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Unverified Commit 6aacf497 authored by Aditya Oke's avatar Aditya Oke Committed by GitHub
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Refactor tests for ops (#6027) 



* Refactor tests

* Remove tol, fix comments

* Add tolerance only where necessary

* Add tolerance only where necessary

* Add tolerance only where necessary

* Refactor to adapt suggestions

* Refactor and add nits

* Refactor box area

* Refactor to one file

* Adapt almost all except area

* final update

* Tighten for jit

* Refactor slightly

* Fix tests
Co-authored-by: default avatarVasilis Vryniotis <datumbox@users.noreply.github.com>
parent 0ada9fa3
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Showing with 287 additions and 432 deletions
test/test_ops.py
View file @ 6aacf497
......@@ -995,7 +995,7 @@ class TestFrozenBNT:
torch.testing.assert_close(fbn(x), bn(x), rtol=1e-5, atol=1e-6)
class TestBoxConversion:
class TestBoxConversionToRoi:
def _get_box_sequences():
# Define here the argument type of `boxes` supported by region pooling operations
box_tensor = torch.tensor([[0, 0, 0, 100, 100], [1, 0, 0, 100, 100]], dtype=torch.float)
......@@ -1021,7 +1021,7 @@ class TestBoxConversion:
assert_equal(ref_tensor, ops._utils.convert_boxes_to_roi_format(box_sequence))
class TestBox:
class TestBoxConvert:
def test_bbox_same(self):
box_tensor = torch.tensor(
[[0, 0, 100, 100], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float
......@@ -1051,7 +1051,7 @@ class TestBox:
assert_equal(box_xyxy, box_tensor)
def test_bbox_xyxy_cxcywh(self):
# Simple test convert boxes to xywh and back. Make sure they are same.
# Simple test convert boxes to cxcywh and back. Make sure they are same.
# box_tensor is in x1 y1 x2 y2 format.
box_tensor = torch.tensor(
[[0, 0, 100, 100], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float
......@@ -1073,7 +1073,6 @@ class TestBox:
[[0, 0, 100, 100], [0, 0, 0, 0], [10, 15, 20, 20], [23, 35, 70, 60]], dtype=torch.float
)
# This is wrong
exp_cxcywh = torch.tensor(
[[50, 50, 100, 100], [0, 0, 0, 0], [20, 25, 20, 20], [58, 65, 70, 60]], dtype=torch.float
)
......@@ -1102,296 +1101,374 @@ class TestBox:
)
scripted_fn = torch.jit.script(ops.box_convert)
TOLERANCE = 1e-3
box_xywh = ops.box_convert(box_tensor, in_fmt="xyxy", out_fmt="xywh")
scripted_xywh = scripted_fn(box_tensor, "xyxy", "xywh")
torch.testing.assert_close(scripted_xywh, box_xywh, rtol=0.0, atol=TOLERANCE)
torch.testing.assert_close(scripted_xywh, box_xywh)
box_cxcywh = ops.box_convert(box_tensor, in_fmt="xyxy", out_fmt="cxcywh")
scripted_cxcywh = scripted_fn(box_tensor, "xyxy", "cxcywh")
torch.testing.assert_close(scripted_cxcywh, box_cxcywh, rtol=0.0, atol=TOLERANCE)
torch.testing.assert_close(scripted_cxcywh, box_cxcywh)
class BoxTestBase(ABC):
@abstractmethod
def _target_fn(self) -> Tuple[bool, Callable]:
pass
INT_BOXES = [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
FLOAT_BOXES = [
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
]
class TestBoxArea:
def area_check(self, box, expected, atol=1e-4):
out = ops.box_area(box)
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=atol)
@pytest.mark.parametrize("dtype", [torch.int8, torch.int16, torch.int32, torch.int64])
def test_int_boxes(self, dtype):
box_tensor = torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=dtype)
expected = torch.tensor([10000, 0], dtype=torch.int32)
self.area_check(box_tensor, expected)
@pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
def test_float_boxes(self, dtype):
box_tensor = torch.tensor(FLOAT_BOXES, dtype=dtype)
expected = torch.tensor([604723.0806, 600965.4666, 592761.0085], dtype=dtype)
self.area_check(box_tensor, expected)
def test_float16_box(self):
box_tensor = torch.tensor(
[[2.825, 1.8625, 3.90, 4.85], [2.825, 4.875, 19.20, 5.10], [2.925, 1.80, 8.90, 4.90]], dtype=torch.float16
)
