Initial docs string (#9)

* Initial round of docstring refactoring

* Add commends to tox

* Fix issues with input_size

Co-authored-by: Ashwin Vaidya <[email protected]>

.pre-commit-config.yaml

@@ -57,3 +57,14 @@ repos:
         args: [--config-file=tox.ini]
         additional_dependencies: [types-PyYAML]
         exclude: "tests/"
+
+  - repo: https://github.com/PyCQA/pydocstyle
+    rev: 6.1.1
+    hooks:
+      - id: pydocstyle
+        name: pydocstyle
+        entry: pydocstyle
+        language: python
+        types: [python]
+        args: [--config=tox.ini]
+        exclude: "tests|docs"

anomalib/__init__.py

@@ -1,6 +1,4 @@
-"""
-Anomalib library for research and benchmarking
-"""
+"""Anomalib library for research and benchmarking."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/config/__init__.py

@@ -1,6 +1,4 @@
-"""
-Utilities to get configurable parameters
-"""
+"""Utilities to get configurable parameters."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/config/config.py

@@ -1,6 +1,4 @@
-"""
-Get configurable parameters
-"""
+"""Get configurable parameters."""
 
 # Copyright (C) 2020 Intel Corporation
 #
@@ -28,16 +26,16 @@
 
 
 def update_input_size_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
-    """
-    Convert integer image size parameters into tuples, calculate the effective input size based on image size and crop
-    size, and set tiling stride if undefined.
+    """Update config with image size as tuple, effective input size and tiling stride.
+
+    Convert integer image size parameters into tuples, calculate the effective input size based on image size
+    and crop size, and set tiling stride if undefined.
 
     Args:
-        config: Dictconfig: Configurable parameters object
+        config (Union[DictConfig, ListConfig]): Configurable parameters object
 
     Returns:
         Configurable parameters with updated values
-
     """
     # handle image size
     if isinstance(config.dataset.image_size, int):
@@ -55,8 +53,7 @@ def update_input_size_config(config: Union[DictConfig, ListConfig]) -> Union[Dic
 
 
 def update_nncf_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
-    """
-    Set the NNCF input size based on the value of the crop_size parameter in the configurable parameters object.
+    """Set the NNCF input size based on the value of the crop_size parameter in the configurable parameters object.
 
     Args:
         config: Dictconfig: Configurable parameters of the current run.
@@ -76,8 +73,9 @@ def update_nncf_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfi
 
 
 def update_multi_gpu_training_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
-    """Updates the config to change learning rate based on number of gpus assigned
-        and ensures only ddp accelerator is used
+    """Updates the config to change learning rate based on number of gpus assigned.
+
+    Current behaviour is to ensure only ddp accelerator is used.
 
     Args:
         config (Union[DictConfig, ListConfig]): Configurable parameters for the current run
@@ -115,9 +113,7 @@ def update_multi_gpu_training_config(config: Union[DictConfig, ListConfig]) -> U
 
 
 def update_device_config(config: Union[DictConfig, ListConfig], openvino: bool) -> Union[DictConfig, ListConfig]:
-    """
-    Update XPU Device Config
-    This function ensures devices are configured correctly by the user.
+    """Update XPU Device Config This function ensures devices are configured correctly by the user.
 
     Args:
         config (Union[DictConfig, ListConfig]): Input config
@@ -150,8 +146,7 @@ def get_configurable_parameters(
     config_filename: Optional[str] = "config",
     config_file_extension: Optional[str] = "yaml",
 ) -> Union[DictConfig, ListConfig]:
-    """
-    Get configurable parameters
+    """Get configurable parameters.
 
     Args:
         model_name: Optional[str]:  (Default value = None)
@@ -163,7 +158,6 @@ def get_configurable_parameters(
 
     Returns:
         Configurable parameters in DictConfig object.
-
     """
     if model_name is None and model_config_path is None:
         raise ValueError(

anomalib/core/__init__.py

@@ -1,6 +1,4 @@
-"""
-This module holds common components such as callbacks, custom modules and utils
-"""
+"""This module holds common components such as callbacks, custom modules and utils."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/core/callbacks/__init__.py

@@ -1,4 +1,4 @@
-"""Callbacks for Anomalib models"""
+"""Callbacks for Anomalib models."""
 
 import os
 from importlib import import_module
@@ -23,13 +23,13 @@
 
 
 def get_callbacks(config: Union[ListConfig, DictConfig]) -> List[Callback]:
-    """Return base callbacks for all the lightning models
+    """Return base callbacks for all the lightning models.
 
