Model3.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nilanshrajput/AC2MC-simulator/blob/master/Model3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJ1YBMPHJxbg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Xpa6LQ6PKZeY",
        "colab": {}
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8-1hWYXuKZed",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2MtfBxzQKZeg",
        "colab": {}
      },
      "source": [
        "\n",
        "!pip install pytorch-lightning\n",
        "!pip install wandb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CLIsYl7mKZeo",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import Dataset,DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "from PIL import Image\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "from argparse import Namespace\n",
        "from typing import *\n",
        "import random\n",
        "from functools import lru_cache\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.core.lightning import LightningModule\n",
        "from pytorch_lightning import Trainer\n",
        "from pytorch_lightning import loggers as pl_loggers\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from pytorch_lightning.metrics import Accuracy"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lQxcNEjnKZer",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "from collections import OrderedDict \n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uBkZ5DqOKZey",
        "colab": {}
      },
      "source": [
        "\n",
        "inventory = ['Deagle', 'Elite', 'FiveSeven', 'Glock', 'Ak47', 'Aug', 'Awp', 'Famas', 'G3sg1', 'GalilAr', 'M249', 'M4a4', 'Mac10', 'P90', 'Mp5sd', 'Ump45', 'Xm1014', 'Bizon', 'Mag7', 'Negev', 'Sawedoff', 'Tec9', 'ZeusX27', 'P2000', 'Mp7', 'Mp9', 'Nova', 'P250', 'Scar20', 'Sg553', 'Ssg08', 'M4a1S', 'UspS', 'Cz75Auto', 'R8Revolver', 'Flashbang', 'HeGrenade', 'SmokeGrenade', 'MolotovGrenade', 'DecoyGrenade', 'IncendiaryGrenade', 'Knife', 'C4']\n",
        "maps = ['de_dust2', 'de_mirage', 'de_nuke', 'de_inferno', 'de_overpass',\n",
        "       'de_vertigo', 'de_train', 'de_cache']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "035E87hUKZe1",
        "colab": {}
      },
      "source": [
        "maps_label = {map:i+1 for i,map in enumerate(maps)}\n",
        "round_status = {'BombPlanted':1, 'FreezeTime':2, 'Normal':3, \"SlackTime\":4}"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "t1kmn6pXKZe4",
        "colab": {}
      },
      "source": [
        "bins = {\n",
        "    'round_time_left':[0, 10, 30, 50, 70, 100,115,4000],\n",
        "    'health':[0,10,20,30,40,50,65,80,90,100],\n",
        "    'money':[0,500,100.,1500,2200,3000,3500,3800,4000,4500,5000,5500,7000,9000,12000,16000],\n",
        "    'clip' : [0,1,2,10,20,30,35,300],\n",
        "    'ammo' :[0,2,30,60,120,300],\n",
        "    'smokes':[0,1,2,3,4,10],\n",
        "    'molotov':[0,1,2,3,4,10],\n",
        "    'armor':[0,10,40,80,100]\n",
        "}\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2MCMM0fyKZe_",
        "colab": {}
      },
      "source": [
        "\n",
        "class CsData(Dataset):\n",
        "    def __init__(self, dfx,dfy):\n",
        "        self.dfx = dfx\n",
        "        self.dfy = dfy\n",
        "        self.dfx['map'] = self.dfx['map'].apply(lambda x: maps_label[x])\n",
        "        self.dfx['round_status'] = self.dfx['round_status'].apply(lambda x: round_status[x])\n",
        "        self.dfx['round_status_time_left_binned']=self.dfx['round_status_time_left'].apply(lambda x: self.encode_time(x))\n",
        "        \n",
        "\n",
        "    def __getitem__(self, id):\n",
        "        row = self.dfx.iloc[id]\n",
        "        outs = self.process_row(row)\n",
        "        #outs = tuple(outs.values())\n",
        "        if len(outs)!= 236:\n",
        "            print(id)\n",
        "        return tuple(outs), torch.FloatTensor([self.dfy.iloc[id]])\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dfx)\n",
        "\n",
        "    def process_row(self, row):\n",
        "        \n",
        "        row_embs = []#OrderedDict()\n",
        "\n",
        "        #map\n",
        "        row_embs.append(torch.LongTensor([row['map']]))\n",
        "\n",
        "        #cts and ts\n",
        "        cts = [p for p in row['alive_players'] if p['team']=='CT']\n",
        "        ts = [p for p in row['alive_players'] if p['team']=='Terrorist']\n",
        "        row_embs.extend(self.encode_alive_players(cts))\n",
        "        row_embs.extend(self.encode_alive_players(ts))\n",
        "\n",
        "        # GRS\n",
        "        \n",
        "        grs = OrderedDict()\n",
        "        grs['molotovs'] = self.label_molotovs(len(row['active_molotovs']))\n",
        "        grs['smokes'] = self.label_smokes(len(row['active_smokes']))\n",
        "        row_embs.extend(grs.values())\n",
        "        \n",
        "        #round\n",
        "        round = OrderedDict()\n",
        "        round['round_status'] = torch.LongTensor([row['round_status']])\n",
        "        round['round_status_time_left_binned']= torch.LongTensor([row['round_status_time_left_binned']])\n",
        "        row_embs.extend(round.values())\n",
        "\n",
        "        #score\n",
        "        row_embs.append(self.encode_score(row['current_score']))\n",
        "\n",
        "\n",
        "        return row_embs\n",
        "\n",
        "    def encode_time(self, time):\n",
        "        bins = [0, 10, 30, 50, 70, 100,115,4000]\n",
        "        return (np.digitize([time],bins)[0])+1\n",
        "\n",
        "    def encode_alive_players(self,alive_pls):\n",
        "        encoding = []\n",
        "        for i,plr in enumerate(alive_pls):\n",
        "            encoding.extend(self.player_encoder(plr))\n",
        "            # bad dta contains more thn 5 in a team\n",
        "            if i==4:\n",
        "                break\n",
        "\n",
        "        for _ in range(len(alive_pls),5):\n",
        "            encoding.extend(self.player_encoder(pad=True))\n",
        "\n",
        "        return encoding\n",
        "\n",
        "    def player_encoder(self, plr=None, pad=False):\n",
        "        embs =[]# OrderedDict()\n",
