Merge branch '69-push-to-pip-repository-after-commit-to-master-off-de…

…v-restructure-hierarchy' into 'dev'

Resolve "Publish NiftyNet v0.1 on Python Package Index (PyPI)"

Closes #69 and #125

See merge request !42

.gitlab-ci.yml

@@ -37,28 +37,28 @@ testjob:
     # run python code with coverage wrapper
     - coverage erase
 
-    - coverage run -a --source . net_segmentation.py train -c config/highres3dnet_config.ini --batch_size 1 --image_size 32 --label_size 32 --queue_length 5 --num_threads 2
-    - coverage run -a --source . net_segmentation.py inference -c config/highres3dnet_config.ini --batch_size 8 --image_size 64 --label_size 64 --queue_length 32
+    - coverage run -a --source . net_segment.py train -c config/highres3dnet_config.ini --batch_size 1 --image_size 32 --label_size 32 --queue_length 5 --num_threads 2
+    - coverage run -a --source . net_segment.py inference -c config/highres3dnet_config.ini --batch_size 8 --image_size 64 --label_size 64 --queue_length 32
 
-    - coverage run -a --source . net_segmentation.py train -c config/scalenet_config.ini --batch_size 1 --image_size 32 --label_size 32 --queue_length 5 --num_threads 2
-    - coverage run -a --source . net_segmentation.py inference -c config/scalenet_config.ini --batch_size 16 --image_size 64 --label_size 64 --queue_length 32
+    - coverage run -a --source . net_segment.py train -c config/scalenet_config.ini --batch_size 1 --image_size 32 --label_size 32 --queue_length 5 --num_threads 2
+    - coverage run -a --source . net_segment.py inference -c config/scalenet_config.ini --batch_size 16 --image_size 64 --label_size 64 --queue_length 32
 
-    - coverage run -a --source . net_segmentation.py train -c config/vnet_config.ini --batch_size 1 --image_size 32 --label_size 32 --queue_length 5 --num_threads 2 --activation_function relu
-    - coverage run -a --source . net_segmentation.py inference -c config/vnet_config.ini --batch_size 16 --image_size 64 --label_size 64 --queue_length 32 --activation_function relu
+    - coverage run -a --source . net_segment.py train -c config/vnet_config.ini --batch_size 1 --image_size 32 --label_size 32 --queue_length 5 --num_threads 2 --activation_function relu
+    - coverage run -a --source . net_segment.py inference -c config/vnet_config.ini --batch_size 16 --image_size 64 --label_size 64 --queue_length 32 --activation_function relu
 
     # need a large GPU to run
-    #- coverage run -a --source . net_segmentation.py train -c config/unet_config.ini --batch_size 1 --image_size 96 --label_size 96 --queue_length 5 --num_threads 2
-    #- coverage run -a --source . net_segmentation.py inference -c config/unet_config.ini --batch_size 1 --image_size 96 --label_size 96 --queue_length 5
+    #- coverage run -a --source . net_segment.py train -c config/unet_config.ini --batch_size 1 --image_size 96 --label_size 96 --queue_length 5 --num_threads 2
+    #- coverage run -a --source . net_segment.py inference -c config/unet_config.ini --batch_size 1 --image_size 96 --label_size 96 --queue_length 5
 
-    #- coverage run -a --source . net_segmentation.py train -c config/deepmedic_config.ini --batch_size 8 --queue_length 16 --num_threads 2
-    #- coverage run -a --source . net_segmentation.py inference -c config/deepmedic_config.ini --batch_size 64 --queue_length 96
+    #- coverage run -a --source . net_segment.py train -c config/deepmedic_config.ini --batch_size 8 --queue_length 16 --num_threads 2
+    #- coverage run -a --source . net_segment.py inference -c config/deepmedic_config.ini --batch_size 64 --queue_length 96
 
-    - coverage run -a --source . net_segmentation.py train -c config/default_config.ini --image_size 42 --label_size 42 --batch_size 3 --queue_length 6
-    - coverage run -a --source . net_segmentation.py train -c config/default_config.ini --image_size 42 --label_size 42 --batch_size 3 --queue_length 6 --starting_iter 10 --max_iter 15
-    - coverage run -a --source . net_segmentation.py inference -c config/default_config.ini --image_size 84 --label_size 84 --batch_size 7 --queue_length 14
+    - coverage run -a --source . net_segment.py train -c config/default_config.ini --image_size 42 --label_size 42 --batch_size 3 --queue_length 6
+    - coverage run -a --source . net_segment.py train -c config/default_config.ini --image_size 42 --label_size 42 --batch_size 3 --queue_length 6 --starting_iter 10 --max_iter 15
+    - coverage run -a --source . net_segment.py inference -c config/default_config.ini --image_size 84 --label_size 84 --batch_size 7 --queue_length 14
 
