CCC: Continuously Changing Corruptions

requirements.txt

@@ -1,4 +1,8 @@
 numpy
 torch
 torchvision
-surgeon_pytorch
+surgeon_pytorch
+lmdb
+pyarrow
+pandas
+pillow

requirements_dev.txt

@@ -10,3 +10,5 @@ sphinx_autodoc_typehints==1.18.1
 sphinx_copybutton==0.5.0
 pydata_sphinx_theme==0.8.1
 interrogate==1.5.0
+pandas-stubs==1.5.1.221024
+types-Pillow==9.3.0.1

setup.cfg

@@ -9,9 +9,6 @@ ignore = E203,E305,E402,E501,E721,E741,F405,F821,F841,F999,W503,W504,C408,E302,W
 [mypy]
 python_version = 3.8
 
-[mypy-numpy]
-ignore_missing_imports = True
-
 [mypy-pytest]
 ignore_missing_imports = True
 
@@ -21,6 +18,27 @@ ignore_missing_imports = True
 [mypy-surgeon_pytorch]
 ignore_missing_imports = True
 
+[mypy-lmdb]
+ignore_missing_imports = True
+
+[mypy-pyarrow]
+ignore_missing_imports = True
+
+[mypy-tqdm]
+ignore_missing_imports = True
+
+[mypy-scipy.*]
+ignore_missing_imports = True
+
+[mypy-skimage.*]
+ignore_missing_imports = True
+
+[mypy-cv2]
+ignore_missing_imports = True
+
+[mypy-wand.*]
+ignore_missing_imports = True
+
 [metadata]
 name = shifthappens
 version = attr: shifthappens.__version__
@@ -66,6 +84,8 @@ dev =
     sphinx_autodoc_typehints==1.18.1
     sphinx_copybutton==0.5.0
     pydata_sphinx_theme==0.8.1
+    pandas-stubs==1.5.1.221024
+    types-Pillow==9.3.0.1
 
 
 [options.packages.find]

shifthappens/tasks/__init__.py

@@ -1,5 +1,6 @@
 """Utility methods and classes for the benchmark's tasks and the individual tasks."""
 
+from shifthappens.tasks import ccc  # noqa: F401
 from shifthappens.tasks import imagenet_c  # noqa: F401
 from shifthappens.tasks import imagenet_r  # noqa: F401
 from shifthappens.tasks import raccoons_ood  # noqa: F401

shifthappens/tasks/ccc/__init__.py

@@ -0,0 +1 @@
+"""The Continuously Changing Corruptions task."""

shifthappens/tasks/ccc/ccc.py

@@ -0,0 +1,98 @@
+"""CCC: Continuously Changing Corruptions
+
+.. note::
+
+    This task only implements the data reading portion of the dataset.
+    In addition to this file, we submitted a file used to generate the
+    data itself.
+"""
+import dataclasses
+
+import numpy as np
+
+import shifthappens.data.torch as sh_data_torch
+from shifthappens import benchmark as sh_benchmark
+from shifthappens.config import imagenet_validation_path
+from shifthappens.data.base import DataLoader
+from shifthappens.models import base as sh_models
+from shifthappens.models.base import PredictionTargets
+from shifthappens.tasks.base import parameter
+from shifthappens.tasks.base import Task
+from shifthappens.tasks.ccc.ccc_utils import WalkLoader
+from shifthappens.tasks.metrics import Metric
+from shifthappens.tasks.task_result import TaskResult
+
+
+@sh_benchmark.register_task(name="CCC", relative_data_folder="ccc", standalone=True)
+@dataclasses.dataclass
+class CCC(Task):
+    """
+    The main task class for the CCC task.
+    This task only implements the data reading portion of the dataset.
+    """
+
+    seed: int = parameter(
+        default=43,
+        options=(43,),
+        description="random seed used in the dataset building process",
+    )
+    frequency: int = parameter(
+        default=5000,
+        options=(5000, 20000),
+        description="represents how many images are sampled from each subset",
+    )
+    base_amount: int = parameter(
+        default=750000,
+        options=(750000,),
+        description="represents how large the base dataset is",
+    )
+    accuracy: int = parameter(
+        default=20,
+        options=(0, 20, 40),
+        description="represents the baseline accuracy of walk",
+    )
+    subset_size: int = parameter(
+        default=5000,
+        options=(5000, 50000),
+        description="represents the sample size of images sampled from ImageNet validation",
+    )
+
+    def setup(self):
+        """Load and prepare the data."""
+
+        self.loader = WalkLoader(
+            imagenet_validation_path,
+            self.data_root,
+            self.seed,
+            self.frequency,
+            self.base_amount,
+            self.accuracy,
+            self.subset_size,
+        )
+
+    def _prepare_dataloader(self) -> DataLoader:
+        data = self.loader.generate_dataset()
+        self.targets = [s[1] for s in data]
+
+        return DataLoader(
+            sh_data_torch.IndexedTorchDataset(
+                sh_data_torch.ImagesOnlyTorchDataset(data)
+            ),
+            max_batch_size=None,
+        )
+
+    def _evaluate(self, model: sh_models.Model) -> TaskResult:
+        dataloader = self._prepare_dataloader()
+
+        all_predicted_labels_list = []
+        for predictions in model.predict(
+            dataloader, PredictionTargets(class_labels=True)
+        ):
+            all_predicted_labels_list.append(predictions.class_labels)
+        all_predicted_labels = np.concatenate(all_predicted_labels_list, 0)
+
+        accuracy = (all_predicted_labels == np.array(self.targets)).mean()
+        print(f"Accuracy: {accuracy}")
+        return TaskResult(
+            accuracy=accuracy, summary_metrics={Metric.Robustness: "accuracy"}
+        )