import json
import argparse
import copy
import joblib
import torch.backends.cudnn as cudnn
import nibabel as nib
import sys
import platform
import multiprocessing
import re
from ivadomed.loader.bids_dataframe import BidsDataframe
from ivadomed import evaluation as imed_evaluation
from ivadomed import config_manager as imed_config_manager
from ivadomed import testing as imed_testing
from ivadomed import training as imed_training
from ivadomed import transforms as imed_transforms
from ivadomed import utils as imed_utils
from ivadomed import metrics as imed_metrics
from ivadomed import inference as imed_inference
from ivadomed.loader import utils as imed_loader_utils, loader as imed_loader, film as imed_film
from ivadomed.keywords import ConfigKW, ModelParamsKW, LoaderParamsKW, ContrastParamsKW, BalanceSamplesKW, \
TrainingParamsKW, ObjectDetectionParamsKW, UncertaintyKW, PostprocessingKW, BinarizeProdictionKW, MetricsKW, \
MetadataKW, OptionKW, SplitDatasetKW
from loguru import logger
from pathlib import Path
cudnn.benchmark = True
# List of not-default available models i.e. different from Unet
MODEL_LIST = ['Modified3DUNet', 'HeMISUnet', 'FiLMedUnet', 'resnet18', 'densenet121', 'Countception']
def get_parser():
parser = argparse.ArgumentParser(add_help=False)
command_group = parser.add_mutually_exclusive_group(required=False)
command_group.add_argument("--train", dest='train', action='store_true',
help="Perform training on data.")
command_group.add_argument("--test", dest='test', action='store_true',
help="Perform testing on trained model.")
command_group.add_argument("--segment", dest='segment', action='store_true',
help="Perform segmentation on data.")
parser.add_argument("-c", "--config", required=True, type=str,
help="Path to configuration file.")
# OPTIONAL ARGUMENTS
optional_args = parser.add_argument_group('OPTIONAL ARGUMENTS')
optional_args.add_argument("-pd", "--path-data", dest="path_data", required=False, type=str,
nargs="*", help="""Path to data in BIDs format. You may list one
or more paths; separate each path with a space, e.g.
--path-data some/path/a some/path/b""")
optional_args.add_argument("-po", "--path-output", required=False, type=str, dest="path_output",
help="Path to output directory.")
optional_args.add_argument('-g', '--gif', required=False, type=int, default=0,
help='Number of GIF files to output. Each GIF file corresponds to a 2D slice showing the '
'prediction over epochs (one frame per epoch). The prediction is run on the '
'validation dataset. GIF files are saved in the output path.')
optional_args.add_argument('-t', '--thr-increment', dest="thr_increment", required=False, type=float,
help='A threshold analysis is performed at the end of the training using the trained '
'model and the training+validation sub-datasets to find the optimal binarization '
'threshold. The specified value indicates the increment between 0 and 1 used during '
'the analysis (e.g. 0.1). Plot is saved under "[PATH_OUTPUT]/thr.png" and the '
'optimal threshold in "[PATH_OUTPUT]/config_file.json as "binarize_prediction" '
'parameter.')
optional_args.add_argument('--resume-training', dest="resume_training", required=False, action='store_true',
help='Load a saved model ("checkpoint.pth.tar" in the output directory specified either with flag "--path-output" or via the config file "output_path" argument) '
'for resume training. This training state is saved everytime a new best model is saved in the output directory specified with flag "--path-output"')
optional_args.add_argument('--no-patch', dest="no_patch", action='store_true', required=False,
help='2D patches are not used while segmenting with models trained with patches '
'(command "--segment" only). The "--no-patch" argument supersedes the "--overlap-2D" argument. '
' This option may not be suitable with large images depending on computer RAM capacity.')
optional_args.add_argument('--overlap-2d', dest="overlap_2d", required=False, type=int, nargs="+",
help='Custom overlap for 2D patches while segmenting (command "--segment" only). '
'Example: "--overlap-2d 48 48" for an overlap of 48 pixels between patches in X and Y respectively. '
'Default model overlap is used otherwise.')
optional_args.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS,
help='Shows function documentation.')
