def test_pad_opset_1():
x = np.ones((2, 2), dtype=np.float32)
y = np.pad(x, pad_width=1, mode="constant")
model = get_node_model("Pad", x, paddings=[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,
mode="constant",
paddings=[0, 0, 1, 3, 0, 0, 2, 4])
ng_results = run_model(model, [x])
assert np.array_equal(ng_results, [y])
# incorrect paddings rank
x = np.ones((2, 2), dtype=np.float32)
model = get_node_model("Pad", x, paddings=[0, 1, 1, 3, 1, 2])
with pytest.raises(RuntimeError):
run_model(model, [x])
# no paddings arttribute
model = get_node_model("Pad", x)
with pytest.raises(RuntimeError):
import_onnx_model(model)
def test_pad_negative_values_end():
x = np.ones((2, 2), dtype=np.float32)
# Axis 1 end
model = get_node_model("Pad", x, opset=2, pads=[0, 0, -1, 0])
ng_result = run_model(model, [x])[0]
assert np.array_equal(ng_result, np.array([[1.0, 1.0]]))
# Axis 2 end
model = get_node_model("Pad", x, opset=2, pads=[0, 0, 0, -1])
ng_result = run_model(model, [x])[0]
assert np.array_equal(ng_result, np.array([[1], [1]]))
def test_clip_default():
np.random.seed(133391)
input_data = -100.0 + np.random.randn(3, 4, 5).astype(np.float32) * 200.0
model = get_node_model("Clip", input_data, opset=10, min=0.0)
result = run_model(model, [input_data])
expected = np.clip(input_data, np.float32(0.0), np.finfo(np.float32).max)
assert np.allclose(result, [expected])
model = get_node_model("Clip", input_data, opset=10, max=0.0)
result = run_model(model, [input_data])
expected = np.clip(input_data, np.finfo(np.float32).min, np.float32(0.0))
assert np.allclose(result, [expected])
def import_and_compute(op_type,
input_data_left,
input_data_right,
opset=7,
**node_attributes):
inputs = [np.array(input_data_left), np.array(input_data_right)]
onnx_node = onnx.helper.make_node(op_type,
inputs=["x", "y"],
outputs=["z"],
**node_attributes)
input_tensors = [
make_tensor_value_info(name, onnx.TensorProto.FLOAT, value.shape)
for name, value in zip(onnx_node.input, inputs)
]
output_tensors = [
make_tensor_value_info(name, onnx.TensorProto.FLOAT, ())
for name in onnx_node.output
]
graph = make_graph([onnx_node], "compute_graph", input_tensors,
output_tensors)
model = make_model(graph, producer_name="ngraph ONNX Importer")
model.opset_import[0].version = opset
return run_model(model, inputs)[0]
def test_clip(min_value, max_value):
np.random.seed(133391)
input_data = np.float32(-100.0) + np.random.randn(3, 4, 5).astype(np.float32) * np.float32(200.0)
model = get_node_model("Clip", input_data, opset=10, min=float(min_value), max=float(max_value))
result = run_model(model, [input_data])
expected = np.clip(input_data, min_value, max_value)
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)
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_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)
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])
