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