return parser
def create_path_model(context, model_params, ds_train, path_output, train_onehotencoder):
path_model = Path(path_output, context[ConfigKW.MODEL_NAME])
if not path_model.is_dir():
logger.info(f'Creating model directory: {path_model}')
path_model.mkdir(parents=True)
if ModelParamsKW.FILM_LAYERS in model_params and any(model_params[ModelParamsKW.FILM_LAYERS]):
joblib.dump(train_onehotencoder, path_model.joinpath("one_hot_encoder.joblib"))
if MetadataKW.METADATA_DICT in ds_train[0][MetadataKW.INPUT_METADATA][0]:
metadata_dict = ds_train[0][MetadataKW.INPUT_METADATA][0][MetadataKW.METADATA_DICT]
joblib.dump(metadata_dict, path_model.joinpath("metadata_dict.joblib"))
else:
logger.info(f'Model directory already exists: {path_model}')
def check_multiple_raters(is_train, loader_params):
if any([isinstance(class_suffix, list) for class_suffix in loader_params[LoaderParamsKW.TARGET_SUFFIX]]):
logger.info(
"Annotations from multiple raters will be used during model training, one annotation from one rater "
"randomly selected at each iteration.\n")
if not is_train:
logger.error(
"Please provide only one annotation per class in 'target_suffix' when not training a model.\n")
sys.exit()
def film_normalize_data(context, model_params, ds_train, ds_valid, path_output):
# Normalize metadata before sending to the FiLM network
results = imed_film.get_film_metadata_models(ds_train=ds_train,
metadata_type=model_params[ModelParamsKW.METADATA],
debugging=context[ConfigKW.DEBUGGING])
ds_train, train_onehotencoder, metadata_clustering_models = results
ds_valid = imed_film.normalize_metadata(ds_valid, metadata_clustering_models, context[ConfigKW.DEBUGGING],
model_params[ModelParamsKW.METADATA])
model_params.update({ModelParamsKW.FILM_ONEHOTENCODER: train_onehotencoder,
ModelParamsKW.N_METADATA: len([ll for l in train_onehotencoder.categories_ for ll in l])})
joblib.dump(metadata_clustering_models, Path(path_output, "clustering_models.joblib"))
joblib.dump(train_onehotencoder, Path(path_output + "one_hot_encoder.joblib"))
return model_params, ds_train, ds_valid, train_onehotencoder
def get_dataset(bids_df, loader_params, data_lst, transform_params, cuda_available, device, ds_type):
ds = imed_loader.load_dataset(bids_df, **{**loader_params, **{'data_list': data_lst,
'transforms_params': transform_params,
'dataset_type': ds_type}}, device=device,
cuda_available=cuda_available)
return ds
def save_config_file(context, path_output):
# Save config file within path_output and path_output/model_name
# Done after the threshold_analysis to propate this info in the config files
with Path(path_output, "config_file.json").open(mode='w') as fp:
json.dump(context, fp, indent=4)
with Path(path_output, context[ConfigKW.MODEL_NAME], context[ConfigKW.MODEL_NAME] + ".json").open(mode='w') as fp:
json.dump(context, fp, indent=4)
def set_loader_params(context, is_train):
loader_params = copy.deepcopy(context[ConfigKW.LOADER_PARAMETERS])
if is_train:
loader_params[LoaderParamsKW.CONTRAST_PARAMS][ContrastParamsKW.CONTRAST_LST] = \
loader_params[LoaderParamsKW.CONTRAST_PARAMS][ContrastParamsKW.TRAINING_VALIDATION]
else:
loader_params[LoaderParamsKW.CONTRAST_PARAMS][ContrastParamsKW.CONTRAST_LST] =\
loader_params[LoaderParamsKW.CONTRAST_PARAMS][ContrastParamsKW.TESTING]
if ConfigKW.FILMED_UNET in context and context[ConfigKW.FILMED_UNET][ModelParamsKW.APPLIED]:
loader_params.update({LoaderParamsKW.METADATA_TYPE: context[ConfigKW.FILMED_UNET][ModelParamsKW.METADATA]})
# Load metadata necessary to balance the loader
if context[ConfigKW.TRAINING_PARAMETERS][TrainingParamsKW.BALANCE_SAMPLES][BalanceSamplesKW.APPLIED] and \
context[ConfigKW.TRAINING_PARAMETERS][TrainingParamsKW.BALANCE_SAMPLES][BalanceSamplesKW.TYPE] != 'gt':
loader_params.update({LoaderParamsKW.METADATA_TYPE:
context[ConfigKW.TRAINING_PARAMETERS][TrainingParamsKW.BALANCE_SAMPLES][BalanceSamplesKW.TYPE]})
return loader_params
def set_model_params(context, loader_params):
model_params = copy.deepcopy(context[ConfigKW.DEFAULT_MODEL])
model_params[ModelParamsKW.FOLDER_NAME] = copy.deepcopy(context[ConfigKW.MODEL_NAME])
model_context_list = [model_name for model_name in MODEL_LIST
if model_name in context and context[model_name][ModelParamsKW.APPLIED]]
if len(model_context_list) == 1:
model_params[ModelParamsKW.NAME] = model_context_list[0]
model_params.update(context[model_context_list[0]])
elif ConfigKW.MODIFIED_3D_UNET in model_context_list and ConfigKW.FILMED_UNET in model_context_list \
and len(model_context_list) == 2:
model_params[ModelParamsKW.NAME] = ConfigKW.MODIFIED_3D_UNET
for i in range(len(model_context_list)):
model_params.update(context[model_context_list[i]])
elif len(model_context_list) > 1:
logger.error(f'ERROR: Several models are selected in the configuration file: {model_context_list}.'
