def Gemm(onnx_node,
ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Calculate general matrix multiplication Y = alpha * (A @ B) + beta * C.
Support is currently limited to 2D matrices only. Higher dimensional tensors will
be flattened to 2D before multiplication.
"""
input_a, input_b, input_c = ng_inputs
alpha = onnx_node.get_attribute_value('alpha',
1) # Scalar multiplier for A @ B
beta = onnx_node.get_attribute_value(
'beta', 1) # Scalar multiplier for input tensor C
trans_a = onnx_node.get_attribute_value('transA',
False) # Should A be transposed?
trans_b = onnx_node.get_attribute_value('transB',
False) # Should B be transposed?
if trans_a:
input_a = transpose(input_a)
if trans_b:
input_b = transpose(input_b)
input_a, input_b = reshape_for_matmul(onnx_node, input_a, input_b)
a_dot_b = ng.dot(input_a, input_b)
if alpha != 1:
a_dot_b = alpha * a_dot_b
if beta != 1:
input_c = beta * input_c
_, input_c = numpy_style_broadcast_for_binary_operation(
onnx_node, [a_dot_b, input_c])
return a_dot_b + input_c
def And(onnx_node,
ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Perform the `and` logical operation elementwise on two input tensors with numpy-style broadcasting."""
left, right = numpy_style_broadcast_for_binary_operation(
onnx_node, ng_inputs)
left = ng.convert(ng.not_equal(left, 0), int)
right = ng.convert(ng.not_equal(right, 0), int)
return ng.convert(left * right, bool)
def Less(onnx_node,
ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Perform the `less` logical operation elementwise on two input tensors with numpy-style broadcasting."""
left, right = numpy_style_broadcast_for_binary_operation(
onnx_node, ng_inputs)
return ng.less(left, right)
def Div(onnx_node,
ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Perform element-wise binary division with numpy-style broadcasting."""
left, right = numpy_style_broadcast_for_binary_operation(
onnx_node, ng_inputs)
return ng.divide(left, right)
def Pow(onnx_node,
ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode
"""Perform element-wise binary power with numpy-style broadcasting."""
base, exponent = numpy_style_broadcast_for_binary_operation(
onnx_node, ng_inputs)
return ng.power(base, exponent)
