Merge pull request #207 from NifTK/refactoring-data-app

window sampler using Dataset APIs and other non-breaking fixes.

.gitlab-ci.yml

@@ -19,7 +19,7 @@ pylintjob:
     - 223-put-bug-fixes-under-fixed-header-in-changelog
   script:
     - pylint --rcfile=tests/pylintrc niftynet/engine
-    - pylint --rcfile=tests/pylintrc niftynet/io/image_*py
+    - pylint --rcfile=tests/pylintrc niftynet/io
     - pylint --rcfile=tests/pylintrc niftynet/utilities/user_parameters_*py
     - pylint --rcfile=tests/pylintrc niftynet/utilities/download.py
     - pylint --rcfile=tests/pylintrc niftynet/utilities/niftynet_global_config.py
@@ -31,8 +31,8 @@ testjob:
   only:
     - master
     - dev
-    - refactoring-engine-with-blinker
     - tags
+    - refactoring-data-app
   script:
     # !!kill coverage in case of hanging processes
     - if pgrep coverage; then pkill -f coverage; fi
@@ -208,8 +208,8 @@ quicktest:
   except:
     - master
     - dev
-    - refactoring-engine-with-blinker
     - tags
+    - refactoring-data-app
     - 147-revise-contribution-guidelines-to-include-github
     - 150-properly-format-the-bibtex-entry-to-the-ipmi-2017-paper-on-the-main-readme
     - 148-publish-niftynet-v0-2-0-on-python-package-index-pypi
@@ -261,9 +261,8 @@ pip-installer:
   only:
     - master
     - dev
-    - refactoring-engine-with-blinker
     - tags
-    - 117-support-for-user-defined-networks-using-pip-installed-niftynet
+    - refactoring-data-app
   script:
     # get the shortened version of last commit's hash
     - LAST_COMMIT=$(git rev-parse --short HEAD)
@@ -303,7 +302,7 @@ pip-installer:
     - set -e
     - cd $venv_dir
     # install TF
-    - pip install tensorflow-gpu==1.9
+    - pip install tensorflow-gpu==1.10
     # install using built NiftyNet wheel
     - pip install $niftynet_wheel
     # install SimpleITK for package importer test to work properly
@@ -403,7 +402,7 @@ pip-installer:
     - set -e
     - cd $venv_dir
     # install TF
-    - pip install tensorflow-gpu==1.9
+    - pip install tensorflow-gpu==1.10
     # install using built NiftyNet wheel
     - pip install $niftynet_wheel
     # install SimpleITK for package importer test to work properly

CONTRIBUTING.md

@@ -142,7 +142,7 @@ Please send a merge request with only relevant changes to a particular unit test
 
 ### Making a release
 
-NiftyNet versions are numbered following [Semantic Versioning (semver)](http://semver.org/spec/v2.0.0.html).
+NiftyNet versions are numbered following [Semantic Versioning (semver)](https://semver.org/spec/v2.0.0.html).
 After adding notes for the current release to the [NiftyNet changelog][changelog], the current release should be [tagged][git-tag] with a [PEP440][pep440]-compliant semver number preceded by the letter `v` (for "version").
 
 [pep440]: https://www.python.org/dev/peps/pep-0440/

README.md

@@ -44,8 +44,8 @@ HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium
 ### Installation
 
 1. Please install the appropriate [TensorFlow][tf] package*:
-   * [`pip install tensorflow-gpu==1.9`][tf-pypi-gpu] for TensorFlow with GPU support
-   * [`pip install tensorflow==1.9`][tf-pypi] for CPU-only TensorFlow
+   * [`pip install tensorflow-gpu==1.10`][tf-pypi-gpu] for TensorFlow with GPU support
+   * [`pip install tensorflow==1.10`][tf-pypi] for CPU-only TensorFlow
 1. [`pip install niftynet`](https://pypi.org/project/NiftyNet/)
 
  <sup>All other NiftyNet dependencies are installed automatically as part of the pip installation process.

config/default_segmentation.ini

@@ -3,6 +3,7 @@
 csv_file=
 path_to_search = ./example_volumes/monomodal_parcellation
 filename_contains = T1
+filename_removefromid = _T1
 filename_not_contains =
 spatial_window_size = (20, 42, 42)
 interp_order = 3
@@ -13,6 +14,7 @@ axcodes=(A, R, S)
 path_to_search = ./example_volumes/monomodal_parcellation
 filename_contains = Label
 filename_not_contains =
+filename_removefromid = _Label
 spatial_window_size = (20, 42, 42)
 interp_order = 0
 pixdim=(1.0, 1.0, 1.0)

