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[POT] References & golds update (#10937)

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* Update references

* Update golds & add stats dumping

* Statistics_data upd

* Enable densenet in nightly

* Pylint fixes

* Update try-except WA

* Update simplified gold
This commit is contained in:
Nikita Malinin authored and GitHub committed 2022-03-15 16:55:48 +03:00
1 parent 4e8e56e887
commit e103af056e
62 files changed
+202 -199
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tools/pot/openvino/tools/pot/engines/ie_engine.py
+3 -10
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@@ -328,18 +328,11 @@ class IEEngine(Engine):
start_time = time()
progress_log_fn = logger.info if print_progress else logger.debug
try:
self._ie.set_property(self._device,
{'CPU_THROUGHPUT_STREAMS': 'CPU_THROUGHPUT_AUTO', 'CPU_BIND_THREAD': 'YES'})
except AttributeError:
self._ie.set_config({'CPU_THROUGHPUT_STREAMS': 'CPU_THROUGHPUT_AUTO', 'CPU_BIND_THREAD': 'YES'},
self._device)
self._ie.set_property(self._device,
{'CPU_THROUGHPUT_STREAMS': 'CPU_THROUGHPUT_AUTO', 'CPU_BIND_THREAD': 'YES'})
# Load model to the plugin
compiled_model = self._ie.compile_model(model=self._model, device_name=self._device)
try:
optimal_requests_num = compiled_model.get_property('OPTIMAL_NUMBER_OF_INFER_REQUESTS')
except AttributeError:
optimal_requests_num = compiled_model.get_metric('OPTIMAL_NUMBER_OF_INFER_REQUESTS')
optimal_requests_num = compiled_model.get_property('OPTIMAL_NUMBER_OF_INFER_REQUESTS')
requests_num = optimal_requests_num if requests_num == 0 else requests_num
logger.debug('Async mode requests number: %d', requests_num)
infer_queue = AsyncInferQueue(compiled_model, requests_num)
tools/pot/openvino/tools/pot/graph/graph_utils.py
+1 -4
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@@ -9,10 +9,7 @@ from openvino.tools.mo.utils.ir_reader.restore_graph import restore_graph_from_i
from openvino.tools.mo.utils.logger import init_logger
from openvino.runtime import Core # pylint: disable=E0401,E0611
from openvino.runtime.passes import Manager # pylint: disable=E0401,E0611
try:
from openvino.offline_transformations import apply_pot_transformations # pylint: disable=import-error,no-name-in-module
except ImportError:
from openvino.offline_transformations_pybind import apply_pot_transformations # pylint: disable=import-error,no-name-in-module
from openvino.offline_transformations import apply_pot_transformations # pylint: disable=import-error,no-name-in-module
from ..graph.passes import ModelPreprocessor, remove_converts, add_removed_converts
from ..utils.logger import stdout_redirect
tools/pot/tests/data/models/1_input_model/1_input_model.json
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tools/pot/tests/data/reference_scale/mobilenet-v2-pytorch_clipped_activations.json
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tools/pot/tests/data/reference_scale/mobilenet-v2-pytorch_performance_activations.json
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tools/pot/tests/data/reference_scale/mobilenet-v2-pytorch_weights.json
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{"Conv_0/WithoutBiases/fq_weights_1": {"low_level": [-0.0053907595574855804, -0.2611284554004669, -0.23381225764751434, -0.3634822368621826, -0.03721718490123749, -0.05057509243488312, -0.06728402525186539, -0.018891500309109688, -0.09281398355960846, -0.00012975696881767362, -0.14555448293685913, -0.008148729801177979, -0.2995051443576813, -0.03391457349061966, -0.0007948838174343109, -0.0005652799736708403, -0.0010670478222891688, -0.0006718520307913423, -0.008362662978470325, -0.126137837767601, -0.09869376569986343, -0.09116945415735245, -0.1943969875574112, -0.0006791173946112394, -0.11312229186296463, -0.1889931857585907, -0.08442160487174988, -0.1949506402015686, -0.07025709003210068, -0.1299339085817337, -0.09471405297517776, -0.02030201070010662], "high_level": [0.004716914612799883, 0.32297468185424805, 0.32340386509895325, 0.3334197998046875, 0.014642827212810516, 0.07109140604734421, 0.13456805050373077, 0.06139737740159035, 0.10278573632240295, 0.0010519583011046052, 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0.39003786444664, 0.24512203037Line truncated