expected = torch.tensor([3.2170, 3.7108, 18.5071], dtype=torch.float16)
self.area_check(box_tensor, expected, atol=0.01)
def _perform_box_operation(self, box: Tensor, run_as_script: bool = False) -> Tensor:
is_binary_fn = self._target_fn()[0]
target_fn = self._target_fn()[1]
box_operation = torch.jit.script(target_fn) if run_as_script else target_fn
return box_operation(box, box) if is_binary_fn else box_operation(box)
def test_box_area_jit(self):
box_tensor = torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=torch.float)
expected = ops.box_area(box_tensor)
scripted_fn = torch.jit.script(ops.box_area)
scripted_area = scripted_fn(box_tensor)
torch.testing.assert_close(scripted_area, expected)
def _run_test(self, test_input: List, dtypes: List[torch.dtype], tolerance: float, expected: List) -> None:
def assert_close(box: Tensor, expected: Tensor, tolerance):
out = self._perform_box_operation(box)
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=tolerance)
class TestIouBase:
@staticmethod
def _run_test(target_fn: Callable, test_input: List, dtypes: List[torch.dtype], atol: float, expected: List):
for dtype in dtypes:
actual_box = torch.tensor(test_input, dtype=dtype)
expected_box = torch.tensor(expected)
assert_close(actual_box, expected_box, tolerance)
out = target_fn(actual_box, actual_box)
torch.testing.assert_close(out, expected_box, rtol=0.0, check_dtype=False, atol=atol)
def _run_jit_test(self, test_input: List) -> None:
@staticmethod
def _run_jit_test(target_fn: Callable, test_input: List):
box_tensor = torch.tensor(test_input, dtype=torch.float)
expected = self._perform_box_operation(box_tensor, True)
scripted_area = self._perform_box_operation(box_tensor, True)
torch.testing.assert_close(scripted_area, expected, rtol=0.0, atol=1e-3)
expected = target_fn(box_tensor, box_tensor)
scripted_fn = torch.jit.script(target_fn)
scripted_out = scripted_fn(box_tensor, box_tensor)
torch.testing.assert_close(scripted_out, expected)
class TestBoxArea(BoxTestBase):
def _target_fn(self) -> Tuple[bool, Callable]:
return (False, ops.box_area)
def _generate_int_input() -> List[List[int]]:
return [[0, 0, 100, 100], [0, 0, 0, 0]]
class TestBoxIou(TestIouBase):
int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
def _generate_int_expected() -> List[int]:
return [10000, 0]
def _generate_float_input(index: int) -> List[List[float]]:
return [
@pytest.mark.parametrize(
"test_input, dtypes, atol, expected",
[
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
],
[[285.25, 185.625, 1194.0, 851.5], [285.25, 188.75, 1192.0, 851.0], [279.25, 198.0, 1189.0, 849.0]],
][index]
)
def test_iou(self, test_input, dtypes, atol, expected):
self._run_test(ops.box_iou, test_input, dtypes, atol, expected)
def _generate_float_expected(index: int) -> List[float]:
return [[604723.0806, 600965.4666, 592761.0085], [605113.875, 600495.1875, 592247.25]][index]
def test_iou_jit(self):
self._run_jit_test(ops.box_iou, INT_BOXES)
class TestGeneralizedBoxIou(TestIouBase):
int_expected = [[1.0, 0.25, -0.7778], [0.25, 1.0, -0.8611], [-0.7778, -0.8611, 1.0]]
float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
@pytest.mark.parametrize(
"test_input, dtypes, tolerance, expected",
"test_input, dtypes, atol, expected",
[
pytest.param(
_generate_int_input(),
[torch.int8, torch.int16, torch.int32, torch.int64],
1e-4,
_generate_int_expected(),
),
pytest.param(_generate_float_input(0), [torch.float32, torch.float64], 0.05, _generate_float_expected(0)),
pytest.param(_generate_float_input(1), [torch.float16], 1e-4, _generate_float_expected(1)),
pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
],
)
def test_box_area(self, test_input: List, dtypes: List[torch.dtype], tolerance: float, expected: List) -> None:
self._run_test(test_input, dtypes, tolerance, expected)
def test_iou(self, test_input, dtypes, atol, expected):
self._run_test(ops.generalized_box_iou, test_input, dtypes, atol, expected)
