![](\"https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.png\"){kind=icon}
\n",
"" ] }, { "cell_type": "markdown", "metadata": { "id": "jJ8E26da-GP1", "colab_type": "text" }, "source": [ ""Image segmentation using deep learning", guess this is the hottest topic in MIDL #MIDL2019 @midl_conference pic.twitter.com/64smdMjnxY
— Hua Ma (@forever_pippo) July 8, 2019
" ] }, { "cell_type": "markdown", "metadata": { "id": "v16_WPuKmtDm", "colab_type": "text" }, "source": [ "### HighRes3DNet" ] }, { "cell_type": "markdown", "metadata": { "id": "i_trk4Gv-GP4", "colab_type": "text" }, "source": [ "Just like #miccai2018! pic.twitter.com/3ZTHxj9iPT
— Julia Schnabel (@ja_schnabel) July 8, 2019
![\"drawing\"](\"https://niftynet.io/img/niftynet-logo.png\"){kind=logo}
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IHNg6sBD-GP7",
"colab_type": "text"
},
"source": [
"HighRes3DNet is a residual convolutional neural network architecture designed to have a large receptive field and preserve a high resolution using a relatively small number of parameters. It was presented in 2017 by Li et al. at IPMI: [*On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task*](https://arxiv.org/abs/1707.01992)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vOJMIC9j-GP8",
"colab_type": "text"
},
"source": [
"![\"HighRes3DNet\"](\"https://github.com/fepegar/tf2pt/raw/master/images/network.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CW59Mlpf-GP9",
"colab_type": "text"
},
"source": [
"The authors used NiftyNet to implement and train a model based on this architecture to perform [brain parcellation](https://ieeexplore.ieee.org/document/7086081?arnumber=7086081) using $T_1$-weighted MR images from the [ADNI dataset](http://adni.loni.usc.edu/). They achieved competitive segmentation performance compared with state-of-the-art architectures such as [DeepMedic](https://biomedia.doc.ic.ac.uk/software/deepmedic/) or [U-Net](https://arxiv.org/abs/1606.06650).\n",
"\n",
"This figure from the paper shows a parcellation produced by HighRes3DNet:\n",
"\n",
"![\"Input](\"https://github.com/fepegar/tf2pt/raw/master/images/li-000.png\")
\n",
"![\"Output](\"https://github.com/fepegar/tf2pt/raw/master/images/li-001.png\")
\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4gDgyxQH-GP-",
"colab_type": "text"
},
"source": [
"The code of the architecture is on [NiftyNet GitHub repository](https://github.com/NifTK/NiftyNet/blob/dev/niftynet/network/highres3dnet.py). The authors have uploaded the parameters and configuration file to the [Model Zoo](https://github.com/NifTK/NiftyNetModelZoo/tree/5-reorganising-with-lfs/highres3dnet_brain_parcellation).\n",
"\n",
"After reading the paper and the code, it is relatively straightforward to [implement the same architecture using PyTorch](https://github.com/fepegar/highresnet)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jfnsSHJh-GRn",
"colab_type": "text"
},
"source": [
"## Running the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BAbMnglFJJx3",
"colab_type": "text"
},
"source": [
"All the code is hosted in a GitHub repository:\n",
"[`fepegar/miccai-educational-challenge-2019`](https://github.com/fepegar/miccai-educational-challenge-2019).\n",
"\n",
"The latest release can also be found on the Zenodo repository under this DOI: [10.5281/zenodo.3352316](https://doi.org/10.5281/zenodo.3352316)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0w-5ghoyBZN1",
"colab_type": "text"
},
"source": [
"### Online"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WyKog0VdBhUo",
"colab_type": "text"
},
"source": [
"If you have a Google account, the best way to run this notebook seamlessly is using [Google Colab](https://colab.research.google.com/drive/1vqDojKuC4Svb97LdoEyZQygm3jccX4hr). You will need to click on \"Open in playground\", at the top left:\n",
"\n",
"\n",
"\n",
"You will also get a couple of warnings that you can safely ignore. The first one warns about this notebook not being authored by Google and the second one asks for confirmation to reset all runtimes. These are valid points, but will not affect this tutorial.\n",
"\n",
"[](https://colab.research.google.com/drive/1vqDojKuC4Svb97LdoEyZQygm3jccX4hr)\n",
"\n",
"---\n",
"\n",
"Please [report any issues on GitHub](https://github.com/fepegar/miccai-educational-challenge-2019/issues/new) and I will fix them. You can also [drop me an email](mailto:fernando.perez.garcia.17@ucl.ac.uk?subject=Combining%20the%20power%20of%20PyTorch%20and%20NiftyNet) if you have any questions or comments.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aNVkCt5vBe0V",
"colab_type": "text"
},
"source": [
"### Locally"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JuJmtAHl-GRn",
"colab_type": "text"
},
"source": [
"To write this notebook I used Ubuntu 18.04 installed on an Alienware 13 R3 laptop, which includes a 6-GB GeForce GTX 1060 NVIDIA GPU. I am using CUDA 9.0.\n",
"\n",
"Inference using PyTorch took 5725 MB of GPU memory. TensorFlow usually takes as much as possible beforehand."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pzitKNDD-GRq",
"colab_type": "text"
},
"source": [
"To run this notebook locally, I recommend downloading the repository and creating a [`conda`](https://docs.conda.io/en/latest/miniconda.html) environment:\n",
"\n",
"```shell\n",
"git clone https://github.com/fepegar/miccai-educational-challenge-2019.git\n",
"cd miccai-educational-challenge-2019\n",
"conda create -n mec2019 python=3.6 -y\n",
"conda activate mec2019 && conda install jupyterlab -y && jupyter lab\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ne0BYvIHFJzR",
"colab_type": "text"
},
"source": [
"### nbviewer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t4c313Z_FPeb",
"colab_type": "text"
},
"source": [
"An already executed version of the notebook can be rendered using [nbviewer](https://nbviewer.jupyter.org/github/fepegar/miccai-educational-challenge-2019/blob/master/Combining_the_power_of_PyTorch_and_NiftyNet.ipynb?flush_cache=true).\n",
"\n",
"\n",
" \n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vooxt1Jt4efL",
"colab_type": "text"
},
"source": [
"### Interactive volume plots"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sMEKcXj14iy0",
"colab_type": "text"
},
"source": [
"If you run the notebook, you can use interactive plots to navigate through the volume slices by setting this variable to `True`. You might need to run the volume visualization cells individually after running the whole notebook. This feature is experimental and therefore disabled by default."
