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[PYTHON] Create graph and generate image in tests (#11569)

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* Change read_image() into generate_image()

* Move test utils from testdata repo to local files

* Minor changes

* Remove unnecessary code

* Minor changes

* Fix compatibility tests

* Fix imports for Azure pipeline

* Move model generation into test_utils

* Minor changes

* Minor changes

* Update linux.yml CI

* Remove testdata repo from .ci/linux.yml

* Remove testdata repo from pipelines

* Fix Azure compatibility tests

* Reset linux.yml

* Remove testdata repo from linux CI

* Try eliminating one of configs

* Attempt at fixing Azure tests

* Add separate utils for compatibility

* xfail comp if op tests

* Minor changes

* Revert changes to .ci files

* minor changes

* Remove xfails

* Remove unecessary import

* Skip if op tests

Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com>
This commit is contained in:
Przemyslaw Wysocki 2022-06-15 23:31:05 +02:00 committed by GitHub
parent 7114863cbc
commit 09a0fb7890
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GPG Key ID: 4AEE18F83AFDEB23
35 changed files with 2159 additions and 265 deletions
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44
src/bindings/python/tests/conftest.py
View File

@ -4,7 +4,6 @@
import os
import pytest
import numpy as np
import tests
@ -13,25 +12,6 @@ from sys import platform
from openvino.runtime import Core
def image_path():
path_to_repo = os.environ["DATA_PATH"]
path_to_img = os.path.join(path_to_repo, "validation_set", "224x224", "dog.bmp")
return path_to_img
def read_image():
import cv2
n, c, h, w = (1, 3, 32, 32)
image = cv2.imread(image_path())
if image is None:
raise FileNotFoundError("Input image not found")
image = cv2.resize(image, (h, w)) / 255
image = image.transpose((2, 0, 1)).astype(np.float32)
image = image.reshape((n, c, h, w))
return image
def get_model_with_template_extension():
core = Core()
ir = bytes(b"""<net name="Activation" version="10">
@ -89,27 +69,27 @@ def get_model_with_template_extension():
def model_path(is_myriad=False):
path_to_repo = os.environ["MODELS_PATH"]
if not is_myriad:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
base_path = os.path.dirname(__file__)
if is_myriad:
test_xml = os.path.join(base_path, "test_utils", "utils", "test_model_fp16.xml")
test_bin = os.path.join(base_path, "test_utils", "utils", "test_model_fp16.bin")
else:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
test_xml = os.path.join(base_path, "test_utils", "utils", "test_model_fp32.xml")
test_bin = os.path.join(base_path, "test_utils", "utils", "test_model_fp32.bin")
return (test_xml, test_bin)
def model_onnx_path():
path_to_repo = os.environ["MODELS_PATH"]
test_onnx = os.path.join(path_to_repo, "models", "test_model", "test_model.onnx")
base_path = os.path.dirname(__file__)
test_onnx = os.path.join(base_path, "test_utils", "utils", "test_model.onnx")
return test_onnx
def plugins_path():
path_to_repo = os.environ["DATA_PATH"]
plugins_xml = os.path.join(path_to_repo, "ie_class", "plugins.xml")
plugins_win_xml = os.path.join(path_to_repo, "ie_class", "plugins_win.xml")
plugins_osx_xml = os.path.join(path_to_repo, "ie_class", "plugins_apple.xml")
base_path = os.path.dirname(__file__)
plugins_xml = os.path.join(base_path, "test_utils", "utils", "plugins.xml")
plugins_win_xml = os.path.join(base_path, "test_utils", "utils", "plugins_win.xml")
plugins_osx_xml = os.path.join(base_path, "test_utils", "utils", "plugins_apple.xml")
return (plugins_xml, plugins_win_xml, plugins_osx_xml)

40
src/bindings/python/tests/test_inference_engine/test_compiled_model.py
View File

@ -6,7 +6,9 @@ import os
import pytest
import numpy as np
from ..conftest import model_path, read_image, get_model_with_template_extension
# TODO: refactor into absolute paths
from ..conftest import model_path, get_model_with_template_extension
from ..test_utils.test_utils import generate_image
from openvino.runtime import Model, ConstOutput, Shape
from openvino.runtime import Core, Tensor
@ -51,10 +53,10 @@ def test_export_import():
new_compiled = core.import_model(user_stream, "CPU")
img = read_image()
img = generate_image()
res = new_compiled.infer_new_request({"data": img})
assert np.argmax(res[new_compiled.outputs[0]]) == 2
assert np.argmax(res[new_compiled.outputs[0]]) == 9
def test_export_import_advanced():
@ -70,10 +72,10 @@ def test_export_import_advanced():
new_compiled = core.import_model(user_stream, "CPU")
img = read_image()
img = generate_image()
res = new_compiled.infer_new_request({"data": img})
assert np.argmax(res[new_compiled.outputs[0]]) == 2
assert np.argmax(res[new_compiled.outputs[0]]) == 9
def test_get_input_i(device):
@ -246,41 +248,41 @@ def test_inputs_docs(device):
def test_infer_new_request_numpy(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = generate_image()
exec_net = ie.compile_model(func, device)
res = exec_net.infer_new_request({"data": img})
assert np.argmax(res[list(res)[0]]) == 2
assert np.argmax(res[list(res)[0]]) == 9
def test_infer_new_request_tensor_numpy_copy(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = generate_image()
tensor = Tensor(img)
exec_net = ie.compile_model(func, device)
res_tensor = exec_net.infer_new_request({"data": tensor})
res_img = exec_net.infer_new_request({"data": tensor})
assert np.argmax(res_tensor[list(res_tensor)[0]]) == 2
assert np.argmax(res_tensor[list(res_tensor)[0]]) == 9
assert np.argmax(res_tensor[list(res_tensor)[0]]) == np.argmax(res_img[list(res_img)[0]])
def test_infer_tensor_numpy_shared_memory(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = generate_image()
img = np.ascontiguousarray(img)
tensor = Tensor(img, shared_memory=True)
exec_net = ie.compile_model(func, device)
res_tensor = exec_net.infer_new_request({"data": tensor})
res_img = exec_net.infer_new_request({"data": tensor})
assert np.argmax(res_tensor[list(res_tensor)[0]]) == 2
assert np.argmax(res_tensor[list(res_tensor)[0]]) == 9
assert np.argmax(res_tensor[list(res_tensor)[0]]) == np.argmax(res_img[list(res_img)[0]])
def test_infer_new_request_wrong_port_name(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = generate_image()
tensor = Tensor(img)
exec_net = ie.compile_model(func, device)
with pytest.raises(RuntimeError) as e:
@ -291,7 +293,7 @@ def test_infer_new_request_wrong_port_name(device):
def test_infer_tensor_wrong_input_data(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = generate_image()
img = np.ascontiguousarray(img)
tensor = Tensor(img, shared_memory=True)
exec_net = ie.compile_model(func, device)
@ -307,10 +309,10 @@ def test_infer_numpy_model_from_buffer(device):
with open(test_net_xml, "rb") as f:
xml = f.read()
func = core.read_model(model=xml, weights=weights)
img = read_image()
img = generate_image()
exec_net = core.compile_model(func, device)
res = exec_net.infer_new_request({"data": img})
assert np.argmax(res[list(res)[0]]) == 2
assert np.argmax(res[list(res)[0]]) == 9
def test_infer_tensor_model_from_buffer(device):
@ -320,11 +322,11 @@ def test_infer_tensor_model_from_buffer(device):
with open(test_net_xml, "rb") as f:
xml = f.read()
func = core.read_model(model=xml, weights=weights)
img = read_image()
img = generate_image()
tensor = Tensor(img)
exec_net = core.compile_model(func, device)
res = exec_net.infer_new_request({"data": tensor})
assert np.argmax(res[list(res)[0]]) == 2
assert np.argmax(res[list(res)[0]]) == 9
def test_direct_infer(device):
@ -334,11 +336,11 @@ def test_direct_infer(device):
with open(test_net_xml, "rb") as f:
xml = f.read()
model = core.read_model(model=xml, weights=weights)
img = read_image()
img = generate_image()
tensor = Tensor(img)
comp_model = core.compile_model(model, device)
res = comp_model({"data": tensor})
assert np.argmax(res[comp_model.outputs[0]]) == 2
assert np.argmax(res[comp_model.outputs[0]]) == 9
ref = comp_model.infer_new_request({"data": tensor})
assert np.array_equal(ref[comp_model.outputs[0]], res[comp_model.outputs[0]])

