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Update prototypes.py

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Changes:
1. Change single-quotes to double-quotes.
This commit is contained in:
Jensun Ravichandran 2021-04-15 12:35:06 +02:00
parent db842b79bb
commit 101b50f4e6
1 changed files with 37 additions and 37 deletions
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74
prototorch/modules/prototypes.py
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@ -14,11 +14,11 @@ class _Prototypes(torch.nn.Module):
def _validate_prototype_distribution(self):
if 0 in self.prototype_distribution:
warnings.warn('Are you sure about the `0` in '
'`prototype_distribution`?')
warnings.warn("Are you sure about the `0` in "
"`prototype_distribution`?")
def extra_repr(self):
return f'prototypes.shape: {tuple(self.prototypes.shape)}'
return f"prototypes.shape: {tuple(self.prototypes.shape)}"
def forward(self):
return self.prototypes, self.prototype_labels
@ -31,7 +31,7 @@ class Prototypes1D(_Prototypes):
"""
def __init__(self,
prototypes_per_class=1,
prototype_initializer='ones',
prototype_initializer="ones",
prototype_distribution=None,
data=None,
dtype=torch.float32,
@ -44,25 +44,25 @@ class Prototypes1D(_Prototypes):
prototype_distribution = prototype_distribution.tolist()
if data is None:
if 'input_dim' not in kwargs:
raise NameError('`input_dim` required if '
'no `data` is provided.')
if "input_dim" not in kwargs:
raise NameError("`input_dim` required if "
"no `data` is provided.")
if prototype_distribution:
kwargs_nclasses = sum(prototype_distribution)
else:
if 'nclasses' not in kwargs:
raise NameError('`prototype_distribution` required if '
'both `data` and `nclasses` are not '
'provided.')
kwargs_nclasses = kwargs.pop('nclasses')
input_dim = kwargs.pop('input_dim')
if "nclasses" not in kwargs:
raise NameError("`prototype_distribution` required if "
"both `data` and `nclasses` are not "
"provided.")
kwargs_nclasses = kwargs.pop("nclasses")
input_dim = kwargs.pop("input_dim")
if prototype_initializer in [
'stratified_mean', 'stratified_random'
"stratified_mean", "stratified_random"
]:
warnings.warn(
f'`prototype_initializer`: `{prototype_initializer}` '
'requires `data`, but `data` is not provided. '
'Using randomly generated data instead.')
f"`prototype_initializer`: `{prototype_initializer}` "
"requires `data`, but `data` is not provided. "
"Using randomly generated data instead.")
x_train = torch.rand(kwargs_nclasses, input_dim)
y_train = torch.arange(kwargs_nclasses)
if one_hot_labels:
@ -75,39 +75,39 @@ class Prototypes1D(_Prototypes):
nclasses = torch.unique(y_train, dim=-1).shape[-1]
if nclasses == 1:
warnings.warn('Are you sure about having one class only?')
warnings.warn("Are you sure about having one class only?")
if x_train.ndim != 2:
raise ValueError('`data[0].ndim != 2`.')
raise ValueError("`data[0].ndim != 2`.")
if y_train.ndim == 2:
if y_train.shape[1] == 1 and one_hot_labels:
raise ValueError('`one_hot_labels` is set to `True` '
'but target labels are not one-hot-encoded.')
raise ValueError("`one_hot_labels` is set to `True` "
"but target labels are not one-hot-encoded.")
if y_train.shape[1] != 1 and not one_hot_labels:
raise ValueError('`one_hot_labels` is set to `False` '
'but target labels in `data` '
'are one-hot-encoded.')
raise ValueError("`one_hot_labels` is set to `False` "
"but target labels in `data` "
"are one-hot-encoded.")
if y_train.ndim == 1 and one_hot_labels:
raise ValueError('`one_hot_labels` is set to `True` '
'but target labels are not one-hot-encoded.')
raise ValueError("`one_hot_labels` is set to `True` "
"but target labels are not one-hot-encoded.")
# Verify input dimension if `input_dim` is provided
if 'input_dim' in kwargs:
input_dim = kwargs.pop('input_dim')
if "input_dim" in kwargs:
input_dim = kwargs.pop("input_dim")
if input_dim != x_train.shape[1]:
raise ValueError(f'Provided `input_dim`={input_dim} does '
'not match data dimension '
f'`data[0].shape[1]`={x_train.shape[1]}')
raise ValueError(f"Provided `input_dim`={input_dim} does "
"not match data dimension "
f"`data[0].shape[1]`={x_train.shape[1]}")
# Verify the number of classes if `nclasses` is provided
if 'nclasses' in kwargs:
kwargs_nclasses = kwargs.pop('nclasses')
if "nclasses" in kwargs:
kwargs_nclasses = kwargs.pop("nclasses")
if kwargs_nclasses != nclasses:
raise ValueError(f'Provided `nclasses={kwargs_nclasses}` does '
'not match data labels '
'`torch.unique(data[1]).shape[0]`'
f'={nclasses}')
raise ValueError(f"Provided `nclasses={kwargs_nclasses}` does "
"not match data labels "
"`torch.unique(data[1]).shape[0]`"
f"={nclasses}")
super().__init__(**kwargs)