A PyTorch framework for developing memory efficient deep invertible networks.
Project description
A PyTorch framework for developing memory efficient deep invertible networks
Free software: MIT license (please cite our work if you use it)
Documentation: https://memcnn.readthedocs.io.
Installation: https://memcnn.readthedocs.io/en/latest/installation.html
Features
Simple ReversibleBlock wrapper class to wrap and convert arbitrary PyTorch Modules into invertible versions.
Simple switching between additive and affine invertible coupling schemes and different implementations.
Simple toggling of memory saving by setting the keep_input property of the ReversibleBlock.
Training and evaluation code for reproducing RevNet experiments using MemCNN.
CI tests for Python v2.7 and v3.6 and torch v0.4, v1.0, and v1.1 and good test coverage.
Example usage: ReversibleBlock
# some required imports
import torch
import torch.nn as nn
import numpy as np
import memcnn.models.revop
# define a new class of operation(s) PyTorch style
class ExampleOperation(nn.Module):
def __init__(self, channels):
super(ExampleOperation, self).__init__()
self.seq = nn.Sequential(
nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(num_features=channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.seq(x)
# generate some random input data (b, c, y, x)
data = np.random.random((2, 10, 8, 8)).astype(np.float32)
X = torch.from_numpy(data)
# application of the operation(s) the normal way
Y = ExampleOperation(channels=10)(X)
# application of the operation(s) using the reversible block
F, G = ExampleOperation(channels=10 // 2), ExampleOperation(channels=10 // 2)
Y = memcnn.models.revop.ReversibleBlock(F, G, coupling='additive')(X)
Run PyTorch Experiments
./train.py [MODEL] [DATASET] --fresh
Available values for DATASET are cifar10 and cifar100.
Available values for MODEL are resnet32, resnet110, resnet164, revnet38, revnet110, revnet164
If not available datasets are automatically downloaded.
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