Skip to content

Latest commit

 

History

History
141 lines (108 loc) · 7.65 KB

README.md

File metadata and controls

141 lines (108 loc) · 7.65 KB
Raw

Neural Network Compression Framework (NNCF)

This module contains a PyTorch*-based framework and samples for neural networks compression. The framework is organized as a Python* package that can be built and used in a standalone mode. The framework architecture is unified to make it easy to add different compression methods. The samples demonstrate the usage of compression algorithms for three different use cases on public models and datasets: Image Classification, Object Detection and Semantic Segmentation.

Key Features

  • Support of various compression algorithms, applied during a model fine-tuning process to achieve best compression parameters and accuracy:
  • Automatic, configurable model graph transformation to obtain the compressed model. The source model is wrapped by the custom class and additional compression-specific layers are inserted in the graph.
  • Common interface for compression methods
  • GPU-accelerated layers for faster compressed model fine-tuning
  • Distributed training support
  • Configuration file examples for each supported compression algorithm.
  • Git patches for prominent third-party repositories (mmdetection, huggingface-transformers) demonstrating the process of integrating NNCF into custom training pipelines
  • Exporting compressed models to ONNX* checkpoints ready for usage with OpenVINO™ toolkit.

Usage

The NNCF is organized as a regular Python package that can be imported in your target training pipeline script. The basic workflow is loading a JSON configuration script containing NNCF-specific parameters determining the compression to be applied to your model, and then passing your model along with the configuration script to the nncf.create_compressed_model function. This function returns a wrapped model ready for compression fine-tuning, and handle to the object allowing you to control the compression during the training process:

import nncf
from nncf import create_compressed_model, Config as NNCFConfig

# Instantiate your uncompressed model
from torchvision.models.resnet import resnet50
model = resnet50()

# Load a configuration file to specify compression
nncf_config = NNCFConfig.from_json("resnet50_int8.json")

# Provide data loaders for compression algorithm initialization, if necessary
nncf_config = register_default_init_args(nncf_config, loss_criterion, train_loader)

# Apply the specified compression algorithms to the model
comp_ctrl, compressed_model = create_compressed_model(model, nncf_config)

# Now use compressed_model as a usual torch.nn.Module to fine-tune compression parameters along with the model weights

# ... the rest of the usual PyTorch-powered training pipeline

# Export to ONNX or .pth when done fine-tuning
comp_ctrl.export_model("compressed_model.onnx")
torch.save(compressed_model.state_dict(), "compressed_model.pth")

For a more detailed description of NNCF usage in your training code, see Usage.md. For in-depth examples of NNCF integration, browse the [sample scripts](#Model Compression Samples) code, or the [example patches](#Third-party repository integration) to third-party repositories.

For more details about the framework architecture, refer to the NNCFArchitecture.md.

Model Compression Samples

For a quicker start with NNCF-powered compression, you can also try the sample scripts, each of which provides a basic training pipeline for classification, semantic segmentation and object detection neural network training correspondingly.

To run the samples please refer to the corresponding tutorials:

Third-party repository integration

NNCF may be straightforwardly integrated into training/evaluation pipelines of third-party repositories. See third_party_integration for examples of code modifications (Git patches and base commit IDs are provided) that are necessary to integrate NNCF into select repositories.

System requirements

  • Ubuntu* 16.04 or later (64-bit)
  • Python* 3.6 or later
  • NVidia CUDA* Toolkit 10.2 or later
  • PyTorch* 1.5 or higher.

Installation

We suggest to install or use the package in the Python virtual environment.

As a package built from checked-out repository:

  1. Install the following system dependencies:

sudo apt-get install python3-dev

  1. Install the package and its dependencies by running the following in the repository root directory:
  • For CPU & GPU-powered execution: python setup.py install
  • For CPU-only installation python setup.py install --cpu-only

As a Docker image

Use one of the Dockerfiles in the docker directory to build an image with an environment already set up and ready for running NNCF [sample scripts](#Model Compression Samples).

NNCF compression results

Achieved using sample scripts and NNCF configuration files provided with this repository. See README.md files for [sample scripts](#Model Compression Samples) for links to exact configuration files and final PyTorch checkpoints.

Model Compression algorithm Dataset PyTorch FP32 baseline PyTorch compressed accuracy
ResNet-50 None ImageNet - 76.13
ResNet-50 INT8 ImageNet 76.13 76.05
ResNet-50 Mixed, 44.8% INT8 / 55.2% INT4 ImageNet 76.13 76.3
ResNet-50 INT8 + Sparsity 61% (RB) ImageNet 76.13 75.28
ResNet-50 Filter pruning, 30%, magnitude criterion ImageNet 76.13 75.7
ResNet-50 Filter pruning, 30%, geometric median criterion ImageNet 76.13 75.7
Inception V3 None ImageNet - 77.32
Inception V3 INT8 ImageNet 77.32 76.92
Inception V3 INT8 + Sparsity 61% (RB) ImageNet 77.32 76.98
MobileNet V2 None ImageNet - 71.81
MobileNet V2 INT8 ImageNet 71.81 71.34
MobileNet V2 Mixed, 46.6% INT8 / 53.4% INT4 ImageNet 71.81 70.89
MobileNet V2 INT8 + Sparsity 52% (RB) ImageNet 71.81 70.99
SqueezeNet V1.1 None ImageNet - 58.18
SqueezeNet V1.1 INT8 ImageNet 58.18 58.02
SqueezeNet V1.1 Mixed, 54.7% INT8 / 45.3% INT4 ImageNet 58.18 58.84
ResNet-18 None ImageNet - 69.76
ResNet-18 XNOR (weights), scale/threshold (activations) ImageNet 69.76 61.61
ResNet-18 DoReFa (weights), scale/threshold (activations) ImageNet 69.76 61.59
ResNet-18 Filter pruning, 30%, magnitude criterion ImageNet 69.76 68.69
ResNet-18 Filter pruning, 30%, geometric median criterion ImageNet 69.76 68.97
ResNet-34 None ImageNet - 73.31
ResNet-34 Filter pruning, 30%, magnitude criterion ImageNet 73.31 72.54
ResNet-34 Filter pruning, 30%, geometric median criterion ImageNet 73.31 72.60
SSD300-BN None VOC12+07 - 78.28
SSD300-BN INT8 VOC12+07 78.28 78.07
SSD300-BN INT8 + Sparsity 70% (Magnitude) VOC12+07 78.28 78.01
SSD512-BN None VOC12+07 - 80.26
SSD512-BN INT8 VOC12+07 80.26 80.02
SSD512-BN INT8 + Sparsity 70% (Magnitude) VOC12+07 80.26 79.98
UNet None CamVid - 71.95
UNet INT8 CamVid 71.95 71.66
UNet INT8 + Sparsity 60% (Magnitude) CamVid 71.95 71.72
ICNet None CamVid - 67.89
ICNet INT8 CamVid 67.89 67.87
ICNet INT8 + Sparsity 60% (Magnitude) CamVid 67.89 67.24
UNet None Mapillary - 56.23
UNet INT8 Mapillary 56.23 56.12
UNet INT8 + Sparsity 60% (Magnitude) Mapillary 56.23 56.0