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

# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import math

import numpy as np

from mo.front.common.partial_infer.utils import int64_array, dynamic_dimension, dynamic_dimension_value
from mo.front.extractor import bool_to_str
from mo.graph.graph import Node, Graph
from mo.graph.perm_inputs import PermuteInputs
from mo.ops.op import Op, PermuteAttrs


def infer_for_opset4(node: Node):
    assert len([p for p in node.in_ports().values() if not p.disconnected()]) in [3, 4], \
        "Interpolate-4 node {} must have 3 or 4 inputs".format(node.soft_get(node.name, node.id))
    assert node.has_valid('mode')
    assert node.has_valid('shape_calculation_mode')
    src_shape = node.in_port(0).data.get_shape()
    assert src_shape is not None

    input_rank = len(src_shape)

    pads_begin = correct_pad(node.soft_get('pads_begin', [0]), input_rank)
    pads_end = correct_pad(node.soft_get('pads_end', [0]), input_rank)
    node['pads_begin'] = pads_begin
    node['pads_end'] = pads_end

    if len(node.in_ports()) == 3:
        axes = list(range(0, input_rank))
    else:
        axes = node.in_port(3).get_source().data.get_value()
        assert axes is not None, \
            "Interpolate-4 node with name {} has None as 'axes' input".format(node.soft_get('name', node.id))

    axes = int64_array(axes)
    output_shape = src_shape + pads_begin + pads_end
    if node.shape_calculation_mode == 'sizes':
        dst_shape = node.in_port(1).data.get_value()
        assert dst_shape is not None
        correct_scales_using_dst_shape(node, dst_shape, src_shape, axes)
        for i, axis in enumerate(axes):
            output_shape[axis] = dst_shape[i]
    else:
        scales = node.in_port(2).data.get_value()
        assert scales is not None
        for i, axis in enumerate(axes):
            if output_shape[axis] is not dynamic_dimension and scales[i] is not dynamic_dimension:
                output_shape[axis] = math.floor(scales[i] * output_shape[axis] + 1.0e-5)
            else:
                output_shape[axis] = dynamic_dimension_value

    if node.is_in_port_connected(3):
        PermuteInputs().set_input_permutation(node.in_node(3), node, 'input:0', 'axis')

    node.out_port(0).data.set_shape(output_shape)


def infer_for_opset1(node: Node):
    assert len([p for p in node.in_ports().values() if not p.disconnected()]) == 2
    assert node.has_valid('mode')
    assert node.has_valid('axes')

    src_shape = node.in_port(0).data.get_shape()

    assert src_shape is not None
    dst_shape = node.in_port(1).data.get_value()
    assert dst_shape is not None

    output_shape = src_shape.copy()
    for ind, axis in enumerate(node.axes):
        output_shape[axis] = dst_shape[ind]

    node.out_port(0).data.set_shape(output_shape)

    PermuteAttrs.create_permute_attrs(node, attrs=[('axes', 'input:0')])


def pad_attribute_to_str(node: Node, attr: str):
    return ','.join(map(str, node[attr])) if node.has_valid(attr) else None


def correct_pad(pad, rank):
    pad_len = len(pad)
    if pad_len < rank:
        return np.pad(pad, (0, rank - pad_len), 'constant').astype(np.int64)
    elif pad_len > rank:
        return np.array(pad[: rank]).astype(np.int64)
    else:
        return np.array(pad, dtype=np.int64)


def correct_scales_using_dst_shape(node, dst_shape, src_shape, axes):
    scales_value = node.in_port(2).data.get_value()
    if scales_value is None or len(scales_value) != len(dst_shape):
        corrected_scales = np.zeros(len(dst_shape))
        for i, axis in enumerate(list(axes)):
            corrected_scales[i] = dst_shape[i] / src_shape[axis]
        node.in_port(2).data.set_value(corrected_scales)


class Interpolate(Op):
    op = 'Interpolate'
    enabled = False
    infers = {
        'opset1': infer_for_opset1,
        'opset4': infer_for_opset4
    }

    def __init__(self, graph: Graph, attrs: dict):
        self.attributes_for_opsets = {
            'opset1': [
                ('axes', lambda node: ','.join(map(str, node.axes))),
                ('antialias', lambda node: bool_to_str(node, 'antialias')),
                ('align_corners', lambda node: bool_to_str(node, 'align_corners')),
                'mode', 'pads_begin', 'pads_end',
            ],
            'opset4': [
                'mode', 'nearest_mode', 'cube_coeff', 'coordinate_transformation_mode',
                'shape_calculation_mode',
                ('antialias', lambda node: bool_to_str(node, 'antialias')),
                ('pads_begin', lambda node: pad_attribute_to_str(node, 'pads_begin')),
                ('pads_end', lambda node: pad_attribute_to_str(node, 'pads_end')),
            ]
        }

        mandatory_props = {
            'op': self.op,
            'type': self.op,
            'version': 'opset1',

            'axes': None,
            'mode': None,
            'align_corners': 0,
            'antialias': 0,
            'pads_begin': 0,
            'pads_end': 0,

            'infer': self.infer,

            'force_precision_in_ports': {1: 'int64'},
            'in_ports_count': 2,
            'out_ports_count': 1,
        }
        super().__init__(graph, mandatory_props, attrs)

    def supported_attrs(self):
        opset = self.get_opset()
        key = opset if opset in self.attributes_for_opsets else 'opset1'
        return self.attributes_for_opsets[key]

    def infer(self, node: Node):
        opset = self.get_opset()
        key = opset if opset in self.infers else 'opset1'
        self.infers[key](node)

    @staticmethod
    def get_axes(node: Node) -> np.ndarray:
        opset = node.get_opset()
        if opset == 'opset1':
            interp_axes = node.soft_get('axes', None)
            return interp_axes if interp_axes is None else int64_array(interp_axes)

        src_shape = node.in_port(0).data.get_shape()
        assert src_shape is not None
        input_rank = len(src_shape)

        if len(node.in_ports()) == 3:
            axes = list(range(0, input_rank))
        else:
            axes = node.in_port(3).get_source().data.get_value()
        return int64_array(axes)