IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[POT] Update IEEngine with the Dynamic model support (#10717)

Browse Source 
* Update IEEngine with the Dynamic models support

* Update with the batch

* Method naming fix

* Update image_loader & tests with dynamic models

* Update test_sanity.py

* Replace custom_mo_config from the model
This commit is contained in:
Nikita Malinin 2022-03-05 15:49:21 +03:00 committed by GitHub
parent 3b8e960b10
commit 41818a377f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 48 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
tools/pot/openvino/tools/pot/data_loaders/image_loader.py
View File

@ -5,6 +5,7 @@ from cv2 import imread, IMREAD_GRAYSCALE
from openvino.runtime import Layout, Dimension # pylint: disable=E0611,E0401
from openvino.tools.mo.utils.cli_parser import get_layout_values
from openvino.tools.mo.front.common.partial_infer.utils import is_fully_defined
from ..api.data_loader import DataLoader
from ..data_loaders.utils import prepare_image, collect_img_files
from ..utils.logger import get_logger
@ -34,6 +35,7 @@ class ImageLoader(DataLoader):
@shape.setter
def shape(self, shape):
self._is_shape_static = is_fully_defined(shape)
self._shape = tuple(shape)
def _read_and_preproc_image(self, img_path):
@ -47,7 +49,9 @@ class ImageLoader(DataLoader):
if image is None:
raise Exception('Can not read the image: {}'.format(img_path))
return prepare_image(image, self._layout, (self.shape[H], self.shape[W]),
dst_shape = (self.shape[H], self.shape[W]) if self._is_shape_static else None
return prepare_image(image, self._layout, dst_shape,
self._crop_central_fraction, self._shape[C] == 1)
def get_layout(self, input_node=None):

4
tools/pot/openvino/tools/pot/data_loaders/utils.py
View File

@ -35,11 +35,13 @@ def crop(image, central_fraction):
return image[start_height:start_height + dst_height, start_width:start_width + dst_width]
def prepare_image(image, layout, dst_shape, central_fraction=None, grayscale=False):
def prepare_image(image, layout, dst_shape=None, central_fraction=None, grayscale=False):
if central_fraction:
image = crop(image, central_fraction)
if dst_shape:
image = cv.resize(image, dst_shape[::-1])
if grayscale:
image = np.expand_dims(image, 2)

17
tools/pot/openvino/tools/pot/engines/ie_engine.py
View File

@ -230,20 +230,26 @@ class IEEngine(Engine):
"""
input_info = model.inputs
def is_dynamic_input(input_blob):
return input_blob.partial_shape.is_dynamic
def process_input(input_blob, input_data):
return input_data if is_dynamic_input(input_blob) else np.reshape(input_data, input_blob.shape)
if isinstance(image_batch[0], dict):
feed_dict = {}
input_blobs = {get_clean_name(in_node.get_node().friendly_name): in_node for in_node in input_info}
for input_name in image_batch[0].keys():
input_blob = input_blobs[input_name]
input_blob_name = self._get_input_any_name(input_blob)
feed_dict[input_blob_name] = np.reshape(image_batch[0][input_name], input_blob.shape)
feed_dict[input_blob_name] = process_input(input_blob, image_batch[0][input_name])
return feed_dict
if len(input_info) == 1:
input_blob = next(iter(input_info))
input_blob_name = self._get_input_any_name(input_blob)
image_batch = {input_blob_name: np.reshape(image_batch, input_blob.shape)}
if Shape(image_batch[input_blob_name].shape) != input_info[0].shape:
image_batch = {input_blob_name: process_input(input_blob, image_batch)}
if not is_dynamic_input(input_blob) and Shape(image_batch[input_blob_name].shape) != input_info[0].shape:
raise ValueError(f"Incompatible input shapes. "
f"Cannot infer {Shape(image_batch[input_blob_name].shape)} into {input_info[0].shape}."
f"Try to specify the layout of the model.")
@ -262,8 +268,9 @@ class IEEngine(Engine):
lambda x: x.get_any_name() != image_info_name, input_info)))
image_tensor_name = image_tensor_node.get_any_name()
image_tensor = (image_tensor_name, np.reshape(image_batch, input_blob.shape))
if Shape(image_tensor[1].shape) != image_tensor_node.shape:
image_tensor = (image_tensor_name, process_input(image_tensor_node, image_batch))
if not is_dynamic_input(image_tensor_node) and \
Shape(image_tensor[1].shape) != image_tensor_node.shape:
raise ValueError(f"Incompatible input shapes. "
f"Cannot infer {Shape(image_tensor[1].shape)} into {image_tensor_node.shape}."
f"Try to specify the layout of the model.")

