openvino/model-optimizer/extensions/ops/elementwise.py

"""
 Copyright (C) 2018-2020 Intel Corporation

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

import logging as log

import numpy as np

from mo.front.common.partial_infer.eltwise import eltwise_infer, bias_add_infer
from mo.graph.graph import Graph, Node
from mo.middle.passes.infer import copy_type_infer
from mo.ops.op import Op
from mo.pipeline.common import convert_const_node_value_type


def override_data_type_of_constant(node: Node):
    in_type_0 = node.in_port(0).get_data_type()
    in_type_1 = node.in_port(1).get_data_type()
    if in_type_0 != in_type_1:
        # in case of input values data type mismatch we try to change the type of the constant to match the type of
        # another input. The input values data type mismatch occur when the MO performs replacement of some
        # operations like SquaredDifference of inputs with floating point data type to Power layer with the integer
        # power value 2, or when replacing Neg operation with Mul with -1 as second input.
        in_node_0 = node.in_port(0).get_source().node
        in_node_1 = node.in_port(1).get_source().node
        if in_node_0.op == 'Const':
            convert_const_node_value_type(in_node_0, in_type_1)
        elif in_node_1.op == 'Const':
            convert_const_node_value_type(in_node_1, in_type_0)
        else:
            log.error('Elementwise operation {} has inputs of different data types: {} and {}'.format(
                node.soft_get('name'), in_type_0, in_type_1))


class Elementwise(Op):
    enabled = False
    operation = None
    op = None
    op_type = None
    version = 'opset1'

    def __init__(self, graph: Graph, attrs: dict):
        super().__init__(graph, {
            'op': self.op,
            'type': self.op_type,
            'version': self.version,
            'infer': lambda node: eltwise_infer(node, self.operation),
            'type_infer': self.type_infer,
            'can_be_bias': True,
            'can_be_fused': True,
            'in_ports_count': 2,
            'out_ports_count': 1,
            'is_eltwise': True,
            'stop_value_propagation': False
        }, attrs)

    @staticmethod
    def type_infer(node):
        override_data_type_of_constant(node)
        node.out_port(0).set_data_type(node.in_port(0).get_data_type())


class UnaryElementwise(Elementwise):
    def __init__(self, graph: Graph, attrs: dict):
        super().__init__(graph, {**{
            'in_ports_count': 1,
        }, **attrs})

    @staticmethod
    def type_infer(node):
        copy_type_infer(node)


class Add(Elementwise):
    op = 'Add'
    op_type = 'Add'
    operation = staticmethod(lambda a, b: a + b)


class BiasAdd(Add):
    op_type = 'BiasAdd'

    def __init__(self, graph: Graph, attrs: dict):
        attrs.update({'infer': lambda node: bias_add_infer(node, self.operation)})
        super().__init__(graph, attrs)


class Sub(Elementwise):
    op = 'Sub'
    op_type = 'Subtract'
    operation = staticmethod(lambda a, b: a - b)


class Mul(Elementwise):
    op = 'Mul'
    op_type = 'Multiply'
    operation = staticmethod(lambda a, b: a * b)


def both_types_are_integer(a, b):
    return np.issubdtype(a.dtype, np.integer) and np.issubdtype(b.dtype, np.integer)


class Div(Elementwise):
    op = 'Div'
    op_type = 'Divide'
    operation = staticmethod(lambda a, b: a // b if both_types_are_integer(a, b) else a / b)


class SquaredDifference(Elementwise):
    op = 'SquaredDifference'
    op_type = 'SquaredDifference'
    operation = staticmethod(lambda a, b: (a - b) * (a - b))


class Pow(Elementwise):
    op = 'Pow'
    op_type = 'Power'

    @staticmethod
    def operation(a, b):
        if np.any(b < 0) and np.issubdtype(a.dtype, np.signedinteger):
            return np.array(a.astype(np.float32) ** b, dtype=np.float32)
        return a ** b