def test_box_area_jit(self) -> None:
self._run_jit_test([[0, 0, 100, 100], [0, 0, 0, 0]])
def test_iou_jit(self):
self._run_jit_test(ops.generalized_box_iou, INT_BOXES)
class TestBoxIou(BoxTestBase):
def _target_fn(self) -> Tuple[bool, Callable]:
return (True, ops.box_iou)
class TestDistanceBoxIoU(TestIouBase):
int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
def _generate_int_input() -> List[List[int]]:
return [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
@pytest.mark.parametrize(
"test_input, dtypes, atol, expected",
[
pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
],
)
def test_iou(self, test_input, dtypes, atol, expected):
self._run_test(ops.distance_box_iou, test_input, dtypes, atol, expected)
def _generate_int_expected() -> List[List[float]]:
return [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
def test_iou_jit(self):
self._run_jit_test(ops.distance_box_iou, INT_BOXES)
def _generate_float_input() -> List[List[float]]:
return [
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
]
def _generate_float_expected() -> List[List[float]]:
return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
class TestCompleteBoxIou(TestIouBase):
int_expected = [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
float_expected = [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
@pytest.mark.parametrize(
"test_input, dtypes, tolerance, expected",
"test_input, dtypes, atol, expected",
[
pytest.param(
_generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
),
pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
pytest.param(_generate_float_input(), [torch.float32, torch.float64], 1e-4, _generate_float_expected()),
pytest.param(INT_BOXES, [torch.int16, torch.int32, torch.int64], 1e-4, int_expected),
pytest.param(FLOAT_BOXES, [torch.float16], 0.002, float_expected),
pytest.param(FLOAT_BOXES, [torch.float32, torch.float64], 1e-3, float_expected),
],
)
def test_iou(self, test_input: List, dtypes: List[torch.dtype], tolerance: float, expected: List) -> None:
self._run_test(test_input, dtypes, tolerance, expected)
def test_iou(self, test_input, dtypes, atol, expected):
self._run_test(ops.complete_box_iou, test_input, dtypes, atol, expected)
def test_iou_jit(self) -> None:
self._run_jit_test([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
def test_iou_jit(self):
self._run_jit_test(ops.complete_box_iou, INT_BOXES)
class TestGenBoxIou(BoxTestBase):
def _target_fn(self) -> Tuple[bool, Callable]:
return (True, ops.generalized_box_iou)
def get_boxes(dtype, device):
box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)
box2 = torch.tensor([0, 0, 1, 1], dtype=dtype, device=device)
box3 = torch.tensor([0, 1, 1, 2], dtype=dtype, device=device)
box4 = torch.tensor([1, 1, 2, 2], dtype=dtype, device=device)
def _generate_int_input() -> List[List[int]]:
return [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
box1s = torch.stack([box2, box2], dim=0)
box2s = torch.stack([box3, box4], dim=0)
def _generate_int_expected() -> List[List[float]]:
return [[1.0, 0.25, -0.7778], [0.25, 1.0, -0.8611], [-0.7778, -0.8611, 1.0]]
return box1, box2, box3, box4, box1s, box2s
def _generate_float_input() -> List[List[float]]:
return [
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
]
def _generate_float_expected() -> List[List[float]]:
return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
def assert_iou_loss(iou_fn, box1, box2, expected_loss, device, reduction="none"):
computed_loss = iou_fn(box1, box2, reduction=reduction)
expected_loss = torch.tensor(expected_loss, device=device)
torch.testing.assert_close(computed_loss, expected_loss)
@pytest.mark.parametrize(
"test_input, dtypes, tolerance, expected",
[
pytest.param(
_generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
),
pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
pytest.param(_generate_float_input(), [torch.float32, torch.float64], 0.001, _generate_float_expected()),
