Replace asserts with exceptions (#5587)

* replace most asserts with exceptions

* fix formating issues

* fix linting and remove more asserts

* fix regresion

* fix regresion

* fix bug

* apply ufmt

* apply ufmt

* fix tests

* fix format

* fix None check

* fix detection models tests

* non scriptable any

* add more checks for None values

* fix retinanet test

* fix retinanet test

* Update references/classification/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/classification/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* make value checks more pythonic:

* Update references/optical_flow/transforms.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* make value checks more pythonic

* make more checks pythonic

* fix bug

* appy ufmt

* fix tracing issues

* fib typos

* fix lint

* remove unecessary f-strings

* fix bug

* Update torchvision/datasets/mnist.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/datasets/mnist.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/ops/boxes.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/ops/poolers.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/utils.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* address PR comments

* Update torchvision/io/_video_opt.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/models/detection/generalized_rcnn.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/models/feature_extraction.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Update torchvision/models/optical_flow/raft.py
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* address PR comments

* addressing further pr comments

* fix bug

* remove unecessary else

* apply ufmt

* last pr comment

* replace RuntimeErrors
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

torchvision/ops/boxes.py

@@ -300,8 +300,10 @@ def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
         _log_api_usage_once(generalized_box_iou)
     # degenerate boxes gives inf / nan results
     # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    if (boxes1[:, 2:] < boxes1[:, :2]).any():
+        raise ValueError("Some of the input boxes1 are invalid.")
+    if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
+        raise ValueError("Some of the input boxes2 are invalid.")
 
     inter, union = _box_inter_union(boxes1, boxes2)
     iou = inter / union

torchvision/ops/feature_pyramid_network.py

@@ -95,7 +95,8 @@ class FeaturePyramidNetwork(nn.Module):
                 nn.init.constant_(m.bias, 0)
 
         if extra_blocks is not None:
-            assert isinstance(extra_blocks, ExtraFPNBlock)
+            if not isinstance(extra_blocks, ExtraFPNBlock):
+                raise TypeError(f"extra_blocks should be of type ExtraFPNBlock not {type(extra_blocks)}")
         self.extra_blocks = extra_blocks
 
     def get_result_from_inner_blocks(self, x: Tensor, idx: int) -> Tensor:

torchvision/ops/poolers.py

@@ -104,7 +104,6 @@ def _infer_scale(feature: Tensor, original_size: List[int]) -> float:
         approx_scale = float(s1) / float(s2)
         scale = 2 ** float(torch.tensor(approx_scale).log2().round())
         possible_scales.append(scale)
-    assert possible_scales[0] == possible_scales[1]
     return possible_scales[0]
 
 
@@ -112,7 +111,8 @@ def _infer_scale(feature: Tensor, original_size: List[int]) -> float:
 def _setup_scales(
     features: List[Tensor], image_shapes: List[Tuple[int, int]], canonical_scale: int, canonical_level: int
 ) -> Tuple[List[float], LevelMapper]:
-    assert len(image_shapes) != 0
+    if not image_shapes:
+        raise ValueError("images list should not be empty")
     max_x = 0
     max_y = 0
     for shape in image_shapes:
@@ -166,8 +166,8 @@ def _multiscale_roi_align(
     Returns:
         result (Tensor)
     """
-    assert scales is not None
-    assert mapper is not None
+    if scales is None or mapper is None:
+        raise ValueError("scales and mapper should not be None")
 
     num_levels = len(x_filtered)
     rois = _convert_to_roi_format(boxes)

torchvision/prototype/models/detection/ssdlite.py

@@ -98,7 +98,10 @@ def ssdlite320_mobilenet_v3_large(
     anchor_generator = DefaultBoxGenerator([[2, 3] for _ in range(6)], min_ratio=0.2, max_ratio=0.95)
     out_channels = det_utils.retrieve_out_channels(backbone, size)
     num_anchors = anchor_generator.num_anchors_per_location()
-    assert len(out_channels) == len(anchor_generator.aspect_ratios)
+    if len(out_channels) != len(anchor_generator.aspect_ratios):
+        raise ValueError(
+            f"The length of the output channels from the backbone {len(out_channels)} do not match the length of the anchor generator aspect ratios {len(anchor_generator.aspect_ratios)}"
+        )
 