-    - coverage run -a --source . net_segmentation.py train -c config/default_multimodal_config.ini --image_size 42 --label_size 42 --batch_size 3
-    - coverage run -a --source . net_segmentation.py inference -c config/default_multimodal_config.ini --image_size 84 --label_size 84 --batch_size 7
+    - coverage run -a --source . net_segment.py train -c config/default_multimodal_config.ini --image_size 42 --label_size 42 --batch_size 3
+    - coverage run -a --source . net_segment.py inference -c config/default_multimodal_config.ini --image_size 84 --label_size 84 --batch_size 7
 
     - coverage run -a --source . -m tests.mean_variance_normalisation_test
     - coverage run -a --source . -m tests.binary_masking_test
@@ -103,9 +103,9 @@ testjob:
 pip-installer:
   stage: pip_test
   only:
-    - 69-push-to-pip-repository-after-commit-to-master-off-dev
-    - 69-push-to-pip-repository-after-commit-to-master-off-dev-restructure-hierarchy
-    - 131-pip-bundle-does-not-work-with-cpu-only-tensorflow-drop-tensorflow-auto-install
+    - master
+    - dev
+    - dev-staging
     - tags
   script:
     # source utils
@@ -154,8 +154,8 @@ pip-installer:
     - python $package_importer
     # test niftynet command
     - ln -s /home/gitlab-runner/environments/niftynet/data/example_volumes ./example_volumes
-    - net_segmentation train -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 42 --label_size 42 --batch_size 1 --save_every_n 10
-    - net_segmentation inference -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 80 --label_size 80 --batch_size 8
+    - net_segment train -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 42 --label_size 42 --batch_size 1 --save_every_n 10
+    - net_segment inference -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 80 --label_size 80 --batch_size 8
     # deactivate virtual environment
     - deactivate
     - cd $niftynet_dir
@@ -188,8 +188,8 @@ pip-installer:
     - python $package_importer
     # test niftynet command
     - ln -s /home/gitlab-runner/environments/niftynet/data/example_volumes ./example_volumes
-    - net_segmentation train -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 42 --label_size 42 --batch_size 1 --save_every_n 10
-    - net_segmentation inference -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 80 --label_size 80 --batch_size 8
+    - net_segment train -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 42 --label_size 42 --batch_size 1 --save_every_n 10
+    - net_segment inference -c $niftynet_dir/config/default_config.ini --net_name toynet --image_size 80 --label_size 80 --batch_size 8
     # deactivate virtual environment
     - deactivate
     - cd $niftynet_dir

demos/PROMISE12/PROMISE12_Demo_Notebook.ipynb

@@ -52,9 +52,9 @@
    "metadata": {},
    "source": [
     "## Training a network from the command line\n",
-    "The simplest way to use NiftyNet is via the commandline net_segmentation.py script. Normally, this is done on the command line with a command like this from the NiftyNet root directory:\n",
+    "The simplest way to use NiftyNet is via the commandline net_segment.py script. Normally, this is done on the command line with a command like this from the NiftyNet root directory:\n",
     "\n",
-    "```python net_segmentation.py train --conf demo/PROMISE12/promise12_demo_train_config.ini --image_size 32 --label_size 32 --max_iter 10```\n",
+    "```python net_segment.py train --conf demo/PROMISE12/promise12_demo_train_config.ini --image_size 32 --label_size 32 --max_iter 10```\n",
     "\n",
     "Notice that we use configuration file that is specific to this experiment. This file contains default settings. Also note that we can override these settings on the command line.\n",
     "\n",
@@ -78,7 +78,7 @@
    "source": [
     "Now you have trained (a few iterations of) a deep learning network for medical image segmentation. If you have some time on your hands, you can finish training the network (by leaving off the max_iter argument) and try it out, by running the following command\n",
     "\n",
-    "```python net_segmentation.py inference --conf demo/PROMISE12/promise12_demo_inference_config.ini --image_size 32 --label_size 32```\n",
+    "```python net_segment.py inference --conf demo/PROMISE12/promise12_demo_inference_config.ini --image_size 32 --label_size 32```\n",
     "\n",
     "or the following python code in the Notebook"
    ]
@@ -102,7 +102,7 @@
    "source": [
     "Otherwise, you can load up some pre-trained weights for the network:\n",
     "\n",
-    "```python net_segmentation.py inference --conf demo/PROMISE12/promise12_demo_config.ini --model_dir demo/PROMISE12/pretrained```\n",
+    "```python net_segment.py inference --conf demo/PROMISE12/promise12_demo_config.ini --model_dir demo/PROMISE12/pretrained```\n",
     "or the following python code in the Notebook"
    ]
   },
@@ -318,7 +318,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can use helper functions to parse commandline parameters and automatically match patient data (which is what net_segmentation.py did)."
+    "You can use helper functions to parse commandline parameters and automatically match patient data (which is what net_segment.py did)."
    ]
   },
   {

