def test_get_set_input():
model_path = get_models(model_name='4in2out', arch=get_arch(), kind='tvm')
device = 'cpu'
model = DLRModel(model_path, device)
input1 = np.asarray([1., 2.])
input2 = np.asarray([3., 4.])
input3 = np.asarray([5., 6., 7])
input4 = np.asarray([8., 9., 10])
model.run({'data1': input1, 'data2': input2, 'data3': input3, 'data4': input4})
assert np.array_equal(model.get_input('data1'), input1)
assert np.array_equal(model.get_input('data2'), input2)
assert np.array_equal(model.get_input('data3'), input3)
assert np.array_equal(model.get_input('data4'), input4)
X, _ = load_svmlight_file(data_file, zero_based=True)
expected = np.array([
1.372033834457397461e+00, -2.448803186416625977e+00,
8.579480648040771484e-01, 1.369985580444335938e+00,
-7.058695554733276367e-01, 4.134958684444427490e-01,
-2.247941017150878906e+00, -2.461995363235473633e+00,
-2.394921064376831055e+00, -1.191793322563171387e+00,
9.672126173973083496e-02, 2.687671184539794922e-01,
1.417675256729125977e+00, -1.832636356353759766e+00,
-5.582004785537719727e-02, -9.497703313827514648e-01,
-1.219825387001037598e+00, 1.512521862983703613e+00,
-1.179921030998229980e-01, -2.383430719375610352e+00,
-9.094548225402832031e-01
])
expected = expected.reshape((-1, 1))
print('Testing inference on XGBoost LETOR...')
assert np.allclose(model.run(_sparse_to_dense(X))[0], expected)
if __name__ == '__main__':
arch = get_arch()
model_names = [
'xgboost-mnist-1.10.0', 'xgboost-iris-1.10.0', 'xgboost-letor-1.10.0'
]
for model_name in model_names:
get_models(model_name, arch, kind='treelite')
test_mnist()
test_iris()
test_letor()
print('All tests passed!')
def test_assign_op():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'assign_op')
device = 'cpu'
model = DLRModel(model_path, device)
print('Testing _assign() operator...')
# Example from https://github.com/dmlc/tvm/blob/bb87f044099ba61ba4782d17dd9127b869936373/nnvm/tests/python/compiler/test_top_assign.py
np.random.seed(seed=0)
input1 = np.random.random(size=(5, 3, 18, 18))
model.run({'w': input1})
input1_next = model.get_input('w2', shape=(5, 3, 18, 18))
assert np.allclose(input1_next, input1 + 2)
model.run({})
input1_next = model.get_input('w2', shape=(5, 3, 18, 18))
assert np.allclose(input1_next, input1 + 3)
if __name__ == '__main__':
arch = get_arch()
model_names = ['resnet18_v1', '4in2out', 'assign_op']
for model_name in model_names:
get_models(model_name, arch, kind='tvm')
test_resnet()
test_multi_input_multi_output()
test_assign_op()
print('All tests passed!')
def set_up():
arch = get_arch()
model_names = ['resnet18_v1', '4in2out', 'assign_op']
for model_name in model_names:
get_models(model_name, arch, kind='tvm')
