4.to_onnx(inputs=[('X', FloatTensorType([None, None]))],
target_opset=op_version)
return onx
def test_onnx_remove_unused_outputs_new(self):
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def0 = cop4.to_onnx({'X': x})
model_def = select_model_inputs_outputs(model_def0,
"inter",
infer_shapes=True,
remove_unused=False)
stats = onnx_statistics(model_def, optim=True)
c1 = model_def.SerializeToString()
new_model = select_model_inputs_outputs(model_def0,
"inter",
infer_shapes=True)
c2 = model_def.SerializeToString()
self.assertEqual(c1, c2)
stats2 = onnx_statistics(model_def, optim=True)
stats3 = onnx_statistics(new_model, optim=False)
self.assertEqual(stats['ninits'], 2)
self.assertEqual(stats2['ninits'], 2)
self.assertEqual(stats3['ninits'], 1)
self.assertEqual(stats2['nnodes'], 1)
self.assertEqual(stats3['nnodes'], 1)
oinf1 = OnnxInference(model_def)
y1 = oinf1.run({'X': x})
oinf2 = OnnxInference(new_model)
y2 = oinf2.run({'X': x})
self.assertNotIn('final', y1)
self.assertNotIn('final', y2)
self.assertIn('inter', y1)
self.assertIn('inter', y2)
self.assertEqualArray(y1['inter'], y2['inter'])
def nmf_to_onnx(W, H):
"""
The function converts a NMF described by matrices
*W*, *H* (*WH* approximate training data *M*).
into a function which takes two indices *(i, j)*
and returns the predictions for it. It assumes
these indices applies on the training data.
"""
col = OnnxArrayFeatureExtractor(H, 'col')
row = OnnxArrayFeatureExtractor(W.T, 'row')
dot = OnnxMul(col, row)
res = OnnxReduceSum(dot, output_names="rec")
indices_type = np.array([0], dtype=np.int64)
onx = res.to_onnx(inputs={'col': indices_type,
'row': indices_type},
outputs=[('rec', FloatTensorType((None, 1)))])
return onx
def test_onnx_remove_two_outputs(self):
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=get_opset_number_from_onnx())
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
output_names=['keep'],
op_version=get_opset_number_from_onnx())
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=get_opset_number_from_onnx())
cop4 = OnnxSub(OnnxMul(cop,
cop3,
op_version=get_opset_number_from_onnx()),
cop2,
output_names=['final'],
op_version=get_opset_number_from_onnx())
model_def = cop4.to_onnx({'X': x},
outputs=[('keep', FloatTensorType([None, 2])),
('final', FloatTensorType([None,
2]))])
c1 = model_def.SerializeToString()
self.assertEqual(len(model_def.graph.output), 2)
c2 = model_def.SerializeToString()
self.assertEqual(c1, c2)
stats = onnx_statistics(model_def, optim=True)
new_model = onnx_remove_node_redundant(model_def, max_hash_size=10)
stats2 = onnx_statistics(model_def, optim=True)
stats3 = onnx_statistics(new_model, optim=False)
self.assertEqual(stats['ninits'], 2)
self.assertEqual(stats2['ninits'], 2)
self.assertEqual(stats3['ninits'], 2)
self.assertEqual(stats2['nnodes'], 6)
self.assertEqual(stats3['nnodes'], 6)
oinf1 = OnnxInference(model_def)
y1 = oinf1.run({'X': x})
oinf2 = OnnxInference(new_model)
y2 = oinf2.run({'X': x})
self.assertEqualArray(y1['final'], y2['final'])
self.assertEqualArray(y1['keep'], y2['keep'])
def conv(scope, operator, container):
X = operator.inputs[0]
out = operator.outputs
op = operator.raw_operator
dtype = guess_numpy_type(X.type)
C = op.cluster_centers_
C2 = row_norms(C, squared=True).astype(dtype)
C = C.astype(dtype)
rs = OnnxReduceSumSquare(X,
axes=[1],
keepdims=1,
op_version=container.target_opset)
N = X.type.shape[0]
if isinstance(N, int):
zeros = np.zeros((N, ))
else:
zeros = OnnxMul(rs,
np.array([0], dtype=np.float32),
op_version=container.target_opset)
z = OnnxAdd(rs,
OnnxGemm(X,
C,
zeros,
alpha=-2.,
transB=1,
op_version=container.target_opset),
op_version=container.target_opset)
y2 = OnnxAdd(C2, z, op_version=container.target_opset)
lo = OnnxArgMin(y2,
axis=1,
keepdims=0,
output_names=out[:1],
op_version=container.target_opset)
y2s = OnnxSqrt(y2,
output_names=out[1:],
op_version=container.target_opset)
lo.add_to(scope, container)
y2s.add_to(scope, container)
def test_onnx_rename_names_exc(self):