{"Convolution_1461/fq_weights_1": {"low_level": [-0.10124634206295013, -0.09384729713201523, -0.07418366521596909, -0.06697019934654236, -0.05964021384716034, -0.059035152196884155, -0.06410862505435944, -0.10846225917339325, -0.1463303118944168, -0.11256258934736252, -0.05575663223862648, -0.11843226104974747, -0.04708372801542282, -0.052086636424064636, -0.04127328842878342, -0.07696791738271713, -0.07077835500240326, -0.10934167355298996, -0.050280481576919556, -0.08503344655036926, -0.09334193915128708, -0.08863487839698792, -0.020289357751607895, -0.1646938920021057, -0.10073158144950867, -0.047992460429668427, -0.045145172625780106, -0.1261540949344635, -0.11176442354917526, -0.08247034251689911, -0.08723096549510956, -0.11493869870901108, -0.09521769732236862, -0.07843150198459625, -0.06798263639211655, -0.07498446851968765, -0.10869022458791733, -0.06748296320438385, -0.10401124507188797, -0.06601454317569733, -0.07349853217601776, -0.05323268845677376, -0.0952429547905922, 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-0.12425515800714493, -0.1041104793548584, -0.06806732714176178, -0.09114668518304825, -0.05920841172337532, -0.09796324372291565, -0.0805300921201706, -0.06533420830965042, -0.09788458794355392, -0.0672326534986496, -0.08414079248905182, -0.08002050966024399, -0.10979483276605606, -0.0635126531124115], "high_level": [0.17636460065841675, 0.05955693870782852, 0.05109338462352753, 0.13394039869308472, 0.06924329698085785, 0.038717858493328094, 0.056095048785209656, 0.06770255416631699, 0.09286346286535263, 0.0691048800945282, 0.0923469215631485, 0.07767310738563538, 0.06726247072219849, 0.036460645496845245, 0.037859708070755005, 0.10476188361644745, 0.07133566588163376, 0.09719260036945343, 0.059305183589458466, 0.1642025113105774, 0.11729694902896881, 0.059089917689561844, 0.02282552793622017, 0.115285724401474, 0.06184154003858566, 0.03469725325703621, 0.09029034525156021, 0.08410273492336273, 0.0674804076552391, 0.13659150898456573, 0.10288780182600021, 0.0929291620850563, 0.07457610219717026, 0.11958862841129303, 0.09Line truncated
tools/pot/tests/data/reference_unify/mobilenet-v2-pytorch_to_unify.json
+30 -30
View File
@@ -1,92 +1,92 @@
[
[
[
"Add_114"
"351"
],
[
"Add_114/fq_input_0",
"Add_114/fq_input_1"
"351/fq_input_0",
"351/fq_input_1"
]
],
[
[
"Add_124"
"376"
],
[
"Add_124/fq_input_0",
"Add_124/fq_input_1"
"376/fq_input_0",
"376/fq_input_1"
]
],
[
[
"Add_143"
"389"
],
[
"Add_143/fq_input_0",
"Add_143/fq_input_1"
"389/fq_input_0",
"389/fq_input_1"
]
],
[
[
"Add_153"
"414"
],
[
"Add_153/fq_input_0",
"Add_153/fq_input_1"
"414/fq_input_0",
"414/fq_input_1"
]
],
[
[
"Add_27"
"427"
],
[
"Add_27/fq_input_0",
"Add_27/fq_input_1"
"427/fq_input_0",
"427/fq_input_1"
]
],
[
[
"Add_46"
"440"
],
[
"Add_46/fq_input_0",
"Add_46/fq_input_1"
"440/fq_input_0",
"440/fq_input_1"
]
],
[
[
"Add_56"
"465"
],
[
"Add_56/fq_input_0",
"Add_56/fq_input_1"
"465/fq_input_0",
"465/fq_input_1"
]
],
[
[
"Add_75"
"478"
],
[
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"Add_75/fq_input_1"
"478/fq_input_0",
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]
],
[
[
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"503"
],
[
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]
],
[
[
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],
[
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"516/fq_input_0",
"516/fq_input_1"
]
]
]
tools/pot/tests/data/test_cases_refs/statistics_data.json
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@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
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size 3423642
tools/pot/tests/data/test_cases_refs/statistics_data.txt