        "        if not pad:\n",
        "            health = self.label_health(plr['health'])\n",
        "            embs.append( health)\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= self.label_armor(plr['armor'])\n",
        "            embs.append(armor)\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = self.label_money(plr['money'])\n",
        "            embs.append(money)\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = self.label_has_defuser(plr['has_defuser'])\n",
        "            embs.append(has_defuser)\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = self.label_has_helmet(plr['has_helmet'])\n",
        "            embs.append(has_helmet)\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            #embs['inventory']=[]\n",
        "            for i,item in enumerate(plr['inventory']):\n",
        "                embs.extend(self.encode_inventory(item).values())\n",
        "                # max 6 items considered\n",
        "                if i==5:\n",
        "                    break\n",
        "            for _ in range(len(plr['inventory']),6):\n",
        "                embs.extend(self.encode_inventory(pad=True).values())\n",
        "\n",
        "        else:\n",
        "\n",
        "            health = torch.LongTensor([0])\n",
        "            embs.append(health)\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= torch.LongTensor([0])\n",
        "            embs.append(armor)\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = torch.LongTensor([0])\n",
        "            embs.append(money)\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = torch.LongTensor([0])\n",
        "            embs.append(has_defuser)\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = torch.LongTensor([0])\n",
        "            embs.append(has_helmet)\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            #embs['inventory']=[]\n",
        "            for _ in range(6):\n",
        "                embs.extend(self.encode_inventory(pad=True).values())\n",
        "\n",
        "        return embs\n",
        "\n",
        "\n",
        "    def encode_inventory(self, item=None, pad = False):\n",
        "        embs = OrderedDict()\n",
        "        if not pad:\n",
        "            typ = item['item_type']\n",
        "            typ = torch.LongTensor([inventory.index(typ)])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = self.label_clip(item['clip_ammo'])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = self.label_ammo(item['clip_ammo'])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "\n",
        "        else: \n",
        "            typ = torch.LongTensor([0])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = torch.LongTensor([0])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = torch.LongTensor([0])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "        return embs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def encode_score(self, score):\n",
        "        score = torch.tensor(score,dtype = torch.float)/16\n",
        "        return score\n",
        "\n",
        "    def label_health(self, health):\n",
        "        health_bins=[0,10,20,30,40,50,65,80,90,100]\n",
        "        return torch.LongTensor((np.digitize([health],health_bins)))+1\n",
        "\n",
        "    def label_money(self, money):\n",
        "        bins = [0,500,1000,1500,2200,3000,3500,3800,4000,4500,5000,5500,7000,9000,12000,16000]\n",
        "        return torch.LongTensor((np.digitize([money],bins)))+1\n",
        "\n",
        "    def label_clip(self, clip):\n",
        "        bins = [0,1,2,10,20,30,35,300]\n",
        "        return torch.LongTensor((np.digitize([clip],bins)))+1\n",
        "\n",
        "    def label_ammo(self, ammo):\n",
        "        bins = [0,2,30,60,120,300]\n",
        "        return torch.LongTensor((np.digitize([ammo],bins)))+1\n",
        "\n",
        "    def label_smokes(self, num_smokes):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_smokes],bins)))+1\n",
        "\n",
        "    def label_molotovs(self, num_molotov):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_molotov],bins)))+1\n",
        "\n",
        "    def cal_dis(self, pos1, pos2 ):\n",
        "        pos1 = np.array([pos1['x'],pos1['y'],pos1['z']])\n",
        "        pos2 = np.array([pos2['x'],pos2['y'],pos2['z']])\n",
        "        dist = np.linalg.norm(pos1-pos2)\n",
        "        return dist\n",
        "\n",
        "    def label_armor(self, armor):\n",
        "        bins=[0,10,40,80,100]\n",
        "        return torch.LongTensor((np.digitize([armor],bins)))+1\n",
        "\n",
        "    def label_has_helmet(self, has_helmet:bool):\n",
        "        if has_helmet:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "    def label_has_defuser(self, has_defuser:bool):\n",
        "        if has_defuser:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "\n",
        "            "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2IaAkGYxKZfF",
        "colab": {}
      },
      "source": [
        "\n",
        "class CsData(Dataset):\n",
        "    def __init__(self, dfx,dfy):\n",
        "        self.dfx = dfx\n",
        "        self.dfy = dfy\n",
        "        self.dfx['map'] = self.dfx['map'].apply(lambda x: maps_label[x])\n",
        "        self.dfx['round_status'] = self.dfx['round_status'].apply(lambda x: round_status[x])\n",
        "        self.dfx['round_status_time_left_binned']=self.dfx['round_status_time_left'].apply(lambda x: self.encode_time(x))\n",
        "        \n",
        "\n",
        "    def __getitem__(self, id):\n",
        "        row = self.dfx.iloc[id]\n",
        "        outs = self.process_row(row)\n",
        "        #outs = tuple(outs.values())\n",
        "        return outs, torch.FloatTensor([self.dfy.iloc[id]])\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dfx)\n",
        "\n",
        "    def process_row(self, row):\n",
        "        \n",
        "        row_embs = OrderedDict()\n",
        "\n",
        "        #map\n",
        "        row_embs['map'] = torch.LongTensor([row['map']])\n",
        "\n",
        "        #cts and ts\n",
        "        cts = [p for p in row['alive_players'] if p['team']=='CT']\n",
