551 Fix premerge py version issue (Project-MONAI#552)

Fixes Project-MONAI#551 

### Description
This PR does the following three changes:
1. fix the python version issue in permerge cpu test
2. update two bundles to support black 24.1
3. update weekly cpu test to enable new bundles tests

### Status
**Ready**

### Please ensure all the checkboxes:
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Codeformat tests passed locally by running `./runtests.sh
--codeformat`.
- [ ] In-line docstrings updated.
- [ ] Update `version` and `changelog` in `metadata.json` if changing an
existing bundle.
- [ ] Please ensure the naming rules in config files meet our
requirements (please refer to: `CONTRIBUTING.md`).
- [ ] Ensure versions of packages such as `monai`, `pytorch` and `numpy`
are correct in `metadata.json`.
- [ ] Descriptions should be consistent with the content, such as
`eval_metrics` of the provided weights and TorchScript modules.
- [ ] Files larger than 25MB are excluded and replaced by providing
download links in `large_file.yml`.
- [ ] Avoid using path that contains personal information within config
files (such as use `/home/your_name/` for `"bundle_root"`).

---------

Signed-off-by: Yiheng Wang <vennw@nvidia.com>

.github/workflows/premerge-cpu.yml

@@ -17,10 +17,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: 3.9
     - name: cache weekly timestamp
       id: pip-cache
       run: |
@@ -34,8 +34,6 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip wheel
-        python -m pip install -r requirements-dev.txt
-        python -m pip install cffi && python -m pip install pipenv
     - name: check
       run: |
         # clean up temporary files

ci/get_bundle_list.py

@@ -15,7 +15,7 @@
 from utils import get_sub_folders
 
 # new added bundles should temporarily be added to this list, and remove until they can be downloaded successfully
-EXCLUDE_LIST = ["segmentation_template", "classification_template"]
+EXCLUDE_LIST = []
 
 
 def main(models_path):

ci/run_premerge_cpu.sh

@@ -30,7 +30,11 @@ elif [[ $# -gt 1 ]]; then
 fi
 
 verify_bundle() {
-    rm -rf /opt/hostedtoolcache
+    for dir in /opt/hostedtoolcache/*; do
+        if [[ $dir != "/opt/hostedtoolcache/Python" ]]; then
+            rm -rf "$dir"
+        fi
+    done
     echo 'Run verify bundle...'
     pip install -r requirements.txt
     head_ref=$(git rev-parse HEAD)

ci/unit_tests/test_spleen_deepedit_annotation.py

@@ -124,9 +124,9 @@ def test_infer_config(self, override):
     @parameterized.expand([TEST_CASE_2])
     def test_infer_click_config(self, override):
         override["dataset_dir"] = self.dataset_dir
-        override[
-            "dataset#data"
-        ] = "$[{'image': i, 'background': [], 'spleen': [[6, 6, 6], [8, 8, 8]]} for i in @datalist]"
+        override["dataset#data"] = (
+            "$[{'image': i, 'background': [], 'spleen': [[6, 6, 6], [8, 8, 8]]} for i in @datalist]"
+        )
         bundle_root = override["bundle_root"]
         sys.path = [bundle_root] + sys.path
 

models/renalStructures_UNEST_segmentation/configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.3",
+    "version": "0.2.4",
     "changelog": {
+        "0.2.4": "fix black 24.1 format error",
         "0.2.3": "update AddChanneld with EnsureChannelFirstd and remove meta_dict",
         "0.2.2": "add name tag",
         "0.2.1": "fix license Copyright error",

models/renalStructures_UNEST_segmentation/scripts/networks/nest/utils.py

@@ -37,9 +37,7 @@ def drop_block_2d(
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
     # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     # Forces the block to be inside the feature map.
     w_i, h_i = torch.meshgrid(torch.arange(w).to(x.device), torch.arange(h).to(x.device))
@@ -89,9 +87,7 @@ def drop_block_fast_2d(
     b, c, h, w = x.shape
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     block_mask = torch.empty_like(x).bernoulli_(gamma)
     block_mask = F.max_pool2d(

models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.3",
+    "version": "0.2.4",
     "changelog": {
+        "0.2.4": "fix black 24.1 format error",
         "0.2.3": "fix PYTHONPATH in readme.md",
         "0.2.2": "add name tag",
         "0.2.1": "fix license Copyright error",

models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest/utils.py

@@ -37,9 +37,7 @@ def drop_block_2d(
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
     # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     # Forces the block to be inside the feature map.
     w_i, h_i = torch.meshgrid(torch.arange(w).to(x.device), torch.arange(h).to(x.device))
@@ -89,9 +87,7 @@ def drop_block_fast_2d(
     b, c, h, w = x.shape
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     block_mask = torch.empty_like(x).bernoulli_(gamma)
     block_mask = F.max_pool2d(