def download_checkpoints_from_run(
azure_config: AzureConfig,
config: ModelConfigBase,
run: Run,
output_subdir_name: Optional[str] = None) -> RunRecovery:
"""
Downloads checkpoints of the provided run or, if applicable, its children.
:param azure_config: Azure related configs.
:param config: Model related configs.
:param run: Run whose checkpoints should be recovered
:return: run recovery information
"""
child_runs: List[Run] = fetch_child_runs(run)
logging.debug(f"Run has ID {run.id} and initial child runs are:")
for child_run in child_runs:
logging.debug(f" {child_run.id}")
checkpoint_subdir_name: Optional[str]
if output_subdir_name:
# From e.g. parent_dir/checkpoints we want parent_dir/output_subdir_name, to which we will
# append split_index / checkpoints below to create child_dst.
checkpoint_path = Path(config.checkpoint_folder)
parent_path = checkpoint_path.parent
checkpoint_subdir_name = checkpoint_path.name
root_output_dir = parent_path / output_subdir_name
else:
root_output_dir = Path(config.checkpoint_folder) / run.id
checkpoint_subdir_name = None
# download checkpoints for the run
download_outputs_from_run(blobs_path=Path(CHECKPOINT_FOLDER),
destination=root_output_dir,
run=run)
if len(child_runs) > 0:
tag_to_use = 'cross_validation_split_index'
can_use_split_indices = tag_values_all_distinct(
child_runs, tag_to_use)
# download checkpoints for the child runs in the root of the parent
child_runs_checkpoints_roots: List[Path] = []
for child in child_runs:
if child.id == RUN_CONTEXT.id:
# We expect to find the file(s) we need in config.checkpoint_folder
child_dst = Path(config.checkpoint_folder)
else:
subdir = str(child.tags[tag_to_use]
if can_use_split_indices else child.number)
if checkpoint_subdir_name:
child_dst = root_output_dir / subdir / checkpoint_subdir_name
else:
child_dst = root_output_dir / subdir
download_outputs_from_run(
blobs_path=Path(CHECKPOINT_FOLDER),
destination=child_dst,
run=child)
child_runs_checkpoints_roots.append(child_dst)
return RunRecovery(checkpoints_roots=child_runs_checkpoints_roots)
else:
return RunRecovery(checkpoints_roots=[root_output_dir])
def get_comparison_baselines(outputs_folder: Path, azure_config: AzureConfig,
comparison_blob_storage_paths: List[Tuple[str, str]]) -> \
List[ComparisonBaseline]:
workspace = azure_config.get_workspace()
comparison_baselines = []
for (comparison_name, comparison_path) in comparison_blob_storage_paths:
# Discard the experiment part of the run rec ID, if any.
comparison_path = comparison_path.split(":")[-1]
run_rec_id, blob_path_str = comparison_path.split("/", 1)
run_rec_id = strip_prefix(run_rec_id, AZUREML_RUN_FOLDER_PREFIX)
blob_path = Path(strip_prefix(blob_path_str, DEFAULT_AML_UPLOAD_DIR + "/"))
run = fetch_run(workspace, run_rec_id)
# We usually find dataset.csv in the same directory as metrics.csv, but we sometimes
# have to look higher up.
comparison_dataset_path: Optional[Path] = None
comparison_metrics_path: Optional[Path] = None
destination_folder = outputs_folder / run_rec_id / blob_path
# Look for dataset.csv inside epoch_NNN/Test, epoch_NNN/ and at top level
for blob_path_parent in step_up_directories(blob_path):
try:
comparison_dataset_path = download_outputs_from_run(
blob_path_parent / DATASET_CSV_FILE_NAME, destination_folder, run, True)
break
except ValueError:
logging.warning(f"cannot find {DATASET_CSV_FILE_NAME} at {blob_path_parent} in {run_rec_id}")
pass
except NotADirectoryError:
logging.warning(f"{blob_path_parent} is not a directory")
break
if comparison_dataset_path is None:
logging.warning(f"cannot find {DATASET_CSV_FILE_NAME} at or above {blob_path} in {run_rec_id}")
# Look for epoch_NNN/Test/metrics.csv
try:
comparison_metrics_path = download_outputs_from_run(
blob_path / METRICS_FILE_NAME, destination_folder, run, True)
except ValueError:
logging.warning(f"cannot find {METRICS_FILE_NAME} at {blob_path} in {run_rec_id}")
