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openvino/tests/layer_tests/onnx_tests/test_slice.py
Daria Ilina e4f44b19fd Mark all failed ONNX layer tests as skip (#16188) 
* Mark all failed ONNX layer tests as XFail

* Add additional xfailed marks

* Add one more failed tests into XFail

* Add conditions for CPU/GPU failures

* Revert "Add conditions for CPU/GPU failures"

This reverts commit 790524c59c.

* Add failures separation for CPU/GPU

* Replace all xfail with skip
2023-03-15 12:22:32 +06:00

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# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
from common.onnx_layer_test_class import OnnxRuntimeLayerTest
class TestSlice(OnnxRuntimeLayerTest):
def create_net(self, shape, axes, ends, starts, ir_version, opset=6, steps=None):
"""
ONNX net IR net
Input->Slice->Output => Input->Crop
"""
#
# Create ONNX model
#
import onnx
from onnx import helper
from onnx import TensorProto
# calculate output shape
test_arr = np.zeros(shape)
slice_idx = [None] * len(shape)
for i, axis in enumerate(axes):
slice_idx[axis] = slice(starts[i], ends[i], steps[i] if steps is not None else 1)
for axis, s in enumerate(slice_idx):
if s is None:
slice_idx[axis] = slice(0, shape[axis], 1)
test_arr = test_arr[tuple(slice_idx)]
output_shape = list(test_arr.shape)
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
nodes = list()
if opset < 10:
node_def = onnx.helper.make_node(
'Slice',
inputs=['input'],
outputs=['slice'],
starts=starts,
ends=ends,
axes=axes
)
nodes.append(node_def)
else:
node_starts_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['starts'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(starts)],
vals=starts
)
)
node_ends_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['ends'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(ends)],
vals=ends
)
)
node_axes_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['axes'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(axes)],
vals=axes
)
)
inputs = ['input', 'starts', 'ends', 'axes']
if steps:
node_steps_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['steps'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(steps)],
vals=steps
)
)
nodes.append(node_steps_def)
inputs.append('steps')
node_def = onnx.helper.make_node(
'Slice',
inputs=inputs,
outputs=['slice']
)
nodes.extend([node_starts_def, node_ends_def, node_axes_def, node_def])
elu_def = onnx.helper.make_node(
'Elu',
inputs=['slice'],
outputs=['output']
)
nodes.append(elu_def)
# Create the graph (GraphProto)
graph_def = helper.make_graph(
nodes,
'test_model',
[input],
[output]
)
# Create the model (ModelProto)
args = dict(producer_name='test_model')
if opset:
args['opset_imports'] = [helper.make_opsetid("", opset)]
onnx_net = helper.make_model(graph_def, **args)
#
# Create reference IR net
#
ref_net = None
return onnx_net, ref_net
def create_net_const(self, shape, axes, ends, starts, ir_version, opset=6, steps=None):
"""
ONNX net IR net
Input->Concat(+sliced const)->Output => Input->Concat(+const)
"""
#
# Create ONNX model
#
import onnx
from onnx import helper
from onnx import TensorProto
# calculate output shape
constant = np.random.randint(-127, 127, shape).astype(float)
slice_idx = [None] * len(shape)
for i, axis in enumerate(axes):
slice_idx[axis] = slice(starts[i], ends[i], steps[i] if steps is not None else 1)
for axis, s in enumerate(slice_idx):
if s is None:
slice_idx[axis] = slice(0, shape[axis], 1)
constant_after = constant[tuple(slice_idx)]
output_shape = list(constant_after.shape)
concat_axis = 0
concat_output_shape = output_shape.copy()
concat_output_shape[concat_axis] *= 2
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, output_shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
node_const_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['const1'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.FLOAT,
dims=shape,
vals=constant.flatten(),
),
)
nodes = [node_const_def]
if opset < 10:
node_def = onnx.helper.make_node(
'Slice',
inputs=['const1'],
outputs=['slice'],
starts=starts,
ends=ends,
axes=axes
)
nodes.append(node_def)
else:
node_starts_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['starts'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(starts)],
vals=starts
)
)
