This is a conversion tool for EfficientNet from TensorFlow to BVLC Caffe, based on efficientnet_tf_pytorch_caffe by xxworkspace. This fork uses only BVLC standard layers, instead of broadcast_Mull and Swish layers.
pip install tensorflow torch torchvision --user
pip3 install tensorflow torch torchvision --user
sudo apt update
sudo apt install caffe-cpu
| Preprocessing | Architecture | Download file |
|---|---|---|
| Standard (ckpts) | efficientnet-b[1-5] | efficientnet-b[1-5] |
| AutoAugment (ckptsaug) | efficientnet-b[1-7] | efficientnet-b[1-7] |
| RandAugment (randaug) | efficientnet-b[57] | efficientnet-b[57]-randaug |
| AdvProp (advprop) | efficientnet-b[0-8] | efficientnet-b[0-8] |
pushd pretrained_tensorflow
download.sh ckptsaug efficientnet-b0
popd
mkdir -p pretrained_pytorch/ckptsaug
pushd convert_tf_pt
python3 convert_params_tf_pytorch.py \
--model_name efficientnet-b0 \
--tf_checkpoint ../pretrained_tensorflow/ckptsaug/efficientnet-b0/ \
--output_file ../pretrained_pytorch/ckptsaug/efficientnet-b0.pth
popd
pushd convert_tf_pt
python3 test_pytorch.py ckptsaug efficientnet-b0
popd
mkdir -p caffemodel/ckptsaug
pushd convert_tf_pt
python3 pytorch2caffe.py ckptsaug efficientnet-b0
popd
pushd convert_tf_pt
python3 pytorch2caffe_model.py ckptsaug efficientnet-b0
popd
pushd convert_tf_pt
python3 test_caffe.py ckptsaug efficientnet-b0
popd
- mean = [0.485, 0.456, 0.406] x 255
- std = [0.229, 0.224, 0.225] x 255
| Input size | Tensorflow layer | Caffe layers |
|---|---|---|
| Odd | conv2d (kernel_size: 2, stride: 2, pad: same) | Convolution (kernel_size: 2, stride: 2, pad: 1) |
| Even | conv2d (kernel_size: 2, stride: 2, pad: same) | Convolution (kernel_size: 2, stride: 2, pad: 2) + Convolution (kernel_size: 2, stride: 2, pad: 1) + Crop (offset: 1) |
| ---------- | depthwise_conv2d | Convolution (group=num_output) |
| ---------- | swish | Sigmoid + Eltwise (operation: PROD) |
| ---------- | broadcast_mul | Flatten + Scale (with 2 bottom blobs) |
https://kentanabe.github.io/netscope/#/preset/efficientnet-b0
