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timm2tflite

This repository is for converting and optionally quantizing models from pytorch-image-models to TFLite using ai-edge-torch.

As for quantization, models are firstly quantized with PyTorch PT2E quantization and then converted to TFLite with ai-edge-torch. See tools/quantization_utils.py for the quantization process.

Results

Randomly selected 5000 images from val set of ImageNet dataset are used for evaluation. Inference is done with CPU and batch size 1 for all models.

Table 1: Top-1 accuracy (%) of the original PyTorch model and the converted TFLite model.

model (from timm v1.0.9) PyTorch fp32 TFLite fp32
resnet18.a1_in1k 73.94 73.94
convnextv2_tiny.fcmae_ft_in22k_in1k 84.82 84.82
tf_efficientnetv2_s.in21k_ft_in1k 84.42 84.42
efficientnet_lite0.ra_in1k 75.44 75.44
mobilenetv4_conv_small.e2400_r224_in1k 75.06 75.06
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k.tflite 83.04 83.04
maxvit_small_tf_224.in1k 84.54 84.54
efficientformerv2_s0.snap_dist_in1k 76.02 2.82

Table 2: Top-1 accuracy (%) of PyTorch fp32, PyTorch int8, and TFLite int8 models.

model (from timm v1.0.9) PyTorch fp32 PyTorch int8 TFLite int8
resnet18.a1_in1k 73.94 68.70 67.96
efficientnet_lite0.ra_in1k 75.44 68.20 62.90

Environment Setup

docker compose build dev
docker compose run dev

Prepare ImageNet Dataset (optional)

If you are going to quantize or evaluate the models, you need to prepare ImageNet dataset (you can skip this section if you want to do simple TFLite conversion without quantization or evaluation).

prepare ImageNet dataset

Download ImageNet dataset under $HOME/data/imagenet.

$ tree $HOME/data/imagenet -L 1

/home/motoki_kimura/data/imagenet
├── test
├── train
└── val

You may use kaggle/imagenet-object-localization-challenge dataset to download ImageNet dataset. After downloading the dataset, apply valprep.sh to val directory to make it ImageFolder format.

mkdir -p $HOME/data/tmp_imagenet
cd $HOME/data/tmp_imagenet

# to run `kaggle competitions download`, you must first authenticate using an API token.
# see `Authentication` section in https://www.kaggle.com/docs/api
# or you can download the dataset manually from https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data
pip install kaggle
kaggle competitions download -c imagenet-object-localization-challenge

unzip imagenet-object-localization-challenge.zip

cd ILSVRC/Data/CLC-LOC/val
wget https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
sh valprep.sh
rm -f valprep.sh

mkdir -p $HOME/data/imagenet
cd $HOME/data/imagenet
mv $HOME/data/tmp_imagenet/ILSVRC/Data/CLC-LOC/train .
mv $HOME/data/tmp_imagenet/ILSVRC/Data/CLC-LOC/val .
mv $HOME/data/tmp_imagenet/ILSVRC/Data/CLC-LOC/test .

rm -rf $HOME/data/tmp_imagenet

You can remove test directory if you want to save disk space.

Generate mini val dataset

To reduce evaluation time, you can use tools/gen_mini_imagenet.py to generate a subset of val dataset:

python tools/gen_mini_imagenet.py --data-dir /data/imagenet --out-dir /data/mini_imagenet_5000 --n-img-per-class 5

If you are evaluating with the mini dataset, please change --data-dir /data/imagenet to --data-dir /data/mini_imagenet_5000 in the subsequent commands.

Prepare calibration dataset for quantization

If you are going to quantize the models, use tools/prep_calib.py to generate calibration dataset:

python tools/prep_calib.py /data/imagenet --n-img 512

With the command above, 512 images from train directory of ImageNet dataset will be copied to $HOME/data/imagenet/calib directory (/data/imagenet/calib in the container).

Usage

Convert to TFLite

python tools/tflite_export.py -m resnet18 --check-forward

Specify the model you want to convert with -m or --model option. You can find the available model names from pytorch-image-models/results/results-imagenet.csv etc.

You can check if the output of the PyTorch model and the TFLite model are consistent (whether the conversion was successful) by adding --check-forward option.

With the command above, resnet18.a1_in1k.tflite (TFLite-fp32 model) will be generated under the current directory.

Evaluate TFLite models

(ImageNet dataset is required. See Prepare ImageNet Dataset section for details.)

After the conversion, you can evaluate the converted TFLite model on ImageNet:

python tools/tflite_validate.py resnet18.a1_in1k.tflite --data-dir /data/imagenet

Convert to TFLite with quantization

(ImageNet dataset is required. See Prepare ImageNet Dataset section for details.)

You can generate a quantized TFLite model by adding --quant option:

python tools/tflite_export.py -m resnet18 --quant --calib-data-dir /data/imagenet/calib

resnet18.a1_in1k.tflite (TFLite-int8 model) will be generated under the current directory.

After the conversion, you can evaluate the quantized TFLite model on ImageNet:

python tools/tflite_validate.py resnet18.a1_in1k.tflite --data-dir /data/imagenet

Evaluate PyTorch models (optionally with PT2E quantization)

(ImageNet dataset is required. See Prepare ImageNet Dataset section for details.)

To evaluate PyTorch models, use tools/validate.py:

python tools/validate.py -m resnet18 --data-dir /data/imagenet

For evaluating PyTorch models, the default is to use GPU. You can also use CPU with --device cpu option. Also, you can specify the batch size with -b or --batch-size option (note that the default batch size for evaluation of TFLite models is 1):

python tools/validate.py -m resnet18 --data-dir /data/imagenet --device cpu -b 1

To evaluate PyTorch models quantized with PT2E, add --quant option:

python tools/validate.py -m resnet18 --data-dir /data/imagenet --device cpu -b 1 --quant --calib-data-dir /data/imagenet/calib