     Args:
-        config (DictConfig): model config
+        config (DictConfig): Model config
 
-    Returns:
-        (List[Callback]): List of callbacks
+    Return:
+        (List[Callback]): List of callbacks.
     """
     callbacks: List[Callback] = []
 
@@ -65,7 +65,7 @@ def get_callbacks(config: Union[ListConfig, DictConfig]) -> List[Callback]:
         if config.optimization.compression.apply:
             callbacks.append(
                 CompressModelCallback(
-                    config=config,
+                    input_size=config.model.input_size,
                     dirpath=os.path.join(config.project.path, "compressed"),
                     filename="compressed_model",
                 )

anomalib/core/callbacks/compress.py

@@ -1,27 +1,32 @@
 """Callback that compresses a trained model by first exporting to .onnx format, and then converting to OpenVINO IR."""
 import os
-from typing import Union
+from typing import Tuple
 
 import torch
-from omegaconf import DictConfig, ListConfig
 from pytorch_lightning import Callback, LightningModule
 
 
 class CompressModelCallback(Callback):
-    """
-    Callback that compresses a trained model by first exporting to .onnx format, and then converting to OpenVINO IR.
+    """Callback to compresses a trained model.
+
+    Model is first exported to .onnx format, and then converted to OpenVINO IR.
+
+    Args:
+        input_size (Tuple[int, int]): Tuple of image height, width
+        dirpath (str): Path for model output
+        filename (str): Name of output model
     """
 
-    def __init__(self, config: Union[ListConfig, DictConfig], dirpath: str, filename: str):
-        self.config = config
+    def __init__(self, input_size: Tuple[int, int], dirpath: str, filename: str):
+        self.input_size = input_size
         self.dirpath = dirpath
         self.filename = filename
 
     def on_train_end(self, trainer, pl_module: LightningModule) -> None:  # pylint: disable=W0613
-        """Called when the train ends."""
+        """Call when the train ends."""
         os.makedirs(self.dirpath, exist_ok=True)
         onnx_path = os.path.join(self.dirpath, self.filename + ".onnx")
-        height, width = self.config.model.input_size
+        height, width = self.input_size
         torch.onnx.export(
             pl_module.model, torch.zeros((1, 3, height, width)).to(pl_module.device), onnx_path, opset_version=11
         )

anomalib/core/callbacks/model_loader.py

@@ -10,5 +10,5 @@ def __init__(self, weights_path):
         self.weights_path = weights_path
 
     def on_test_start(self, trainer, pl_module: LightningModule) -> None:  # pylint: disable=W0613
-        """Called when the test begins."""
+        """Call when the test begins."""
         pl_module.load_state_dict(torch.load(self.weights_path)["state_dict"])

anomalib/core/callbacks/nncf_callback.py

@@ -1,6 +1,4 @@
-"""
-NNCF Callback
-"""
+"""NNCF Callback."""
 
 import os
 from typing import Any, Dict, Iterator, Optional, Tuple
@@ -19,16 +17,7 @@
 
 
 def criterion_fn(outputs, criterion):
-    """
-    criterion_fn [summary]
-
-    Args:
-        outputs ([type]): [description]
-        criterion ([type]): [description]
-
-    Returns:
-        [type]: [description]
-    """
+    """Calls the criterion function on outputs."""
     return criterion(outputs)
 
 
@@ -40,25 +29,26 @@ def __init__(self, data_loader: DataLoader):
         self._data_loader_iter: Iterator
 
     def __iter__(self):
+        """Create iterator for dataloader."""
         self._data_loader_iter = iter(self._data_loader)
         return self
 
     def __next__(self) -> Any:
+        """Return next item from dataloader iterator."""
         loaded_item = next(self._data_loader_iter)
         return loaded_item["image"]
 
     def get_inputs(self, dataloader_output) -> Tuple[Tuple, Dict]:
-        """
+        """Get input to model.
+
         Returns:
             (dataloader_output,), {}: Tuple[Tuple, Dict]: The current model call to be made during
             the initialization process
         """
         return (dataloader_output,), {}
 
     def get_target(self, _):
-        """
-        Parses the generic data loader output and returns a structure to be used as
-        ground truth in the loss criterion.
+        """Return structure for ground truth in loss criterion based on dataloader output.
 
         Returns:
             None
@@ -69,8 +59,8 @@ def get_target(self, _):
 class NNCFCallback(Callback):
     """Callback for NNCF compression.
 