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def = cop4.to_onnx({'X': x})
self.assertRaise(lambda: onnx_rename_names(model_def, strategy="none"),
ValueError)
def _onnx_grad_square_error(target_opset=None,
dtype=numpy.float32,
weight_name=None):
"""
Returns the ONNX graph for the gradient of function
:math:`Y = f(X1, X2) = \\lVert X1 - X2 \\rVert ^2` or
:math:`Y = f(X1, X2) = \\lVert X1 - X2 \\rVert ^2 w` if
*weight_name* is not None
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_square_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import OnnxSub, OnnxMul, OnnxReshape
diff = OnnxSub('X1', 'X2', op_version=target_opset)
if weight_name is None:
res = OnnxMul(diff,
numpy.array([-2], dtype=dtype),
op_version=target_opset,
output_names=['Y_grad'])
else:
res = OnnxMul(OnnxMul(diff,
numpy.array([-2], dtype=dtype),
op_version=target_opset),
OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset),
op_version=target_opset,
output_names=['Y_grad'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type([None, None])), ('X2', var_type([None, None]))]
if weight_name is not None:
varsx.append((weight_name, var_type([None])))
onx = res.to_onnx(varsx,
outputs=[('Y_grad', var_type())],
target_opset=target_opset)
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx
def test_prepare_c_profiling(self):
opset = TestOrt.opset
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X', numpy.array([1], dtype=dtype), op_version=opset)
cop2 = OnnxAdd('X', numpy.array([1], dtype=dtype), op_version=opset)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=opset,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=opset),
cop2,
output_names=['final'],
op_version=13)
model_def = cop4.to_onnx({'X': x}, target_opset=opset)
temp = get_temp_folder(__file__, "temp_prepare_c_profiling")
cmd = prepare_c_profiling(model_def, [x], dest=temp)
self.assertStartsWith("onnx", cmd)
self.assertExists(os.path.join(temp, "model.onnx"))
self.assertExists(os.path.join(temp, "test_data_set_0", "input_0.pb"))
self.assertExists(os.path.join(temp, "test_data_set_0", "output_0.pb"))
def test_onnx_remove_redundant(self):
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET)
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def = cop4.to_onnx({'X': x})
stats = onnx_statistics(model_def, optim=True)
c1 = model_def.SerializeToString()
new_model = onnx_remove_node_redundant(model_def, max_hash_size=10)
c2 = model_def.SerializeToString()
self.assertEqual(c1, c2)
stats2 = onnx_statistics(model_def, optim=True)
stats3 = onnx_statistics(new_model, optim=False)
self.assertEqual(stats['ninits'], 2)
self.assertEqual(stats2['ninits'], 2)
self.assertEqual(stats3['ninits'], 2)
self.assertEqual(stats2['nnodes'], 6)
self.assertEqual(stats3['nnodes'], 6)
oinf1 = OnnxInference(model_def)
y1 = oinf1.run({'X': x})
oinf2 = OnnxInference(new_model)
y2 = oinf2.run({'X': x})
self.assertEqualArray(y1['final'], y2['final'])
def test_enumerate_model_node_outputs(self):
dtype = numpy.float32
x = numpy.array([1, 2, 4, 5, 5, 4]).astype(numpy.float32).reshape(
(3, 2))
cop = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop2 = OnnxAdd('X',
numpy.array([1], dtype=dtype),
op_version=TARGET_OPSET)
cop3 = OnnxAdd('X',
numpy.array([2], dtype=dtype),
op_version=TARGET_OPSET,
output_names=['inter'])
cop4 = OnnxSub(OnnxMul(cop, cop3, op_version=TARGET_OPSET),
cop2,
output_names=['final'],
op_version=TARGET_OPSET)
model_def = cop4.to_onnx({'X': x})
nodes1 = list(enumerate_model_node_outputs(model_def))
nodes2 = list(enumerate_model_node_outputs(model_def, order=True))
self.assertEqual(list(sorted(nodes1)), list(sorted(nodes2)))
expected = ['Ad_Addcst2', 'Ad_C0', 'inter', 'Ad_C02', 'Mu_C0', 'final']
self.assertEqual(nodes2, expected)
def get_onnx_mul(self):
mul = OnnxMul('X', 'X', output_names=['Y'])
onx = mul.to_onnx(inputs=[('X', FloatTensorType())])
return onx.SerializeToString()
def build_ort_op(op_version=14, save=None, **kwargs): # opset=13, 14, ...
