From 828c36b32a74084ebc737549e849d597de272b69 Mon Sep 17 00:00:00 2001
From: Jan Zavadil <hzavadil98@gmail.com>
Date: Tue, 25 Feb 2025 11:01:52 +0100
Subject: [PATCH 1/4] .github - job file added

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 4cb8d55..78ce176 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,9 @@ doc/autoapi
 
 *.DS_Store
 
+# Jan
+job.yaml
+sync.sh
 
 #Magnus specific
 job*

From ddccc26c78a8b6160be7f5dadc1a7c7b0403da08 Mon Sep 17 00:00:00 2001
From: Jan Zavadil <hzavadil98@gmail.com>
Date: Wed, 26 Feb 2025 11:31:53 +0100
Subject: [PATCH 2/4] Finetuning docstrings

---
 CollaborativeCoding/dataloaders/download.py  |  8 +-
 CollaborativeCoding/dataloaders/mnist_0_3.py | 12 +--
 CollaborativeCoding/load_data.py             |  2 +-
 CollaborativeCoding/load_metric.py           | 10 ++-
 CollaborativeCoding/metrics/accuracy.py      | 77 ++++++++++++++------
 CollaborativeCoding/models/jan_model.py      |  6 +-
 6 files changed, 78 insertions(+), 37 deletions(-)

diff --git a/CollaborativeCoding/dataloaders/download.py b/CollaborativeCoding/dataloaders/download.py
index e165d5f..6b82b1e 100644
--- a/CollaborativeCoding/dataloaders/download.py
+++ b/CollaborativeCoding/dataloaders/download.py
@@ -16,12 +16,12 @@
 
 class Downloader:
     """
-    Class to download and load the USPS dataset.
+    Class used to verify availability and potentially download implemented datasets.
 
     Methods
     -------
     mnist(data_dir: Path) -> tuple[np.ndarray, np.ndarray]
-        Download the MNIST dataset and save it as an HDF5 file to `data_dir`.
+        Checks the availability of mnist dataset. If not present downloads it into MNIST folder in `data_dir`.
     svhn(data_dir: Path) -> tuple[np.ndarray, np.ndarray]
         Download the SVHN dataset and save it as an HDF5 file to `data_dir`.
     usps(data_dir: Path) -> tuple[np.ndarray, np.ndarray]
@@ -42,6 +42,10 @@ class Downloader:
     """
 
     def mnist(self, data_dir: Path) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Check the availability of mnist dataset. If not present downloads it into MNIST folder in `data_dir`.
+        """
+
         def _chech_is_downloaded(path: Path) -> bool:
             path = path / "MNIST"
             if path.exists():
diff --git a/CollaborativeCoding/dataloaders/mnist_0_3.py b/CollaborativeCoding/dataloaders/mnist_0_3.py
index 7fefa28..2f4ad2e 100644
--- a/CollaborativeCoding/dataloaders/mnist_0_3.py
+++ b/CollaborativeCoding/dataloaders/mnist_0_3.py
@@ -10,20 +10,20 @@
 class MNISTDataset0_3(Dataset):
     """
     A custom Dataset class for loading a subset of the MNIST dataset containing digits 0 to 3.
-    Parameters
+
+    Args
     ----------
     data_path : Path
-        The root directory where the MNIST data is stored.
+        The root directory where the MNIST folder with data is stored.
     sample_ids : list
         A list of indices specifying which samples to load.
     train : bool, optional
         If True, load training data, otherwise load test data. Default is False.
     transform : callable, optional
         A function/transform to apply to the images. Default is None.
+
     Attributes
     ----------
-    data_path : Path
-        The root directory where the MNIST data is stored.
     mnist_path : Path
         The directory where the MNIST dataset is located within the root directory.
     idx : list
@@ -40,6 +40,7 @@ class MNISTDataset0_3(Dataset):
         The path to the label file (train or test) based on the `train` flag.
     length : int
         The number of samples in the dataset.
+
     Methods
     -------
     __len__()
@@ -58,8 +59,7 @@ def __init__(
     ):
         super().__init__()
 
-        self.data_path = data_path
-        self.mnist_path = self.data_path / "MNIST"
+        self.mnist_path = data_path / "MNIST"
         self.idx = sample_ids
         self.train = train
         self.transform = transform
diff --git a/CollaborativeCoding/load_data.py b/CollaborativeCoding/load_data.py
index 80fe43c..8fed9b8 100644
--- a/CollaborativeCoding/load_data.py
+++ b/CollaborativeCoding/load_data.py
@@ -17,7 +17,7 @@ def filter_labels(samples: list, wanted_labels: list) -> list:
 
 def load_data(dataset: str, *args, **kwargs) -> tuple:
     """
-    load the dataset based on the dataset name.
+    Load the dataset based on the dataset name.
 
