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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: ml_samples_automl_image.py
DESCRIPTION:
These samples demonstrate how to use AutoML Image functions
USAGE:
python ml_samples_automl_image.py
"""
class AutoMLImageSamples(object):
def automl_image_jobs(self):
# [START automl.image_classification]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ClassificationMultilabelPrimaryMetrics
image_classification_job = automl.image_classification(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="label",
primary_metric=ClassificationMultilabelPrimaryMetrics.ACCURACY,
tags={"my_custom_tag": "My custom value"},
)
# [END automl.image_classification]
# [START automl.image_classification_multilabel]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ClassificationMultilabelPrimaryMetrics
image_classification_multilabel_job = automl.image_classification_multilabel(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="label",
primary_metric=ClassificationMultilabelPrimaryMetrics.IOU,
tags={"my_custom_tag": "My custom value"},
)
# [END automl.image_classification_multilabel]
# [START automl.image_object_detection]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ObjectDetectionPrimaryMetrics
image_object_detection_job = automl.image_object_detection(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="label",
primary_metric=ObjectDetectionPrimaryMetrics.MEAN_AVERAGE_PRECISION,
tags={"my_custom_tag": "My custom value"},
)
# [END automl.image_object_detection]
# [START automl.image_instance_segmentation]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import InstanceSegmentationPrimaryMetrics
image_instance_segmentation_job = automl.image_instance_segmentation(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="label",
primary_metric=InstanceSegmentationPrimaryMetrics.MEAN_AVERAGE_PRECISION,
tags={"my_custom_tag": "My custom value"},
)
# [END automl.image_instance_segmentation]
# [START automl.automl_image_job.image_classification_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ClassificationMultilabelPrimaryMetrics
image_classification_job = automl.ImageClassificationJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="label",
primary_metric=ClassificationMultilabelPrimaryMetrics.ACCURACY,
tags={"my_custom_tag": "My custom value"},
)
# [END automl.automl_image_job.image_classification_job]
# [START automl.automl_image_job.image_classification_multilabel_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ClassificationMultilabelPrimaryMetrics
image_classification_multilabel_job = automl.ImageClassificationMultilabelJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="terms",
primary_metric=ClassificationMultilabelPrimaryMetrics.IOU,
tags={"my_custom_tag": "My custom value"},
)
# [END automl.automl_image_job.image_classification_multilabel_job]
# [START automl.automl_image_job.image_object_detection_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ObjectDetectionPrimaryMetrics
image_object_detection_job = automl.ImageObjectDetectionJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
tags={"my_custom_tag": "My custom value"},
primary_metric=ObjectDetectionPrimaryMetrics.MEAN_AVERAGE_PRECISION,
)
# [END automl.automl_image_job.image_object_detection_job]
# [START automl.automl_image_job.image_instance_segmentation_job]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
from azure.ai.ml.automl import ObjectDetectionPrimaryMetrics
image_instance_segmentation_job = automl.ImageInstanceSegmentationJob(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
tags={"my_custom_tag": "My custom value"},
primary_metric=ObjectDetectionPrimaryMetrics.MEAN_AVERAGE_PRECISION,
)
# [END automl.automl_image_job.image_instance_segmentation_job]
# [START automl.automl_image_job.image_sweep_settings]
from azure.ai.ml import automl
from azure.ai.ml.sweep import BanditPolicy
image_sweep_settings = automl.ImageSweepSettings(
sampling_algorithm="Grid",
early_termination=BanditPolicy(evaluation_interval=2, slack_factor=0.05, delay_evaluation=6),
)
# [END automl.automl_image_job.image_sweep_settings]
# [START automl.automl_image_job.image_classification_search_space]
from azure.ai.ml import automl
from azure.ai.ml.sweep import Uniform, Choice
image_classification_search_space = automl.ImageClassificationSearchSpace(
model_name="vitb16r224",
number_of_epochs=Choice([15, 30]),
weight_decay=Uniform(0.01, 0.1),
)
# [END automl.automl_image_job.image_classification_search_space]
# [START automl.automl_image_job.image_object_detection_search_space]
from azure.ai.ml import automl
from azure.ai.ml.sweep import Uniform
image_detection_search_space = automl.ImageObjectDetectionSearchSpace(
learning_rate=Uniform(0.005, 0.05),
model_name="yolov5",
weight_decay=Uniform(0.01, 0.1),
)
# [END automl.automl_image_job.image_object_detection_search_space]
# [START automl.automl_image_job.image_limit_settings]
from azure.ai.ml import automl, Input
from azure.ai.ml.constants import AssetTypes
# Create the AutoML job with the related factory-function.
image_job = automl.image_instance_segmentation(
experiment_name="my_experiment",
compute="my_compute",
training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
target_column_name="label",
primary_metric="MeanAveragePrecision",
tags={"my_custom_tag": "custom value"},
)
# Set the limits for the AutoML job.
image_job.set_limits(
max_trials=10,
max_concurrent_trials=2,
)
# [END automl.automl_image_job.image_limit_settings]
# [START automl.automl_image_job.image_classification_model_settings]
from azure.ai.ml import automl
image_classification_model_settings = automl.ImageModelSettingsClassification(
checkpoint_frequency=5,
early_stopping=False,
gradient_accumulation_step=2,
)
# [END automl.automl_image_job.image_classification_model_settings]
# [START automl.automl_image_job.image_object_detection_model_settings]
from azure.ai.ml import automl
object_detection_model_settings = automl.ImageModelSettingsObjectDetection(min_size=600, max_size=1333)
# [END automl.automl_image_job.image_object_detection_model_settings]
if __name__ == "__main__":
sample = AutoMLImageSamples()
sample.automl_image_jobs()
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