Refactor tests (#1011)

* Add parallel test deps

* Update signature

* Add encoders tests

* Update gitignore

* Update encoders for timm-universal

* Add parallel tests run

* Disable models tests

* Add uv to CI

* Add uv to minimum

* Add show-install-packages

* Increase to 3 workers

* Fix show-packages

* Change back for 2 workers

* Add coverage

* Basic model test

* Fix

* Move model archs

* Add base params test

* Fix timm test for minimum version

* Remove deprecated utils from coverage

* Fix

* Fix

* Exclude conversion script

* Add save-load test, add aux head test

* Remove custom encoder

* Set encoder for models tests

* Docs + flag for anyres

* Fix loading from config

* Bump min hf-hub to 0.25.0

* Fix minimal

* Add test with hub checkpoint

* Fixing minimum

* Fix

* Fix torch for minimum tests

* Update torch version and run-slow

* run-slow

* Show skipped

* [run-slow] Fixing minimum

* [run-slow] Fixing minimum

* Fix decorator

* Raise error

* [run-slow] Fixing run slow

* [run-slow] Fixing run slow

* Run slow tests in separate job

* FIx

* Fixes

* Add device

* Bum tolerance

* Add device

* Fixup

Author: qubvel

.github/workflows/tests.yml

@@ -36,24 +36,61 @@ jobs:
     runs-on: ${{ matrix.os }}
     steps:
     - uses: actions/checkout@v4
+
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
+
     - name: Install dependencies
-      run: python -m pip install -r requirements/required.txt -r requirements/test.txt
-    - name: Test with pytest
-      run: pytest
+      run: |
+        python -m pip install uv
+        python -m uv pip install --system -r requirements/required.txt -r requirements/test.txt
+
+    - name: Show installed packages
+      run: |
+        python -m pip list
+
+    - name: Test with PyTest
+      run: |
+        pytest -v -rsx -n 2 --cov=segmentation_models_pytorch --cov-report=xml --cov-config=pyproject.toml -k "not logits_match"
+
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        slug: qubvel-org/segmentation_models.pytorch
+      if: matrix.os == 'macos-latest' && matrix.python-version == '3.12'
+
+  test_logits_match:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install uv
+        python -m uv pip install --system -r requirements/required.txt -r requirements/test.txt
+    - name: Test with PyTest
+      run: RUN_SLOW=1 pytest -v -rsx -n 2 -k "logits_match"
 
   minimum:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
-    - name: Set up Python ${{ matrix.python-version }}
+    - name: Set up Python
       uses: actions/setup-python@v5
       with:
         python-version: "3.9"
     - name: Install dependencies
-      run: python -m pip install -r requirements/minimum.old -r requirements/test.txt
+      run: |
+        python -m pip install uv
+        python -m uv pip install --system -r requirements/minimum.old -r requirements/test.txt
+    - name: Show installed packages
+      run: |
+        python -m pip list
     - name: Test with pytest
-      run: pytest
+      run: pytest -v -rsx -n 2 -k "not logits_match"

.gitignore

@@ -93,6 +93,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+.vscode/
 
 # Spyder project settings
 .spyderproject

Makefile

@@ -7,7 +7,10 @@ install_dev: .venv
 	.venv/bin/pip install -e ".[test]"
 
 test: .venv
-	.venv/bin/pytest -p no:cacheprovider tests/
+	.venv/bin/pytest -v -rsx -n 2 tests/ -k "not logits_match"
+
+test_all: .venv
+	RUN_SLOW=1 .venv/bin/pytest -v -rsx -n 2 tests/
 
 table:
 	.venv/bin/python misc/generate_table.py

misc/generate_test_models.py

@@ -0,0 +1,41 @@
+import os
+import torch
+import tempfile
+import huggingface_hub
+import segmentation_models_pytorch as smp
+
+HUB_REPO = "smp-test-models"
+ENCODER_NAME = "tu-resnet18"
+
+api = huggingface_hub.HfApi(token=os.getenv("HF_TOKEN"))
+
+for model_name, model_class in smp.MODEL_ARCHITECTURES_MAPPING.items():
+    model = model_class(encoder_name=ENCODER_NAME)
+    model = model.eval()
+
+    # generate test sample
+    torch.manual_seed(423553)
+    sample = torch.rand(1, 3, 256, 256)
+
+    with torch.no_grad():
+        output = model(sample)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # save model
+        model.save_pretrained(f"{tmpdir}")
+
+        # save input and output
+        torch.save(sample, f"{tmpdir}/input-tensor.pth")
+        torch.save(output, f"{tmpdir}/output-tensor.pth")
+
+        # create repo
+        repo_id = f"{HUB_REPO}/{model_name}-{ENCODER_NAME}"
+        if not api.repo_exists(repo_id=repo_id):
+            api.create_repo(repo_id=repo_id, repo_type="model")
+
+        # upload to hub
+        api.upload_folder(
+            folder_path=tmpdir,
+            repo_id=f"{HUB_REPO}/{model_name}-{ENCODER_NAME}",
+            repo_type="model",
+        )

pyproject.toml

@@ -40,6 +40,8 @@ docs = [
 ]
 test = [
     'pytest',
+    'pytest-cov',
+    'pytest-xdist',
     'ruff',
 ]
 