]
},
{
"cell_type": "code",
"metadata": {
"id": "FT17pIB249jn",
"colab_type": "code",
"colab": {}
},
"source": [
"interactive_plots = False"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FZzLgIR80mVQ"
},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8BIsvPiS4ghW"
},
"source": [
"### Install and import libraries"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Qtauo5uO70v",
"colab_type": "text"
},
"source": [
"Clone NiftyNet and some custom Python libraries for this notebook. This might take one or two minutes."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "X2uoosvx1uxh",
"colab": {}
},
"source": [
"%%capture --no-stderr\n",
"# This might take about 30 seconds\n",
"!rm -rf NiftyNet && git clone https://github.com/NifTK/NiftyNet --depth 1\n",
"!cd NiftyNet && git checkout df0f86733357fdc92bbc191c8fec0dcf49aa5499 && cd ..\n",
"!pip install -r NiftyNet/requirements-gpu.txt\n",
"!curl -O https://raw.githubusercontent.com/fepegar/miccai-educational-challenge-2019/master/requirements.txt\n",
"!curl -O https://raw.githubusercontent.com/fepegar/miccai-educational-challenge-2019/master/tf2pt.py\n",
"!curl -O https://raw.githubusercontent.com/fepegar/miccai-educational-challenge-2019/master/utils.py\n",
"!curl -O https://raw.githubusercontent.com/fepegar/miccai-educational-challenge-2019/master/visualization.py\n",
"!curl -O https://raw.githubusercontent.com/fepegar/miccai-educational-challenge-2019/master/highresnet_mapping.py\n",
"!curl -O https://raw.githubusercontent.com/fepegar/highresnet/master/GIFNiftyNet.ctbl\n",
"!pip install -r requirements.txt\n",
"!pip install --upgrade numpy\n",
"!pip install ipywidgets\n",
"import sys\n",
"sys.path.insert(0, 'NiftyNet')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "TdjKHmBb82qG",
"outputId": "9cd83db3-e0e2-44bf-a34f-456fe36bc5d4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format='retina'\n",
"\n",
"import os\n",
"import tempfile\n",
"from pathlib import Path\n",
"from configparser import ConfigParser\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"try:\n",
" # Fancy rendering of Pandas tables\n",
" import google.colab.data_table\n",
" %load_ext google.colab.data_table\n",
" print(\"We are on Google Colab\")\n",
"except ModuleNotFoundError:\n",
" print(\"We are not on Google Colab\")\n",
" pd.set_option('display.max_colwidth', -1) # do not truncate strings when displaying data frames\n",
" pd.set_option('display.max_rows', None) # show all rows\n",
"\n",
"import torch\n",
"\n",
"from highresnet import HighRes3DNet"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"We are on Google Colab\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pNpcQ8mZJm6H",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
"from tensorflow.python.util import deprecation\n",
"deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
"\n",
"import tf2pt\n",
"import utils\n",
"import visualization\n",
"import highresnet_mapping\n",
"\n",
"if interactive_plots: # for Colab or Jupyter\n",
" plot_volume = visualization.plot_volume_interactive\n",
"else: # for HTML, GitHub or nbviewer\n",
" plot_volume = visualization.plot_volume\n",
"\n",
"from niftynet.io.image_reader import ImageReader\n",
"from niftynet.engine.sampler_grid_v2 import GridSampler\n",
"from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator\n",
"from niftynet.layer.pad import PadLayer\n",
"from niftynet.layer.binary_masking import BinaryMaskingLayer\n",
"from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer\n",
"from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "O6Z5ftE48fxv"
},
"source": [
"### Download NiftyNet model and test data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YHb8yWM7-GQJ",
"colab_type": "text"
},
"source": [
"We can use NiftyNet's `net_download` to get all we need from the [Model Zoo entry](https://github.com/NifTK/NiftyNetModelZoo/tree/5-reorganising-with-lfs/highres3dnet_brain_parcellation#downloading-model-zoo-files) corresponding to brain parcellation using HighRes3DNet:"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "fA92bog98ewS",
"colab": {}
},
"source": [
"%%capture\n",
"%run NiftyNet/net_download.py highres3dnet_brain_parcellation_model_zoo"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zz1V4CKy-GQM",
"colab_type": "code",
"outputId": "371eb9fb-e327-4844-9cc4-e85e30dc6223",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 459
}
},
"source": [
"niftynet_dir = Path('~/niftynet').expanduser()\n",