node_ends_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['ends'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(ends)],
vals=ends
)
)
node_axes_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['axes'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(axes)],
vals=axes
)
)
inputs = ['const1', 'starts', 'ends', 'axes']
if steps:
node_steps_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['steps'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[len(steps)],
vals=steps
)
)
nodes.append(node_steps_def)
inputs.append('steps')
node_def = onnx.helper.make_node(
'Slice',
inputs=inputs,
outputs=['slice']
)
nodes.extend([node_starts_def, node_ends_def, node_axes_def, node_def])
node_concat_def = onnx.helper.make_node(
'Concat',
inputs=['input', 'slice'],
outputs=['output'],
axis=concat_axis
)
nodes.append(node_concat_def)
# Create the graph (GraphProto)
graph_def = helper.make_graph(
nodes,
'test_reshape_model',
[input],
[output],
)
# Create the model (ModelProto)
args = dict(producer_name='test_model')
if opset:
args['opset_imports'] = [helper.make_opsetid("", opset)]
onnx_net = helper.make_model(graph_def, **args)
#
# Create reference IR net
# Please, specify 'type': 'Input' for input node
# Moreover, do not forget to validate ALL layer attributes!!!
#
ref_net = None
return onnx_net, ref_net
test_data_no_steps = [
dict(shape=[10, 12], axes=[0], starts=[1], ends=[9]),
dict(shape=[10, 12], axes=[1], starts=[1], ends=[11]),
dict(shape=[10, 12], axes=[0, 1], starts=[1, 1], ends=[9, 11]),
dict(shape=[8, 10, 12], axes=[0], starts=[1], ends=[7]),
dict(shape=[8, 10, 12], axes=[1], starts=[1], ends=[9]),
dict(shape=[8, 10, 12], axes=[2], starts=[1], ends=[11]),
dict(shape=[8, 10, 12], axes=[0, 1], starts=[1, 1], ends=[7, 9]),
dict(shape=[8, 10, 12], axes=[1, 2], starts=[1, 1], ends=[9, 11]),
dict(shape=[8, 10, 12], axes=[0, 2], starts=[1, 1], ends=[7, 11]),
dict(shape=[8, 10, 12], axes=[0, 1, 2], starts=[1, 1, 1], ends=[7, 9, 11]),
dict(shape=[6, 8, 10, 12], axes=[0], starts=[1], ends=[5]),
dict(shape=[6, 8, 10, 12], axes=[1], starts=[1], ends=[7]),
dict(shape=[6, 8, 10, 12], axes=[2], starts=[1], ends=[9]),
dict(shape=[6, 8, 10, 12], axes=[3], starts=[1], ends=[11]),
dict(shape=[6, 8, 10, 12], axes=[0, 1], starts=[1, 1], ends=[5, 7]),
dict(shape=[6, 8, 10, 12], axes=[1, 2], starts=[1, 1], ends=[7, 9]),
dict(shape=[6, 8, 10, 12], axes=[2, 3], starts=[1, 1], ends=[9, 11]),
dict(shape=[6, 8, 10, 12], axes=[0, 2], starts=[1, 1], ends=[5, 9]),
dict(shape=[6, 8, 10, 12], axes=[0, 3], starts=[1, 1], ends=[5, 11]),
dict(shape=[6, 8, 10, 12], axes=[1, 3], starts=[1, 1], ends=[7, 11]),
dict(shape=[6, 8, 10, 12], axes=[0, 1, 2], starts=[1, 1, 1], ends=[5, 7, 9]),
dict(shape=[6, 8, 10, 12], axes=[1, 2, 3], starts=[1, 1, 1], ends=[7, 9, 11]),
dict(shape=[6, 8, 10, 12], axes=[0, 2, 3], starts=[1, 1, 1], ends=[5, 9, 11]),
dict(shape=[6, 8, 10, 12], axes=[0, 1, 3], starts=[1, 1, 1], ends=[5, 7, 11]),
dict(shape=[6, 8, 10, 12], axes=[0, 1, 2, 3], starts=[1, 1, 1, 1], ends=[5, 7, 9, 11]),
dict(shape=[4, 6, 8, 10, 12], axes=[0], starts=[1], ends=[3]),
dict(shape=[4, 6, 8, 10, 12], axes=[1], starts=[1], ends=[5]),
dict(shape=[4, 6, 8, 10, 12], axes=[2], starts=[1], ends=[7]),
dict(shape=[4, 6, 8, 10, 12], axes=[3], starts=[1], ends=[9]),
dict(shape=[4, 6, 8, 10, 12], axes=[4], starts=[1], ends=[11]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1], starts=[1, 1], ends=[3, 5]),
dict(shape=[4, 6, 8, 10, 12], axes=[2, 3], starts=[1, 1], ends=[7, 9]),
dict(shape=[4, 6, 8, 10, 12], axes=[3, 4], starts=[1, 1], ends=[9, 11]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1, 2], starts=[1, 1, 1], ends=[3, 5, 7]),
dict(shape=[4, 6, 8, 10, 12], axes=[1, 2, 3], starts=[1, 1, 1], ends=[5, 7, 9]),
dict(shape=[4, 6, 8, 10, 12], axes=[2, 3, 4], starts=[1, 1, 1], ends=[7, 9, 11]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1, 2, 3], starts=[1, 1, 1, 1], ends=[3, 5, 7, 9]),
dict(shape=[4, 6, 8, 10, 12], axes=[1, 2, 3, 4], starts=[1, 1, 1, 1], ends=[5, 7, 9, 11]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1, 2, 3, 4], starts=[1, 1, 1, 1, 1],
ends=[3, 5, 7, 9, 11]),
]
test_data_with_steps = [
dict(shape=[10, 12], axes=[0, 1], starts=[1, 1], ends=[9, 11], steps=[2, 2]),
dict(shape=[10, 12], axes=[0, 1], starts=[9, 11], ends=[1, 1], steps=[-1, -1]),