     defaults = {
         "score_thresh": 0.001,

torchvision/transforms/_functional_video.py

@@ -24,12 +24,14 @@ def crop(clip, i, j, h, w):
     Args:
         clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
     """
-    assert len(clip.size()) == 4, "clip should be a 4D tensor"
+    if len(clip.size()) != 4:
+        raise ValueError("clip should be a 4D tensor")
     return clip[..., i : i + h, j : j + w]
 
 
 def resize(clip, target_size, interpolation_mode):
-    assert len(target_size) == 2, "target size should be tuple (height, width)"
+    if len(target_size) != 2:
+        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
     return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)
 
 
@@ -46,17 +48,20 @@ def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
     Returns:
         clip (torch.tensor): Resized and cropped clip. Size is (C, T, H, W)
     """
-    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
+    if not _is_tensor_video_clip(clip):
+        raise ValueError("clip should be a 4D torch.tensor")
     clip = crop(clip, i, j, h, w)
     clip = resize(clip, size, interpolation_mode)
     return clip
 
 
 def center_crop(clip, crop_size):
-    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
+    if not _is_tensor_video_clip(clip):
+        raise ValueError("clip should be a 4D torch.tensor")
     h, w = clip.size(-2), clip.size(-1)
     th, tw = crop_size
-    assert h >= th and w >= tw, "height and width must be no smaller than crop_size"
+    if h < th or w < tw:
+        raise ValueError("height and width must be no smaller than crop_size")
 
     i = int(round((h - th) / 2.0))
     j = int(round((w - tw) / 2.0))
@@ -87,7 +92,8 @@ def normalize(clip, mean, std, inplace=False):
     Returns:
         normalized clip (torch.tensor): Size is (C, T, H, W)
     """
-    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
+    if not _is_tensor_video_clip(clip):
+        raise ValueError("clip should be a 4D torch.tensor")
     if not inplace:
         clip = clip.clone()
     mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
@@ -103,5 +109,6 @@ def hflip(clip):
     Returns:
         flipped clip (torch.tensor): Size is (C, T, H, W)
     """
-    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
+    if not _is_tensor_video_clip(clip):
+        raise ValueError("clip should be a 4D torch.tensor")
     return clip.flip(-1)

torchvision/transforms/_transforms_video.py

@@ -59,7 +59,8 @@ class RandomResizedCropVideo(RandomResizedCrop):
         interpolation_mode="bilinear",
     ):
         if isinstance(size, tuple):
-            assert len(size) == 2, "size should be tuple (height, width)"
+            if len(size) != 2:
+                raise ValueError(f"size should be tuple (height, width), instead got {size}")
             self.size = size
         else:
             self.size = (size, size)

torchvision/utils.py

@@ -82,10 +82,8 @@ def make_grid(
 
     if normalize is True:
         tensor = tensor.clone()  # avoid modifying tensor in-place
-        if value_range is not None:
-            assert isinstance(
-                value_range, tuple
-            ), "value_range has to be a tuple (min, max) if specified. min and max are numbers"
+        if value_range is not None and not isinstance(value_range, tuple):
+            raise TypeError("value_range has to be a tuple (min, max) if specified. min and max are numbers")
 
         def norm_ip(img, low, high):
             img.clamp_(min=low, max=high)
@@ -103,7 +101,8 @@ def make_grid(
         else:
             norm_range(tensor, value_range)
 
-    assert isinstance(tensor, torch.Tensor)
+    if not isinstance(tensor, torch.Tensor):
+        raise TypeError("tensor should be of type torch.Tensor")
     if tensor.size(0) == 1:
         return tensor.squeeze(0)