"utils.list_files(niftynet_dir)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"niftynet/\n",
" data/\n",
" OASIS/\n",
" license\n",
" OAS1_0145_MR2_mpr_n4_anon_sbj_111.nii.gz\n",
" models/\n",
" highres3dnet_brain_parcellation/\n",
" inference_niftynet_log\n",
" databrain_std_hist_models_otsu.txt\n",
" settings_inference.txt\n",
" Modality0.csv\n",
" parcellation_output/\n",
" window_seg_OAS1_0145_MR2_mpr_n4_anon_sbj_111__niftynet_out.nii.gz\n",
" inferred.csv\n",
" logs/\n",
" models/\n",
" model.ckpt-33000.meta\n",
" model.ckpt-33000.data-00000-of-00001\n",
" model.ckpt-33000.index\n",
" extensions/\n",
" __init__.py\n",
" highres3dnet_brain_parcellation/\n",
" __init__.py\n",
" highres3dnet_config_eval.ini\n",
" network/\n",
" __init__.py\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yjpKmyji-GQO",
"colab_type": "text"
},
"source": [
"There are three directories under `~/niftynet`:\n",
"1. `extensions` is a Python package that contains the [configuration file].(https://niftynet.readthedocs.io/en/dev/config_spec.html)\n",
"2. `models` contains the landmarks for [histogram standardization](https://ieeexplore.ieee.org/document/836373) (an MRI preprocessing step) and the network parameters.\n",
"3. `data` contains an [OASIS](https://www.oasis-brains.org/) MRI sample that can be used to test the model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c8vms-m2p5d3",
"colab_type": "text"
},
"source": [
"Here are the paths to the downloaded files and to the files that will be generated by the notebook. I use `nn` for NiftyNet, `tf` for TensorFlow and `pt` for PyTorch."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "e3Ll7LVl4WtC",
"colab": {}
},
"source": [
"models_dir = niftynet_dir / 'models'\n",
"zoo_entry = 'highres3dnet_brain_parcellation'\n",
"input_checkpoint_tf_name = 'model.ckpt-33000'\n",
"input_checkpoint_tf_path = models_dir / zoo_entry / 'models' / input_checkpoint_tf_name\n",
"data_dir = niftynet_dir / 'data' / 'OASIS'\n",
"config_path = niftynet_dir / 'extensions' / zoo_entry / 'highres3dnet_config_eval.ini'\n",
"histogram_landmarks_path = models_dir / zoo_entry / 'databrain_std_hist_models_otsu.txt'\n",
"tempdir = Path(tempfile.gettempdir()) / 'miccai_niftynet_pytorch'\n",
"tempdir.mkdir(exist_ok=True)\n",
"output_csv_tf_path = tempdir / 'variables_tf.csv'\n",
"output_state_dict_tf_path = tempdir / 'state_dict_tf.pth'\n",
"output_state_dict_pt_path = tempdir / 'state_dict_pt.pth'\n",
"prediction_pt_dir = tempdir / 'prediction'\n",
"prediction_pt_dir.mkdir(exist_ok=True)\n",
"color_table_path = 'GIFNiftyNet.ctbl'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "IwdjnYc--GQR",
"colab_type": "text"
},
"source": [
"Note that the path to the checkpoint is not a path to an existing filename, but the basename of the three checkpoint files."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fpb_C4qt-GQR",
"colab_type": "text"
},
"source": [
"## Transferring parameters from NiftyNet to PyTorch"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "svG_jtqn0KGU"
},
"source": [
"### Variables in TensorFlow world \n",
"\n",
"![\"drawing\"](\"https://static.nvidiagrid.net/ngc/containers/tensorflow.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JfZfj1zs-GQT",
"colab_type": "text"
},
"source": [
"There are two modules that are relevant for this section in the\n",
"[repository](https://github.com/fepegar/miccai-educational-challenge-2019).\n",
"[`tf2pt`](https://github.com/fepegar/miccai-educational-challenge-2019/blob/master/tf2pt.py) contains generic functions that can be used to transform any TensorFlow model to PyTorch.\n",
"[`highresnet_mapping`](https://github.com/fepegar/miccai-educational-challenge-2019/blob/master/highresnet_mapping.py) contains custom functions that are specific for HighRes3DNet.\n",
"\n",
"Let's see what variables are stored in the checkpoint.\n",
"\n",
"These are filtered out by\n",
"[`highresnet_mapping.is_not_valid()`](https://github.com/fepegar/miccai-educational-challenge-2019/blob/c96777d654ac577c0dba218038f76c2497de946a/highresnet_mapping.py#L4-L11)\n",
"for clarity:\n",
"* Variables used by the Adam optimizer during training\n",
"* Variables with no shape. They won't help much.\n",
"* Variables containing `biased` or `ExponentialMovingAverage`. I have experimented with them and the results using these variables turned out to be different to the ones generated by NiftyNet."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XLFf7VkP-GQT",
