black formatted files before changes

Deep-SAD-PyTorch/src/utils/config.py

@@ -10,7 +10,7 @@ class Config(object):
     def load_config(self, import_json):
         """Load settings dict from import_json (path/filename.json) JSON-file."""
 
-        with open(import_json, 'r') as fp:
+        with open(import_json, "r") as fp:
             settings = json.load(fp)
 
         for key, value in settings.items():
@@ -19,5 +19,5 @@ class Config(object):
     def save_config(self, export_json):
         """Save settings dict to export_json (path/filename.json) JSON-file."""
 
-        with open(export_json, 'w') as fp:
+        with open(export_json, "w") as fp:
             json.dump(self.settings, fp)

Deep-SAD-PyTorch/src/utils/misc.py

@@ -38,7 +38,9 @@ def log_sum_exp(tensor, dim=-1, sum_op=torch.sum):
     :return: LSE
     """
     max, _ = torch.max(tensor, dim=dim, keepdim=True)
-    return torch.log(sum_op(torch.exp(tensor - max), dim=dim, keepdim=True) + 1e-8) + max
+    return (
+        torch.log(sum_op(torch.exp(tensor - max), dim=dim, keepdim=True) + 1e-8) + max
+    )
 
 
 def binary_cross_entropy(x, y):

Deep-SAD-PyTorch/src/utils/visualization/plot_images_grid.py

@@ -1,26 +1,37 @@
 import torch
 import matplotlib
-matplotlib.use('Agg')  # or 'PS', 'PDF', 'SVG'
+
+matplotlib.use("Agg")  # or 'PS', 'PDF', 'SVG'
 
 import matplotlib.pyplot as plt
 import numpy as np
 from torchvision.utils import make_grid
 
 
-def plot_images_grid(x: torch.tensor, export_img, title: str = '', nrow=8, padding=2, normalize=False, pad_value=0):
+def plot_images_grid(
+    x: torch.tensor,
+    export_img,
+    title: str = "",
+    nrow=8,
+    padding=2,
+    normalize=False,
+    pad_value=0,
+):
     """Plot 4D Tensor of images of shape (B x C x H x W) as a grid."""
 
-    grid = make_grid(x, nrow=nrow, padding=padding, normalize=normalize, pad_value=pad_value)
+    grid = make_grid(
+        x, nrow=nrow, padding=padding, normalize=normalize, pad_value=pad_value
+    )
     npgrid = grid.cpu().numpy()
 
-    plt.imshow(np.transpose(npgrid, (1, 2, 0)), interpolation='nearest')
+    plt.imshow(np.transpose(npgrid, (1, 2, 0)), interpolation="nearest")
 
     ax = plt.gca()
     ax.xaxis.set_visible(False)
     ax.yaxis.set_visible(False)
 
-    if not (title == ''):
+    if not (title == ""):
         plt.title(title)
 
-    plt.savefig(export_img, bbox_inches='tight', pad_inches=0.1)
+    plt.savefig(export_img, bbox_inches="tight", pad_inches=0.1)
     plt.clf()