Merge branch 'main' into fix/exportable_center_crop

Author: samet-akcay

CHANGELOG.md

@@ -10,17 +10,46 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
-- 🔨 Replace "./dtasets/BTech" to "./dtasets/BTech"
+### Deprecated
+
+### Fixed
+
+### New Contributors
+
+**Full Changelog**:
+
+## [v1.1.1]
+
+### Added
+
+- 📚Ppipelines how-to guide by @ashwinvaidya17 in https://github.com/openvinotoolkit/anomalib/pull/2109
+
+### Changed
+
+- Set permissions for github workflows by @djdameln in https://github.com/openvinotoolkit/anomalib/pull/2127
+- Update timm requirement from <=1.0.3,>=0.5.4 to >=0.5.4,<=1.0.7 by @dependabot in https://github.com/openvinotoolkit/anomalib/pull/2151
+- 🚀 Use gh actions runners for pre-commit checks by @ashwinvaidya17 in https://github.com/openvinotoolkit/anomalib/pull/2160
+- Bump AlexanderDokuchaev/md-dead-link-check from 0.8 to 0.9 by @dependabot in https://github.com/openvinotoolkit/anomalib/pull/2162
+- Added accuracy control quantization by @adrianboguszewski in https://github.com/openvinotoolkit/anomalib/pull/2070
 
 ### Deprecated
 
+- 🔨Remove device postfix by @ashwinvaidya17 in https://github.com/openvinotoolkit/anomalib/pull/2233
+
 ### Fixed
 
-- 🔨 Fix uncorrect download path of btech dataset
+- Fix: get MLFLOW_TRACKING_UTI from env variables as default by @CarlosNacher in https://github.com/openvinotoolkit/anomalib/pull/2107
+- Fix normalization by @alexriedel1 in https://github.com/openvinotoolkit/anomalib/pull/2130
+- Fix image-level heatmap normalization by @djdameln in https://github.com/openvinotoolkit/anomalib/pull/2131
+- Fix: efficient ad model_size str fixes by @Gornoka in https://github.com/openvinotoolkit/anomalib/pull/2159
+- Fix the CI by @samet-akcay in https://github.com/openvinotoolkit/anomalib/pull/2178
+- Fix BTech Dataset by @samet-akcay in https://github.com/openvinotoolkit/anomalib/pull/2180
 
 ### New Contributors
 
-**Full Changelog**:
+- @CarlosNacher made their first contribution in https://github.com/openvinotoolkit/anomalib/pull/2107
+
+**Full Changelog**: https://github.com/openvinotoolkit/anomalib/compare/v1.1.0...v1.1.1
 
 ## [v1.1.0]
 

notebooks/500_use_cases/501_dobot/501a_training_a_model_with_cubes_from_a_robotic_arm.ipynb

@@ -437,6 +437,45 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For optimization and quantization process, we are using a seamless integration with [NNCF Library](https://github.com/openvinotoolkit/nncf) in the backend of Anomalib. Select one of the following options for optimization or quantization. Replace the openvino_model_path line above in order to export the optimized/quantized model:\n",
+    "\n",
+    "```\n",
+    "# Exporting optimized/quantized models\n",
+    "\n",
+    "# Post Training Quantization\n",
+    "openvino_model_path = engine.export(\n",
+    "    model, \n",
+    "    ExportType.OPENVINO, \n",
+    "    str(Path.cwd()) + \"_optimized\", \n",
+    "    compression_type=CompressionType.INT8_PTQ, \n",
+    "    datamodule=datamodule\n",
+    "    )\n",
+    "\n",
+    "# Accuracy-Control Quantization\n",
+    "openvino_model_path=engine.export(\n",
+    "    model, \n",
+    "    ExportType.OPENVINO, \n",
+    "    str(Path.cwd()) + \"_optimized\", \n",
+    "    compression_type=CompressionType.INT8_ACQ, \n",
+    "    datamodule=datamodule, \n",
+    "    metric=\"F1Score\"\n",
+    "    )\n",
+    "\n",
+    "# Weight Compression\n",
+    "openvino_model_path=engine.export(\n",
+    "    model, \n",
+    "    ExportType.OPENVINO, \n",
+    "    str(Path.cwd()) + \"_WEIGHTS\", \n",
+    "    compression_type=CompressionType.FP16, \n",
+    "    datamodule=datamodule\n",
+    "    )\n",
+    "```"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",

