[Doc] Fix tanh_loc in docstrings (pytorch#1203)

ai4co · May 29, 2023 · 3528ffc · 3528ffc
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Showing 13 changed files with 20 additions and 20 deletions.
diff --git a/torchrl/data/replay_buffers/samplers.py b/torchrl/data/replay_buffers/samplers.py
@@ -88,7 +88,7 @@ class SamplerWithoutReplacement(Sampler):
 
     When the sampler reaches the end of the list of available indices, a new sample order
     will be generated and the resulting indices will be completed with this new draw, which
-    can lead to duplicated indices, unless the :obj:`drop_last` argument is set to :obj:`True`.
+    can lead to duplicated indices, unless the :obj:`drop_last` argument is set to ``True``.
 
     """
 

diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py
@@ -2389,7 +2389,7 @@ class CompositeSpec(TensorSpec):
             This is useful to build nested CompositeSpecs with tuple indices.
         **kwargs (key (str): value (TensorSpec)): dictionary of tensorspecs
             to be stored. Values can be None, in which case is_in will be assumed
-            to be :obj:`True` for the corresponding tensors, and :obj:`project()` will have no
+            to be ``True`` for the corresponding tensors, and :obj:`project()` will have no
             effect. `spec.encode` cannot be used with missing values.
 
     Examples:

diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py
@@ -1106,7 +1106,7 @@ def rollout(
             callback (callable, optional): function to be called at each iteration with the given TensorDict.
             auto_reset (bool, optional): if ``True``, resets automatically the environment
                 if it is in a done state when the rollout is initiated.
-                Default is :obj:`True`.
+                Default is ``True``.
             auto_cast_to_device (bool, optional): if ``True``, the device of the tensordict is automatically cast to the
                 policy device before the policy is used. Default is ``False``.
             break_when_any_done (bool): breaks if any of the done state is True. If False, a reset() is

diff --git a/torchrl/envs/libs/utils.py b/torchrl/envs/libs/utils.py
@@ -32,7 +32,7 @@ class GymPixelObservationWrapper(ObservationWrapper):
 
     Args:
         env: The environment to wrap.
-        pixels_only: If :obj:`True` (default), the original observation returned
+        pixels_only: If ``True`` (default), the original observation returned
             by the wrapped environment will be discarded, and a dictionary
             observation will only include pixels. If ``False``, the
             observation dictionary will contain both the original

diff --git a/torchrl/envs/transforms/r3m.py b/torchrl/envs/transforms/r3m.py
@@ -213,7 +213,7 @@ class R3MTransform(Compose):
             Defaults to 244.
         stack_images (bool, optional): if False, the images given in the :obj:`in_keys`
              argument will be treaded separetely and each will be given a single,
-             separated entry in the output tensordict. Defaults to :obj:`True`.
+             separated entry in the output tensordict. Defaults to ``True``.
         download (bool, torchvision Weights config or corresponding string):
             if ``True``, the weights will be downloaded using the torch.hub download
             API (i.e. weights will be cached for future use).

diff --git a/torchrl/envs/transforms/vip.py b/torchrl/envs/transforms/vip.py
@@ -179,7 +179,7 @@ class VIPTransform(Compose):
             Defaults to 244.
         stack_images (bool, optional): if False, the images given in the :obj:`in_keys`
              argument will be treaded separetely and each will be given a single,
-             separated entry in the output tensordict. Defaults to :obj:`True`.
+             separated entry in the output tensordict. Defaults to ``True``.
         download (bool, torchvision Weights config or corresponding string):
             if ``True``, the weights will be downloaded using the torch.hub download
             API (i.e. weights will be cached for future use).

diff --git a/torchrl/modules/distributions/continuous.py b/torchrl/modules/distributions/continuous.py
@@ -57,9 +57,9 @@ class IndependentNormal(D.Independent):
 
             Default is 5.0
 
-        tanh_loc (bool, optional): if ``True``, the above formula is used for the location scaling, otherwise the raw value
-            is kept.
-            Default is :obj:`True`;
+        tanh_loc (bool, optional): if ``False``, the above formula is used for
+            the location scaling, otherwise the raw value
+            is kept. Default is ``False``;
     """
 
     num_params: int = 2
@@ -69,7 +69,7 @@ def __init__(
         loc: torch.Tensor,
         scale: torch.Tensor,
         upscale: float = 5.0,
-        tanh_loc: bool = True,
+        tanh_loc: bool = False,
         event_dim: int = 1,
         **kwargs,
     ):
@@ -187,7 +187,7 @@ class TruncatedNormal(D.Independent):
         max (torch.Tensor or number, optional): maximum value of the distribution. Default = 1.0;
         tanh_loc (bool, optional): if ``True``, the above formula is used for
             the location scaling, otherwise the raw value is kept.
-            Default is :obj:`True`;
+            Default is ``False``;
     """
 
