mmyolo/tests/test_datasets/test_utils.py

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

import numpy as np
import torch
from mmdet.structures import DetDataSample
from mmdet.structures.bbox import HorizontalBoxes
from mmengine.structures import InstanceData

from mmyolo.datasets import BatchShapePolicy, yolov5_collate


def _rand_bboxes(rng, num_boxes, w, h):
    cx, cy, bw, bh = rng.rand(num_boxes, 4).T

    tl_x = ((cx * w) - (w * bw / 2)).clip(0, w)
    tl_y = ((cy * h) - (h * bh / 2)).clip(0, h)
    br_x = ((cx * w) + (w * bw / 2)).clip(0, w)
    br_y = ((cy * h) + (h * bh / 2)).clip(0, h)

    bboxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
    return bboxes


class TestYOLOv5Collate(unittest.TestCase):

    def test_yolov5_collate(self):
        rng = np.random.RandomState(0)

        inputs = torch.randn((3, 10, 10))
        data_samples = DetDataSample()
        gt_instances = InstanceData()
        bboxes = _rand_bboxes(rng, 4, 6, 8)
        gt_instances.bboxes = HorizontalBoxes(bboxes, dtype=torch.float32)
        labels = rng.randint(1, 2, size=len(bboxes))
        gt_instances.labels = torch.LongTensor(labels)
        data_samples.gt_instances = gt_instances

        out = yolov5_collate([dict(inputs=inputs, data_samples=data_samples)])
        self.assertIsInstance(out, dict)
        self.assertTrue(out['inputs'].shape == (1, 3, 10, 10))
        self.assertTrue(out['data_samples'].shape == (4, 6))

        out = yolov5_collate([dict(inputs=inputs, data_samples=data_samples)] *
                             2)
        self.assertIsInstance(out, dict)
        self.assertTrue(out['inputs'].shape == (2, 3, 10, 10))
        self.assertTrue(out['data_samples'].shape == (8, 6))

    def test_yolov5_collate_with_multi_scale(self):
        rng = np.random.RandomState(0)

        inputs = torch.randn((3, 10, 10))
        data_samples = DetDataSample()
        gt_instances = InstanceData()
        bboxes = _rand_bboxes(rng, 4, 6, 8)
        gt_instances.bboxes = HorizontalBoxes(bboxes, dtype=torch.float32)
        labels = rng.randint(1, 2, size=len(bboxes))
        gt_instances.labels = torch.LongTensor(labels)
        data_samples.gt_instances = gt_instances

        out = yolov5_collate([dict(inputs=inputs, data_samples=data_samples)],
                             use_ms_training=True)
        self.assertIsInstance(out, dict)
        self.assertTrue(out['inputs'][0].shape == (3, 10, 10))
        print(out['data_samples'].shape)
        self.assertTrue(out['data_samples'].shape == (4, 6))
        self.assertIsInstance(out['inputs'], list)
        self.assertIsInstance(out['data_samples'], torch.Tensor)

        out = yolov5_collate(
            [dict(inputs=inputs, data_samples=data_samples)] * 2,
            use_ms_training=True)
        self.assertIsInstance(out, dict)
        self.assertTrue(out['inputs'][0].shape == (3, 10, 10))
        self.assertTrue(out['data_samples'].shape == (8, 6))
        self.assertIsInstance(out['inputs'], list)
        self.assertIsInstance(out['data_samples'], torch.Tensor)


class TestBatchShapePolicy(unittest.TestCase):

    def test_batch_shape_policy(self):
        src_data_infos = [{
            'height': 20,
            'width': 100,
        }, {
            'height': 11,
            'width': 100,
        }, {
            'height': 21,
            'width': 100,
        }, {
            'height': 30,
            'width': 100,
        }, {
            'height': 10,
            'width': 100,
        }]

        expected_data_infos = [{
            'height': 10,
            'width': 100,
            'batch_shape': np.array([96, 672])
        }, {
            'height': 11,
            'width': 100,
            'batch_shape': np.array([96, 672])
        }, {
            'height': 20,
            'width': 100,
            'batch_shape': np.array([160, 672])
        }, {
            'height': 21,
            'width': 100,
            'batch_shape': np.array([160, 672])
        }, {
            'height': 30,
            'width': 100,
            'batch_shape': np.array([224, 672])
        }]

        batch_shapes_policy = BatchShapePolicy(batch_size=2)
        out_data_infos = batch_shapes_policy(src_data_infos)

        for i in range(5):
            self.assertEqual(
                (expected_data_infos[i]['height'],
                 expected_data_infos[i]['width']),
                (out_data_infos[i]['height'], out_data_infos[i]['width']))
            self.assertTrue(
                np.allclose(expected_data_infos[i]['batch_shape'],
                            out_data_infos[i]['batch_shape']))