26
src/bindings/python/tests/test_inference_engine/test_core.py
View File

@ -24,43 +24,41 @@ from ..conftest import (
model_path,
model_onnx_path,
plugins_path,
read_image,
get_model_with_template_extension,
)
from ..test_utils.test_utils import (
generate_image,
generate_relu_model,
)
plugins_xml, plugins_win_xml, plugins_osx_xml = plugins_path()
test_net_xml, test_net_bin = model_path()
test_net_onnx = model_onnx_path()
plugins_xml, plugins_win_xml, plugins_osx_xml = plugins_path()
def test_compact_api_xml():
img = read_image()
img = generate_image()
model = compile_model(test_net_xml)
assert isinstance(model, CompiledModel)
results = model.infer_new_request({"data": img})
assert np.argmax(results[list(results)[0]]) == 2
assert np.argmax(results[list(results)[0]]) == 9
def test_compact_api_onnx():
img = read_image()
img = generate_image()
model = compile_model(test_net_onnx)
assert isinstance(model, CompiledModel)
results = model.infer_new_request({"data": img})
assert np.argmax(results[list(results)[0]]) == 2
assert np.argmax(results[list(results)[0]]) == 9
def test_core_class():
input_shape = [1, 3, 4, 4]
param = ov.parameter(input_shape, np.float32, name="parameter")
relu = ov.relu(param, name="relu")
func = Model([relu], [param], "test")
func.get_ordered_ops()[2].friendly_name = "friendly"
core = Core()
model = core.compile_model(func, "CPU", {})
model = generate_relu_model(input_shape)
request = model.create_infer_request()
input_data = np.random.rand(*input_shape).astype(np.float32) - 0.5
@ -390,7 +388,7 @@ def test_read_model_from_buffer_no_weights(device):
def test_infer_new_request_return_type(device):
ie = Core()
func = ie.read_model(model=test_net_xml, weights=test_net_bin)
img = read_image()
img = generate_image()
exec_net = ie.compile_model(func, device)
res = exec_net.infer_new_request({"data": img})
arr = res[list(res)[0]][0]

31
src/bindings/python/tests/test_inference_engine/test_function.py
View File

@ -20,12 +20,7 @@ from openvino.runtime import (
get_batch,
)
def create_test_model():
param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
add = ops.add(param1, param2)
return Model(add, [param1, param2], "TestFunction")
from ..test_utils.test_utils import generate_add_model # TODO: reformat into an absolute path
def test_test_descriptor_tensor():
@ -196,7 +191,7 @@ def test_add_outputs_incorrect_outputs_list():
def test_validate_nodes_and_infer_types():
model = create_test_model()
model = generate_add_model()
invalid_shape = Shape([3, 7])
param3 = ops.parameter(invalid_shape, dtype=np.float32, name="data3")
model.replace_parameter(0, param3)
@ -262,7 +257,7 @@ def test_replace_parameter():
def test_evaluate():
model = create_test_model()
model = generate_add_model()
input1 = np.array([2, 1], dtype=np.float32).reshape(2, 1)
input2 = np.array([3, 7], dtype=np.float32).reshape(2, 1)
out_tensor = Tensor("float32", Shape([2, 1]))
@ -272,7 +267,7 @@ def test_evaluate():
def test_evaluate_invalid_input_shape():
model = create_test_model()
model = generate_add_model()
with pytest.raises(RuntimeError) as e:
assert model.evaluate(
[Tensor("float32", Shape([2, 1]))],
@ -282,7 +277,7 @@ def test_evaluate_invalid_input_shape():
def test_get_batch():
model = create_test_model()
model = generate_add_model()
param = model.get_parameters()[0]
param.set_layout(Layout("NC"))
assert get_batch(model) == 2
@ -301,7 +296,7 @@ def test_get_batch_chwn():
def test_set_batch_dimension():
model = create_test_model()
model = generate_add_model()
model_param1 = model.get_parameters()[0]
model_param2 = model.get_parameters()[1]
# check batch == 2
@ -317,7 +312,7 @@ def test_set_batch_dimension():
def test_set_batch_int():
model = create_test_model()
model = generate_add_model()
model_param1 = model.get_parameters()[0]
model_param2 = model.get_parameters()[1]
# check batch == 2
@ -333,7 +328,7 @@ def test_set_batch_int():
def test_set_batch_default_batch_size():
model = create_test_model()
model = generate_add_model()
model_param1 = model.get_parameters()[0]
model_param1.set_layout(Layout("NC"))
set_batch(model)
@ -341,7 +336,7 @@ def test_set_batch_default_batch_size():
def test_reshape_with_ports():
model = create_test_model()
model = generate_add_model()
new_shape = PartialShape([1, 4])
for model_input in model.inputs:
assert isinstance(model_input, Output)
@ -350,7 +345,7 @@ def test_reshape_with_ports():
def test_reshape_with_indexes():
model = create_test_model()
model = generate_add_model()
new_shape = PartialShape([1, 4])
for index, model_input in enumerate(model.inputs):
model.reshape({index: new_shape})
@ -358,7 +353,7 @@ def test_reshape_with_indexes():
def test_reshape_with_names():
model = create_test_model()
model = generate_add_model()
new_shape = PartialShape([1, 4])
for model_input in model.inputs:
model.reshape({model_input.any_name: new_shape})
@ -378,7 +373,7 @@ def test_reshape(device):
def test_reshape_with_python_types(device):
model = create_test_model()
model = generate_add_model()
def check_shape(new_shape):
for model_input in model.inputs:
@ -395,7 +390,7 @@ def test_reshape_with_python_types(device):
check_shape(PartialShape(shape2))
shape3 = [1, 8]
new_shapes = {i: shape3 for i, input in enumerate(model.inputs)}
new_shapes = {i: shape3 for i, _ in enumerate(model.inputs)}
model.reshape(new_shapes)
check_shape(PartialShape(shape3))

26
src/bindings/python/tests/test_inference_engine/test_infer_request.py
View File

