Fix super_resolution example for torch>=2.6

Author: framoncg

run_python_examples.sh

@@ -137,6 +137,7 @@ function fx() {
 
 function super_resolution() {
   uv run main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 1 --lr 0.001 --mps || error "super resolution failed"
+  uv run super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_1.pth --output_filename out.png || error "super resolution upscaling failed"
 }
 
 function time_sequence_prediction() {

super_resolution/README.md

@@ -22,14 +22,18 @@ optional arguments:
   --seed                random seed to use. Default=123
 ```
 
-This example trains a super-resolution network on the [BSD300 dataset](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/), using crops from the 200 training images, and evaluating on crops of the 100 test images. A snapshot of the model after every epoch with filename model*epoch*<epoch_number>.pth
+This example trains a super-resolution network on the [BSD300 dataset](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/), using crops from the 200 training images, and evaluating on crops of the 100 test images. A snapshot of the model after every epoch with filename `model_epoch_<epoch_number>.pth`.
 
 ## Example Usage:
 
 ### Train
 
-`python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 30 --lr 0.001`
+```bash
+python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 30 --lr 0.001
+```
 
 ### Super Resolve
 
-`python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_500.pth --output_filename out.png`
+```bash
+python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_30.pth --output_filename out.png
+```

super_resolution/super_resolve.py

@@ -3,6 +3,7 @@
 import torch
 from PIL import Image
 from torchvision.transforms import ToTensor
+from model import Net
 
 import numpy as np
 
@@ -18,7 +19,16 @@
 img = Image.open(opt.input_image).convert('YCbCr')
 y, cb, cr = img.split()
 
-model = torch.load(opt.model)
+with open(opt.model, 'rb') as f:
+    safe_globals = [
+        Net,
+        torch.nn.modules.activation.ReLU,
+        torch.nn.modules.conv.Conv2d,
+        torch.nn.modules.pixelshuffle.PixelShuffle,
+    ]
+    with torch.serialization.safe_globals(safe_globals):
+        model = torch.load(f)
+
 img_to_tensor = ToTensor()
 input = img_to_tensor(y).view(1, -1, y.size[1], y.size[0])