daily tests for mmar loading

.github/workflows/cron-mmar.yml

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+name: cron-mmar
+
+on:
+  schedule:
+    - cron: "0 2 * * *"  # at 02:00 UTC
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+concurrency:
+  # automatically cancel the previously triggered workflows when there's a newer version
+  group: mmar-tests-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  cron-load:
+    if: github.repository == 'Project-MONAI/MONAI'
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: cache weekly timestamp
+      id: pip-cache
+      run: echo "::set-output name=datew::$(date '+%Y-%V')"
+    - name: cache for pip
+      uses: actions/cache@v2
+      id: cache
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ steps.pip-cache.outputs.datew }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip wheel
+        python -m pip install -r requirements-dev.txt
+    - name: Loading MMARs
+      run: |
+        # clean up temporary files
+        $(pwd)/runtests.sh --clean
+        # run tests
+        python -m tests.ngc_mmar_loading

tests/ngc_mmar_loading.py

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+# Copyright 2020 - 2021 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
+from parameterized import parameterized
+
+from monai.apps.mmars import MODEL_DESC, load_from_mmar
+from monai.config import print_debug_info
+
+
+class TestAllDownloadingMMAR(unittest.TestCase):
+    def setUp(self):
+        print_debug_info()
+        self.test_dir = "./"
+
+    @parameterized.expand((item,) for item in MODEL_DESC)
+    def test_loading_mmar(self, item):
+        pretrained_model = load_from_mmar(item=item, mmar_dir="./", map_location="cpu")
+        self.assertTrue(isinstance(pretrained_model, torch.nn.Modules))
+
+    def tearDown(self):
+        print(os.listdir(self.test_dir))
+
+
+if __name__ == "__main__":
+    unittest.main()