def test_compress(self):
if legacy_opset_pre_ver(9):
raise unittest.SkipTest(
"ONNX version {} doesn't support Compress.".format(
defs.onnx_opset_version()))
axis = 1
node_def = helper.make_node(
"Compress", inputs=['X', 'condition'], outputs=['Y'], axis=axis)
x = self._get_rnd([5, 5, 5])
cond = np.array([1, 0, 1])
output = run_node(node_def, inputs=[x, cond])
np.testing.assert_almost_equal(output['Y'], np.compress(cond, x, axis=axis))
def test_shrink(self):
if legacy_opset_pre_ver(9):
raise unittest.SkipTest(
"ONNX version {} doesn't support Shrink.".format(
defs.onnx_opset_version()))
node_def = helper.make_node("Shrink", ["X"], ["Y"], bias=1.5, lambd=1.5)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
output = run_node(node_def, [X])
np.testing.assert_almost_equal(output["Y"], Y)
def test_mod(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest("ONNX version {} doesn't support Mod.".format(
defs.onnx_opset_version()))
x = self._get_rnd([5, 5])
y = self._get_rnd([5, 5])
node_def = helper.make_node("Mod", ["X", "Y"], ["Z"], fmod=0)
output = run_node(node_def, [x, y])
np.testing.assert_almost_equal(output["Z"], np.mod(x, y))
node_def = helper.make_node("Mod", ["X", "Y"], ["Z"], fmod=1)
output = run_node(node_def, [x, y])
np.testing.assert_almost_equal(output["Z"], np.fmod(x, y))
def test_eye_like(self):
if legacy_opset_pre_ver(9):
raise unittest.SkipTest("ONNX version {} doesn't support EyeLike.".format(
defs.onnx_opset_version()))
for shape in [[6, 10], [10, 6]]:
for off_diagonal_offset in [-10, -6, -3, 0, 3, 6, 7, 10]:
node_def = helper.make_node(
"EyeLike", ['x'], ['y'], dtype=1, k=off_diagonal_offset)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
output = run_node(node_def, [x])
np.testing.assert_equal(output['y'], y)
def test_cast(self):
if legacy_onnx_pre_ver(1, 2) or legacy_opset_pre_ver(6):
test_cases = [("FLOAT", tf.float32), ("UINT8", tf.uint8),
("INT8", tf.int8), ("UINT16", tf.uint16), ("INT16",
tf.int16),
("INT32", tf.int32), ("INT64", tf.int64), ("BOOL", tf.bool),
("FLOAT16", tf.float16), ("DOUBLE", tf.float64),
("COMPLEX64", tf.complex64), ("COMPLEX128", tf.complex128)]
else:
test_cases = [(TensorProto.FLOAT,
tf.float32), (TensorProto.UINT8,
tf.uint8), (TensorProto.INT8, tf.int8),
(TensorProto.UINT16,
tf.uint16), (TensorProto.INT16,
tf.int16), (TensorProto.INT32, tf.int32),
(TensorProto.INT64,
tf.int64), (TensorProto.BOOL,
tf.bool), (TensorProto.FLOAT16, tf.float16),
(TensorProto.DOUBLE,
tf.float64), (TensorProto.COMPLEX64,
tf.complex64), (TensorProto.COMPLEX128,
tf.complex128)]
if not legacy_opset_pre_ver(9):
test_cases.append((TensorProto.STRING, tf.string))
for ty, tf_type in test_cases:
node_def = helper.make_node("Cast", ["input"], ["output"], to=ty)
vector = [2, 3]
output = run_node(node_def, [vector])
np.testing.assert_equal(output["output"].dtype, tf_type)
if not legacy_opset_pre_ver(9):
test_cases2 = [(TensorProto.FLOAT, tf.float32),
(TensorProto.INT32, tf.int32),
(TensorProto.INT64, tf.int64),
(TensorProto.DOUBLE, tf.float64)]
for ty, tf_type in test_cases2:
node_def = helper.make_node("Cast", ["input"], ["output"], to=ty)
vector = ['2', '3']
output = run_node(node_def, [vector])
np.testing.assert_equal(output["output"].dtype, tf_type)
def test_non_max_suppression(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest(
"ONNX version {} doesn't support NonMaxSuppression.".format(
defs.onnx_opset_version()))
boxes = np.array([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0]],
[[0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0,
101.0]]]).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]],
[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [1, 0, 3],
[1, 0, 0]]).astype(np.int64)
node_def = helper.make_node("NonMaxSuppression", [
"boxes", "scores", "max_output_boxes_per_class", "iou_threshold",
"score_threshold"
], ["selected_indices"],
center_point_box=0)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("boxes", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("scores", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("max_output_boxes_per_class",
TensorProto.INT64, [None]),
helper.make_tensor_value_info("iou_threshold", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("score_threshold", TensorProto.FLOAT,
[None])
],
outputs=[
helper.make_tensor_value_info("selected_indices", TensorProto.INT64,
[None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({
