Merge branch 'main' into transforms/pad

Author: pmeier

references/classification/transforms.py

@@ -21,8 +21,14 @@ class RandomMixup(torch.nn.Module):
 
     def __init__(self, num_classes: int, p: float = 0.5, alpha: float = 1.0, inplace: bool = False) -> None:
         super().__init__()
-        assert num_classes > 0, "Please provide a valid positive value for the num_classes."
-        assert alpha > 0, "Alpha param can't be zero."
+
+        if num_classes < 1:
+            raise ValueError(
+                f"Please provide a valid positive value for the num_classes. Got num_classes={num_classes}"
+            )
+
+        if alpha <= 0:
+            raise ValueError("Alpha param can't be zero.")
 
         self.num_classes = num_classes
         self.p = p
@@ -99,8 +105,10 @@ class RandomCutmix(torch.nn.Module):
 
     def __init__(self, num_classes: int, p: float = 0.5, alpha: float = 1.0, inplace: bool = False) -> None:
         super().__init__()
-        assert num_classes > 0, "Please provide a valid positive value for the num_classes."
-        assert alpha > 0, "Alpha param can't be zero."
+        if num_classes < 1:
+            raise ValueError("Please provide a valid positive value for the num_classes.")
+        if alpha <= 0:
+            raise ValueError("Alpha param can't be zero.")
 
         self.num_classes = num_classes
         self.p = p

references/detection/coco_eval.py

@@ -12,7 +12,8 @@
 
 class CocoEvaluator:
     def __init__(self, coco_gt, iou_types):
-        assert isinstance(iou_types, (list, tuple))
+        if not isinstance(iou_types, (list, tuple)):
+            raise TypeError(f"This constructor expects iou_types of type list or tuple, instead  got {type(iou_types)}")
         coco_gt = copy.deepcopy(coco_gt)
         self.coco_gt = coco_gt
 

references/detection/coco_utils.py

@@ -126,7 +126,10 @@ def _has_valid_annotation(anno):
             return True
         return False
 
-    assert isinstance(dataset, torchvision.datasets.CocoDetection)
+    if not isinstance(dataset, torchvision.datasets.CocoDetection):
+        raise TypeError(
+            f"This function expects dataset of type torchvision.datasets.CocoDetection, instead  got {type(dataset)}"
+        )
     ids = []
     for ds_idx, img_id in enumerate(dataset.ids):
         ann_ids = dataset.coco.getAnnIds(imgIds=img_id, iscrowd=None)

references/optical_flow/transforms.py

@@ -7,16 +7,21 @@ class ValidateModelInput(torch.nn.Module):
     # Pass-through transform that checks the shape and dtypes to make sure the model gets what it expects
     def forward(self, img1, img2, flow, valid_flow_mask):
 
-        assert all(isinstance(arg, torch.Tensor) for arg in (img1, img2, flow, valid_flow_mask) if arg is not None)
-        assert all(arg.dtype == torch.float32 for arg in (img1, img2, flow) if arg is not None)
+        if not all(isinstance(arg, torch.Tensor) for arg in (img1, img2, flow, valid_flow_mask) if arg is not None):
+            raise TypeError("This method expects all input arguments to be of type torch.Tensor.")
+        if not all(arg.dtype == torch.float32 for arg in (img1, img2, flow) if arg is not None):
+            raise TypeError("This method expects the tensors img1, img2 and flow of be of dtype torch.float32.")
 
-        assert img1.shape == img2.shape
+        if img1.shape != img2.shape:
+            raise ValueError("img1 and img2 should have the same shape.")
         h, w = img1.shape[-2:]
-        if flow is not None:
-            assert flow.shape == (2, h, w)
+        if flow is not None and flow.shape != (2, h, w):
+            raise ValueError(f"flow.shape should be (2, {h}, {w}) instead of {flow.shape}")
         if valid_flow_mask is not None:
-            assert valid_flow_mask.shape == (h, w)
-            assert valid_flow_mask.dtype == torch.bool
+            if valid_flow_mask.shape != (h, w):
+                raise ValueError(f"valid_flow_mask.shape should be ({h}, {w}) instead of {valid_flow_mask.shape}")
+            if valid_flow_mask.dtype != torch.bool:
+                raise TypeError("valid_flow_mask should be of dtype torch.bool instead of {valid_flow_mask.dtype}")
 
