Merge pull request #36 from SFI-Visual-Intelligence/christian/sphinx-autoapi

Add autoapi to crawl our modules and populate the docs, closes #14

Author: hzavadil98

doc/about.md

@@ -1,3 +1,3 @@
 # About this code
 
-Work in progress ...
+Work is still in progress ...

doc/conf.py

@@ -7,8 +7,12 @@
 
 extensions = [
     "myst_parser",  # in order to use markdown
+    "autoapi.extension",  # in order to generate API documentation
 ]
 
+# search this directory for Python files
+autoapi_dirs = ["../utils"]
+
 myst_enable_extensions = [
     "colon_fence",  # ::: can be used instead of ``` for better rendering
 ]

main.py

@@ -109,7 +109,7 @@ def main():
             metrics(y, preds)
 
             break
-        print(metrics.__getmetrics__())
+        print(metrics.accumulate())
         print("Dry run completed successfully.")
         exit(0)
 
@@ -135,8 +135,8 @@ def main():
             preds = th.argmax(logits, dim=1)
             metrics(y, preds)
 
-        wandb.log(metrics.__getmetrics__(str_prefix="Train "))
-        metrics.__resetvalues__()
+        wandb.log(metrics.accumulate(str_prefix="Train "))
+        metrics.reset()
 
         evalloss = []
         # Eval loop start
@@ -151,8 +151,8 @@ def main():
                 preds = th.argmax(logits, dim=1)
                 metrics(y, preds)
 
-        wandb.log(metrics.__getmetrics__(str_prefix="Evaluation "))
-        metrics.__resetvalues__()
+        wandb.log(metrics.accumulate(str_prefix="Evaluation "))
+        metrics.reset()
 
         wandb.log(
             {

utils/dataloaders/datasources.py

@@ -1,3 +1,10 @@
+"""This module contains the data sources for the datasets used in the experiments.
+
+The data sources are defined as dictionaries with the following keys
+- train: A list containing the URL, filename, and MD5 hash of the training data.
+- test: A list containing the URL, filename, and MD5 hash of the test data.
+"""
+
 USPS_SOURCE = {
     "train": [
         "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.bz2",

utils/dataloaders/mnist_0_3.py

@@ -10,6 +10,7 @@
 class MNISTDataset0_3(Dataset):
     """
     A custom dataset class for loading MNIST data, specifically for digits 0 through 3.
+
     Parameters
     ----------
     data_path : Path
@@ -20,6 +21,7 @@ class MNISTDataset0_3(Dataset):
         A function/transform that takes in an image and returns a transformed version. Default is None.
     download : bool, optional
         If True, downloads the dataset if it is not already present in the specified data_path. Default is False.
+
     Attributes
     ----------
     data_path : Path
@@ -40,6 +42,7 @@ class MNISTDataset0_3(Dataset):
         Indices of the labels that are less than 4.
     length : int
         The number of samples in the dataset.
+
     Methods
     -------
     _parse_labels(train)

utils/dataloaders/usps_0_6.py

@@ -26,7 +26,7 @@ class USPSDataset0_6(Dataset):
     Args
     ----
     data_path : pathlib.Path
-        Path to the USPS dataset file.
+        Path to the data directory.
     train : bool, optional
         Mode of the dataset.
     transform : callable, optional
@@ -60,18 +60,29 @@ class USPSDataset0_6(Dataset):
 
     Examples
     --------
+    >>> from torchvision import transforms
     >>> from src.datahandlers import USPSDataset0_6
-    >>> dataset = USPSDataset0_6(path="data/usps.h5", mode="train")
+    >>> transform = transforms.Compose([
+    ...     transforms.Resize((16, 16)),
+    ...     transforms.ToTensor()
+    ... ])
+    >>> dataset = USPSDataset0_6(
+    ...     data_path="data",
+    ...     transform=transform
+    ...     download=True,
+    ...     train=True,
+    ... )
     >>> len(dataset)
     5460
     >>> data, target = dataset[0]
     >>> data.shape
-    (16, 16)
+    (1, 16, 16)
     >>> target
-    6
+    tensor([1., 0., 0., 0., 0., 0., 0.])
     """
 
     filename = "usps.h5"
+    num_classes = 7
 
     def __init__(
         self,
@@ -85,7 +96,6 @@ def __init__(
         path = data_path if isinstance(data_path, Path) else Path(data_path)
         self.filepath = path / self.filename
         self.transform = transform
-        self.num_classes = 7  # 0-6
         self.mode = "train" if train else "test"
 
         # Download the dataset if it does not exist in a temporary directory
@@ -116,7 +126,24 @@ def _dataset_ok(self):
         return True
 
     def download(self, url, filepath, checksum, mode):
-        """Download the USPS dataset."""
+        """Download the USPS dataset, and save it as an HDF5 file.
+
+        Args
+        ----
+        url : str
+            URL to download the dataset from.
+        filepath : pathlib.Path
+            Path to save the downloaded dataset.
+        checksum : str
+            MD5 checksum of the downloaded file.
+        mode : str
+            Mode of the dataset, either train or test.
+
+        Raises
+        ------
+        ValueError
+            If the checksum of the downloaded file does not match the expected checksum.
+        """
 
         def reporthook(blocknum, blocksize, totalsize):
             """Report download progress."""
@@ -164,7 +191,20 @@ def reporthook(blocknum, blocksize, totalsize):
 