"boxes": boxes,
"scores": scores,
"max_output_boxes_per_class": max_output_boxes_per_class,
"iou_threshold": iou_threshold,
"score_threshold": score_threshold
})
np.testing.assert_almost_equal(output["selected_indices"], selected_indices)
def test_topk(self):
x = np.arange(15, dtype=np.float32).reshape(3, 5)
values = np.array([[4, 3], [9, 8], [14, 13]], dtype=np.float32)
indices = np.array([[4, 3],[4, 3],[4, 3]], dtype=np.int64)
if legacy_opset_pre_ver(10): # for opset = 1
node_def = helper.make_node("TopK", ["x"], ["values", "indices"], k=2)
output = run_node(node_def, [x])
elif legacy_opset_pre_ver(11): # for opset = 10
k = np.array([2], dtype=np.int64)
node_def = helper.make_node("TopK", ["x", "k"], ["values", "indices"])
output = run_node(node_def, [x, k])
else: # for opset = 11
x = np.array([[3, 2, 5, 10, 7], [12, 15, 10, 7, 20], [21, 16, 5, 3, 6]],
dtype=np.float32)
values = np.array([[3, 2], [10, 7], [5, 3]], dtype=np.float32)
indices = np.array([[0, 1], [2, 3], [2, 3]], dtype=np.int64)
k = np.array([2], dtype=np.int64)
node_def = helper.make_node(
"TopK", ["x", "k"], ["values", "indices"], largest=0, sorted=0)
output = run_node(node_def, [x, k])
np.testing.assert_almost_equal(output["values"], values)
np.testing.assert_almost_equal(output["indices"], indices)
def test_dropout(self):
# Since current ONNX only support inference and
# dropout at inference mode is a no-op,
# therefore dropout is always a no-op operator
# in ONNX.
node_def = helper.make_node("Dropout", ["X"], ["Y"])
if legacy_opset_pre_ver(7):
# at inference mode, is_test is always set to 1
node_def = helper.make_node("Dropout", ["X"], ["Y"], is_test=1)
x = self._get_rnd([3, 4, 5])
y = x
output = run_node(node_def, [x])
np.testing.assert_equal(output["Y"], y)
def test_max_pool_with_argmax_2d_dilations_ceil_pads(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest(
"ONNX version {} doesn't support dilations nor ceil mode.".format(
defs.onnx_opset_version()))
kernel_shape = [3, 3]
strides = [2, 2]
dilations = [3, 3]
pads = [1, 1, 2, 2]
ceil_mode = True
input_shape = [10, 3, 23, 23]
x = self._get_rnd_float32(shape=input_shape) - 2
node_def = helper.make_node("MaxPool", ["X"], ["Y", "Ind"],
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
pads=pads,
ceil_mode=ceil_mode)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None]),
helper.make_tensor_value_info("Ind", TensorProto.INT64,
[None, None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({"X": x})
test_output, test_ind = py_pool(x,
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
padding=pads,
ceil_mode=ceil_mode,
pooling_type="MAX")
np.testing.assert_almost_equal(output["Y"], test_output)
np.testing.assert_almost_equal(output["Ind"], test_ind)
def test_onehot(self):
if legacy_opset_pre_ver(9):
raise unittest.SkipTest("ONNX version {} doesn't support OneHot.".format(
defs.onnx_opset_version()))
indices = np.array([[0, 2], [1, 2], [0, 1]])
depth = np.array([5], dtype=np.int32)
on_value = 6.0
off_value = 2.0
values = np.array([off_value, on_value])
node_def = helper.make_node(
'OneHot', inputs=['indices', 'depth', 'values'], outputs=['y'], axis=-1)
y = (np.arange(depth) == indices[..., None]).astype(int)
y = y * (on_value - off_value) + off_value
output = run_node(node_def, inputs=[indices, depth, values])
np.testing.assert_equal(output['y'], y)
def test_topk(self):
x = np.arange(15, dtype=np.float32).reshape(3, 5)
values = np.array([[4, 3], [9, 8], [14, 13]], dtype=np.float32)
indices = np.array([[4, 3], [4, 3], [4, 3]], dtype=np.int64)
if legacy_opset_pre_ver(10): # for opset = 1
node_def = helper.make_node("TopK", ["x"], ["values", "indices"],
k=2)
output = run_node(node_def, [x])
else: # for opset = 10
k = np.array([2], dtype=np.int64)
node_def = helper.make_node("TopK", ["x", "k"],
["values", "indices"])
output = run_node(node_def, [x, k])
np.testing.assert_almost_equal(output["values"], values)
np.testing.assert_almost_equal(output["indices"], indices)
def test_mean_variance_normalization(self):
if legacy_opset_pre_ver(9):
raise unittest.SkipTest(
"ONNX version {} doesn't have test for MeanVarianceNormalization"
.format(defs.onnx_opset_version()))
input_data = self._get_rnd([2,2,2,2])
# Calculate expected output data using formula:
# (Input - Mean)/SD
mean = np.mean(input_data, keepdims=1, axis=(0,2,3))
std = np.std(input_data, keepdims=1, axis=(0,2,3))
