def concatenation(self, node: onnx.NodeProto) -> spec.Spec:
input_shapes, _, attributes = self.get_inputs_for_gen_spec(node)
operator_spec_option = spec.Concatenation(
tensors=list(map(list, input_shapes)),
axis=spec_utils.implicit_axis_to_explicit(attributes['axis'],
input_shapes[0]))
return spec.Spec(spec_utils.node_identifier(node),
operator_spec_option)
def flatten(self, node: onnx.NodeProto) -> spec.Spec:
input_shapes, _, attributes = self.get_inputs_for_gen_spec(node)
assert len(input_shapes) == 1
input_shape = input_shapes[0]
operator_spec_option = spec.Flatten(
shape=[*input_shape],
axis=spec_utils.implicit_axis_to_explicit(attributes['axis'],
input_shape))
return spec.Spec(spec_utils.node_identifier(node),
operator_spec_option)
def transpose(self, node: onnx.NodeProto) -> spec.Spec:
input_shapes, _, attributes = self.get_inputs_for_gen_spec(node)
assert len(input_shapes) == 1
input_shape = input_shapes[0]
operator_spec_option = spec.Transpose(
shape=[*input_shape],
permutation=spec_utils.implicit_axis_to_explicit(
[*attributes['perm']], input_shape))
return spec.Spec(spec_utils.node_identifier(node),
operator_spec_option)
def softmax(self, node: onnx.NodeProto) -> spec.Spec:
input_shapes, _, attributes = self.get_inputs_for_gen_spec(node)
assert len(input_shapes) == 1
input_shape = input_shapes[0]
operator_spec_option = spec.Softmax(
input_shape=[*input_shape],
beta=attributes.get('beta', float(1.0)),
axis=spec_utils.implicit_axis_to_explicit(attributes['axis'],
input_shape))
return spec.Spec(spec_utils.node_identifier(node),
operator_spec_option)
