ContextQMD
Back to Oneflow

oneflow.nn

docs/source/nn.rst

1.0.07.0 KB
Original Source

oneflow.nn

.. The documentation is referenced from: https://pytorch.org/docs/1.10/nn.html

These are the basic building blocks for graphs:

.. contents:: oneflow.nn :depth: 2 :local: :class: this-will-duplicate-information-and-it-is-still-useful-here :backlinks: top

.. currentmodule:: oneflow.nn .. autosummary:: :toctree: generated :nosignatures: :template:

Parameter

Containers

.. currentmodule:: oneflow.nn

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

Module
Sequential
ModuleList
ModuleDict
ParameterList
ParameterDict

nn.Module

.. currentmodule:: oneflow.nn.Module

.. autosummary:: :toctree: generated :nosignatures:

add_module
apply
buffers
children
cpu
cuda
double
train
eval
extra_repr
float
forward
load_state_dict
modules
named_buffers
named_children
named_modules
named_parameters
parameters
register_buffer
register_forward_hook
register_forward_pre_hook
register_backward_hook
register_full_backward_hook
register_state_dict_pre_hook
register_parameter
requires_grad_
state_dict
to
zero_grad

Containers

Convolution Layers

.. currentmodule:: oneflow .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.Conv1d 
nn.Conv2d 
nn.Conv3d
nn.ConvTranspose1d 
nn.ConvTranspose2d 
nn.ConvTranspose3d
nn.Unfold
nn.Fold

Pooling Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.MaxPool1d 
nn.MaxPool2d 
nn.MaxPool3d 
nn.MaxUnpool1d
nn.MaxUnpool2d
nn.MaxUnpool3d
nn.AdaptiveAvgPool1d 
nn.AdaptiveAvgPool2d 
nn.AdaptiveAvgPool3d
nn.AdaptiveMaxPool1d 
nn.AdaptiveMaxPool2d 
nn.AdaptiveMaxPool3d
nn.AvgPool1d 
nn.AvgPool2d 
nn.AvgPool3d

Padding Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.ConstantPad1d 
nn.ConstantPad2d 
nn.ConstantPad3d
nn.ReflectionPad1d
nn.ReflectionPad2d
nn.ReplicationPad1d
nn.ReplicationPad2d
nn.ZeroPad2d

Non-linear Activations (weighted sum, nonlinearity)

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.ELU 
nn.Hardshrink
nn.Hardsigmoid 
nn.Hardswish 
nn.Hardtanh 
nn.LeakyReLU 
nn.LogSigmoid 
nn.PReLU 
nn.ReLU
nn.ReLU6 
nn.SELU 
nn.CELU 
nn.GELU 
nn.QuickGELU 
nn.SquareReLU
nn.SiLU 
nn.Sigmoid 
nn.Mish 
nn.Softplus 
nn.Softshrink 
nn.Softsign 
nn.Tanh 
nn.Threshold 
nn.GLU

Non-linear Activations (other)

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.Softmax
nn.LogSoftmax

Normalization Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.BatchNorm1d 
nn.BatchNorm2d 
nn.BatchNorm3d
nn.SyncBatchNorm
nn.FusedBatchNorm1d 
nn.FusedBatchNorm2d
nn.FusedBatchNorm3d 
nn.GroupNorm 
nn.InstanceNorm1d 
nn.InstanceNorm2d 
nn.InstanceNorm3d 
nn.LayerNorm
nn.RMSLayerNorm
nn.RMSNorm

Recurrent Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.RNN
nn.LSTM
nn.GRU
nn.RNNCell
nn.LSTMCell
nn.GRUCell

Linear Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.Identity
nn.Linear

Dropout Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.Dropout
nn.Dropout1d
nn.Dropout2d
nn.Dropout3d

Sparse Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.Embedding

Distance Functions

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.CosineSimilarity
nn.PairwiseDistance

Loss Functions

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.BCELoss 
nn.BCEWithLogitsLoss 
nn.CTCLoss 
nn.CombinedMarginLoss 
nn.CrossEntropyLoss 
nn.KLDivLoss 
nn.L1Loss 
nn.MSELoss 
nn.MarginRankingLoss 
nn.NLLLoss 
nn.SmoothL1Loss 
nn.TripletMarginLoss

Vision Layers

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.PixelShuffle 
nn.Upsample 
nn.UpsamplingBilinear2d 
nn.UpsamplingNearest2d

DataParallel Layers (multi-GPU, distributed)

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.parallel.DistributedDataParallel

Data loading and preprocessing Layers

.. autosummary:: :toctree: generated :nosignatures:

nn.COCOReader
nn.CoinFlip
nn.CropMirrorNormalize
nn.OFRecordBytesDecoder
nn.OFRecordImageDecoder
nn.OFRecordImageDecoderRandomCrop
nn.OFRecordRawDecoder
nn.OFRecordReader

Quantization Aware Training

.. autosummary:: :toctree: generated :nosignatures:

nn.MinMaxObserver
nn.MovingAverageMinMaxObserver
nn.FakeQuantization
nn.QatConv1d
nn.QatConv2d
nn.QatConv3d

Utilities

From the oneflow.nn.utils module

.. currentmodule:: oneflow.nn.utils .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

clip_grad_norm_
clip_grad_value_
weight_norm
remove_weight_norm

Utility functions in other modules

.. currentmodule:: oneflow .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.utils.rnn.PackedSequence
nn.utils.rnn.pack_padded_sequence
nn.utils.rnn.pad_packed_sequence
nn.utils.rnn.pad_sequence
nn.utils.rnn.pack_sequence

.. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst

nn.Flatten

Quantized Functions

Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision.

.. autosummary:: :toctree: generated :nosignatures: :template:

nn.FakeQuantization
nn.MinMaxObserver
nn.MovingAverageMinMaxObserver
nn.Quantization