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)
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)
def test_mobilenet_v1_0_75_224_quant():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'mobilenet_v1_0.75_224_quant')
device = 'cpu'
model = DLRModel(model_path, device)
# load image (dtype: uint8)
image = np.load(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'cat_224_uint8.npy'))
print('Testing inference on mobilenet_v1_0.75_224_quant...')
probabilities = model.run({'input': image})
assert probabilities[0].argmax() == 282
assert model.get_input_names() == ["input"]
assert model.get_input_dtypes() == ["uint8"]
assert model.get_output_dtypes() == ["uint8"]
assert model.get_input_dtype(0) == "uint8"
assert model.get_output_dtype(0) == "uint8"
input2 = model.get_input("input")
assert input2.dtype == 'uint8'
assert input2.shape == (1, 224, 224, 3)
assert (input2 == image).all()
def test_tf_model(dev_type=None, dev_id=None):
_generate_frozen_graph()
model = DLRModel(FROZEN_GRAPH_PATH, dev_type, dev_id)
inp_names = model.get_input_names()
assert inp_names == ['import/input1:0', 'import/input2:0']
out_names = model.get_output_names()
assert out_names == [
'import/preproc/output1:0', 'import/preproc/output2:0'
]
inp1 = [[4., 1.], [3., 2.]]
inp2 = [[0., 1.], [1., 0.]]
res = model.run({'import/input1:0': inp1, 'import/input2:0': inp2})
assert res is not None
assert len(res) == 2
assert np.alltrue(res[0] == [[36., 361.], [49., 324.]])
assert res[1] == 1
m_inp1 = model.get_input('import/input1:0')
m_inp2 = model.get_input('import/input2:0')
assert np.alltrue(m_inp1 == inp1)
assert np.alltrue(m_inp2 == inp2)
def test_tflite_model():
_generate_tflite_file()
m = DLRModel(TFLITE_FILE_PATH)
inp_names = m.get_input_names()
assert sorted(inp_names) == ['input1', 'input2']
out_names = m.get_output_names()
assert sorted(out_names) == ['preproc/output1', 'preproc/output2']
inp1 = np.array([[4., 1.], [3., 2.]]).astype("float32")
inp2 = np.array([[0., 1.], [1., 0.]]).astype("float32")
res = m.run({'input1': inp1, 'input2': inp2})
assert res is not None
assert len(res) == 2
exp_out0 = np.array([[36., 361.], [49., 324.]]).astype("float32")
assert np.alltrue(res[0] == exp_out0)
assert res[1] == 1
m_inp1 = m.get_input('input1')
m_inp2 = m.get_input('input2')
assert np.alltrue(m_inp1 == inp1)
assert np.alltrue(m_inp2 == inp2)