],
)
def test_gen_iou(self, test_input: List, dtypes: List[torch.dtype], tolerance: float, expected: List) -> None:
self._run_test(test_input, dtypes, tolerance, expected)
def test_giou_jit(self) -> None:
self._run_jit_test([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
def assert_empty_loss(iou_fn, dtype, device):
box1 = torch.randn([0, 4], dtype=dtype, device=device).requires_grad_()
box2 = torch.randn([0, 4], dtype=dtype, device=device).requires_grad_()
loss = iou_fn(box1, box2, reduction="mean")
loss.backward()
torch.testing.assert_close(loss, torch.tensor(0.0, device=device))
assert box1.grad is not None, "box1.grad should not be None after backward is called"
assert box2.grad is not None, "box2.grad should not be None after backward is called"
loss = iou_fn(box1, box2, reduction="none")
assert loss.numel() == 0, f"{str(iou_fn)} for two empty box should be empty"
class TestDistanceBoxIoU(BoxTestBase):
def _target_fn(self):
return (True, ops.distance_box_iou)
class TestGeneralizedBoxIouLoss:
# We refer to original test: https://github.com/facebookresearch/fvcore/blob/main/tests/test_giou_loss.py
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_giou_loss(self, dtype, device):
def _generate_int_input():
return [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
box1, box2, box3, box4, box1s, box2s = get_boxes(dtype, device)
def _generate_int_expected():
return [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
# Identical boxes should have loss of 0
assert_iou_loss(ops.generalized_box_iou_loss, box1, box1, 0.0, device=device)
def _generate_float_input():
return [
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
]
# quarter size box inside other box = IoU of 0.25
assert_iou_loss(ops.generalized_box_iou_loss, box1, box2, 0.75, device=device)
def _generate_float_expected():
return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
# Two side by side boxes, area=union
# IoU=0 and GIoU=0 (loss 1.0)
assert_iou_loss(ops.generalized_box_iou_loss, box2, box3, 1.0, device=device)
@pytest.mark.parametrize(
"test_input, dtypes, tolerance, expected",
[
pytest.param(
_generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
),
pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
pytest.param(_generate_float_input(), [torch.float32, torch.float64], 0.001, _generate_float_expected()),
],
)
def test_distance_iou(self, test_input, dtypes, tolerance, expected):
self._run_test(test_input, dtypes, tolerance, expected)
# Two diagonally adjacent boxes, area=2*union
# IoU=0 and GIoU=-0.5 (loss 1.5)
assert_iou_loss(ops.generalized_box_iou_loss, box2, box4, 1.5, device=device)
def test_distance_iou_jit(self):
self._run_jit_test([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
# Test batched loss and reductions
assert_iou_loss(ops.generalized_box_iou_loss, box1s, box2s, 2.5, device=device, reduction="sum")
assert_iou_loss(ops.generalized_box_iou_loss, box1s, box2s, 1.25, device=device, reduction="mean")
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_empty_inputs(self, dtype, device):
assert_empty_loss(ops.generalized_box_iou_loss, dtype, device)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_distance_iou_loss(dtype, device):
box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)
box2 = torch.tensor([0, 0, 1, 1], dtype=dtype, device=device)
box3 = torch.tensor([0, 1, 1, 2], dtype=dtype, device=device)
box4 = torch.tensor([1, 1, 2, 2], dtype=dtype, device=device)
box1s = torch.stack(
[box2, box2],
dim=0,
)
box2s = torch.stack(
[box3, box4],
dim=0,
)
class TestCompleteBoxIouLoss:
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_ciou_loss(self, dtype, device):
box1, box2, box3, box4, box1s, box2s = get_boxes(dtype, device)
def assert_distance_iou_loss(box1, box2, expected_output, reduction="none"):
output = ops.distance_box_iou_loss(box1, box2, reduction=reduction)
