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Unverified Commit 426e229d authored by q.yao's avatar q.yao Committed by GitHub
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[Fix]: Update test data of test_tin_shift (#1426) 



* Update test data of test_tin_shift

* Delete tmp.engine

* add pytest raises asserterror test

* raise valueerror, update test log

* add more comment

* Apply suggestions from code review
Co-authored-by: default avatarZaida Zhou <58739961+zhouzaida@users.noreply.github.com>
Co-authored-by: default avatarZaida Zhou <58739961+zhouzaida@users.noreply.github.com>
parent f9be3bc3
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Showing with 137 additions and 35 deletions
mmcv/ops/tin_shift.py
View file @ 426e229d
...@@ -18,6 +18,11 @@ class TINShiftFunction(Function): ...@@ -18,6 +18,11 @@ class TINShiftFunction(Function):
@staticmethod @staticmethod
def forward(ctx, input, shift): def forward(ctx, input, shift):
C = input.size(2)
num_segments = shift.size(1)
if C // num_segments <= 0 or C % num_segments != 0:
raise ValueError('C should be a multiple of num_segments, '
f'but got C={C} and num_segments={num_segments}.')
ctx.save_for_backward(shift) ctx.save_for_backward(shift)
......
tests/test_ops/test_tin_shift.py
View file @ 426e229d
...@@ -14,49 +14,127 @@ except ImportError: ...@@ -14,49 +14,127 @@ except ImportError:
cur_dir = os.path.dirname(os.path.abspath(__file__)) cur_dir = os.path.dirname(os.path.abspath(__file__))
inputs = ([[[[0.4369, -3.7571], [-1.1835, -1.6374], [0.9534, -0.1321]], inputs = ([[[[0.88572276, 0.46422583], [0.97408265, 0.59547687],
[[-0.4658, 0.2162], [-0.8135, -0.3903], [-0.1720, -0.0599]], [0.030812204, 0.96236038], [0.75418317, 0.44058233],
[[0.4851, 1.8224], [0.8973, 0.3779], [2.3454, 1.0319]], [0.33279222, 0.00084149837], [0.7069388, 0.23255438],
[[0.0420, 0.3574], [0.7641, 0.2384], [0.2759, 0.4931]]], [0.13547045, 0.81549376], [0.40174931, 0.36317211]],
[[[-0.5897, 0.7544], [1.0593, 0.8388], [-0.5732, 0.5692]], [[0.57444429, 0.15905505], [0.39897251, 0.25790238],
[[-0.6766, -1.4657], [1.2362, 0.4913], [-1.1820, -1.4341]], [0.93282568, 0.18451685], [0.92526674, 0.18283755],
[[0.6476, -0.7391], [1.4314, -0.3522], [0.8401, -0.7757]], [0.31664443, 0.59323865], [0.1957739, 0.42505842],
[[1.4306, 0.9726], [1.0518, -0.8820], [-0.5129, -0.7876]]]]) [0.081158757, 0.81340349], [0.43456328, 0.30195212]],
[[0.8198145, 0.05990988], [0.98062474, 0.34803438],
[0.10412294, 0.37183142], [0.15021622, 0.038857818],
[0.40985721, 0.42253625], [0.71150124, 0.59778064],
[0.83851069, 0.15194464], [0.097513378, 0.74820143]],
[[0.80680406, 0.49327564], [0.17821097, 0.12980539],
[0.50657678, 0.14446253], [0.04178369, 0.53071898],
[0.84983683, 0.3826949], [0.32193625, 0.91275406],
