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openvino/tests/layer_tests/onnx_tests/test_loop.py
Roman Kazantsev 0d201376df [Tests] Fix rest of NumPy deprecated types (#15245) 
Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
2023-01-21 11:17:52 +00:00

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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 TestLoop(OnnxRuntimeLayerTest):
@staticmethod
def create_const(name, tensor_type, value):
from onnx import helper
from onnx import TensorProto
if tensor_type == TensorProto.INT64:
np_type = np.int64
elif tensor_type == TensorProto.FLOAT:
np_type = np.float
elif tensor_type == TensorProto.BOOL:
np_type = bool
else:
return None
return helper.make_node('Constant', inputs=[], outputs=[name],
value=helper.make_tensor(name='const_tensor',
data_type=tensor_type,
dims=value.shape,
vals=value.flatten().astype(np_type)))
@staticmethod
def create_body_graph(input_nodes, output_nodes, input_names, output_names, input_shape,
graph_name):
# input_nodes - list of input nodes with structure {counter, condition, <other inputs>}
# output_nodes - list of output nodes with structure {condition, <back edges>, <external outputs>}.
# In this function I assume that every <other input> have <back edge> and <external output>
# input_shape - shape of all inputs from <other inputs>
from onnx import helper
from onnx import TensorProto
assert len(input_nodes) > 2
assert len(output_nodes) == (len(input_nodes) - 2) * 2 + 1
assert len(input_nodes) == len(input_names)
assert len(output_nodes) == len(output_names)
other_inputs_count = len(input_nodes) - 2
one_value = np.ones(input_shape, dtype=float)
one = TestLoop.create_const('one_' + graph_name, TensorProto.FLOAT, one_value)
one_int = TestLoop.create_const('one_int_' + graph_name, TensorProto.INT64, np.ones([1]))
# add one to all inputs except counter and condition
add_one_nodes = []
for i in range(2, len(input_names)):
add_one_nodes.append(
helper.make_node('Add', inputs=[input_names[i], 'one_' + graph_name],
outputs=[output_names[other_inputs_count + i - 1]]))
# add 1 to counter
add_one_to_m_node = helper.make_node(
'Add',
inputs=[input_names[0], 'one_int_' + graph_name],
outputs=['counter_plus_1_' + graph_name]
)
# map inputs to outputs - back edges
identity_nodes = []
for i in range(1, len(input_nodes)):
identity_nodes.append(helper.make_node('Identity',
inputs=[input_names[i]],
outputs=[output_names[i - 1]]))
body_nodes = [one, one_int]
body_nodes.extend(add_one_nodes)
body_nodes.append(add_one_to_m_node)
body_nodes.extend(identity_nodes)
body_graph = helper.make_graph(
body_nodes,
graph_name,
input_nodes,
output_nodes
)
return body_graph
def create_loop(self):
"""
ONNX net
Input->Loop->Output => Only accuracy check
"""
from onnx import helper
from onnx import TensorProto
# Create ONNX model
# Input ---> Loop ---> Identity ---> Result
input_shape = [1, 4, 64, 54]
in_1 = helper.make_tensor_value_info('IN_1', TensorProto.FLOAT, input_shape)
in_1_int = helper.make_tensor_value_info('in_1_int', TensorProto.FLOAT, input_shape)
in_1_int_out = helper.make_tensor_value_info('in_1_int_out', TensorProto.FLOAT, input_shape)
out_1 = helper.make_tensor_value_info('OUT_1', TensorProto.FLOAT, None)
res = helper.make_tensor_value_info('res', TensorProto.FLOAT, None)
m_1 = helper.make_tensor_value_info('m_1', TensorProto.INT64, [1])
cond_int_1 = helper.make_tensor_value_info('cond_int_1', TensorProto.BOOL, [1])
cond_out_1 = helper.make_tensor_value_info('cond_out_1', TensorProto.BOOL, [1])
m_1_value = np.array([10], dtype=np.int64)
cond_value = np.array([True], bool)
M_1 = self.create_const('M_1', TensorProto.INT64, m_1_value)
cond = self.create_const('cond', TensorProto.BOOL, cond_value)
body_graph_1 = self.create_body_graph([m_1, cond_int_1, in_1_int],
[cond_out_1, in_1_int_out, out_1],
['m_1', 'cond_int_1', 'in_1_int'],
['cond_out_1', 'in_1_int_out', 'OUT_1'],
input_shape, 'body_graph_1')
node_loop_1 = helper.make_node(
'Loop',
inputs=['M_1', 'cond', 'IN_1'],
outputs=['cond_out_1', 'OUT_1'],
body=body_graph_1
)
res_node = helper.make_node(
'Identity',
inputs=['OUT_1'],
outputs=['res'],
)
graph_def = helper.make_graph(
[M_1, cond, node_loop_1, res_node],
'graph',
[in_1],
[res]
)
onnx_net = helper.make_model(graph_def, producer_name='test_loop_model')
# We do not create reference graph, as it's too complicated to construct it
# So we return None to skip IR comparision
return onnx_net, None