src/anomalib/callbacks/checkpoint.py

@@ -35,7 +35,7 @@ def _should_skip_saving_checkpoint(self, trainer: Trainer) -> bool:
         Overrides the parent method to allow saving during both the ``FITTING`` and ``VALIDATING`` states, and to allow
         saving when the global step and last_global_step_saved are both 0 (only for zero-/few-shot models).
         """
-        is_zero_or_few_shot = trainer.model.learning_type in [LearningType.ZERO_SHOT, LearningType.FEW_SHOT]
+        is_zero_or_few_shot = trainer.lightning_module.learning_type in [LearningType.ZERO_SHOT, LearningType.FEW_SHOT]
         return (
             bool(trainer.fast_dev_run)  # disable checkpointing with fast_dev_run
             or trainer.state.fn not in [TrainerFn.FITTING, TrainerFn.VALIDATING]  # don't save anything during non-fit
@@ -52,7 +52,7 @@ def _should_save_on_train_epoch_end(self, trainer: Trainer) -> bool:
         if self._save_on_train_epoch_end is not None:
             return self._save_on_train_epoch_end
 
-        if trainer.model.learning_type in [LearningType.ZERO_SHOT, LearningType.FEW_SHOT]:
+        if trainer.lightning_module.learning_type in [LearningType.ZERO_SHOT, LearningType.FEW_SHOT]:
             return False
 
         return super()._should_save_on_train_epoch_end(trainer)

src/anomalib/cli/utils/installation.py

@@ -276,81 +276,6 @@ def get_hardware_suffix(with_available_torch_build: bool = False, torch_version:
     return hardware_suffix
 
 
-def add_hardware_suffix_to_torch(
-    requirement: Requirement,
-    hardware_suffix: str | None = None,
-    with_available_torch_build: bool = False,
-) -> str:
-    """Add hardware suffix to the torch requirement.
-
-    Args:
-        requirement (Requirement): Requirement object comprising requirement
-            details.
-        hardware_suffix (str | None): Hardware suffix. If None, it will be set
-            to the correct hardware suffix. Defaults to None.
-        with_available_torch_build (bool): To check whether the installed
-            CUDA version is supported by the latest available PyTorch build.
-            Defaults to False.
-
-    Examples:
-        >>> from pkg_resources import Requirement
-        >>> req = "torch>=1.13.0, <=2.0.1"
-        >>> requirement = Requirement.parse(req)
-        >>> requirement.name, requirement.specs
-        ('torch', [('>=', '1.13.0'), ('<=', '2.0.1')])
-
-        >>> add_hardware_suffix_to_torch(requirement)
-        'torch>=1.13.0+cu121, <=2.0.1+cu121'
-
-        ``with_available_torch_build=True`` will use the latest available PyTorch build.
-        >>> req = "torch==2.0.1"
-        >>> requirement = Requirement.parse(req)
-        >>> add_hardware_suffix_to_torch(requirement, with_available_torch_build=True)
-        'torch==2.0.1+cu118'
-
-        It is possible to pass the ``hardware_suffix`` manually.
-        >>> req = "torch==2.0.1"
-        >>> requirement = Requirement.parse(req)
-        >>> add_hardware_suffix_to_torch(requirement, hardware_suffix="cu121")
-        'torch==2.0.1+cu111'
-
-    Raises:
-        ValueError: When the requirement has more than two version criterion.
-
-    Returns:
-        str: Updated torch package with the right cuda suffix.
-    """
-    name = requirement.unsafe_name
-    updated_specs: list[str] = []
-
-    for operator, version in requirement.specs:
-        hardware_suffix = hardware_suffix or get_hardware_suffix(with_available_torch_build, version)
-        updated_version = version + f"+{hardware_suffix}" if not version.startswith(("2.1", "2.2")) else version
-
-        # ``specs`` contains operators and versions as follows:
-        # These are to be concatenated again for the updated version.
-        updated_specs.append(operator + updated_version)
-
-    updated_requirement: str = ""
-
-    if updated_specs:
-        # This is the case when specs are e.g. ['<=1.9.1+cu111']
-        if len(updated_specs) == 1:
-            updated_requirement = name + updated_specs[0]
-        # This is the case when specs are e.g., ['<=1.9.1+cu111', '>=1.8.1+cu111']
-        elif len(updated_specs) == 2:
-            updated_requirement = name + updated_specs[0] + ", " + updated_specs[1]
-        else:
-            msg = (
-                "Requirement version can be a single value or a range. \n"
-                "For example it could be torch>=1.8.1 "
-                "or torch>=1.8.1, <=1.9.1\n"
-                f"Got {updated_specs} instead."
-            )
-            raise ValueError(msg)
-    return updated_requirement
-
-
 def get_torch_install_args(requirement: str | Requirement) -> list[str]:
     """Get the install arguments for Torch requirement.
 