@ -14,7 +14,9 @@ from openvino.runtime import Core, AsyncInferQueue, Tensor, ProfilingInfo, Model
from openvino.runtime import Type, PartialShape, Shape, Layout
from openvino.preprocess import PrePostProcessor
from ..conftest import model_path, read_image
# TODO: reformat into absolute paths
from ..conftest import model_path
from ..test_utils.test_utils import generate_image
is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
test_net_xml, test_net_bin = model_path(is_myriad)
@ -89,7 +91,7 @@ def test_get_profiling_info(device):
model = core.read_model(test_net_xml, test_net_bin)
core.set_property(device, {"PERF_COUNT": "YES"})
compiled = core.compile_model(model, device)
img = read_image()
img = generate_image()
request = compiled.create_infer_request()
tensor_name = compiled.input("data").any_name
request.infer({tensor_name: img})
@ -110,7 +112,7 @@ def test_tensor_setter(device):
compiled_2 = core.compile_model(model=model, device_name=device)
compiled_3 = core.compile_model(model=model, device_name=device)
img = read_image()
img = generate_image()
tensor = Tensor(img)
request1 = compiled_1.create_infer_request()
@ -152,7 +154,7 @@ def test_set_tensors(device):
model = core.read_model(test_net_xml, test_net_bin)
compiled = core.compile_model(model, device)
data1 = read_image()
data1 = generate_image()
tensor1 = Tensor(data1)
data2 = np.ones(shape=(1, 10), dtype=np.float32)
tensor2 = Tensor(data2)
@ -281,7 +283,7 @@ def test_cancel(device):
core = Core()
model = core.read_model(test_net_xml, test_net_bin)
compiled = core.compile_model(model, device)
img = read_image()
img = generate_image()
request = compiled.create_infer_request()
request.start_async({0: img})
@ -301,7 +303,7 @@ def test_start_async(device):
core = Core()
model = core.read_model(test_net_xml, test_net_bin)
compiled = core.compile_model(model, device)
img = read_image()
img = generate_image()
jobs = 3
requests = []
for _ in range(jobs):
@ -354,7 +356,7 @@ def test_infer_mixed_keys(device):
core.set_property(device, {"PERF_COUNT": "YES"})
model = core.compile_model(model, device)
img = read_image()
img = generate_image()
tensor = Tensor(img)
data2 = np.ones(shape=img.shape, dtype=np.float32)
@ -362,7 +364,7 @@ def test_infer_mixed_keys(device):
request = model.create_infer_request()
res = request.infer({0: tensor2, "data": tensor})
assert np.argmax(res[model.output()]) == 2
assert np.argmax(res[model.output()]) == 9
@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
@ -471,7 +473,7 @@ def test_infer_queue(device):
jobs_done[job_id]["finished"] = True
jobs_done[job_id]["latency"] = request.latency
img = read_image()
img = generate_image()
infer_queue.set_callback(callback)
for i in range(jobs):
infer_queue.start_async({"data": img}, i)
@ -508,7 +510,7 @@ def test_infer_queue_fail_on_cpp_model(device):
def callback(request, _):
request.get_tensor("Unknown")
img = read_image()
img = generate_image()
infer_queue.set_callback(callback)
with pytest.raises(RuntimeError) as e:
@ -530,7 +532,7 @@ def test_infer_queue_fail_on_py_model(device):
def callback(request, _):
request = request + 21
img = read_image()
img = generate_image()
infer_queue.set_callback(callback)
with pytest.raises(TypeError) as e:
@ -641,7 +643,7 @@ def test_results_async_infer(device):
jobs_done[job_id]["finished"] = True
jobs_done[job_id]["latency"] = request.latency
img = read_image()
img = generate_image()
infer_queue.set_callback(callback)
for i in range(jobs):
infer_queue.start_async({"data": img}, i)

11
src/bindings/python/tests/test_inference_engine/test_input_node.py
View File

@ -15,17 +15,6 @@ is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
test_net_xml, test_net_bin = model_path(is_myriad)
def model_path(is_myriad=False):
path_to_repo = os.environ["MODELS_PATH"]
if not is_myriad:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
else:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
return (test_xml, test_bin)
def test_input_type(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)

11
src/bindings/python/tests/test_inference_engine/test_output_const_node.py
View File

@ -22,17 +22,6 @@ is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
test_net_xml, test_net_bin = model_path(is_myriad)
def model_path(is_myriad=False):
path_to_repo = os.environ["MODELS_PATH"]
if not is_myriad:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
else:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
return (test_xml, test_bin)
def test_const_output_type(device):
core = Core()
func = core.read_model(model=test_net_xml, weights=test_net_bin)

11
src/bindings/python/tests/test_inference_engine/test_output_node.py
View File

@ -12,17 +12,6 @@ is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
test_net_xml, test_net_bin = model_path(is_myriad)
def model_path(is_myriad=False):
path_to_repo = os.environ["MODELS_PATH"]
if not is_myriad:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
else:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
return (test_xml, test_bin)
def test_output_replace(device):
param = ops.parameter([1, 64], Type.i64)
param.output(0).get_tensor().set_names({"a", "b"})

8
src/bindings/python/tests/test_inference_engine/test_tensor.py
View File

@ -14,7 +14,7 @@ from openvino.helpers import pack_data, unpack_data
import pytest
from ..conftest import read_image
from ..test_utils.test_utils import generate_image # TODO: reformat into an absolute path
@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
@ -97,7 +97,7 @@ def test_init_with_numpy_dtype(ov_type, numpy_dtype):
(ov.Type.boolean, np.bool),
])
def test_init_with_numpy_shared_memory(ov_type, numpy_dtype):
arr = read_image().astype(numpy_dtype)
arr = generate_image().astype(numpy_dtype)
shape = arr.shape
arr = np.ascontiguousarray(arr)
ov_tensor = Tensor(array=arr, shared_memory=True)
@ -134,7 +134,7 @@ def test_init_with_numpy_shared_memory(ov_type, numpy_dtype):
(ov.Type.boolean, np.bool),
])
def test_init_with_numpy_copy_memory(ov_type, numpy_dtype):
arr = read_image().astype(numpy_dtype)
arr = generate_image().astype(numpy_dtype)
shape = arr.shape
ov_tensor = Tensor(array=arr, shared_memory=False)
assert tuple(ov_tensor.shape) == shape
@ -149,7 +149,7 @@ def test_init_with_numpy_copy_memory(ov_type, numpy_dtype):
def test_init_with_numpy_fail():
arr = np.asfortranarray(read_image())
arr = np.asfortranarray(generate_image())
with pytest.raises(RuntimeError) as e:
_ = Tensor(array=arr, shared_memory=True)
assert "Tensor with shared memory must be C contiguous" in str(e.value)

30
src/bindings/python/tests/test_utils/test_utils.py
View File

@ -2,10 +2,12 @@
# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from openvino.runtime import Model
from openvino.runtime import Shape, Type
import openvino
from openvino.runtime import Model, Core, Shape, Type
from openvino.runtime.op import Parameter
import openvino.runtime.opset8 as ops
from typing import Tuple, Union, List
import numpy as np
def get_test_function():
@ -21,7 +23,29 @@ def test_compare_functions():
try:
from openvino.test_utils import compare_functions
func = get_test_function()
status, msg = compare_functions(func, func)
status, _ = compare_functions(func, func)
assert status
except RuntimeError:
print("openvino.test_utils.compare_functions is not available")
def generate_image(shape: Tuple = (1, 3, 32, 32), dtype: Union[str, np.dtype] = "float32") -> np.array:
np.random.seed(42)
return np.random.rand(*shape).astype(dtype)
def generate_relu_model(input_shape: List[int]) -> openvino.runtime.ie_api.CompiledModel:
param = ops.parameter(input_shape, np.float32, name="parameter")
relu = ops.relu(param, name="relu")
func = Model([relu], [param], "test")
func.get_ordered_ops()[2].friendly_name = "friendly"
core = Core()
return core.compile_model(func, "CPU", {})
def generate_add_model() -> openvino.pyopenvino.Model:
param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
add = ops.add(param1, param2)
return Model(add, [param1, param2], "TestFunction")

11
src/bindings/python/tests/test_utils/utils/plugins.xml Normal file
View File

@ -0,0 +1,11 @@
<!--
Copyright (C) 2020 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<ie>
<plugins>
<plugin location="libopenvino_intel_cpu_plugin.so" name="CUSTOM">
</plugin>
</plugins>
</ie>