[0.75628334, 0.52934098], [0.27994192, 0.3053292]]],
[[[0.082397044, 0.4210068], [0.23563534, 0.7938987],
[0.63669145, 0.69397897], [0.8844561, 0.97854084],
[0.79027033, 0.60640401], [0.63528901, 0.72172403],
[0.0097346902, 0.70800996], [0.87891227, 0.13674974]],
[[0.74329448, 0.0243572], [0.82178867, 0.85750699],
[0.7568835, 0.73146772], [0.5031184, 0.30479157],
[0.28713053, 0.47414285], [0.4682079, 0.067471564],
[0.48368263, 0.14590704], [0.25397325, 0.19946373]],
[[0.4291026, 0.068739474], [0.7159555, 0.79903615],
[0.76412082, 0.85348046], [0.081224024, 0.82264912],
[0.97173303, 0.24291694], [0.48957139, 0.43488795],
[0.67382395, 0.21889746], [0.36712623, 0.67127824]],
[[0.12054044, 0.18096751], [0.86675781, 0.54755616],
[0.68208277, 0.15164375], [0.79991871, 0.80811197],
[0.85256428, 0.68253738], [0.185983, 0.95642138],
[0.48102546, 0.28009653], [0.35726011, 0.58168036]]]])
shifts = [([[1, 0, 1, -2], [-2, 1, -1, 1]]), ([[2, 1, 2, -1], [-1, 2, 0, 2]])] shifts = [([[1, 0, 1, -2], [-2, 1, -1, 1]]), ([[2, 1, 2, -1], [-1, 2, 0, 2]])]
outputs = [([[[[0.4369, -3.7571], [-1.1835, -1.6374], [0.9534, -0.1321]], outputs = [([[[[0.0, 0.0], [0.0, 0.0], [0.030812, 0.96236], [0.75418, 0.44058],
[[-0.4658, 0.2162], [-0.8135, -0.3903], [-0.1720, -0.0599]], [0.0, 0.0], [0.0, 0.0], [0.83851, 0.15194], [0.097513, 0.7482]],
[[0.4851, 1.8224], [0.8973, 0.3779], [2.3454, 1.0319]], [[0.88572, 0.46423], [0.97408, 0.59548], [0.93283, 0.18452],
[[0.0420, 0.3574], [0.7641, 0.2384], [0.2759, 0.4931]]], [0.92527, 0.18284], [0.33279, 0.0008415], [0.70694, 0.23255],
[[[0.6476, -0.7391], [1.4314, -0.3522], [0.8401, -0.7757]], [0.75628, 0.52934], [0.27994, 0.30533]],
[[1.4306, 0.9726], [1.0518, -0.8820], [-0.5129, -0.7876]], [[0.57444, 0.15906], [0.39897, 0.2579], [0.10412, 0.37183],
[[0.0000, 0.0000], [0.0000, 0.0000], [0.0000, 0.0000]], [0.15022, 0.038858], [0.31664, 0.59324], [0.19577, 0.42506],
[[0.0000, 0.0000], [0.0000, 0.0000], [0.0000, 0.0000]]]]), [0.0, 0.0], [0.0, 0.0]],
([[[[0.4369, -3.7571], [-1.1835, -1.6374], [0.9534, -0.1321]], [[0.81981, 0.05991], [0.98062, 0.34803], [0.50658, 0.14446],
[[-0.4658, 0.2162], [-0.8135, -0.3903], [-0.1720, -0.0599]], [0.041784, 0.53072], [0.40986, 0.42254], [0.7115, 0.59778],
[[0.4851, 1.8224], [0.8973, 0.3779], [2.3454, 1.0319]], [0.0, 0.0], [0.0, 0.0]]],
[[0.0420, 0.3574], [0.7641, 0.2384], [0.2759, 0.4931]]], [[[0.4291, 0.068739], [0.71596, 0.79904], [0.0, 0.0], [0.0, 0.0],
[[[-0.6766, -1.4657], [1.2362, 0.4913], [-1.1820, -1.4341]], [0.28713, 0.47414], [0.46821, 0.067472], [0.0, 0.0], [0.0,
[[0.6476, -0.7391], [1.4314, -0.3522], [0.8401, -0.7757]], 0.0]],
[[1.4306, 0.9726], [1.0518, -0.8820], [-0.5129, -0.7876]], [[0.12054, 0.18097], [0.86676, 0.54756], [0.63669, 0.69398],