        "        ts = [p for p in row['alive_players'] if p['team']=='Terrorist']\n",
        "        row_embs['cts'] = self.encode_alive_players(cts)\n",
        "        row_embs['ts'] = self.encode_alive_players(ts)\n",
        "\n",
        "        # GRS\n",
        "        \n",
        "        grs = OrderedDict()\n",
        "        grs['molotovs'] = self.label_molotovs(len(row['active_molotovs']))\n",
        "        grs['smokes'] = self.label_smokes(len(row['active_smokes']))\n",
        "        row_embs['grs'] = grs\n",
        "        \n",
        "        #round\n",
        "        round = OrderedDict()\n",
        "        round['round_status'] = torch.LongTensor([row['round_status']])\n",
        "        round['round_status_time_left_binned']= torch.LongTensor([row['round_status_time_left_binned']])\n",
        "        row_embs['round_info'] = round\n",
        "\n",
        "        #score\n",
        "        row_embs['score'] = self.encode_score(row['current_score'])\n",
        "\n",
        "\n",
        "        return row_embs\n",
        "\n",
        "    def encode_time(self, time):\n",
        "        bins = [0, 10, 30, 50, 70, 100,115,4000]\n",
        "        return (np.digitize([time],bins)[0])+1\n",
        "\n",
        "    def encode_alive_players(self,alive_pls):\n",
        "        encoding = []\n",
        "        for i,plr in enumerate(alive_pls):\n",
        "            encoding.append(self.player_encoder(plr))\n",
        "\n",
        "            # bad dta contains more thn 5 in a team\n",
        "            if i==4:\n",
        "                break\n",
        "\n",
        "        for _ in range(len(alive_pls),5):\n",
        "            encoding.append(self.player_encoder(pad=True))\n",
        "\n",
        "        return encoding\n",
        "\n",
        "    def player_encoder(self, plr=None, pad=False):\n",
        "        embs = OrderedDict()\n",
        "        if not pad:\n",
        "            health = self.label_health(plr['health'])\n",
        "            embs['health_bin'] = health\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= self.label_armor(plr['armor'])\n",
        "            embs['armor'] = armor\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = self.label_money(plr['money'])\n",
        "            embs['money'] = money\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = self.label_has_defuser(plr['has_defuser'])\n",
        "            embs['has_defuser'] = has_defuser\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = self.label_has_helmet(plr['has_helmet'])\n",
        "            embs['has_helmet'] = has_helmet\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            embs['inventory']=[]\n",
        "            for i,item in enumerate(plr['inventory']):\n",
        "                embs['inventory'].append(self.encode_inventory(item))\n",
        "                #considering only 6 items\n",
        "                if i==5:\n",
        "                    break\n",
        "            for _ in range(len(plr['inventory']),6):\n",
        "                embs['inventory'].append(self.encode_inventory(pad=True))\n",
        "\n",
        "        else:\n",
        "\n",
        "            health = torch.LongTensor([0])\n",
        "            embs['health_bin'] = health\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= torch.LongTensor([0])\n",
        "            embs['armor'] = armor\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = torch.LongTensor([0])\n",
        "            embs['money'] = money\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = torch.LongTensor([0])\n",
        "            embs['has_defuser'] = has_defuser\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = torch.LongTensor([0])\n",
        "            embs['has_helmet'] = has_helmet\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            embs['inventory']=[]\n",
        "            for _ in range(6):\n",
        "                embs['inventory'].append(self.encode_inventory(pad=True))\n",
        "\n",
        "        return embs\n",
        "\n",
        "\n",
        "    def encode_inventory(self, item=None, pad = False):\n",
        "        embs = OrderedDict()\n",
        "        if not pad:\n",
        "            typ = item['item_type']\n",
        "            typ = torch.LongTensor([inventory.index(typ)])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = self.label_clip(item['clip_ammo'])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = self.label_ammo(item['clip_ammo'])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "\n",
        "        else: \n",
        "            typ = torch.LongTensor([0])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = torch.LongTensor([0])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = torch.LongTensor([0])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "        return embs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def encode_score(self, score):\n",
        "        score = torch.tensor(score,dtype = torch.float)/16\n",
        "        return score\n",
        "\n",
        "    def label_health(self, health):\n",
        "        health_bins=[0,10,20,30,40,50,65,80,90,100]\n",
        "        return torch.LongTensor((np.digitize([health],health_bins)))+1\n",
        "\n",
        "    def label_money(self, money):\n",
        "        bins = [0,500,1000,1500,2200,3000,3500,3800,4000,4500,5000,5500,7000,9000,12000,16000]\n",
        "        return torch.LongTensor((np.digitize([money],bins)))+1\n",
        "\n",
        "    def label_clip(self, clip):\n",
        "        bins = [0,1,2,10,20,30,35,300]\n",
        "        return torch.LongTensor((np.digitize([clip],bins)))+1\n",
        "\n",
        "    def label_ammo(self, ammo):\n",
        "        bins = [0,2,30,60,120,300]\n",
        "        return torch.LongTensor((np.digitize([ammo],bins)))+1\n",
        "\n",
        "    def label_smokes(self, num_smokes):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_smokes],bins)))+1\n",
        "\n",
        "    def label_molotovs(self, num_molotov):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_molotov],bins)))+1\n",
        "\n",
        "    def cal_dis(self, pos1, pos2 ):\n",