expected_output = (input_data - mean) / std
# Testing without "axes" argument should default to axes=[0,2,3]
node_def = helper.make_node("MeanVarianceNormalization", ["X"], ["Y"])
output = run_node(node_def, [input_data])
np.testing.assert_almost_equal(output["Y"], expected_output, decimal=5)
def test_batch_normalization(self):
if legacy_opset_pre_ver(6):
raise unittest.SkipTest("Backend doesn't support consumed flag")
node_def = helper.make_node("BatchNormalization",
["X", "scale", "bias", "mean", "var"],
["Y"],
epsilon=0.001)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
helper.make_tensor_value_info("scale", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("bias", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("mean", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("var", TensorProto.FLOAT, [None])
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None])
])
x_shape = [3, 5, 4, 2]
param_shape = [5]
_param_shape = [1, 5, 1, 1]
x = self._get_rnd_float32(0, 1, shape=x_shape)
m = self._get_rnd_float32(0, 1, shape=param_shape)
_m = m.reshape(_param_shape)
v = self._get_rnd_float32(0, 1, shape=param_shape)
_v = v.reshape(_param_shape)
scale = self._get_rnd_float32(0, 1, shape=param_shape)
_scale = scale.reshape(_param_shape)
bias = self._get_rnd_float32(0, 1, shape=param_shape)
_bias = bias.reshape(_param_shape)
golden = self._batch_normalization(x, _m, _v, _bias, _scale, 0.001)
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/batch_normalization'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
# run the model
tf_model_output = tf_model(X=x, scale=scale, bias=bias, mean=m, var=v)
np.testing.assert_almost_equal(tf_model_output[0], golden, decimal=5)
def test_batch_normalization(self):
if legacy_opset_pre_ver(6):
raise unittest.SkipTest("Backend doesn't support consumed flag")
node_def = helper.make_node("BatchNormalization",
["X", "scale", "bias", "mean", "var"],
["Y"],
epsilon=0.001)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
helper.make_tensor_value_info("scale", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("bias", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("mean", TensorProto.FLOAT,
[None]),
helper.make_tensor_value_info("var", TensorProto.FLOAT, [None])
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None])
])
x_shape = [3, 5, 4, 2]
param_shape = [5]
_param_shape = [1, 5, 1, 1]
x = self._get_rnd_float32(0, 1, shape=x_shape)
m = self._get_rnd_float32(0, 1, shape=param_shape)
_m = m.reshape(_param_shape)
v = self._get_rnd_float32(0, 1, shape=param_shape)
_v = v.reshape(_param_shape)
scale = self._get_rnd_float32(0, 1, shape=param_shape)
_scale = scale.reshape(_param_shape)
bias = self._get_rnd_float32(0, 1, shape=param_shape)
_bias = bias.reshape(_param_shape)
golden = self._batch_normalization(x, _m, _v, _bias, _scale, 0.001)
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({
"X": x,
"scale": scale,
"bias": bias,
"mean": m,
"var": v
})
np.testing.assert_almost_equal(output["Y"], golden, decimal=5)
def test_is_inf(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest("ONNX version {} doesn't support IsInf.".format(
defs.onnx_opset_version()))
inp = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
dtype=np.float32)
expected_output = np.isinf(inp)
node_def = helper.make_node("IsInf", ["X"], ["Y"])
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT, [None]),
],
outputs=[helper.make_tensor_value_info("Y", TensorProto.BOOL, [None])])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({"X": inp})
np.testing.assert_equal(output["Y"], expected_output)
def test_scatter_nd(self):
if legacy_opset_pre_ver(11):
raise unittest.SkipTest(
"ONNX version {} doesn't support ScatterND.".format(
defs.onnx_opset_version()))
# valid positive and negative indices for slices
data = np.reshape(np.arange(1, 25, dtype=np.float32), [2, 3, 4])
indices = np.array([[-1]], dtype=np.int64)
updates = np.array(
[[[43, 44, 45, 46], [47, 48, 49, 50], [51, 52, 53, 54]]],
dtype=np.float32)
ref_output = np.array(
[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]],
[[43, 44, 45, 46], [47, 48, 49, 50], [51, 52, 53, 54]]],
dtype=np.float32)
node_def = helper.make_node("ScatterND",
["data", "indices", "updates"],
["outputs"])
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("data", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("indices", TensorProto.INT64,