11
src/bindings/python/tests/test_utils/utils/plugins_apple.xml Normal file
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@ -0,0 +1,11 @@
<!--
Copyright (C) 2020 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<ie>
<plugins>
<plugin location="libopenvino_intel_cpu_plugin.so" name="CUSTOM">
</plugin>
</plugins>
</ie>

11
src/bindings/python/tests/test_utils/utils/plugins_win.xml Normal file
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@ -0,0 +1,11 @@
<!--
Copyright (C) 2020 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<ie>
<plugins>
<plugin location="openvino_intel_cpu_plugin.dll" name="CUSTOM">
</plugin>
</plugins>
</ie>

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src/bindings/python/tests/test_utils/utils/test_model_fp16.xml Normal file
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@ -0,0 +1,467 @@
<?xml version="1.0" ?>
<net name="test_model" version="10">
<layers>
<layer id="0" name="data" type="Parameter" version="opset1">
<data element_type="f16" shape="1,3,32,32"/>
<output>
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<dim>3</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="1" name="20/mean/Fused_Mul_614616_const" type="Const" version="opset1">
<data element_type="f16" offset="0" shape="16,3,5,5" size="2400"/>
<output>
<port id="1" precision="FP16">
<dim>16</dim>
<dim>3</dim>
<dim>5</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="2" name="19/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
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</port>
<port id="1">
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<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="3" name="data_add_575/copy_const" type="Const" version="opset1">
<data element_type="f16" offset="2400" shape="1,16,1,1" size="32"/>
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<port id="1" precision="FP16">
<dim>1</dim>
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<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="19/Fused_Add_" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1">
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</port>
</input>
<output>
<port id="2" precision="FP16">
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</output>
</layer>
<layer id="5" name="21" type="ReLU" version="opset1">
<input>
<port id="0">
<dim>1</dim>
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<dim>32</dim>
<dim>32</dim>
</port>
</input>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
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<dim>32</dim>
</port>
</output>
</layer>
<layer id="6" name="22" type="MaxPool" version="opset1">
<data kernel="2,2" pads_begin="0,0" pads_end="0,0" rounding_type="floor" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
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</input>
<output>
<port id="1" precision="FP16">
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</output>
</layer>
<layer id="7" name="onnx_initializer_node_8/Output_0/Data__const" type="Const" version="opset1">
<data element_type="f16" offset="2432" shape="32,16,5,5" size="25600"/>
<output>
<port id="1" precision="FP16">
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</output>
</layer>
<layer id="8" name="23/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
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<dim>16</dim>
</port>
<port id="1">
<dim>32</dim>
<dim>16</dim>
<dim>5</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
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<dim>16</dim>
</port>
</output>
</layer>
<layer id="9" name="23/Dims351/copy_const" type="Const" version="opset1">
<data element_type="f16" offset="28032" shape="1,32,1,1" size="64"/>
<output>
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<dim>1</dim>
</port>
</output>
</layer>
<layer id="10" name="23" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
<port id="1">
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</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>32</dim>
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<dim>16</dim>
</port>
</output>
</layer>
<layer id="11" name="25/mean/Fused_Mul_618620_const" type="Const" version="opset1">
<data element_type="f16" offset="28096" shape="64,32,3,3" size="36864"/>
<output>
<port id="1" precision="FP16">
<dim>64</dim>
<dim>32</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="12" name="24/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
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<dim>16</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>64</dim>
<dim>32</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
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<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="13" name="data_add_578583/copy_const" type="Const" version="opset1">
<data element_type="f16" offset="64960" shape="1,64,1,1" size="128"/>
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<layer id="14" name="24/Fused_Add_" type="Add" version="opset1">
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<port id="0">
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</input>
<output>
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<dim>18</dim>
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</output>
</layer>
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<input>
<port id="0">
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<data kernel="2,2" pads_begin="0,0" pads_end="0,0" rounding_type="floor" strides="2,2"/>
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<port id="0">
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</input>
<output>
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<dim>9</dim>
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</port>
</output>
</layer>
<layer id="17" name="28/Reshape/Cast_1955_const" type="Const" version="opset1">
<data element_type="i64" offset="65088" shape="2" size="16"/>
<output>
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<dim>2</dim>
</port>
</output>
</layer>
<layer id="18" name="28/Reshape" type="Reshape" version="opset1">
<data special_zero="True"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>9</dim>
<dim>9</dim>
</port>
<port id="1">
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</port>
</input>
<output>
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<dim>5184</dim>
</port>
</output>
</layer>
<layer id="19" name="onnx_initializer_node_17/Output_0/Data__const" type="Const" version="opset1">
<data element_type="f16" offset="65104" shape="10,5184" size="103680"/>
<output>
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<dim>5184</dim>
</port>
</output>
</layer>
<layer id="20" name="29/WithoutBiases" type="MatMul" version="opset1">
<data transpose_a="0" transpose_b="1"/>
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</port>
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</input>
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</port>
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<data axis="1"/>
<input>
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<edge from-layer="23" from-port="1" to-layer="24" to-port="0"/>
</edges>
<meta_data>
<MO_version value="unknown version"/>
<cli_parameters>
<blobs_as_inputs value="True"/>
<data_type value="FP16"/>
<disable_resnet_optimization value="False"/>
<disable_weights_compression value="False"/>
<enable_concat_optimization value="False"/>
<extensions value="DIR"/>
<framework value="onnx"/>
<freeze_placeholder_with_value value="{}"/>
<generate_deprecated_IR_V2 value="False"/>
<generate_deprecated_IR_V7 value="False"/>
<generate_experimental_IR_V10 value="True"/>
<input_model value="DIR/test_model.onnx"/>
<keep_quantize_ops_in_IR value="True"/>
<keep_shape_ops value="False"/>
<log_level value="ERROR"/>
<mean_scale_values value="{}"/>
<mean_values value="()"/>
<model_name value="test_model"/>
<move_to_preprocess value="False"/>
<output_dir value="DIR"/>
<placeholder_data_types value="{}"/>
<progress value="False"/>
<reverse_input_channels value="False"/>
<scale_values value="()"/>
<silent value="False"/>
<stream_output value="False"/>
<unset unset_cli_parameters="batch, disable_fusing, disable_gfusing, finegrain_fusing, input, input_shape, output, placeholder_shapes, scale, transformations_config"/>
</cli_parameters>
</meta_data>
</net>