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@@ -1,3 +0,0 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1fb8615789b3270b5273485aed2effc85c33190a298cb14ce60d23153aeb4f31
size 3522564
tools/pot/tests/data/test_cases_refs/test_ig_border_case_with_fq_moving.bin
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version https://git-lfs.github.com/spec/v1
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tools/pot/tests/data/test_cases_refs/test_ig_border_case_with_fq_moving.xml
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version https://git-lfs.github.com/spec/v1
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tools/pot/tests/data/test_cases_refs/test_ig_border_case_without_fq_moving.bin
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version https://git-lfs.github.com/spec/v1
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tools/pot/tests/data/test_cases_refs/test_ig_border_case_without_fq_moving.xml
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version https://git-lfs.github.com/spec/v1
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size 14462
tools/pot/tests/test_graph.py
+50 -55
View File
@@ -11,7 +11,7 @@ from openvino.tools.pot.configs.hardware_config import HardwareConfig
from openvino.tools.pot.graph.transformer import GraphTransformer
from openvino.tools.pot.graph.model_utils import get_nodes_by_type, get_node_by_name
from openvino.tools.pot.graph.node_utils import get_node_inputs, get_first_convolutions
from tests.utils.path import TEST_ROOT, HARDWARE_CONFIG_PATH
from tests.utils.path import HARDWARE_CONFIG_PATH
from tests.utils.check_graph import check_model
CPU_CONFIG_PATH = HARDWARE_CONFIG_PATH / 'cpu.json'
@@ -86,17 +86,16 @@ def test_build_quantization_graph_with_ignored_params(
{
'type': 'Convolution',
'attributes': {
'output': 1280,
'group': 1
'output': 1280
}
}
]
}
if model_name == 'resnet_example':
ignored_params['scope'] = ['Conv_11/WithoutBiases', 'Conv_29/WithoutBiases']
ignored_params['scope'] = ['Convolution_283', 'Convolution_724']
elif model_name == 'googlenet_example':
node_name = 'Conv_10/WithoutBiases'
node_name = 'Convolution_289'
ignored_params['scope'] = [node_name]
elif model_name == 'mtcnn':
ignored_params = {
@@ -160,29 +159,23 @@ def test_build_quantization_graph_with_ignored_agnostic_params(
TEST_MODELS_REMOVAL = [
('mobilenetv2_ssd_example', 'caffe', ['Conv_8/WithoutBiases',
'Conv_172/WithoutBiases',
'Conv_129/WithoutBiases']),
('squeezenet1_1_example', 'pytorch', ['Conv_14/WithoutBiases',
'Conv_51/WithoutBiases']),
('mobilenet_example', 'pytorch', ['Conv_10/WithoutBiases',
'Conv_35/WithoutBiases',
'Conv_73/WithoutBiases']),
('googlenet_example', 'tf', ['Conv_3/WithoutBiases',
'Conv_57/WithoutBiases',
'Conv_65/WithoutBiases',
'Conv_104/WithoutBiases',
'Conv_39/WithoutBiases']),
('mobilenetv2_ssd_example', 'pytorch', ['Convolution_448',
'GroupConvolution_840',
'Convolution_1160']),
('squeezenet1_1_example', 'pytorch', ['Convolution_150',
'Convolution_991']),
('mobilenetv2_example', 'pytorch', ['Convolution_521',
'GroupConvolution_863',
'Convolution_2450']),
('googlenet_example', 'pytorch', ['Convolution_190',
'Convolution_289',
'Convolution_486',
'Convolution_1917',
'Convolution_2017']),
('multiple_out_ports_net', 'tf', ['add_indices'])
]
@pytest.fixture(scope='module', params=TEST_MODELS_REMOVAL,
ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MODELS_REMOVAL])
def _params(request):
return request.param
def cut_fq_node(model, node_list, graph_transformer, tmp_path):
model_ = load_model(model.model_params)
quantized_model = graph_transformer.insert_fake_quantize(model_)
@@ -197,8 +190,10 @@ def cut_fq_node(model, node_list, graph_transformer, tmp_path):
check_model(tmp_path, cropped_model, model.model_name + '_cut_fq', model.framework)
def test_cutting_fq_layers(_params, tmp_path, models):
model_name, model_framework, node_list = _params
@pytest.mark.parametrize(