demos/README.md

@@ -28,15 +28,15 @@ To train a "toynet" specified in `network/toynet.py`:
 cd NiftyNet/
 wget -N https://www.dropbox.com/s/y7mdh4m9ptkibax/example_volumes.tar.gz
 tar -xzvf example_volumes.tar.gz
-net_segmentation train --net_name toynet \
+net_segment train --net_name toynet \
     --image_size 42 --label_size 42 --batch_size 1
 ```
 (GPU computing is enabled by default; to train with CPU only please use `--num_gpus 0`)
 
 After the training process, to do segmentation with a trained "toynet":
 ``` sh
 cd NiftyNet/
-net_segmentation inference --net_name toynet \
+net_segment inference --net_name toynet \
     --save_seg_dir ./seg_output \
     --image_size 80 --label_size 80 --batch_size 8
 ```
@@ -51,11 +51,11 @@ Alternatively, to run with a customised config file:
 ``` sh
 cd NiftyNet/
 # training
-net_segmentation train -c /path/to/customised_config
+net_segment train -c /path/to/customised_config
 # inference
-net_segmentation inference -c /path/to/customised_config
+net_segment inference -c /path/to/customised_config
 ```
 where `/path/to/customised_config` implements all parameters listed by running:
 ```sh
-net_segmentation -h
+net_segment -h
 ```

demos/brain_parcellation/run_parcellation.sh

@@ -2,7 +2,7 @@
 
 # please make sure you installed all dependencies of NiftyNet.
 # cd NiftyNet/; pip install -r requirements-gpu.txt
-NIFTYNET=../../net_segmentation.py
+NIFTYNET=../../net_segment.py
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
 cd "$DIR"

niftynet/network/README.md

@@ -56,4 +56,4 @@ image_ size = 57, label_ size = 9, d_ factor = 3
 1. Create a `niftynet/network/new_net.py` inheriting `BaseNet` from `niftynet.layer.base_net`
 1. Implement `layer_op()` function using the building blocks in `niftynet/layer/` or creating new layers
 1. Import `niftynet.network.new_net` to the `NetFactory` class in `niftynet/__init__.py`
-1. Train the network with `python net_segmentation.py train -c /path/to/customised_config`
+1. Train the network with `python net_segment.py train -c /path/to/customised_config`

pip/long_description.rst

@@ -66,7 +66,7 @@ Acknowledgements
 This project is grateful for the support from the `Wellcome Trust`_, the `Engineering and Physical Sciences Research Council (EPSRC)`_, the `National Institute for Health Research (NIHR)`_, the `Department of Health (DoH)`_, `University College London (UCL)`_, the `Science and Engineering South Consortium (SES)`_, the `STFC Rutherford-Appleton Laboratory`_, and `NVIDIA`_.
 
 .. _`TensorFlow`: https://www.tensorflow.org/
-.. _`Wellcome EPSRC Centre for Interventional and Surgical Sciences`: http://www.ucl.ac.uk/surgical-interventional-sciences
+.. _`Wellcome EPSRC Centre for Interventional and Surgical Sciences`: http://www.ucl.ac.uk/weiss
 .. _`NiftyNet source code repository`: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet
 .. _`Centre for Medical Image Computing`: http://cmic.cs.ucl.ac.uk/
 .. _`Centre for Medical Image Computing (CMIC)`: http://cmic.cs.ucl.ac.uk/

run_application.py

@@ -2,7 +2,7 @@
 import warnings
 import time
 warnings.simplefilter('always', DeprecationWarning)
-warnings.warn('run_application.py is deprecated and will be removed; please use net_segmentation.py instead.', DeprecationWarning, stacklevel=2)
+warnings.warn('run_application.py is deprecated and will be removed; please use net_segment.py instead.', DeprecationWarning, stacklevel=2)
 warnings.simplefilter('ignore', DeprecationWarning)
 time.sleep(3)
 

setup.py

@@ -26,7 +26,7 @@
 info_module.write('"""\n')
 info_module.write('\n')
 info_module.write('\n')
-info_module.write('version = "{}"\n'.format(version_buf))
+info_module.write('VERSION_DESCRIPTOR = "{}"\n'.format(version_buf))
 info_module.close()
 
 # Regex for checking PEP 440 conformity
@@ -121,7 +121,7 @@
 
     entry_points={
         'console_scripts': [
-            'net_segmentation=niftynet:main',
+            'net_segment=niftynet:main',
         ],
     },
 )