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(ValueError):
run_model(model, [x])
# no paddings arttribute
model = get_node_model('Pad', x)
with pytest.raises(ValueError):
import_onnx_model(model)[0]
def import_and_compute(op_type,
input_data_left,
input_data_right,
opset=4,
**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, value.shape)
for name, value in zip(onnx_node.output, ())
] # type: ignore
graph = make_graph([onnx_node], 'compute_graph', input_tensors,
output_tensors)
model = make_model(graph, producer_name='NgraphBackend')
model.opset_import[0].version = opset
return run_model(model, inputs)[0]
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_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_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(ValueError):
run_model(model, [x])
# negative pads values
model = get_node_model('Pad', x, opset=2, pads=[0, -1, -1, 3])
with pytest.raises(NotImplementedError):
run_model(model, [x])
# no pads attribute
model = get_node_model('Pad', x, opset=2)
with pytest.raises(ValueError):
import_onnx_model(model)
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)