def _test_roialign_rotated_allclose(device, dtype):
if not torch.cuda.is_available() and device == 'cuda':
pytest.skip('unittest does not support GPU yet.')
try:
from mmcv.ops import roi_align_rotated
except ModuleNotFoundError:
pytest.skip('test requires compilation')
pool_h = 2
pool_w = 2
spatial_scale = 1.0
sampling_ratio = 2
for case, output in zip(inputs, outputs):
np_input = np.array(case[0])
np_rois = np.array(case[1])
np_output = np.array(output[0])
np_grad = np.array(output[1])
x = torch.tensor(
np_input, dtype=dtype, device=device, requires_grad=True)
rois = torch.tensor(np_rois, dtype=dtype, device=device)
output = roi_align_rotated(x, rois, (pool_h, pool_w), spatial_scale,
sampling_ratio, True)
output.backward(torch.ones_like(output))
assert np.allclose(
output.data.type(torch.float).cpu().numpy(), np_output, atol=1e-3)
assert np.allclose(
x.grad.data.type(torch.float).cpu().numpy(), np_grad, atol=1e-3)
def warpped_function(torch_input, torch_rois):
return roi_align_rotated(torch_input, torch_rois, (pool_w, pool_h),
spatial_scale, sampling_ratio, True, False)
def test_roialign_rotated():
if torch.__version__ == 'parrots':
pytest.skip('onnx is not supported in parrots directly')
try:
from mmcv.ops import roi_align_rotated
from mmcv.ops import get_onnxruntime_op_path
except (ImportError, ModuleNotFoundError):
pytest.skip('roi_align_aligned op is not successfully compiled')
ort_custom_op_path = get_onnxruntime_op_path()
if not os.path.exists(ort_custom_op_path):
pytest.skip('custom ops for onnxruntime are not compiled.')
# roi align config
pool_h = 2
pool_w = 2
spatial_scale = 1.0
sampling_ratio = 2
inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0.5, 0.5, 1., 1., 0]]),
([[[[1., 2.], [3., 4.]]]], [[0., 0.5, 0.5, 1., 1., np.pi / 2]]),
([[[[1., 2.], [3., 4.]],
[[4., 3.], [2., 1.]]]], [[0., 0.5, 0.5, 1., 1., 0]]),
([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.],
[11., 12., 15., 16.]]]], [[0., 1.5, 1.5, 3., 3., 0]]),
([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.],
[11., 12., 15., 16.]]]], [[0., 1.5, 1.5, 3., 3.,
np.pi / 2]])]
def warpped_function(torch_input, torch_rois):
return roi_align_rotated(torch_input, torch_rois, (pool_w, pool_h),
spatial_scale, sampling_ratio, True, False)
for case in inputs:
np_input = np.array(case[0], dtype=np.float32)
np_rois = np.array(case[1], dtype=np.float32)
input = torch.from_numpy(np_input)
rois = torch.from_numpy(np_rois)
# compute pytorch_output
with torch.no_grad():
pytorch_output = roi_align_rotated(input, rois, (pool_w, pool_h),
spatial_scale, sampling_ratio,
True, False)
# export and load onnx model
wrapped_model = WrapFunction(warpped_function)
with torch.no_grad():
torch.onnx.export(wrapped_model, (input, rois),
onnx_file,
export_params=True,
keep_initializers_as_inputs=True,
input_names=['features', 'rois'],
opset_version=11)
onnx_model = onnx.load(onnx_file)
session_options = rt.SessionOptions()
if os.path.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
# compute onnx_output
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 2)
sess = rt.InferenceSession(onnx_file, session_options)
onnx_output = sess.run(None, {
'features': input.detach().numpy(),
'rois': rois.detach().numpy()
})
onnx_output = onnx_output[0]
# allclose
os.remove(onnx_file)
assert np.allclose(pytorch_output, onnx_output, atol=1e-3)
