def setUp(self):
# these parameters are referenced from chainer test
in_shape = (3, 3, 12, 8)
self.x = input_generator.positive_increasing(*in_shape)
# In chainer test, x is shuffled and normalize-like conversion,
# In this test, those operations are skipped.
# If x includes negative value, not match with onnxruntime output.
# You can reproduce this issue by changing `positive_increasing` to
# `increase`
self.rois = np.array([
[0, 1, 1, 6, 6],
[2, 6, 2, 7, 11],
[1, 3, 1, 5, 10],
[0, 3, 3, 3, 3]], dtype=np.float32)
kwargs = {
'outh': 3,
'outw': 7,
'spatial_scale': 0.6
}
class Model(chainer.Chain):
def __init__(self, kwargs):
super(Model, self).__init__()
self.kwargs = kwargs
def __call__(self, x, rois):
return F.roi_pooling_2d(x, rois, **self.kwargs)
self.model = Model(kwargs)
def setup_test_case(self):
class Model(chainer.Chain):
def __init__(self, ops):
super(Model, self).__init__()
self.ops = ops
def __call__(self, a):
if not isinstance(a, chainer.Variable):
a = chainer.Variable(a)
return eval(self.ops)
self.model = Model(self.ops)
self.a = input_generator.positive_increasing(2, 3) / 10.0
name = self.op_name.lower()
if hasattr(self, 'condition'):
name += '_' + self.condition
self.name = name
skip_opset_version = []
if self.op_name == 'Cosh' or self.op_name == 'Sinh':
skip_opset_version.append(7)
skip_opset_version.append(8)
if self.op_name == 'BroadcastTo':
skip_opset_version.append(7)
self.skip_opset_version = skip_opset_version
def setUp(self):
class Model(chainer.Chain):
def __init__(self, ops):
super(Model, self).__init__()
self.ops = ops
def __call__(self, a):
if not isinstance(a, chainer.Variable):
a = chainer.Variable(a)
return eval(self.ops)
self.model = Model(self.ops)
self.a = chainer.Variable(input_generator.positive_increasing(2, 3))
def test_export_external_converters_overwrite(tmpdir, check_model_expect):
path = str(tmpdir)
model = chainer.Sequential(chainer.functions.sigmoid)
x = input_generator.positive_increasing(2, 5)
def custom_converter(params):
return onnx_helper.make_node('Tanh', params.input_names,
params.output_names),
addon_converters = {'Sigmoid': custom_converter}
export_testcase(model, x, path, external_converters=addon_converters)
tanh_outputs = chainer.functions.tanh(x).array
output_path = os.path.join(path, 'test_data_set_0', 'output_0.pb')
onnx_helper.write_tensor_pb(output_path, '', tanh_outputs) # overwrite
check_model_expect(path)
def vector(self):
a = chainer.Variable(input_generator.positive_increasing(2, ))
b = chainer.Variable(input_generator.nonzero_increasing(2, ) * 0.3)
return (a, b)
def matrix(self):
a = chainer.Variable(input_generator.positive_increasing(5, 2, 3))
b = chainer.Variable(input_generator.nonzero_increasing(5, 2, 3) * 0.3)
return (a, b)
