pytorch-stuff/Pruning_LeNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        # 1 input image channel, 6 output channels, 3x3 square conv kernel\n",
    "        self.conv1 = nn.Conv2d(1, 6, 3)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 3)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5x5 image dimension\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "        \n",
    "        def forward(self, x):\n",
    "            x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
    "            x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
    "            x = x.view(-1, int(x.nelement() / x.shape[0]))\n",
    "            x = F.relu(self.fc1(x))\n",
    "            x = F.relu(self.fc2(x))\n",
    "            x = self.fc3(x)\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LeNet().to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('weight', Parameter containing:\n",
      "tensor([[[[ 0.0854,  0.1762, -0.0814],\n",
      "          [-0.0859, -0.2473, -0.3291],\n",
      "          [-0.2670, -0.0962, -0.2002]]],\n",
      "\n",
      "\n",
      "        [[[-0.2353,  0.2884, -0.0267],\n",
      "          [-0.2879,  0.0165, -0.2282],\n",
      "          [-0.2628, -0.0222,  0.2331]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1070,  0.2683,  0.2301],\n",
      "          [ 0.1637,  0.2600,  0.1026],\n",
      "          [ 0.3013, -0.2336,  0.0121]]],\n",
      "\n",
      "\n",
      "        [[[-0.2488,  0.2017, -0.1913],\n",
      "          [-0.0723,  0.0911, -0.2306],\n",
      "          [-0.0578,  0.3193,  0.1617]]],\n",
      "\n",
      "\n",
      "        [[[-0.0321,  0.1383,  0.2564],\n",
      "          [-0.1842, -0.3065,  0.1342],\n",
      "          [ 0.0815, -0.0757,  0.2290]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1121, -0.2876, -0.1017],\n",
      "          [ 0.2958,  0.3092, -0.2187],\n",
      "          [ 0.3065,  0.1924,  0.2755]]]], device='cuda:0', requires_grad=True)), ('bias', Parameter containing:\n",
      "tensor([-0.1582, -0.0629, -0.1216, -0.3196,  0.2949,  0.2759], device='cuda:0',\n",
      "       requires_grad=True))]\n"
     ]
    }
   ],
   "source": [
    "module = model.conv1\n",
    "print(list(module.named_parameters()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(list(module.named_buffers()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.utils.prune as prune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prune.random_unstructured"
   ]
  }
 ],
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