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Pytorch Network Slimming

TL;DR: Try mobilenetv3 first before you want to pruning a big model :)

This repository contains tools to make implement Learning Efficient Convolutional Networks Through Network Slimming on different backbones easier.

Features

  • Auto generate pruning schema. Tracker code is inspired by torch2trt
  • Channel pruning
  • Save and load pruned network graph and params (without pruning schema)
  • Export pruned model to onnx
  • Channels round up to 8x (for train/infer speed) (TODO: handle shortcuts channels round up)
  • Layer pruning

Supported project:

Supported model arches:

  • ResNet
  • RepVGG
  • vgg_bn
  • mobilenet_v2
  • mobilenet_v3(doc)

Supported layers:

  • Conv2d
  • depthwise/pointwise Conv2d
  • SqueezeExcitation(SE block)
  • Linear
  • BatchNorm2d
  • torch.cat

Quick start on cifar10

  1. Install pns as a python package: python3 setup.py develop

  2. Install requirements.txt, this is the dependency needed to train the cifar10 demo, pns itself only depends on pytorch

  3. Generate pruning schema of resnet18:

    python3 gen_schema.py --net resnet18 --save_path ./schema/resnet18.json

  4. Train on cifar10，and do fine tuning after apply Network Slimming.

    python3 main.py \
--save_dir output \
--dataset cifar10 \
--net resnet18 \
--epochs 120 \
--batch_size 64 \
--learning_rate 0.01 \
--sparsity_train \
--s 0.0001 \
--fine_tune \
--prune_schema ./schema/resnet18.json \
--fine_tune_epochs 120 \
--fine_tune_learning_rate 0.001 \
--prune_ratio 0.75

  5. After apply Network Slimming, pruning result will be saved in checkpoint with _slim_pruning_result key and pruned params will be saved in another checkpoint.

Eval model without pruning result:

python3 main.py \
--dataset cifar10 \
--net resnet18 \
--ckpt ./output/last.ckpt

Eval model with pruning result:

python3 main.py \
--dataset cifar10 \
--net resnet18 \
--ckpt ./output/pruned_0.75/model_with_pruning_result.ckpt \
--ckpt_pruned ./output/pruned_0.75/last.ckpt

Export pruned model to ONNX

python3 main.py \
--net resnet18 \
--ckpt ./output/pruned_0.75/model_with_pruning_result.ckpt \
--ckpt_pruned ./output/pruned_0.75/last.ckpt \
--export_onnx_path ./output/pruned_0.75/last.onnx

Eval ONNX model(demo script only support CPU)

python3 main.py \
--dataset cifar10 \
--net resnet18 \
--ckpt ./output/pruned_0.75/last.onnx \
--device cpu

Benchmark onnx model performance

python3 benchmark.py \
/path/to/onnx_model1.onnx \
/path/to/onnx_model2.onnx

Experiments Result on CIFAR10

  • batch_size: 64
  • epochs: 120
  • sparsity: 1e-4
  • learning rate: 0.01
  • fine tune epochs: 120
  • fine tune learning rate: 0.01
  • onnx latency: input shape=1x3x224x224, OMP_NUM_THREADS=4, cpu=Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz
Net Prune Ratio Test Acc Test Acc Diff Params Params Reduce ONNX File size(MB) ONNX Latency ONNX Memory
0 RepVGG-A0-woid 0 93.6 0 7.8 M
1 RepVGG-A0-woid 0.75 93.88 +0.28 2.2 M 71.78%
2 RepVGG-A0-woid 0.5 93.86 +0.26 3.8 M 52.14%
3 resnet18 0 94.48 0 11.2 M
4 resnet18 0.75 94.14 -0.34 4.5 M 59.29%
5 resnet18 0.5 94.8 +0.32 3.5 M 68.83%
6 resnet50 0 94.65 0 23.5 M
7 resnet50 0.75 95.29 +0.64 5.3 M 77.59%
8 resnet50 0.5 95.42 +0.77 14.8 M 37.04%
9 vgg11_bn 0 91.7 0 128 M
10 vgg11_bn 0.75 89.85 -1.85 28.9 M 77.53%
11 vgg11_bn 0.5 91.46 -0.24 58.5 M 54.58%
12 mobilenet_v2 0 94.52 0 2.2M 8.5
13 mobilenet_v2 0.75 91.17 -3.35 661K 70.41% 2.6
14 mobilenet_v2(s=1e-5) 0 94.42 0 2.2M 8.5
15 mobilenet_v2(s=1e-5) 0.75 93.12 -1.3 597k 73.30% 2.3
16 mobilenet_v3_large_nose 0 92.61 0 2.7 M
18 mobilenet_v3_large_nose 0.5 93.33 +0.72 1.9 M 30.09%
17 mobilenet_v3_large_nose 0.75 91.42 -1.19 1.4 M 48.33%
19 mobilenet_v3_small_nose 0 90.69 0 1.1 M
20 mobilenet_v3_small_nose 0.5 91.08 +0.39 777 K 27.11%
21 mobilenet_v3_small_nose 0.75 87.25 -3.44 564 K 47.11%
22 mobilenet_v3_large 0 92.96 0 4.2 M 16 100ms 165mb
23 mobilenet_v3_large 0.75 92.18 -0.78 1.6 M 63.12% 5.9 65ms 151mb
24 mobilenet_v3_large 0.5 92.87 -0.09 2.3 M 45.57% 8.7 81ms 123mb
  • for RepVGG-A0-woid(prune ratio 0.7), fine tune learning rate = 0.001
  • woid：RepVGGBlock without identity layer
  • nose: MobileNetV3 without SE block

