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Refactor test BoxOps #4596

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Refactor test BoxOps #4596

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80 changes: 38 additions & 42 deletions test/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -996,30 +996,30 @@ def test_bbox_convert_jit(self):
torch.testing.assert_close(scripted_cxcywh, box_cxcywh, rtol=0.0, atol=TOLERANCE)


class TestBoxArea:
def test_box_area(self):
class TestBoxOps:
@pytest.mark.parametrize("int_dtype", [torch.int8, torch.int16, torch.int32, torch.int64])
@pytest.mark.parametrize("float_dtype", [torch.float32, torch.float64])
Comment on lines +1000 to +1001
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As explained above I think this isn't the parametrization we're looking for, but just a note: this parametrization here will make a cross-product over the int values and the float values, equivalent to this:

for int_dtype in int_values:
    for float_dtype in float_values:
        the_test_body_here()

So this isn't equivalent to the previous tests, which looked like this:

for int_dtype in int_values:
    do_something;
for float_dtype in float_values:
    do_something_else()

def test_box_area(self, int_dtype, float_dtype):
def area_check(box, expected, tolerance=1e-4):
out = ops.box_area(box)
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=tolerance)

# Check for int boxes
for dtype in [torch.int8, torch.int16, torch.int32, torch.int64]:
box_tensor = torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=dtype)
expected = torch.tensor([10000, 0])
area_check(box_tensor, expected)
box_tensor = torch.tensor([[0, 0, 100, 100], [0, 0, 0, 0]], dtype=int_dtype)
expected = torch.tensor([10000, 0])
area_check(box_tensor, expected)

# Check for float32 and float64 boxes
for dtype in [torch.float32, torch.float64]:
box_tensor = torch.tensor(
[
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
],
dtype=dtype,
)
expected = torch.tensor([604723.0806, 600965.4666, 592761.0085], dtype=torch.float64)
area_check(box_tensor, expected, tolerance=0.05)
box_tensor = torch.tensor(
[
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
],
dtype=float_dtype,
)
expected = torch.tensor([604723.0806, 600965.4666, 592761.0085], dtype=torch.float64)
area_check(box_tensor, expected, tolerance=0.05)

# Check for float16 box
box_tensor = torch.tensor(
Expand All @@ -1037,31 +1037,29 @@ def test_box_area_jit(self):
scripted_area = scripted_fn(box_tensor)
torch.testing.assert_close(scripted_area, expected, rtol=0.0, atol=TOLERANCE)


class TestBoxIou:
def test_iou(self):
@pytest.mark.parametrize("int_dtype", [torch.int16, torch.int32, torch.int64])
@pytest.mark.parametrize("float_dtype", [torch.float16, torch.float32, torch.float64])
def test_iou(self, int_dtype, float_dtype):
def iou_check(box, expected, tolerance=1e-4):
out = ops.box_iou(box, box)
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=tolerance)

# Check for int boxes
for dtype in [torch.int16, torch.int32, torch.int64]:
box = torch.tensor([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]], dtype=dtype)
expected = torch.tensor([[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]])
iou_check(box, expected)
box = torch.tensor([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]], dtype=int_dtype)
expected = torch.tensor([[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]])
iou_check(box, expected)

# Check for float boxes
for dtype in [torch.float16, torch.float32, torch.float64]:
box_tensor = torch.tensor(
[
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
],
dtype=dtype,
)
expected = torch.tensor([[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]])
iou_check(box_tensor, expected, tolerance=0.002 if dtype == torch.float16 else 1e-4)
box_tensor = torch.tensor(
[
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
],
dtype=float_dtype,
)
expected = torch.tensor([[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]])
iou_check(box_tensor, expected, tolerance=0.002 if dtype == torch.float16 else 1e-4)

def test_iou_jit(self):
box_tensor = torch.tensor([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]], dtype=torch.float)
Expand All @@ -1071,28 +1069,26 @@ def test_iou_jit(self):
scripted_iou = scripted_fn(box_tensor, box_tensor)
torch.testing.assert_close(scripted_iou, expected, rtol=0.0, atol=TOLERANCE)


class TestGenBoxIou:
def test_gen_iou(self):
@pytest.mark.parametrize("int_dtype", [torch.int16, torch.int32, torch.int64])
@pytest.mark.parametrize("float_dtype", [torch.float16, torch.float32, torch.float64])
def test_gen_iou(self, int_dtype, float_dtype):
def gen_iou_check(box, expected, tolerance=1e-4):
out = ops.generalized_box_iou(box, box)
torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=tolerance)

# Check for int boxes
for dtype in [torch.int16, torch.int32, torch.int64]:
box = torch.tensor([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]], dtype=dtype)
box = torch.tensor([[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]], dtype=int_dtype)
expected = torch.tensor([[1.0, 0.25, -0.7778], [0.25, 1.0, -0.8611], [-0.7778, -0.8611, 1.0]])
gen_iou_check(box, expected)

# Check for float boxes
for dtype in [torch.float16, torch.float32, torch.float64]:
box_tensor = torch.tensor(
[
[285.3538, 185.5758, 1193.5110, 851.4551],
[285.1472, 188.7374, 1192.4984, 851.0669],
[279.2440, 197.9812, 1189.4746, 849.2019],
],
dtype=dtype,
dtype=float_dtype,
)
expected = torch.tensor([[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]])
gen_iou_check(box_tensor, expected, tolerance=0.002 if dtype == torch.float16 else 1e-3)
Expand Down