'Please select only one (i.e. only one where: "applied": true).')
exit()
model_params[ModelParamsKW.IS_2D] = False if ConfigKW.MODIFIED_3D_UNET in model_params[ModelParamsKW.NAME] \
else model_params[ModelParamsKW.IS_2D]
# Get in_channel from contrast_lst
if loader_params[LoaderParamsKW.MULTICHANNEL]:
model_params[ModelParamsKW.IN_CHANNEL] = \
len(loader_params[LoaderParamsKW.CONTRAST_PARAMS][ContrastParamsKW.CONTRAST_LST])
else:
model_params[ModelParamsKW.IN_CHANNEL] = 1
# Get out_channel from target_suffix
model_params[ModelParamsKW.OUT_CHANNEL] = len(loader_params[LoaderParamsKW.TARGET_SUFFIX])
# If multi-class output, then add background class
if model_params[ModelParamsKW.OUT_CHANNEL] > 1:
model_params.update({ModelParamsKW.OUT_CHANNEL: model_params[ModelParamsKW.OUT_CHANNEL] + 1})
# Display for spec' check
imed_utils.display_selected_model_spec(params=model_params)
# Update loader params
if ConfigKW.OBJECT_DETECTION_PARAMS in context:
object_detection_params = context[ConfigKW.OBJECT_DETECTION_PARAMS]
object_detection_params.update({ObjectDetectionParamsKW.GPU_IDS: context[ConfigKW.GPU_IDS][0],
ObjectDetectionParamsKW.PATH_OUTPUT: context[ConfigKW.PATH_OUTPUT]})
loader_params.update({ConfigKW.OBJECT_DETECTION_PARAMS: object_detection_params})
loader_params.update({LoaderParamsKW.MODEL_PARAMS: model_params})
return model_params, loader_params
def set_output_path(context):
path_output = copy.deepcopy(context[ConfigKW.PATH_OUTPUT])
if not Path(path_output).is_dir():
logger.info(f'Creating output path: {path_output}')
Path(path_output).mkdir(parents=True)
else:
logger.info(f'Output path already exists: {path_output}')
return path_output
def update_film_model_params(context, ds_test, model_params, path_output):
clustering_path = Path(path_output, "clustering_models.joblib")
metadata_clustering_models = joblib.load(clustering_path)
# Model directory
ohe_path = Path(path_output, context[ConfigKW.MODEL_NAME], "one_hot_encoder.joblib")
one_hot_encoder = joblib.load(ohe_path)
ds_test = imed_film.normalize_metadata(ds_test, metadata_clustering_models, context[ConfigKW.DEBUGGING],
model_params[ModelParamsKW.METADATA])
model_params.update({ModelParamsKW.FILM_ONEHOTENCODER: one_hot_encoder,
ModelParamsKW.N_METADATA: len([ll for l in one_hot_encoder.categories_ for ll in l])})
return ds_test, model_params
def run_segment_command(context, model_params, no_patch, overlap_2d):
# BIDSDataframe of all image files
# Indexing of derivatives is False for command segment
# split_method is unused for command segment
bids_df = BidsDataframe(
context.get(ConfigKW.LOADER_PARAMETERS),
context.get(ConfigKW.PATH_OUTPUT),
derivatives=False,
split_method=None
)
# Append subjects filenames into a list
bids_subjects = sorted(bids_df.df.get('filename').to_list())
# Add postprocessing to packaged model
path_model = Path(context[ConfigKW.PATH_OUTPUT], context[ConfigKW.MODEL_NAME])
path_model_config = Path(path_model, context[ConfigKW.MODEL_NAME] + ".json")
model_config = imed_config_manager.load_json(str(path_model_config))
model_config[ConfigKW.POSTPROCESSING] = context.get(ConfigKW.POSTPROCESSING)
with path_model_config.open(mode='w') as fp:
