# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
from random import Random
from typing import Any
from azureml.core import ScriptRunConfig
from azureml.train.hyperdrive import BanditPolicy, HyperDriveConfig, PrimaryMetricGoal, RandomParameterSampling, uniform
import pandas as pd
from InnerEye.Common.fixed_paths_for_tests import full_ml_test_data_path
from InnerEye.Common.metrics_constants import TrackedMetrics
from InnerEye.ML.config import PhotometricNormalizationMethod, SegmentationModelBase, equally_weighted_classes
from InnerEye.ML.utils.model_metadata_util import generate_random_colours_list
from InnerEye.ML.utils.split_dataset import DatasetSplits
[docs]class HelloWorld(SegmentationModelBase):
"""
This is a very basic model that is pre-configured to train on the CPU for 2 epochs on a dummy dataset
../Tests/ML/test_data/dataset.csv
The aim of this config is to demonstrate how to:
1) Subclass SegmentationModelBase which is the base config for all segmentation model configs
2) Configure the UNet3D implemented in this package
3) Configure Azure HyperDrive based parameter search
* This model can be trained from the commandline: python InnerEye/runner.py --model=HelloWorld
* If you want to test that your AzureML workspace is working, please follow the instructions in
<repo_root>/docs/hello_world_model.md.
In this example, the model is trained on 1 input image channels "channel1", and
predicts 2 foreground classes region, region_1.
"""
def __init__(self, **kwargs: Any) -> None:
fg_classes = ["region", "region_1"]
image_channels = kwargs.pop("image_channels", ["channel1"])
super().__init__(
# Data definition - in this section we define where to load the dataset from
local_dataset=full_ml_test_data_path(),
azure_dataset_id="hello_world",
# Model definition - in this section we define what model to use and some related configurations
architecture="UNet3D",
feature_channels=[4],
crop_size=(64, 64, 64),
image_channels=image_channels,
ground_truth_ids=fg_classes,
class_weights=equally_weighted_classes(fg_classes, background_weight=0.02),
mask_id="mask",
# Model training and testing - in this section we define configurations pertaining to the model
# training loop (ie: batch size, how many epochs to train, number of epochs to save)
# and testing (ie: how many epochs to test)
num_dataload_workers=0,
train_batch_size=2,
num_epochs=2,
use_mixed_precision=True,
# Pre-processing - in this section we define how to normalize our inputs, in this case we are doing
# CT Level and Window based normalization.
norm_method=PhotometricNormalizationMethod.CtWindow,
level=50,
window=200,
# Post-processing - in this section we define our post processing configurations, in this case
# we are filling holes in the generated segmentation masks for all of the foreground classes.
fill_holes=[True] * len(fg_classes),
roi_interpreted_types=["ORGAN"] * len(fg_classes),
# Output - in this section we define settings that determine how our output looks like in this case
# we define the structure names and colours to use.
ground_truth_ids_display_names=fg_classes,
colours=generate_random_colours_list(Random(5), len(fg_classes)),
# The HelloWorld model uses dummy data with overlapping segmentation labels
check_exclusive=False
)
self.add_and_validate(kwargs)
[docs] def get_model_train_test_dataset_splits(self, dataset_df: pd.DataFrame) -> DatasetSplits:
return DatasetSplits.from_subject_ids(
df=dataset_df,
train_ids=['1', '2', '3'],
val_ids=['4', '5'],
test_ids=['6'],
)
[docs] def get_parameter_search_hyperdrive_config(self, run_config: ScriptRunConfig) -> HyperDriveConfig:
"""
Specify an Azure HyperDrive configuration.
Further details are described in the tutorial
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters
A reference is provided at https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train
.hyperdrive?view=azure-ml-py
:param run_config: The configuration for running an individual experiment.
:return: An Azure HyperDrive run configuration (configured PyTorch environment).
"""
parameter_space = {
'l_rate': uniform(0.0005, 0.01)
}
param_sampling = RandomParameterSampling(parameter_space)
# early terminate poorly performing runs
early_termination_policy = BanditPolicy(slack_factor=0.15, evaluation_interval=1, delay_evaluation=10)
return HyperDriveConfig(
run_config=run_config,
hyperparameter_sampling=param_sampling,
policy=early_termination_policy,
primary_metric_name=TrackedMetrics.Val_Loss.value,
primary_metric_goal=PrimaryMetricGoal.MINIMIZE,
max_total_runs=10,
max_concurrent_runs=2
)