@@ -368,7 +293,7 @@ def get_torch_install_args(requirement: str | Requirement) -> list[str]:
         >>> requriment = "torch>=1.13.0"
         >>> get_torch_install_args(requirement)
         ['--extra-index-url', 'https://download.pytorch.org/whl/cpu',
-        'torch==1.13.0+cpu', 'torchvision==0.14.0+cpu']
+        'torch>=1.13.0', 'torchvision==0.14.0']
 
     Returns:
         list[str]: The install arguments.
@@ -401,21 +326,15 @@ def get_torch_install_args(requirement: str | Requirement) -> list[str]:
         # Create the PyTorch Index URL to download the correct wheel.
         index_url = f"https://download.pytorch.org/whl/{hardware_suffix}"
 
-        # Create the PyTorch version depending on the CUDA version. For example,
-        # If CUDA version is 11.2, then the PyTorch version is 1.8.0+cu112.
-        # If CUDA version is None, then the PyTorch version is 1.8.0+cpu.
-        torch_version = add_hardware_suffix_to_torch(requirement, hardware_suffix, with_available_torch_build=True)
+        torch_version = f"{requirement.name}{operator}{version}"  # eg: torch==1.13.0
 
         # Get the torchvision version depending on the torch version.
         torchvision_version = AVAILABLE_TORCH_VERSIONS[version]["torchvision"]
         torchvision_requirement = f"torchvision{operator}{torchvision_version}"
-        if isinstance(torchvision_version, str) and not torchvision_version.startswith("0.16"):
-            torchvision_requirement += f"+{hardware_suffix}"
 
         # Return the install arguments.
         install_args += [
             "--extra-index-url",
-            # "--index-url",
             index_url,
             torch_version,
             torchvision_requirement,

src/anomalib/data/base/datamodule.py

@@ -270,8 +270,8 @@ def train_transform(self) -> Transform:
         """
         if self._train_transform:
             return self._train_transform
-        if getattr(self, "trainer", None) and self.trainer.model and self.trainer.model.transform:
-            return self.trainer.model.transform
+        if getattr(self, "trainer", None) and self.trainer.lightning_module and self.trainer.lightning_module.transform:
+            return self.trainer.lightning_module.transform
         if self.image_size:
             return Resize(self.image_size, antialias=True)
         return None
@@ -284,8 +284,8 @@ def eval_transform(self) -> Transform:
         """
         if self._eval_transform:
             return self._eval_transform
-        if getattr(self, "trainer", None) and self.trainer.model and self.trainer.model.transform:
-            return self.trainer.model.transform
+        if getattr(self, "trainer", None) and self.trainer.lightning_module and self.trainer.lightning_module.transform:
+            return self.trainer.lightning_module.transform
         if self.image_size:
             return Resize(self.image_size, antialias=True)
         return None