'model_name, model_framework, node_list', TEST_MODELS_REMOVAL,
ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MODELS_REMOVAL])
def test_cutting_fq_layers(tmp_path, models, model_name, model_framework, node_list):
model = models.get(model_name, model_framework, tmp_path)
hardware_config = HardwareConfig.from_json(CPU_CONFIG_PATH.as_posix())
graph_transformer = GraphTransformer(hardware_config)
@@ -229,24 +224,26 @@ def test_build_quantization_graph_with_ignored_blocks(tmp_path, models, model_na
check_model(tmp_path, quantization_model, model_name + '_ig_pt', model_framework)
def test_multibranch_propagation_without_fq_moving():
# TODO: Enable this test after IRReader solve the problem with MaxPool #9613
pytest.skip()
TEST_CASES_PATH = TEST_ROOT / 'data' / 'test_cases_refs'
model_path = (TEST_CASES_PATH / 'test_ig_border_case_without_fq_moving.xml').as_posix()
weights_path = (TEST_CASES_PATH / 'test_ig_border_case_without_fq_moving.bin').as_posix()
TEST_MODELS_WITHOUT_FQ_MOVING = [
('test_multibranch_propogation_without_fq_moving', 'pytorch')
]
@pytest.mark.parametrize(
'model_name, model_framework', TEST_MODELS_WITHOUT_FQ_MOVING,
ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MODELS_WITHOUT_FQ_MOVING])
def test_multibranch_propagation_without_fq_moving(tmp_path, models, model_name, model_framework):
ignored_params = {
"scope": ['8/WithoutBiases', '9/WithoutBiases', '10/WithoutBiases', '11/WithoutBiases']
"scope": ['Convolution_104', 'Convolution_152', 'Convolution_8', 'Convolution_56']
}
config = Dict({'model': model_path, 'weights': weights_path})
model = load_model(config)
model = models.get(model_name, model_framework, tmp_path)
model = load_model(model.model_params)
hardware_config = HardwareConfig.from_json((HARDWARE_CONFIG_PATH / 'cpu.json').as_posix())
quantized_model = GraphTransformer(hardware_config).insert_fake_quantize(model, ignored_params)
node = get_node_by_name(quantized_model, '13/WithoutBiases')
node = get_node_by_name(quantized_model, 'Convolution_201')
for node_input in get_node_inputs(node)[:2]:
assert node_input.type == 'FakeQuantize'
assert len(get_nodes_by_type(quantized_model, ['FakeQuantize'])) == 2
@@ -279,19 +276,21 @@ def test_lstm_ends(tmp_path, models):
assert sorted(lstm_ends_names) == sorted(lstm_ends_ref[read_value.name])
def test_multibranch_propagation_with_fq_moving():
# TODO: Enable this test after IRReader solve the problem with MaxPool #9613
pytest.skip()
TEST_CASES_PATH = TEST_ROOT / 'data' / 'test_cases_refs'
model_path = (TEST_CASES_PATH / 'test_ig_border_case_with_fq_moving.xml').as_posix()
weights_path = (TEST_CASES_PATH / 'test_ig_border_case_with_fq_moving.bin').as_posix()
TEST_MODELS_WITHOUT_FQ_MOVING = [
('test_multibranch_propogation_with_fq_moving', 'pytorch')
]
@pytest.mark.parametrize(
'model_name, model_framework', TEST_MODELS_WITHOUT_FQ_MOVING,
ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MODELS_WITHOUT_FQ_MOVING])
def test_multibranch_propagation_with_fq_moving(tmp_path, models, model_name, model_framework):
ignored_params = {
"scope": ['8/WithoutBiases', '9/WithoutBiases', '10/WithoutBiases', '11/WithoutBiases']
"scope": ['Convolution_104', 'Convolution_152', 'Convolution_8', 'Convolution_56']
}
config = Dict({'model': model_path, 'weights': weights_path})
model = load_model(config)
model = models.get(model_name, model_framework, tmp_path)
model = load_model(model.model_params)
hardware_config = HardwareConfig.from_json((HARDWARE_CONFIG_PATH / 'cpu.json').as_posix())
quantized_model = GraphTransformer(hardware_config).insert_fake_quantize(model, ignored_params)
@@ -309,19 +308,15 @@ def test_multibranch_propagation_with_fq_moving():
MODELS_FOR_FIRST_CONV_TEST = [
('1_input_model', 'onnx', ['Conv_3/WithoutBiases']),
('3_inputs_model', 'onnx', ['Conv_3/WithoutBiases', 'Conv_5/WithoutBiases', 'Conv_7/WithoutBiases']),
('1_input_model', 'onnx', ['Convolution_19']),
('3_inputs_model', 'onnx', ['Convolution_172', 'Convolution_123', 'Convolution_27']),
]