[None, None]),
helper.make_tensor_value_info("updates", TensorProto.FLOAT,
[None, None, None])
],
outputs=[
helper.make_tensor_value_info("outputs", TensorProto.FLOAT,
[None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/scatter_nd'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
# run the model
tf_model_output = tf_model(data=data, indices=indices, updates=updates)
np.testing.assert_almost_equal(tf_model_output[0], ref_output)
def test_batch_normalization(self):
if legacy_opset_pre_ver(6):
raise unittest.SkipTest("Backend doesn't support consumed flag")
node_def = helper.make_node(
"BatchNormalization", ["X", "scale", "bias", "mean", "var"], ["Y"],
epsilon=0.001)
x_shape = [3, 5, 4, 2]
param_shape = [5]
_param_shape = [1, 5, 1, 1]
x = self._get_rnd(x_shape, 0, 1)
m = self._get_rnd(param_shape, 0, 1)
_m = m.reshape(_param_shape)
v = self._get_rnd(param_shape, 0, 1)
_v = v.reshape(_param_shape)
scale = self._get_rnd(param_shape, 0, 1)
_scale = scale.reshape(_param_shape)
bias = self._get_rnd(param_shape, 0, 1)
_bias = bias.reshape(_param_shape)
golden = self._batch_normalization(x, _m, _v, _bias, _scale, 0.001)
output = run_node(node_def, [x, scale, bias, m, v])
np.testing.assert_almost_equal(output["Y"], golden, decimal=5)
def test_constant(self):
shape = [16, 16]
values = np.random.randn(*shape).flatten().astype(float)
const2_onnx = helper.make_tensor("const2", TensorProto.DOUBLE, shape,
values)
node_def = helper.make_node("Constant", [], ["Y"], value=const2_onnx)
output = run_node(node_def, [])
np.testing.assert_equal(output["Y"].shape, shape)
np.testing.assert_almost_equal(output["Y"].flatten(), values)
# test sparse tensor
if not legacy_opset_pre_ver(11):
expected = np.array([[1, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0]])
x = np.array([[0, 0], [1, 2]]).flatten().astype(np.int64)
values = helper.make_tensor("values", TensorProto.INT32, [2], [1, 2])
indices = helper.make_tensor("indices", TensorProto.INT64, [2, 2], x)
a = helper.make_sparse_tensor(values, indices,[3, 4])
node_def = helper.make_node("Constant", [], ["Y"], sparse_value=a)
output = run_node(node_def, [])
b = tf.sparse_to_dense(output["Y"].indices, output["Y"].dense_shape, output["Y"].values)
result = b.eval(session=tf.Session())
np.testing.assert_equal(result, expected)
def test_is_inf(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest(
"ONNX version {} doesn't support IsInf.".format(
defs.onnx_opset_version()))
input = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
dtype=np.float32)
expected_output = {
"node_def": np.isinf(input),
"node_def_neg_false": np.isposinf(input),
"node_def_pos_false": np.isneginf(input)}
node_defs = {
"node_def" :
helper.make_node("IsInf", ["X"], ["Y"]),
"node_def_neg_false" :
helper.make_node("IsInf", ["X"], ["Y"], detect_negative = 0),
"node_def_pos_false" :
helper.make_node("IsInf", ["X"], ["Y"], detect_positive = 0)
}
for key in node_defs:
output = run_node(node_defs[key], [input])
np.testing.assert_equal(output["Y"], expected_output[key])
def test_constant_fill(self):
if not legacy_opset_pre_ver(9):
raise unittest.SkipTest(
"ONNX version {} doesn't support ConstantFill.".format(
defs.onnx_opset_version()))
shape = [1, 2, 3, 4]
extra_shape = [5, 6]
value = 3.
node_def = helper.make_node(
"ConstantFill",
["X"],
["Y"],
value=value,
extra_shape=extra_shape,
dtype=1,
)
x = self._get_rnd(shape)
y = np.zeros(shape + extra_shape)
y.fill(value)
output = run_node(node_def, [x])
np.testing.assert_equal(output["Y"].dtype, tf.float32)
np.testing.assert_equal(output["Y"], y)
def test_eye_like(self):
if legacy_opset_pre_ver(9):
raise unittest.SkipTest("ONNX version {} doesn't support EyeLike.".format(
defs.onnx_opset_version()))
shape = [6, 10]
off_diagonal_offset = -3
x = self._get_rnd_int(0, 100, shape=shape)
y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
node_def = helper.make_node("EyeLike", ["x"], ["y"],
dtype=TensorProto.FLOAT,
k=off_diagonal_offset)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("x", TensorProto.INT32, [None, None])
],
outputs=[
helper.make_tensor_value_info("y", TensorProto.FLOAT, [None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({"x": x})
np.testing.assert_equal(output["y"], y)
def test_arg_max(self):
if legacy_opset_pre_ver(12):
raise unittest.SkipTest(
"ONNX version {} doesn't support select_last_index attribute for ArgMax that depends on shape."