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467
src/bindings/python/tests/test_utils/utils/test_model_fp32.xml Normal file
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@ -0,0 +1,467 @@
<?xml version="1.0" ?>
<net name="test_model" version="10">
<layers>
<layer id="0" name="data" type="Parameter" version="opset1">
<data element_type="f32" shape="1,3,32,32"/>
<output>
<port id="0" precision="FP32">
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<output>
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</output>
</layer>
<layer id="9" name="23/Dims357/copy_const" type="Const" version="opset1">
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<output>
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</output>
</layer>
<layer id="10" name="23" type="Add" version="opset1">
<input>
<port id="0">
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<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>32</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
</output>
</layer>
<layer id="11" name="25/mean/Fused_Mul_618620_const" type="Const" version="opset1">
<data element_type="f32" offset="56192" shape="64,32,3,3" size="73728"/>
<output>
<port id="1" precision="FP32">
<dim>64</dim>
<dim>32</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="12" name="24/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>64</dim>
<dim>32</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="13" name="data_add_578583/copy_const" type="Const" version="opset1">
<data element_type="f32" offset="129920" shape="1,64,1,1" size="256"/>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="14" name="24/Fused_Add_" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="15" name="26" type="ReLU" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="16" name="27" type="MaxPool" version="opset1">
<data kernel="2,2" pads_begin="0,0" pads_end="0,0" rounding_type="floor" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>9</dim>
<dim>9</dim>
</port>
</output>
</layer>
<layer id="17" name="28/Reshape/Cast_1955_const" type="Const" version="opset1">
<data element_type="i64" offset="130176" shape="2" size="16"/>
<output>
<port id="1" precision="I64">
<dim>2</dim>
</port>
</output>
</layer>
<layer id="18" name="28/Reshape" type="Reshape" version="opset1">
<data special_zero="True"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>9</dim>
<dim>9</dim>
</port>
<port id="1">
<dim>2</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>5184</dim>
</port>
</output>
</layer>
<layer id="19" name="onnx_initializer_node_17/Output_0/Data__const" type="Const" version="opset1">
<data element_type="f32" offset="130192" shape="10,5184" size="207360"/>
<output>
<port id="1" precision="FP32">
<dim>10</dim>
<dim>5184</dim>
</port>
</output>
</layer>
<layer id="20" name="29/WithoutBiases" type="MatMul" version="opset1">
<data transpose_a="0" transpose_b="1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>5184</dim>
</port>
<port id="1">
<dim>10</dim>
<dim>5184</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>10</dim>
</port>
</output>
</layer>
<layer id="21" name="onnx_initializer_node_18/Output_0/Data_/copy_const" type="Const" version="opset1">
<data element_type="f32" offset="337552" shape="1,10" size="40"/>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>10</dim>
</port>
</output>
</layer>
<layer id="22" name="29" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>10</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>10</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>10</dim>
</port>
</output>
</layer>
<layer id="23" name="fc_out" type="SoftMax" version="opset1">
<data axis="1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>10</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>10</dim>
</port>
</output>
</layer>
<layer id="24" name="fc_out/sink_port_0" type="Result" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>10</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
<edge from-layer="1" from-port="1" to-layer="2" to-port="1"/>
<edge from-layer="2" from-port="2" to-layer="4" to-port="0"/>
<edge from-layer="3" from-port="1" to-layer="4" to-port="1"/>
<edge from-layer="4" from-port="2" to-layer="5" to-port="0"/>
<edge from-layer="5" from-port="1" to-layer="6" to-port="0"/>
<edge from-layer="6" from-port="1" to-layer="8" to-port="0"/>
<edge from-layer="7" from-port="1" to-layer="8" to-port="1"/>
<edge from-layer="8" from-port="2" to-layer="10" to-port="0"/>
<edge from-layer="9" from-port="1" to-layer="10" to-port="1"/>
<edge from-layer="10" from-port="2" to-layer="12" to-port="0"/>
<edge from-layer="11" from-port="1" to-layer="12" to-port="1"/>
<edge from-layer="12" from-port="2" to-layer="14" to-port="0"/>
<edge from-layer="13" from-port="1" to-layer="14" to-port="1"/>
<edge from-layer="14" from-port="2" to-layer="15" to-port="0"/>
<edge from-layer="15" from-port="1" to-layer="16" to-port="0"/>
<edge from-layer="16" from-port="1" to-layer="18" to-port="0"/>
<edge from-layer="17" from-port="1" to-layer="18" to-port="1"/>
<edge from-layer="18" from-port="2" to-layer="20" to-port="0"/>
<edge from-layer="19" from-port="1" to-layer="20" to-port="1"/>
<edge from-layer="20" from-port="2" to-layer="22" to-port="0"/>
<edge from-layer="21" from-port="1" to-layer="22" to-port="1"/>
<edge from-layer="22" from-port="2" to-layer="23" to-port="0"/>
<edge from-layer="23" from-port="1" to-layer="24" to-port="0"/>
</edges>
<meta_data>
<MO_version value="unknown version"/>
<cli_parameters>
<blobs_as_inputs value="True"/>
<data_type value="FP32"/>
<disable_resnet_optimization value="False"/>
<disable_weights_compression value="False"/>
<enable_concat_optimization value="False"/>
<extensions value="DIR"/>
<framework value="onnx"/>
<freeze_placeholder_with_value value="{}"/>
<generate_deprecated_IR_V2 value="False"/>
<generate_deprecated_IR_V7 value="False"/>
<generate_experimental_IR_V10 value="True"/>
<input_model value="DIR/test_model.onnx"/>
<keep_quantize_ops_in_IR value="True"/>
<keep_shape_ops value="False"/>
<log_level value="ERROR"/>
<mean_scale_values value="{}"/>
<mean_values value="()"/>
<model_name value="test_model"/>
<move_to_preprocess value="False"/>
<output_dir value="DIR"/>
<placeholder_data_types value="{}"/>
<progress value="False"/>
<reverse_input_channels value="False"/>
<scale_values value="()"/>
<silent value="False"/>
<stream_output value="False"/>
<unset unset_cli_parameters="batch, disable_fusing, disable_gfusing, finegrain_fusing, input, input_shape, output, placeholder_shapes, scale, transformations_config"/>
</cli_parameters>
</meta_data>
</net>

1
src/bindings/python/tests_compatibility/__init__.py
View File

@ -146,3 +146,4 @@ xfail_issue_81976 = xfail_test(reason="RuntimeError: z node not found in graph c
xfail_issue_82038 = xfail_test(reason="ScatterElements, ScatterND, AssertionError: Result mismatch")
xfail_issue_82039 = xfail_test(reason="Unsupported data type Optional, RuntimeError: [ NOT_IMPLEMENTED ] "
"CPU plugin: Input image format UNSPECIFIED is not supported yet...")
skip_issue_86384 = pytest.mark.skip(reason="If op compatibility tests failing only when triggering whole test suite")

30
src/bindings/python/tests_compatibility/conftest.py
View File

@ -11,34 +11,28 @@ import tests_compatibility
from pathlib import Path
def image_path():
path_to_repo = os.environ["DATA_PATH"]
path_to_img = os.path.join(path_to_repo, "validation_set", "224x224", "dog.bmp")
return path_to_img
def model_path(is_myriad=False):
path_to_repo = os.environ["MODELS_PATH"]
if not is_myriad:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
base_path = os.path.dirname(__file__)
if is_myriad:
test_xml = os.path.join(base_path, "test_utils", "utils", "test_model_fp16.xml")
test_bin = os.path.join(base_path, "test_utils", "utils", "test_model_fp16.bin")
else:
test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
test_xml = os.path.join(base_path, "test_utils", "utils", "test_model_fp32.xml")
test_bin = os.path.join(base_path, "test_utils", "utils", "test_model_fp32.bin")
return (test_xml, test_bin)
def model_onnx_path():
path_to_repo = os.environ["MODELS_PATH"]
test_onnx = os.path.join(path_to_repo, "models", "test_model", "test_model.onnx")
base_path = os.path.dirname(__file__)
test_onnx = os.path.join(base_path, "test_utils", "utils", "test_model.onnx")
return test_onnx
def plugins_path():
path_to_repo = os.environ["DATA_PATH"]
plugins_xml = os.path.join(path_to_repo, "ie_class", "plugins.xml")
plugins_win_xml = os.path.join(path_to_repo, "ie_class", "plugins_win.xml")
plugins_osx_xml = os.path.join(path_to_repo, "ie_class", "plugins_apple.xml")
base_path = os.path.dirname(__file__)
plugins_xml = os.path.join(base_path, "test_utils", "utils", "plugins.xml")
plugins_win_xml = os.path.join(base_path, "test_utils", "utils", "plugins_win.xml")
plugins_osx_xml = os.path.join(base_path, "test_utils", "utils", "plugins_apple.xml")
return (plugins_xml, plugins_win_xml, plugins_osx_xml)

21
src/bindings/python/tests_compatibility/test_inference_engine/test_Blob.py
View File

