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)