Aimbot-PPO/Aimbot-PPO-Python/Pytorch/test2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyNet(\n",
      "  (fc1): Linear(in_features=10, out_features=20, bias=True)\n",
      "  (fc2): Linear(in_features=20, out_features=10, bias=True)\n",
      ")\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个神经网络\n",
    "class MyNet(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(10, 20)\n",
    "        self.fc2 = torch.nn.Linear(20, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "net = MyNet()\n",
    "\n",
    "# 打印神经网络结构\n",
    "print(net)\n",
    "\n",
    "# 获取第一层权重张量\n",
    "weights = net.state_dict()['fc1.weight']\n",
    "\n",
    "# 将权重张量转换为numpy数组，并可视化\n",
    "plt.imshow(weights.numpy())\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version:  3.11.3 | packaged by Anaconda, Inc. | (main, Apr 19 2023, 23:46:34) [MSC v.1916 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "# print python version\n",
    "import sys\n",
    "print('python version: ', sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import wandb\n",
    "import time\n",
    "import numpy as np\n",
    "import random\n",
    "import uuid\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "from AimbotEnv import Aimbot\n",
    "from tqdm import tqdm\n",
    "from torch.distributions.normal import Normal\n",
    "from torch.distributions.categorical import Categorical\n",
    "from distutils.util import strtobool\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from mlagents_envs.environment import UnityEnvironment\n",
    "from mlagents_envs.side_channel.side_channel import (\n",
    "    SideChannel,\n",
    "    IncomingMessage,\n",
    "    OutgoingMessage,\n",
    ")\n",
    "from typing import List\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'aaa' object has no attribute 'outa'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m asd \u001b[39m=\u001b[39m aaa(outa, outb)\n\u001b[0;32m     13\u001b[0m asd\u001b[39m.\u001b[39mfunc()\n\u001b[1;32m---> 14\u001b[0m \u001b[39mprint\u001b[39m(asd\u001b[39m.\u001b[39;49mouta)  \u001b[39m# 输出 100\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'aaa' object has no attribute 'outa'"
     ]
    }
   ],
   "source": [
    "class aaa():\n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "\n",
    "    def func(self):\n",
    "        global outa\n",
    "        outa = 100\n",
    "\n",
    "outa = 1\n",
    "outb = 2\n",
    "asd = aaa(outa, outb)\n",
    "asd.func()\n",
    "print(asd.outa)  # 输出 100"
   ]
  }
 ],
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