        "        pos1 = np.array([pos1['x'],pos1['y'],pos1['z']])\n",
        "        pos2 = np.array([pos2['x'],pos2['y'],pos2['z']])\n",
        "        dist = np.linalg.norm(pos1-pos2)\n",
        "        return dist\n",
        "\n",
        "    def label_armor(self, armor):\n",
        "        bins=[0,10,40,80,100]\n",
        "        return torch.LongTensor((np.digitize([armor],bins)))+1\n",
        "\n",
        "    def label_has_helmet(self, has_helmet:bool):\n",
        "        if has_helmet:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "    def label_has_defuser(self, has_defuser:bool):\n",
        "        if has_defuser:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "\n",
        "            "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "04c4tHBmKZf7",
        "colab": {}
      },
      "source": [
        "class Submodule(nn.Module):\n",
        "    def __init__(self, hidden1, hidden2, dropout = 0.3):\n",
        "        super(Submodule, self).__init__()\n",
        "        self.lnr1 = nn.Linear(hidden1, hidden1)\n",
        "        self.lnr2 = nn.Linear(hidden1, hidden2)\n",
        "        self.act = nn.LeakyReLU()\n",
        "        self.drop = nn.Dropout(p=dropout)\n",
        "        self.bn = nn.BatchNorm1d(hidden2)\n",
        "\n",
        "        \n",
        "    def forward(self, x):\n",
        "        #print(x.shape)\n",
        "        x = self.lnr1(x)\n",
        "        x = self.drop(x)\n",
        "        x = self.act(x)\n",
        "        x = self.lnr2(x)\n",
        "        x = self.bn(x)\n",
        "        return x\n",
        "\n",
        "class FC_model(LightningModule):\n",
        "\n",
        "    def __init__(self,hparams) -> None:\n",
        "        super(FC_model, self).__init__()\n",
        "        self.params = hparams\n",
        "        self.hparams = hparams\n",
        "        self.embeddings =  nn.ModuleDict()\n",
        "        self.embeddings['map'] = nn.Embedding(len(maps)+3, 12,padding_idx =0)\n",
        "        #self.embeddings['map_crc'] = nn.Embedding(25, 2,padding_idx =0)\n",
        "        #self.embeddings['patch_version'] = nn.Embedding(15,2,padding_idx =0)\n",
        "        self.embeddings['round_status_time_left_binned'] = nn.Embedding(len(bins['round_time_left'])+3,16,padding_idx =0)\n",
        "        self.embeddings['round_status'] = nn.Embedding(4+2, 16,padding_idx =0)\n",
        "        self.embeddings['health_bin'] = nn.Embedding(10+4,16,padding_idx =0)\n",
        "        self.embeddings['has_helmet'] = nn.Embedding(2+2,16,padding_idx =0)\n",
        "        self.embeddings['has_defuser'] = nn.Embedding(2+2,4,padding_idx =0)\n",
        "        #self.embeddings['team'] = nn.Embedding(2+2,12,padding_idx =0)\n",
        "        self.embeddings['armor'] = nn.Embedding(len(bins['armor'])+2,12,padding_idx =0)\n",
        "        self.embeddings['item'] = nn.Embedding(len(inventory)+2,12,padding_idx =0)\n",
        "        self.embeddings['money'] = nn.Embedding(len(bins['money'])+2,16,padding_idx =0)\n",
        "        self.embeddings['smokes'] = nn.Embedding(len(bins['smokes'])+2,4,padding_idx =0)\n",
        "        self.embeddings['clip'] = nn.Embedding(len(bins['clip'])+2,4,padding_idx =0)\n",
        "        self.embeddings['ammo'] = nn.Embedding(len(bins['ammo'])+2,2,padding_idx =0)\n",
        "        self.embeddings['molotovs'] = nn.Embedding(len(bins['molotov'])+2,4,padding_idx =0)\n",
        "        \n",
        "\n",
        "        self.map_nn = Submodule(12,64,0.5)\n",
        "        self.ct_nn = Submodule(860, 1024,0.3)\n",
        "        self.t_nn = Submodule(860, 1024,0.3)\n",
        "        self.gr_nn  = Submodule(8,64,0.8)\n",
        "        self.round_nn = Submodule(32,64)\n",
        "        self.score_nn = Submodule(2,64,0.3)\n",
        "\n",
        "        self.drop = nn.Dropout(p=0.15)\n",
        "        self.fc1 = Submodule(64*4+1024*2, 3072,0.3)\n",
        "        self.fc2 = Submodule(3072, 1024,0.3)\n",
        "        self.classify  = nn.Linear(1024, 1)\n",
        "        self.loss = nn.BCEWithLogitsLoss()\n",
        "        self.acc = Accuracy(num_classes=2)\n",
        "\n",
        "    def forward(self, input):\n",
        "        input = self.embed_batch(input)\n",
        "        map, ct,t,gr,round, score = input\n",
        "        \"\"\"print(map.shape)\n",
        "        print(ct.shape)\n",
        "        print(t.shape)\n",
        "        print(gr.shape)\n",
        "        print(round.shape)\n",
        "        print(score.shape)\"\"\"\n",
        "        map = self.map_nn(map)\n",
        "        ct = self.ct_nn(ct)\n",
        "        t = self.t_nn(t)\n",
        "        gr = self.gr_nn(gr)\n",
        "        round = self.round_nn(round)\n",
        "        score = self.score_nn(score)\n",
        "        \n",
        "\n",
        "        cat = torch.cat((ct,t,round, map,gr,score), dim =-1)\n",
        "        cat = self.fc1(cat)\n",
        "        cat = self.fc2(cat)\n",
        "        \n",
        "        out = self.drop(cat)\n",
        "        output = self.classify(out)\n",
        "\n",
        "        return output\n",
        "\n",
        "    def embed_input(self, input):\n",
        "        i= 0\n",
        "        map  = self.embeddings[keys[0]](input[0]).squeeze(1)\n",
        "        i=1\n",
        "        ct = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(plr_keys*5,input[1:5*len(plr_keys)+1])], dim=-1)\n",
        "        i+= 5*len(plr_keys)\n",
        "        ts = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(plr_keys*5,input[i:5*len(plr_keys)+i])], dim=-1)\n",
        "        i+= 5*len(plr_keys)\n",
        "        grs = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(gr_keys,input[i:i+len(gr_keys)])], dim=-1)\n",
        "        i+= len(gr_keys)\n",
        "        round = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(round_keys,input[i:i+len(round_keys)])], dim=-1)\n",
        "        i+=len(round_keys)\n",
        "\n",
        "        return map,ct,ts,grs,round, input[-1]\n",
        "\n",
        "    def embed_batch(self,batch):\n",
        "        embs=batch.copy()\n",
        "        for key, val in batch.items():\n",