@@ -55,3 +57,25 @@ version = {attr = 'segmentation_models_pytorch.__version__.__version__'}
 
 [tool.setuptools.packages.find]
 include = ['segmentation_models_pytorch*']
+
+[tool.pytest.ini_options]
+markers = [
+    "deeplabv3",
+    "deeplabv3plus",
+    "fpn",
+    "linknet",
+    "manet",
+    "pan",
+    "psp",
+    "segformer",
+    "unet",
+    "unetplusplus",
+    "upernet",
+    "logits_match",
+]
+
+[tool.coverage.run]
+omit = [
+    "segmentation_models_pytorch/utils/*",
+    "**/convert_*",
+]

requirements/minimum.old

@@ -8,3 +8,4 @@ timm==0.9.0
 torch==1.9.0
 torchvision==0.10.0
 tqdm==4.42.1
+Jinja2==3.0.0

requirements/test.txt

@@ -1,2 +1,4 @@
 pytest==8.3.4
-ruff==0.8.4
+pytest-xdist==3.6.1
+pytest-cov==6.0.0
+ruff==0.8.4

segmentation_models_pytorch/__init__.py

@@ -30,6 +30,21 @@
     "ignore", message=r'"is" with \'str\' literal.*', category=SyntaxWarning
 )  # for python >= 3.12
 
+_MODEL_ARCHITECTURES = [
+    Unet,
+    UnetPlusPlus,
+    MAnet,
+    Linknet,
+    FPN,
+    PSPNet,
+    DeepLabV3,
+    DeepLabV3Plus,
+    PAN,
+    UPerNet,
+    Segformer,
+]
+MODEL_ARCHITECTURES_MAPPING = {a.__name__.lower(): a for a in _MODEL_ARCHITECTURES}
+
 
 def create_model(
     arch: str,
@@ -43,26 +58,12 @@ def create_model(
     parameters, without using its class
     """
 
-    archs = [
-        Unet,
-        UnetPlusPlus,
-        MAnet,
-        Linknet,
-        FPN,
-        PSPNet,
-        DeepLabV3,
-        DeepLabV3Plus,
-        PAN,
-        UPerNet,
-        Segformer,
-    ]
-    archs_dict = {a.__name__.lower(): a for a in archs}
     try:
-        model_class = archs_dict[arch.lower()]
+        model_class = MODEL_ARCHITECTURES_MAPPING[arch.lower()]
     except KeyError:
         raise KeyError(
             "Wrong architecture type `{}`. Available options are: {}".format(
-                arch, list(archs_dict.keys())
+                arch, list(MODEL_ARCHITECTURES_MAPPING.keys())
             )
         )
     return model_class(

segmentation_models_pytorch/base/hub_mixin.py

@@ -136,3 +136,14 @@ def from_pretrained(pretrained_model_name_or_path: str, *args, **kwargs):
 
     model_class = getattr(smp, model_class_name)
     return model_class.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
+
+
+def supports_config_loading(func):
+    """Decorator to filter special config kwargs"""
+
+    @wraps(func)
+    def wrapper(self, *args, **kwargs):
+        kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
+        return func(self, *args, **kwargs)
+
+    return wrapper

segmentation_models_pytorch/base/model.py

@@ -5,13 +5,22 @@
 
 
 class SegmentationModel(torch.nn.Module, SMPHubMixin):
+    """Base class for all segmentation models."""
+
+    # if model supports shape not divisible by 2 ^ n
+    # set to False
+    requires_divisible_input_shape = True
+
     def initialize(self):
         init.initialize_decoder(self.decoder)
         init.initialize_head(self.segmentation_head)
         if self.classification_head is not None:
             init.initialize_head(self.classification_head)
 
     def check_input_shape(self, x):
+        """Check if the input shape is divisible by the output stride.
+        If not, raise a RuntimeError.
+        """
         h, w = x.shape[-2:]
         output_stride = self.encoder.output_stride
         if h % output_stride != 0 or w % output_stride != 0:
@@ -33,7 +42,7 @@ def check_input_shape(self, x):
     def forward(self, x):
         """Sequentially pass `x` trough model`s encoder, decoder and heads"""
 
-        if not torch.jit.is_tracing():
+        if not torch.jit.is_tracing() or self.requires_divisible_input_shape:
             self.check_input_shape(x)
 
         features = self.encoder(x)