.format(defs.onnx_opset_version()))
axis = 1
node_def = helper.make_node("ArgMax",
inputs=['X'],
outputs=['Y'],
axis=axis,
keepdims=0,
select_last_index=1)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None])
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None])
])
x = np.array([[1, 2, 3, 5, 3, 4, 5, 1], [2, 9, 3, 5, 9, 4, 5,
1]]).astype(np.float32)
# get tf_rep
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/arg_max'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
# run the model
tf_model_output = tf_model(X=x)
expected_output = np.argmax(np.flip(x, axis), axis=axis)
expected_output = x.shape[axis] - expected_output - 1
np.testing.assert_almost_equal(tf_model_output[0], expected_output)
backend_test.exclude(r'test_mod_[a-z,_]*uint[0-9]+')
backend_test.exclude(r'test_mod_[a-z,_]*int(8|(16))+')
# TF doesn't support most of the attributes in resize op
# test_node.py will cover the test
backend_test.exclude(r'test_resize_[a-z,_]*')
# range is using loop in the model test but all the outputs datatype are
# missing in the body attribute of the loop
backend_test.exclude(r'test_range_float_type_positive_delta_expanded[a-z,_]*')
backend_test.exclude(r'test_range_int32_type_negative_delta_expanded[a-z,_]*')
# skip all the cumsum testcases because all the axis in the testcases
# are created as a 1-D 1 element tensor, but the spec clearly state
# that axis should be a 0-D tensor(scalar)
if legacy_opset_pre_ver(13):
backend_test.exclude(r'test_cumsum_[a-z,_]*')
# TF session run does not support sequence/RaggedTensor as model inputs
backend_test.exclude(r'test_loop13_seq[a-z,_]*')
# TF minimum/maximum do not support uint64 when auto-cast is False (default)
backend_test.exclude(r'test_min_uint64_[a-z,_]*')
backend_test.exclude(r'test_max_uint64_[a-z,_]*')
if legacy_opset_pre_ver(7):
backend_test.exclude(r'[a-z,_]*Upsample[a-z,_]*')
if 'TRAVIS' in os.environ:
backend_test.exclude('test_vgg19')
backend_test.exclude('zfnet512')
model = onnx_model
tf_rep = prepare(model)
output_onnx_tf = tf_rep.run(backend_feed_dict)
assert len(output_tf) == len(output_onnx_tf)
for tf_output, onnx_backend_output in zip(output_tf, output_onnx_tf):
np.testing.assert_allclose(tf_output,
onnx_backend_output,
rtol=1e-3,
atol=1e-7)
return do_test_expected
if legacy_opset_pre_ver(10):
dir_path = os.path.dirname(os.path.realpath(__file__))
with open(dir_path + "/test_model.yaml", 'r') as config:
try:
for test_model in yaml.safe_load_all(config):
for device in test_model["devices"]:
if supports_device(device):
test_method = create_test(test_model)
test_name_parts = ["test", test_model["name"], device]
test_name = str("_".join(map(str, test_name_parts)))
test_method.__name__ = test_name
setattr(TestModel, test_method.__name__, test_method)
except yaml.YAMLError as exception:
print(exception)
if __name__ == '__main__':
def test_if_with_sequence(self):
if legacy_opset_pre_ver(14):
raise unittest.SkipTest(
"ONNX version {} doesn't support helper.make_tensor_sequence_value_info."