dict(shape=[10, 12], axes=[0], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[10, 12], axes=[1], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[10, 12], axes=[0, 1], starts=[9, 11], ends=[1, 1], steps=[-2, -2]),
dict(shape=[8, 10, 12], axes=[0, 1, 2], starts=[1, 1, 1], ends=[7, 9, 11], steps=[2, 2, 2]),
dict(shape=[8, 10, 12], axes=[0, 1, 2], starts=[7, 9, 11], ends=[1, 1, 1],
steps=[-1, -1, -1]),
dict(shape=[8, 10, 12], axes=[0], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[8, 10, 12], axes=[1], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[8, 10, 12], axes=[2], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[8, 10, 12], axes=[0, 1, 2], starts=[7, 9, 11], ends=[1, 1, 1],
steps=[-2, -2, -2]),
dict(shape=[6, 8, 10, 12], axes=[0, 1, 2, 3], starts=[1, 1, 1, 1], ends=[5, 7, 9, 11],
steps=[2, 2, 2, 2]),
dict(shape=[6, 8, 10, 12], axes=[0, 1, 2, 3], starts=[5, 7, 9, 11], ends=[1, 1, 1, 1],
steps=[-1, -1, -1, -1]),
dict(shape=[6, 8, 10, 12], axes=[0], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[6, 8, 10, 12], axes=[1], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[6, 8, 10, 12], axes=[2], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[6, 8, 10, 12], axes=[3], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[6, 8, 10, 12], axes=[0, 1, 2, 3], starts=[5, 7, 9, 11], ends=[1, 1, 1, 1],
steps=[-2, -2, -2, -2]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1, 2, 3, 4], starts=[1, 1, 1, 1, 1],
ends=[3, 5, 7, 9, 11],
steps=[2, 2, 2, 2, 2]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1, 2, 3, 4], starts=[3, 5, 7, 9, 11],
ends=[1, 1, 1, 1, 1],
steps=[-1, -1, -1, -1, -1]),
dict(shape=[4, 6, 8, 10, 12], axes=[0], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[4, 6, 8, 10, 12], axes=[1], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[4, 6, 8, 10, 12], axes=[2], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[4, 6, 8, 10, 12], axes=[3], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[4, 6, 8, 10, 12], axes=[4], starts=[-1], ends=[-9999], steps=[-1]),
dict(shape=[4, 6, 8, 10, 12], axes=[0, 1, 2, 3, 4], starts=[3, 5, 7, 9, 11],
ends=[1, 1, 1, 1, 1],
steps=[-2, -2, -2, -2, -2]),
]
@pytest.mark.parametrize("params", test_data_no_steps)
@pytest.mark.nightly
def test_slice_opset6(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
self._test(*self.create_net(**params, opset=6, ir_version=ir_version), ie_device, precision,
ir_version,
temp_dir=temp_dir, use_old_api=use_old_api)
@pytest.mark.parametrize("params", test_data_no_steps)
@pytest.mark.nightly
def test_slice_const_opset6(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
self._test(*self.create_net_const(**params, opset=6, ir_version=ir_version), ie_device,
precision, ir_version,
temp_dir=temp_dir, use_old_api=use_old_api)
@pytest.mark.parametrize("params", test_data_no_steps + test_data_with_steps)
@pytest.mark.nightly
def test_slice_opset10(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
if ie_device == 'GPU':
pytest.skip('GREEN_SUITE')
self._test(
*self.create_net(**params, opset=10, ir_version=ir_version), ie_device, precision,
ir_version,
temp_dir=temp_dir, use_old_api=use_old_api)
@pytest.mark.parametrize("params", test_data_no_steps + test_data_with_steps)
@pytest.mark.nightly
def test_slice_const_opset10(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
if ie_device == 'GPU':
pytest.skip('GREEN_SUITE')
self._test(*self.create_net_const(**params, opset=10, ir_version=ir_version),
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
@pytest.mark.parametrize("params", test_data_no_steps + test_data_with_steps)
@pytest.mark.nightly
def test_slice_opset11(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
if ie_device == 'GPU':
pytest.skip('GREEN_SUITE')
self._test(
*self.create_net(**params, opset=11, ir_version=ir_version), ie_device, precision,
ir_version,
temp_dir=temp_dir, use_old_api=use_old_api)
@pytest.mark.parametrize("params", test_data_no_steps + test_data_with_steps)
@pytest.mark.nightly
def test_slice_const_opset11(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
self._test(*self.create_net_const(**params, opset=11, ir_version=ir_version),
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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