        "            if key=='map':\n",
        "                \n",
        "                embs[key]=self.embeddings[key](val).squeeze(1)\n",
        "\n",
        "            if key == 'grs':\n",
        "                mol = self.embeddings['molotovs'](val['molotovs']).squeeze(1)\n",
        "                smoke = self.embeddings['smokes'](val['smokes']).squeeze(1)\n",
        "                embs[key] = torch.cat((smoke, mol), dim =-1)\n",
        "\n",
        "            if key == 'round_info':\n",
        "                round_status_time = self.embeddings['round_status_time_left_binned'](val['round_status_time_left_binned']).squeeze(1)\n",
        "                round_status= self.embeddings['round_status'](val['round_status']).squeeze(1)\n",
        "                embs['round_info'] = torch.cat((round_status_time,round_status), dim=-1)\n",
        "\n",
        "            if key in ('cts' or 'ts'):\n",
        "                plr_embs=[]\n",
        "                for pl in val:\n",
        "                    plr_embs.extend(self.embed_player(pl))\n",
        "                embs[key] = torch.cat(plr_embs, dim=-1)\n",
        "\n",
        "        return tuple(embs.values())\n",
        "            \n",
        "\n",
        "    def embed_player(self, plr):\n",
        "        embs = []\n",
        "        for key, val in plr.items():\n",
        "            if key=='inventory':\n",
        "                for item in val:\n",
        "                    embs.extend(self.embed_inventory(item))\n",
        "                continue\n",
        "            embs.append(self.embeddings[key](val).squeeze(1))    \n",
        "\n",
        "        return embs           \n",
        "\n",
        "    def embed_inventory(self, item):\n",
        "        embs = []\n",
        "        for key, val in item.items():\n",
        "            embs.append(self.embeddings[key](val).squeeze(1))\n",
        "\n",
        "        return embs\n",
        "\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        input, target  = batch\n",
        "        #input = self.embed_batch(input)\n",
        "        pred = self(input)\n",
        "        \n",
        "        loss = self.loss(pred, target)\n",
        "        logs = {'train_loss': loss,}\n",
        "        \"\"\"result = pl.TrainResult(minimize=loss,)\n",
        "        result.prediction = pred\n",
        "        result.target = target\n",
        "        result.log('train_loss', loss, prog_bar=True, on_epoch=True)\"\"\"\n",
        "        return {'loss': loss, 'log': logs,\"progress_bar\": logs, \"preds\":pred, \"targets\": target}\n",
        "\n",
        "    \n",
        "    def training_epoch_end(self, outputs):\n",
        "        \n",
        "        \"\"\"y_hat = training_step_outputs.prediction\n",
        "        y_hat = torch.round(torch.sigmoid(y_hat))\n",
        "        y= training_step_outputs.target\"\"\"\n",
        "        #acc = self.acc(y_hat, y)\n",
        "        loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds))\n",
        "        #print(preds)\n",
        "        #print(targets)\n",
        "        acc = self.acc(preds, targets)\n",
        "        logs = {'train_loss': loss,'train_acc':acc}\n",
        "        #training_step_outputs.log('train_acc', acc, on_step = False,prog_bar=True, on_epoch=True)\n",
        "        #training_step_outputs.acc = acc\n",
        "        \n",
        "        return {'loss': loss, 'log': logs,\"progress_bar\": logs, \"acc\":acc}\n",
        "\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        input, target  = batch\n",
        "        #input = self.embed_batch(input)\n",
        "        pred = self(input)\n",
        "\n",
        "        loss = self.loss(pred, target)\n",
        "        logs = {'val_loss': loss,}\n",
        "        \"\"\"result = pl.EvalResult(checkpoint_on=loss)\n",
        "        result.log('val_loss', loss, prog_bar=True, on_epoch=True)\n",
        "        result.prediction = pred\n",
        "        result.target = target\"\"\"\n",
        "\n",
        "        return {'val_loss': loss, 'log': logs,\"progress_bar\": logs, \"preds\":pred, \"targets\": target}\n",
        "    \n",
        "        \"\"\"    \n",
        "        def validation_end(self, outputs: List[dict]):\n",
        "        loss = torch.stack([x[\"val_loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds)\n",
        "        acc = self.acc(preds, targets)\n",
        "        return {\"val_loss\": loss, acc =}\"\"\"\n",
        "\n",
        "    def validation_epoch_end(self, outputs):\n",
        "        \"\"\"y_hat = validation_step_outputs.prediction\n",
        "        y_hat = torch.round(torch.sigmoid(y_hat))\n",
        "        y= validation_step_outputs.target\n",
        "        acc = self.acc(y_hat, y)\n",
        "        \n",
        "        #validation_step_outputs.log('val_acc', acc, on_step = False,prog_bar=True, on_epoch=True)\n",
        "        #validation_step_outputs.acc = acc\"\"\"\n",
        "        loss = torch.stack([x[\"val_loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds))\n",
        "        acc = self.acc(preds, targets)\n",
        "        logs = {'val_loss': loss,'val_acc':acc}\n",
        "\n",
        "        return {\"val_loss\": loss, 'log': logs,\"progress_bar\": logs, \"val_acc\":acc}\n",
        "\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        input, target  = batch\n",
        "        #input = self.embed_batch(input)\n",
        "        pred = self(input)\n",
        "\n",
        "        loss = self.loss(pred, target)\n",
        "        logs = {'test_loss': loss,}\n",
        "        \n",
        "\n",
        "        return {'test_loss': loss, 'log': logs,\"progress_bar\": logs, \"preds\":pred, \"targets\": target}\n",
        "    \n",
        "        \n",
        "\n",
        "    def test_end(self, outputs):\n",
        "        loss = torch.stack([x[\"test_loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds))\n",
        "        acc = self.acc(preds, targets)\n",
        "        logs = {'test_loss': loss,'test_acc':acc}\n",
        "\n",
        "        return {\"test_loss\": loss, 'log': logs,\"progress_bar\": logs, \"test_acc\":acc}\n",
        "\n",