.format(defs.onnx_opset_version()))
# S = [a]
# if cond is True
# S = [a,b]
# else
# S = [a,c]
a = np.random.randn(2, 1, 2).astype(np.float32)
b = np.random.randn(1, 1, 2).astype(np.float32)
c = np.random.randn(3, 1, 2).astype(np.float32)
seq_construct_node = helper.make_node('SequenceConstruct', ['a'],
['S'])
seq_insert_node1 = helper.make_node('SequenceInsert', ['S', 'b'],
['Sb'])
seq_insert_node2 = helper.make_node('SequenceInsert', ['S', 'c'],
['Sc'])
a_in = helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT,
[2, 1, 2])
b_in = helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT,
[1, 1, 2])
c_in = helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT,
[3, 1, 2])
cond_in = helper.make_tensor_value_info('cond', TensorProto.BOOL, [])
s_in = helper.make_sequence_value_info('S', TensorProto.FLOAT,
[None, None, None, None])
sb_out = helper.make_sequence_value_info('Sb', TensorProto.FLOAT,
[None, None, None, None])
sc_out = helper.make_sequence_value_info('Sc', TensorProto.FLOAT,
[None, None, None, None])
s_final_out = helper.make_sequence_value_info('S_final',
TensorProto.FLOAT,
[None, None, None, None])
then_graph = helper.make_graph(nodes=[seq_insert_node1],
name="then_graph",
inputs=[s_in, b_in],
outputs=[sb_out])
else_graph = helper.make_graph(nodes=[seq_insert_node2],
name="else_graph",
inputs=[s_in, c_in],
outputs=[sc_out])
if_node = helper.make_node('If', ['cond'], ['S_final'],
then_branch=then_graph,
else_branch=else_graph)
graph_def = helper.make_graph(nodes=[seq_construct_node, if_node],
name='test_if',
inputs=[a_in, b_in, c_in, cond_in],
outputs=[s_final_out])
tf_rep = prepare(helper.make_model(graph_def))
output = tf_rep.run({
'a': a,
'b': b,
'c': c,
'cond': np.array(True, dtype=np.bool)
})
np.testing.assert_almost_equal(output['S_final'][0], a)
np.testing.assert_almost_equal(output['S_final'][1], b)
output = tf_rep.run({
'a': a,
'b': b,
'c': c,
'cond': np.array(False, dtype=np.bool)
})
np.testing.assert_almost_equal(output['S_final'][0], a)
np.testing.assert_almost_equal(output['S_final'][1], c)
# https://github.com/onnx/onnx/issues/349
backend_test.exclude(r'[a-z,_]*GLU[a-z,_]*')
# TF does not support dialation and strides at the same time:
# Will produce strides > 1 not supported in conjunction with dilation_rate > 1
backend_test.exclude(r'[a-z,_]*dilated_strided[a-z,_]*')
backend_test.exclude(r'[a-z,_]*Conv2d_dilated[a-z,_]*')
# TF does not have column major max_pool_with_argmax
backend_test.exclude(
r'[a-z,_]*maxpool_with_argmax_2d_precomputed_strides[a-z,_]*')
# PRelu OnnxBackendPyTorchConvertedModelTest has wrong dim for broadcasting
backend_test.exclude(r'[a-z,_]*PReLU_[0-9]d_multiparam[a-z,_]*')
if legacy_opset_pre_ver(7):
backend_test.exclude(r'[a-z,_]*Upsample[a-z,_]*')
if 'TRAVIS' in os.environ:
backend_test.exclude('test_vgg19')
backend_test.exclude('zfnet512')
if legacy_onnx_pre_ver(1, 2):
# These following tests fails by a tiny margin with onnx<1.2:
backend_test.exclude('test_operator_add_broadcast_cpu')
backend_test.exclude('test_operator_add_size1_broadcast_cpu')
backend_test.exclude('test_operator_add_size1_right_broadcast_cpu')
backend_test.exclude('test_operator_add_size1_singleton_broadcast_cpu')
backend_test.exclude('test_averagepool_3d_default_cpu')
# Do not support consumed flag:
backend_test.exclude('test_batch_normalization')
def test_slice(self):
# test case 1 with normal inputs
axes = [0, 1, 2]
starts = [0, 0, 0]
ends = [2, 2, 2]
if legacy_opset_pre_ver(10):
node_def = helper.make_node("Slice", ["X"], ["S"],
axes=axes,
starts=starts,
ends=ends)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None])
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
else:
node_def = helper.make_node("Slice",
["X", "starts", "ends", "axes"], ["S"])
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("starts", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("ends", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("axes", TensorProto.INT32,
[None]),
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
if legacy_opset_pre_ver(10):
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
output = tf_rep.run({"X": x})
np.testing.assert_almost_equal(output["S"], x[0:2, 0:2, 0:2])
else:
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
output = tf_rep.run({
"X": x,
"starts": starts,
"ends": ends,
"axes": axes
})
np.testing.assert_almost_equal(output["S"], x[0:2, 0:2, 0:2])
