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Quantization for image classification

Install

  1. Clone the repo (change the FASTDIR as perfered):
export FASTDIR=/workspace
cd $FASTDIR/git/
git clone https://github.com/aim-uofa/model-quantization 
git clone https://github.com/blueardour/pytorch-utils
cd model-quantization
ln -s ../pytorch-utils utils

# create separate log and weight folders (optional, if symbol link not created, the script will create these folders under the project path)
#mkdir -p /data/pretrained/pytorch/model-quantization/{exp,weights}
#ln -s /data/pretrained/pytorch/model-quantization/exp .
#ln -s /data/pretrained/pytorch/model-quantization/weights .

  1. Install prerequisite packages

    cd $FASTDIR/git/model-quantization
# python 3 is required
pip install -r requirement.txt

    Quantization for the classification task has not strict requirement on the pytorch version. However, other tasks such as detection and segmentation require a higher version pytorch. detectron2 currently require Torch 1.4+. Besides, the CUDA version on the machine is advised to keep the same with the one compiling the pytorch.

  2. Install Nvidia image pre-processing packages and mix precision training packages (optional, highly recommend)

    Nvidia Dali

    Nvidia Mix precision training package

Dataset

This repo supports the Imagenet dataset and CIFAR dataset. Create necessary folders and prepare the datasets. Example:

# dataset
mkdir -p /data/cifar
mkdir -p /data/imagenet
# download imagnet and move the train and evaluation data in in /data/imagenet/{train,val}, respectively.
# cifar dataset can be downloaded on the fly

Pretrained models and Quantization Results

Some of the quantization results are listed in result_cls.md. We provide pretrained models in google drive

Quick Start

Both training and testing employ the train.sh script. Directly call the main.py is also possible.

bash train.sh config.xxxx

config.xxxx is the configuration file, which contains network architecture, quantization related and training related parameters. For more about the supported options, refer below Training script options and config.md. Also refer the examples in config subfolder.

Training is often time-consuming . Try our start_on_terminate.sh script which can be used to pend a second task. New round training will start automatically when last training process is terminated.

# wait in a screen shell
screen -S next-round
bash start_on_terminate.sh [current training thread pid] [next round config.xxxx]
# Ctl+A D to detach screen to backend

Besides, tools.py provides many useful functions for debug / verbose / model convert. Refer tools.md for detailed usage.

Known Issues

See know issues

Training script options

  • From 2020.07.28 Dynamic loading of the training options by policy file is supported.

  • Option parsing

    Common options are parsed in util/config.py. Quantization related options are separated in the main.py.

  • Keyword (choosing quantization method)

    The --keyword option is one of most important variables to control the model architecture and quantization algorithm choice.

    We currently support quantization algorithms by adding the following options in the keyword:

    a. lq for LQ-Nets

    b. pact for PACT

    c. dorefa for DoReFa-Net. Besides, additional keyword of lsq for learned step size, non-uniform for FATNN.

    d. xnor for XNOR-Net. If gamma is combined with the xnor in the keyword, a separated learnable scale coefficient is added (It namely becomes the XNor-net++).

  • Keyword (structure control):

    The network structure can be chosen by --arch or --model. For ResNet, the official ResNet model is provided with pytorch-resnetxx and more flexible ResNet architecture can be realized by setting the --arch or --model with resnetxx. For the latter case, a lot of options can be combined to customize the network structure:

    a. origin exists / not exists in keyword is to choose whether the bi-real skip connection is preferred (Block-wise skip connection versus layer-wise skip connection).

    b. bacs or cbas, etc, indicate the layer order in a ResNet block. For example, bacs is a kind of pre-activation structure, representing in a ResNet block, first normalization layer, then activation layer, then convolution layer and last skip connection layer. For pre-activation structure, preBN is required for the first ResNet block. Refer resnet.md for more information.

    c. By default all layers except the first and last layers are quantized, real_skip can be added to keep the skip connection layers in ResNet to full precision, which is widely used in Xnor-net and Bi-Real net.

    d. For the normalization layer and activation layer, we also provide some keyword for different variants. For example, NRelU means do not include ReLU activation in the network and PRelU indicates PReLU is employed. Refer model/layer.py for details.

    e. Padding and quantization order. I think it is an error if padding the feature map with 0 after quantization, especially in BNNs. From my perspective, the strategy makes BNNs to become TNNs. Thus, I advocate to pad the feature map with zero first and then go through the quantization step. To keep compatible with the publication as well as providing a revised method, padding_after_quant can be set to control the order between padding and quantization. Refer line 445 in model/quant.py for the implementation.

    f. Skip connection realization. Two choices are provided. One is the avgpooling with stride followed by a conv1x1 with stride=1. Another is just one conv1x1 with stride as demanded. singleconv in keyword is used for the choice.

    g. fixup is used to enable the architecture in Fixup Initialization.

    h. The option base which is a standalone option rather a word in the keyword list is used to realize the branch configuration in Group-Net.

    Self-defined keyword is supported and can be easily realized according the user's own desire. As introduced above, the options can be combined to build up different variant architectures. Examples can be found in the config subfolder.

  • Activation and weight quantization options

    The script provides independent configurations for activations and weights respectively. We here explain some advanced options.

    1. xx_quant_group indicates the group amount for the quantization parameter along the channel dimension.

    2. xx_adaptive in most cases, indicates the additional normalization operation which shows great potential to increase the performance.

    3. xx_grad_type defines custom gadient approximation method. In general, the quantization step is not differentiable, techniques such as the STE are used to approximate the gradient. Other types of approximation exist. Besides, in some works, it is advocated to add some scale coefficient to the gradient in order to stabilize the training.

  • Weight decay

    Three major related options.

    1. --wd set the default L2 weight decay value.

    2. Weight decay is originally proposed to avoid overfit for the large number of parameters. For some small tensors, for example the parameters in BatchNorm layer (as well as custom defined quantization parameters, such as clip-value), weight decay is advocated to be zero. --decay_small is for whether decay those small tensors or not.

    3. --custom_decay_list and --custom_decay are combined for specific custom decay value to certain parameters. For example, in PACT, the clip_boundary can own its independent weight decay for regularization.

  • Learning rate

    1. multi-step decay

    2. ploy decay

    3. sgdr (with restart)

    4. --custom_lr_list and --custom_lr are provided similarly with before mentioned weight decay to specific custom learning rate for certain parameters.

  • Mixed precision training options --fp16 and --opt_level [O1] are provided for mix precision traning.

    1. FP32

    2. FP16 with custom level, recommend O1 level.