     Args
     ----
diff --git a/CollaborativeCoding/load_metric.py b/CollaborativeCoding/load_metric.py
index 002260c..ea0c574 100644
--- a/CollaborativeCoding/load_metric.py
+++ b/CollaborativeCoding/load_metric.py
@@ -9,26 +9,32 @@ class MetricWrapper(nn.Module):
     This class allows you to compute several metrics simultaneously on given
     true and predicted labels. It supports a variety of common metrics and
     provides methods to accumulate results and reset the state.
+
     Args
     ----
         num_classes : int
             The number of classes in the classification task.
         metrics : list[str]
             A list of metric names to be evaluated.
+        macro_averaging : bool
+            Whether to compute macro-averaged metrics for multi-class classification.
+
     Attributes
     ----------
         metrics : dict
             A dictionary mapping metric names to their corresponding functions.
         num_classes : int
             The number of classes for the classification task.
+
     Methods
     -------
         __call__(y_true, y_pred)
-            Computes the specified metrics on the provided true and predicted labels.
+            Passes the true and predicted labels to the metric functions.
         getmetrics(str_prefix: str = None)
-            Retrieves the computed metrics, optionally prefixed with a string.
+            Retrieves the dictionary of computed metrics, optionally all keys can be prefixed with a string.
         resetmetric()
             Resets the state of all metric computations.
+
     Examples
     --------
     >>> from CollaborativeCoding import MetricWrapperProposed
diff --git a/CollaborativeCoding/metrics/accuracy.py b/CollaborativeCoding/metrics/accuracy.py
index 2b23dff..5afc230 100644
--- a/CollaborativeCoding/metrics/accuracy.py
+++ b/CollaborativeCoding/metrics/accuracy.py
@@ -4,6 +4,45 @@
 
 
 class Accuracy(nn.Module):
+    """
+    Computes the accuracy of a model's predictions.
+
+    Args
+    ----------
+    num_classes : int
+        The number of classes in the classification task.
+    macro_averaging : bool, optional
+        If True, computes macro-average accuracy. Otherwise, computes micro-average accuracy. Default is False.
+
+
+    Methods
+    -------
+    forward(y_true, y_pred)
+        Stores the true and predicted labels. Typically called for each batch during the forward pass of a model.
+    _macro_acc()
+        Computes the macro-average accuracy.
+    _micro_acc()
+        Computes the micro-average accuracy.
+    __returnmetric__()
+        Returns the computed accuracy based on the averaging method for all stored predictions.
+    __reset__()
+        Resets the stored true and predicted labels.
+
+    Examples
+    --------
+    >>> y_true = torch.tensor([0, 1, 2, 3, 3])
+    >>> y_pred = torch.tensor([0, 1, 2, 3, 0])
+    >>> accuracy = Accuracy(num_classes=4)
+    >>> accuracy(y_true, y_pred)
+    >>> accuracy.__returnmetric__()
+    0.8
+    >>> accuracy.__reset__()
+    >>> accuracy.macro_averaging = True
+    >>> accuracy(y_true, y_pred)
+    >>> accuracy.__returnmetric__()
+    0.875
+    """
+
     def __init__(self, num_classes, macro_averaging=False):
         super().__init__()
         self.num_classes = num_classes
@@ -13,19 +52,14 @@ def __init__(self, num_classes, macro_averaging=False):
 
     def forward(self, y_true, y_pred):
         """
-        Compute the accuracy of the model.
+        Store the true and predicted labels.
 
         Parameters
         ----------
         y_true : torch.Tensor
             True labels.
         y_pred : torch.Tensor
-            Predicted labels.
-
-        Returns
-        -------
-        float
-            Accuracy score.
+            Predicted labels. Either a 1D tensor of shape (batch_size,) or a 2D tensor of shape (batch_size, num_classes).
         """
         if y_pred.dim() > 1:
             y_pred = y_pred.argmax(dim=1)
@@ -34,14 +68,7 @@ def forward(self, y_true, y_pred):
 
     def _macro_acc(self):
         """
-        Compute the macro-average accuracy.
-
-        Parameters
-        ----------
-        y_true : torch.Tensor
-            True labels.
-        y_pred : torch.Tensor
-            Predicted labels.
+        Compute the macro-average accuracy on the stored predictions.
 
         Returns
         -------
@@ -63,14 +90,7 @@ def _macro_acc(self):
 
     def _micro_acc(self):
         """
-        Compute the micro-average accuracy.
-
-        Parameters
-        ----------
-        y_true : torch.Tensor
-            True labels.
-        y_pred : torch.Tensor
-            Predicted labels.
+        Compute the micro-average accuracy on the stored predictions.
 
