Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_rcnn_helper.py 4.03 KB
Newer Older
facebook-github-bot's avatar
Initial commit  
facebook-github-bot committed
1
2
3
4
5
6
7
8 
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved


import unittest

import numpy as np
import torch
Yanghan Wang's avatar
move test utils to core library  
Yanghan Wang committed
9 
from d2go.utils.testing import rcnn_helper as rh
facebook-github-bot's avatar
Initial commit  
facebook-github-bot committed
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116 
from detectron2.structures import Boxes


class TestRCNNHelper(unittest.TestCase):
    def test_get_instances_from_image(self):
        boxes = Boxes(torch.Tensor([[50, 40, 100, 80], [150, 60, 200, 120]]))
        gt_kpts = torch.Tensor([75, 60, 1.0] * 21 + [175, 90, 1.0] * 21).reshape(
            2, 21, 3
        )
        batched_inputs = rh.get_batched_inputs(2, boxes=boxes)
        instances = rh.get_detected_instances_from_image(batched_inputs)
        self.assertEqual(len(instances), 2)
        self.assertArrayEqual(instances[0].pred_boxes.tensor, boxes.tensor)
        self.assertArrayEqual(instances[0].pred_keypoints, gt_kpts)

    def test_get_instances_from_image_scale_image(self):
        H, W = 398, 224
        all_boxes = Boxes(torch.Tensor([[50, 40, 100, 80], [150, 60, 200, 120]]))
        image = rh.get_batched_inputs(1, (H, W), (H, W), all_boxes)[0]["image"]

        boxes = rh.get_detected_instances_from_image([{"image": image}])[0].pred_boxes
        self.assertArrayEqual(boxes.tensor, all_boxes.tensor)

        # scale image by 0.5
        scale_image = torch.nn.functional.interpolate(
            torch.unsqueeze(image, 0),
            scale_factor=(0.5, 0.5),
            mode="bilinear",
            align_corners=False,
            recompute_scale_factor=False,
        )[0]
        sub_boxes = rh.get_detected_instances_from_image([{"image": scale_image}])[
            0
        ].pred_boxes
        self.assertArrayEqual(sub_boxes.tensor, [[25, 20, 50, 40], [75, 30, 100, 60]])

        # scale image by 0.75
        scale_image = torch.nn.functional.interpolate(
            torch.unsqueeze(image, 0),
            scale_factor=(0.75, 0.75),
            mode="bilinear",
            align_corners=False,
            recompute_scale_factor=False,
        )[0]
        sub_boxes = rh.get_detected_instances_from_image([{"image": scale_image}])[
            0
        ].pred_boxes
        # [[37.5, 30, 75, 60], [112.5, 45, 150, 90]])
        self.assertArrayEqual(sub_boxes.tensor, [[37, 30, 75, 60], [112, 45, 150, 90]])

    def test_mock_rcnn_inference(self):
        image_size = (1920, 1080)
        resize_size = (398, 224)
        scale_xy = (1080.0 / 224, 1920.0 / 398)

        gt_boxes = Boxes(torch.Tensor([[50, 40, 100, 80], [150, 60, 200, 120]]))
        gt_kpts = torch.Tensor([75, 60, 1.0] * 21 + [175, 90, 1.0] * 21).reshape(
            2, 21, 3
        )

        # create inputs
        batched_inputs = rh.get_batched_inputs(2, image_size, resize_size, gt_boxes)

        # create model
        model = rh.MockRCNNInference(image_size, resize_size)

        # run without post processing
        det_instances = model(batched_inputs, None, do_postprocess=False)

        self.assertArrayAllClose(
            det_instances[0].pred_boxes.tensor,
            gt_boxes.tensor,
            atol=1e-4,
        )
        self.assertArrayAllClose(
            det_instances[0].pred_keypoints,
            gt_kpts,
            atol=1e-4,
        )

        # run with post processing
        det_instances = model(batched_inputs, None, do_postprocess=True)

        gt_boxes_scaled = gt_boxes.clone()
        gt_boxes_scaled.scale(*scale_xy)

        gt_kpts_scaled = torch.Tensor(
            [75 * scale_xy[0], 60 * scale_xy[1], 1.0] * 21
            + [175 * scale_xy[0], 90 * scale_xy[1], 1.0] * 21
        ).reshape(2, 21, 3)

        self.assertArrayAllClose(
            det_instances[0]["instances"].pred_boxes.tensor,
            gt_boxes_scaled.tensor,
            atol=1e-4,
        )
        self.assertArrayAllClose(
            det_instances[0]["instances"].pred_keypoints,
            gt_kpts_scaled,
            atol=1e-4,
        )

    def assertArrayEqual(self, a1, a2):
        self.assertTrue(np.array_equal(a1, a2))

    def assertArrayAllClose(self, a1, a2, rtol=1.0e-5, atol=1.0e-8):
        self.assertTrue(np.allclose(a1, a2, rtol=rtol, atol=atol))