        "   \n",
        "    \n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optims = []\n",
        "        scheds = []\n",
        "        no_decay = ['bias','LayerNorm.bias', 'LayerNorm.weight']\n",
        "        optimizer_grouped_parameters = [\n",
        "            {'params': [p for n, p in self.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "            {'params': [p for n, p in self.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "        ]\n",
        "        optimizer = optim.Adam(optimizer_grouped_parameters, self.params.lr)\n",
        "\n",
        "        optims.append(optimizer)\n",
        "        scheduler=torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=self.params.milestones, gamma=0.01)\n",
        "        \n",
        "        scheds.append(scheduler)\n",
        "                \n",
        "        return optims\n",
        "\n",
        "    def read_dataset(self, template, start_idx, end_idx):\n",
        "        frames = [ pd.read_json(f) for f in [template.format(i) for i in range(start_idx, end_idx+1)] ]\n",
        "        return pd.concat(frames, ignore_index = True)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        dftrain = self.read_dataset(self.params.root+\"/dataset_initial/dataset_{:02}.json\", 9, 14)\n",
        "        y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\n",
        "        dataset = CsData(dftrain, y_train)\n",
        "        return DataLoader(dataset,\n",
        "                          batch_size= self.params.batch_size,\n",
        "                          num_workers=self.params.num_workers,\n",
        "                          drop_last=True,\n",
        "                          shuffle = True)\n",
        "        \n",
        "    def val_dataloader(self):\n",
        "        dftrain = self.read_dataset(self.params.root+\"/dataset_initial/dataset_{:02}.json\", 15, 16)\n",
        "        y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\n",
        "        dataset = CsData(dftrain, y_train)\n",
        "        return DataLoader(dataset,\n",
        "                          batch_size= self.params.val_batch_size,\n",
        "                          num_workers=self.params.num_workers,\n",
        "                          drop_last=True)\n",
        "        \n",
        "    def test_dataloader(self):\n",
        "        dftrain = self.read_dataset(self.params.root+\"/dataset_initial/dataset_{:02}.json\", 16, 17)\n",
        "        y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\n",
        "        dataset = CsData(dftrain, y_train)\n",
        "        return DataLoader(dataset,\n",
        "                          batch_size= self.params.val_batch_size,\n",
        "                          num_workers=self.params.num_workers,\n",
        "                          drop_last=True)\n",
        "    \n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LcrmwD8RKZf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          ]
        },
        "outputId": "2cf07ccd-2428-45d6-80bb-cf34b6761429"
      },
      "source": [
        "root = '/gdrive/My Drive/dl_projects/CSGOAI'\n",
        "path = '/gdrive/My Drive/dl_projects/CSGOAI/model3'\n",
        "path2 = '/gdrive/My Drive/dl_projects/CSGOAI/checkpts'\n",
        "\n",
        "hparams = Namespace(\n",
        "        root=root,\n",
        "        batch_size= 256,\n",
        "        val_batch_size=256,\n",
        "        lr= 1e-3,\n",
        "        epochs=40,\n",
        "        save_top_k=30,\n",
        "        accumulate_grad_batches=1,\n",
        "        num_workers = 4,\n",
        "        num_warmup_steps=100,\n",
        "        milestones=[1,3]\n",
        "      \n",
        "    )\n",
        "#wandb_logger = pl_loggers.TensorBoardLogger(path,name = 'lightning_logs',)\n",
        "wandb_logger = WandbLogger(project='CSGO_AI', save_dir = path)\n",
        "# initialize Model Checkpoint Saver\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    filepath=path+'/{epoch}-{val_loss:.3f}-{val_acc:0.3f}-model3',\n",
        "    save_top_k=hparams.save_top_k,\n",
        "    verbose=True,\n",
        "    monitor=\"val_acc\",\n",
        "    period=1,\n",
        "    mode=\"max\",\n",
        ")\n",
        "model = FC_model(hparams)\n",
        "trainer = Trainer(gpus=1,default_root_dir = path2, max_epochs=hparams.epochs,logger = wandb_logger,checkpoint_callback=checkpoint_callback,log_gpu_memory=True,weights_summary=None)\n",
        "#trainer = Trainer(fast_dev_run=True, gpus=1, auto_scale_batch_size= None)\n",
        "trainer.fit(model)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "CUDA_VISIBLE_DEVICES: [0]\n"
          ],
          "name": "stderr"
        },
        {
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              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
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              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
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              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://app.wandb.ai/authorize'\n",
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              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Not authenticated.  Copy a key from https://app.wandb.ai/authorize\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "API Key: ··········\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/nilansh/CSGO_AI\" target=\"_blank\">https://app.wandb.ai/nilansh/CSGO_AI</a><br/>\n",
              "                Run page: <a href=\"https://app.wandb.ai/nilansh/CSGO_AI/runs/3rzl0wx4\" target=\"_blank\">https://app.wandb.ai/nilansh/CSGO_AI/runs/3rzl0wx4</a><br/>\n",
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        {
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          "text": [
            "\n",
            "Epoch 00000: val_acc reached 0.67248 (best 0.67248), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=0-val_loss=0.586-val_acc=0.672-model3.ckpt as top 30\n"
