[TorchFix] Add weights_only to torch.load (#8105)

Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>

references/classification/train.py

@@ -127,7 +127,8 @@ def load_data(traindir, valdir, args):
     if args.cache_dataset and os.path.exists(cache_path):
         # Attention, as the transforms are also cached!
         print(f"Loading dataset_train from {cache_path}")
-        dataset, _ = torch.load(cache_path)
+        # TODO: this could probably be weights_only=True
+        dataset, _ = torch.load(cache_path, weights_only=False)
     else:
         # We need a default value for the variables below because args may come
         # from train_quantization.py which doesn't define them.
@@ -159,7 +160,8 @@ def load_data(traindir, valdir, args):
     if args.cache_dataset and os.path.exists(cache_path):
         # Attention, as the transforms are also cached!
         print(f"Loading dataset_test from {cache_path}")
-        dataset_test, _ = torch.load(cache_path)
+        # TODO: this could probably be weights_only=True
+        dataset_test, _ = torch.load(cache_path, weights_only=False)
     else:
         if args.weights and args.test_only:
             weights = torchvision.models.get_weight(args.weights)
@@ -337,7 +339,7 @@ def main(args):
         model_ema = utils.ExponentialMovingAverage(model_without_ddp, device=device, decay=1.0 - alpha)
 
     if args.resume:
-        checkpoint = torch.load(args.resume, map_location="cpu")
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=True)
         model_without_ddp.load_state_dict(checkpoint["model"])
         if not args.test_only:
             optimizer.load_state_dict(checkpoint["optimizer"])

references/classification/train_quantization.py

@@ -74,7 +74,7 @@ def main(args):
         model_without_ddp = model.module
 
     if args.resume:
-        checkpoint = torch.load(args.resume, map_location="cpu")
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=True)
         model_without_ddp.load_state_dict(checkpoint["model"])
         optimizer.load_state_dict(checkpoint["optimizer"])
         lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])

references/classification/utils.py

@@ -287,8 +287,7 @@ def average_checkpoints(inputs):
     for fpath in inputs:
         with open(fpath, "rb") as f:
             state = torch.load(
-                f,
-                map_location=(lambda s, _: torch.serialization.default_restore_location(s, "cpu")),
+                f, map_location=(lambda s, _: torch.serialization.default_restore_location(s, "cpu")), weights_only=True
             )
         # Copies over the settings from the first checkpoint
         if new_state is None:
@@ -367,7 +366,7 @@ def store_model_weights(model, checkpoint_path, checkpoint_key="model", strict=T
 
     # Deep copy to avoid side effects on the model object.
     model = copy.deepcopy(model)
-    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+    checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
 
     # Load the weights to the model to validate that everything works
     # and remove unnecessary weights (such as auxiliaries, etc.)

references/depth/stereo/cascade_evaluation.py

@@ -262,7 +262,7 @@ def load_checkpoint(args):
     utils.setup_ddp(args)
 
     if not args.weights:
-        checkpoint = torch.load(args.checkpoint, map_location=torch.device("cpu"))
+        checkpoint = torch.load(args.checkpoint, map_location=torch.device("cpu"), weights_only=True)
         if "model" in checkpoint:
             experiment_args = checkpoint["args"]
             model = torchvision.prototype.models.depth.stereo.__dict__[experiment_args.model](weights=None)

references/depth/stereo/train.py

@@ -498,7 +498,7 @@ def main(args):
     # load them from checkpoint if needed
     args.start_step = 0
     if args.resume_path is not None:
-        checkpoint = torch.load(args.resume_path, map_location="cpu")
+        checkpoint = torch.load(args.resume_path, map_location="cpu", weights_only=True)
         if "model" in checkpoint:
             # this means the user requested to resume from a training checkpoint
             model_without_ddp.load_state_dict(checkpoint["model"])

references/detection/train.py

@@ -288,7 +288,7 @@ def main(args):
         )
 
     if args.resume:
-        checkpoint = torch.load(args.resume, map_location="cpu")
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=True)
         model_without_ddp.load_state_dict(checkpoint["model"])
         optimizer.load_state_dict(checkpoint["optimizer"])
         lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])

references/optical_flow/train.py

@@ -226,7 +226,7 @@ def main(args):
         model_without_ddp = model
 
     if args.resume is not None:
-        checkpoint = torch.load(args.resume, map_location="cpu")
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=True)
         model_without_ddp.load_state_dict(checkpoint["model"])
 
     if args.test_only:

references/segmentation/train.py

@@ -223,7 +223,7 @@ def main(args):
         lr_scheduler = main_lr_scheduler
 
     if args.resume:
-        checkpoint = torch.load(args.resume, map_location="cpu")
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=True)
         model_without_ddp.load_state_dict(checkpoint["model"], strict=not args.test_only)
         if not args.test_only:
             optimizer.load_state_dict(checkpoint["optimizer"])

references/similarity/train.py

@@ -101,7 +101,7 @@ def main(args):
 
     model = EmbeddingNet()
     if args.resume:
-        model.load_state_dict(torch.load(args.resume))
+        model.load_state_dict(torch.load(args.resume, weights_only=True))
 
     model.to(device)
 

