step_30_prune_tinyyolov2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pruning TinyYolov2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn.utils import prune\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from models.q_tinyyolov2 import QTinyYoloV2\n",
    "from models.tinyyolov2 import TinyYoloV2\n",
    "\n",
    "from utils.ap import ap\n",
    "from utils.dataloader import VOCDataLoaderPerson\n",
    "from utils.yolo import test\n",
    "\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "loader = VOCDataLoaderPerson(train=True, batch_size=128, shuffle=True)\n",
    "loader_test = VOCDataLoaderPerson(train=False, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_sparsity(net):\n",
    "    sd = net.state_dict()\n",
    "\n",
    "    sums = 0\n",
    "    nelem_count = 0\n",
    "    for key, value in sd.items():\n",
    "        if \"conv\" in key and \"weight\" in key:\n",
    "            sum_i = torch.sum(value == 0)\n",
    "            nelem_i = value.nelement()\n",
    "            print(\n",
    "                \"Sparsity in {:s}: {:.2f}%\".format(\n",
    "                    key,\n",
    "                    100. * float(sum_i)\n",
    "                    / float(nelem_i)\n",
    "                )\n",
    "            )\n",
    "            sums += sum_i\n",
    "            nelem_count += nelem_i\n",
    "\n",
    "\n",
    "    print(\n",
    "        \"Global sparsity: {:.2f}%\".format(\n",
    "            100. * float(sums)\n",
    "            / float(nelem_count)\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unstructured L1 pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------Prune 10.0% of connections-------------\n",
      "dict_keys(['anchors', 'conv1.weight_mask', 'bn1.running_mean', 'bn1.running_var', 'bn1.num_batches_tracked', 'conv2.weight_mask', 'bn2.running_mean', 'bn2.running_var', 'bn2.num_batches_tracked', 'conv3.weight_mask', 'bn3.running_mean', 'bn3.running_var', 'bn3.num_batches_tracked', 'conv4.weight_mask', 'bn4.running_mean', 'bn4.running_var', 'bn4.num_batches_tracked', 'conv5.weight_mask', 'bn5.running_mean', 'bn5.running_var', 'bn5.num_batches_tracked', 'conv6.weight_mask', 'bn6.running_mean', 'bn6.running_var', 'bn6.num_batches_tracked', 'conv7.weight_mask', 'bn7.running_mean', 'bn7.running_var', 'bn7.num_batches_tracked', 'conv8.weight_mask', 'bn8.running_mean', 'bn8.running_var', 'bn8.num_batches_tracked', 'conv9.weight_mask'])\n",
      "dict_keys(['anchors', 'bn1.running_mean', 'bn1.running_var', 'bn1.num_batches_tracked', 'bn2.running_mean', 'bn2.running_var', 'bn2.num_batches_tracked', 'bn3.running_mean', 'bn3.running_var', 'bn3.num_batches_tracked', 'bn4.running_mean', 'bn4.running_var', 'bn4.num_batches_tracked', 'bn5.running_mean', 'bn5.running_var', 'bn5.num_batches_tracked', 'bn6.running_mean', 'bn6.running_var', 'bn6.num_batches_tracked', 'bn7.running_mean', 'bn7.running_var', 'bn7.num_batches_tracked', 'bn8.running_mean', 'bn8.running_var', 'bn8.num_batches_tracked'])\n",
      "Sparsity in conv1.weight: 9.95%\n",
      "Sparsity in conv2.weight: 10.00%\n",
      "Sparsity in conv3.weight: 10.00%\n",
      "Sparsity in conv4.weight: 10.00%\n",
      "Sparsity in conv5.weight: 10.00%\n",
      "Sparsity in conv6.weight: 10.00%\n",
      "Sparsity in conv7.weight: 10.00%\n",
      "Sparsity in conv8.weight: 10.00%\n",
      "Sparsity in conv9.weight: 10.00%\n",
      "Global sparsity: 10.00%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2232 [00:00<?, ?it/s]c:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\functional.py:512: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ..\\aten\\src\\ATen\\native\\TensorShape.cpp:3588.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      " 21%|██        | 465/2232 [00:54<03:28,  8.47it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 25\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mdict\u001b[39m(net\u001b[38;5;241m.\u001b[39mnamed_buffers())\u001b[38;5;241m.