         return img1, img2, flow, valid_flow_mask
 
@@ -109,7 +114,8 @@ class RandomErasing(T.RandomErasing):
     def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False, max_erase=1):
         super().__init__(p=p, scale=scale, ratio=ratio, value=value, inplace=inplace)
         self.max_erase = max_erase
-        assert self.max_erase > 0
+        if self.max_erase <= 0:
+            raise ValueError("max_raise should be greater than 0")
 
     def forward(self, img1, img2, flow, valid_flow_mask):
         if torch.rand(1) > self.p:

references/optical_flow/utils.py

@@ -71,7 +71,10 @@ def update(self, **kwargs):
         for k, v in kwargs.items():
             if isinstance(v, torch.Tensor):
                 v = v.item()
-            assert isinstance(v, (float, int))
+            if not isinstance(v, (float, int)):
+                raise TypeError(
+                    f"This method expects the value of the input arguments to be of type float or int, instead  got {type(v)}"
+                )
             self.meters[k].update(v)
 
     def __getattr__(self, attr):

references/segmentation/coco_utils.py

@@ -68,7 +68,11 @@ def _has_valid_annotation(anno):
         # if more than 1k pixels occupied in the image
         return sum(obj["area"] for obj in anno) > 1000
 
-    assert isinstance(dataset, torchvision.datasets.CocoDetection)
+    if not isinstance(dataset, torchvision.datasets.CocoDetection):
+        raise TypeError(
+            f"This function expects dataset of type torchvision.datasets.CocoDetection, instead  got {type(dataset)}"
+        )
+
     ids = []
     for ds_idx, img_id in enumerate(dataset.ids):
         ann_ids = dataset.coco.getAnnIds(imgIds=img_id, iscrowd=None)

references/segmentation/utils.py

@@ -118,7 +118,10 @@ def update(self, **kwargs):
         for k, v in kwargs.items():
             if isinstance(v, torch.Tensor):
                 v = v.item()
-            assert isinstance(v, (float, int))
+            if not isinstance(v, (float, int)):
+                raise TypeError(
+                    f"This method expects the value of the input arguments to be of type float or int, instead  got {type(v)}"
+                )
             self.meters[k].update(v)
 
     def __getattr__(self, attr):

references/similarity/sampler.py

@@ -47,7 +47,8 @@ def __init__(self, groups, p, k):
         self.groups = create_groups(groups, self.k)
 
         # Ensures there are enough classes to sample from
-        assert len(self.groups) >= p
+        if len(self.groups) < p:
+            raise ValueError("There are not enought classes to sample from")
 
     def __iter__(self):
         # Shuffle samples within groups

references/video_classification/utils.py

@@ -76,7 +76,10 @@ def update(self, **kwargs):
         for k, v in kwargs.items():
             if isinstance(v, torch.Tensor):
                 v = v.item()
-            assert isinstance(v, (float, int))
+            if not isinstance(v, (float, int)):
+                raise TypeError(
+                    f"This method expects the value of the input arguments to be of type float or int, instead  got {type(v)}"
+                )
             self.meters[k].update(v)
 
     def __getattr__(self, attr):

test/test_backbone_utils.py

@@ -144,16 +144,16 @@ def test_build_fx_feature_extractor(self, model_name):
             model, train_return_nodes=train_return_nodes, eval_return_nodes=eval_return_nodes
         )
         # Check must specify return nodes
-        with pytest.raises(AssertionError):
+        with pytest.raises(ValueError):
             self._create_feature_extractor(model)
         # Check return_nodes and train_return_nodes / eval_return nodes
         # mutual exclusivity
-        with pytest.raises(AssertionError):
+        with pytest.raises(ValueError):
             self._create_feature_extractor(
                 model, return_nodes=train_return_nodes, train_return_nodes=train_return_nodes
             )
         # Check train_return_nodes / eval_return nodes must both be specified
-        with pytest.raises(AssertionError):
+        with pytest.raises(ValueError):
             self._create_feature_extractor(model, train_return_nodes=train_return_nodes)
         # Check invalid node name raises ValueError
         with pytest.raises(ValueError):