def test_invalid_param_combinations(self, depth, height, width,
distortion_scale, device, dtype):
random_perspective_generator3d(batch_size=8,
height=height,
width=width,
distortion_scale=distortion_scale.to(
device=device, dtype=dtype))
def test_same_on_batch(self, device, dtype):
torch.manual_seed(42)
batch_size = 2
res = random_perspective_generator3d(batch_size,
200,
200,
200,
torch.tensor(0.5,
device=device,
dtype=dtype),
same_on_batch=True)
expected = dict(
start_points=torch.tensor(
[[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]],
[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]]],
device=device,
dtype=dtype),
end_points=torch.tensor(
[[[2.9077, 3.1455, 6.1793], [196.3710, 26.3086, 23.8392],
[151.2382, 152.5624, 4.1772], [6.6320, 191.1473, 18.7684],
[42.1260, 43.5251, 180.1234], [168.3782, 4.4055, 163.9409],
[167.8298, 177.1361, 195.2633],
[34.1715, 183.3881, 183.5471]],
[[2.9077, 3.1455, 6.1793], [196.3710, 26.3086, 23.8392],
[151.2382, 152.5624, 4.1772], [6.6320, 191.1473, 18.7684],
[42.1260, 43.5251, 180.1234], [168.3782, 4.4055, 163.9409],
[167.8298, 177.1361, 195.2633],
[34.1715, 183.3881, 183.5471]]],
device=device,
dtype=dtype))
assert res.keys() == expected.keys()
assert_allclose(res['start_points'],
expected['start_points'],
atol=1e-4,
rtol=1e-4)
assert_allclose(res['end_points'],
expected['end_points'],
atol=1e-4,
rtol=1e-4)
def test_same_on_batch(self, device, dtype):
torch.manual_seed(42)
batch_size = 2
res = random_perspective_generator3d(batch_size,
200,
200,
200,
torch.tensor(0.5,
device=device,
dtype=dtype),
same_on_batch=True)
expected = dict(
start_points=torch.tensor(
[[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]],
[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]]],
device=device,
dtype=dtype),
end_points=torch.tensor(
[[[44.1135, 45.7502, 19.1432], [151.0347, 19.5224, 30.0448],
[186.1714, 159.3179, 47.0386], [6.6593, 152.2701, 29.6790],
[43.4702, 28.3858, 161.9453], [177.5298, 44.2721, 170.3048],
[185.6710, 167.6275, 185.5184],
[22.0682, 184.1540, 157.4157]],
[[44.1135, 45.7502, 19.1432], [151.0347, 19.5224, 30.0448],
[186.1714, 159.3179, 47.0386], [6.6593, 152.2701, 29.6790],
[43.4702, 28.3858, 161.9453], [177.5298, 44.2721, 170.3048],
[185.6710, 167.6275, 185.5184],
[22.0682, 184.1540, 157.4157]]],
device=device,
dtype=dtype))
assert res.keys() == expected.keys()
assert_allclose(res['start_points'],
expected['start_points'],
atol=1e-4,
rtol=1e-4)
assert_allclose(res['end_points'],
expected['end_points'],
atol=1e-4,
rtol=1e-4)
def test_random_gen(self, device, dtype):
torch.manual_seed(42)
batch_size = 2
res = random_perspective_generator3d(
batch_size, 200, 200, 200,
torch.tensor(0.5, device=device, dtype=dtype))
expected = dict(
start_points=torch.tensor(
[[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]],
[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]]],
device=device,
dtype=dtype),
end_points=torch.tensor(
[[[2.9077, 3.1455, 6.1793], [196.3710, 26.3086, 23.8392],
[151.2382, 152.5624, 4.1772], [6.6320, 191.1473, 18.7684],
[42.1260, 43.5251, 180.1234], [168.3782, 4.4055, 163.9409],
[167.8298, 177.1361, 195.2633],
[34.1715, 183.3881, 183.5471]],
[[1.5670, 2.0183, 46.5954], [191.3937, 13.2522, 6.5217],
[186.4072, 187.3296, 11.2833], [15.8784, 168.2796, 29.8886],
[42.4501, 1.6248, 183.5378], [177.3848, 46.7149, 192.8844],
[177.6152, 184.5908, 149.9299],
[42.2815, 179.7861, 194.0347]]],
device=device,
dtype=dtype),
)
assert res.keys() == expected.keys()
assert_allclose(res['start_points'],
expected['start_points'],
atol=1e-4,
rtol=1e-4)
assert_allclose(res['end_points'],
expected['end_points'],
atol=1e-4,
rtol=1e-4)
def test_random_gen(self, device, dtype):
torch.manual_seed(42)
batch_size = 2
res = random_perspective_generator3d(
batch_size, 200, 200, 200,
torch.tensor(0.5, device=device, dtype=dtype))
expected = dict(
start_points=torch.tensor(
[[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]],
[[0., 0., 0.], [199., 0., 0.], [199., 199., 0.],
[0., 199., 0.], [0., 0., 199.], [199., 0., 199.],
[199., 199., 199.], [0., 199., 199.]]],
device=device,
dtype=dtype),
end_points=torch.tensor(
[[[44.1135, 45.7502, 19.1432], [151.0347, 19.5224, 30.0448],
[186.1714, 159.3179, 47.0386], [6.6593, 152.2701, 29.6790],
[43.4702, 28.3858, 161.9453], [177.5298, 44.2721, 170.3048],
[185.6710, 167.6275, 185.5184],
[22.0682, 184.1540, 157.4157]],
[[5.2657, 13.4747, 17.9406], [189.0318, 27.3596, 0.3080],
[151.4223, 195.2367, 44.3007], [29.1605, 182.1176, 40.4487],
[28.8963, 45.1991, 171.2670], [181.8843, 31.7171, 180.7795],
[163.4786, 151.6794, 159.5485],
[14.0707, 159.5684, 169.5268]]],
device=device,
dtype=dtype),
)
assert res.keys() == expected.keys()
assert_allclose(res['start_points'],
expected['start_points'],
atol=1e-4,
rtol=1e-4)
assert_allclose(res['end_points'],
expected['end_points'],
atol=1e-4,
rtol=1e-4)