@pytest.fixture(scope='module', params=MODELS_FOR_FIRST_CONV_TEST,
ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_FOR_FIRST_CONV_TEST])
def _params(request):
return request.param
def test_first_convolutions_search(_params, tmp_path, models):
model_name, model_framework, first_convs_ref = _params
@pytest.mark.parametrize(
'model_name, model_framework, first_convs_ref', MODELS_FOR_FIRST_CONV_TEST,
ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_FOR_FIRST_CONV_TEST])
def test_first_convolutions_search(tmp_path, models, model_name, model_framework, first_convs_ref):
model = models.get(model_name, model_framework, tmp_path)
model = load_model(model.model_params)
input_nodes = get_nodes_by_type(model, ['Parameter'])
tools/pot/tests/test_nightly.py
+3 -3
View File
@@ -35,9 +35,9 @@ TEST_MODELS_DEFAULT = [
('googlenet-v3-pytorch', 'pytorch', 'FP32', {
'performance': {'accuracy@top1': 0.77562, 'accuracy@top5': 0.9363},
'mixed': {'accuracy@top1': 0.77562, 'accuracy@top5': 0.9363}}),
# ('densenet-121', 'caffe', 'FP32', {
# 'performance': {'accuracy@top1': 0.73908, 'accuracy@top5': 0.91728},
# 'mixed': {'accuracy@top1': 0.7389, 'accuracy@top5': 0.91714}}),
('densenet-121', 'caffe', 'FP32', {
'performance': {'accuracy@top1': 0.73908, 'accuracy@top5': 0.91728},
'mixed': {'accuracy@top1': 0.7389, 'accuracy@top5': 0.91714}}),
('mobilenet-ssd', 'caffe', 'FP32', {
'performance': {'map': 0.666},
'mixed': {'map': 0.664}}),
tools/pot/tests/test_sanity.py
+3 -3
View File
@@ -23,8 +23,8 @@ from .utils.config import get_engine_config, merge_configs, \
get_dataset_info, PATHS2DATASETS_CONFIG, make_algo_config
TEST_MODELS = [
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance', 300, {'accuracy@top1': 0.731,
'accuracy@top5': 0.907},
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance', 300, {'accuracy@top1': 0.737,
'accuracy@top5': 0.909},
{}, 'CPU'),
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'mixed', 300, {'accuracy@top1': 0.731,
@@ -173,7 +173,7 @@ SIMPLIFIED_TEST_MODELS = [
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance',
{'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,?,?]'])
{'accuracy@top1': 0.709, 'accuracy@top5': 0.906}, ['--input_shape=[1,3,?,?]'])
]
tools/pot/tests/test_statistic_builder.py
+1 -1
View File
@@ -105,4 +105,4 @@ def test_statistics_collector_subsets(tmp_path, models, model_name, model_framew
ir_name = f'{model_name}_stat_{type_max}_{type_min}' if type_min is not None \
else f'{model_name}_stat_mean'
check_model(tmp_path, model_with_nodes, ir_name, model_framework)
assert len(set(nodes_names[model.name])) == add_output_nodes
assert len(set(nodes_names[model.models[0]['model'].name])) == add_output_nodes
tools/pot/tests/test_statistics_collector.py
+14 -2
View File
@@ -2,6 +2,9 @@
# SPDX-License-Identifier: Apache-2.0
import json
import os
from copy import copy
from pathlib import Path
import pytest
import numpy as np
@@ -54,11 +57,20 @@ def test_statistics_collector_subsets(tmp_path, models, model_name, model_framew
out = {'MinMaxQuantization': collector.get_statistics_for_algorithm('MinMaxQuantization'),
'BiasCorrection': collector.get_statistics_for_algorithm('BiasCorrection')}
refs_file = Path(__file__).parent / 'data/test_cases_refs/statistics_data.txt'
refs_file = Path(__file__).parent / 'data/test_cases_refs/statistics_data.json'
local_path = os.path.join(tmp_path, '{}_{}.json'.format(model_name, 'statistics_data'))
local_file = open(local_path, 'w')
with open(refs_file.as_posix()) as file:
refs = json.loads(json.load(file))
refs = json.load(file)
eps = 1e-3
local_out = copy(out)
for algo_name, algo_val in local_out.items():
for node_name, node_val in algo_val.items():
for stats_name, stats_val in node_val.items():
local_out[algo_name][node_name][stats_name] = [v.tolist() for v in stats_val]
json.dump(local_out, local_file)
for algo_name, algo_val in out.items():
for node_name, node_val in algo_val.items():
for stats_name, stats_val in node_val.items():