# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import unittest

import numpy as np
import onnx

from openvino.tools.mo.front.onnx.detection_output_ext import DetectionOutputFrontExtractor
from openvino.tools.mo.ops.DetectionOutput import DetectionOutput
from openvino.tools.mo.ops.op import Op
from unit_tests.utils.extractors import PB


class TestDetectionOutputExt(unittest.TestCase):
    @staticmethod
    def _create_do_node(num_classes=0, share_location=0, background_label_id=0,
                        code_type="", variance_encoded_in_target=0, keep_top_k=0,
                        confidence_threshold=0, nms_threshold=0, top_k=0, eta=0):
        pb = onnx.helper.make_node(
            'DetectionOutput',
            inputs=['x'],
            outputs=['y'],
            num_classes=num_classes,
            share_location=share_location,
            background_label_id=background_label_id,
            code_type=code_type,
            variance_encoded_in_target=variance_encoded_in_target,
            keep_top_k=keep_top_k,
            confidence_threshold=confidence_threshold,
            # nms_param
            nms_threshold=nms_threshold,
            top_k=top_k,
            eta=eta,
        )
        
        node = PB({'pb': pb})
        return node

    @classmethod
    def setUpClass(cls):
        Op.registered_ops['DetectionOutput'] = DetectionOutput

    def test_do_no_pb_no_ml(self):
        self.assertRaises(AttributeError, DetectionOutputFrontExtractor.extract, None)

    def test_do_ext_ideal_numbers(self):
        node = self._create_do_node(num_classes=21, share_location=1,
                                    code_type="CENTER_SIZE", keep_top_k=200,
                                    confidence_threshold=0.01, nms_threshold=0.45, top_k=400, eta=1.0)
        
        DetectionOutputFrontExtractor.extract(node)
        
        exp_res = {
            'op': 'DetectionOutput',
            'type': 'DetectionOutput',
            'num_classes': 21,
            'share_location': 1,
            'background_label_id': 0,
            'code_type': "caffe.PriorBoxParameter.CENTER_SIZE",
            'variance_encoded_in_target': 0,
            'keep_top_k': 200,
            'confidence_threshold': 0.01,
            'visualize_threshold': 0.6,
            # nms_param
            'nms_threshold': 0.45,
            'top_k': 400,
            'eta': 1.0,
            # ONNX have not such parameters
            # save_output_param.resize_param
            'prob': 0,
            'resize_mode': "",
            'height': 0,
            'width': 0,
            'height_scale': 0,
            'width_scale': 0,
            'pad_mode': "",
            'pad_value': "",
            'interp_mode': "",
            'input_width': 1,
            'input_height': 1,
            'normalized': 1,            
        }

        for key in exp_res.keys():
            if key in ['confidence_threshold', 'visualise_threshold', 'nms_threshold', 'eta']:
                np.testing.assert_almost_equal(node[key], exp_res[key])
            else:
                self.assertEqual(node[key], exp_res[key])