# test case 2 with negative, out-of-bound and default inputs
axes = [0, 2]
starts = [0, -7]
ends = [-8, 20]
steps = [1, 1]
if legacy_opset_pre_ver(10):
node_def = helper.make_node("Slice", ["X"], ["S"],
axes=axes,
starts=starts,
ends=ends)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None])
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
else:
node_def = helper.make_node(
"Slice", ["X", "starts", "ends", "axes", "steps"], ["S"])
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("starts", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("ends", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("axes", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("steps", TensorProto.INT32,
[None]),
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
if legacy_opset_pre_ver(10):
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
output = tf_rep.run({"X": x})
np.testing.assert_almost_equal(output["S"], x[0:-8, :, -7:20])
else:
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
output = tf_rep.run({
"X": x,
"starts": starts,
"ends": ends,
"axes": axes,
"steps": steps
})
np.testing.assert_almost_equal(output["S"], x[0:-8, :, -7:20])
# test case 3 with non-default steps
axes = [0, 1, 2]
starts = [0, 0, 0]
ends = [2, 2, 2]
steps = [2, -2, -1]
if not legacy_opset_pre_ver(10):
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
output = tf_rep.run({
"X": x,
"starts": starts,
"ends": ends,
"axes": axes,
"steps": steps
})
np.testing.assert_almost_equal(output["S"], x[0:2:2, 0:2:-2,
0:2:-1])
def test_matmul_integer(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest(
"ONNX version {} doesn't support MatMulInteger.".format(
defs.onnx_opset_version()))
node_def = helper.make_node("MatMulInteger",
["A", "B", "a_zero_point", "b_zero_point"],
["Z"])
# A & B are 3-D tensor and a_zero_point & b_zero_point are scalar
A = self._get_rnd_int(-20, 20, shape=(2, 3, 4), dtype=np.int8)
B = self._get_rnd_int(-20, 20, shape=(2, 4, 6), dtype=np.int8)
a_zero_point = self._get_rnd_int(-20, 20, dtype=np.int8)
b_zero_point = self._get_rnd_int(-20, 20, dtype=np.int8)
A_minus_zero_point = np.subtract(A.astype(np.int32),
a_zero_point.astype(np.int32))
B_minus_zero_point = np.subtract(B.astype(np.int32),
b_zero_point.astype(np.int32))
z = np.matmul(A_minus_zero_point, B_minus_zero_point)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("A", TensorProto.INT8,
[None, None, None]),
helper.make_tensor_value_info("B", TensorProto.INT8,
[None, None, None]),
helper.make_tensor_value_info("a_zero_point", TensorProto.INT8,
[]),
helper.make_tensor_value_info("b_zero_point", TensorProto.INT8,
[])
],
outputs=[
helper.make_tensor_value_info("Z", TensorProto.INT32,
[None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({
"A": A,
"B": B,
"a_zero_point": a_zero_point,
"b_zero_point": b_zero_point
})
np.testing.assert_almost_equal(output["Z"], z)
# A & B are 4-D tensor and a_zero_point & b_zero_point are 1-D tensor
A = self._get_rnd_int(-20, 20, shape=(2, 5, 3, 4), dtype=np.int8)
B = self._get_rnd_int(-20, 20, shape=(2, 1, 4, 6), dtype=np.int8)
a_zero_point = self._get_rnd_int(-20,
20,
shape=(A.shape[-2]),
dtype=np.int8)
b_zero_point = self._get_rnd_int(-20,
20,
shape=(B.shape[-1]),
dtype=np.int8)
a_zero_point_with_reshape = np.reshape(a_zero_point, [A.shape[-2], 1])
A_minus_zero_point = np.subtract(
A.astype(np.int32), a_zero_point_with_reshape.astype(np.int32))
B_minus_zero_point = np.subtract(B.astype(np.int32),
b_zero_point.astype(np.int32))
z = np.matmul(A_minus_zero_point, B_minus_zero_point)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("A", TensorProto.INT8,
[None, None, None, None]),
helper.make_tensor_value_info("B", TensorProto.INT8,
[None, None, None, None]),
helper.make_tensor_value_info("a_zero_point", TensorProto.INT8,
[None]),
helper.make_tensor_value_info("b_zero_point", TensorProto.INT8,
[None])
],
outputs=[
helper.make_tensor_value_info("Z", TensorProto.INT32,
[None, None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({
"A": A,
"B": B,
"a_zero_point": a_zero_point,
"b_zero_point": b_zero_point
})
np.testing.assert_almost_equal(output["Z"], z)
("test_sqrt", tf.sqrt, "Sqrt", [get_rnd([10, 10])], {}),
("test_squeeze", tf.squeeze, "Squeeze", [get_rnd([1, 1, 10, 10])], {"axis":[0, 1]}),
("test_subtract", tf.subtract, "Sub", [get_rnd([10, 10]), get_rnd([10, 10])], {}),
