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A PyTorch framework for developing memory efficient deep invertible networks.

Project description

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A PyTorch framework for developing memory-efficient invertible neural networks.

Features

  • Enable memory savings during training by wrapping arbitrary invertible PyTorch functions with the InvertibleModuleWrapper class.
  • Simple toggling of memory saving by setting the keep_input property of the InvertibleModuleWrapper.
  • Turn arbitrary non-linear PyTorch functions into invertible versions using the AdditiveCoupling or the AffineCoupling classes.
  • Training and evaluation code for reproducing RevNet experiments using MemCNN.
  • CI tests for Python v2.7 and v3.7 and torch v0.4, v1.0, v1.1, and v1.4 with good code coverage.

Examples

Creating an AdditiveCoupling with memory savings

import torch
import torch.nn as nn
import memcnn


# define a new torch Module with a sequence of operations: Relu o BatchNorm2d o Conv2d
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 (batch_size, num_channels, y_elements, x_elements)
X = torch.rand(2, 10, 8, 8)

# application of the operation(s) the normal way
model_normal = ExampleOperation(channels=10)
model_normal.eval()

Y = model_normal(X)

# turn the ExampleOperation invertible using an additive coupling
invertible_module = memcnn.AdditiveCoupling(
    Fm=ExampleOperation(channels=10 // 2),
    Gm=ExampleOperation(channels=10 // 2)
)

# test that it is actually a valid invertible module (has a valid inverse method)
assert memcnn.is_invertible_module(invertible_module, test_input_shape=X.shape)

# wrap our invertible_module using the InvertibleModuleWrapper and benefit from memory savings during training
invertible_module_wrapper = memcnn.InvertibleModuleWrapper(fn=invertible_module, keep_input=True, keep_input_inverse=True)

# by default the module is set to training, the following sets this to evaluation
# note that this is required to pass input tensors to the model with requires_grad=False (inference only)
invertible_module_wrapper.eval()

# test that the wrapped module is also a valid invertible module
assert memcnn.is_invertible_module(invertible_module_wrapper, test_input_shape=X.shape)

# compute the forward pass using the wrapper
Y2 = invertible_module_wrapper.forward(X)

# the input (X) can be approximated (X2) by applying the inverse method of the wrapper on Y2
X2 = invertible_module_wrapper.inverse(Y2)

# test that the input and approximation are similar
assert torch.allclose(X, X2, atol=1e-06)

Run PyTorch Experiments

After installing MemCNN run:

python -m memcnn.train [MODEL] [DATASET] [--fresh] [--no-cuda]
  • Available values for DATASET are cifar10 and cifar100.
  • Available values for MODEL are resnet32, resnet110, resnet164, revnet38, revnet110, revnet164
  • Use the --fresh flag to remove earlier experiment results.
  • Use the --no-cuda flag to train on the CPU rather than the GPU through CUDA.

Datasets are automatically downloaded if they are not available.

When using Python 3.* replace the python directive with the appropriate Python 3 directive. For example when using the MemCNN docker image use python3.6.

When MemCNN was installed using pip or from sources you might need to setup a configuration file before running this command. Read the corresponding section about how to do this here: https://memcnn.readthedocs.io/en/latest/installation.html

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