json.dump(model_config, fp, indent=4)
options = {}
# Initialize a list of already seen subject ids for multichannel
seen_subj_ids = []
for subject in bids_subjects:
if context.get(ConfigKW.LOADER_PARAMETERS).get(LoaderParamsKW.MULTICHANNEL):
# Get subject_id for multichannel
df_sub = bids_df.df.loc[bids_df.df['filename'] == subject]
subj_id = re.sub(r'_' + df_sub['suffix'].values[0] + '.*', '', subject)
if subj_id not in seen_subj_ids:
# if subj_id has not been seen yet
fname_img = []
provided_contrasts = []
contrasts = context[ConfigKW.LOADER_PARAMETERS][LoaderParamsKW.CONTRAST_PARAMS][ContrastParamsKW.TESTING]
# Keep contrast order
for c in contrasts:
df_tmp = bids_df.df[
bids_df.df['filename'].str.contains(subj_id) & bids_df.df['suffix'].str.contains(c)]
if ~df_tmp.empty:
provided_contrasts.append(c)
fname_img.append(df_tmp['path'].values[0])
seen_subj_ids.append(subj_id)
if len(fname_img) != len(contrasts):
logger.warning(f"Missing contrast for subject {subj_id}. {provided_contrasts} were provided but "
f"{contrasts} are required. Skipping subject.")
continue
else:
# Returns an empty list for subj_id already seen
fname_img = []
else:
fname_img = bids_df.df[bids_df.df['filename'] == subject]['path'].to_list()
# Add film metadata to options for segment_volume
if ModelParamsKW.FILM_LAYERS in model_params and any(model_params[ModelParamsKW.FILM_LAYERS]) \
and model_params[ModelParamsKW.METADATA]:
metadata = bids_df.df[bids_df.df['filename'] == subject][model_params[ModelParamsKW.METADATA]].values[0]
options[OptionKW.METADATA] = metadata
# Add microscopy pixel size and pixel size units metadata to options for segment_volume
if MetadataKW.PIXEL_SIZE in bids_df.df.columns:
options[OptionKW.PIXEL_SIZE] = \
bids_df.df.loc[bids_df.df['filename'] == subject][MetadataKW.PIXEL_SIZE].values[0]
if MetadataKW.PIXEL_SIZE_UNITS in bids_df.df.columns:
options[OptionKW.PIXEL_SIZE_UNITS] = \
bids_df.df.loc[bids_df.df['filename'] == subject][MetadataKW.PIXEL_SIZE_UNITS].values[0]
# Add 'no_patch' and 'overlap-2d' argument to options
if no_patch:
options[OptionKW.NO_PATCH] = no_patch
if overlap_2d:
options[OptionKW.OVERLAP_2D] = overlap_2d
if fname_img:
pred_list, target_list = imed_inference.segment_volume(str(path_model),
fname_images=fname_img,
gpu_id=context[ConfigKW.GPU_IDS][0],
options=options)
pred_path = Path(context[ConfigKW.PATH_OUTPUT], "pred_masks")
if not pred_path.exists():
pred_path.mkdir(parents=True)
# Reformat target list to include class index and be compatible with multiple raters
target_list = ["_class-%d" % i for i in range(len(target_list))]
for pred, target in zip(pred_list, target_list):
filename = subject.split('.')[0] + target + "_pred" + ".nii.gz"
nib.save(pred, Path(pred_path, filename))
# For Microscopy PNG/TIF files (TODO: implement OMETIFF behavior)
extension = imed_loader_utils.get_file_extension(subject)
if "nii" not in extension:
imed_inference.pred_to_png(pred_list,
target_list,
str(Path(pred_path, subject)).replace(extension, ''),
suffix="_pred.png")
[docs]
def run_command(context, n_gif=0, thr_increment=None, resume_training=False, no_patch=False, overlap_2d=None):
"""Run main command.
