"""Sample Code of otx training for detection."""
# Copyright (C) 2021-2022 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.
import argparse
import sys
from random import randint
import numpy as np
from otx.algorithms.common.utils import get_task_class
from otx.api.configuration.helper import create
from otx.api.entities.annotation import (
Annotation,
AnnotationSceneEntity,
AnnotationSceneKind,
NullAnnotationSceneEntity,
)
from otx.api.entities.dataset_item import DatasetItemEntity
from otx.api.entities.datasets import DatasetEntity
from otx.api.entities.image import Image
from otx.api.entities.inference_parameters import InferenceParameters
from otx.api.entities.label import Domain, LabelEntity
from otx.api.entities.label_schema import LabelSchemaEntity
from otx.api.entities.model import ModelEntity
from otx.api.entities.model_template import parse_model_template
from otx.api.entities.optimization_parameters import OptimizationParameters
from otx.api.entities.resultset import ResultSetEntity
from otx.api.entities.scored_label import ScoredLabel
from otx.api.entities.shapes.rectangle import Rectangle
from otx.api.entities.subset import Subset
from otx.api.entities.task_environment import TaskEnvironment
from otx.api.usecases.tasks.interfaces.export_interface import ExportType
from otx.api.usecases.tasks.interfaces.optimization_interface import OptimizationType
from otx.utils.logger import get_logger
logger = get_logger()
[docs]
def parse_args():
"""Parse function for getting model template & check export."""
parser = argparse.ArgumentParser(description="Sample showcasing the new API")
parser.add_argument("template_file_path", help="path to template file")
parser.add_argument("--export", action="store_true")
return parser.parse_args()
colors = dict(red=(255, 0, 0), green=(0, 255, 0))
[docs]
def load_test_dataset():
"""Load Sample dataset for detection."""
def gen_image(resolution, x1, y1, x2, y2, color):
width, height = resolution
image = np.full([height, width, 3], fill_value=255, dtype=np.uint8)
image[int(y1 * height) : int(y2 * height), int(x1 * width) : int(x2 * width), :] = np.full(
[int(height * y2) - int(height * y1), int(width * x2) - int(width * x1), 3],
fill_value=colors[color],
dtype=np.uint8,
)
return (image, Rectangle(x1=x1, y1=y1, x2=x2, y2=y2))
labels = [
LabelEntity(name="red", domain=Domain.DETECTION, id=0), # OLD class
LabelEntity(name="green", domain=Domain.DETECTION, id=1),
]
def get_image(subset, label_id):
def get_randcoord():
# disable B311 random - used for the random sampling not for security/crypto
x1 = randint(0, 9) # nosec B311
y1 = randint(0, 9) # nosec B311
x2 = min(x1 + 2, 10)
y2 = min(y1 + 2, 10)
coord = (x1 / 10, y1 / 10, x2 / 10, y2 / 10)
return coord
coord = get_randcoord()
image, bbox = gen_image((640, 480), *coord, labels[label_id].name)
if subset != Subset.UNLABELED:
return DatasetItemEntity(
media=Image(data=image),
annotation_scene=AnnotationSceneEntity(
annotations=[Annotation(bbox, labels=[ScoredLabel(label=labels[label_id])])],
kind=AnnotationSceneKind.ANNOTATION,
),
subset=subset,
)
return DatasetItemEntity(
media=Image(data=image),
annotation_scene=NullAnnotationSceneEntity(),
subset=subset,
)
train = [get_image(Subset.TRAINING, 0) for i in range(10)]
train += [get_image(Subset.TRAINING, 1) for i in range(10)]
val = [get_image(Subset.VALIDATION, 0) for i in range(2)]
val += [get_image(Subset.VALIDATION, 1) for i in range(2)]
val += [get_image(Subset.TESTING, 0) for i in range(2)]
val += [get_image(Subset.TESTING, 1) for i in range(2)]
unlabeled = [get_image(Subset.UNLABELED, 0) for i in range(100)]
unlabeled += [get_image(Subset.UNLABELED, 1) for i in range(100)]
return DatasetEntity(train + val + unlabeled), labels
# pylint: disable=too-many-locals, too-many-statements
[docs]
def main(args):
"""Main function of Detection Sample."""