slices = kwargs['slices']
slice1, slice2 = slices
slice1 = slice(0, None) if slice1 is None else slice(*slice1)
slice2 = slice(0, None) if slice2 is None else slice(*slice2)
axes = []
starts = []
ends = []
for i in [0, 1]:
if slices[i] is None:
continue
axes.append(i)
starts.append(slices[i][0])
ends.append(slices[i][1])
starts = numpy.array(starts, dtype=numpy.int64)
ends = numpy.array(ends, dtype=numpy.int64)
axes = numpy.array(axes, dtype=numpy.int64)
node1 = OnnxSlice('X', starts, ends, axes, op_version=op_version)
node2 = OnnxAdd(node1,
numpy.array([1], dtype=numpy.float32),
op_version=op_version)
node3 = OnnxSlice(node2, starts, ends, axes, op_version=op_version)
node4 = OnnxMul(node3,
numpy.array([2], dtype=numpy.float32),
op_version=op_version,
output_names=['Y'])
onx = node4.to_onnx(inputs=[('X', FloatTensorType([None, None]))],
target_opset=op_version)
sess = InferenceSession(onx.SerializeToString(),
providers=["CPUExecutionProvider"])
if save is not None:
with open(save, "wb") as f:
f.write(onx.SerializeToString())
def npy_fct(x):
return ((x[slice1, slice2] + 1)[slice1, slice2] * 2).copy()
rnd = numpy.random.randn(10, 10).astype(numpy.float32)
expected = npy_fct(rnd)
got = sess.run(None, {'X': rnd})[0]
try:
assert_almost_equal(expected, got)
except AssertionError as e:
raise AssertionError("kwargs=%r slice1=%r slice2=%r shapes=%r ? %r "
"(x[slice1, slice2].shape)=%r" %
(kwargs, slice1, slice2, expected.shape,
got.shape, rnd[slice1, slice2].shape)) from e
if get_device().upper() == 'GPU':
sessg = InferenceSession(onx.SerializeToString(),
providers=["CUDAExecutionProvider"])
io_binding = sessg.io_binding()._iobinding
device = get_ort_device('cuda:0')
def run_gpu(x):
io_binding.bind_input('X', device, numpy.float32, x.shape(),
x.data_ptr())
io_binding.bind_output('Y', device)
return sessg._sess.run_with_iobinding(io_binding, None)
return onx, lambda x: sess.run(None, {'X': x}), npy_fct, run_gpu
else:
return onx, lambda x: sess.run(None, {'X': x}), npy_fct, None
def live_decorrelate_transformer_converter(scope, operator, container):
# shortcuts
op = operator.raw_operator
opv = container.target_opset
out = operator.outputs
# We retrieve the unique input.
X = operator.inputs[0]
# We guess its type. If the operator ingests float (or double),
# it outputs float (or double).
proto_dtype = guess_proto_type(X.type)
dtype = guess_numpy_type(X.type)