This function is central in the ivadomed project as training / testing / evaluation commands
are run via this function. All the process parameters are defined in the config.
Args:
context (dict): Dictionary containing all parameters that are needed for a given process. See
:doc:`configuration_file` for more details.
n_gif (int): Generates a GIF during training if larger than zero, one frame per epoch for a given slice. The
parameter indicates the number of 2D slices used to generate GIFs, one GIF per slice. A GIF shows
predictions of a given slice from the validation sub-dataset. They are saved within the output path.
thr_increment (float): A threshold analysis is performed at the end of the training using the trained model and
the training + validation sub-dataset to find the optimal binarization threshold. The specified value
indicates the increment between 0 and 1 used during the ROC analysis (e.g. 0.1).
resume_training (bool): Load a saved model ("checkpoint.pth.tar" in the output directory specified with flag
"--path-output" or via the config file "output_path". This training state is saved everytime a new best
model is saved in the log argument) for resume training directory.
no_patch (bool): If True, 2D patches are not used while segmenting with models trained with patches
(command "--segment" only). Default: False (i.e. segment with patches). The "no_patch" option supersedes
the "overlap_2D" option.
overlap_2d (list of int): Custom overlap for 2D patches while segmenting (command "--segment" only).
Default model overlap is used otherwise.
Returns:
float or pandas.DataFrame or None:
* If "train" command: Returns floats: best loss score for both training and validation.
* If "test" command: Returns a pandas Dataframe: of metrics computed for each subject of
the testing sub-dataset and return the prediction metrics before evaluation.
* If "segment" command: No return value.
"""
command = copy.deepcopy(context[ConfigKW.COMMAND])
path_output = set_output_path(context)
path_log = Path(context.get('path_output'), context.get('log_file'))
logger.remove()
logger.add(str(path_log))
logger.add(sys.stdout)
# Create a log with the version of the Ivadomed software and the version of the Annexed dataset (if present)
create_dataset_and_ivadomed_version_log(context)
cuda_available, device = imed_utils.define_device(context[ConfigKW.GPU_IDS][0])
# BACKWARDS COMPATIBILITY: If bids_path is string, assign to list - Do this here so it propagates to all functions
context[ConfigKW.LOADER_PARAMETERS][LoaderParamsKW.PATH_DATA] =\
imed_utils.format_path_data(context[ConfigKW.LOADER_PARAMETERS][LoaderParamsKW.PATH_DATA])
# Loader params
loader_params = set_loader_params(context, command == "train")
# Get transforms for each subdataset
transform_train_params, transform_valid_params, transform_test_params = \
imed_transforms.get_subdatasets_transforms(context[ConfigKW.TRANSFORMATION])
# MODEL PARAMETERS
model_params, loader_params = set_model_params(context, loader_params)
if command == 'segment':
run_segment_command(context, model_params, no_patch, overlap_2d)
return
# BIDSDataframe of all image files
# Indexing of derivatives is True for commands train and test
# split_method is used for removing unused subject files in bids_df for commands train and test
bids_df = BidsDataframe(loader_params, path_output, derivatives=True,
split_method=context.get(ConfigKW.SPLIT_DATASET).get(SplitDatasetKW.SPLIT_METHOD))
# Get subject filenames lists. "segment" command uses all participants of data path, hence no need to split
train_lst, valid_lst, test_lst = imed_loader_utils.get_subdatasets_subject_files_list(context[ConfigKW.SPLIT_DATASET],
bids_df.df,
path_output,
context.get(ConfigKW.LOADER_PARAMETERS).get(
LoaderParamsKW.SUBJECT_SELECTION))
# Generating sha256 for the training files
imed_utils.generate_sha_256(context, bids_df.df, train_lst)