logger.info("[Semi-SL] Train model with unlabeled dataset")
dataset, labels_list = load_test_dataset()
labels_schema = LabelSchemaEntity.from_labels(labels_list)
logger.info(f"Train dataset: {len(dataset.get_subset(Subset.TRAINING))} items")
logger.info(f"Validation dataset: {len(dataset.get_subset(Subset.VALIDATION))} items")
logger.info("Load model template")
model_template = parse_model_template(args.template_file_path)
logger.info("Set hyperparameters")
params = create(model_template.hyper_parameters.data)
params.learning_parameters.num_iters = 5
params.learning_parameters.learning_rate = 0.01
params.learning_parameters.learning_rate_warmup_iters = 1
params.learning_parameters.batch_size = 4
logger.info("Setup environment")
environment = TaskEnvironment(
model=None,
hyper_parameters=params,
label_schema=labels_schema,
model_template=model_template,
)
logger.info("Create base Task")
task_impl_path = model_template.entrypoints.base
task_cls = get_task_class(task_impl_path)
task = task_cls(task_environment=environment)
logger.info("Train model")
output_model = ModelEntity(
dataset,
environment.get_model_configuration(),
)
task.train(dataset, output_model)
logger.info("Get predictions on the validation set")
validation_dataset = dataset.get_subset(Subset.VALIDATION)
predicted_validation_dataset = task.infer(
validation_dataset.with_empty_annotations(),
InferenceParameters(is_evaluation=False),
)
resultset = ResultSetEntity(
model=output_model,
ground_truth_dataset=validation_dataset,
prediction_dataset=predicted_validation_dataset,
)
logger.info("Estimate quality on validation set")
task.evaluate(resultset)
logger.info(str(resultset.performance))
if args.export:
logger.info("Export model")
exported_model = ModelEntity(
dataset,
environment.get_model_configuration(),
)
task.export(ExportType.OPENVINO, exported_model)
logger.info("Create OpenVINO Task")
environment.model = exported_model
openvino_task_impl_path = model_template.entrypoints.openvino
openvino_task_cls = get_task_class(openvino_task_impl_path)
openvino_task = openvino_task_cls(environment)
logger.info("Get predictions on the validation set")
predicted_validation_dataset = openvino_task.infer(
validation_dataset.with_empty_annotations(),
InferenceParameters(is_evaluation=True),
)
resultset = ResultSetEntity(
model=output_model,
ground_truth_dataset=validation_dataset,
prediction_dataset=predicted_validation_dataset,
)
logger.info("Estimate quality on validation set")
openvino_task.evaluate(resultset)
logger.info(str(resultset.performance))
logger.info("Run POT optimization")
optimized_model = ModelEntity(
dataset,
environment.get_model_configuration(),
)
openvino_task.optimize(
OptimizationType.POT,
dataset.get_subset(Subset.TRAINING),
optimized_model,
OptimizationParameters(),
)
logger.info("Get predictions on the validation set")
predicted_validation_dataset = openvino_task.infer(
validation_dataset.with_empty_annotations(),
InferenceParameters(is_evaluation=True),
)
resultset = ResultSetEntity(
model=optimized_model,
ground_truth_dataset=validation_dataset,
prediction_dataset=predicted_validation_dataset,
)
logger.info("Performance of optimized model:")
openvino_task.evaluate(resultset)
logger.info(str(resultset.performance))
logger.info("Running the NNCF optimization")
environment.model = output_model
nncf_task_impl_path = model_template.entrypoints.nncf
nncf_task_cls = get_task_class(nncf_task_impl_path)
nncf_task = nncf_task_cls(environment)
optimized_model = ModelEntity(
dataset,
configuration=environment.get_model_configuration(),
)
nncf_task.optimize(OptimizationType.NNCF, dataset, optimized_model)
logger.info("Inferring the optimised model on the validation set.")
predicted_validation_dataset = nncf_task.infer(
validation_dataset.with_empty_annotations(),
InferenceParameters(is_evaluation=True),
)
resultset = ResultSetEntity(
model=optimized_model,
ground_truth_dataset=validation_dataset,
prediction_dataset=predicted_validation_dataset,
)
logger.info("Evaluating the optimized model on the validation set.")
nncf_task.evaluate(resultset)
logger.info(str(resultset.performance))
logger.info("Exporting the model.")
exported_model = ModelEntity(
train_dataset=dataset,
configuration=environment.get_model_configuration(),
)
nncf_task.export(ExportType.OPENVINO, exported_model)
environment.model = exported_model
logger.info("Creating the OpenVINO Task.")
environment.model = exported_model
openvino_task_impl_path = model_template.entrypoints.openvino
nncf_openvino_task_cls = get_task_class(openvino_task_impl_path)
nncf_openvino_task = nncf_openvino_task_cls(environment)
logger.info("Inferring the exported model on the validation set.")
predicted_validation_dataset = nncf_openvino_task.infer(
validation_dataset.with_empty_annotations(),
InferenceParameters(is_evaluation=True),
)
logger.info("Evaluating the exported model on the validation set.")
resultset = ResultSetEntity(
model=exported_model,
ground_truth_dataset=validation_dataset,
prediction_dataset=predicted_validation_dataset,
)
nncf_openvino_task.evaluate(resultset)
logger.info(str(resultset.performance))
if __name__ == "__main__":
sys.exit(main(parse_args()) or 0)
