Merge branch 'main' of github.com:SFI-Visual-Intelligence/Collaborative-Coding-Exam into mag-branch

Author: Seilmast

.github/workflows/build-image.yml

@@ -0,0 +1,69 @@
+name: Create and publish a Docker image
+
+# Configures this workflow to run every time a change is pushed to the branch called `main`.
+on:
+  push:
+    paths:
+      - "pyproject.toml"
+      - "Dockerfile"
+  pull_request:
+    paths:
+      - "pyproject.toml"
+      - "Dockerfile"
+
+# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job.
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+      #
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
+      - name: Log in to the Container registry
+        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=branch
+            type=sha
+      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
+      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see [Usage](https://github.com/docker/build-push-action#usage) in the README of the `docker/build-push-action` repository.
+      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+      
+      # This step generates an artifact attestation for the image, which is an unforgeable statement about where and how it was built. It increases supply chain security for people who consume the image. For more information, see [Using artifact attestations to establish provenance for builds](/actions/security-guides/using-artifact-attestations-to-establish-provenance-for-builds).
+      - name: Generate artifact attestation
+        uses: actions/attest-build-provenance@v2
+        with:
+          subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
+          subject-digest: ${{ steps.push.outputs.digest }}
+          push-to-registry: true
+      
+

.github/workflows/format.yml

@@ -4,9 +4,11 @@ on:
   push:
     paths:
       - 'CollaborativeCoding/**'
+      - 'tests/**'
   pull_request:
     paths:
       - 'CollaborativeCoding/**'
+      - 'tests/**'
 
 jobs:
   format:

.github/workflows/test.yml

@@ -2,9 +2,13 @@ name: Test
 
 on:
   push:
-    branches: [ main ]
+    paths:
+      - 'CollaborativeCoding/**'
+      - 'tests/**'
   pull_request:
-    branches: [ main ]
+    paths:
+      - 'CollaborativeCoding/**'
+      - 'tests/**'
 
 jobs:
   test:

CollaborativeCoding/dataloaders/mnist_4_9.py

@@ -19,6 +19,10 @@ class MNISTDataset4_9(Dataset):
         Array of indices spcifying which samples to load. This determines the samples used by the dataloader.
     train : bool, optional
         Whether to train the model or not, by default False
+    transorm : callable, optional
+        Transform to apply to the images, by default None
+    nr_channels : int, optional
+        Number of channels in the images, by default 1
     """
 
     def __init__(

CollaborativeCoding/dataloaders/uspsh5_7_9.py

@@ -2,10 +2,8 @@
 
 import h5py
 import numpy as np
-import torch
 from PIL import Image
 from torch.utils.data import Dataset
-from torchvision import transforms
 
 
 class USPSH5_Digit_7_9_Dataset(Dataset):
@@ -55,6 +53,7 @@ def __init__(
         self.h5_path = data_path / self.filename
         self.sample_ids = sample_ids
         self.nr_channels = nr_channels
+        self.num_classes = 3
 
         # Load the dataset from the HDF5 file
         with h5py.File(self.filepath, "r") as hf:
@@ -103,34 +102,3 @@ def __getitem__(self, id):
             image = self.transform(image)
 
         return image, label
-
-
-def main():
-    # Example Usage:
-    transform = transforms.Compose(
-        [
-            transforms.Resize((16, 16)),  # Ensure images are 16x16
-            transforms.ToTensor(),
-            transforms.Normalize((0.5,), (0.5,)),  # Normalize to [-1, 1]
-        ]
-    )
-    indices = np.array([7, 8, 9])
-    # Load the dataset
-    dataset = USPSH5_Digit_7_9_Dataset(
-        data_path="C:/Users/Solveig/OneDrive/Dokumente/UiT PhD/Courses/Git",
-        sample_ids=indices,
-        train=False,
-        transform=transform,
-    )
-    data_loader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True)
-    batch = next(iter(data_loader))  # grab a batch from the dataloader
-    img, label = batch
-    print(img.shape)
-    print(label.shape)
-
-    # Check dataset size
-    print(f"Dataset size: {len(dataset)}")
-
-
-if __name__ == "__main__":
-    main()