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: val_acc reached 0.67949 (best 0.67949), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=1-val_loss=0.561-val_acc=0.679-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00002: val_acc reached 0.66577 (best 0.67949), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=2-val_loss=0.602-val_acc=0.666-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00003: val_acc reached 0.67188 (best 0.67949), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=3-val_loss=0.551-val_acc=0.672-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
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          "text": [
            "\n",
            "Epoch 00004: val_acc reached 0.67979 (best 0.67979), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=4-val_loss=0.571-val_acc=0.680-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
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            "\n",
            "Epoch 00005: val_acc reached 0.65134 (best 0.67979), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=5-val_loss=0.593-val_acc=0.651-model3.ckpt as top 30\n"
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          "text": [
            "\n",
            "Epoch 00006: val_acc reached 0.68630 (best 0.68630), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=6-val_loss=0.557-val_acc=0.686-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
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          "text": [
            "\n",
            "Epoch 00007: val_acc reached 0.68930 (best 0.68930), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=7-val_loss=0.568-val_acc=0.689-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
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            "\n",
            "Epoch 00008: val_acc reached 0.68700 (best 0.68930), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=8-val_loss=0.557-val_acc=0.687-model3.ckpt as top 30\n"
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            "Epoch 00009: val_acc reached 0.69000 (best 0.69000), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=9-val_loss=0.569-val_acc=0.690-model3.ckpt as top 30\n"
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            "\n",
            "Epoch 00010: val_acc reached 0.68680 (best 0.69000), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=10-val_loss=0.561-val_acc=0.687-model3.ckpt as top 30\n"
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            "\n",
            "Epoch 00011: val_acc reached 0.68349 (best 0.69000), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=11-val_loss=0.573-val_acc=0.683-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
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            "\n",
            "Epoch 00012: val_acc reached 0.69201 (best 0.69201), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=12-val_loss=0.549-val_acc=0.692-model3.ckpt as top 30\n"
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            "Epoch 00013: val_acc reached 0.67899 (best 0.69201), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=13-val_loss=0.574-val_acc=0.679-model3.ckpt as top 30\n"
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            "\n",
            "Epoch 00014: val_acc reached 0.67097 (best 0.69201), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=14-val_loss=0.574-val_acc=0.671-model3.ckpt as top 30\n"
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            "\n",
            "Epoch 00015: val_acc reached 0.67167 (best 0.69201), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=15-val_loss=0.572-val_acc=0.672-model3.ckpt as top 30\n"
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          "name": "stderr"
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            "\n",
            "Epoch 00016: val_acc reached 0.69281 (best 0.69281), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=16-val_loss=0.544-val_acc=0.693-model3.ckpt as top 30\n"
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            "\n",
            "Epoch 00017: val_acc reached 0.68299 (best 0.69281), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=17-val_loss=0.561-val_acc=0.683-model3.ckpt as top 30\n"
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          "text": [
            "\n",
            "Epoch 00018: val_acc reached 0.69121 (best 0.69281), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=18-val_loss=0.549-val_acc=0.691-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00019: val_acc reached 0.69641 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=19-val_loss=0.551-val_acc=0.696-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00020: val_acc reached 0.68389 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=20-val_loss=0.562-val_acc=0.684-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00021: val_acc reached 0.68440 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=21-val_loss=0.548-val_acc=0.684-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48aaab4d35b148eba1e82c4a89d364a3",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00022: val_acc reached 0.68079 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=22-val_loss=0.556-val_acc=0.681-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "1d014a2e103c4bb187242eef9a21d3c2",
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              "version_major": 2
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          },
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00023: val_acc reached 0.68199 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=23-val_loss=0.547-val_acc=0.682-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "912db6570f5f42cdb044c1b3dc2f34cc",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00024: val_acc reached 0.68379 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=24-val_loss=0.566-val_acc=0.684-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8fc8fb59d4b420c94d2ea16b5a7ab25",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00025: val_acc reached 0.68470 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=25-val_loss=0.564-val_acc=0.685-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9757bf402afc4c7cbabd148ec8117372",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00026: val_acc reached 0.68229 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=26-val_loss=0.555-val_acc=0.682-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9824c06b34904c4dbc189e0a848855c9",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00027: