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PyTorch Network Slimming

This repo implements the following paper in PyTorch:

Learning Efficient Convolutional Networks Through Network Slimming

Different with most other popular network slimming repos, this implementation enables training & pruning models in hand with a few lines of new codes. Writing codes specifically for pruning is not required. Moreover, the pruned model can be further improved and deployed with toolkits supporting ONNX. An example of further acceleration with OpenVINO is included.

BN layers are automatically identified by 1) parse the traced graph by TorchScript, and 2) identify prunable BN layers which only have Convolution(groups=1)/Linear in preceding & succeeding layers. Example of a prunable BN:

        conv/linear --> ... --> BN --> ... --> conv/linear
                                 |
                                ...
                                 | --> relu --> ... --> conv/linear
                                         |
                                         | --> ... --> maxpool --> ... --> conv/linear

Note that without coding for patches, such as channel_selection, the prunable BNs for some network architectures (ResNet/DenseNet/...) will be less than the official implementation. For more details please refer to source code.

It is supposed to support user defined models with Convolution(groups=1)/Linear and BN layers. The package is tested with the CIFAR-100 examples in this repo, and an in-house Conv3d based model for video classification.

* DataParalell is not supported

Known Issue

Node without an explicit name in traced graph

This code depends on traced graph by TorchScript, so any graph without an explicit module name will fail. For example:

def foward(self, x)
    ...
    for name, child in self.backbone.named_children():
        x = child(x)
    ...

shortcut from BN to BN

will be fixed later ...

Results on CIFAR-100

Accuracy (%)

Original L1 on BN PR=0.3 PR=0.5 PR=0.7 PR=0.3 (TFS) PR=0.5 (TFS) PR=0.7 (TFS)
ResNet-18 78.90 78.21 78.60 78.02 74.97 78.31 76.84 73.93
ResNet-18 (L1 on all BNs) 78.90 78.51 79.07 77.58 75.30 78.29 76.77 74.26
VGG-11 71.04 70.66 71.69 69.16 58.95 70.74 68.50 61.13
simplified VGG-11 71.72 71.62 71.55 68.80 F 70.58 67.37 52.87
DenseNet-63 78.34 78.06 78.11 77.76 76.33 78.12 77.65 76.01

Params (M)

Original PR=0.3 PR=0.5 PR=0.7
ResNet-18 10.70 7.91 6.44 4.56
ResNet-18 (L1 on all BNs) 10.70 7.84 6.39 4.52
VGG-11 27.20 21.75 19.35 18.00
simplified VGG-11 8.85 4.63 2.09 0.54
DenseNet-63 2.19 1.63 1.32 0.93

GFLOPS

Original PR=0.3 PR=0.5 PR=0.7
ResNet-18 0.52 0.32 0.21 0.12
ResNet-18 (L1 on all BNs) 0.52 0.33 0.22 0.12
VGG-11 0.19 0.14 0.09 0.06
simplified VGG-11 0.16 0.06 0.03 0.01
DenseNet-63 0.30 0.18 0.12 0.07

* F: Failed to converge

* PR: Prune Ratio

* TFS: Train-from-scratch as proposed in Liu's later paper on ICLR 2019 Rethinking the Value of Network Pruning.

Requirements

Python >= 3.6
torch >= 1.1.0
torchvision >= 0.3.0

Usage

  1. Import from netslim in your training script

    from netslim import update_bn

  2. Insert updat_bn between loss.backward() and optimizer.step(). The following is an example:

    before

    ...
loss.backward()
optimizer.step()
...

    after

    ...
loss.backward()
update_bn(model)  # or update_bn(model, s), specify s to control sparsity on BN
optimizer.step()
...

    * update_bn puts L1 regularization on all BNs. Sparsity on prunable BNs only is also supported for networks with complex connections, such as ResNet. Check examples for more details.

  3. Prune the model after training

    from netslim import prune
# For example, input_shape for CIFAR is (3, 32, 32)
pruned_model = prune(model, input_shape, prune_ratio=0.5)

  4. Fine-tune & export model

  5. Load the pruned model and have fun

    from netslim import load_pruned_model
model = MyModel()
weights = torch.load("/path/to/pruned-weights.pth")
pruned_model = load_pruned_model(model, weights)
...

Run CIFAR-100 examples

ResNet-18

sh experiment-resnet.sh

VGG-11

sh experiment-vgg11.sh
sh experiment-vgg11s.sh % simplified VGG-11 by replacing classifier with a Linear

DenseNet-63

sh experiment-densenet.sh

Train from scratch

Run shell scripts end with "_tfs". For example, train pruned ResNet-18 after running L1 regularized training:

sh experiment_resnet_tfs.sh

Load & test pruned model (ResNet-18 example)

python test.py --arch resnet18 --resume_path output-resnet18-bn-pr05/ckpt_best.pth

-all-bn refers to L1 sparsity on all BN layers

Inference with OpenVINO

The efficiency of pruned model can be further improved by using OpenVINO, if you are working with Intel processors/accelerators.

  1. Download OpenVINO from the official website OpenVINO.

  2. After installation, initialize the environment:

    source /opt/intel/openvino/bin/setupvars.sh

  3. Take simplified VGG-11 as an example, after finished running experiment-vgg11s.sh, convert pruned model to OpenVINO IR:

    python export2openvino.py --arch vgg11s --weights output-vgg11s-bn-pr05/ckpt_best.pth --outname vgg11s-pr05
python export2openvino.py --arch vgg11s --weights output-vgg11s-bn-pr05/ckpt_best.pth --outname vgg11s-pr05 --fp16

  4. Test with CIFAR-100

    python test-openvino.py vgg11s-FP32
python test-openvino.py vgg11s-FP16

  5. Compare runtime

    python cpu-runtime-benchmark-vgg11s.py

On a desktop with Xeon 8160 CPU, the FPS results are:

PyTorch Full Model PyTorch PR=0.5 OpenVINO PR=0.5 FP32 OpenVINO PR=0.5 FP16
89.50 146.21 960.49 973.29

Acknowledgement

The implementation of udpate_bn is referred to pytorch-slimming.