# TODO: When passing the dtype, the torch.half fails as usual.
expected_output = torch.tensor(expected_output, device=device)
tol = 1e-5 if dtype != torch.half else 1e-3
torch.testing.assert_close(output, expected_output, rtol=tol, atol=tol)
assert_iou_loss(ops.complete_box_iou_loss, box1, box1, 0.0, device=device)
assert_iou_loss(ops.complete_box_iou_loss, box1, box2, 0.8125, device=device)
assert_iou_loss(ops.complete_box_iou_loss, box1, box3, 1.1923, device=device)
assert_iou_loss(ops.complete_box_iou_loss, box1, box4, 1.2500, device=device)
assert_iou_loss(ops.complete_box_iou_loss, box1s, box2s, 1.2250, device=device, reduction="mean")
assert_iou_loss(ops.complete_box_iou_loss, box1s, box2s, 2.4500, device=device, reduction="sum")
assert_distance_iou_loss(box1, box1, 0.0)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_empty_inputs(self, dtype, device):
assert_empty_loss(ops.complete_box_iou_loss, dtype, device)
assert_distance_iou_loss(box1, box2, 0.8125)
assert_distance_iou_loss(box1, box3, 1.1923)
class TestDistanceBoxIouLoss:
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_distance_iou_loss(self, dtype, device):
box1, box2, box3, box4, box1s, box2s = get_boxes(dtype, device)
assert_distance_iou_loss(box1, box4, 1.2500)
assert_iou_loss(ops.distance_box_iou_loss, box1, box1, 0.0, device=device)
assert_iou_loss(ops.distance_box_iou_loss, box1, box2, 0.8125, device=device)
assert_iou_loss(ops.distance_box_iou_loss, box1, box3, 1.1923, device=device)
assert_iou_loss(ops.distance_box_iou_loss, box1, box4, 1.2500, device=device)
assert_iou_loss(ops.distance_box_iou_loss, box1s, box2s, 1.2250, device=device, reduction="mean")
assert_iou_loss(ops.distance_box_iou_loss, box1s, box2s, 2.4500, device=device, reduction="sum")
assert_distance_iou_loss(box1s, box2s, 1.2250, reduction="mean")
assert_distance_iou_loss(box1s, box2s, 2.4500, reduction="sum")
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_empty_distance_iou_inputs(self, dtype, device):
assert_empty_loss(ops.distance_box_iou_loss, dtype, device)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_empty_distance_iou_inputs(dtype, device) -> None:
box1 = torch.randn([0, 4], dtype=dtype, device=device).requires_grad_()
box2 = torch.randn([0, 4], dtype=dtype, device=device).requires_grad_()
class TestFocalLoss:
def _generate_diverse_input_target_pair(self, shape=(5, 2), **kwargs):
def logit(p):
return torch.log(p / (1 - p))
loss = ops.distance_box_iou_loss(box1, box2, reduction="mean")
loss.backward()
def generate_tensor_with_range_type(shape, range_type, **kwargs):
if range_type != "random_binary":
low, high = {
"small": (0.0, 0.2),
"big": (0.8, 1.0),
"zeros": (0.0, 0.0),
"ones": (1.0, 1.0),
"random": (0.0, 1.0),
}[range_type]
return torch.testing.make_tensor(shape, low=low, high=high, **kwargs)
else:
return torch.randint(0, 2, shape, **kwargs)
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(loss, torch.tensor(0.0, device=device), rtol=tol, atol=tol)
assert box1.grad is not None, "box1.grad should not be None after backward is called"
assert box2.grad is not None, "box2.grad should not be None after backward is called"
# This function will return inputs and targets with shape: (shape[0]*9, shape[1])
inputs = []
targets = []
for input_range_type, target_range_type in [
("small", "zeros"),
("small", "ones"),
("small", "random_binary"),
("big", "zeros"),
("big", "ones"),
("big", "random_binary"),
("random", "zeros"),
("random", "ones"),
("random", "random_binary"),
]:
inputs.append(logit(generate_tensor_with_range_type(shape, input_range_type, **kwargs)))
targets.append(generate_tensor_with_range_type(shape, target_range_type, **kwargs))
loss = ops.distance_box_iou_loss(box1, box2, reduction="none")
assert loss.numel() == 0, "diou_loss for two empty box should be empty"
return torch.cat(inputs), torch.cat(targets)
@pytest.mark.parametrize("alpha", [-1.0, 0.0, 0.58, 1.0])
@pytest.mark.parametrize("gamma", [0, 2])
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("seed", [0, 1])
def test_correct_ratio(self, alpha, gamma, device, dtype, seed):
if device == "cpu" and dtype is torch.half:
pytest.skip("Currently torch.half is not fully supported on cpu")
# For testing the ratio with manual calculation, we require the reduction to be "none"
reduction = "none"