"colab_type": "text"
},
"source": [
"We will store the variables names in a data frame to list them in this notebook and the values in a Python dictionary to retrieve them later. I figured out the code in\n",
"[`tf2pt.checkpoint_tf_to_state_dict_tf()`](https://github.com/fepegar/miccai-educational-challenge-2019/blob/c96777d654ac577c0dba218038f76c2497de946a/tf2pt.py#L90-L129)\n",
"reading the corresponding [TensorFlow docs](https://www.tensorflow.org/api_docs/python/tf/train/list_variables) and [Stack Overflow answers](https://stackoverflow.com/search?q=restore+tensorflow)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "N74zhuFYPwff",
"colab_type": "code",
"outputId": "330fba6e-fb22-4800-cc6b-82b9b3d18554",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 190
}
},
"source": [
"# %%capture\n",
"tf2pt.checkpoint_tf_to_state_dict_tf(\n",
" input_checkpoint_tf_path=input_checkpoint_tf_path,\n",
" output_csv_tf_path=output_csv_tf_path,\n",
" output_state_dict_tf_path=output_state_dict_tf_path,\n",
" filter_out_function=highresnet_mapping.is_not_valid,\n",
" replace_string='HighRes3DNet/',\n",
")\n",
"data_frame_tf = pd.read_csv(output_csv_tf_path)\n",
"state_dict_tf = torch.load(output_state_dict_tf_path)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"W0824 12:06:24.860505 140162299176832 deprecation_wrapper.py:119] From /content/tf2pt.py:106: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
"\n",
"W0824 12:06:24.872821 140162299176832 deprecation_wrapper.py:119] From /content/tf2pt.py:114: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
"\n",
"W0824 12:06:25.661258 140162299176832 deprecation_wrapper.py:119] From /content/tf2pt.py:122: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
"\n",
"W0824 12:06:25.749863 140162299176832 deprecation_wrapper.py:119] From /content/tf2pt.py:124: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"I0824 12:06:29.631742 140162299176832 saver.py:1280] Restoring parameters from /root/niftynet/models/highres3dnet_brain_parcellation/models/model.ckpt-33000\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5GoBd5Qq-GQU",
"colab_type": "code",
"outputId": "ba845b60-587f-4adc-9ec3-89493dbf2994",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 623
}
},
"source": [
"data_frame_tf"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
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| 81 | \n", "81 | \n", "res_3_0/bn_1/moving_mean | \n", "64 | \n", "
| 82 | \n", "82 | \n", "res_3_0/bn_1/moving_variance | \n", "64 | \n", "
| 83 | \n", "83 | \n", "res_3_0/conv_0/w | \n", "3, 3, 3, 32, 64 | \n", "
| 84 | \n", "84 | \n", "res_3_0/conv_1/w | \n", "3, 3, 3, 64, 64 | \n", "
| 85 | \n", "85 | \n", "res_3_1/bn_0/beta | \n", "64 | \n", "
| 86 | \n", "86 | \n", "res_3_1/bn_0/gamma | \n", "64 | \n", "
| 87 | \n", "87 | \n", "res_3_1/bn_0/moving_mean | \n", "64 | \n", "
| 88 | \n", "88 | \n", "res_3_1/bn_0/moving_variance | \n", "64 | \n", "
| 89 | \n", "89 | \n", "res_3_1/bn_1/beta | \n", "64 | \n", "
| 90 | \n", "90 | \n", "res_3_1/bn_1/gamma | \n", "64 | \n", "
| 91 | \n", "91 | \n", "res_3_1/bn_1/moving_mean | \n", "64 | \n", "
| 92 | \n", "92 | \n", "res_3_1/bn_1/moving_variance | \n", "64 | \n", "
| 93 | \n", "93 | \n", "res_3_1/conv_0/w | \n", "3, 3, 3, 64, 64 | \n", "
| 94 | \n", "94 | \n", "res_3_1/conv_1/w | \n", "3, 3, 3, 64, 64 | \n", "
| 95 | \n", "95 | \n", "res_3_2/bn_0/beta | \n", "64 | \n", "
| 96 | \n", "96 | \n", "res_3_2/bn_0/gamma | \n", "64 | \n", "
| 97 | \n", "97 | \n", "res_3_2/bn_0/moving_mean | \n", "64 | \n", "
| 98 | \n", "98 | \n", "res_3_2/bn_0/moving_variance | \n", "64 | \n", "
| 99 | \n", "99 | \n", "res_3_2/bn_1/beta | \n", "64 | \n", "
| 100 | \n", "100 | \n", "res_3_2/bn_1/gamma | \n", "64 | \n", "
| 101 | \n", "101 | \n", "res_3_2/bn_1/moving_mean | \n", "64 | \n", "
| 102 | \n", "102 | \n", "res_3_2/bn_1/moving_variance | \n", "64 | \n", "
| 103 | \n", "103 | \n", "res_3_2/conv_0/w | \n", "3, 3, 3, 64, 64 | \n", "
| 104 | \n", "104 | \n", "res_3_2/conv_1/w | \n", "3, 3, 3, 64, 64 | \n", "
105 rows × 3 columns
\n", "![\"drawing\"](\"https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2019/01/pytorch-logo.png\"){kind=logo}
\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KvsUcgkNS4dw",
"colab_type": "text"
},
"source": [