# TESTING PARAMS
# Aleatoric uncertainty
if context[ConfigKW.UNCERTAINTY][UncertaintyKW.ALEATORIC] \
and context[ConfigKW.UNCERTAINTY][UncertaintyKW.N_IT] > 0:
transformation_dict = transform_train_params
else:
transformation_dict = transform_test_params
undo_transforms = imed_transforms.UndoCompose(imed_transforms.Compose(transformation_dict, requires_undo=True))
testing_params = copy.deepcopy(context[ConfigKW.TRAINING_PARAMETERS])
testing_params.update({ConfigKW.UNCERTAINTY: context[ConfigKW.UNCERTAINTY]})
testing_params.update({LoaderParamsKW.TARGET_SUFFIX: loader_params[LoaderParamsKW.TARGET_SUFFIX],
ConfigKW.UNDO_TRANSFORMS: undo_transforms,
LoaderParamsKW.SLICE_AXIS: loader_params[LoaderParamsKW.SLICE_AXIS]})
if command == "train":
imed_utils.display_selected_transfoms(transform_train_params, dataset_type=["training"])
imed_utils.display_selected_transfoms(transform_valid_params, dataset_type=["validation"])
elif command == "test":
imed_utils.display_selected_transfoms(transformation_dict, dataset_type=["testing"])
# Check if multiple raters
check_multiple_raters(command == "train", loader_params)
if command == 'train':
# Get Validation dataset
ds_valid = get_dataset(bids_df, loader_params, valid_lst, transform_valid_params, cuda_available, device,
'validation')
# Get Training dataset
ds_train = get_dataset(bids_df, loader_params, train_lst, transform_train_params, cuda_available, device,
'training')
metric_fns = imed_metrics.get_metric_fns(ds_train.task)
# If FiLM, normalize data
if ModelParamsKW.FILM_LAYERS in model_params and any(model_params[ModelParamsKW.FILM_LAYERS]):
model_params, ds_train, ds_valid, train_onehotencoder = \
film_normalize_data(context, model_params, ds_train, ds_valid, path_output)
else:
train_onehotencoder = None
# Model directory
create_path_model(context, model_params, ds_train, path_output, train_onehotencoder)
save_config_file(context, path_output)
# RUN TRAINING
best_training_dice, best_training_loss, best_validation_dice, best_validation_loss = imed_training.train(
model_params=model_params,
dataset_train=ds_train,
dataset_val=ds_valid,
training_params=context[ConfigKW.TRAINING_PARAMETERS],
wandb_params=context.get(ConfigKW.WANDB),
path_output=path_output,
device=device,
cuda_available=cuda_available,
metric_fns=metric_fns,
n_gif=n_gif,
resume_training=resume_training,
debugging=context[ConfigKW.DEBUGGING])
if thr_increment:
# LOAD DATASET
if command != 'train': # If command == train, then ds_valid already load
# Get Validation dataset
ds_valid = get_dataset(bids_df, loader_params, valid_lst, transform_valid_params, cuda_available, device,
'validation')
# Get Training dataset with no Data Augmentation
ds_train = get_dataset(bids_df, loader_params, train_lst, transform_valid_params, cuda_available, device,
'training')
# Choice of optimisation metric
if model_params[ModelParamsKW.NAME] in imed_utils.CLASSIFIER_LIST:
metric = MetricsKW.RECALL_SPECIFICITY
else:
metric = MetricsKW.DICE
# Model path
model_path = Path(path_output, "best_model.pt")
# Run analysis
thr = imed_testing.threshold_analysis(model_path=str(model_path),
ds_lst=[ds_train, ds_valid],
model_params=model_params,
testing_params=testing_params,
metric=metric,
increment=thr_increment,
fname_out=str(Path(path_output, "roc.png")),
cuda_available=cuda_available)
# Update threshold in config file
context[ConfigKW.POSTPROCESSING][PostprocessingKW.BINARIZE_PREDICTION] = {BinarizeProdictionKW.THR: thr}
save_config_file(context, path_output)
if command == 'train':
return best_training_dice, best_training_loss, best_validation_dice, best_validation_loss
if command == 'test':
# LOAD DATASET
# Warn user that the input-level dropout is set during inference
if loader_params[LoaderParamsKW.IS_INPUT_DROPOUT]:
logger.warning("Input-level dropout is set during testing. To turn this option off, set 'is_input_dropout'"
"to 'false' in the configuration file.")