# If both dataset.csv and metrics.csv were downloaded successfully, read their contents and
# add a tuple to the comparison data.
if comparison_dataset_path is not None and comparison_metrics_path is not None and \
comparison_dataset_path.exists() and comparison_metrics_path.exists():
comparison_baselines.append(ComparisonBaseline(
comparison_name,
pd.read_csv(comparison_dataset_path),
pd.read_csv(comparison_metrics_path),
run_rec_id))
else:
logging.warning(f"could not find comparison data for run {run_rec_id}")
for key, path in ("dataset", comparison_dataset_path), ("metrics", comparison_metrics_path):
logging.warning(f"path to {key} data is {path}")
# noinspection PyUnresolvedReferences
if path is not None and not path.exists():
logging.warning(" ... but it does not exist")
return comparison_baselines
def download_or_get_local_file(self,
run: Optional[Run],
blob_to_download: PathOrString,
destination: Path,
local_src_subdir: Optional[Path] = None) -> Optional[Path]:
"""
Downloads a file from the results folder of an AzureML run, or copies it from a local results folder.
Returns the path to the downloaded file if it exists, or None if the file was not found.
If the blobs_path contains folders, the same folder structure will be created inside the destination folder.
For example, downloading "foo.txt" to "/c/temp" will create "/c/temp/foo.txt". Downloading "foo/bar.txt"
to "/c/temp" will create "/c/temp/foo/bar.txt"
:param blob_to_download: path of data to download within the run
:param destination: directory to write to
:param run: The AzureML run to download from.
:param local_src_subdir: if not None, then if we copy from a local results folder, that folder is
self.outputs_directory/local_src_subdir/blob_to_download instead of self.outputs_directory/blob_to_download
:return: The path to the downloaded file, or None if the file was not found.
"""
blob_path = Path(blob_to_download)
blob_parent = blob_path.parent
if blob_parent != Path("."):
destination = destination / blob_parent
downloaded_file = destination / blob_path.name
# If we've already downloaded the data, leave it as it is
if downloaded_file.exists():
logging.info(f"Download of '{blob_path}' to '{downloaded_file}: not needed, already exists'")
return downloaded_file
logging.info(f"Download of '{blob_path}' to '{downloaded_file}': proceeding")
# If the provided run is the current run, then there is nothing to download.
# Just copy the provided path in the outputs directory to the destination.
if not destination.exists():
destination.mkdir(parents=True)
if run is None or Run.get_context().id == run.id or is_parent_run(run) or is_offline_run_context(run):
if run is None:
assert self.local_run_results is not None, "Local run results must be set in unit testing"
local_src = Path(self.local_run_results)
if self.local_run_result_split_suffix:
local_src = local_src / self.local_run_result_split_suffix
else:
local_src = Path(self.outputs_directory)
if local_src_subdir is not None:
local_src = local_src / local_src_subdir
local_src = local_src / blob_path
if local_src.exists():
logging.info(f"Copying files from {local_src} to {destination}")
return Path(shutil.copy(local_src, destination))
return None
else:
try:
return download_outputs_from_run(
blobs_path=blob_path,
destination=destination,
run=run,
is_file=True
)
except Exception as ex:
logging.warning(f"File {blob_to_download} not found in output of run {run.id}: {ex}")
return None
def download_checkpoints_from_run(config: DeepLearningConfig,
run: Run) -> RunRecovery:
"""
Downloads checkpoints of the provided run or, if applicable, its children.
When downloading from a run that does not have sibling runs, a single folder inside the checkpoints folder
will be created that contains the downloaded checkpoints.
When downloading from a run that has sibling runs, the checkpoints for the sibling runs will go into
folder 'OTHER_RUNS/<cross_validation_split>'
:param config: Model related configs.