         Returns
         -------
@@ -80,6 +100,14 @@ def _micro_acc(self):
         return (self.y_true == self.y_pred).float().mean().item()
 
     def __returnmetric__(self):
+        """
+        Return the computed accuracy based on the averaging method for all stored predictions.
+
+        Returns
+        -------
+        float
+            Computed accuracy score.
+        """
         if self.y_true == [] or self.y_pred == []:
             return np.nan
         if isinstance(self.y_true, list):
@@ -92,6 +120,9 @@ def __returnmetric__(self):
         return self._micro_acc() if not self.macro_averaging else self._macro_acc()
 
     def __reset__(self):
+        """
+        Reset the stored true and predicted labels.
+        """
         self.y_true = []
         self.y_pred = []
         return None
diff --git a/CollaborativeCoding/models/jan_model.py b/CollaborativeCoding/models/jan_model.py
index ef4ac66..69dab54 100644
--- a/CollaborativeCoding/models/jan_model.py
+++ b/CollaborativeCoding/models/jan_model.py
@@ -3,12 +3,12 @@
 
 
 class JanModel(nn.Module):
-    """A simple MLP network model for image classification tasks.
+    """A simple MLP network model for image classification tasks. Two hidden layers with 100 neurons.
 
     Args
     ----
-    in_channels : int
-        Number of input channels.
+    mage_shape : tuple(int, int, int)
+        Shape of the input image (C, H, W).
     num_classes : int
         Number of classes in the dataset.
 

From 8afe7b6e0f3b72ff923251d959ec7821588c9ccb Mon Sep 17 00:00:00 2001
From: Jan Zavadil <hzavadil98@gmail.com>
Date: Wed, 26 Feb 2025 13:55:04 +0100
Subject: [PATCH 3/4] Added saving the model and metrics locally into
 corresponding folders

---
 main.py |   6 +-
 uv.lock | 213 +++++++++++++++++++++++++++++++++++++++++++++++++++++---
 2 files changed, 208 insertions(+), 11 deletions(-)

diff --git a/main.py b/main.py
index c34d317..18b56aa 100644
--- a/main.py
+++ b/main.py
@@ -1,11 +1,11 @@
 import numpy as np
 import torch as th
 import torch.nn as nn
-import wandb
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from tqdm import tqdm
 
+import wandb
 from CollaborativeCoding import (
     MetricWrapper,
     createfolders,
@@ -132,6 +132,7 @@ def main():
     wandb.init(
         entity="ColabCode",
         project=args.run_name,
+        dir=args.resultfolder,
         tags=[args.modelname, args.dataset],
         config=args,
     )
@@ -178,6 +179,9 @@ def main():
         train_metrics.resetmetric()
         val_metrics.resetmetric()
 
+    if args.savemodel:
+        th.save(model, args.modelfolder / f"{args.modelname}_run:{args.run_name}.pth")
+
     testloss = []
     model.eval()
     with th.no_grad():
diff --git a/uv.lock b/uv.lock
index 4d42378..97ea764 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,8 +1,10 @@
 version = 1
-requires-python = ">=3.12"
+requires-python = ">=3.11.5"
 resolution-markers = [
-    "sys_platform == 'linux'",
-    "sys_platform != 'linux'",
+    "python_full_version >= '3.12' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and sys_platform != 'linux'",
+    "python_full_version < '3.12' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and sys_platform != 'linux'",
 ]
 
 [[package]]
@@ -163,6 +165,10 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/94/49/26a7b0f3f35da4b5a65f081943b7bcd22d7002f5f0fb8098ec1ff21cb6ef/black-25.1.0.tar.gz", hash = "sha256:33496d5cd1222ad73391352b4ae8da15253c5de89b93a80b3e2c8d9a19ec2666", size = 649449 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/e7/d0/2c34c36190b741c59c901e56ab7f6e54dad8df05a6272a9747ecef7c6036/black-25.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:96c1c7cd856bba8e20094e36e0f948718dc688dba4a9d78c3adde52b9e6c2299", size = 1442865 },
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+    { url = "https://files.pythonhosted.org/packages/58/db/4f5beb989b547f79096e035c4981ceb36ac2b552d0ac5f2620e941501c99/black-25.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:172b1dbff09f86ce6f4eb8edf9dede08b1fce58ba194c87d7a4f1a5aa2f5b3c2", size = 1428028 },
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@@ -209,6 +215,18 @@ dependencies = [
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 wheels = [
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@@ -239,6 +257,19 @@ version = "3.4.1"
 source = { registry = "https://pypi.org/simple" }
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From d7dbe28f90b18f9893d387b7b62c17e9034492e5 Mon Sep 17 00:00:00 2001
From: Jan Zavadil <79144013+hzavadil98@users.noreply.github.com>
Date: Wed, 26 Feb 2025 16:45:06 +0100
Subject: [PATCH 4/4] Update CollaborativeCoding/models/jan_model.py

Co-authored-by: Christian Salomonsen <55956280+salomaestro@users.noreply.github.com>
---
 CollaborativeCoding/models/jan_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CollaborativeCoding/models/jan_model.py b/CollaborativeCoding/models/jan_model.py
index 69dab54..a68d6b6 100644
--- a/CollaborativeCoding/models/jan_model.py
+++ b/CollaborativeCoding/models/jan_model.py
@@ -7,7 +7,7 @@ class JanModel(nn.Module):
 
     Args
     ----
-    mage_shape : tuple(int, int, int)
+    image_shape : tuple(int, int, int)
         Shape of the input image (C, H, W).
     num_classes : int
         Number of classes in the dataset.