Merge branch 'dev' into dev

Author: KumoLiu

.coderabbit.yaml

@@ -0,0 +1,53 @@
+# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
+
+# This file configures CodeRabbit with the various options described in https://docs.coderabbit.ai/configure-coderabbit.
+# CodeRabbit also has a set of commands here: https://docs.coderabbit.ai/guides/commands/
+
+language: "en-US"
+early_access: false
+tone_instructions: "Be terse and to the point in all statements and commentary."
+reviews:
+  # chill is less verbose, assertive is more verbose with more nitpick feedback
+  profile: chill
+  high_level_summary: false
+  high_level_summary_placeholder: "@coderabbitai summary"
+  sequence_diagrams: false
+  auto_apply_labels: false
+  suggested_reviewers: false
+  changed_files_summary: false
+  suggested_labels: false
+  abort_on_close: true
+  poem: false
+  path_instructions:
+    - path: '**/*.md'
+      instructions: Remember that documentation must be updated with the latest information.
+    - path: '**/*.rst'
+      instructions: Remember that documentation must be updated with the latest information.
+    - path: '**/*.py'
+      instructions: >-
+        Review the Python code for quality and correctness. Ensure variable names adhere to PEP8 style guides, are
+        sensible and informative in regards to their function, though permitting simple names for loop and comprehension
+        variables. Ensure routine names are meaningful in regards to their function and use verbs, adjectives, and
+        nouns in a semantically appropriate way. Docstrings should be present for all definition which describe each
+        variable, return value, and raised exception in the appropriate section of the Google-style of docstrings.
+        Examine code for logical error or inconsistencies, and suggest what may be changed to addressed these. Suggest
+        any enhancements for code improving efficiency, maintainability, comprehensibility, and correctness. Ensure new
+        or modified definitions will be covered by existing or new unit tests.
+
+  auto_review:
+    # Automatic Review | Automatic code review
+    enabled: true
+    # Review draft PRs/MRs.
+    drafts: false
+    # ignore PRs with these in the title, these sorts of PRs should be drafts anyway
+    ignore_title_keywords:
+    - "WIP"
+    - "DO NOT MERGE"
+
+# opt out for now until it's clear this isn't too much info and is useful
+knowledge_base:
+  opt_out: true
+
+# chat is allowed
+chat:
+  auto_reply: true

.github/workflows/pythonapp-min.yml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        pytorch-version: ['2.4.1', '2.5.1', '2.6.0']  # FIXME: add 'latest' back once PyTorch 2.7 issues are resolved
+        pytorch-version: ['2.4.1', '2.5.1', '2.6.0', '2.7.1']
     timeout-minutes: 40
     steps:
     - uses: actions/checkout@v4
@@ -151,11 +151,7 @@ jobs:
     - name: Install the dependencies
       run: |
         # min. requirements
-        if [ ${{ matrix.pytorch-version }} == "latest" ]; then
-          python -m pip install torch
-        else
-          python -m pip install torch==${{ matrix.pytorch-version }}
-        fi
+        python -m pip install torch==${{ matrix.pytorch-version }}
         python -m pip install -r requirements-min.txt
         python -m pip list
         BUILD_MONAI=0 python setup.py develop  # no compile of extensions

CONTRIBUTING.md

@@ -1,5 +1,5 @@
 -f https://download.pytorch.org/whl/cpu/torch-2.4.1%2Bcpu-cp39-cp39-linux_x86_64.whl
-torch>=2.4.1, <2.7.0
+torch>=2.4.1
 pytorch-ignite==0.4.11
 numpy>=1.20
 itk>=5.2

docs/requirements.txt

@@ -232,7 +232,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         if self.print_info:
             logger.info(f"Number of splits: {self.num_splits}")
 
-        if self.dim_split <= 1 and self.num_splits <= 1:
+        if self.num_splits <= 1:
             x = self.conv(x)
             return x
 

monai/apps/generation/maisi/networks/autoencoderkl_maisi.py

@@ -2,7 +2,7 @@
 requires = [
   "wheel",
   "setuptools",
-  "torch>=2.4.1, <2.7.0",
+  "torch>=2.4.1",
   "ninja",
   "packaging"
 ]

pyproject.toml

@@ -20,7 +20,7 @@ pyflakes
 black>=25.1.0
 isort>=5.1, <6.0
 ruff
-pytype>=2020.6.1; platform_system != "Windows"
+pytype>=2020.6.1, <=2024.4.11; platform_system != "Windows"
 types-setuptools
 mypy>=1.5.0, <1.12.0
 ninja

requirements-dev.txt

@@ -1,2 +1,3 @@
-torch>=2.4.1, <2.7.0
+torch>=2.4.1; platform_system != "Windows"
+torch>=2.4.1, !=2.7.0; platform_system == "Windows"
 numpy>=1.24,<3.0

requirements.txt

@@ -42,7 +42,7 @@ setup_requires =
     ninja
     packaging
 install_requires =
-    torch>=2.4.1, <2.7.0
+    torch>=2.4.1
     numpy>=1.24,<3.0
 
 [options.extras_require]

setup.cfg

@@ -20,7 +20,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets import resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200
 from monai.utils import ensure_tuple, optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_onnx_save, test_script_save
+from tests.test_utils import SkipIfBeforePyTorchVersion, dict_product, skip_if_quick, test_onnx_save, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -86,15 +86,12 @@
     (2, 1, 32, 64),
 ]
 
-TEST_CASES = []
-for case in [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_2_A, TEST_CASE_3_A]:
-    for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]:
-        TEST_CASES.append([model, *case])
+# Create all test case combinations using dict_product
+CASE_LIST = [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_2_A, TEST_CASE_3_A]
+MODEL_LIST = [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]
 
-TEST_CASES_TS = []
-for case in [TEST_CASE_1]:
-    for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]:
-        TEST_CASES_TS.append([model, *case])
+TEST_CASES = [[params["model"], *params["case"]] for params in dict_product(model=MODEL_LIST, case=CASE_LIST)]
+TEST_CASES_TS = [[params["model"], *params["case"]] for params in dict_product(model=MODEL_LIST, case=[TEST_CASE_1])]
 
 
 @SkipIfBeforePyTorchVersion((1, 12))