# Copyright 2020 MONAI Consortium
# 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 os
import numpy as np
import torch
from ignite.engine import Events
import logging
from monai.data.nifti_writer import write_nifti
[docs]class SegmentationSaver:
"""
Event handler triggered on completing every iteration to save the segmentation predictions as nifti files.
"""
def __init__(self, output_path='./', dtype='float32', output_postfix='seg', output_ext='.nii.gz',
batch_transform=lambda x: x, output_transform=lambda x: x, name=None):
"""
Args:
output_path (str): output image directory.
dtype (str): to convert the image to save to this datatype.
output_postfix (str): a string appended to all output file names.
output_ext (str): output file extension name.
batch_transform (Callable): a callable that is used to transform the
ignite.engine.batch into expected format to extract the meta_data dictionary.
output_transform (Callable): a callable that is used to transform the
ignite.engine.output into the form expected nifti image data.
The first dimension of this transform's output will be treated as the
batch dimension. Each item in the batch will be saved individually.
name (str): identifier of logging.logger to use, defaulting to `engine.logger`.
"""
self.output_path = output_path
self.dtype = dtype
self.output_postfix = output_postfix
self.output_ext = output_ext
self.batch_transform = batch_transform
self.output_transform = output_transform
self.logger = None if name is None else logging.getLogger(name)
def attach(self, engine):
if self.logger is None:
self.logger = engine.logger
if not engine.has_event_handler(self, Events.ITERATION_COMPLETED):
engine.add_event_handler(Events.ITERATION_COMPLETED, self)
@staticmethod
def _create_file_basename(postfix, input_file_name, folder_path, data_root_dir=""):
"""
Utility function to create the path to the output file based on the input
filename (extension is added by lib level writer before writing the file)
Args:
postfix (str): output name's postfix
input_file_name (str): path to the input image file
folder_path (str): path for the output file
data_root_dir (str): if not empty, it specifies the beginning parts of the input file's
absolute path. This is used to compute `input_file_rel_path`, the relative path to the file from
`data_root_dir` to preserve folder structure when saving in case there are files in different
folders with the same file names.
"""
# get the filename and directory
filedir, filename = os.path.split(input_file_name)
# jettison the extension to have just filename
filename, ext = os.path.splitext(filename)
while ext != "":
filename, ext = os.path.splitext(filename)
# use data_root_dir to find relative path to file
filedir_rel_path = ""
if data_root_dir:
filedir_rel_path = os.path.relpath(filedir, data_root_dir)
# sub-folder path will be original name without the extension
subfolder_path = os.path.join(folder_path, filedir_rel_path, filename)
if not os.path.exists(subfolder_path):
os.makedirs(subfolder_path)
# add the sub-folder plus the postfix name to become the file basename in the output path
return os.path.join(subfolder_path, filename + "_" + postfix)
def __call__(self, engine):
"""
This method assumes self.batch_transform will extract Metadata from the input batch.
Metadata should have the following keys:
- ``'filename_or_obj'`` -- for output file name creation
- ``'original_affine'`` (optional) for data orientation handling
- ``'affine'`` (optional) for data output affine.
output file datatype is determined from ``engine.state.output.dtype``.
"""
meta_data = self.batch_transform(engine.state.batch)
filenames = meta_data['filename_or_obj']
original_affine = meta_data.get('original_affine', None)
affine = meta_data.get('affine', None)
engine_output = self.output_transform(engine.state.output)
for batch_id, filename in enumerate(filenames): # save a batch of files
seg_output = engine_output[batch_id]
affine_ = affine[batch_id]
original_affine_ = original_affine[batch_id]
if isinstance(seg_output, torch.Tensor):
seg_output = seg_output.detach().cpu().numpy()
output_filename = self._create_file_basename(self.output_postfix, filename, self.output_path)
output_filename = '{}{}'.format(output_filename, self.output_ext)
# change output to "channel last" format and write to nifti format file
to_save = np.moveaxis(seg_output, 0, -1)
write_nifti(to_save, affine_, output_filename, original_affine_, dtype=seg_output.dtype)
self.logger.info('saved: {}'.format(output_filename))