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lint fix: raise from None
Summary: New linter warning is complaining about `raise` inside `except`. Reviewed By: kjchalup Differential Revision: D37819264 fbshipit-source-id: 56ad5d0558ea39e1125f3c76b43b7376aea2bc7c
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@ -276,7 +276,7 @@ def _read_binvox_header(f): # pragma: no cover
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try:
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dims = [int(d) for d in dims[1:]]
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except ValueError:
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raise ValueError("Invalid header (line 2)")
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raise ValueError("Invalid header (line 2)") from None
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if len(dims) != 3 or dims[0] != dims[1] or dims[0] != dims[2]:
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raise ValueError("Invalid header (line 2)")
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size = dims[0]
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@ -291,7 +291,7 @@ def _read_binvox_header(f): # pragma: no cover
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try:
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translation = tuple(float(t) for t in translation[1:])
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except ValueError:
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raise ValueError("Invalid header (line 3)")
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raise ValueError("Invalid header (line 3)") from None
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# Fourth line of the header should be "scale [float]"
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line = f.readline().strip()
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@ -107,7 +107,7 @@ class Autodecoder(Configurable, torch.nn.Module):
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device=next(self.parameters()).device,
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)
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except StopIteration:
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raise ValueError("Not enough n_instances in the autodecoder")
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raise ValueError("Not enough n_instances in the autodecoder") from None
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# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
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return self._autodecoder_codes(x)
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@ -238,7 +238,7 @@ class Stats(object):
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"could not extract prediction %s\
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from the prediction dictionary"
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% stat
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)
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) from None
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else:
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val = None
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@ -85,7 +85,7 @@ def _read_faces_lump(
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if n_faces > 1 and "Wrong number of columns" in e.args[0]:
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file.seek(old_offset)
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return None
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raise ValueError("Not enough face data.")
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raise ValueError("Not enough face data.") from None
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if len(data) != n_faces:
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raise ValueError("Not enough face data.")
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@ -247,11 +247,11 @@ def _load_off_stream(file) -> dict:
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try:
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n_verts = int(items[0])
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except ValueError:
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raise ValueError("Invalid counts line: %s" % header)
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raise ValueError("Invalid counts line: %s" % header) from None
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try:
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n_faces = int(items[1])
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except ValueError:
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raise ValueError("Invalid counts line: %s" % header)
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raise ValueError("Invalid counts line: %s" % header) from None
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if (len(items) > 3 and not items[3].startswith(b"#")) or n_verts < 0 or n_faces < 0:
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raise ValueError("Invalid counts line: %s" % header)
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@ -236,7 +236,7 @@ class _PlyHeader:
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count = int(items[2])
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except ValueError:
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msg = "Number of items for %s was not a number."
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raise ValueError(msg % items[1])
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raise ValueError(msg % items[1]) from None
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self.elements.append(_PlyElementType(items[1], count))
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@ -409,12 +409,12 @@ def _parse_heterogeneous_property_ascii(datum, line_iter, property: _Property):
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else:
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datum.append(int(value))
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except ValueError:
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raise ValueError("Bad numerical data.")
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raise ValueError("Bad numerical data.") from None
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else:
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try:
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length = int(value)
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except ValueError:
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raise ValueError("A list length was not a number.")
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raise ValueError("A list length was not a number.") from None
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list_value = np.zeros(length, dtype=_PLY_TYPES[property.data_type].np_type)
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for i in range(length):
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inner_value = next(line_iter, None)
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@ -423,7 +423,7 @@ def _parse_heterogeneous_property_ascii(datum, line_iter, property: _Property):
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try:
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list_value[i] = float(inner_value)
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except ValueError:
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raise ValueError("Bad numerical data.")
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raise ValueError("Bad numerical data.") from None
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datum.append(list_value)
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@ -141,7 +141,7 @@ def iterative_closest_point(
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"(minibatch, dim, dim), T is a batch of dim-dimensional "
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"translations of shape (minibatch, dim) and s is a batch "
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"of scalars of shape (minibatch,)."
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)
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) from None
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# apply the init transform to the input point cloud
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Xt = _apply_similarity_transform(Xt, R, T, s)
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else:
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