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[Hub Tests] Apply Python code formatting (#21286)

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* Apply Python code formatting

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Apply Python code formatting

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
This commit is contained in:
Roman Kazantsev 2023-11-28 16:54:02 +04:00 committed by GitHub
parent a7de95a8a4
commit 7c590bf180
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
12 changed files with 59 additions and 50 deletions
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2
tests/model_hub_tests/models_hub_common/multiprocessing_utils.py
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@ -3,7 +3,6 @@
import logging as log
import os
import platform
import signal
import sys
import traceback
@ -11,6 +10,7 @@ from multiprocessing import Process, Queue, TimeoutError, ProcessError
from queue import Empty as QueueEmpty
from typing import Callable, Union
def _mp_wrapped_func(func: Callable, func_args: list, queue: Queue, logger_queue: Queue):
"""
Wraps callable object with exception handling. Current wrapper is a target for

17
tests/model_hub_tests/models_hub_common/test_performance_model.py
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@ -4,16 +4,14 @@
import sys
import time
from enum import Enum
import traceback
import pytest
from openvino.runtime.utils.types import openvino_to_numpy_types_map
from enum import Enum
import numpy as np
from models_hub_common.multiprocessing_utils import multiprocessing_run
import openvino as ov
# noinspection PyUnresolvedReferences
import pytest
from models_hub_common.multiprocessing_utils import multiprocessing_run
from openvino.runtime.utils.types import openvino_to_numpy_types_map
# set seed to have deterministic input data generation
# to avoid sporadic issues in inference results
@ -179,7 +177,7 @@ class TestModelPerformance:
print('read model time infer {}'.format(read_model_time))
print('read model time infer var {}'.format(read_model_time_variance))
infer_time_ratio = converted_model_time/read_model_time
infer_time_ratio = converted_model_time / read_model_time
results.converted_infer_time = converted_model_time
results.converted_model_time_variance = converted_model_time_variance
@ -200,13 +198,14 @@ class TestModelPerformance:
except:
ex_type, ex_value, tb = sys.exc_info()
results.error_message = "{tb}\n{ex_type}: {ex_value}".format(tb=''.join(traceback.format_tb(tb)),
ex_type=ex_type.__name__, ex_value=ex_value)
ex_type=ex_type.__name__, ex_value=ex_value)
return results
def run(self, model_name, model_link, ie_device):
self.result = Results()
t0 = time.time()
self.result = multiprocessing_run(self._run, [model_name, model_link, ie_device], model_name, self.infer_timeout)
self.result = multiprocessing_run(self._run, [model_name, model_link, ie_device], model_name,
self.infer_timeout)
t1 = time.time()
print('test running time {}'.format(t1 - t0))
if self.result.status == Status.OK:

4
tests/model_hub_tests/models_hub_common/utils.py
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@ -1,11 +1,11 @@
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import itertools
import os
import shutil
import itertools
import numpy as np
import numpy as np
from models_hub_common.constants import test_device

4
tests/model_hub_tests/performance_tests/conftest.py
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@ -2,10 +2,10 @@
# SPDX-License-Identifier: Apache-2.0
import inspect
import pytest
from py.xml import html
import pytest
from models_hub_common.utils import get_params
from py.xml import html
def pytest_generate_tests(metafunc):

8
tests/model_hub_tests/performance_tests/test_tf_hub_perfomance_model.py
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@ -2,18 +2,16 @@
# SPDX-License-Identifier: Apache-2.0
import gc
import os
import shutil
import gc
import pytest
import tensorflow_hub as hub
# noinspection PyUnresolvedReferences
from models_hub_common.constants import no_clean_cache_dir
from models_hub_common.constants import tf_hub_cache_dir
from models_hub_common.test_performance_model import TestModelPerformance
from models_hub_common.utils import get_models_list
from models_hub_common.constants import tf_hub_cache_dir
from models_hub_common.constants import no_clean_cache_dir
def clean_cache():

