"""Configuration file of OTX Segmentation."""
# Copyright (C) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
from attr import attrs
from otx.algorithms.common.configs import (
BaseConfig,
LearningRateSchedule,
POTQuantizationPreset,
)
from otx.api.configuration.elements import (
add_parameter_group,
boolean_attribute,
configurable_boolean,
configurable_float,
configurable_integer,
selectable,
string_attribute,
)
from otx.api.configuration.model_lifecycle import ModelLifecycle
# pylint: disable=invalid-name
[docs]
@attrs
class SegmentationConfig(BaseConfig):
"""Configurations of OTX Segmentation."""
header = string_attribute("Configuration for an object semantic segmentation task of OTX")
description = header
@attrs
class __LearningParameters(BaseConfig.BaseLearningParameters):
header = string_attribute("Learning Parameters")
description = header
learning_rate_schedule = selectable(
default_value=LearningRateSchedule.COSINE,
header="Learning rate schedule",
description="Specify learning rate scheduling for the MMDetection task. "
"When training for a small number of epochs (N < 10), the fixed "
"schedule is recommended. For training for 10 < N < 25 epochs, "
"step-wise or exponential annealing might give better results. "
"Finally, for training on large datasets for at least 20 "
"epochs, cyclic annealing could result in the best model.",
editable=True,
visible_in_ui=True,
)
@attrs
class __AlgoBackend(BaseConfig.BaseAlgoBackendParameters):
header = string_attribute("Parameters for the OTX algo-backend")
description = header
@attrs
class __Postprocessing(BaseConfig.BasePostprocessing):
header = string_attribute("Postprocessing")
description = header
blur_strength = configurable_integer(
header="Blur strength",
description="With a higher value, the segmentation output will be smoother, but less accurate.",
default_value=1,
min_value=1,
max_value=25,
affects_outcome_of=ModelLifecycle.INFERENCE,
)
soft_threshold = configurable_float(
default_value=0.5,
header="Soft threshold",
description="The threshold to apply to the probability output of the model, for each pixel. A higher value "
"means a stricter segmentation prediction.",
min_value=0.0,
max_value=1.0,
affects_outcome_of=ModelLifecycle.INFERENCE,
)
@attrs
class __POTParameter(BaseConfig.BasePOTParameter):
header = string_attribute("POT Parameters")
description = header
visible_in_ui = boolean_attribute(False)
stat_subset_size = configurable_integer(
header="Number of data samples",
description="Number of data samples used for post-training optimization",
default_value=300,
min_value=1,
max_value=1000,
)
preset = selectable(
default_value=POTQuantizationPreset.PERFORMANCE,
header="Preset",
description="Quantization preset that defines quantization scheme",
editable=False,
visible_in_ui=False,
)
@attrs
class __NNCFOptimization(BaseConfig.BaseNNCFOptimization):
header = string_attribute("Optimization by NNCF")
description = header
visible_in_ui = boolean_attribute(False)
enable_quantization = configurable_boolean(
default_value=True,
header="Enable quantization algorithm",
description="Enable quantization algorithm",
affects_outcome_of=ModelLifecycle.TRAINING,
)
enable_pruning = configurable_boolean(
default_value=False,
header="Enable filter pruning algorithm",
description="Enable filter pruning algorithm",
affects_outcome_of=ModelLifecycle.TRAINING,
)
pruning_supported = configurable_boolean(
default_value=False,
header="Whether filter pruning is supported",
description="Whether filter pruning is supported",
affects_outcome_of=ModelLifecycle.TRAINING,
)
maximal_accuracy_degradation = configurable_float(
default_value=1.0,
min_value=0.0,
max_value=100.0,
header="Maximum accuracy degradation",
description="The maximal allowed accuracy metric drop",
affects_outcome_of=ModelLifecycle.TRAINING,
)
learning_parameters = add_parameter_group(__LearningParameters)
postprocessing = add_parameter_group(__Postprocessing)
algo_backend = add_parameter_group(__AlgoBackend)
nncf_optimization = add_parameter_group(__NNCFOptimization)
pot_parameters = add_parameter_group(__POTParameter)