"To match the model in the [paper]((https://arxiv.org/abs/1707.01992), we set the number of classes to 160 and enable the flag to add the dropout layer."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Mn72NRUn-GQb",
"colab_type": "code",
"colab": {}
},
"source": [
"num_input_modalities = 1\n",
"num_classes = 160\n",
"model = HighRes3DNet(num_input_modalities, num_classes, add_dropout_layer=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "M1ZHXJZ9-GQd",
"colab_type": "text"
},
"source": [
"Let's see what the variable names created by PyTorch are:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "c08im3mD-GQe",
"colab_type": "code",
"outputId": "de8749e5-d3fc-4427-ebe7-b6b63a763cea",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 623
}
},
"source": [
"state_dict_pt = model.state_dict()\n",
"rows = []\n",
"for name, parameters in state_dict_pt.items():\n",
" if 'num_batches_tracked' in name: # filter out for clarity\n",
" continue\n",
" shape = ', '.join(str(n) for n in parameters.shape)\n",
" row = {'name': name, 'shape': shape}\n",
" rows.append(row)\n",
"df_pt = pd.DataFrame.from_dict(rows)\n",
"df_pt"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
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| 8 | \n", "block.1.dilation_block.0.residual_block.0.conv... | \n", "16 | \n", "
| 9 | \n", "block.1.dilation_block.0.residual_block.0.conv... | \n", "16, 16, 3, 3, 3 | \n", "
| 10 | \n", "block.1.dilation_block.0.residual_block.1.conv... | \n", "16 | \n", "
| 11 | \n", "block.1.dilation_block.0.residual_block.1.conv... | \n", "16 | \n", "
| 12 | \n", "block.1.dilation_block.0.residual_block.1.conv... | \n", "16 | \n", "
| 13 | \n", "block.1.dilation_block.0.residual_block.1.conv... | \n", "16 | \n", "
| 14 | \n", "block.1.dilation_block.0.residual_block.1.conv... | \n", "16, 16, 3, 3, 3 | \n", "
| 15 | \n", "block.1.dilation_block.1.residual_block.0.conv... | \n", "16 | \n", "
| 16 | \n", "block.1.dilation_block.1.residual_block.0.conv... | \n", "16 | \n", "
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| 18 | \n", "block.1.dilation_block.1.residual_block.0.conv... | \n", "16 | \n", "
| 19 | \n", "block.1.dilation_block.1.residual_block.0.conv... | \n", "16, 16, 3, 3, 3 | \n", "
| 20 | \n", "block.1.dilation_block.1.residual_block.1.conv... | \n", "16 | \n", "
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| 23 | \n", "block.1.dilation_block.1.residual_block.1.conv... | \n", "16 | \n", "
| 24 | \n", "block.1.dilation_block.1.residual_block.1.conv... | \n", "16, 16, 3, 3, 3 | \n", "
| 25 | \n", "block.1.dilation_block.2.residual_block.0.conv... | \n", "16 | \n", "
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| 28 | \n", "block.1.dilation_block.2.residual_block.0.conv... | \n", "16 | \n", "
| 29 | \n", "block.1.dilation_block.2.residual_block.0.conv... | \n", "16, 16, 3, 3, 3 | \n", "
| ... | \n", "... | \n", "... | \n", "
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| 85 | \n", "block.3.dilation_block.2.residual_block.0.conv... | \n", "64 | \n", "
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| 94 | \n", "block.3.dilation_block.2.residual_block.1.conv... | \n", "64, 64, 3, 3, 3 | \n", "
| 95 | \n", "block.4.convolutional_block.0.weight | \n", "80, 64, 1, 1, 1 | \n", "
| 96 | \n", "block.4.convolutional_block.1.weight | \n", "80 | \n", "
| 97 | \n", "block.4.convolutional_block.1.bias | \n", "80 | \n", "
| 98 | \n", "block.4.convolutional_block.1.running_mean | \n", "80 | \n", "
| 99 | \n", "block.4.convolutional_block.1.running_var | \n", "80 | \n", "
| 100 | \n", "block.6.convolutional_block.0.weight | \n", "160, 80, 1, 1, 1 | \n", "
| 101 | \n", "block.6.convolutional_block.1.weight | \n", "160 | \n", "
| 102 | \n", "block.6.convolutional_block.1.bias | \n", "160 | \n", "
| 103 | \n", "block.6.convolutional_block.1.running_mean | \n", "160 | \n", "
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105 rows × 2 columns
\n", "![\"drawing\"](\"https://github.com/fepegar/tf2pt/raw/master/images/patch.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gGWzGjBh-GRV",
"colab_type": "text"
},
"source": [
"We will use NiftyNet's grid sampler to get all windows from the volume (blue in the previous image) and a grid samples aggregator (red) to reconstruct the output image from the inferred windows. If you have any memory issues, try reducing the window size.\n",
"\n",
"The [window border](https://niftynet.readthedocs.io/en/dev/window_sizes.html#border) is needed to reduce the border effect in a dense prediction."