torch.random.manual_seed(seed)
inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
focal_loss = ops.sigmoid_focal_loss(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=reduction)
assert torch.all(
focal_loss <= ce_loss
), "focal loss must be less or equal to cross entropy loss with same input"
class TestCompleteBoxIou(BoxTestBase):
def _target_fn(self) -> Tuple[bool, Callable]:
return (True, ops.complete_box_iou)
loss_ratio = (focal_loss / ce_loss).squeeze()
prob = torch.sigmoid(inputs)
p_t = prob * targets + (1 - prob) * (1 - targets)
correct_ratio = (1.0 - p_t) ** gamma
if alpha >= 0:
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
correct_ratio = correct_ratio * alpha_t
def _generate_int_input() -> List[List[int]]:
return [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(correct_ratio, loss_ratio, atol=tol, rtol=tol)
def _generate_int_expected() -> List[List[float]]:
return [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
@pytest.mark.parametrize("reduction", ["mean", "sum"])
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("seed", [2, 3])
def test_equal_ce_loss(self, reduction, device, dtype, seed):
if device == "cpu" and dtype is torch.half:
pytest.skip("Currently torch.half is not fully supported on cpu")
# focal loss should be equal ce_loss if alpha=-1 and gamma=0
alpha = -1
gamma = 0
torch.random.manual_seed(seed)
inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
inputs_fl = inputs.clone().requires_grad_()
targets_fl = targets.clone()
inputs_ce = inputs.clone().requires_grad_()
targets_ce = targets.clone()
focal_loss = ops.sigmoid_focal_loss(inputs_fl, targets_fl, gamma=gamma, alpha=alpha, reduction=reduction)
ce_loss = F.binary_cross_entropy_with_logits(inputs_ce, targets_ce, reduction=reduction)
def _generate_float_input() -> List[List[float]]:
return [
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
]
torch.testing.assert_close(focal_loss, ce_loss)
def _generate_float_expected() -> List[List[float]]:
return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
focal_loss.backward()
ce_loss.backward()
torch.testing.assert_close(inputs_fl.grad, inputs_ce.grad)
@pytest.mark.parametrize(
"test_input, dtypes, tolerance, expected",
[
pytest.param(
_generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
),
pytest.param(_generate_float_input(), [torch.float32, torch.float64], 0.002, _generate_float_expected()),
pytest.param(_generate_float_input(), [torch.float32, torch.float64], 0.001, _generate_float_expected()),
],
)
def test_complete_iou(self, test_input: List, dtypes: List[torch.dtype], tolerance: float, expected: List) -> None:
self._run_test(test_input, dtypes, tolerance, expected)
@pytest.mark.parametrize("alpha", [-1.0, 0.0, 0.58, 1.0])
@pytest.mark.parametrize("gamma", [0, 2])
@pytest.mark.parametrize("reduction", ["none", "mean", "sum"])
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("seed", [4, 5])
def test_jit(self, alpha, gamma, reduction, device, dtype, seed):
if device == "cpu" and dtype is torch.half:
pytest.skip("Currently torch.half is not fully supported on cpu")
script_fn = torch.jit.script(ops.sigmoid_focal_loss)
torch.random.manual_seed(seed)
inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
focal_loss = ops.sigmoid_focal_loss(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
if device == "cpu":
scripted_focal_loss = script_fn(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
else:
with torch.jit.fuser("fuser2"):
# Use fuser2 to prevent a bug on fuser: https://github.com/pytorch/pytorch/issues/75476
# We may remove this condition once the bug is resolved
scripted_focal_loss = script_fn(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
def test_ciou_jit(self) -> None:
self._run_jit_test([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(focal_loss, scripted_focal_loss, rtol=tol, atol=tol)
class TestMasksToBoxes:
def test_masks_box(self):
def masks_box_check(masks, expected, tolerance=1e-4):
def masks_box_check(masks, expected, atol=1e-4):
out = ops.masks_to_boxes(masks)
assert out.dtype == torch.float
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=tolerance)
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=True, atol=atol)
