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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 2022-03-15 16:55:48 +03:00 committed by GitHub
parent 4e8e56e887
commit e103af056e
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GPG Key ID: 4AEE18F83AFDEB23
62 changed files with 202 additions and 199 deletions
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13
tools/pot/openvino/tools/pot/engines/ie_engine.py
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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)

5
tools/pot/openvino/tools/pot/graph/graph_utils.py
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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

3
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2
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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]
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[
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],
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]
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],
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]
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[
[
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],
[
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"Add_27/fq_input_1"
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]
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105
tools/pot/tests/test_graph.py
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@ -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'])

6
tools/pot/tests/test_nightly.py
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}}),

6
tools/pot/tests/test_sanity.py
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,?,?]'])
]

2
tools/pot/tests/test_statistic_builder.py
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

16
tools/pot/tests/test_statistics_collector.py
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():