Adding script to run unit tests and example test cases

.gitignore

@@ -103,3 +103,4 @@ venv.bak/
 # mypy
 .mypy_cache/
 examples/scd_lvsegs.npz
+.idea/

monai/__init__.py

@@ -12,7 +12,7 @@
 import os
 import sys
 
-from .utils.moduleutils import load_submodules, loadSubmodules
+from .utils.moduleutils import load_submodules
 
 __copyright__ = "(c) 2020 MONAI Consortium"
 __version__tuple__ = (0, 0, 1)

runtests.sh

@@ -0,0 +1,102 @@
+#! /bin/bash
+# Test script for running all tests
+
+
+homedir="$( cd -P "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd $homedir
+
+#export PYTHONPATH="$homedir:$PYTHONPATH"
+
+# configuration values
+doCoverage=false
+doQuickTests=false
+doNetTests=false
+doDryRun=false
+doZooTests=false
+
+# testing command to run
+cmd="python"
+cmdprefix=""
+
+
+# parse arguments
+for i in "$@"
+do
+    case $i in
+        --coverage)
+            doCoverage=true
+        ;;
+        --quick)
+            doQuickTests=true
+            doCoverage=true
+            export QUICKTEST=True
+        ;;
+        --net)
+            doNetTests=true
+        ;;
+        --dryrun)
+            doDryRun=true
+        ;;
+        --zoo)
+            doZooTests=true
+        ;;
+        *)
+            echo "runtests.sh [--coverage] [--quick] [--net] [--dryrun] [--zoo]"
+            exit 1
+        ;;
+    esac
+done
+
+
+# commands are echoed instead of run in this case
+if [ "$doDryRun" = 'true' ]
+then
+    echo "Dry run commands:"
+    cmdprefix="dryrun " 
+
+    # create a dry run function which prints the command prepended with spaces for neatness
+    function dryrun { echo "  " $* ; }
+fi
+
+
+# set command and clear previous coverage data
+if [ "$doCoverage" = 'true' ]
+then
+    cmd="coverage run -a --source ."
+    ${cmdprefix} coverage erase
+fi
+
+
+# # download test data if needed
+# if [ ! -d testing_data ] && [ "$doDryRun" != 'true' ]
+# then
+# fi
+
+
+# unit tests
+${cmdprefix}${cmd} -m unittest
+
+
+# network training/inference/eval tests
+if [ "$doNetTests" = 'true' ]
+then
+    for i in examples/*.py
+    do
+        echo $i
+        ${cmdprefix}${cmd} $i
+    done
+fi
+
+
+# # run model zoo tests
+# if [ "$doZooTests" = 'true' ]
+# then
+# fi
+
+
+# report on coverage
+if [ "$doCoverage" = 'true' ]
+then
+    ${cmdprefix}coverage report --omit='*/test/*' --skip-covered -m
+fi
+

tests/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/testconvolutions.py

@@ -0,0 +1,85 @@
+# 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.
+
+
+from .utils import ImageTestCase
+
+from monai.networks.layers.convolutions import Convolution, ResidualUnit
+
+
+class TestConvolution2D(ImageTestCase):
+    def test_conv1(self):
+        conv = Convolution(2, self.input_channels, self.output_channels)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_conv_only1(self):
+        conv = Convolution(2, self.input_channels, self.output_channels, conv_only=True)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_stride1(self):
+        conv = Convolution(2, self.input_channels, self.output_channels, strides=2)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_dilation1(self):
+        conv = Convolution(2, self.input_channels, self.output_channels, dilation=3)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_dropout1(self):
+        conv = Convolution(2, self.input_channels, self.output_channels, dropout=0.15)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_transpose1(self):
+        conv = Convolution(2, self.input_channels, self.output_channels, is_transposed=True)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_transpose2(self):
+        conv = Convolution(2, self.input_channels, self.output_channels, strides=2, is_transposed=True)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0] * 2, self.im_shape[1] * 2)
+        self.assertEqual(out.shape, expected_shape)
+
+
+class TestResidualUnit2D(ImageTestCase):
+    def test_conv_only1(self):
+        conv = ResidualUnit(2, 1, self.output_channels)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_stride1(self):
+        conv = ResidualUnit(2, 1, self.output_channels, strides=2)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_dilation1(self):
+        conv = ResidualUnit(2, 1, self.output_channels, dilation=3)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)
+
+    def test_dropout1(self):
+        conv = ResidualUnit(2, 1, self.output_channels, dropout=0.15)
+        out = conv(self.imt)
+        expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
+        self.assertEqual(out.shape, expected_shape)

tests/utils.py

@@ -0,0 +1,70 @@
+# 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 unittest
+import torch
+import numpy as np
+
+from monai.utils.arrayutils import rescale_array
+
+quick_test_var = "QUICKTEST"
+
+
+def skip_if_quick(obj):
+    is_quick = os.environ.get(quick_test_var, "").lower() == "true"
+
+    return unittest.skipIf(is_quick, "Skipping slow tests")(obj)
+
+
+def create_test_image(width, height, num_objs=12, rad_max=30, noise_max=0.0, num_seg_classes=5):
+    """
+    Return a noisy 2D image with `numObj' circles and a 2D mask image. The maximum radius of the circles is given as
+    `radMax'. The mask will have `numSegClasses' number of classes for segmentations labeled sequentially from 1, plus a
+    background class represented as 0. If `noiseMax' is greater than 0 then noise will be added to the image taken from
+    the uniform distribution on range [0,noiseMax).
+    """
+    image = np.zeros((width, height))
+
+    for i in range(num_objs):
+        x = np.random.randint(rad_max, width - rad_max)
+        y = np.random.randint(rad_max, height - rad_max)
+        rad = np.random.randint(5, rad_max)
+        spy, spx = np.ogrid[-x : width - x, -y : height - y]
+        circle = (spx * spx + spy * spy) <= rad * rad
+
+        if num_seg_classes > 1:
+            image[circle] = np.ceil(np.random.random() * num_seg_classes)
+        else:
+            image[circle] = np.random.random() * 0.5 + 0.5
+
+    labels = np.ceil(image).astype(np.int32)
+
+    norm = np.random.uniform(0, num_seg_classes * noise_max, size=image.shape)
+    noisyimage = rescale_array(np.maximum(image, norm))
+
+    return noisyimage, labels
+
+
+class ImageTestCase(unittest.TestCase):
+    im_shape = (128, 128)
+    input_channels = 1
+    output_channels = 4
+    num_classes = 3
+
+    def setUp(self):
+        im, msk = create_test_image(self.im_shape[0], self.im_shape[1], 4, 20, 0, self.num_classes)
+
+        self.imt = torch.tensor(im[None, None])
+
+        self.seg1 = torch.tensor((msk[None, None] > 0).astype(np.float32))
+        self.segn = torch.tensor(msk[None, None])