]
},
{
"cell_type": "code",
"metadata": {
"id": "qpZLGsgT-GRW",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture\n",
"window_size = 128\n",
"window_size = 3 * (window_size, )\n",
"window_border = 2, 2, 2\n",
"window_size_dict = {'image': window_size}\n",
"batch_size = 1\n",
"\n",
"sampler = GridSampler(\n",
" reader,\n",
" window_size_dict,\n",
" window_border=window_border,\n",
")\n",
"\n",
"prediction_pt_dir = tempdir / 'prediction'\n",
"prediction_pt_dir.mkdir(exist_ok=True)\n",
"aggregator = GridSamplesAggregator(\n",
" image_reader=reader,\n",
" window_border=window_border,\n",
" output_path=prediction_pt_dir,\n",
")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "E4Thnl01amwO",
"colab_type": "text"
},
"source": [
"### Run inference"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cHBl_aTM-GRb",
"colab_type": "text"
},
"source": [
"Now, the most important part: running the parcellation! We will iterate over the windows provided by the grid sampler, pass them through the network and aggregate them to the output volume. With the default parameters, the inference will run for 27 iterations, which might take a couple of minutes:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "gFTMbpRD-GRb",
"colab_type": "code",
"outputId": "e87b82e2-c2c2-48a0-ac86-0be385d17747",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 598
}
},
"source": [
"device = torch.device('cuda') if torch.cuda.is_available() else 'cpu'\n",
"model.to(device)\n",
"model.eval()\n",
"for batch_index, batch_dict in enumerate(sampler()):\n",
" print(f'Running inference iteration {batch_index}...')\n",
" input_tensor = tf2pt.niftynet_batch_to_torch_tensor(batch_dict).to(device)\n",
" with torch.no_grad():\n",
" logits = model(input_tensor)\n",
" labels = tf2pt.torch_logits_to_niftynet_labels(logits)\n",
" window_dict = dict(window=labels)\n",
" aggregator.decode_batch(window_dict, batch_dict['image_location'])\n",
"\n",
"# Release GPU memory\n",
"del model\n",
"del model_pretrained\n",
"del input_tensor\n",
"del logits\n",
"torch.cuda.empty_cache()"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"I0824 12:06:55.225259 140162299176832 sampler_grid_v2.py:77] grid sampling image sizes: {'image': (180, 276, 276, 1, 1)}\n",
"I0824 12:06:55.226367 140162299176832 sampler_grid_v2.py:79] grid sampling window sizes: {'image': (128, 128, 128, 1, 1)}\n",
"I0824 12:06:55.227269 140162299176832 sampler_grid_v2.py:87] yielding 27 locations from image\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Running inference iteration 0...\n",
"Running inference iteration 1...\n",
"Running inference iteration 2...\n",
"Running inference iteration 3...\n",
"Running inference iteration 4...\n",
"Running inference iteration 5...\n",
"Running inference iteration 6...\n",
"Running inference iteration 7...\n",
"Running inference iteration 8...\n",
"Running inference iteration 9...\n",
"Running inference iteration 10...\n",
"Running inference iteration 11...\n",
"Running inference iteration 12...\n",
"Running inference iteration 13...\n",
"Running inference iteration 14...\n",
"Running inference iteration 15...\n",
"Running inference iteration 16...\n",
"Running inference iteration 17...\n",
"Running inference iteration 18...\n",
"Running inference iteration 19...\n",
"Running inference iteration 20...\n",
"Running inference iteration 21...\n",
"Running inference iteration 22...\n",
"Running inference iteration 23...\n",
"Running inference iteration 24...\n",
"Running inference iteration 25...\n",
"Running inference iteration 26...\n",
"Running inference iteration 27...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"W0824 12:10:27.618356 140162299176832 misc_io.py:404] File /tmp/miccai_niftynet_pytorch/prediction/window_OAS1_0145_MR2_mpr_n4_anon_sbj_111_niftynet_out.nii.gz exists, overwriting the file.\n",
"I0824 12:10:27.770883 140162299176832 misc_io.py:409] Saved /tmp/miccai_niftynet_pytorch/prediction/window_OAS1_0145_MR2_mpr_n4_anon_sbj_111_niftynet_out.nii.gz\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xQxvxkaI-GRd",
"colab_type": "text"
},
"source": [
"Let's [run the inference using NiftyNet](https://github.com/NifTK/NiftyNetModelZoo/tree/5-reorganising-with-lfs/highres3dnet_brain_parcellation#generating-segmentations-for-example-data) as well, so that we can compare both results. As before, this step might take a couple of minutes:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aE3ob5z1-GRd",
"colab_type": "code",
"outputId": "b6330d23-c06a-4e27-b504-70da64e67cdb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"%run NiftyNet/net_segment.py inference -c ~/niftynet/extensions/highres3dnet_brain_parcellation/highres3dnet_config_eval.ini"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
"NiftyNet version 0+untagged.1.g299b972\n",
"[CUSTOM]\n",
"-- num_classes: 160\n",
"-- output_prob: False\n",
"-- label_normalisation: False\n",
"-- softmax: True\n",
"-- min_sampling_ratio: 0\n",
"-- compulsory_labels: (0, 1)\n",
"-- rand_samples: 0\n",
"-- min_numb_labels: 1\n",
"-- proba_connect: True\n",
"-- evaluation_units: foreground\n",