23
tests/model_hub_tests/torch_tests/test_aliked.py
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@ -1,19 +1,20 @@
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import os
import sys
import math
import tempfile
import torch
import pytest
import os
import subprocess
from torch_utils import TestTorchConvertModel
from openvino import convert_model, Model, PartialShape, Type
import openvino.runtime.opset12 as ops
from openvino.frontend import ConversionExtension
import numpy as np
import sys
import tempfile
import numpy as np
import openvino.runtime.opset12 as ops
import pytest
import torch
from openvino import convert_model, Model, PartialShape, Type
from openvino.frontend import ConversionExtension
from torch_utils import TestTorchConvertModel
# To make tests reproducible we seed the random generator
torch.manual_seed(0)
@ -79,7 +80,7 @@ class TestAlikedConvertModel(TestTorchConvertModel):
subprocess.check_call(
["git", "checkout", "6008af43942925eec7e32006814ef41fbd0858d8"], cwd=self.repo_dir.name)
subprocess.check_call([sys.executable, "-m", "pip", "install",
"-r", os.path.join(self.repo_dir.name, "requirements.txt")])
"-r", os.path.join(self.repo_dir.name, "requirements.txt")])
subprocess.check_call(["sh", "build.sh"], cwd=os.path.join(
self.repo_dir.name, "custom_ops"))

4
tests/model_hub_tests/torch_tests/test_detectron2.py
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@ -2,11 +2,13 @@
# SPDX-License-Identifier: Apache-2.0
import os
import pytest
import torch
from torch_utils import TestTorchConvertModel, process_pytest_marks
from models_hub_common.utils import get_models_list, compare_two_tensors
from torch_utils import TestTorchConvertModel, process_pytest_marks
class TestDetectron2ConvertModel(TestTorchConvertModel):
def setup_class(self):

13
tests/model_hub_tests/torch_tests/test_hf_transformers.py
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@ -2,12 +2,14 @@
# SPDX-License-Identifier: Apache-2.0
import os
import pytest
import torch
from huggingface_hub import model_info
from torch_utils import TestTorchConvertModel
from models_hub_common.utils import cleanup_dir
from models_hub_common.constants import hf_hub_cache_dir
from models_hub_common.utils import cleanup_dir
from torch_utils import TestTorchConvertModel
from torch_utils import process_pytest_marks
@ -219,13 +221,13 @@ class TestTransformersModel(TestTorchConvertModel):
"Number of movies": ["87", "53", "69"]}
queries = ["What is the name of the first actor?",
"How many movies has George Clooney played in?",
"What is the total number of movies?",]
"What is the total number of movies?", ]
answer_coordinates = [[(0, 0)], [(2, 1)], [
(0, 1), (1, 1), (2, 1)]]
answer_text = [["Brad Pitt"], ["69"], ["209"]]
table = pd.DataFrame.from_dict(data)
encoded_input = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates,
answer_text=answer_text, padding="max_length", return_tensors="pt",)
answer_text=answer_text, padding="max_length", return_tensors="pt", )
example = dict(input_ids=encoded_input["input_ids"],
token_type_ids=encoded_input["token_type_ids"],
attention_mask=encoded_input["attention_mask"])
@ -277,7 +279,8 @@ class TestTransformersModel(TestTorchConvertModel):
def test_convert_model_precommit(self, name, type, ie_device):
self.run(model_name=name, model_link=type, ie_device=ie_device)
@pytest.mark.parametrize("name", process_pytest_marks(os.path.join(os.path.dirname(__file__), "hf_transformers_models")))
@pytest.mark.parametrize("name",
process_pytest_marks(os.path.join(os.path.dirname(__file__), "hf_transformers_models")))
@pytest.mark.nightly
def test_convert_model_all_models(self, name, ie_device):
self.run(model_name=name, model_link=None, ie_device=ie_device)