[[0.0000, 0.0000], [0.0000, 0.0000], [0.0000, 0.0000]]]])] [0.88446, 0.97854], [0.97173, 0.24292], [0.48957, 0.43489],
[0.0097347, 0.70801], [0.87891, 0.13675]],
grads = [[[[[1., 1.], [1., 1.], [1., 1.]], [[1., 1.], [1., 1.], [1., 1.]], [[0.0, 0.0], [0.0, 0.0], [0.75688, 0.73147], [0.50312, 0.30479],
[[1., 1.], [1., 1.], [1., 1.]], [[1., 1.], [1., 1.], [1., 1.]]], [0.85256, 0.68254], [0.18598, 0.95642], [0.48368, 0.14591],
[[[1., 1.], [1., 1.], [1., 1.]], [[1., 1.], [1., 1.], [1., 1.]], [0.25397, 0.19946]],
[[0., 0.], [0., 0.], [0., 0.]], [[0., 0.], [0., 0.], [0., 0.]]]], [[0.0, 0.0], [0.0, 0.0], [0.76412, 0.85348], [0.081224, 0.82265],
[[[[1., 1.], [1., 1.], [1., 1.]], [[1., 1.], [1., 1.], [1., 1.]], [0.0, 0.0], [0.0, 0.0], [0.67382, 0.2189], [0.36713,
[[1., 1.], [1., 1.], [1., 1.]], [[1., 1.], [1., 1.], [1., 1.]]], 0.67128]]]]),
[[[1., 1.], [1., 1.], [1., 1.]], [[1., 1.], [1., 1.], [1., 1.]], ([[[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0],
[[1., 1.], [1., 1.], [1., 1.]], [[0., 0.], [0., 0.], [0., 0.]]]]] [0.0, 0.0], [0.081159, 0.8134], [0.43456, 0.30195]],
[[0.0, 0.0], [0.0, 0.0], [0.030812, 0.96236], [0.75418, 0.44058],
[0.0, 0.0], [0.0, 0.0], [0.83851, 0.15194], [0.097513, 0.7482]],
[[0.88572, 0.46423], [0.97408, 0.59548], [0.93283, 0.18452],
[0.92527, 0.18284], [0.33279, 0.0008415], [0.70694, 0.23255],
[0.75628, 0.52934], [0.27994, 0.30533]],
[[0.57444, 0.15906], [0.39897, 0.2579], [0.10412, 0.37183],
[0.15022, 0.038858], [0.31664, 0.59324], [0.19577, 0.42506],
[0.0, 0.0], [0.0, 0.0]]],
[[[0.74329, 0.024357], [0.82179, 0.85751], [0.0, 0.0], [0.0, 0.0],
[0.79027, 0.6064], [0.63529, 0.72172], [0.0, 0.0], [0.0, 0.0]],
[[0.4291, 0.068739], [0.71596, 0.79904], [0.0, 0.0], [0.0, 0.0],
[0.28713, 0.47414], [0.46821, 0.067472], [0.0, 0.0], [0.0,
0.0]],
[[0.12054, 0.18097], [0.86676, 0.54756], [0.63669, 0.69398],
[0.88446, 0.97854], [0.97173, 0.24292], [0.48957, 0.43489],
[0.0097347, 0.70801], [0.87891, 0.13675]],
[[0.0, 0.0], [0.0, 0.0], [0.75688, 0.73147], [0.50312, 0.30479],
[0.85256, 0.68254], [0.18598, 0.95642], [0.48368, 0.14591],
[0.25397, 0.19946]]]])]
grads = [
[[[[0., 0.], [0., 0.], [1., 1.], [1., 1.], [0., 0.], [0., 0.], [1., 1.],
[1., 1.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.],
[1., 1.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [0., 0.],
[0., 0.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [0., 0.],
[0., 0.]]],
[[[1., 1.], [1., 1.], [0., 0.], [0., 0.], [1., 1.], [1., 1.], [0., 0.],
[0., 0.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.],
[1., 1.]],
[[0., 0.], [0., 0.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.],
[1., 1.]],
[[0., 0.], [0., 0.], [1., 1.], [1., 1.], [0., 0.], [0., 0.], [1., 1.],
[1., 1.]]]],
[[[[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.], [1., 1.],
[1., 1.]],
