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# 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'], dict)
self.assertTrue(out['data_samples']['bboxes_labels'].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'], dict)
self.assertTrue(out['data_samples']['bboxes_labels'].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))
self.assertTrue(out['data_samples'], dict)
self.assertTrue(out['data_samples']['bboxes_labels'].shape == (4, 6))
self.assertIsInstance(out['inputs'], list)
self.assertIsInstance(out['data_samples']['bboxes_labels'],
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'], dict)
self.assertTrue(out['data_samples']['bboxes_labels'].shape == (8, 6))
self.assertIsInstance(out['inputs'], list)
self.assertIsInstance(out['data_samples']['bboxes_labels'],
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']))
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