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from unittest import TestCase
import numpy as np
import torch
from mmengine.testing import assert_allclose
from .utils import ToyBaseBoxes
class TestBaseBoxes(TestCase):
def test_init(self):
box_tensor = torch.rand((3, 4, 4))
boxes = ToyBaseBoxes(box_tensor)
boxes = ToyBaseBoxes(box_tensor, dtype=torch.float64)
self.assertEqual(boxes.tensor.dtype, torch.float64)
if torch.cuda.is_available():
boxes = ToyBaseBoxes(box_tensor, device='cuda')
self.assertTrue(boxes.tensor.is_cuda)
with self.assertRaises(AssertionError):
box_tensor = torch.rand((4, ))
boxes = ToyBaseBoxes(box_tensor)
with self.assertRaises(AssertionError):
box_tensor = torch.rand((3, 4, 3))
boxes = ToyBaseBoxes(box_tensor)
def test_getitem(self):
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
# test single dimension index
# int
new_boxes = boxes[0]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (4, 4))
# list
new_boxes = boxes[[0, 2]]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (2, 4, 4))
# slice
new_boxes = boxes[0:2]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (2, 4, 4))
# torch.LongTensor
new_boxes = boxes[torch.LongTensor([0, 1])]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (2, 4, 4))
# torch.BoolTensor
new_boxes = boxes[torch.BoolTensor([True, False, True])]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (2, 4, 4))
with self.assertRaises(AssertionError):
index = torch.rand((2, 4, 4)) > 0
new_boxes = boxes[index]
# test multiple dimension index
# select single box
new_boxes = boxes[1, 2]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (1, 4))
# select the last dimension
with self.assertRaises(AssertionError):
new_boxes = boxes[1, 2, 1]
# has Ellipsis
new_boxes = boxes[None, ...]
self.assertIsInstance(new_boxes, ToyBaseBoxes)
self.assertEqual(new_boxes.tensor.shape, (1, 3, 4, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes[..., None]
def test_setitem(self):
values = ToyBaseBoxes(torch.rand(3, 4, 4))
tensor = torch.rand(3, 4, 4)
# only support BaseBoxes type
with self.assertRaises(AssertionError):
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[0:2] = tensor[0:2]
# test single dimension index
# int
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[1] = values[1]
assert_allclose(boxes.tensor[1], values.tensor[1])
# list
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[[1, 2]] = values[[1, 2]]
assert_allclose(boxes.tensor[[1, 2]], values.tensor[[1, 2]])
# slice
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[0:2] = values[0:2]
assert_allclose(boxes.tensor[0:2], values.tensor[0:2])
# torch.BoolTensor
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
index = torch.rand(3, 4) > 0.5
boxes[index] = values[index]
assert_allclose(boxes.tensor[index], values.tensor[index])
# multiple dimension index
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[0:2, 0:2] = values[0:2, 0:2]
assert_allclose(boxes.tensor[0:2, 0:2], values.tensor[0:2, 0:2])
# select single box
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[1, 1] = values[1, 1]
assert_allclose(boxes.tensor[1, 1], values.tensor[1, 1])
# select the last dimension
with self.assertRaises(AssertionError):
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[1, 1, 1] = values[1, 1, 1]
# has Ellipsis
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
boxes[0:2, ...] = values[0:2, ...]
assert_allclose(boxes.tensor[0:2, ...], values.tensor[0:2, ...])
def test_tensor_like_functions(self):
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
# new_tensor
boxes.new_tensor([1, 2, 3])
# new_full
boxes.new_full((3, 4), 0)
# new_empty
boxes.new_empty((3, 4))
# new_ones
boxes.new_ones((3, 4))
# new_zeros
boxes.new_zeros((3, 4))
# size
self.assertEqual(boxes.size(0), 3)
self.assertEqual(tuple(boxes.size()), (3, 4, 4))
# dim
self.assertEqual(boxes.dim(), 3)
# device
self.assertIsInstance(boxes.device, torch.device)
# dtype
self.assertIsInstance(boxes.dtype, torch.dtype)
# numpy
np_boxes = boxes.numpy()
self.assertIsInstance(np_boxes, np.ndarray)
self.assertTrue((np_boxes == np_boxes).all())
# to
new_boxes = boxes.to(torch.uint8)
self.assertEqual(new_boxes.tensor.dtype, torch.uint8)
if torch.cuda.is_available():
new_boxes = boxes.to(device='cuda')
self.assertTrue(new_boxes.tensor.is_cuda)
