{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 自定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mod import load_mod\n",
    "# 载入自定义模块\n",
    "load_mod()\n",
    "\n",
    "from pytorch_book.quantization.qat import QuantizableCustom\n",
    "from xinet import CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "qat = QuantizableCustom('resnet18')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = qat.qconfig(quantize=True,\n",
    "                    pretrained=True,\n",
    "                    progress=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'QuantizableResNet' object has no attribute 'quantize_qat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb Cell 5'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=0'>1</a>\u001b[0m batch_size \u001b[39m=\u001b[39m \u001b[39m128\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=1'>2</a>\u001b[0m train_iter, test_iter \u001b[39m=\u001b[39m CV\u001b[39m.\u001b[39mload_data_cifar10(batch_size\u001b[39m=\u001b[39mbatch_size)\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=2'>3</a>\u001b[0m quantized_model \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mquantize_qat(model,\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=3'>4</a>\u001b[0m                                      run_fn\u001b[39m=\u001b[39mCV\u001b[39m.\u001b[39mtrain_fine_tuning,\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=4'>5</a>\u001b[0m                                      run_args\u001b[39m=\u001b[39m[train_iter, test_iter],\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=5'>6</a>\u001b[0m                                      run_kwargs\u001b[39m=\u001b[39m{\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=6'>7</a>\u001b[0m                                          \u001b[39m'\u001b[39m\u001b[39mlearning_rate\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m1e-3\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=7'>8</a>\u001b[0m                                          \u001b[39m'\u001b[39m\u001b[39mnum_epochs\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m100\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=8'>9</a>\u001b[0m                                      },\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=9'>10</a>\u001b[0m                                      inplace\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/transfer-learning/custom.ipynb#ch0000011vscode-remote?line=10'>11</a>\u001b[0m                                      )\n",
      "File \u001b[0;32m~/xinet/anaconda3/envs/torchx/lib/python3.10/site-packages/torch/nn/modules/module.py:1185\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   <a href='file:///home/pc/xinet/anaconda3/envs/torchx/lib/python3.10/site-packages/torch/nn/modules/module.py?line=1182'>1183</a>\u001b[0m     \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m modules:\n\u001b[1;32m   <a href='file:///home/pc/xinet/anaconda3/envs/torchx/lib/python3.10/site-packages/torch/nn/modules/module.py?line=1183'>1184</a>\u001b[0m         \u001b[39mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> <a href='file:///home/pc/xinet/anaconda3/envs/torchx/lib/python3.10/site-packages/torch/nn/modules/module.py?line=1184'>1185</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m object has no attribute \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   <a href='file:///home/pc/xinet/anaconda3/envs/torchx/lib/python3.10/site-packages/torch/nn/modules/module.py?line=1185'>1186</a>\u001b[0m     \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, name))\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'QuantizableResNet' object has no attribute 'quantize_qat'"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "train_iter, test_iter = CV.load_data_cifar10(batch_size=batch_size)\n",
    "quantized_model = model.quantize_qat(model,\n",
    "                                     run_fn=CV.train_fine_tuning,\n",
    "                                     run_args=[train_iter, test_iter],\n",
    "                                     run_kwargs={\n",
    "                                         'learning_rate': 1e-3,\n",
    "                                         'num_epochs': 100,\n",
    "                                     },\n",
    "                                     inplace=False\n",
    "                                     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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