torchex library
(WIP) torchex
library provides advanced Neural Network Layers. You can easily use them like using original pytorch.
Installation
$ pip install torchex
Requirements
- Pytorch >= 1.0
Documentation
How to use
Lazy Style Model Definition
import torch
import torchex.nn as exnn
net = exnn.Linear(10)
# You don't need to give the size of input for this module.
# This network is equivalent to `nn.Linear(100, 10)`.
x = troch.randn(10, 100)
y = net(x)
torchex.nn list
torchex.nn.Pass
torchex.nn.Flatten
-
torchex.nn.Linear
- Lazy style
-
torchex.nn.Conv1d
- Lazy style
-
torchex.nn.Conv2d
- Lazy style
-
torchex.nn.Conv3d
- Lazy style
torchex.nn.Conv2dLocal
torchex.nn.GlobalAvgPool1d
torchex.nn.GlobalAvgPool2d
torchex.nn.GlobalMaxPool1d
torchex.nn.GlobalMaxPool2d
torchex.nn.MaxAvgPool2d
torch.nn.Crop2d
torch.nn.Crop3d
torch.nn.MLPConv2d
torch.nn.UpsampleConvLayer
torch.nn.CordConv2d
torch.nn.DFT1d
torch.nn.DFT2d
torch.nn.PeriodicPad2d
torch.nn.PeriodicPad3d
torch.nn.Highway
torch.nn.Inception
torch.nn.InceptionBN
torch.nn.IndRNNCell
torch.nn.IndRNNTanhCell
torch.nn.IndRNNReLuCell
torch.nn.IndRNN
torch.nn.GraphLinear
torch.nn.GraphConv
torch.nn.SparseMM
torch.nn.GraphBatchNrom
torchex.data.transforms
torchex.data.transforms.PadRandomSift
torchex.data.transforms.RandomResize
torchex.data.attribute
for visualization
torchex.attribute.IntegratedGradients