PR to demonstrate that CI jobs work on gitlab.com w/ GPU runners. Please comment. Thank you.

.gitlab-ci.yml

@@ -5,14 +5,16 @@ stages:
    script:
    - nvidia-smi
    - python -m pip install --upgrade pip
+   - pip uninstall -y torch torchvision
    - pip install -r requirements.txt
+   - pip list
    - 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 --config ./.flake8
+   # - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --config ./.flake8
    # exit-zero treats all errors as warnings.
-   # - flake8 . --count --statistics --config ./.flake8
-   - ./runtests.sh --quick
+   - flake8 . --count --statistics --config ./.flake8
+   - ./runtests.sh --net
    - echo "Done with runtests.sh"
 
 build-ci-test:

README.md

@@ -5,7 +5,6 @@ _Contact: <monai.miccai2019@gmail.com>_
 
 This document identifies key concepts of project MONAI at a high level, the goal is to facilitate further technical discussions of requirements,roadmap, feasibility and trade-offs.
 
-
 ## Vision
    *   Develop a community of academic, industrial and clinical researchers collaborating and working on a common foundation of standardized tools.
    *   Create a state-of-the-art, end-to-end training toolkit for healthcare imaging.
@@ -242,3 +241,7 @@ This document identifies key concepts of project MONAI at a high level, the goal
   </tr>
 </table>
 
+test-string inserted here
+
+This is to test gitlab.com CI configuration and runner
+

monai/utils/generateddata.py

@@ -0,0 +1,44 @@
+# 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 numpy as np
+
+from monai.utils.arrayutils import rescale_array
+
+
+def create_test_image_2d(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

requirements.txt

@@ -1,4 +1,5 @@
-torch
+torch>=1.4
+torchvision
 pytorch-ignite==0.2.1
 numpy
 pyyaml

runtests.sh

@@ -6,7 +6,8 @@ set -e
 homedir="$( cd -P "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 cd $homedir
 
-#export PYTHONPATH="$homedir:$PYTHONPATH"
+export PYTHONPATH="$homedir:$PYTHONPATH"
+echo $PYTHONPATH
 
 # configuration values
 doCoverage=false
@@ -16,7 +17,7 @@ doDryRun=false
 doZooTests=false
 
 # testing command to run
-cmd="python"
+cmd="python3"
 cmdprefix=""
 
 
@@ -75,13 +76,13 @@ fi
 
 
 # unit tests
-${cmdprefix}${cmd} -m unittest
+${cmdprefix}${cmd} -m unittest -v
 
 
 # network training/inference/eval tests
 if [ "$doNetTests" = 'true' ]
 then
-    for i in examples/*.py
+    for i in tests/integration_*.py
     do
         echo $i
         ${cmdprefix}${cmd} $i

tests/integration_unet2d.py

@@ -0,0 +1,60 @@
+# 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 sys
+
+import numpy as np
+import torch
+from ignite.engine import create_supervised_trainer
+from torch.utils.data import DataLoader, IterableDataset
+
+from monai import networks, utils
+
+
+def run_test(batch_size=64, train_steps=100, device=torch.device("cuda:0")):
+
+    class _TestBatch(IterableDataset):
+
+        def __iter__(self):
+            for _ in range(train_steps):
+                im, seg = utils.generateddata.create_test_image_2d(128, 128, noise_max=1, num_objs=4, num_seg_classes=1)
+                yield im[None], seg[None].astype(np.float32)
+
+        def __len__(self):
+            return train_steps
+
+    net = networks.nets.UNet(
+        dimensions=2,
+        in_channels=1,
+        num_classes=1,
+        channels=(4, 8, 16, 32),
+        strides=(2, 2, 2),
+        num_res_units=2,
+    )
+
+    loss = networks.losses.DiceLoss()
+    opt = torch.optim.Adam(net.parameters(), 1e-4)
+    src = DataLoader(_TestBatch(), batch_size=batch_size)
+
+    def loss_fn(pred, grnd):
+        return loss(pred[0], grnd)
+
+    trainer = create_supervised_trainer(net, opt, loss_fn, device, False)
+
+    trainer.run(src, 1)
+
+    return trainer.state.output
+
+
+if __name__ == "__main__":
+    result = run_test()
+
+    sys.exit(0 if result < 1 else 1)

tests/utils.py

@@ -15,7 +15,7 @@
 import numpy as np
 import torch
 
-from monai.utils.arrayutils import rescale_array
+from monai.utils.generateddata import create_test_image_2d
 
 quick_test_var = "QUICKTEST"
 
@@ -26,43 +26,14 @@ def skip_if_quick(obj):
     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 NumpyImageTestCase2D(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)
+        im, msk = create_test_image_2d(self.im_shape[0], self.im_shape[1], 4, 20, 0, self.num_classes)
 
         self.imt = im[None, None]
         self.seg1 = (msk[None, None] > 0).astype(np.float32)