EfficientNet-PyTorch to ONNX

test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.onnx\n",
    "from efficientnet_pytorch import EfficientNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size:  240\n",
      "Loaded pretrained weights for efficientnet-b1\n",
      "Model image size:  240\n"
     ]
    }
   ],
   "source": [
    "# Specify which model to use\n",
    "model_name = 'efficientnet-b1'\n",
    "image_size = EfficientNet.get_image_size(model_name)\n",
    "print('Image size: ', image_size)\n",
    "\n",
    "# Load model\n",
    "model = EfficientNet.from_pretrained(model_name)\n",
    "model.set_swish(memory_efficient=False)\n",
    "model.eval()\n",
    "print('Model image size: ', model._global_params.image_size)\n",
    "\n",
    "# Dummy input for ONNX\n",
    "dummy_input = torch.randn(10, 3, 224, 224)\n",
    "\n",
    "# Export with ONNX\n",
    "torch.onnx.export(model, dummy_input, \"efficientnet-b1.onnx\", verbose=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "df0893f56f349688326838aaeea0de204df53a132722cbd565e54b24a8fec5f6"
    }
   },
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}