:param run: Run whose checkpoints should be recovered
:return: run recovery information
"""
# TODO antonsc: Clarify how we handle the case of multiple checkpoint being downloaded.
child_runs: List[Run] = fetch_child_runs(run)
if child_runs:
logging.info(f"Run has ID {run.id}, child runs: {', '.join(c.id for c in child_runs)}")
tag_to_use = 'cross_validation_split_index'
can_use_split_indices = tag_values_all_distinct(child_runs, tag_to_use)
# download checkpoints for the child runs in the root of the parent
child_runs_checkpoints_roots: List[Path] = []
for child in child_runs:
if child.id == RUN_CONTEXT.id:
# We expect to find the file(s) we need in config.checkpoint_folder
child_dst = config.checkpoint_folder
else:
subdir = str(child.tags[tag_to_use] if can_use_split_indices else child.number)
child_dst = config.checkpoint_folder / OTHER_RUNS_SUBDIR_NAME / subdir
download_outputs_from_run(
blobs_path=Path(CHECKPOINT_FOLDER),
destination=child_dst,
run=child
)
child_runs_checkpoints_roots.append(child_dst)
return RunRecovery(checkpoints_roots=child_runs_checkpoints_roots)
else:
logging.info(f"Run with ID {run.id} has no child runs")
root_output_dir = config.checkpoint_folder / run.id
# download checkpoints for the run
download_outputs_from_run(
blobs_path=Path(CHECKPOINT_FOLDER),
destination=root_output_dir,
run=run
)
return RunRecovery(checkpoints_roots=[root_output_dir])
def download_all_checkpoints_from_run(config: DeepLearningConfig,
run: Run) -> RunRecovery:
"""
Downloads all checkpoints of the provided run: The best checkpoint and the recovery checkpoint.
A single folder inside the checkpoints folder will be created that contains the downloaded checkpoints.
:param config: Model related configs.
:param run: Run whose checkpoints should be recovered
:return: run recovery information
"""
if fetch_child_runs(run):
raise ValueError(
f"AzureML run {run.id} has child runs, this method does not support those."
)
root_output_dir = config.checkpoint_folder / run.id
download_outputs_from_run(blobs_path=Path(CHECKPOINT_FOLDER),
destination=root_output_dir,
run=run)
return RunRecovery(checkpoints_roots=[root_output_dir])
def download_best_checkpoints_from_child_runs(config: DeepLearningConfig,
run: Run) -> RunRecovery:
"""
Downloads the best checkpoints from all child runs of the provided Hyperdrive parent run.
The checkpoints for the sibling runs will go into folder 'OTHER_RUNS/<cross_validation_split>'
in the checkpoint folder. There is special treatment for the child run that is equal to the present AzureML
run, its checkpoints will be read off the checkpoint folder as-is.
:param config: Model related configs.
:param run: The Hyperdrive parent run to download from.
:return: run recovery information
"""
child_runs: List[Run] = fetch_child_runs(run)
if not child_runs:
raise ValueError(
f"AzureML run {run.id} does not have any child runs.")
logging.info(
f"Run {run.id} has {len(child_runs)} child runs: {', '.join(c.id for c in child_runs)}"
)
tag_to_use = 'cross_validation_split_index'
can_use_split_indices = tag_values_all_distinct(child_runs, tag_to_use)
# download checkpoints for the child runs in the root of the parent
child_runs_checkpoints_roots: List[Path] = []
for child in child_runs:
if child.id == RUN_CONTEXT.id:
# We expect to find the file(s) we need in config.checkpoint_folder
child_dst = config.checkpoint_folder
else:
subdir = str(child.tags[tag_to_use]
if can_use_split_indices else child.number)
child_dst = config.checkpoint_folder / OTHER_RUNS_SUBDIR_NAME / subdir
download_outputs_from_run(
blobs_path=Path(CHECKPOINT_FOLDER) /
BEST_CHECKPOINT_FILE_NAME_WITH_SUFFIX,
destination=child_dst,
run=child,
is_file=True)
child_runs_checkpoints_roots.append(child_dst)
return RunRecovery(checkpoints_roots=child_runs_checkpoints_roots)
def download_all_checkpoints_from_run(
config: OutputParams,
run: Run,
subfolder: Optional[str] = None) -> RunRecovery:
"""
Downloads all checkpoints of the provided run inside the checkpoints folder.
:param config: Model related configs.