     @staticmethod
     def check_integrity(filepath, checksum):
-        """Check the integrity of the USPS dataset file."""
+        """Check the integrity of the USPS dataset file.
+
+        Args
+        ----
+        filepath : pathlib.Path
+            Path to the USPS dataset file.
+        checksum : str
+            MD5 checksum of the dataset file.
+
+        Returns
+        -------
+        bool
+            True if the checksum of the file matches the expected checksum, False otherwise
+        """
 
         file_hash = hashlib.md5(filepath.read_bytes()).hexdigest()
 

utils/load_data.py

@@ -4,6 +4,35 @@
 
 
 def load_data(dataset: str, *args, **kwargs) -> Dataset:
+    """
+    Load the dataset based on the dataset name.
+
+    Args
+    ----
+    dataset : str
+        Name of the dataset to load.
+    *args : list
+        Additional arguments for the dataset class.
+    **kwargs : dict
+        Additional keyword arguments for the dataset class.
+
+    Returns
+    -------
+    dataset : torch.utils.data.Dataset
+        Dataset object.
+
+    Raises
+    ------
+    NotImplementedError
+        If the dataset is not implemented.
+
+    Examples
+    --------
+    >>> from utils import load_data
+    >>> dataset = load_data("usps_0-6", data_path="data", train=True, download=True)
+    >>> len(dataset)
+    5460
+    """
     match dataset.lower():
         case "usps_0-6":
             return USPSDataset0_6(*args, **kwargs)
@@ -12,4 +41,4 @@ def load_data(dataset: str, *args, **kwargs) -> Dataset:
         case "usps_7-9":
             return USPSH5_Digit_7_9_Dataset(*args, **kwargs)
         case _:
-            raise ValueError(f"Dataset: {dataset} not implemented.")
+            raise NotImplementedError(f"Dataset: {dataset} not implemented.")

utils/load_metric.py

@@ -7,7 +7,48 @@
 
 
 class MetricWrapper(nn.Module):
+
+    """
+    Wrapper class for metrics, that runs multiple metrics on the same data.
+
+    Args
+    ----
+        metrics : list[str]
+            List of metrics to run on the data.
+
+    Attributes
+    ----------
+        metrics : dict
+            Dictionary containing the metric functions.
+        tmp_scores : dict
+            Dictionary containing the temporary scores of the metrics.
+
+    Methods
+    -------
+        __call__(y_true, y_pred)
+            Call the metric functions on the true and predicted labels.
+        accumulate()
+            Get the average scores of the metrics.
+        reset()
+            Reset the temporary scores of the metrics.
+
+    Examples
+    --------
+    >>> from utils import MetricWrapper
+    >>> metrics = MetricWrapper("entropy", "f1", "precision")
+    >>> y_true = [0, 1, 0, 1]
+    >>> y_pred = [0, 1, 1, 0]
+    >>> metrics(y_true, y_pred)
+    >>> metrics.accumulate()
+    {'entropy': 0.6931471805599453, 'f1': 0.5, 'precision': 0.5}
+    >>> metrics.reset()
+    >>> metrics.accumulate()
+    {'entropy': [], 'f1': [], 'precision': []}
+    """
+
+
     def __init__(self, *metrics, num_classes):
+
         super().__init__()
         self.metrics = {}
         self.num_classes = num_classes
@@ -50,7 +91,7 @@ def __call__(self, y_true, y_pred):
         for key in self.metrics:
             self.tmp_scores[key].append(self.metrics[key](y_true, y_pred))
 
-    def __getmetrics__(self, str_prefix: str = None):
+    def accumulate(self, str_prefix: str = None):
         return_metrics = {}
         for key in self.metrics:
             if str_prefix is not None:
@@ -60,6 +101,6 @@ def __getmetrics__(self, str_prefix: str = None):
 
         return return_metrics
 
-    def __resetvalues__(self):
+    def reset(self):
         for key in self.tmp_scores:
             self.tmp_scores[key] = []

utils/load_model.py

@@ -4,6 +4,37 @@
 
 
 def load_model(modelname: str, *args, **kwargs) -> nn.Module:
+    """
+    Load the model based on the model name.
+
+    Args
+    ----
+    modelname : str
+        Name of the model to load.
+    *args : list
+        Additional arguments for the model class.
+    **kwargs : dict
+        Additional keyword arguments for the model class.
+
+    Returns
+    -------
+    model : torch.nn.Module
+        Model object.
+
+    Raises
+    ------
+    NotImplementedError
+        If the model is not implemented.
+
+    Examples
+    --------
+    >>> from utils import load_model
+    >>> model = load_model("magnusmodel", num_classes=10)
+    >>> model
+    MagnusModel(
+      (fc1): Linear(in_features=784, out_features=100, bias=True)
+      (fc2): Linear(in_features=100, out_features=10, bias=True
+    """
     match modelname.lower():
         case "magnusmodel":
             return MagnusModel(*args, **kwargs)
@@ -14,6 +45,9 @@ def load_model(modelname: str, *args, **kwargs) -> nn.Module:
         case "solveigmodel":
             return SolveigModel(*args, **kwargs)
         case _:
-            raise ValueError(
-                f"Model: {modelname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling"
+            errmsg = (
+                f"Model: {modelname} not implemented. "
+                "Check the documentation for implemented models, "
+                "or check your spelling."
             )
+            raise NotImplementedError(errmsg)