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utils
utils
 
 
README.md
README.md
 
 
__init__.py
__init__.py
 
 
infer.py
infer.py
 
 
parser.py
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setup.py
setup.py
 
 
train.py
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README.md

QAT working example

This example is using QAT library open sourced by nvidia. Github link

Directory overview

  1. This directory contains

    1. dataset : contains code for cifar-10 dataset
    2. layers : contains implementation for inference. More details under layers/README.md
    3. models: contains two models. resnet18 and vanilla_cnn
    4. utils : contains various utility functions for loading state dict, custom wrapper for training and inference & calculating accuracy during training
    5. train.py and infer.py : contains code for training and inference (including trt conversion)

  2. Usually, nvidia quantization library doesn't provide control per layer for quantization. Custom wrapper under utils/utilities.py helps us in quantization selective layers in our model.

Environment

Filename : pytorch_ngc_container_20.09

FROM nvcr.io/nvidia/pytorch:20.09-py3
RUN apt-get update && apt-get install -y software-properties-common && apt-get update
RUN add-apt-repository ppa:git-core/ppa && \
    apt install -y git    

RUN pip install termcolor graphviz

RUN git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git /sw/torch2trt/ && \
    cd /sw/torch2trt/scripts && \
	bash build_contrib.sh

Docker build: docker build -f pytorch_ngc_container_20.09 -t pytorch_ngc_container_20.09 .

docker_image=pytorch_ngc_container_20.09

Docker run : docker run -e NVIDIA_VISIBLE_DEVICES=0 --gpus 0 -it --shm-size=1g --ulimit memlock=-1 --rm -v $PWD:/workspace/work $docker_image

Important Notes :

  • Sparse checkout helps us in checking out a part of the github repo.
  • Patch file can be found under examples/quantization_aware_training/utils

Workflow

Workflow consists of three parts.

  1. Train without quantization:

Here pretrained weights from imagenet are used.

python train.py --m resnet34-tl / resnet18-tl --num_epochs 45 --test_trt --FP16 --INT8PTC

  1. Train with quantization (weights are mapped using a custom function to make sure that each weight is loaded correctly)

python train.py --m resnet34/ resnet18 --netqat --partial_ckpt --tl --load_ckpt /tmp/pytorch_exp/{} --num_epochs 25 --lr 1e-4 --lrdt 10

  1. Infer with and without TRT

python infer.py --m resnet34/resnet18 --load_ckpt /tmp/pytorch_exp_1/ckpt_{} --netqat --INT8QAT

Accuracy Results

Model FP32 FP16 INT8 (QAT) INT(PTC)
Resnet18 83.08 83.12 83.12 83.06
Resnet34 84.65 84.65 83.26 84.5

Please note that the idea behind these experiments is to see if TRT conversion is working properly rather than achieving industry standard accuracy results

Future Work

  • Add results for Resnet50, EfficientNet and Mobilenet