Merge pull request #20 from Project-MONAI/moving-code-from-minimal

moving monai-minimal prototype code to the main repo

Author: atbenmurray

.flake8

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+[flake8]
+select = B,C,E,F,N,P,T4,W,B9
+max-line-length = 120
+# C408 ignored because we like the dict keyword argument syntax
+# E501 is not flexible enough, we're using B950 instead
+ignore =
+    E203,E305,E402,E501,E721,E741,F403,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,
+    # these ignores are from flake8-bugbear; please fix!
+    B007,B008,
+    # these ignores are from flake8-comprehensions; please fix!
+    C400,C401,C402,C403,C404,C405,C407,C411,C413,C414,C415,C416
+per-file-ignores = __init__.py: F401
+exclude = docs/src,*.pyi,.git

.github/workflows/pythonapp.yml

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+name: Python application
+
+on: [push]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python 3.7
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Lint with flake8
+      run: |
+        pip install flake8
+        pip install pep8-naming
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings.
+        flake8 . --count --statistics
+#     - name: Test with pytest
+#       run: |
+#         pip install pytest
+#         pytest

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+examples/scd_lvsegs.npz

examples/cardiac_segmentation.ipynb

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+
+# monai
+
+The framework is divided into a few large modules:
+
+* **application**: Contains the `NetworkManager` type, monitors, and utilities
+
+* **data**: Contains the data stream definition types and example datasets
+
+* **networks**: Contains network definitions, component definitions, and Pytorch specific utilities
+
+* **test**: Contains the unit tests for the framework
+
+* **utils**: Contains a set of utility files for doing namespace aliasing, auto module loading, and other facilities

monai/README.md

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+import os, sys
+from .utils.moduleutils import loadSubmodules
+
+
+__copyright__ = "(c) 2019 MONAI Consortium"
+__version__tuple__ = (0, 0, 1)
+__version__ = "%i.%i.%i" % (__version__tuple__)
+
+__basedir__ = os.path.dirname(__file__)
+
+
+loadSubmodules(sys.modules[__name__], False)  # load directory modules only, skip loading individual files
+loadSubmodules(sys.modules[__name__], True)  # load all modules, this will trigger all export decorations

monai/__init__.py

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+
+# Application
+
+Contains the code for managing the training process. This includes the `NetworkManager` and its subclasses, monitors,
+and other facilities.
+
+* **config**: This has a few facilities for configuration and diagnostic output.
+
+* **metrics**: Defines metric tracking types.
+
+* **handlers**: Defines handlers for implementing functionality at various stages in the training process.
+
+* **engine**: Eventually will have Engine-derived classes for extending Ignite behaviour.

monai/application/README.md

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+
+

monai/application/__init__.py

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+
+

monai/application/config/__init__.py

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+import os, sys
+from collections import OrderedDict
+import monai
+import numpy as np
+import torch
+
+try:
+    import ignite
+    ignite_version=ignite.__version__
+except ImportError:
+    ignite_version='NOT INSTALLED'
+
+export = monai.utils.export("monai.application.config")
+
+
+@export
+def getConfigValues():
+    output = OrderedDict()
+
+    output["MONAI version"] = monai.__version__
+    output["Python version"] = sys.version.replace("\n", " ")
+    output["Numpy version"] = np.version.full_version
+    output["Pytorch version"] = torch.__version__
+    output["Ignite version"] = ignite_version
+
+    return output
+
+
+@export
+def printConfig(file=sys.stdout):
+    for kv in getConfigValues().items():
+        print("%s: %s" % kv, file=file, flush=True)
+
+
+@export
+def setVisibleDevices(*devInds):
+    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, devInds))

monai/application/config/deviceconfig.py

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+
+

monai/application/engine/__init__.py

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+from collections import defaultdict
+
+import monai
+
+
+@monai.utils.export("monai.application.handlers")
+@monai.utils.alias("metriclogger")
+class MetricLogger:
+    def __init__(self,loss_transform=lambda x:x, metric_transform=lambda x:x):
+        self.loss_transform=loss_transform
+        self.metric_transform=metric_transform
+        self.loss=[]
+        self.metrics=defaultdict(list)
+        
+    def attach(self,engine):
+        return engine.add_event_handler(monai.application.engine.Events.ITERATION_COMPLETED,self)
+        
+    def __call__(self,engine):
+        self.loss.append(self.loss_transform(engine.state.output))
+        
+        for m,v in engine.state.metrics.items():
+            v=self.metric_transform(v)
+#             # metrics may not be added on the first timestep, pad the list if this is the case 
+#             # so that each metric list is the same length as self.loss
+#             if len(self.metrics[m])==0: 
+#                 self.metrics[m].append([v[0]]*len(self.loss))
+                
+            self.metrics[m].append(v)
+            

monai/application/handlers/__init__.py

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+
+# Data
+
+This implements the data streams classes and contains a few example datasets. Data streams are iterables which produce
+single data items or batches thereof from source iterables (usually). Chaining these together is how data pipelines are
+implemented in the framework. Data augmentation routines are also provided here which can applied to data items as they
+pass through the stream, either singly or in parallel. 
+
+For example, the following stream reads image/segmentation pairs from `imSrc` (any iterable), applies the augmentations
+to convert the array format and apply simple augmentations (rotation, transposing, flipping, shifting) using mutliple
+threads, and wraps the whole stream in a buffering thread stream:
+
+```
+def normalizeImg(im,seg):
+    im=utils.arrayutils.rescaleArray(im)
+    im=im[None].astype(np.float32)
+    seg=seg[None].astype(np.int32)
+    return im, seg
+
+augs=[
+    normalizeImg,
+    augments.rot90, 
+    augments.transpose, 
+    augments.flip,
+    partial(augments.shift,dimFract=5,order=0,nonzeroIndex=1),
+]
+
+src=data.augments.augmentstream.ThreadAugmentStream(imSrc,200,augments=augs)
+src=data.streams.ThreadBufferStream(src)
+```
+
+In this code, `src` is now going to yield batches of 200 images in a separate thread when iterated over. This can be
+fed directly into a `NetworkManager` class as its `src` parameter. 
+
+Module breakdown:
+
+* **augments**: Contains definitions and stream types for doing data augmentation. An augment is simply a callable which
+accepts one or more Numpy arrays and returns the augmented result. The provided decorators are for adding probability
+and other facilities to a function.
+
+* **readers**: Subclasses of `DataStream` for reading data from arrays and various file formats.
+
+* **streams**: Contains the definitions of the stream classes which implement a number of operations on streams. The
+root of the stream classes is `DataStream` which provides a very simple iterable facility. It iterates over its `src`
+member, passes each item into its `generate()` generator method and yields each resulting value. This allows subclasses
+to implement `generate` to modify data as it moves through the stream. The `streamgen` decorator is provided to simplify
+this by being applied to a generator function to fill this role in a new object. Other subclasses implement buffering,
+batching, merging from multiple sources, cycling between sources, prefetching, and fetching data from the source in a
+separate thread.
+

monai/application/handlers/metric_logger.py

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+
+

monai/application/metrics/__init__.py

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+
+