ixrt

EfficientNetV2 (ixRT)

Model Description

EfficientNetV2 is an improved version of the EfficientNet architecture proposed by Google, aiming to enhance model performance and efficiency. Unlike the original EfficientNet, EfficientNetV2 features a simplified design and incorporates a series of enhancement strategies to further boost performance.

Supported Environments

GPU	IXUCA SDK	Release	Branch
MR-V100	4.4.0	26.03	release/26.03
MR-V100	4.3.0	25.12	release/25.12

Note: 请切换到与您的 SDK 版本对应的 Release 分支进行测试。请勿直接在 master 分支上运行测试，因为 master 分支可能包含与您的本地 SDK 版本不兼容的最新更改。

切换分支命令示例：git checkout release/26.03

Model Preparation

Prepare Resources

Pretrained model: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth

Dataset: https://www.image-net.org/download.php to download the validation dataset.

Install Dependencies

# Install libGL
## CentOS
yum install -y mesa-libGL
## Ubuntu
apt install -y libgl1-mesa-glx

pip3 install -r ../../ixrt_common/requirements.txt
pip3 install timm==1.0.11

Model Conversion

mkdir checkpoints
git clone -b v1.0.11 --depth=1 https://github.com/huggingface/pytorch-image-models.git
cp ./export_onnx.py pytorch-image-models/timm/models
cp ./_builder.py pytorch-image-models/timm/models
cd pytorch-image-models/timm
mkdir -p /root/.cache/torch/hub/checkpoints/
wget -P /root/.cache/torch/hub/checkpoints/ https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth
python3 -m models.export_onnx --output_model ../../checkpoints/efficientnet_v2.onnx
cd ../../

Model Inference

export PROJ_DIR=./
export DATASETS_DIR=/path/to/imagenet_val/
export CHECKPOINTS_DIR=./checkpoints
export RUN_DIR=../../ixrt_common/
export CONFIG_DIR=../../ixrt_common/config/EFFICIENTNET_V2_CONFIG

FP16

# Accuracy
bash scripts/infer_efficientnet_v2_fp16_accuracy.sh
# Performance
bash scripts/infer_efficientnet_v2_fp16_performance.sh

INT8

# Accuracy
bash scripts/infer_efficientnet_v2_int8_accuracy.sh
# Performance
bash scripts/infer_efficientnet_v2_int8_performance.sh

Model Results

Model	BatchSize	Precision	FPS	Top-1(%)	Top-5(%)
EfficientnetV2	32	FP16	1882.87	82.14	96.16
EfficientnetV2	32	INT8	2595.96	81.50	95.96