# Lines in comment specify the numpy computation
# the ONNX code implements.
# mean_ = numpy.mean(X, axis=0, keepdims=True)
mean = OnnxReduceMean(X, axes=[0], keepdims=1, op_version=opv)
# This is trick I often use. The converter automatically
# chooses a name for every output. In big graph,
# it is difficult to know which operator is producing which output.
# This line just tells every node must prefix its ouputs with this string.
# It also applies to all inputs nodes unless this method
# was called for one of these nodes.
mean.set_onnx_name_prefix('mean')
# X2 = X - mean_
X2 = OnnxSub(X, mean, op_version=opv)
# V = X2.T @ X2 / X2.shape[0]
N = OnnxGatherElements(OnnxShape(X, op_version=opv),
numpy.array([0], dtype=numpy.int64),
op_version=opv)
Nf = OnnxCast(N, to=proto_dtype, op_version=opv)
# Every output involved in N and Nf is prefixed by 'N'.
Nf.set_onnx_name_prefix('N')
V = OnnxDiv(OnnxMatMul(OnnxTranspose(X2, op_version=opv),
X2,
op_version=opv),
Nf,
op_version=opv)
V.set_onnx_name_prefix('V1')
# V += numpy.identity(V.shape[0]) * self.alpha
V = OnnxAdd(V,
op.alpha * numpy.identity(op.nf_, dtype=dtype),
op_version=opv)
V.set_onnx_name_prefix('V2')
# L, P = numpy.linalg.eig(V)
LP = OnnxEig(V, eigv=True, op_version=opv)
LP.set_onnx_name_prefix('LP')
# Linv = L ** (-0.5)
# Notation LP[0] means OnnxPow is taking the first output
# of operator OnnxEig, LP[1] would mean the second one
# LP is not allowed as it is ambiguous
Linv = OnnxPow(LP[0], numpy.array([-0.5], dtype=dtype), op_version=opv)
Linv.set_onnx_name_prefix('Linv')
# diag = numpy.diag(Linv)
diag = OnnxMul(OnnxEyeLike(numpy.zeros((op.nf_, op.nf_),
dtype=numpy.int64),
k=0,
op_version=opv),
Linv,
op_version=opv)
diag.set_onnx_name_prefix('diag')
# root = P @ diag @ P.transpose()
trv = OnnxTranspose(LP[1], op_version=opv)
coef_left = OnnxMatMul(LP[1], diag, op_version=opv)
coef_left.set_onnx_name_prefix('coef_left')
coef = OnnxMatMul(coef_left, trv, op_version=opv)
coef.set_onnx_name_prefix('coef')
# Same part as before.
Y = OnnxMatMul(X2, coef, op_version=opv, output_names=out[:1])
Y.set_onnx_name_prefix('Y')
# The last line specifies the final output.
# Every node involved in the computation is added to the ONNX
# graph at this stage.
Y.add_to(scope, container)
def _onnx_grad_sigmoid_neg_log_loss_error(target_opset=None,
dtype=numpy.float32,
eps=1e-5,
weight_name=None):
"""
The function the raw scores from a classifier, uses the
sigmoid function to compute probabilities, then the log function
to compute the loss. It creates the ONNX graph for this function
and the associated gradient of the loss against the raw scores.
Probabilites (class 1): :math:`p(s) = \\frac{1}{1 + \\exp(-s)}`.
Loss (for two classes): :math:`L(y, s) = (1 - y)\\log(1 - p(s)) +
y \\log(p(s))`.
Gradient :math:`\\frac{dL(y, s)}{ds} = y - p(s)`.
To avoid nan values, probabilies are clipped:
:math:`p(s) = \\max(\\min(p(s), 1 - \\epsilon), \\epsilon)`.
:math:`y \\in \\{0, 1\\}` (integer). *s* is a float.