def create_loop_in_loop(self):
"""
ONNX net
Input->Loop(Loop)->Output => Only accuracy check
"""
from onnx import helper
from onnx import TensorProto
# Create ONNX model
input_shape = [1, 4, 64, 54]
in_1 = helper.make_tensor_value_info('IN_1', TensorProto.FLOAT, input_shape)
in_1_int = helper.make_tensor_value_info('in_1_int', TensorProto.FLOAT, input_shape)
in_1_int_out = helper.make_tensor_value_info('in_1_int_out', TensorProto.FLOAT, input_shape)
in_2 = helper.make_tensor_value_info('IN_2', TensorProto.FLOAT, input_shape)
in_2_int = helper.make_tensor_value_info('in_2_int', TensorProto.FLOAT, input_shape)
in_2_int_out = helper.make_tensor_value_info('in_2_int_out', TensorProto.FLOAT, input_shape)
out_1 = helper.make_tensor_value_info('OUT_1', TensorProto.FLOAT, None)
out_2 = helper.make_tensor_value_info('OUT_2', TensorProto.FLOAT, None)
res = helper.make_tensor_value_info('res', TensorProto.FLOAT, None)
m_1 = helper.make_tensor_value_info('m_1', TensorProto.INT64, [1])
m_2 = helper.make_tensor_value_info('m_2', TensorProto.INT64, [1])
cond_int_1 = helper.make_tensor_value_info('cond_int_1', TensorProto.BOOL, [1])
cond_out_1 = helper.make_tensor_value_info('cond_out_1', TensorProto.BOOL, [1])
cond_int_2 = helper.make_tensor_value_info('cond_int_2', TensorProto.BOOL, [1])
cond_out_2 = helper.make_tensor_value_info('cond_out_2', TensorProto.BOOL, [1])
m_1_value = np.array([10], dtype=np.int64)
m_2_value = np.array([5], dtype=np.int64)
cond_value = np.array([True], bool)
one_value = np.ones(input_shape, dtype=float)
M_1 = self.create_const('M_1', TensorProto.INT64, m_1_value)
M_2 = self.create_const('M_2', TensorProto.INT64, m_2_value)
cond = self.create_const('cond', TensorProto.BOOL, cond_value)
one = self.create_const('one', TensorProto.FLOAT, one_value)
one_int = self.create_const('one_int', TensorProto.INT64, one_value)
# create body of external loop
add_one_node = helper.make_node(
'Add',
inputs=['in_1_int', 'one'],
outputs=['in_1_loop_1']
)
add_one_to_m_node = helper.make_node(
'Add',
inputs=['m_1', 'one_int'],
outputs=['m_1_loop_1']
)
cond_2 = self.create_const('cond_2', TensorProto.BOOL, cond_value)
# create body for internal loop
body_graph_2 = self.create_body_graph([m_2, cond_int_2, in_2_int],
[cond_out_2, in_2_int_out, out_2],
['m_2', 'cond_int_2', 'in_2_int'],
['cond_out_2', 'in_2_int_out', 'OUT_2'], input_shape,
'body_graph_2')
node_loop_2 = helper.make_node(
'Loop',
inputs=['M_2', 'cond_2', 'IN_2'],
outputs=['cond_out_2', 'OUT_2'],
body=body_graph_2
)
# internal loop created
out_1_node = helper.make_node(
'Identity',
inputs=['OUT_2'],
outputs=['OUT_1'],
)
cond_1_node = helper.make_node(
'Identity',
inputs=['cond_int_1'],
outputs=['cond_out_1'],
)
in_1_int_node = helper.make_node(
'Identity',
inputs=['in_1_int'],
outputs=['in_1_int_out'],
)
body_graph_1 = helper.make_graph(
[one, add_one_node, one_int, add_one_to_m_node, M_2, cond_2, node_loop_2, out_1_node,
cond_1_node,
in_1_int_node],
'body_graph_1',
[m_1, cond_int_1, in_1_int],
[cond_out_1, in_1_int_out, out_1],
)
node_loop_1 = helper.make_node(
'Loop',
inputs=['M_1', 'cond', 'IN_1'],
outputs=['cond_out_1', 'OUT_1'],
body=body_graph_1
)
# external loop created
res_node = helper.make_node(
'Identity',
inputs=['OUT_1'],
outputs=['res'],
)
graph_def = helper.make_graph(
[M_1, cond, node_loop_1, res_node],
'graph',
[in_1, in_2],
[res],
)
onnx_net = helper.make_model(graph_def, producer_name='test_loop_in_loop_model')
# We do not create reference graph, as it's too complicated to construct it
# So we return None to skip IR comparision
return onnx_net, None
@pytest.mark.precommit
@pytest.mark.timeout(250)
def test_loop_simple_precommit(self, ie_device, precision, ir_version, temp_dir, use_old_api):
self._test(*self.create_loop(), ie_device, precision, ir_version, temp_dir=temp_dir,
infer_timeout=150, use_old_api=use_old_api)
@pytest.mark.precommit
@pytest.mark.timeout(250)
def test_loop_in_loop_simple_precommit(self, ie_device, precision, ir_version, temp_dir, use_old_api):
if ie_device == 'GPU':
pytest.xfail("Program doesn't contain primitive: constant:res/10/M_2 that is input to: loop")
self._test(*self.create_loop_in_loop(), ie_device, precision, ir_version, temp_dir=temp_dir,
infer_timeout=150, use_old_api=use_old_api)
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