# Check for int type boxes.
def _get_image():
......@@ -1579,227 +1656,5 @@ class TestDropBlock:
assert len(graph_node_names[0]) == 1 + op_obj.n_inputs
class TestFocalLoss:
def _generate_diverse_input_target_pair(self, shape=(5, 2), **kwargs):
def logit(p: Tensor) -> Tensor:
return torch.log(p / (1 - p))
def generate_tensor_with_range_type(shape, range_type, **kwargs):
if range_type != "random_binary":
low, high = {
"small": (0.0, 0.2),
"big": (0.8, 1.0),
"zeros": (0.0, 0.0),
"ones": (1.0, 1.0),
"random": (0.0, 1.0),
}[range_type]
return torch.testing.make_tensor(shape, low=low, high=high, **kwargs)
else:
return torch.randint(0, 2, shape, **kwargs)
# This function will return inputs and targets with shape: (shape[0]*9, shape[1])
inputs = []
targets = []
for input_range_type, target_range_type in [
("small", "zeros"),
("small", "ones"),
("small", "random_binary"),
("big", "zeros"),
("big", "ones"),
("big", "random_binary"),
("random", "zeros"),
("random", "ones"),
("random", "random_binary"),
]:
inputs.append(logit(generate_tensor_with_range_type(shape, input_range_type, **kwargs)))
targets.append(generate_tensor_with_range_type(shape, target_range_type, **kwargs))
return torch.cat(inputs), torch.cat(targets)
@pytest.mark.parametrize("alpha", [-1.0, 0.0, 0.58, 1.0])
@pytest.mark.parametrize("gamma", [0, 2])
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("seed", [0, 1])
def test_correct_ratio(self, alpha, gamma, device, dtype, seed) -> None:
if device == "cpu" and dtype is torch.half:
pytest.skip("Currently torch.half is not fully supported on cpu")
# For testing the ratio with manual calculation, we require the reduction to be "none"
reduction = "none"
torch.random.manual_seed(seed)
inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
focal_loss = ops.sigmoid_focal_loss(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=reduction)
assert torch.all(
focal_loss <= ce_loss
), "focal loss must be less or equal to cross entropy loss with same input"
loss_ratio = (focal_loss / ce_loss).squeeze()
prob = torch.sigmoid(inputs)
p_t = prob * targets + (1 - prob) * (1 - targets)
correct_ratio = (1.0 - p_t) ** gamma
if alpha >= 0:
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
correct_ratio = correct_ratio * alpha_t
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(correct_ratio, loss_ratio, rtol=tol, atol=tol)
@pytest.mark.parametrize("reduction", ["mean", "sum"])
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("seed", [2, 3])
def test_equal_ce_loss(self, reduction, device, dtype, seed) -> None:
if device == "cpu" and dtype is torch.half:
pytest.skip("Currently torch.half is not fully supported on cpu")
# focal loss should be equal ce_loss if alpha=-1 and gamma=0
alpha = -1
gamma = 0
torch.random.manual_seed(seed)
inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
inputs_fl = inputs.clone().requires_grad_()
targets_fl = targets.clone()
inputs_ce = inputs.clone().requires_grad_()
targets_ce = targets.clone()
focal_loss = ops.sigmoid_focal_loss(inputs_fl, targets_fl, gamma=gamma, alpha=alpha, reduction=reduction)
ce_loss = F.binary_cross_entropy_with_logits(inputs_ce, targets_ce, reduction=reduction)
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(focal_loss, ce_loss, rtol=tol, atol=tol)
focal_loss.backward()
ce_loss.backward()
torch.testing.assert_close(inputs_fl.grad, inputs_ce.grad, rtol=tol, atol=tol)
@pytest.mark.parametrize("alpha", [-1.0, 0.0, 0.58, 1.0])
@pytest.mark.parametrize("gamma", [0, 2])
@pytest.mark.parametrize("reduction", ["none", "mean", "sum"])
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("seed", [4, 5])
def test_jit(self, alpha, gamma, reduction, device, dtype, seed) -> None:
if device == "cpu" and dtype is torch.half:
pytest.skip("Currently torch.half is not fully supported on cpu")
script_fn = torch.jit.script(ops.sigmoid_focal_loss)
torch.random.manual_seed(seed)
inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
focal_loss = ops.sigmoid_focal_loss(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
if device == "cpu":
scripted_focal_loss = script_fn(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
else:
with torch.jit.fuser("fuser2"):