"-- label: ()\n",
"-- weight: ()\n",
"-- inferred: ()\n",
"-- image: ('Modality0',)\n",
"-- sampler: ()\n",
"-- name: net_segment\n",
"[CONFIG_FILE]\n",
"-- path: /root/niftynet/extensions/highres3dnet_brain_parcellation/highres3dnet_config_eval.ini\n",
"[MODALITY0]\n",
"-- csv_file: \n",
"-- path_to_search: data/OASIS/\n",
"-- filename_contains: ('nii',)\n",
"-- filename_not_contains: ()\n",
"-- filename_removefromid: \n",
"-- interp_order: 0\n",
"-- loader: None\n",
"-- pixdim: (1.0, 1.0, 1.0)\n",
"-- axcodes: ('R', 'A', 'S')\n",
"-- spatial_window_size: (96, 96, 96)\n",
"[SYSTEM]\n",
"-- cuda_devices: \"\"\n",
"-- num_threads: 2\n",
"-- num_gpus: 1\n",
"-- model_dir: /root/niftynet/models/highres3dnet_brain_parcellation\n",
"-- dataset_split_file: ./dataset_split.csv\n",
"-- event_handler: ('model_saver', 'model_restorer', 'sampler_threading', 'apply_gradients', 'output_interpreter', 'console_logger', 'tensorboard_logger', 'performance_logger')\n",
"-- iteration_generator: iteration_generator\n",
"-- action: inference\n",
"[NETWORK]\n",
"-- name: highres3dnet\n",
"-- activation_function: relu\n",
"-- batch_size: 1\n",
"-- smaller_final_batch_mode: pad\n",
"-- decay: 0.0\n",
"-- reg_type: L2\n",
"-- volume_padding_size: (10, 10, 10)\n",
"-- volume_padding_mode: minimum\n",
"-- volume_padding_to_size: (0,)\n",
"-- window_sampling: uniform\n",
"-- force_output_identity_resizing: False\n",
"-- queue_length: 5\n",
"-- multimod_foreground_type: and\n",
"-- histogram_ref_file: databrain_std_hist_models_otsu.txt\n",
"-- norm_type: percentile\n",
"-- cutoff: (0.001, 0.999)\n",
"-- foreground_type: mean_plus\n",
"-- normalisation: True\n",
"-- rgb_normalisation: False\n",
"-- whitening: True\n",
"-- normalise_foreground_only: True\n",
"-- weight_initializer: he_normal\n",
"-- bias_initializer: zeros\n",
"-- keep_prob: 1.0\n",
"-- weight_initializer_args: {}\n",
"-- bias_initializer_args: {}\n",
"[INFERENCE]\n",
"-- spatial_window_size: (128, 128, 128)\n",
"-- inference_iter: 33000\n",
"-- dataset_to_infer: \n",
"-- save_seg_dir: ./parcellation_output\n",
"-- output_postfix: _niftynet_out\n",
"-- output_interp_order: 0\n",
"-- border: (2, 2, 2)\n",
"-- fill_constant: 0.0\n",
"\u001b[1mINFO:niftynet:\u001b[0m starting segmentation application\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.327971 140162299176832 segmentation_application.py:43] starting segmentation application\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m `csv_file = ` not found, writing to \"/root/niftynet/models/highres3dnet_brain_parcellation/Modality0.csv\" instead.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.334575 140162299176832 image_sets_partitioner.py:266] `csv_file = ` not found, writing to \"/root/niftynet/models/highres3dnet_brain_parcellation/Modality0.csv\" instead.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m Overwriting existing: \"/root/niftynet/models/highres3dnet_brain_parcellation/Modality0.csv\".\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.342744 140162299176832 image_sets_partitioner.py:269] Overwriting existing: \"/root/niftynet/models/highres3dnet_brain_parcellation/Modality0.csv\".\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m [Modality0] search file folders, writing csv file /root/niftynet/models/highres3dnet_brain_parcellation/Modality0.csv\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.347836 140162299176832 image_sets_partitioner.py:286] [Modality0] search file folders, writing csv file /root/niftynet/models/highres3dnet_brain_parcellation/Modality0.csv\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m \n",
"\n",
"Number of subjects 1, input section names: ['subject_id', 'Modality0']\n",
"-- using all subjects (without data partitioning).\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.358435 140162299176832 image_sets_partitioner.py:90] \n",
"\n",
"Number of subjects 1, input section names: ['subject_id', 'Modality0']\n",
"-- using all subjects (without data partitioning).\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\rreading datasets headers |----------| 0.0% \r\u001b[1mINFO:niftynet:\u001b[0m Image reader: loading 1 subjects from sections ('Modality0',) as input [image]\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.371430 140162299176832 image_reader.py:178] Image reader: loading 1 subjects from sections ('Modality0',) as input [image]\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m normalisation histogram reference models ready for image:('Modality0',)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.377284 140162299176832 histogram_normalisation.py:109] normalisation histogram reference models ready for image:('Modality0',)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m initialised window instance\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.417274 140162299176832 sampler_grid_v2.py:53] initialised window instance\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m initialised grid sampler {'image': (1, 128, 128, 128, 1, 1), 'image_location': (1, 7)}\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.418755 