     num_params: int = 2
@@ -204,7 +204,7 @@ def __init__(
         upscale: Union[torch.Tensor, float] = 5.0,
         min: Union[torch.Tensor, float] = -1.0,
         max: Union[torch.Tensor, float] = 1.0,
-        tanh_loc: bool = True,
+        tanh_loc: bool = False,
     ):
         err_msg = "TanhNormal max values must be strictly greater than min values"
         if isinstance(max, torch.Tensor) or isinstance(min, torch.Tensor):
@@ -304,7 +304,7 @@ class TanhNormal(FasterTransformedDistribution):
         event_dims (int, optional): number of dimensions describing the action.
             Default is 1;
         tanh_loc (bool, optional): if ``True``, the above formula is used for the location scaling, otherwise the raw
-            value is kept. Default is :obj:`True`;
+            value is kept. Default is ``False``;
     """
 
     arg_constraints = {

diff --git a/torchrl/modules/models/models.py b/torchrl/modules/models/models.py
@@ -884,7 +884,7 @@ class DdpgCnnQNet(nn.Module):
             'bias_last_layer': True,
         }
         use_avg_pooling (bool, optional): if ``True``, a nn.AvgPooling layer is
-            used to aggregate the output. Default is :obj:`True`.
+            used to aggregate the output. Default is ``True``.
         device (Optional[DEVICE_TYPING]): device to create the module on.
     """
 

diff --git a/torchrl/modules/tensordict_module/probabilistic.py b/torchrl/modules/tensordict_module/probabilistic.py
@@ -210,7 +210,7 @@ class SafeProbabilisticTensorDictSequential(
             of the input keys. If so, the only module that will be executed are those
             who can be executed given the keys that are present. Also, if the input
             tensordict is a lazy stack of tensordicts AND if partial_tolerant is
-            :obj:`True` AND if the stack does not have the required keys, then
+            ``True`` AND if the stack does not have the required keys, then
             TensorDictSequential will scan through the sub-tensordicts looking for those
             that have the required keys, if any.
 

diff --git a/torchrl/modules/tensordict_module/sequence.py b/torchrl/modules/tensordict_module/sequence.py
@@ -25,7 +25,7 @@ class SafeSequential(TensorDictSequential, SafeModule):
          partial_tolerant (bool, optional): if ``True``, the input tensordict can miss some of the input keys.
             If so, the only module that will be executed are those who can be executed given the keys that
             are present.
-            Also, if the input tensordict is a lazy stack of tensordicts AND if partial_tolerant is :obj:`True` AND if the
+            Also, if the input tensordict is a lazy stack of tensordicts AND if partial_tolerant is ``True`` AND if the
             stack does not have the required keys, then SafeSequential will scan through the sub-tensordicts
             looking for those that have the required keys, if any.
 

diff --git a/torchrl/objectives/utils.py b/torchrl/objectives/utils.py
@@ -92,7 +92,7 @@ def distance_loss(
         v2 (Tensor): a tensor with a shape compatible with v1
         loss_function (str): One of "l2", "l1" or "smooth_l1" representing which loss function is to be used.
         strict_shape (bool): if False, v1 and v2 are allowed to have a different shape.
-            Default is :obj:`True`.
+            Default is ``True``.
 
     Returns:
          A tensor of the shape v1.view_as(v2) or v2.view_as(v1) with values equal to the distance loss between the

diff --git a/torchrl/objectives/value/advantages.py b/torchrl/objectives/value/advantages.py
@@ -750,7 +750,7 @@ class GAE(ValueEstimatorBase):
 
     GAE will return an :obj:`"advantage"` entry containing the advange value. It will also
     return a :obj:`"value_target"` entry with the return value that is to be used
-    to train the value network. Finally, if :obj:`gradient_mode` is :obj:`True`,
+    to train the value network. Finally, if :obj:`gradient_mode` is ``True``,
     an additional and differentiable :obj:`"value_error"` entry will be returned,
     which simple represents the difference between the return and the value network
     output (i.e. an additional distance loss should be applied to that signed value).

diff --git a/torchrl/trainers/trainers.py b/torchrl/trainers/trainers.py
@@ -114,12 +114,12 @@ class Trainer:
         clip_grad_norm (bool, optional): If True, the gradients will be clipped
             based on the total norm of the model parameters. If False,
             all the partial derivatives will be clamped to
-            (-clip_norm, clip_norm). Default is :obj:`True`.
+            (-clip_norm, clip_norm). Default is ``True``.
         clip_norm (Number, optional): value to be used for clipping gradients.
             Default is None (no clip norm).
         progress_bar (bool, optional): If True, a progress bar will be
             displayed using tqdm. If tqdm is not installed, this option
-            won't have any effect. Default is :obj:`True`
+            won't have any effect. Default is ``True``
         seed (int, optional): Seed to be used for the collector, pytorch and
             numpy. Default is ``None``.
         save_trainer_interval (int, optional): How often the trainer should be