references/video_classification/train.py

@@ -164,7 +164,7 @@ def main(args):
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_train from {cache_path}")
-        dataset, _ = torch.load(cache_path)
+        dataset, _ = torch.load(cache_path, weights_only=True)
         dataset.transform = transform_train
     else:
         if args.distributed:
@@ -201,7 +201,7 @@ def main(args):
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_test from {cache_path}")
-        dataset_test, _ = torch.load(cache_path)
+        dataset_test, _ = torch.load(cache_path, weights_only=True)
         dataset_test.transform = transform_test
     else:
         if args.distributed:
@@ -295,7 +295,7 @@ def main(args):
         model_without_ddp = model.module
 
     if args.resume:
-        checkpoint = torch.load(args.resume, map_location="cpu")
+        checkpoint = torch.load(args.resume, map_location="cpu", weights_only=True)
         model_without_ddp.load_state_dict(checkpoint["model"])
         optimizer.load_state_dict(checkpoint["optimizer"])
         lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])

test/test_functional_tensor.py

@@ -1024,7 +1024,8 @@ def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):
     #     "23_23_1.7": ...
     # }
     p = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "gaussian_blur_opencv_results.pt")
-    true_cv2_results = torch.load(p)
+
+    true_cv2_results = torch.load(p, weights_only=False)
 
     if image_size == "small":
         tensor = (

test/test_models.py

@@ -149,7 +149,7 @@ def _assert_expected(output, name, prec=None, atol=None, rtol=None):
         if binary_size > MAX_PICKLE_SIZE:
             raise RuntimeError(f"The output for {filename}, is larger than 50kb - got {binary_size}kb")
     else:
-        expected = torch.load(expected_file)
+        expected = torch.load(expected_file, weights_only=True)
         rtol = rtol or prec  # keeping prec param for legacy reason, but could be removed ideally
         atol = atol or prec
         torch.testing.assert_close(output, expected, rtol=rtol, atol=atol, check_dtype=False, check_device=False)
@@ -747,7 +747,7 @@ def test_segmentation_model(model_fn, dev):
             # so instead of validating the probability scores, check that the class
             # predictions match.
             expected_file = _get_expected_file(model_name)
-            expected = torch.load(expected_file)
+            expected = torch.load(expected_file, weights_only=True)
             torch.testing.assert_close(
                 out.argmax(dim=1), expected.argmax(dim=1), rtol=prec, atol=prec, check_device=False
             )
@@ -847,7 +847,7 @@ def test_detection_model(model_fn, dev):
             # as in NMSTester.test_nms_cuda to see if this is caused by duplicate
             # scores.
             expected_file = _get_expected_file(model_name)
-            expected = torch.load(expected_file)
+            expected = torch.load(expected_file, weights_only=True)
             torch.testing.assert_close(
                 output[0]["scores"], expected[0]["scores"], rtol=prec, atol=prec, check_device=False, check_dtype=False
             )

test/test_prototype_datasets_builtin.py

@@ -215,7 +215,7 @@ class TestCommon:
         with io.BytesIO() as buffer:
             torch.save(sample, buffer)
             buffer.seek(0)
-            assert_samples_equal(torch.load(buffer), sample)
+            assert_samples_equal(torch.load(buffer, weights_only=True), sample)
 
     @parametrize_dataset_mocks(DATASET_MOCKS)
     def test_infinite_buffer_size(self, dataset_mock, config):

test/test_transforms_v2.py

@@ -3176,7 +3176,8 @@ class TestGaussianBlur:
     #     "26_28_1__23_23_1.7": cv2.GaussianBlur(np_img2, ksize=(23, 23), sigmaX=1.7),
     # }
     REFERENCE_GAUSSIAN_BLUR_IMAGE_RESULTS = torch.load(
-        Path(__file__).parent / "assets" / "gaussian_blur_opencv_results.pt"
+        Path(__file__).parent / "assets" / "gaussian_blur_opencv_results.pt",
+        weights_only=False,
     )
 
     @pytest.mark.parametrize(

test/test_utils.py

@@ -375,7 +375,7 @@ def test_flow_to_image(batch):
     assert img.shape == (2, 3, h, w) if batch else (3, h, w)
 
     path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "expected_flow.pt")
-    expected_img = torch.load(path, map_location="cpu")
+    expected_img = torch.load(path, map_location="cpu", weights_only=True)
 
     if batch:
         expected_img = torch.stack([expected_img, expected_img])

torchvision/datasets/imagenet.py

@@ -84,7 +84,7 @@ def load_meta_file(root: str, file: Optional[str] = None) -> Tuple[Dict[str, str
     file = os.path.join(root, file)
 
     if check_integrity(file):
-        return torch.load(file)
+        return torch.load(file, weights_only=True)
     else:
         msg = (
             "The meta file {} is not present in the root directory or is corrupted. "

torchvision/datasets/mnist.py

@@ -116,7 +116,7 @@ class MNIST(VisionDataset):
         # This is for BC only. We no longer cache the data in a custom binary, but simply read from the raw data
         # directly.
         data_file = self.training_file if self.train else self.test_file
-        return torch.load(os.path.join(self.processed_folder, data_file))
+        return torch.load(os.path.join(self.processed_folder, data_file), weights_only=True)
 
     def _load_data(self):
         image_file = f"{'train' if self.train else 't10k'}-images-idx3-ubyte"

torchvision/datasets/phototour.py

@@ -106,7 +106,7 @@ class PhotoTour(VisionDataset):
             self.cache()
 
         # load the serialized data
-        self.data, self.labels, self.matches = torch.load(self.data_file)
+        self.data, self.labels, self.matches = torch.load(self.data_file, weights_only=True)
 
     def __getitem__(self, index: int) -> Union[torch.Tensor, Tuple[Any, Any, torch.Tensor]]:
         """