:param eps: to clip probabilities and avoid computing `log(0)`
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_sigmoid_neg_log_loss_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxSigmoid,
OnnxLog, OnnxNeg, OnnxReduceSum,
OnnxReshape, OnnxAdd, OnnxCast,
OnnxClip)
p1c = OnnxSigmoid('X2', op_version=target_opset)
p1 = OnnxClip(p1c,
numpy.array([eps], dtype=dtype),
numpy.array([1 - eps], dtype=dtype),
op_version=target_opset)
p0 = OnnxSub(numpy.array([1], dtype=dtype), p1, op_version=target_opset)
y1 = OnnxCast('X1',
to=NP_TYPE_TO_TENSOR_TYPE[numpy.dtype(dtype)],
op_version=target_opset)
y0 = OnnxSub(numpy.array([1], dtype=dtype), y1, op_version=target_opset)
loss_obs = OnnxAdd(OnnxMul(y0,
OnnxLog(p0, op_version=target_opset),
op_version=target_opset),
OnnxMul(y1,
OnnxLog(p1, op_version=target_opset),
op_version=target_opset),
op_version=target_opset)
loss_neg = OnnxNeg(loss_obs, op_version=target_opset)
if weight_name is None:
loss = OnnxReduceSum(loss_neg, op_version=target_opset)
grad = OnnxSub(p1,
y1,
op_version=target_opset,
output_names=['Y_grad'])
else:
loss = OnnxReduceSum(OnnxMul(loss_neg,
OnnxReshape(weight_name,
numpy.array(
[-1, 1],
dtype=numpy.int64),
op_version=target_opset),
op_version=target_opset),
op_version=target_opset)
grad = OnnxMul(OnnxSub(p1, y1, op_version=target_opset),
OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset),
output_names=['Y_grad'],
op_version=target_opset)
res = OnnxReshape(loss,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type_int64 = dtype_to_var_type(numpy.int64)
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type_int64([None, None])),
('X2', var_type([None, None]))]
if weight_name is not None:
varsx.append((weight_name, var_type([None])))
onx = res.to_onnx(varsx,
outputs=[('Y', var_type()), ('Y_grad', var_type())],
target_opset=target_opset,
other_outputs=[grad])
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx
def _onnx_n_penalty_elastic_error(target_opset=None,
dtype=numpy.float32,
weight_name=None,
l1_weight=0.01,
l2_weight=0.01,
n_tensors=1,
loss_shape=(1, 1)):
"""
Returns the ONNX graph for function
:math:`Y = f(W) = \\beta \\lVert W \\rVert +
\\alpha \\lVert W \\rVert^2`
*l1_weight* is :math:`\\beta` and
*l2_weight* is :math:`\\alpha`.
It does that for *n_tensors* and adds all of the results
to an input loss.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph(
'n_penalty_elastic_error', n_tensors=2)
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxMul, OnnxAdd,
OnnxReduceSumSquare, OnnxReduceSum,
OnnxAbs, OnnxReshape)
if n_tensors <= 0:
raise ValueError( # pragma: no cover
"This function is useless if the number of tensors is null.")
var_type = dtype_to_var_type(dtype)
varsx = [('loss', var_type(loss_shape))]
names = ['loss']
for n in range(n_tensors):
name = 'W%d' % n
abs_diff = OnnxAbs(name, op_version=target_opset)
res_l1 = OnnxReduceSum(abs_diff, op_version=target_opset)
# res2_l1 = OnnxSign(diff, op_version=target_opset)
res_l2 = OnnxReduceSumSquare(name, op_version=target_opset)
# res2_l2 = diff
res = OnnxAdd(OnnxMul(res_l1,
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset),
OnnxMul(res_l2,
numpy.array([l2_weight], dtype=dtype),
op_version=target_opset),
op_version=target_opset)
names.append(res)
varsx.append(('W%d' % n, var_type()))
if len(names) == 2:
res = OnnxAdd(*names, op_version=target_opset)
else:
res = OnnxAdd(names[1], names[2], op_version=target_opset)
for i in range(3, len(names)):
res = OnnxAdd(res, names[i], op_version=target_opset)
res = OnnxAdd(names[0], res, op_version=target_opset)
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