@ -7,11 +7,8 @@ import numpy as np
import os
from openvino.inference_engine import TensorDesc, Blob, IECore
from tests_compatibility.conftest import image_path, create_encoder
import ngraph as ng
path_to_image = image_path()
from tests_compatibility.conftest import model_path
from ..test_utils.test_utils import generate_image # TODO: reformat into an absolute path
def test_init_with_tensor_desc():
@ -89,15 +86,7 @@ def test_incompatible_array_and_td():
def test_incompatible_input_precision():
import cv2
n, c, h, w = (1, 3, 32, 32)
image = cv2.imread(path_to_image)
if image is None:
raise FileNotFoundError("Input image not found")
image = cv2.resize(image, (h, w)) / 255
image = image.transpose((2, 0, 1))
image = image.reshape((n, c, h, w))
image = generate_image(dtype="float64")
tensor_desc = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
with pytest.raises(ValueError) as e:
Blob(tensor_desc, image)
@ -109,9 +98,7 @@ def test_incompatible_input_precision():
@pytest.mark.skip(reason="Test will enable when CPU fix will be merge")
@pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device dependent test")
def test_buffer_values_after_add_outputs(device):
path_to_repo = os.environ["MODELS_PATH"]
test_net_xml_fp16 = os.path.join(path_to_repo, "models", "test_model", 'test_model_fp16.xml')
test_net_bin_fp16 = os.path.join(path_to_repo, "models", "test_model", 'test_model_fp16.bin')
test_net_xml_fp16, test_net_bin_fp16 = model_path(is_myriad=True)
ie_core = IECore()
if device == "CPU":
if ie_core.get_metric(device, "FULL_DEVICE_NAME") == "arm_compute::NEON":

54
src/bindings/python/tests_compatibility/test_inference_engine/test_ExecutableNetwork.py
View File

@ -4,38 +4,24 @@
import numpy as np
import os
import pytest
import warnings
import time
from openvino.inference_engine import ie_api as ie
from tests_compatibility.conftest import model_path, image_path
from tests_compatibility.conftest import model_path
from ..test_utils.test_utils import generate_image # TODO: reformat into an absolute path
is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
path_to_image = image_path()
test_net_xml, test_net_bin = model_path(is_myriad)
def read_image():
import cv2
n, c, h, w = (1, 3, 32, 32)
image = cv2.imread(path_to_image)
if image is None:
raise FileNotFoundError("Input image not found")
image = cv2.resize(image, (h, w)) / 255
image = image.transpose((2, 0, 1))
image = image.reshape((n, c, h, w))
return image
def test_infer(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device)
img = read_image()
img = generate_image()
res = exec_net.infer({'data': img})
assert np.argmax(res['fc_out'][0]) == 2
assert np.argmax(res['fc_out'][0]) == 9
del exec_net
del ie_core
@ -50,7 +36,7 @@ def test_infer_net_from_buffer(device):
net2 = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device)
exec_net2 = ie_core.load_network(net2, device)
img = read_image()
img = generate_image()
res = exec_net.infer({'data': img})
res2 = exec_net2.infer({'data': img})
del ie_core
@ -63,7 +49,7 @@ def test_infer_wrong_input_name(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device)
img = read_image()
img = generate_image()
with pytest.raises(AssertionError) as e:
exec_net.infer({'_data_': img})
assert "No input with name _data_ found in network" in str(e.value)
@ -108,11 +94,11 @@ def test_async_infer_one_req(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request_handler = exec_net.start_async(request_id=0, inputs={'data': img})
request_handler.wait()
res = request_handler.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
del exec_net
del ie_core
@ -121,12 +107,12 @@ def test_async_infer_many_req(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=5)
img = read_image()
img = generate_image()
for id in range(5):
request_handler = exec_net.start_async(request_id=id, inputs={'data': img})
request_handler.wait()
res = request_handler.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
del exec_net
del ie_core
@ -136,7 +122,7 @@ def test_async_infer_many_req_get_idle(device):
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
num_requests = 5
exec_net = ie_core.load_network(net, device, num_requests=num_requests)
img = read_image()
img = generate_image()
check_id = set()
for id in range(2*num_requests):
request_id = exec_net.get_idle_request_id()
@ -151,7 +137,7 @@ def test_async_infer_many_req_get_idle(device):
assert status == ie.StatusCode.OK
for id in range(num_requests):
if id in check_id:
assert np.argmax(exec_net.requests[id].output_blobs['fc_out'].buffer) == 2
assert np.argmax(exec_net.requests[id].output_blobs['fc_out'].buffer) == 9
del exec_net
del ie_core
@ -161,7 +147,7 @@ def test_wait_before_start(device):
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
num_requests = 5
exec_net = ie_core.load_network(net, device, num_requests=num_requests)
img = read_image()
img = generate_image()
requests = exec_net.requests
for id in range(num_requests):
status = requests[id].wait()
@ -169,7 +155,7 @@ def test_wait_before_start(device):
request_handler = exec_net.start_async(request_id=id, inputs={'data': img})
status = requests[id].wait()
assert status == ie.StatusCode.OK
assert np.argmax(request_handler.output_blobs['fc_out'].buffer) == 2
assert np.argmax(request_handler.output_blobs['fc_out'].buffer) == 9
del exec_net
del ie_core
@ -185,7 +171,7 @@ def test_wait_for_callback(device):
exec_net = ie_core.load_network(net, device, num_requests=num_requests)
callbacks_info = {}
callbacks_info['finished'] = 0
img = read_image()
img = generate_image()
for request in exec_net.requests:
request.set_completion_callback(callback, callbacks_info)
request.async_infer({'data': img})
@ -198,7 +184,7 @@ def test_wrong_request_id(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
with pytest.raises(ValueError) as e:
exec_net.start_async(request_id=20, inputs={'data': img})
assert "Incorrect request_id specified!" in str(e.value)
@ -230,9 +216,9 @@ def test_plugin_accessible_after_deletion(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device)
img = read_image()
img = generate_image()
res = exec_net.infer({'data': img})
assert np.argmax(res['fc_out'][0]) == 2
assert np.argmax(res['fc_out'][0]) == 9
del exec_net
del ie_core
@ -244,7 +230,7 @@ def test_exec_graph(device):
pytest.skip("Can't run on ARM plugin due-to get_exec_graph_info method isn't implemented")
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device)
img = read_image()
img = generate_image()
res = exec_net.infer({'data': img})
exec_graph = exec_net.get_exec_graph_info()
exec_graph_file = 'exec_graph.xml'
@ -267,7 +253,7 @@ def test_export_import():
assert os.path.exists(exported_net_file)
exec_net = ie_core.import_network(exported_net_file, "MYRIAD")
os.remove(exported_net_file)
img = read_image()
img = generate_image()
res = exec_net.infer({'data': img})
assert np.argmax(res['fc_out'][0]) == 3
del exec_net

4
src/bindings/python/tests_compatibility/test_inference_engine/test_IENetwork.py
View File

@ -5,8 +5,8 @@ import os
import pytest
import ngraph as ng
from openvino.inference_engine import IECore, IENetwork, DataPtr, InputInfoPtr, PreProcessInfo
from tests_compatibility.conftest import model_path, create_relu
from openvino.inference_engine import IECore, DataPtr, InputInfoPtr, PreProcessInfo
from tests_compatibility.conftest import model_path
test_net_xml, test_net_bin = model_path()

91
src/bindings/python/tests_compatibility/test_inference_engine/test_InferRequest.py
View File

