def test_onnxt_runtime_argmin(self):
X = numpy.array([[2, 1], [0, 1]], dtype=float)
onx = OnnxArgMin('X', output_names=['Y'], keepdims=0)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmin(X, axis=0), got['Y'], decimal=6)
onx = OnnxArgMin('X', output_names=['Y'], axis=1, keepdims=0)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(
numpy.argmin(X, axis=1).ravel(), got['Y'].ravel())
onx = OnnxArgMin('X', output_names=['Y'], axis=1, keepdims=1)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(
numpy.argmin(X, axis=1).ravel(), got['Y'].ravel())
def test_algebra_argmin(self):
op = OnnxArgMin('I0', op_version=TARGET_OPSET)
onx = op.to_onnx({'I0': numpy.ones((1, 2), dtype=numpy.float32)})
assert onx is not None
sonx = str(onx)
assert len(sonx) > 0
import onnxruntime as ort
sess = ort.InferenceSession(onx.SerializeToString())
X = numpy.array([[0, 2], [0, -2]])
exp = numpy.array([[0, 1]])
Y = sess.run(None, {'I0': X.astype(numpy.float32)})[0]
assert_almost_equal(exp, Y)
def test_algebra_normalizer_argmin_named_output(self):
op = OnnxArgMin(OnnxNormalizer('I0', norm='L1', output_names=['Y']))
onx = op.to_onnx({'I0': numpy.ones((1, 2), dtype=numpy.float32)})
assert onx is not None
sonx = str(onx)
assert len(sonx) > 0
import onnxruntime as ort
sess = ort.InferenceSession(onx.SerializeToString())
X = numpy.array([[0, 2], [0, -2]])
exp = numpy.array([[0, 1]])
Y = sess.run(None, {'I0': X.astype(numpy.float32)})[0]
assert_almost_equal(exp, Y)
def conv(scope, operator, container):
X = operator.inputs[0]
out = operator.outputs
op = operator.raw_operator
C = op.cluster_centers_
C2 = row_norms(C, squared=True)
N = X.type.shape[0]
zeros = np.zeros((N, ))
rs = OnnxReduceSumSquare(X, axes=[1], keepdims=1)
z = OnnxAdd(rs, OnnxGemm(X, C, zeros, alpha=-2., transB=1))
y2 = OnnxAdd(C2, z)
lo = OnnxArgMin(y2, axis=1, keepdims=0, output_names=out[:1])
y2s = OnnxSqrt(y2, output_names=out[1:])
lo.add_to(scope, container)
y2s.add_to(scope, container)
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)