onx = res.to_onnx(varsx,
outputs=[('Y', var_type([None]))],
target_opset=target_opset)
return onx
def _onnx_grad_penalty_elastic_error(target_opset=None,
dtype=numpy.float32,
l1_weight=0.01,
l2_weight=0.01):
"""
Returns the ONNX graph for function
:math:`Y = f(W) = \\beta \\lVert W \\rVert +
\\alpha \\lVert W \\rVert^2`
*l1_weight* is :math:`\\beta` and
*l2_weight* is :math:`\\alpha`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_penalty_elastic_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxMul, OnnxAdd,
OnnxReduceSumSquare, OnnxReduceSum,
OnnxSign, OnnxAbs, OnnxReshape)
diff = 'X'
abs_diff = OnnxAbs(diff, op_version=target_opset)
res_l1 = OnnxReduceSum(abs_diff, op_version=target_opset)
res2_l1 = OnnxSign(diff, op_version=target_opset)
res_l2 = OnnxReduceSumSquare(diff, op_version=target_opset)
res2_l2 = diff
res = OnnxAdd(OnnxMul(res_l1,
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset),
OnnxMul(res_l2,
numpy.array([l2_weight], dtype=dtype),
op_version=target_opset),
op_version=target_opset)
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
res2 = OnnxAdd(OnnxMul(res2_l1,
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset),
OnnxMul(res2_l2,
numpy.array([l2_weight * (2)], dtype=dtype),
op_version=target_opset),
op_version=target_opset,
output_names=['Y_grad'])
var_type = dtype_to_var_type(dtype)
varsx = [('X', var_type([None, None]))]
onx = res.to_onnx(varsx,
outputs=[('Y', var_type([None])),
('Y_grad', var_type())],
target_opset=target_opset,
other_outputs=[res2])
return onx
def _onnx_grad_loss_elastic_error(target_opset=None,
dtype=numpy.float32,
weight_name=None,
l1_weight=0.01,
l2_weight=0.01):
"""
Returns the ONNX graph for function
:math:`Y = f(X1, X2) = \\beta \\lVert X1 - X2 \\rVert +
\\alpha \\lVert X1 - X2 \\rVert^2` or
:math:`Y = f(X1, X2) = \\beta \\lVert w(X1 - X2) \\rVert +
\\alpha \\lVert (\\sqrt{w})(X1 - X2) \\rVert^2` if
*weight_name* is not None and its gradient.
*l1_weight* is :math:`\\beta` and
*l2_weight* is :math:`\\alpha`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_loss_elastic_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxAdd,
OnnxIdentity, OnnxReduceSum,
OnnxReshape, OnnxSign, OnnxAbs)
diff = OnnxSub('X1', 'X2', op_version=target_opset)
abs_diff = OnnxAbs(diff, op_version=target_opset)
# loss
abs_diff_l1 = OnnxMul(abs_diff,
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset)
diff_l2 = OnnxMul(OnnxMul(diff, diff, op_version=target_opset),
numpy.array([l2_weight], dtype=dtype),
op_version=target_opset)
score = OnnxAdd(abs_diff_l1, diff_l2, op_version=target_opset)
# gradient
grad_l1 = OnnxMul(OnnxSign(diff, op_version=target_opset),
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset)
grad_l2 = OnnxMul(diff,
numpy.array([l2_weight * -2], dtype=dtype),
op_version=target_opset)
grad = OnnxAdd(grad_l1, grad_l2, op_version=target_opset)
if weight_name is None:
res = OnnxReduceSum(score, op_version=target_opset)
res2 = OnnxIdentity(grad,
op_version=target_opset,
output_names=['Y_grad'])
else:
resh = OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset)
res = OnnxReduceSum(OnnxMul(score, resh, op_version=target_opset),
op_version=target_opset)
res2 = OnnxMul(grad,
resh,
op_version=target_opset,
output_names=['Y_grad'])
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type([None, None])), ('X2', var_type([None, None]))]
if weight_name is not None:
varsx.append((weight_name, var_type([None])))
onx = res.to_onnx(varsx,
outputs=[('Y', var_type()), ('Y_grad', var_type())],
target_opset=target_opset,
other_outputs=[res2])
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx