openvino/model-optimizer/extensions/back/InterpolateReshape.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 numpy as np

from extensions.ops.elementwise import Mul
from extensions.ops.gather import Gather
from extensions.ops.interpolate import Interpolate
from mo.back.replacement import BackReplacementPattern
from mo.front.caffe.extractors.utils import get_canonical_axis_index
from mo.front.common.partial_infer.utils import int64_array
from mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
from mo.graph.graph import Graph
from mo.ops.shape import Shape


class InterpolateConcat(BackReplacementPattern):
    """
    Replaces hard-coded 1-port input of Interpolate with reshape-able sub-graph using the following Concat inputs

    BEFORE:
            input                   Const
    shape=[1, 3, 30, 40]      value=[60, 160]
            \                   /
           Interpolate(axes=(2, 3))     input_1
            shape=[1, 3, 60, 160]    shape=[1, 4, 60, 160]
                        \           /
                        Concat(axis=1)
                    shape=[1, 7, 60, 160]
    AFTER:
                input
            shape=[1, 3, 30, 40]           input_1
               |                     shape=[1, 4, 60, 160]
               |                      /        |
               |                  ShapeOf      |
               |                    |          |
               |               Gather          |
               |     indices=(2, 3); axis=0    |
               \                    |          |
                Interpolate(axes=(2, 3))      |
            shape=[1, 3, 60, 160]             |
                        \                   /
                           Concat(axis=1)
                        shape=[1, 7, 60, 160]
    """
    enabled = True
    graph_condition = [lambda graph: not graph.graph['cmd_params'].static_shape]
    force_shape_inference = True
    id = 'reshape_interpolate_through_concat'

    @staticmethod
    def make_interpolate_reshapeable(interpolate, concat):
        assert interpolate.soft_get('type') == 'Interpolate'
        assert concat.soft_get('type') == 'Concat'

        output_shape = interpolate.out_port(0).data.get_shape()

        interp_axes = [get_canonical_axis_index(output_shape, axis) for axis in Interpolate.get_axes(interpolate)]
        concat_axis = get_canonical_axis_index(output_shape, concat.axis)
        if concat_axis in interp_axes:
            return

        concat_srcs = [port.get_source() for port in concat.in_ports().values() if not port.disconnected()]
        non_interp_concat_srcs = [src for src in concat_srcs if src.node.soft_get('type') != 'Interpolate']
        if len(non_interp_concat_srcs) == 0:
            return

        graph = interpolate.graph
        src = non_interp_concat_srcs[0]

        shape = Shape(graph, {'name': src.node.soft_get('name', src.node.id) + '/Shape'}).create_node()
        shape.in_port(0).connect(src)
        gather = create_op_with_const_inputs(graph, Gather,
                                             {1: np.array(interp_axes, dtype=np.int32), 2: int64_array(0)},
                                             {'name': shape.name + '/Gathered'}, shape)
        interpolate.in_port(1).get_connection().set_source(gather.out_port(0))

    def find_and_replace_pattern(self, graph: Graph):
        for interpolate in graph.get_op_nodes(type='Interpolate'):
            if interpolate.in_port(1).get_source().node.soft_get('type') != 'Const':
                continue
            dsts = interpolate.out_port(0).get_destinations()
            if len(dsts) == 1 and dsts[0].node.soft_get('type') == 'Concat':
                self.make_interpolate_reshapeable(interpolate, dsts[0].node)


class InterpolateReshapeWA(BackReplacementPattern):
    """
    Replaces hard-coded 1-port input of Interpolate with reshape-able sub-graph.
    WARNING: Could cause troubles if model has hard-coded Interpolate intentionally -- rare situation
    BEFORE:
        input                   Const
    shape=[1, 3, 30, 40]      value=[60, 160]
            \                   /
           Interpolate(axes=(2, 3))
            shape=[1, 3, 60, 160]
    AFTER:
            input
    shape=[1, 3, 30, 40]
        |                \
        |              ShapeOf
        |                |
        |              Gather                Const
        |        indices=(2, 3); axis=0    value=[2, 4]
        |                \                /
        |                    Multiply
        |                   /
    Interpolate(axes=(2, 3))
      shape=[1, 3, 60, 160]
    """
    enabled = False
    graph_condition = [lambda graph: not graph.graph['cmd_params'].static_shape]
    force_shape_inference = True
    id = 'reshape_interpolate_wa'

    def run_after(self):
        return [InterpolateConcat]

    @staticmethod
    def make_interpolate_reshapeable(interpolate):
        assert interpolate.soft_get('type') == 'Interpolate'
        axes = Interpolate.get_axes(interpolate)
        input_shape = interpolate.in_port(0).data.get_shape()
        output_shape = interpolate.out_port(0).data.get_shape()
        if not np.all(np.remainder(output_shape, input_shape) == 0) and \
                not np.all(np.remainder(input_shape, output_shape) == 0):
            return
        graph = interpolate.graph
        name = interpolate.soft_get('name', interpolate.id)
        shape = Shape(graph, {'name': name + '/ShapeOf'}).create_node()
        shape.in_port(0).connect(interpolate.in_port(0).get_source())
        gather = create_op_with_const_inputs(graph, Gather, {1: np.array(axes, dtype=np.int32), 2: int64_array(0)},
                                             {'name': shape.name + '/Gathered'}, shape)
        multipliers = output_shape[axes] / input_shape[axes]
        mul = create_op_node_with_second_input(graph, Mul, multipliers, {'name': gather.name + '/Multiplied'}, gather)
        interpolate.in_port(1).get_connection().set_source(mul.out_port(0))

    def find_and_replace_pattern(self, graph: Graph):
        for interpolate in graph.get_op_nodes(type='Interpolate'):
            if interpolate.in_port(1).get_source().node.soft_get('type') == 'Const':
                self.make_interpolate_reshapeable(interpolate)