Solveig branch

main.py

@@ -30,9 +30,7 @@ def main():
 
     device = args.device
 
-
     if "usps" in args.dataset.lower():
-
         transform = transforms.Compose(
             [
                 transforms.Resize((28, 28)),
@@ -47,7 +45,6 @@ def main():
         data_dir=args.datafolder,
         transform=transform,
         val_size=args.val_size,
-
     )
 
     train_metrics = MetricWrapper(
@@ -129,7 +126,6 @@ def main():
         project=args.run_name,
         tags=[args.modelname, args.dataset],
         config=args,
-
     )
     wandb.watch(model)
 

tests/test_models.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from utils.models import ChristianModel, JanModel, MagnusModel
+from utils.models import ChristianModel, JanModel, MagnusModel, SolveigModel
 
 
 @pytest.mark.parametrize(
@@ -34,6 +34,21 @@ def test_jan_model(image_shape, num_classes):
     assert y.shape == (n, num_classes), f"Shape: {y.shape}"
 
 
+@pytest.mark.parametrize(
+    "image_shape, num_classes",
+    [((3, 16, 16), 3), ((3, 16, 16), 7)],
+)
+def test_solveig_model(image_shape, num_classes):
+    n, c, h, w = 5, *image_shape
+
+    model = SolveigModel(image_shape, num_classes)
+
+    x = torch.randn(n, c, h, w)
+    y = model(x)
+
+    assert y.shape == (n, num_classes), f"Shape: {y.shape}"
+
+
 @pytest.mark.parametrize("image_shape", [(3, 28, 28)])
 def test_magnus_model(image_shape):
     import torch as th

utils/arg_parser.py

@@ -33,7 +33,6 @@ def get_args():
         help="Whether model should be saved or not.",
     )
 
-
     # Data/Model specific values
     parser.add_argument(
         "--modelname",
@@ -83,7 +82,6 @@ def get_args():
         "--macro_averaging",
         action="store_true",
         help="If the flag is included, the metrics will be calculated using macro averaging.",
-
     )
 
     # Training specific values

utils/dataloaders/svhn.py

@@ -1,6 +1,5 @@
 import os
 
-
 import h5py
 import numpy as np
 from PIL import Image
@@ -95,7 +94,6 @@ def __getitem__(self, index):
             img = Image.fromarray(h5f["images"][index])
 
         if self.nr_channels == 1:
-
             img = img.convert("L")
         if self.transforms is not None:
             img = self.transforms(img)

utils/dataloaders/uspsh5_7_9.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import h5py
 import numpy as np
 import torch
@@ -30,7 +32,7 @@ class USPSH5_Digit_7_9_Dataset(Dataset):
         A transform function to apply to the images.
     """
 
-    def __init__(self, h5_path, mode, transform=None):
+    def __init__(self, data_path, train=False, transform=None):
         super().__init__()
         """
         Initializes the USPS dataset by loading images and labels from the given `.h5` file.
@@ -43,12 +45,13 @@ def __init__(self, h5_path, mode, transform=None):
         transform : callable, optional, default=None
             A transform function to apply on images.
         """
-
+        self.filename = "usps.h5"
+        path = data_path if isinstance(data_path, Path) else Path(data_path)
+        self.filepath = path / self.filename
         self.transform = transform
-        self.mode = mode
-        self.h5_path = h5_path
+        self.mode = "train" if train else "test"
         # Load the dataset from the HDF5 file
-        with h5py.File(self.h5_path, "r") as hf:
+        with h5py.File(self.filepath, "r") as hf:
             images = hf[self.mode]["data"][:]
             labels = hf[self.mode]["target"][:]
 
@@ -105,8 +108,8 @@ def main():
 
     # Load the dataset
     dataset = USPSH5_Digit_7_9_Dataset(
-        h5_path="C:/Users/Solveig/OneDrive/Dokumente/UiT PhD/Courses/Git/usps.h5",
-        mode="train",
+        data_path="C:/Users/Solveig/OneDrive/Dokumente/UiT PhD/Courses/Git",
+        train=False,
         transform=transform,
     )
     data_loader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True)

utils/metrics/F1.py

@@ -112,6 +112,7 @@ def _macro_F1(self):
 
     def forward(self, preds, target):
         """
+
         Update the True Positives, False Positives, and False Negatives, and compute the F1 score.
 
         This method computes the F1 score based on the predictions and true labels. It can compute either the