"""Configuration file of OTX Visual Prompting."""
# Copyright (C) 2023 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, POTQuantizationPreset
from otx.api.configuration.elements import (
ParameterGroup,
add_parameter_group,
boolean_attribute,
configurable_boolean,
configurable_float,
configurable_integer,
selectable,
string_attribute,
)
from otx.api.configuration.model_lifecycle import ModelLifecycle
[docs]
@attrs
class VisualPromptingBaseConfig(BaseConfig):
"""Configurations of OTX Visual Prompting."""
header = string_attribute("Configuration for a visual prompting task of OTX")
description = header
@attrs
class __LearningParameters(BaseConfig.BaseLearningParameters):
header = string_attribute("Learning Parameters")
description = header
@attrs
class __AlgoBackend(BaseConfig.BaseAlgoBackendParameters):
header = string_attribute("Parameters for the OTX algo-backend")
description = header
@attrs
class __Postprocessing(ParameterGroup):
header = string_attribute("Postprocessing")
description = header
image_size = configurable_integer(
header="Image size",
description="The size of the input image to the model.",
default_value=1024,
min_value=0,
max_value=2048,
affects_outcome_of=ModelLifecycle.INFERENCE,
)
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,
)
embedded_processing = configurable_boolean(
default_value=True,
header="Embedded processing",
description="Flag that pre/postprocessing embedded.",
affects_outcome_of=ModelLifecycle.INFERENCE,
)
orig_width = configurable_integer(
header="Original width",
description="Model input width before embedding processing.",
default_value=64,
affects_outcome_of=ModelLifecycle.INFERENCE,
)
orig_height = configurable_integer(
header="Original height",
description="Model input height before embedding processing.",
default_value=64,
affects_outcome_of=ModelLifecycle.INFERENCE,
)
mask_threshold = configurable_float(
default_value=0.0,
header="Mask threshold",
description=(
"The threshold to apply to the raw logit 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,
)
downsizing = configurable_integer(
default_value=64,
header="The downsizing ratio",
description="The downsizing ratio of image encoder.",
min_value=1,
max_value=1024,
affects_outcome_of=ModelLifecycle.INFERENCE,
)
@attrs
class __POTParameter(BaseConfig.BasePOTParameter):
header = string_attribute("POT Parameters")
description = header
visible_in_ui = boolean_attribute(False)
preset = selectable(
default_value=POTQuantizationPreset.MIXED,
header="Preset",
description="Quantization preset that defines quantization scheme",
editable=True,
visible_in_ui=True,
)
learning_parameters = add_parameter_group(__LearningParameters)
algo_backend = add_parameter_group(__AlgoBackend)
postprocessing = add_parameter_group(__Postprocessing)
pot_parameters = add_parameter_group(__POTParameter)