Experiments result without sparsity train + prune:

Net Sparsity Prune Ratio Test Acc Test Acc Diff Params Size Reduce
0 resnet18 0 0 93.65 0 11.2 M
1 resnet18 0 0.75 91.07 -2.58 389 K 96.52%

TODO: Understand why the size of the model is reduced so much?

How to use pns in your project

  1. Understand the content of this paper Learning Efficient Convolutional Networks Through Network Slimming，install pns by run python3 setup.py install
  2. Refer to the gen_schema.py script to generate the pruning schema. You may need to implement your own build_model section
  3. Training: call update_bn_grad after loss.backward()
...
loss.backward()
update_bn_grad(model, s=0.0001)
...

  1. Fine tune:

    • Restore model weights according to the normal flow of your project
    • Pruning according to the prune_schema of your network
    • Save pruning result anywhere you like
    • Fine tune pruned model
    pruner = SlimPruner(restored_model, prune_schema)
pruning_result: List[Dict] = pruner.run(prune_ratio=0.75)
# save pruning_result
...
# fine tune pruned_model
pruner.pruned_model
...
# save pruned_model state_dict()
torch.save(pruner.pruned_model.state_dict())

  2. Loading pruning result/params when do forward or pruning again:

#  build model
pruner = SlimPruner(model)
# load pruning_result from some where to get a slim network
pruning_result: List[Dict]
pruner.apply_pruning_result(pruning_result)
# load pruned state_dict from some where
pruned_state_dict: Dict
pruner.pruned_model.load_state_dict(pruned_state_dict)
# do forward or train with update_bn_grad again
pruner.pruned_model

Pruning Schema

{
  "prefix": "model.",
  "channel_rounding": "eight",
  "modules": [
    {
      "name": "conv1",
      "prev_bn": "",
      "next_bn": "bn1"
    }
  ],
  "shortcuts": [
    {
      "names": ["bn1", "layer1.0.bn2", "layer1.1.bn2"],
      "method": "or"
    }
  ],
  "depthwise_conv_adjacent_bn": [
    {
      "names": ["bn1", "layer1.0.bn2", "layer1.1.bn2"],
      "method": "or"
    }
  ],
  "fixed_bn_ratio": [
    {
      "name": "name1",
      "ratio": 0.8
    },
    {
      "name": ["name2"],
      "ratio": 0.8
    }
  ]
}
  • prefix: common prefix added to all module name
  • channel_rounding: none/eight/two_pow
  • modules: Conv2d or Linear layers
  • shortcuts/depthwise_conv_adjacent_bn: BatchNorm2d Layers
    • or: All bn layer reserved channels take the merged set
    • and: All bn layer reserved channels take the intersection set
  • fixed_bn_ratio: BatchNorm2d Layers fix prune percent, will applied before merge shortcuts
    • name: string or List[string]
    • ratio: prune ratio

Development

Run test

pytest -v tests