29
tools/pot/tests/test_sanity.py
View File

@ -113,7 +113,9 @@ def test_compression(_params, tmp_path, models):
TEST_SAMPLE_MODELS = [
('mobilenet-v2-1.0-224', 'tf', 'DefaultQuantization', 'performance', {'accuracy@top1': 0.716})
('mobilenet-v2-1.0-224', 'tf', 'DefaultQuantization', 'performance', {'accuracy@top1': 0.716}, []),
('mobilenet-v2-1.0-224', 'tf', 'DefaultQuantization', 'performance', {'accuracy@top1': 0.716},
['--input_shape=[1,?,?,3]'])
]
@ -124,7 +126,7 @@ def _sample_params(request):
def test_sample_compression(_sample_params, tmp_path, models):
model_name, model_framework, algorithm, preset, expected_accuracy = _sample_params
model_name, model_framework, algorithm, preset, expected_accuracy, custom_mo_config = _sample_params
# hack for sample imports because sample app works only from sample directory
pot_dir = Path(__file__).parent.parent
@ -132,7 +134,7 @@ def test_sample_compression(_sample_params, tmp_path, models):
# pylint: disable=C0415
from openvino.tools.pot.api.samples.classification.classification_sample import optimize_model
model = models.get(model_name, model_framework, tmp_path)
model = models.get(model_name, model_framework, tmp_path, custom_mo_config=custom_mo_config)
data_source, annotations = get_dataset_info('imagenet_1001_classes')
args = Dict({
@ -167,15 +169,17 @@ def test_sample_compression(_sample_params, tmp_path, models):
SIMPLIFIED_TEST_MODELS = [
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance',
{'accuracy@top1': 0.701, 'accuracy@top5': 0.91})
{'accuracy@top1': 0.701, 'accuracy@top5': 0.91}, []),
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance',
{'accuracy@top1': 0.707, 'accuracy@top5': 0.904}, ['--input_shape=[1,3,?,?]'])
]
def launch_simplified_mode(tmp_path, models, engine_config):
model_name, model_framework, algorithm, preset, _ = SIMPLIFIED_TEST_MODELS[0]
def launch_simplified_mode(_simplified_params, tmp_path, models, engine_config):
model_name, model_framework, algorithm, preset, _, custom_mo_config = _simplified_params
algorithm_config = make_algo_config(algorithm, preset)
model = models.get(model_name, model_framework, tmp_path)
model = models.get(model_name, model_framework, tmp_path, custom_mo_config=custom_mo_config)
config = merge_configs(model.model_params, engine_config, algorithm_config)
_ = optimize(config)
@ -205,7 +209,12 @@ def launch_simplified_mode(tmp_path, models, engine_config):
return metrics
def test_simplified_mode(tmp_path, models):
@pytest.fixture(scope='module', params=SIMPLIFIED_TEST_MODELS,
ids=['{}_{}_{}_{}'.format(*m) for m in SIMPLIFIED_TEST_MODELS])
def _simplified_params(request):
return request.param
def test_simplified_mode(_simplified_params, tmp_path, models):
with open(PATHS2DATASETS_CONFIG.as_posix()) as f:
data_source = Dict(json.load(f))['ImageNet2012'].pop('source_dir')
@ -214,8 +223,8 @@ def test_simplified_mode(tmp_path, models):
'device': 'CPU',
'central_fraction': 0.875})
_, _, _, _, expected_accuracy = SIMPLIFIED_TEST_MODELS[0]
metrics = launch_simplified_mode(tmp_path, models, engine_config)
_, _, _, _, expected_accuracy, _ = _simplified_params
metrics = launch_simplified_mode(_simplified_params, tmp_path, models, engine_config)
assert metrics == pytest.approx(expected_accuracy, abs=0.006)

4
tools/pot/tests/utils/model_store.py
View File

@ -23,7 +23,7 @@ class ModelStore:
# load model description to self.models
self._load_models_description()
def get(self, name, framework, tmp_path, model_precision='FP32'):
def get(self, name, framework, tmp_path, model_precision='FP32', custom_mo_config=None):
for model in self.models:
if framework != model.framework:
continue
@ -38,7 +38,7 @@ class ModelStore:
'Couldn\'t load model {} from the framework {}'.format(model.name, model.framework))
assert omz_model_download(model) == 0,\
'Can not download model: {}'.format(model.name)
convert_value = omz_model_convert(model)
convert_value = omz_model_convert(model, custom_mo_config)
assert convert_value == 0, 'Can not convert model: {}'.format(model.name)
model_path = tmp_path.joinpath(
model.subdirectory.as_posix(), model.precision, model.name)

9
tools/pot/tests/utils/open_model_zoo.py
View File

@ -58,7 +58,7 @@ def download(config):
return runner.run()
def command_line_for_convert(config):
def command_line_for_convert(config, custom_mo_config=None):
python_path = DOWNLOAD_PATH.as_posix()
executable = OMZ_DOWNLOADER_PATH.joinpath('converter.py').as_posix()
cli_args = ' -o ' + config.model_params.output_dir
@ -66,6 +66,9 @@ def command_line_for_convert(config):
cli_args += ' --name ' + config.name
cli_args += ' --mo ' + MO_PATH.joinpath('mo.py').as_posix()
cli_args += ' --precisions ' + config.precision
if custom_mo_config:
for custom_mo_arg in custom_mo_config:
cli_args += ' --add_mo_arg=' + custom_mo_arg
script_launch_cli = '{python_exe} {main_py} {args}'.format(
python_exe=sys.executable, main_py=executable, args=cli_args
)
@ -77,8 +80,8 @@ def command_line_for_convert(config):
)
def convert(config):
runner = Command(command_line_for_convert(config))
def convert(config, custom_mo_config=None):
runner = Command(command_line_for_convert(config, custom_mo_config))
return runner.run()