@ -9,13 +9,13 @@ from datetime import datetime
import time
from openvino.inference_engine import ie_api as ie
from tests_compatibility.conftest import model_path, image_path, create_encoder
from tests_compatibility.conftest import model_path, create_encoder
from ..test_utils.test_utils import generate_image # TODO: reformat into an absolute path
import ngraph as ng
from ngraph.impl import Function, Type
is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
test_net_xml, test_net_bin = model_path(is_myriad)
path_to_img = image_path()
def create_function_with_memory(input_shape, data_type):
@ -29,19 +29,6 @@ def create_function_with_memory(input_shape, data_type):
return caps
def read_image():
import cv2
n, c, h, w = (1, 3, 32, 32)
image = cv2.imread(path_to_img)
if image is None:
raise FileNotFoundError("Input image not found")
image = cv2.resize(image, (h, w)) / 255
image = image.transpose((2, 0, 1)).astype(np.float32)
image = image.reshape((n, c, h, w))
return image
def load_sample_model(device, num_requests=1):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
@ -117,7 +104,7 @@ def test_write_to_input_blobs_directly(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
executable_network = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = executable_network.requests[0]
input_data = request.input_blobs["data"]
input_data.buffer[:] = img
@ -131,7 +118,7 @@ def test_write_to_input_blobs_copy(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
executable_network = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = executable_network.requests[0]
request.input_blobs["data"].buffer[:] = img
assert np.allclose(executable_network.requests[0].input_blobs["data"].buffer, img)
@ -144,11 +131,11 @@ def test_infer(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.infer({'data': img})
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
del exec_net
del ie_core
del net
@ -158,12 +145,12 @@ def test_async_infer_default_timeout(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.async_infer({'data': img})
request.wait()
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
del exec_net
del ie_core
del net
@ -173,12 +160,12 @@ def test_async_infer_wait_finish(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.async_infer({'data': img})
request.wait(ie.WaitMode.RESULT_READY)
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
del exec_net
del ie_core
del net
@ -188,7 +175,7 @@ def test_async_infer_wait_time(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=2)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.async_infer({'data': img})
start_time = datetime.utcnow()
@ -207,7 +194,7 @@ def test_async_infer_wait_time(device):
i += 1
assert status == ie.StatusCode.OK
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
del exec_net
del ie_core
del net
@ -217,12 +204,12 @@ def test_async_infer_wait_status(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.async_infer({'data': img})
request.wait(ie.WaitMode.RESULT_READY)
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
status = request.wait(ie.WaitMode.STATUS_ONLY)
assert status == ie.StatusCode.OK
del exec_net
@ -234,14 +221,14 @@ def test_async_infer_fill_inputs(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.input_blobs['data'].buffer[:] = img
request.async_infer()
status_end = request.wait()
assert status_end == ie.StatusCode.OK
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res[0]) == 2
assert np.argmax(res[0]) == 9
del exec_net
del ie_core
del net
@ -251,18 +238,18 @@ def test_infer_modify_outputs(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
outputs0 = exec_net.infer({'data': img})
status_end = request.wait()
assert status_end == ie.StatusCode.OK
assert np.argmax(outputs0['fc_out']) == 2
assert np.argmax(outputs0['fc_out']) == 9
outputs0['fc_out'][:] = np.zeros(shape=(1, 10), dtype=np.float32)
outputs1 = request.output_blobs
assert np.argmax(outputs1['fc_out'].buffer) == 2
assert np.argmax(outputs1['fc_out'].buffer) == 9
outputs1['fc_out'].buffer[:] = np.ones(shape=(1, 10), dtype=np.float32)
outputs2 = request.output_blobs
assert np.argmax(outputs2['fc_out'].buffer) == 2
assert np.argmax(outputs2['fc_out'].buffer) == 9
del exec_net
del ie_core
del net
@ -284,14 +271,14 @@ def test_async_infer_callback(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.set_completion_callback(callback)
request.async_infer({'data': img})
status = request.wait()
assert status == ie.StatusCode.OK
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
assert callback.callback_called == 1
del exec_net
del ie_core
@ -312,7 +299,7 @@ def test_async_infer_callback_wait_before_start(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.set_completion_callback(callback)
status = request.wait()
@ -321,7 +308,7 @@ def test_async_infer_callback_wait_before_start(device):
status = request.wait()
assert status == ie.StatusCode.OK
res = request.output_blobs['fc_out'].buffer
assert np.argmax(res) == 2
assert np.argmax(res) == 9
assert callback.callback_called == 1
del exec_net
del ie_core
@ -354,7 +341,7 @@ def test_async_infer_callback_wait_in_callback(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=1)
img = read_image()
img = generate_image()
request_wrap = InferReqWrap(exec_net.requests[0])
request_wrap.execute({'data': img})
del exec_net
@ -373,7 +360,7 @@ def test_async_infer_wait_while_callback_will_not_finish(device):
callback_status['finished'] = False
request = exec_net.requests[0]
request.set_completion_callback(callback, py_data=callback_status)
img = read_image()
img = generate_image()
request.async_infer({'data': img})
request.wait()
assert callback_status['finished'] == True
@ -384,7 +371,7 @@ def test_get_perf_counts(device):
net = ie_core.read_network(test_net_xml, test_net_bin)
ie_core.set_config({"PERF_COUNT": "YES"}, device)
exec_net = ie_core.load_network(net, device)
img = read_image()
img = generate_image()
request = exec_net.requests[0]
request.infer({'data': img})
pc = request.get_perf_counts()
@ -406,11 +393,11 @@ def test_set_batch_size(device):
net.batch_size = 10
data = np.zeros(shape=net.input_info['data'].input_data.shape)
exec_net = ie_core.load_network(net, device)
data[0] = read_image()[0]
data[0] = generate_image()[0]
request = exec_net.requests[0]
request.set_batch(1)
request.infer({'data': data})
assert np.allclose(int(round(request.output_blobs['fc_out'].buffer[0][2])), 1), "Incorrect data for 1st batch"
assert np.allclose(int(round(request.output_blobs['fc_out'].buffer[0][2])), 0), "Incorrect data for 1st batch"
del exec_net
del ie_core
del net
@ -453,7 +440,7 @@ def test_blob_setter(device):
net.input_info['data'].layout = "NHWC"
exec_net_2 = ie_core.load_network(network=net, device_name=device, num_requests=1)
img = read_image()
img = generate_image()
res_1 = np.sort(exec_net_1.infer({"data": img})['fc_out'])
img = np.transpose(img, axes=(0, 2, 3, 1)).astype(np.float32)
@ -471,7 +458,7 @@ def test_blob_setter_with_preprocess(device):
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(network=net, device_name=device, num_requests=1)
img = read_image()
img = generate_image()
tensor_desc = ie.TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
img_blob = ie.Blob(tensor_desc, img)
preprocess_info = ie.PreProcessInfo()
@ -498,25 +485,15 @@ def test_resize_algorithm_work(device):
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net_1 = ie_core.load_network(network=net, device_name=device, num_requests=1)
img = read_image()
img = generate_image()
res_1 = np.sort(exec_net_1.infer({"data": img})['fc_out'])
net.input_info['data'].preprocess_info.resize_algorithm = ie.ResizeAlgorithm.RESIZE_BILINEAR
exec_net_2 = ie_core.load_network(net, device)
import cv2
image = cv2.imread(path_to_img)
if image is None:
raise FileNotFoundError("Input image not found")
image = image / 255
image = image.transpose((2, 0, 1)).astype(np.float32)
image = np.expand_dims(image, 0)
tensor_desc = ie.TensorDesc("FP32", [1, 3, image.shape[2], image.shape[3]], "NCHW")
img_blob = ie.Blob(tensor_desc, image)
tensor_desc = ie.TensorDesc("FP32", [1, 3, img.shape[2], img.shape[3]], "NCHW")
img_blob = ie.Blob(tensor_desc, img)
request = exec_net_2.requests[0]
assert request.preprocess_info["data"].resize_algorithm == ie.ResizeAlgorithm.RESIZE_BILINEAR
request.set_blob('data', img_blob)

