tensorplay.nn
These are the basic building blocks for graphs:
Containers
Global Hooks For Module
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Register a forward pre-hook common to all modules. |
Register a global forward hook for all the modules. |
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Register a backward pre-hook common to all the modules. |
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Register a backward hook common to all the modules. |
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Register a buffer registration hook common to all modules. |
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Register a module registration hook common to all modules. |
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Register a parameter registration hook common to all modules. |
Utilities
From the tensorplay.nn.utils module:
Utility functions to clip parameter gradients.
Utility functions to flatten and unflatten Module parameters to and from a single vector.
Utility functions to fuse Modules with BatchNorm modules.
Utility functions to convert Module parameter memory formats.
Utility functions to apply and remove weight normalization from Module parameters.
Utility functions for initializing Module parameters.
Utility classes and functions for pruning Module parameters.
Parametrizations implemented using the new parametrization functionality
in tensorplay.nn.utils.parameterize.register_parametrization().
Utility functions to parametrize Tensors on existing Modules.
Note that these functions can be used to parametrize a given Parameter
or Buffer given a specific function that maps from an input space to the
parametrized space. They are not parameterizations that would transform
an object into a parameter. See the
Parametrizations tutorial
for more information on how to implement your own parametrizations.
Utility functions to call a given Module in a stateless manner.
Utility functions in other modules
Calls |
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Holds the data and list of |
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Packs a Tensor containing padded sequences of variable length. |
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Pad a packed batch of variable length sequences. |
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Pad a list of variable length Tensors with |
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Packs a list of variable length Tensors. |
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Unpack PackedSequence into a list of variable length Tensors. |
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Unpad padded Tensor into a list of variable length Tensors. |
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Returns the inverse of |
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Returns whether |
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Flattens a contiguous range of dims into a tensor. |
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Unflattens a tensor dim expanding it to a desired shape. |
Quantized Functions
Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision. TensorPlay supports both per tensor and per channel asymmetric linear quantization. To learn more how to use quantized functions in TensorPlay, please refer to the Quantization documentation.
Lazy Modules Initialization
A mixin for modules that lazily initialize parameters, also known as "lazy modules". |