    @staticmethod
    def type_infer(node):
        in_type_0 = node.in_port(0).get_data_type()
        in_type_1 = node.in_port(1).get_data_type()
        assert in_type_0 == in_type_1, \
            'Power operation {} has inputs of different data types: {} and {}'.format(
                node.soft_get('name'), in_type_0, in_type_1)
        node.out_port(0).set_data_type(in_type_0)


class LogicalElementwise(Elementwise):
    @staticmethod
    def type_infer(node):
        override_data_type_of_constant(node)
        node.out_port(0).set_data_type(np.bool)


class Greater(LogicalElementwise):
    op = 'Greater'
    op_type = 'Greater'
    operation = staticmethod(lambda a, b: a > b)


class GreaterEqual(LogicalElementwise):
    op = 'GreaterEqual'
    op_type = 'GreaterEqual'
    operation = staticmethod(lambda a, b: a >= b)


class Less(LogicalElementwise):
    op = 'Less'
    op_type = 'Less'
    operation = staticmethod(lambda a, b: a < b)


class LessEqual(LogicalElementwise):
    op = 'LessEqual'
    op_type = 'LessEqual'
    operation = staticmethod(lambda a, b: a <= b)


class Equal(LogicalElementwise):
    op = 'Equal'
    op_type = 'Equal'
    operation = staticmethod(lambda a, b: a == b)


class NotEqual(LogicalElementwise):
    op = 'NotEqual'
    op_type = 'NotEqual'
    operation = staticmethod(lambda a, b: a != b)


class Maximum(Elementwise):
    op = 'Maximum'
    op_type = 'Maximum'
    operation = staticmethod(lambda a, b: np.maximum(a, b))


class Minimum(Elementwise):
    op = 'Minimum'
    op_type = 'Minimum'
    operation = staticmethod(lambda a, b: np.minimum(a, b))


class Round(UnaryElementwise):
    op = 'Round'
    op_type = 'Round'
    version = 'opset5'

    def __init__(self, graph: Graph, attrs):
        round_attrs = {'mode': 'half_to_even',
                       'infer': self.infer
                       }
        round_attrs.update(attrs)
        super().__init__(graph, round_attrs)

    def backend_attrs(self):
        return ['mode']

    @classmethod
    def infer(cls, node: Node):
        node.out_port(0).data.set_shape(node.in_port(0).data.get_shape())

        a = node.in_port(0).data.get_value()
        if a is not None:
            assert node.soft_get('mode') in ['half_to_even', 'half_away_from_zero'], \
                'Round node {} has unsupported "mode" attribute value: {}'.format(node.soft_get('name', node.id),
                                                                                  node.soft_get('mode'))
            if node.mode == 'half_away_from_zero':
                mask = (a >= 0)
                out = np.empty_like(a)
                out[mask] = np.floor(a[mask] + 0.5)
                out[~mask] = np.ceil(a[~mask] - 0.5)
            else:
                out = np.round(a)
            node.out_port(0).data.set_value(out)


class LogicalOr(LogicalElementwise):
    op = 'LogicalOr'
    op_type = 'LogicalOr'
    operation = staticmethod(lambda a, b: np.logical_or(a, b))


class LogicalXor(Elementwise):
    op = 'LogicalXor'
    op_type = 'LogicalXor'
    operation = staticmethod(lambda a, b: np.logical_xor(a, b))


class LogicalAnd(LogicalElementwise):
    op = 'LogicalAnd'
    op_type = 'LogicalAnd'
    operation = staticmethod(lambda a, b: np.logical_and(a, b))


class FloorMod(Elementwise):
    op = 'FloorMod'
    op_type = 'FloorMod'
    operation = staticmethod(lambda a, b: a % b)


class Negative(UnaryElementwise):
    op = 'Negative'
    op_type = 'Negative'
    operation = staticmethod(lambda a: -a)