\u001b[39mkeys()) \u001b[38;5;66;03m# to verify the final modules\u001b[39;00m\n\u001b[0;32m     23\u001b[0m check_sparsity(net)\n\u001b[1;32m---> 25\u001b[0m test_precision, test_recall \u001b[38;5;241m=\u001b[39m \u001b[43mtest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloader_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     26\u001b[0m avg_precision \u001b[38;5;241m=\u001b[39m ap(test_precision, test_recall)\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAP:\u001b[39m\u001b[38;5;124m\"\u001b[39m, avg_precision)\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\utils\\yolo.py:105\u001b[0m, in \u001b[0;36mtest\u001b[1;34m(net, loader_test, conf_threshold, iou_threshold, device)\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m    104\u001b[0m target\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m--> 105\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myolo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    107\u001b[0m \u001b[38;5;66;03m#The right threshold values can be adjusted for the target application\u001b[39;00m\n\u001b[0;32m    108\u001b[0m output \u001b[38;5;241m=\u001b[39m filter_boxes(output, conf_threshold)\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\models\\tinyyolov2.py:80\u001b[0m, in \u001b[0;36mTinyYoloV2.forward\u001b[1;34m(self, x, yolo)\u001b[0m\n\u001b[0;32m     77\u001b[0m x \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mmax_pool2d(x, kernel_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, stride\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     78\u001b[0m x \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mleaky_relu(x, negative_slope\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 80\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv7\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     81\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn7(x)\n\u001b[0;32m     82\u001b[0m x \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mleaky_relu(x, negative_slope\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 460\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\codau\\Home\\Uni\\master_4\\Embedded ML Lab\\ml-lab-ss24-challenge\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m    453\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[0;32m    454\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[0;32m    455\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[1;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    457\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ratios = np.arange(start=0.1, stop=0.3, step=0.1)\n",
    "final_net = None\n",
    "final_ratio = 0.1\n",
    "max_ap = 0\n",
    "for i, ratio in enumerate(ratios):\n",
    "    net = TinyYoloV2(num_classes=1)\n",
    "    sd = torch.load(\"models/configs/voc_finetuned.pt\")\n",
    "    net.load_state_dict(sd)\n",
    "    print(f\"-------------Prune {ratio*100}% of connections-------------\")\n",
    "    for name, module in net.named_modules():\n",
    "        # prune ratio of connections in all 2D-conv layers\n",
    "        if isinstance(module, torch.nn.Conv2d):\n",
    "            prune.l1_unstructured(module, name='weight', amount=ratio)\n",
    "\n",
    "    print(dict(net.named_buffers()).keys())  # to verify that all masks exist\n",
    "\n",
    "    # make pruning permanent\n",
    "    for name, module in net.named_modules():\n",
    "        if isinstance(module, torch.nn.Conv2d):\n",
    "            prune.remove(module, \"weight\")\n",
    "    print(dict(net.named_buffers()).keys()) # to verify the final modules\n",
    "\n",
    "    check_sparsity(net)\n",
    "\n",
    "    test_precision, test_recall = test(net, loader_test)\n",
    "    avg_precision = ap(test_precision, test_recall)\n",
    "    print(\"AP:\", avg_precision)\n",
    "    # save best net so far\n",
    "    if avg_precision > max_ap:\n",
    "        print(\"Saved net!\")\n",
    "        max_ap = avg_precision\n",
    "        final_ratio = ratio\n",
    "        final_net = net\n",
    "\n",
    "if final_net:\n",
    "    check_sparsity(final_net)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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