"""Configurable parameters for STFPM anomaly base task."""
# 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.anomaly.configs.base.configuration import BaseAnomalyConfig
from otx.algorithms.anomaly.configs.base.configuration_enums import (
EarlyStoppingMetrics,
ModelBackbone,
)
from otx.api.configuration.elements import (
ParameterGroup,
add_parameter_group,
configurable_float,
configurable_integer,
selectable,
string_attribute,
)
from otx.api.configuration.model_lifecycle import ModelLifecycle
[docs]
@attrs
class STFPMAnomalyBaseConfig(BaseAnomalyConfig):
"""Configurable parameters for STFPM anomaly base task."""
header = string_attribute("Configuration for STFPM")
description = header
[docs]
@attrs
class LearningParameters(BaseAnomalyConfig.LearningParameters):
"""Parameters that can be tuned using HPO."""
lr = configurable_float(
default_value=0.4,
header="Learning Rate",
min_value=1e-3,
max_value=1,
description="Learning rate used for optimizing the Student network.",
)
momentum = configurable_float(
default_value=0.9,
header="Momentum",
min_value=0.1,
max_value=1.0,
description="Momentum used for SGD optimizer",
)
weight_decay = configurable_float(
default_value=0.0001,
header="Weight Decay",
min_value=1e-5,
max_value=1,
description="Decay for SGD optimizer",
)
backbone = selectable(
default_value=ModelBackbone.RESNET18,
header="Model Backbone",
description="Pre-trained backbone used for feature extraction",
)
[docs]
@attrs
class EarlyStoppingParameters(ParameterGroup):
"""Early stopping parameters."""
header = string_attribute("Early Stopping Parameters")
description = header
metric = selectable(
default_value=EarlyStoppingMetrics.IMAGE_F1,
header="Early Stopping Metric",
description="The metric used to determine if the model should stop training",
)
patience = configurable_integer(
default_value=10,
min_value=1,
max_value=100,
header="Early Stopping Patience",
description="Number of epochs to wait for an improvement in the monitored metric. If the metric has "
"not improved for this many epochs, the training will stop and the best model will be "
"returned.",
warning="Setting this value too low might lead to underfitting. Setting the value too high will "
"increase the training time and might lead to overfitting.",
affects_outcome_of=ModelLifecycle.TRAINING,
)
early_stopping = add_parameter_group(EarlyStoppingParameters)
max_epochs = configurable_integer(
default_value=100,
header="Max Epochs",
min_value=1,
max_value=500,
description="Maximum number of epochs to train the model for.",
warning="Training for very few epochs might lead to poor performance. If Early Stopping is enabled then "
"increasing the value of max epochs might not lead to desired result.",
affects_outcome_of=ModelLifecycle.TRAINING,
)
learning_parameters = add_parameter_group(LearningParameters)