# Use fuser2 to prevent a bug on fuser: https://github.com/pytorch/pytorch/issues/75476
# We may remove this condition once the bug is resolved
scripted_focal_loss = script_fn(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(focal_loss, scripted_focal_loss, rtol=tol, atol=tol)
class TestGeneralizedBoxIouLoss:
# We refer to original test: https://github.com/facebookresearch/fvcore/blob/main/tests/test_giou_loss.py
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_giou_loss(self, dtype, device) -> None:
box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)
box2 = torch.tensor([0, 0, 1, 1], dtype=dtype, device=device)
box3 = torch.tensor([0, 1, 1, 2], dtype=dtype, device=device)
box4 = torch.tensor([1, 1, 2, 2], dtype=dtype, device=device)
box1s = torch.stack([box2, box2], dim=0)
box2s = torch.stack([box3, box4], dim=0)
def assert_giou_loss(box1, box2, expected_loss, reduction="none"):
tol = 1e-3 if dtype is torch.half else 1e-5
computed_loss = ops.generalized_box_iou_loss(box1, box2, reduction=reduction)
expected_loss = torch.tensor(expected_loss, device=device)
torch.testing.assert_close(computed_loss, expected_loss, rtol=tol, atol=tol)
# Identical boxes should have loss of 0
assert_giou_loss(box1, box1, 0.0)
# quarter size box inside other box = IoU of 0.25
assert_giou_loss(box1, box2, 0.75)
# Two side by side boxes, area=union
# IoU=0 and GIoU=0 (loss 1.0)
assert_giou_loss(box2, box3, 1.0)
# Two diagonally adjacent boxes, area=2*union
# IoU=0 and GIoU=-0.5 (loss 1.5)
assert_giou_loss(box2, box4, 1.5)
# Test batched loss and reductions
assert_giou_loss(box1s, box2s, 2.5, reduction="sum")
assert_giou_loss(box1s, box2s, 1.25, reduction="mean")
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_empty_inputs(self, dtype, device) -> None:
box1 = torch.randn([0, 4], dtype=dtype).requires_grad_()
box2 = torch.randn([0, 4], dtype=dtype).requires_grad_()
loss = ops.generalized_box_iou_loss(box1, box2, reduction="mean")
loss.backward()
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(loss, torch.tensor(0.0), rtol=tol, atol=tol)
assert box1.grad is not None, "box1.grad should not be None after backward is called"
assert box2.grad is not None, "box2.grad should not be None after backward is called"
loss = ops.generalized_box_iou_loss(box1, box2, reduction="none")
assert loss.numel() == 0, "giou_loss for two empty box should be empty"
class TestCIOULoss:
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_ciou_loss(self, dtype, device):
box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)
box2 = torch.tensor([0, 0, 1, 1], dtype=dtype, device=device)
box3 = torch.tensor([0, 1, 1, 2], dtype=dtype, device=device)
box4 = torch.tensor([1, 1, 2, 2], dtype=dtype, device=device)
box1s = torch.stack([box2, box2], dim=0)
box2s = torch.stack([box3, box4], dim=0)
def assert_ciou_loss(box1, box2, expected_output, reduction="none"):
output = ops.complete_box_iou_loss(box1, box2, reduction=reduction)
# TODO: When passing the dtype, the torch.half test doesn't pass...
expected_output = torch.tensor(expected_output, device=device)
tol = 1e-5 if dtype != torch.half else 1e-3
torch.testing.assert_close(output, expected_output, rtol=tol, atol=tol)
assert_ciou_loss(box1, box1, 0.0)
assert_ciou_loss(box1, box2, 0.8125)
assert_ciou_loss(box1, box3, 1.1923)
assert_ciou_loss(box1, box4, 1.2500)
assert_ciou_loss(box1s, box2s, 1.2250, reduction="mean")
assert_ciou_loss(box1s, box2s, 2.4500, reduction="sum")
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", [torch.float32, torch.half])
def test_empty_inputs(self, dtype, device) -> None:
box1 = torch.randn([0, 4], dtype=dtype).requires_grad_()
box2 = torch.randn([0, 4], dtype=dtype).requires_grad_()
loss = ops.complete_box_iou_loss(box1, box2, reduction="mean")
loss.backward()
tol = 1e-3 if dtype is torch.half else 1e-5
torch.testing.assert_close(loss, torch.tensor(0.0), rtol=tol, atol=tol)
assert box1.grad is not None, "box1.grad should not be None after backward is called"
assert box2.grad is not None, "box2.grad should not be None after backward is called"
loss = ops.complete_box_iou_loss(box1, box2, reduction="none")
assert loss.numel() == 0, "ciou_loss for two empty box should be empty"
if __name__ == "__main__":
pytest.main([__file__])
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