src/anomalib/engine/engine.py

@@ -183,7 +183,7 @@ def model(self) -> AnomalyModule:
         Returns:
             AnomalyModule: Anomaly model.
         """
-        if not self.trainer.model:
+        if not self.trainer.lightning_module:
             msg = "Trainer does not have a model assigned yet."
             raise UnassignedError(msg)
         return self.trainer.lightning_module

tests/unit/cli/test_installation.py

@@ -12,7 +12,6 @@
 from pytest_mock import MockerFixture
 
 from anomalib.cli.utils.installation import (
-    add_hardware_suffix_to_torch,
     get_cuda_suffix,
     get_cuda_version,
     get_hardware_suffix,
@@ -122,29 +121,6 @@ def test_get_hardware_suffix(mocker: MockerFixture) -> None:
     assert get_hardware_suffix() == "cpu"
 
 
-def test_add_hardware_suffix_to_torch(mocker: MockerFixture) -> None:
-    """Test that add_hardware_suffix_to_torch returns the expected updated requirement."""
-    mocker.patch("anomalib.cli.utils.installation.get_hardware_suffix", return_value="cu121")
-    requirement = Requirement.parse("torch>=1.13.0, <=2.0.1")
-    updated_requirement = add_hardware_suffix_to_torch(requirement)
-    assert "torch" in updated_requirement
-    assert ">=1.13.0+cu121" in updated_requirement
-    assert "<=2.0.1+cu121" in updated_requirement
-
-    requirement = Requirement.parse("torch==2.0.1")
-    mocker.patch("anomalib.cli.utils.installation.get_hardware_suffix", return_value="cu118")
-    updated_requirement = add_hardware_suffix_to_torch(requirement, with_available_torch_build=True)
-    assert updated_requirement == "torch==2.0.1+cu118"
-
-    requirement = Requirement.parse("torch==2.0.1")
-    updated_requirement = add_hardware_suffix_to_torch(requirement, hardware_suffix="cu111")
-    assert updated_requirement == "torch==2.0.1+cu111"
-
-    requirement = Requirement.parse("torch>=1.13.0, <=2.0.1, !=1.14.0")
-    with pytest.raises(ValueError, match="Requirement version can be a single value or a range."):
-        add_hardware_suffix_to_torch(requirement)
-
-
 def test_get_torch_install_args(mocker: MockerFixture) -> None:
     """Test that get_torch_install_args returns the expected install arguments."""
     requirement = Requirement.parse("torch>=2.1.1")
@@ -174,8 +150,8 @@ def test_get_torch_install_args(mocker: MockerFixture) -> None:
     expected_args = [
         "--extra-index-url",
         "https://download.pytorch.org/whl/cu111",
-        "torch==2.0.1+cu111",
-        "torchvision==0.15.2+cu111",
+        "torch==2.0.1",
+        "torchvision==0.15.2",
     ]
     install_args = get_torch_install_args(requirement)
     for arg in expected_args:

tests/unit/metrics/test_adaptive_threshold.py

@@ -55,5 +55,5 @@ def test_manual_threshold() -> None:
         devices=1,
     )
     engine.fit(model=model, datamodule=datamodule)
-    assert engine.trainer.model.image_metrics.F1Score.threshold == image_threshold
-    assert engine.trainer.model.pixel_metrics.F1Score.threshold == pixel_threshold
+    assert engine.trainer.lightning_module.image_metrics.F1Score.threshold == image_threshold
+    assert engine.trainer.lightning_module.pixel_metrics.F1Score.threshold == pixel_threshold