ds_test = imed_loader.load_dataset(bids_df, **{**loader_params, **{'data_list': test_lst,
'transforms_params': transformation_dict,
'dataset_type': 'testing',
'requires_undo': True}}, device=device,
cuda_available=cuda_available)
eval_params = context[ConfigKW.EVALUATION_PARAMETERS]
metric_fns = imed_metrics.get_metric_fns(ds_test.task, eval_params)
if ModelParamsKW.FILM_LAYERS in model_params and any(model_params[ModelParamsKW.FILM_LAYERS]):
ds_test, model_params = update_film_model_params(context, ds_test, model_params, path_output)
# RUN INFERENCE
pred_metrics = imed_testing.test(model_params=model_params,
dataset_test=ds_test,
testing_params=testing_params,
path_output=path_output,
device=device,
cuda_available=cuda_available,
metric_fns=metric_fns,
postprocessing=context[ConfigKW.POSTPROCESSING])
# RUN EVALUATION
df_results = imed_evaluation.evaluate(bids_df, path_output=path_output,
target_suffix=loader_params[LoaderParamsKW.TARGET_SUFFIX],
eval_params=eval_params)
return df_results, pred_metrics
def create_dataset_and_ivadomed_version_log(context):
path_data = context.get(ConfigKW.LOADER_PARAMETERS).get(LoaderParamsKW.PATH_DATA)
ivadomed_version = imed_utils._version_string()
datasets_version = []
if isinstance(path_data, str):
datasets_version = [imed_utils.__get_commit(path_to_git_folder=path_data)]
elif isinstance(path_data, list):
for Dataset in path_data:
datasets_version.append(imed_utils.__get_commit(path_to_git_folder=Dataset))
path_log = Path(context.get(ConfigKW.PATH_OUTPUT), 'version_info.log')
try:
f = path_log.open(mode="w")
except OSError as err:
logger.error(f"OS error: {err}")
raise Exception("Have you selected a log folder, and do you have write permissions for that folder?")
# IVADOMED
f.write('IVADOMED TOOLBOX\n----------------\n(' + ivadomed_version + ')')
# DATASETS
path_data = imed_utils.format_path_data(path_data)
f.write('\n\n\nDATASET VERSION\n---------------\n')
f.write('The following BIDS dataset(s) were used for training.\n')
for i_dataset in range(len(path_data)):
if datasets_version[i_dataset] not in ['', '?!?']:
f.write(str(i_dataset + 1) + '. ' + path_data[i_dataset] + ' - Dataset Annex version: ' + datasets_version[
i_dataset] + '\n')
else:
f.write(str(i_dataset + 1) + '. ' + path_data[i_dataset] + ' - Dataset is not Annexed.\n')
# SYSTEM INFO
f.write('\n\nSYSTEM INFO\n-------------\n')
platform_running = sys.platform
if platform_running.find('darwin') != -1:
os_running = 'osx'
elif platform_running.find('linux') != -1:
os_running = 'linux'
elif platform_running.find('win32') or platform_running.find('win64'):
os_running = 'windows'
else:
os_running = 'NA'
f.write('OS: ' + os_running + ' (' + platform.platform() + ')\n')
# Display number of CPU cores
f.write('CPU cores: Available: {}\n\n\n\n\n'.format(multiprocessing.cpu_count()))
# USER INPUTS
f.write('CONFIG INPUTS\n-------------\n')
if sys.version_info[0] > 2:
for k, v in context.items():
f.write(str(k) + ': ' + str(v) + '\n') # Making sure all numbers are converted to strings
else:
for k, v in context.viewitems(): # Python2
f.write(str(k) + ': ' + str(v) + '\n')
f.close()
def run_main():
imed_utils.init_ivadomed()
parser = get_parser()
args = parser.parse_args()
# Get context from configuration file
path_config_file = args.config
context = imed_config_manager.ConfigurationManager(path_config_file).get_config()
context[ConfigKW.COMMAND] = imed_utils.get_command(args, context)
context[ConfigKW.PATH_OUTPUT] = imed_utils.get_path_output(args, context)
context[ConfigKW.LOADER_PARAMETERS][LoaderParamsKW.PATH_DATA] = imed_utils.get_path_data(args, context)
# Run command
run_command(context=context,
n_gif=args.gif if args.gif is not None else 0,
thr_increment=args.thr_increment if args.thr_increment else None,
resume_training=bool(args.resume_training),
no_patch=bool(args.no_patch),
overlap_2d=args.overlap_2d if args.overlap_2d else None)
if __name__ == "__main__":
run_main()