:param run: Run whose checkpoints should be recovered
:param subfolder: optional subfolder name, if provided the checkpoints will be downloaded to
CHECKPOINT_FOLDER / subfolder. If None, the checkpoint are downloaded to CHECKPOINT_FOLDER of the current run.
:return: run recovery information
"""
if fetch_child_runs(run):
raise ValueError(
f"AzureML run {run.id} has child runs, this method does not support those."
)
destination_folder = config.checkpoint_folder / subfolder if subfolder else config.checkpoint_folder
download_outputs_from_run(blobs_path=Path(CHECKPOINT_FOLDER),
destination=destination_folder,
run=run)
time.sleep(60) # Needed because AML is not fast enough to download
return RunRecovery(checkpoints_roots=[destination_folder])
def download_pytest_result(
run: Run, destination_folder: Path = Path.cwd()) -> Path:
"""
Downloads the pytest result file that is stored in the output folder of the given AzureML run.
If there is no pytest result file, throw an Exception.
:param run: The run from which the files should be read.
:param destination_folder: The folder into which the PyTest result file is downloaded.
:return: The path (folder and filename) of the downloaded file.
"""
logging.info(f"Downloading pytest result file: {PYTEST_RESULTS_FILE}")
try:
return download_outputs_from_run(PYTEST_RESULTS_FILE,
destination=destination_folder,
run=run,
is_file=True)
except:
raise ValueError(
f"No pytest result file {PYTEST_RESULTS_FILE} was found for run {run.id}"
)
def load_predictions(run_type: SurfaceDistanceRunType,
azure_config: AzureConfig,
model_config: SegmentationModelBase,
execution_mode: ModelExecutionMode,
extended_annotators: List[str],
outlier_range: float) -> List[Segmentation]:
"""
For each run type (IOV or outliers), instantiate a list of predicted Segmentations and return
:param run_type: either "iov" or "outliers:
:param azure_config: AzureConfig
:param model_config: GenericConfig
:param execution_mode: ModelExecutionMode: Either Test, Train or Val
:param extended_annotators: List of annotators plus model_name to load segmentations for
:param outlier_range: The standard deviation from the mean which the points have to be below
to be considered an outlier.
:return: list of [(subject_id, structure name and dice_scores)]
"""
predictions = []
if run_type == SurfaceDistanceRunType.OUTLIERS:
first_child_run = sd_util.get_first_child_run(azure_config)
output_dir = sd_util.get_run_output_dir(azure_config, model_config)
metrics_path = sd_util.get_metrics_path(azure_config, model_config)
# Load the downloaded metrics CSV as dataframe and determine worst performing outliers for the Test run
df = load_csv(metrics_path, [
MetricsFileColumns.Patient.value,
MetricsFileColumns.Structure.value
])
test_run_df = df[df['mode'] == execution_mode.value]
worst_performers = get_worst_performing_outliers(
test_run_df,
outlier_range,
MetricsFileColumns.Dice.value,
max_n_outliers=-50)
for (subject_id, structure_name, dice_score, _) in worst_performers:
subject_prefix = sd_util.get_subject_prefix(
model_config, execution_mode, subject_id)
# if not already present, download data for subject
download_outputs_from_run(blobs_path=subject_prefix,
destination=output_dir,
run=first_child_run)
# check it has been downloaded
segmentation_path = output_dir / subject_prefix / f"{structure_name}.nii.gz"
predictions.append(
Segmentation(structure_name=structure_name,
subject_id=subject_id,
segmentation_path=segmentation_path,
dice_score=float(dice_score)))
elif run_type == SurfaceDistanceRunType.IOV:
subject_id = 0
iov_dir = Path("outputs") / SurfaceDistanceRunType.IOV.value.lower()
all_structs = model_config.class_and_index_with_background()
structs_to_plot = [
struct_name for struct_name in all_structs.keys()
if struct_name not in ['background', 'external']
]
for annotator in extended_annotators:
for struct_name in structs_to_plot:
segmentation_path = iov_dir / f"{struct_name + annotator}.nii.gz"
if not segmentation_path.is_file():
logging.warning(f"No such file {segmentation_path}")
continue
predictions.append(
Segmentation(structure_name=struct_name,
subject_id=subject_id,
segmentation_path=segmentation_path,
annotator=annotator))
return predictions