config/default_segmentation_bf.ini

@@ -0,0 +1,77 @@
+############################ input configuration sections
+[modality1]
+csv_file=
+path_to_search = ./example_volumes/monomodal_parcellation
+filename_contains = T1
+filename_not_contains =
+spatial_window_size = (20, 42, 42)
+interp_order = 3
+pixdim=(1.0, 1.0, 1.0)
+axcodes=(A, R, S)
+
+[label]
+path_to_search = ./example_volumes/monomodal_parcellation
+filename_contains = Label
+filename_not_contains =
+spatial_window_size = (20, 42, 42)
+interp_order = 0
+pixdim=(1.0, 1.0, 1.0)
+axcodes=(A, R, S)
+
+############################## system configuration sections
+[SYSTEM]
+cuda_devices = ""
+num_threads = 2
+num_gpus = 1
+model_dir = ./models/model_monomodal_toy
+
+[NETWORK]
+name = toynet
+activation_function = prelu
+batch_size = 1
+decay = 0.1
+reg_type = L2
+
+# volume level preprocessing
+volume_padding_size = 21
+# histogram normalisation
+histogram_ref_file = ./example_volumes/monomodal_parcellation/standardisation_models.txt
+norm_type = percentile
+cutoff = (0.01, 0.99)
+normalisation = False
+whitening = False
+normalise_foreground_only=True
+foreground_type = otsu_plus
+multimod_foreground_type = and
+
+queue_length = 20
+
+
+[TRAINING]
+sample_per_volume = 32
+rotation_angle =
+scaling_percentage =
+bf_order = 3
+bias_field_range = (-0.5, 0.5)
+random_flipping_axes= 1
+lr = 0.01
+loss_type = Dice
+starting_iter = 0
+save_every_n = 100
+max_iter = 10
+max_checkpoints = 20
+
+[INFERENCE]
+border = (0, 0, 1)
+#inference_iter = 10
+save_seg_dir = ./output/toy
+output_interp_order = 0
+spatial_window_size = (0, 0, 3)
+
+############################ custom configuration sections
+[SEGMENTATION]
+image = modality1
+label = label
+output_prob = False
+num_classes = 160
+label_normalisation = True

demos/module_examples/ImageReader.ipynb

@@ -56,7 +56,7 @@
    "source": [
     "import sys\n",
     "niftynet_path = '/Users/bar/Documents/Niftynet/'\n",
-    "sys.path.append(niftynet_path)\n",
+    "sys.path.insert(0, niftynet_path)\n",
     "\n",
     "from niftynet.io.image_reader import ImageReader"
    ]

demos/module_examples/ImageSampler.ipynb

@@ -64,7 +64,7 @@
    "source": [
     "import sys\n",
     "niftynet_path = '/Users/foo/Documents/Niftynet/'\n",
-    "sys.path.append(niftynet_path)\n",
+    "sys.path.insert(0, niftynet_path)\n",
     "\n",
     "from niftynet.utilities.download import download\n",
     "download('mr_ct_regression_model_zoo_data')\n",
@@ -101,7 +101,7 @@
    ],
    "source": [
     "from niftynet.io.image_reader import ImageReader\n",
-    "from niftynet.contrib.dataset_sampler.image_window_dataset import ImageWindowDataset\n",
+    "from niftynet.engine.image_window_dataset import ImageWindowDataset\n",
     "\n",
     "# creating an image reader.\n",
     "data_param = \\\n",
@@ -258,7 +258,7 @@
    ],
    "source": [
     "from niftynet.io.image_reader import ImageReader\n",
-    "from niftynet.contrib.dataset_sampler.sampler_uniform_v2 import UniformSampler\n",
+    "from niftynet.engine.sampler_uniform_v2 import UniformSampler\n",
     "\n",
     "# creating an image reader.\n",
     "# creating an image reader.\n",
@@ -342,7 +342,7 @@
    ],
    "source": [
     "from niftynet.io.image_reader import ImageReader\n",
-    "from niftynet.contrib.dataset_sampler.sampler_grid_v2 import GridSampler\n",
+    "from niftynet.engine.sampler_grid_v2 import GridSampler\n",
     "\n",
     "# creating an image reader.\n",
     "data_param = \\\n",
@@ -471,7 +471,7 @@
    ],
    "source": [
     "from niftynet.io.image_reader import ImageReader\n",
-    "from niftynet.contrib.dataset_sampler.sampler_weighted_v2 import WeightedSampler\n",
+    "from niftynet.engine.sampler_weighted_v2 import WeightedSampler\n",
     "\n",
     "# creating an image reader.\n",
     "data_param = \\\n",
@@ -588,7 +588,7 @@
    ],
    "source": [
     "from niftynet.io.image_reader import ImageReader\n",
-    "from niftynet.contrib.dataset_sampler.sampler_balanced_v2 import BalancedSampler\n",
+    "from niftynet.engine.sampler_balanced_v2 import BalancedSampler\n",
     "\n",
     "# creating an image reader.\n",
     "data_param = \\\n",