Merge pull request #113 from SFI-Visual-Intelligence/mag-branch

SVHN bug fix

Author: Seilmast

CollaborativeCoding/dataloaders/download.py

@@ -98,11 +98,11 @@ def download_svhn(path, train: bool = True):
         train_data = parent_path / "train_32x32.mat"
         test_data = parent_path / "test_32x32.mat"
 
-        if not train_data.is_file():
+        if not train_data.exists():
             download_svhn(parent_path, train=True)
-        if not test_data.is_file():
+        if not test_data.exists():
             download_svhn(parent_path, train=False)
-        print(test_data)
+
         train_labels = loadmat(train_data)["y"]
         test_labels = loadmat(test_data)["y"]
 

CollaborativeCoding/dataloaders/svhn.py

@@ -31,7 +31,7 @@ def __init__(
         """
         super().__init__()
 
-        self.data_path = data_path
+        self.data_path = data_path / "SVHN"
         self.indexes = sample_ids
         self.split = "train" if train else "test"
 
@@ -41,7 +41,7 @@ def __init__(
         if not os.path.exists(
             os.path.join(self.data_path, f"svhn_{self.split}data.h5")
         ):
-            self._download_data(self.data_path)
+            self._create_h5py(self.data_path)
 
         assert os.path.exists(
             os.path.join(self.data_path, f"svhn_{self.split}data.h5")
@@ -53,15 +53,14 @@ def __init__(
 
         self.num_classes = len(np.unique(self.labels))
 
-    def _download_data(self, path: str):
+    def _create_h5py(self, path: str):
         """
         Downloads the SVHN dataset to the specified directory.
         Args:
             path (str): The directory where the dataset will be downloaded.
         """
         print(f"Downloading SVHN data into {path}")
 
-        SVHN(path, split=self.split, download=True)
         data = loadmat(os.path.join(path, f"{self.split}_32x32.mat"))
 
         images, labels = data["X"], data["y"]

CollaborativeCoding/dataloaders/uspsh5_7_9.py

@@ -102,4 +102,3 @@ def __getitem__(self, id):
             image = self.transform(image)
 
         return image, label
-

CollaborativeCoding/metrics/F1.py

@@ -23,7 +23,6 @@ def __init__(self, num_classes, macro_averaging=False):
         self.y_true = []
         self.y_pred = []
 
-
     def forward(self, target, preds):
         """
         Stores predictions and targets for computing the F1 score.
@@ -57,7 +56,11 @@ def compute_f1(self):
         y_true = torch.cat(self.y_true)
         y_pred = torch.cat(self.y_pred)
 
-        return self._macro_F1(y_true, y_pred) if self.macro_averaging else self._micro_F1(y_true, y_pred)
+        return (
+            self._macro_F1(y_true, y_pred)
+            if self.macro_averaging
+            else self._micro_F1(y_true, y_pred)
+        )
 
     def _micro_F1(self, target, preds):
         """Computes Micro F1 Score (global TP, FP, FN)."""
@@ -111,9 +114,13 @@ def __returnmetric__(self):
         y_true = torch.cat([t.unsqueeze(0) if t.dim() == 0 else t for t in self.y_true])
         y_pred = torch.cat([t.unsqueeze(0) if t.dim() == 0 else t for t in self.y_pred])
 
-        return self._macro_F1(y_true, y_pred) if self.macro_averaging else self._micro_F1(y_true, y_pred)
+        return (
+            self._macro_F1(y_true, y_pred)
+            if self.macro_averaging
+            else self._micro_F1(y_true, y_pred)
+        )
 
     def __reset__(self):
         """Resets stored predictions and targets."""
         self.y_true = []
-        self.y_pred = []
+        self.y_pred = []

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,