# cpu
if torch.cuda.is_available():
new_boxes = boxes.to(device='cuda')
new_boxes = new_boxes.cpu()
self.assertFalse(new_boxes.tensor.is_cuda)
# cuda
if torch.cuda.is_available():
new_boxes = boxes.cuda()
self.assertTrue(new_boxes.tensor.is_cuda)
# clone
boxes.clone()
# detach
boxes.detach()
# view
new_boxes = boxes.view(12, 4)
self.assertEqual(tuple(new_boxes.size()), (12, 4))
new_boxes = boxes.view(-1, 4)
self.assertEqual(tuple(new_boxes.size()), (12, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.view(-1)
# reshape
new_boxes = boxes.reshape(12, 4)
self.assertEqual(tuple(new_boxes.size()), (12, 4))
new_boxes = boxes.reshape(-1, 4)
self.assertEqual(tuple(new_boxes.size()), (12, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.reshape(-1)
# expand
new_boxes = boxes[None, ...].expand(4, -1, -1, -1)
self.assertEqual(tuple(new_boxes.size()), (4, 3, 4, 4))
# repeat
new_boxes = boxes.repeat(2, 2, 1)
self.assertEqual(tuple(new_boxes.size()), (6, 8, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.repeat(2, 2, 2)
# transpose
new_boxes = boxes.transpose(0, 1)
self.assertEqual(tuple(new_boxes.size()), (4, 3, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.transpose(1, 2)
# permute
new_boxes = boxes.permute(1, 0, 2)
self.assertEqual(tuple(new_boxes.size()), (4, 3, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.permute(2, 1, 0)
# split
boxes_list = boxes.split(1, dim=0)
for box in boxes_list:
self.assertIsInstance(box, ToyBaseBoxes)
self.assertEqual(tuple(box.size()), (1, 4, 4))
boxes_list = boxes.split([1, 2], dim=0)
with self.assertRaises(AssertionError):
boxes_list = boxes.split(1, dim=2)
# chunk
boxes_list = boxes.split(3, dim=1)
self.assertEqual(len(boxes_list), 2)
for box in boxes_list:
self.assertIsInstance(box, ToyBaseBoxes)
with self.assertRaises(AssertionError):
boxes_list = boxes.split(3, dim=2)
# unbind
boxes_list = boxes.unbind(dim=1)
self.assertEqual(len(boxes_list), 4)
for box in boxes_list:
self.assertIsInstance(box, ToyBaseBoxes)
self.assertEqual(tuple(box.size()), (3, 4))
with self.assertRaises(AssertionError):
boxes_list = boxes.unbind(dim=2)
# flatten
new_boxes = boxes.flatten()
self.assertEqual(tuple(new_boxes.size()), (12, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.flatten(end_dim=2)
# squeeze
boxes = ToyBaseBoxes(torch.rand(1, 3, 1, 4, 4))
new_boxes = boxes.squeeze()
self.assertEqual(tuple(new_boxes.size()), (3, 4, 4))
new_boxes = boxes.squeeze(dim=2)
self.assertEqual(tuple(new_boxes.size()), (1, 3, 4, 4))
# unsqueeze
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
new_boxes = boxes.unsqueeze(0)
self.assertEqual(tuple(new_boxes.size()), (1, 3, 4, 4))
with self.assertRaises(AssertionError):
new_boxes = boxes.unsqueeze(3)
# cat
with self.assertRaises(ValueError):
ToyBaseBoxes.cat([])
box_list = []
box_list.append(ToyBaseBoxes(torch.rand(3, 4, 4)))
box_list.append(ToyBaseBoxes(torch.rand(1, 4, 4)))
with self.assertRaises(AssertionError):
ToyBaseBoxes.cat(box_list, dim=2)
cat_boxes = ToyBaseBoxes.cat(box_list, dim=0)
self.assertIsInstance(cat_boxes, ToyBaseBoxes)
self.assertEqual((cat_boxes.size()), (4, 4, 4))
# stack
with self.assertRaises(ValueError):
ToyBaseBoxes.stack([])
box_list = []
box_list.append(ToyBaseBoxes(torch.rand(3, 4, 4)))
box_list.append(ToyBaseBoxes(torch.rand(3, 4, 4)))
with self.assertRaises(AssertionError):
ToyBaseBoxes.stack(box_list, dim=3)
stack_boxes = ToyBaseBoxes.stack(box_list, dim=1)
self.assertIsInstance(stack_boxes, ToyBaseBoxes)
self.assertEqual((stack_boxes.size()), (3, 2, 4, 4))
def test_misc(self):
boxes = ToyBaseBoxes(torch.rand(3, 4, 4))
# __len__
self.assertEqual(len(boxes), 3)
# __repr__
repr(boxes)
# fake_boxes
new_boxes = boxes.fake_boxes((3, 4, 4), 1)
self.assertEqual(tuple(new_boxes.size()), (3, 4, 4))
self.assertEqual(boxes.dtype, new_boxes.dtype)
self.assertEqual(boxes.device, new_boxes.device)
self.assertTrue((new_boxes.tensor == 1).all())
with self.assertRaises(AssertionError):
new_boxes = boxes.fake_boxes((3, 4, 1))
new_boxes = boxes.fake_boxes((3, 4, 4), dtype=torch.uint8)
self.assertEqual(new_boxes.dtype, torch.uint8)
if torch.cuda.is_available():
new_boxes = boxes.fake_boxes((3, 4, 4), device='cuda')
self.assertTrue(new_boxes.tensor.is_cuda)
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