12
tests/model_hub_tests/torch_tests/test_speech-transformer.py
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@ -2,17 +2,15 @@
# SPDX-License-Identifier: Apache-2.0
import os
import subprocess
import sys
import tempfile
import torch
import pytest
import subprocess
import torch
from models_hub_common.test_convert_model import TestConvertModel
from openvino import convert_model
from torch_utils import TestTorchConvertModel
# To make tests reproducible we seed the random generator
torch.manual_seed(0)
@ -39,8 +37,8 @@ class TestSpeechTransformerConvertModel(TestTorchConvertModel):
torch.stack(sorted(torch.randint(55, 250, [32]), reverse=True)),
torch.randint(-1, 4232, [32, 20]))
self.inputs = (torch.randn(32, 209, 320),
torch.stack(sorted(torch.randint(55, 400, [32]), reverse=True)),
torch.randint(-1, 4232, [32, 25]))
torch.stack(sorted(torch.randint(55, 400, [32]), reverse=True)),
torch.randint(-1, 4232, [32, 25]))
return m
def infer_fw_model(self, model_obj, inputs):

9
tests/model_hub_tests/torch_tests/test_timm.py
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@ -2,19 +2,22 @@
# SPDX-License-Identifier: Apache-2.0
import os
import pytest
import timm
import torch
import pytest
from torch_utils import TestTorchConvertModel, process_pytest_marks
from models_hub_common.constants import hf_hub_cache_dir
from models_hub_common.utils import cleanup_dir, get_models_list
from torch_utils import TestTorchConvertModel, process_pytest_marks
def filter_timm(timm_list: list) -> list:
unique_models = set()
filtered_list = []
ignore_set = {"base", "mini", "small", "xxtiny", "xtiny", "tiny", "lite", "nano", "pico", "medium", "big",
"large", "xlarge", "xxlarge", "huge", "gigantic", "giant", "enormous", "xs", "xxs", "s", "m", "l", "xl"}
"large", "xlarge", "xxlarge", "huge", "gigantic", "giant", "enormous", "xs", "xxs", "s", "m", "l",
"xl"}
for name in sorted(timm_list):
# first: remove datasets
name_parts = name.split(".")

7
tests/model_hub_tests/torch_tests/test_torchvision_models.py
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@ -2,10 +2,12 @@
# SPDX-License-Identifier: Apache-2.0
import os
import tempfile
import pytest
import torch
import tempfile
import torchvision.transforms.functional as F
from torch_utils import process_pytest_marks, TestTorchConvertModel
@ -102,7 +104,8 @@ class TestTorchHubConvertModel(TestTorchConvertModel):
def test_convert_model_precommit(self, model_name, ie_device):
self.run(model_name, None, ie_device)
@pytest.mark.parametrize("name", process_pytest_marks(os.path.join(os.path.dirname(__file__), "torchvision_models")))
@pytest.mark.parametrize("name",
process_pytest_marks(os.path.join(os.path.dirname(__file__), "torchvision_models")))
@pytest.mark.nightly
def test_convert_model_all_models(self, name, ie_device):
self.run(name, None, ie_device)

6
tests/model_hub_tests/torch_tests/torch_utils.py
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@ -3,8 +3,8 @@
import pytest
import torch
from models_hub_common.utils import get_models_list
from models_hub_common.test_convert_model import TestConvertModel
from models_hub_common.utils import get_models_list
from openvino import convert_model
@ -26,7 +26,9 @@ def flattenize_structure(outputs):
def process_pytest_marks(filepath: str):
return [pytest.param(n, marks=pytest.mark.xfail(reason=r) if m == "xfail" else pytest.mark.skip(reason=r)) if m else n for n, _, m, r in get_models_list(filepath)]
return [
pytest.param(n, marks=pytest.mark.xfail(reason=r) if m == "xfail" else pytest.mark.skip(reason=r)) if m else n
for n, _, m, r in get_models_list(filepath)]
class TestTorchConvertModel(TestConvertModel):