("test_tanh", tf.tanh, "Tanh", [get_rnd([10, 10])], {}),
("test_top_k", tf.nn.top_k, "TopKV2", [get_rnd([10, 10, 10, 10])], {"k": 3}),
# Use reverse to test ignore_unimplemented
("test_unimplemented", tf.reverse, "ReverseV2", [get_rnd([1, 2, 3, 4]), [3]], {}, {"ignore_unimplemented": True}),
("test_unpack", tf.unstack, "unstack", [get_rnd([2, 3, 4])], {}),
("test_xor", tf.logical_xor, "LogicalXor", [get_rnd([10, 10], dtype=np.bool_), get_rnd([10, 10], dtype=np.bool_)], {}),
("test_transpose", tf.transpose, "transpose", [get_rnd([2, 10])], {"perm":[1, 0]}),
("test_concat", tf.concat, "concat", [[get_rnd([1, 10]),get_rnd([10, 10]),get_rnd([20, 10])], 0], {}),
("test_bias_add_nchw", tf.nn.bias_add, "BiasAdd", [get_rnd([10, 32, 10, 10]),get_rnd([32])], {"data_format":"NCHW"}),
("test_bias_add_nhwc", tf.nn.bias_add, "BiasAdd", [get_rnd([10, 10, 10, 32]),get_rnd([32])], {"data_format":"NHWC"}),
]
if not legacy_opset_pre_ver(6):
test_cases.append(("test_tile", tf.tile, "Tile", [get_rnd([1, 2, 3, 4]), np.random.randint(1, 10, (4,), dtype=np.int32)], {}))
if not legacy_opset_pre_ver(9):
test_cases.append(("test_strided_slice", tf.strided_slice, "StridedSlice", [get_rnd([5, 5]), [0, 0], [1, 5], [1, 1]], {}))
test_cases.append(("test_strided_slice_shrink", tf.strided_slice, "StridedSlice", [get_rnd([5, 5]), [0, 0], [1, 3], [1, 1]], {"shrink_axis_mask":1}))
# yapf: enable
for k, val in enumerate(test_cases):
test_method = create_test(val)
test_method.__name__ = str(val[0])
setattr(TestNode, test_method.__name__, test_method)
if __name__ == '__main__':
unittest.main()
def test_slice(self):
# test case 1 with normal inputs
axes = [0, 1, 2]
starts = [0, 0, 0]
ends = [2, 2, 2]
if legacy_opset_pre_ver(10):
node_def = helper.make_node("Slice", ["X"], ["S"],
axes=axes,
starts=starts,
ends=ends)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None])
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
else:
node_def = helper.make_node("Slice",
["X", "starts", "ends", "axes"], ["S"])
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("starts", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("ends", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("axes", TensorProto.INT32,
[None]),
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/slice'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
if legacy_opset_pre_ver(10):
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
tf_model_output = tf_model(X=x)
np.testing.assert_almost_equal(tf_model_output[0], x[0:2, 0:2,
0:2])
else:
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
tf_model_output = tf_model(X=x,
starts=starts,
ends=ends,
axes=axes)
np.testing.assert_almost_equal(tf_model_output[0], x[0:2, 0:2,
0:2])
# test case 2 with negative, out-of-bound and default inputs
axes = [0, 2]
starts = [0, -7]
ends = [-8, 20]
steps = [1, 1]
if legacy_opset_pre_ver(10):
node_def = helper.make_node("Slice", ["X"], ["S"],
axes=axes,
starts=starts,
ends=ends)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None])
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
else:
node_def = helper.make_node(
"Slice", ["X", "starts", "ends", "axes", "steps"], ["S"])
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None]),
helper.make_tensor_value_info("starts", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("ends", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("axes", TensorProto.INT32,
[None]),
helper.make_tensor_value_info("steps", TensorProto.INT32,
[None]),
],
outputs=[
helper.make_tensor_value_info("S", TensorProto.FLOAT,
[None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/slice'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
if legacy_opset_pre_ver(10):
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
tf_model_output = tf_model(X=x)
np.testing.assert_almost_equal(tf_model_output[0], x[0:-8, :,
-7:20])
else:
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
tf_model_output = tf_model(X=x,
starts=starts,
ends=ends,
axes=axes,
steps=steps)
np.testing.assert_almost_equal(tf_model_output[0], x[0:-8, :,
-7:20])
# test case 3 with non-default steps
axes = [0, 1, 2]
starts = [0, 0, 0]
ends = [2, 2, 2]
steps = [2, -2, -1]
if not legacy_opset_pre_ver(10):
x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
tf_model_output = tf_model(X=x,
starts=starts,
ends=ends,
axes=axes,
steps=steps)
np.testing.assert_almost_equal(tf_model_output[0], x[0:2:2, 0:2:-2,
0:2:-1])