def test_cast_to_bool(val_type, input_data):
expected = np.array(input_data, dtype=val_type)
model = get_node_model('Cast',
input_data,
opset=6,
to=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[val_type])
result = run_model(model, [input_data])
assert np.allclose(result, expected)
model = get_node_model('Cast',
input_data,
opset=5,
to=np_dtype_to_tensor_type_name(val_type))
result = run_model(model, [input_data])
assert np.allclose(result, expected)
def test_cast_to_uint(val_type):
np.random.seed(133391)
input_data = np.ceil(np.random.rand(2, 3, 4) * 16)
expected = np.array(input_data, dtype=val_type)
model = get_node_model('Cast',
input_data,
opset=6,
to=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[val_type])
result = run_model(model, [input_data])
assert np.allclose(result, expected)
model = get_node_model('Cast',
input_data,
opset=5,
to=np_dtype_to_tensor_type_name(val_type))
result = run_model(model, [input_data])
assert np.allclose(result, expected)
def test_cast_to_float(val_type, range_start, range_end, in_dtype):
np.random.seed(133391)
input_data = np.random.randint(range_start,
range_end,
size=(2, 2),
dtype=in_dtype)
expected = np.array(input_data, dtype=val_type)
model = get_node_model('Cast',
input_data,
opset=6,
to=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[val_type])
result = run_model(model, [input_data])
assert np.allclose(result, expected)
model = get_node_model('Cast',
input_data,
opset=5,
to=np_dtype_to_tensor_type_name(in_dtype))
result = run_model(model, [input_data])
assert np.allclose(result, expected)