140162299176832 sampler_grid_v2.py:54] initialised grid sampler {'image': (1, 128, 128, 128, 1, 1), 'image_location': (1, 7)}\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m using HighRes3DNet\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.432965 140162299176832 base_net.py:29] using HighRes3DNet\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mWARNING:niftynet:\u001b[0m From NiftyNet/niftynet/application/base_application.py:203: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"W0824 12:10:28.434620 140162299176832 deprecation_wrapper.py:119] From NiftyNet/niftynet/application/base_application.py:203: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mINFO:niftynet:\u001b[0m Initialising dataset from generator...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"I0824 12:10:28.444165 140162299176832 image_window_dataset.py:318] Initialising dataset from generator...\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mWARNING:niftynet:\u001b[0m From NiftyNet/niftynet/layer/convolution.py:89: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"W0824 12:10:28.746025 140162299176832 deprecation_wrapper.py:119] From NiftyNet/niftynet/layer/convolution.py:89: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mWARNING:niftynet:\u001b[0m From NiftyNet/niftynet/layer/bn.py:75: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"W0824 12:10:28.798005 140162299176832 deprecation_wrapper.py:119] From NiftyNet/niftynet/layer/bn.py:75: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[1mWARNING:niftynet:\u001b[0m From NiftyNet/niftynet/engine/application_variables.py:188: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"W0824 12:10:30.033472 140162299176832 deprecation_wrapper.py:119] From NiftyNet/niftynet/engine/application_variables.py:188: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
"\n"
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"\u001b[1mWARNING:niftynet:\u001b[0m From NiftyNet/niftynet/engine/handler_model.py:108: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
"\n"
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"W0824 12:10:30.041137 140162299176832 deprecation_wrapper.py:119] From NiftyNet/niftynet/engine/handler_model.py:108: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
"\n"
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"text": [
"\u001b[1mINFO:niftynet:\u001b[0m Restoring parameters from /root/niftynet/models/highres3dnet_brain_parcellation/models/model.ckpt-33000\n"
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"I0824 12:10:30.175667 140162299176832 saver.py:1280] Restoring parameters from /root/niftynet/models/highres3dnet_brain_parcellation/models/model.ckpt-33000\n"
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"\u001b[1mINFO:niftynet:\u001b[0m grid sampling image sizes: {'image': (180, 276, 276, 1, 1)}\n"
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"I0824 12:10:33.128409 140156488800000 sampler_grid_v2.py:77] grid sampling image sizes: {'image': (180, 276, 276, 1, 1)}\n"
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"I0824 12:10:33.131004 140156488800000 sampler_grid_v2.py:79] grid sampling window sizes: {'image': (128, 128, 128, 1, 1)}\n"
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"\u001b[1mINFO:niftynet:\u001b[0m yielding 27 locations from image\n"
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"I0824 12:10:33.132872 140156488800000 sampler_grid_v2.py:87] yielding 27 locations from image\n"
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"W0824 12:11:22.753985 140162299176832 misc_io.py:404] File /root/niftynet/models/highres3dnet_brain_parcellation/parcellation_output/window_seg_OAS1_0145_MR2_mpr_n4_anon_sbj_111__niftynet_out.nii.gz exists, overwriting the file.\n"
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"\u001b[1mINFO:niftynet:\u001b[0m Saved /root/niftynet/models/highres3dnet_brain_parcellation/parcellation_output/window_seg_OAS1_0145_MR2_mpr_n4_anon_sbj_111__niftynet_out.nii.gz\n"
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"I0824 12:11:23.047438 140162299176832 misc_io.py:409] Saved /root/niftynet/models/highres3dnet_brain_parcellation/parcellation_output/window_seg_OAS1_0145_MR2_mpr_n4_anon_sbj_111__niftynet_out.nii.gz\n"
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"\u001b[1mINFO:niftynet:\u001b[0m stopping -- event handler: OutputInterpreter.\n"
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"I0824 12:11:23.052951 140162299176832 application_driver.py:336] stopping -- event handler: OutputInterpreter.\n"
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"I0824 12:11:23.055260 140162299176832 application_driver.py:220] cleaning up...\n"
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"I0824 12:11:23.057387 140162299176832 handler_sampler.py:53] stopping sampling threads\n"
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"I0824 12:11:23.083722 140162299176832 application_driver.py:232] SegmentationApplication stopped (time in second 53.05).\n"
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