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)
def test_pad_opset_2():
x = np.ones((2, 2), dtype=np.float32)
y = np.pad(x, pad_width=1, mode="constant")
model = get_node_model("Pad", x, opset=2, pads=[1, 1, 1, 1])
ng_results = run_model(model, [x])
assert np.array_equal(ng_results, [y])
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
y = np.pad(x, pad_width=((0, 0), (0, 0), (1, 2), (3, 4)), mode="constant")
model = get_node_model("Pad",
x,
opset=2,
mode="constant",
pads=[0, 0, 1, 3, 0, 0, 2, 4])
ng_results = run_model(model, [x])
assert np.array_equal(ng_results, [y])
# incorrect pads rank
x = np.ones((2, 2), dtype=np.float32)
model = get_node_model("Pad", x, opset=2, pads=[0, 1, 1, 3, 1, 2])
with pytest.raises(RuntimeError):
run_model(model, [x])
def test_slice_opset1():
data = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
expected_output = np.array([[5, 6, 7]])
model = get_node_model("Slice", data, axes=[0, 1], starts=[1, 0], ends=[2, 3])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
expected_output = np.array([[2, 3, 4]])
model = get_node_model("Slice", data, starts=[0, 1], ends=[-1, 1000])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
data = np.random.randn(20, 10, 5).astype(np.float32)
expected_output = data[0:3, 0:10]
model = get_node_model("Slice", data, axes=[0, 1], starts=[0, 0], ends=[3, 10])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
# default axes
data = np.random.randn(20, 10, 5).astype(np.float32)
expected_output = data[:, :, 3:4]
model = get_node_model("Slice", data, starts=[0, 0, 3], ends=[20, 10, 4])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
# end out of bounds
data = np.random.randn(20, 10, 5).astype(np.float32)
expected_output = data[:, 1:1000]
model = get_node_model("Slice", data, axes=[1], starts=[1], ends=[1000])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
# negative value
data = np.random.randn(20, 10, 5).astype(np.float32)
expected_output = data[:, 0:-1]
model = get_node_model("Slice", data, axes=[1], starts=[0], ends=[-1])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
# start ouf of bounds
data = np.random.randn(20, 10, 5).astype(np.float32)
expected_output = data[:, 1000:1000]
model = get_node_model("Slice", data, axes=[1], starts=[1000], ends=[1000])
ng_results = run_model(model, [data])
assert np.array_equal(ng_results, [expected_output])
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)