-    Assumes that the pl module contains a 'model' attribute, which is the PyTorch module
-    that must be compressed.
+    Assumes that the pl module contains a 'model' attribute, which is
+    the PyTorch module that must be compressed.
     """
 
     def __init__(self, config, dirpath, filename):
@@ -88,7 +78,7 @@ def __init__(self, config, dirpath, filename):
         self.compression_scheduler: CompressionScheduler
 
     def setup(self, _: pl.Trainer, pl_module: pl.LightningModule, __: Optional[str] = None) -> None:
-        """Called when fit or test begins"""
+        """Call when fit or test begins."""
         if self.comp_ctrl is None:
             init_loader = InitLoader(self.train_loader)
             nncf_config = register_default_init_args(
@@ -101,13 +91,13 @@ def setup(self, _: pl.Trainer, pl_module: pl.LightningModule, __: Optional[str]
     def on_train_batch_start(
         self, trainer, _pl_module: pl.LightningModule, _batch: Any, _batch_idx: int, _dataloader_idx: int
     ) -> None:
-        """Called when the train batch begins."""
+        """Call when the train batch begins."""
         self.compression_scheduler.step()
         if self.comp_ctrl is not None:
             trainer.model.loss_val = self.comp_ctrl.loss()
 
     def on_train_end(self, _trainer, _pl_module: pl.LightningModule) -> None:
-        """Called when the train ends."""
+        """Call when the train ends."""
         os.makedirs(self.dirpath, exist_ok=True)
         onnx_path = os.path.join(self.dirpath, self.filename + ".onnx")
         if self.comp_ctrl is not None:
@@ -118,5 +108,5 @@ def on_train_end(self, _trainer, _pl_module: pl.LightningModule) -> None:
     def on_train_epoch_end(
         self, _trainer: pl.Trainer, _pl_module: pl.LightningModule, _unused: Optional[Any] = None
     ) -> None:
-        """Called when the train epoch ends."""
+        """Call when the train epoch ends."""
         self.compression_scheduler.epoch_step()

anomalib/core/callbacks/save_to_csv.py

@@ -1,6 +1,4 @@
-"""
-SaveToCSV Callback
-"""
+"""Callback to save metrics to CSV."""
 from pathlib import Path
 
 import numpy as np
@@ -11,21 +9,22 @@
 
 
 class SaveToCSVCallback(Callback):
-    """
-    Callback that saves the inference results of a model. The callback generates a csv file that saves different
-    performance metrics and results.
+    """Callback that saves the inference results of a model.
+
+    The callback generates a csv file that saves different performance
+    metrics and results.
     """
 
     def __init__(self):
-        """SaveToCSV callback"""
+        """Callback to save metrics to CSV."""
 
     def on_test_epoch_end(self, _trainer: Trainer, pl_module: AnomalyModule) -> None:
-        """Save Results at the end of training
+        """Save Results at the end of training.
+
         Args:
             _trainer (Trainer): Pytorch lightning trainer object (unused)
             pl_module (LightningModule): Lightning modules derived from BaseAnomalyLightning object.
         """
-
         results = pl_module.results
         data_frame = pd.DataFrame(
             {

anomalib/core/callbacks/timer.py

@@ -1,4 +1,4 @@
-"""Callback to measure training and testing time of a PyTorch Lightning module"""
+"""Callback to measure training and testing time of a PyTorch Lightning module."""
 import time
 
 from pytorch_lightning import Callback, LightningModule
@@ -11,17 +11,17 @@ def __init__(self):
         self.start = None
 
     def on_fit_start(self, trainer, pl_module: LightningModule) -> None:  # pylint: disable=W0613
-        """Called when fit begins"""
+        """Call when fit begins."""
         self.start = time.time()
 
     def on_fit_end(self, trainer, pl_module: LightningModule) -> None:  # pylint: disable=W0613
-        """Called when fit ends"""
+        """Call when fit ends."""
         print(f"Training took {time.time() - self.start} seconds")
 
     def on_test_start(self, trainer, pl_module: LightningModule) -> None:  # pylint: disable=W0613
-        """Called when the test begins."""
+        """Call when the test begins."""
         self.start = time.time()
 
     def on_test_end(self, trainer, pl_module: LightningModule) -> None:  # pylint: disable=W0613
-        """Called when the test ends."""
+        """Call when the test ends."""
         print(f"Testing took {time.time() - self.start} seconds.")