[[0., 0.], [0., 0.], [1., 1.], [1., 1.], [0., 0.], [0., 0.], [1., 1.],
[1., 1.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.],
[1., 1.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [0., 0.],
[0., 0.]]],
[[[1., 1.], [1., 1.], [0., 0.], [0., 0.], [1., 1.], [1., 1.], [0., 0.],
[0., 0.]],
[[1., 1.], [1., 1.], [0., 0.], [0., 0.], [1., 1.], [1., 1.], [0., 0.],
[0., 0.]],
[[1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.],
[1., 1.]],
[[0., 0.], [0., 0.], [1., 1.], [1., 1.], [1., 1.], [1., 1.], [1., 1.],
[1., 1.]]]]
]
def _test_tinshift_gradcheck(dtype): def _test_tinshift_gradcheck(dtype):
try: try:
from mmcv.ops import tin_shift from mmcv.ops import tin_shift
except ModuleNotFoundError: except ModuleNotFoundError:
pytest.skip('TinShift op is not successfully compiled') pytest.skip('TINShift op is not successfully compiled')
if dtype == torch.half: if dtype == torch.half:
pytest.skip('"add_cpu/sub_cpu" not implemented for Half') pytest.skip('"add_cpu/sub_cpu" not implemented for Half')
...@@ -78,7 +156,7 @@ def _test_tinshift_allclose(dtype): ...@@ -78,7 +156,7 @@ def _test_tinshift_allclose(dtype):
try: try:
from mmcv.ops import tin_shift from mmcv.ops import tin_shift
except ModuleNotFoundError: except ModuleNotFoundError:
pytest.skip('TinShift op is not successfully compiled') pytest.skip('TINShift op is not successfully compiled')
for shift, output, grad in zip(shifts, outputs, grads): for shift, output, grad in zip(shifts, outputs, grads):
np_input = np.array(inputs) np_input = np.array(inputs)
...@@ -98,9 +176,28 @@ def _test_tinshift_allclose(dtype): ...@@ -98,9 +176,28 @@ def _test_tinshift_allclose(dtype):
x.grad.data.type(torch.float).cpu().numpy(), np_grad, 1e-3) x.grad.data.type(torch.float).cpu().numpy(), np_grad, 1e-3)
def _test_tinshift_assert(dtype):
try:
from mmcv.ops import tin_shift
except ModuleNotFoundError:
pytest.skip('TINShift op is not successfully compiled')
inputs = [torch.rand(2, 3, 4, 2), torch.rand(2, 3, 4, 2)]
shifts = [torch.rand(2, 3), torch.rand(2, 5)]
for x, shift in zip(inputs, shifts):
x = x.cuda()
shift = shift.cuda()
# A ValueError should be raised if ops get inputs with wrong shapes.
with pytest.raises(ValueError):
tin_shift(x, shift)
@pytest.mark.skipif( @pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support') not torch.cuda.is_available(), reason='requires CUDA support')
@pytest.mark.parametrize('dtype', [torch.float, torch.double, torch.half]) @pytest.mark.parametrize('dtype', [torch.float, torch.double, torch.half])
def test_tinshift(dtype): def test_tinshift(dtype):
_test_tinshift_allclose(dtype=dtype) _test_tinshift_allclose(dtype=dtype)
_test_tinshift_gradcheck(dtype=dtype) _test_tinshift_gradcheck(dtype=dtype)
_test_tinshift_assert(dtype=dtype)
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