3
src/bindings/python/tests_compatibility/test_inference_engine/test_NGraph.py
View File

@ -3,8 +3,7 @@
from openvino.inference_engine import IECore, IENetwork
import ngraph as ng
from ngraph.impl.op import Parameter
from ngraph.impl import Function, Shape, Type
from ngraph.impl import Function
from tests_compatibility.conftest import model_path, create_relu

5
src/bindings/python/tests_compatibility/test_ngraph/test_if.py
View File

@ -9,6 +9,7 @@ from ngraph.utils.tensor_iterator_types import (
TensorIteratorInvariantInputDesc,
TensorIteratorBodyOutputDesc,
)
from tests_compatibility import skip_issue_86384
from tests_compatibility.runtime import get_runtime
@ -150,6 +151,7 @@ def check_if(if_model, cond_val, exp_results):
check_results(results, exp_results)
@skip_issue_86384
def test_if_with_two_outputs():
check_if(create_simple_if_with_two_outputs, True,
[np.array([10], dtype=np.float32), np.array([-20], dtype=np.float32)])
@ -157,6 +159,7 @@ def test_if_with_two_outputs():
[np.array([17], dtype=np.float32), np.array([16], dtype=np.float32)])
@skip_issue_86384
def test_diff_if_with_two_outputs():
check_if(create_diff_if_with_two_outputs, True,
[np.array([10], dtype=np.float32), np.array([6, 4], dtype=np.float32)])
@ -164,11 +167,13 @@ def test_diff_if_with_two_outputs():
[np.array([4], dtype=np.float32), np.array([12, 16], dtype=np.float32)])
@skip_issue_86384
def test_simple_if():
check_if(simple_if, True, [np.array([6, 4], dtype=np.float32)])
check_if(simple_if, False, [np.array([5, 5], dtype=np.float32)])
@skip_issue_86384
def test_simple_if_without_body_parameters():
check_if(simple_if_without_parameters, True, [np.array([0.7], dtype=np.float32)])
check_if(simple_if_without_parameters, False, [np.array([9.0], dtype=np.float32)])

43
src/bindings/python/tests_compatibility/test_utils/test_utils.py Normal file
View File

@ -0,0 +1,43 @@
# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import openvino
from openvino.runtime import Model, Core, Shape, Type
from openvino.runtime.op import Parameter
from typing import Tuple, Union, List
import openvino.runtime.opset8 as ops
import numpy as np
def get_test_function():
element_type = Type.f32
param = Parameter(element_type, Shape([1, 3, 22, 22]))
relu = ops.relu(param)
func = Model([relu], [param], "test")
assert func is not None
return func
def test_compare_functions():
try:
from openvino.test_utils import compare_functions
func = get_test_function()
status, _ = compare_functions(func, func)
assert status
except RuntimeError:
print("openvino.test_utils.compare_functions is not available")
def generate_image(shape: Tuple = (1, 3, 32, 32), dtype: Union[str, np.dtype] = "float32") -> np.array:
np.random.seed(42)
return np.random.rand(*shape).astype(dtype)
def generate_model(input_shape: List[int]) -> openvino.runtime.ie_api.CompiledModel:
param = ops.parameter(input_shape, np.float32, name="parameter")
relu = ops.relu(param, name="relu")
func = Model([relu], [param], "test")
func.get_ordered_ops()[2].friendly_name = "friendly"
core = Core()
return core.compile_model(func, "CPU", {})

11
src/bindings/python/tests_compatibility/test_utils/utils/plugins.xml Normal file
View File

@ -0,0 +1,11 @@
<!--
Copyright (C) 2020 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<ie>
<plugins>
<plugin location="libopenvino_intel_cpu_plugin.so" name="CUSTOM">
</plugin>
</plugins>
</ie>

11
src/bindings/python/tests_compatibility/test_utils/utils/plugins_apple.xml Normal file
View File

@ -0,0 +1,11 @@
<!--
Copyright (C) 2020 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<ie>
<plugins>
<plugin location="libopenvino_intel_cpu_plugin.so" name="CUSTOM">
</plugin>
</plugins>
</ie>

11
src/bindings/python/tests_compatibility/test_utils/utils/plugins_win.xml Normal file
View File

@ -0,0 +1,11 @@
<!--
Copyright (C) 2020 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<ie>
<plugins>
<plugin location="openvino_intel_cpu_plugin.dll" name="CUSTOM">
</plugin>
</plugins>
</ie>

BIN
src/bindings/python/tests_compatibility/test_utils/utils/test_model.onnx Normal file
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BIN
src/bindings/python/tests_compatibility/test_utils/utils/test_model_fp16.bin Normal file
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467
src/bindings/python/tests_compatibility/test_utils/utils/test_model_fp16.xml Normal file
View File

@ -0,0 +1,467 @@
<?xml version="1.0" ?>
<net name="test_model" version="10">
<layers>
<layer id="0" name="data" type="Parameter" version="opset1">
<data element_type="f16" shape="1,3,32,32"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>3</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="1" name="20/mean/Fused_Mul_614616_const" type="Const" version="opset1">
<data element_type="f16" offset="0" shape="16,3,5,5" size="2400"/>
<output>
<port id="1" precision="FP16">
<dim>16</dim>
<dim>3</dim>
<dim>5</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="2" name="19/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1">
<dim>16</dim>
<dim>3</dim>
<dim>5</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="3" name="data_add_575/copy_const" type="Const" version="opset1">
<data element_type="f16" offset="2400" shape="1,16,1,1" size="32"/>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="19/Fused_Add_" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="5" name="21" type="ReLU" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</input>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="6" name="22" type="MaxPool" version="opset1">
<data kernel="2,2" pads_begin="0,0" pads_end="0,0" rounding_type="floor" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</input>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
<dim>16</dim>
<dim>16</dim>
<dim>16</dim>
</port>
</output>
</layer>
<layer id="7" name="onnx_initializer_node_8/Output_0/Data__const" type="Const" version="opset1">
<data element_type="f16" offset="2432" shape="32,16,5,5" size="25600"/>
<output>
<port id="1" precision="FP16">
<dim>32</dim>
<dim>16</dim>
<dim>5</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="8" name="23/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
<dim>16</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>32</dim>
<dim>16</dim>
<dim>5</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
</output>
</layer>
<layer id="9" name="23/Dims351/copy_const" type="Const" version="opset1">
<data element_type="f16" offset="28032" shape="1,32,1,1" size="64"/>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
<dim>32</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="10" name="23" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>32</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
</output>
</layer>
<layer id="11" name="25/mean/Fused_Mul_618620_const" type="Const" version="opset1">
<data element_type="f16" offset="28096" shape="64,32,3,3" size="36864"/>
<output>
<port id="1" precision="FP16">
<dim>64</dim>
<dim>32</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="12" name="24/WithoutBiases" type="Convolution" version="opset1">
<data dilations="1,1" output_padding="0,0" pads_begin="2,2" pads_end="2,2" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>32</dim>
<dim>16</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>64</dim>
<dim>32</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="13" name="data_add_578583/copy_const" type="Const" version="opset1">
<data element_type="f16" offset="64960" shape="1,64,1,1" size="128"/>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="14" name="24/Fused_Add_" type="Add" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="15" name="26" type="ReLU" version="opset1">
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</input>
<output>
<port id="1" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
</port>
</output>
</layer>
<layer id="16" name="27" type="MaxPool" version="opset1">
<data kernel="2,2" pads_begin="0,0" pads_end="0,0" rounding_type="floor" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>18</dim>
<dim>18</dim>
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