# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase

import torch
from parameterized import parameterized

from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules


class TestRPN(TestCase):

    def setUp(self):
        register_all_modules()

    @parameterized.expand(['rpn/rpn_r50_fpn_1x_coco.py'])
    def test_init(self, cfg_file):
        model = get_detector_cfg(cfg_file)
        # backbone convert to ResNet18
        model.backbone.depth = 18
        model.neck.in_channels = [64, 128, 256, 512]
        model.backbone.init_cfg = None

        from mmdet.registry import MODELS
        detector = MODELS.build(model)
        self.assertTrue(detector.backbone)
        self.assertTrue(detector.neck)
        self.assertTrue(detector.bbox_head)

        # if rpn.num_classes > 1, force set rpn.num_classes = 1
        model.rpn_head.num_classes = 2
        detector = MODELS.build(model)
        self.assertEqual(detector.bbox_head.num_classes, 1)

    @parameterized.expand([('rpn/rpn_r50_fpn_1x_coco.py', ('cpu', 'cuda'))])
    def test_rpn_forward_loss_mode(self, cfg_file, devices):
        model = get_detector_cfg(cfg_file)
        # backbone convert to ResNet18
        model.backbone.depth = 18
        model.neck.in_channels = [64, 128, 256, 512]
        model.backbone.init_cfg = None

        from mmdet.registry import MODELS
        assert all([device in ['cpu', 'cuda'] for device in devices])

        for device in devices:
            detector = MODELS.build(model)

            if device == 'cuda':
                if not torch.cuda.is_available():
                    return unittest.skip('test requires GPU and torch+cuda')
                detector = detector.cuda()

            packed_inputs = demo_mm_inputs(2, [[3, 128, 128], [3, 125, 130]])
            data = detector.data_preprocessor(packed_inputs, True)
            # Test forward train
            losses = detector.forward(**data, mode='loss')
            self.assertIsInstance(losses, dict)

    @parameterized.expand([('rpn/rpn_r50_fpn_1x_coco.py', ('cpu', 'cuda'))])
    def test_rpn_forward_predict_mode(self, cfg_file, devices):
        model = get_detector_cfg(cfg_file)
        # backbone convert to ResNet18
        model.backbone.depth = 18
        model.neck.in_channels = [64, 128, 256, 512]
        model.backbone.init_cfg = None

        from mmdet.registry import MODELS
        assert all([device in ['cpu', 'cuda'] for device in devices])

        for device in devices:
            detector = MODELS.build(model)

            if device == 'cuda':
                if not torch.cuda.is_available():
                    return unittest.skip('test requires GPU and torch+cuda')
                detector = detector.cuda()

            packed_inputs = demo_mm_inputs(2, [[3, 128, 128], [3, 125, 130]])
            data = detector.data_preprocessor(packed_inputs, False)
            # Test forward test
            detector.eval()
            with torch.no_grad():
                batch_results = detector.forward(**data, mode='predict')
                self.assertEqual(len(batch_results), 2)
                self.assertIsInstance(batch_results[0], DetDataSample)

    @parameterized.expand([('rpn/rpn_r50_fpn_1x_coco.py', ('cpu', 'cuda'))])
    def test_rpn_forward_tensor_mode(self, cfg_file, devices):
        model = get_detector_cfg(cfg_file)
        # backbone convert to ResNet18
        model.backbone.depth = 18
        model.neck.in_channels = [64, 128, 256, 512]
        model.backbone.init_cfg = None

        from mmdet.registry import MODELS
        assert all([device in ['cpu', 'cuda'] for device in devices])

        for device in devices:
            detector = MODELS.build(model)

            if device == 'cuda':
                if not torch.cuda.is_available():
                    return unittest.skip('test requires GPU and torch+cuda')
                detector = detector.cuda()

            packed_inputs = demo_mm_inputs(2, [[3, 128, 128], [3, 125, 130]])
            data = detector.data_preprocessor(packed_inputs, False)
            batch_results = detector.forward(**data, mode='tensor')
            self.assertIsInstance(batch_results, tuple)