val_acc reached 0.67648 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=27-val_loss=0.593-val_acc=0.676-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a40b6d5946384cc4a979b70ca7dce746",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00028: val_acc reached 0.67538 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=28-val_loss=0.599-val_acc=0.675-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0b9276e2721e46b29be62632497bbe49",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00029: val_acc reached 0.67177 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=29-val_loss=0.603-val_acc=0.672-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cb8964c9c90486cbc6be28eab4054da",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00030: val_acc reached 0.68139 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=30-val_loss=0.583-val_acc=0.681-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e8833a7fc9594d1e88c16844798634ef",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00031: val_acc reached 0.68409 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=31-val_loss=0.583-val_acc=0.684-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6134a506b04341029a4d867e624f1de0",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00032: val_acc  was not in top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98e6279fd1374caea84dd24940208aef",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00033: val_acc  was not in top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "64e6a8e1709444f7afcb367c730950df",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00034: val_acc reached 0.68229 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=34-val_loss=0.587-val_acc=0.682-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "525159380728475a9ad99dd1704e49bc",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00035: val_acc reached 0.67488 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=35-val_loss=0.654-val_acc=0.675-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1bdfd1419612478997c09150a5442fff",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00036: val_acc  was not in top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5045479741b4e2988b043976546d740",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00037: val_acc reached 0.67788 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=37-val_loss=0.606-val_acc=0.678-model3.ckpt as top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fffab68c96ed456393434dcbf220aecd",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00038: val_acc  was not in top 30\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "552af67c9b064990844c4e718a5f29c7",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00039: val_acc reached 0.67378 (best 0.69641), saving model to /gdrive/My Drive/dl_projects/CSGOAI/model3/epoch=39-val_loss=0.642-val_acc=0.674-model3.ckpt as top 30\n",
            "Saving latest checkpoint..\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rkwyuOc6KZf_",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lgUR8lNtKZgC",
        "colab": {}
      },
      "source": [
        "model=model.load_from_checkpoint('/gdrive/My Drive/dl_projects/CSGOAI/epoch=39-val_model2_loss=0.000.ckpt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7DXZ7kFfKZgF",
        "colab": {}
      },
      "source": [
        "hparams = Namespace(\n",
        "        root=root,\n",
        "        batch_size= 256,\n",
        "        val_batch_size=256,\n",
        "        lr= 1e-4,\n",
        "        epochs=5,\n",
        "        save_top_k=20,\n",
        "        accumulate_grad_batches=1,\n",
        "        num_workers = 4,\n",
        "        num_warmup_steps=100,\n",
        "        milestones=[1]\n",
        "      \n",
        "    )\n",
        "model.params=hparams"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Jvm8OAORKZgH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d75c65b53b1049bab7ef4c9dfc3522e3",
            "140c71577c7c4ae99450f051b6c8af71",
            "0824381c0fd14c518c4f9b446cd79056",
            "32dd95854fa6431392449e1d478846af",
            "fbfc9e4a8c1947df8988b2d874190362",
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            "54d89bc3b44242569907703615cbd18b",
            "a2e5817d81ab41f0a43f6fa4ba60315c",
            "f44248df08a544a28d76a1bd483c3bfb",
            "012a241f62aa4300aba1d8ae57354940",
            "aa51c20e00494f9e8eb5e1656b2f327d",
            "c1716bb3201f4122918a7b3d49168a2d",
            "3f0c7f92d8044c5891f977c246cf7c3a"
          ]
        },
        "outputId": "53dc9ff3-6b46-472a-e0d8-5eb60de646f6"
      },
      "source": [
        "trainer.fit(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d75c65b53b1049bab7ef4c9dfc3522e3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43ad024a645b48b9bc2716e7ed9dd560",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cut_WCk1KZgJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "b20252a404e44b628f028297a1442546",
            "2a815880ae9f489c817592b92f3b1ada",
            "341c071811414aaa928c5e9bfc97ed61",
            "65d4f11b3c74463cb4383598aec8db44",
            "9e5ccde2ffe84271a2f177bdb9520ee6",
            "8bb1afbbff424439b3ba6b7c792038cb",
            "5c7548f3fdcc4af18d9293214f94e3bb",
            "ff865cd396a648d2b4f9ef6b32886c98"
          ]
        },
        "outputId": "f561ba64-d675-4ea8-965c-24b5cd496822"
      },
      "source": [
        "model=model.load_from_checkpoint('/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata/epoch=3-val_loss=0.526-val_acc=0.702.ckpt')\n",
        "model.params=hparams\n",
        "trainer.test(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b20252a404e44b628f028297a1442546",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Testing', layout=Layout(flex='2'), max=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
            "{'test_acc': tensor(0.6978, device='cuda:0'),\n",
            " 'test_loss': tensor(0.5293, device='cuda:0')}\n",
            "--------------------------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_acc': 0.6977983117103577, 'test_loss': 0.529349148273468}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FDnnVG5SKZgM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43cf3be9-f381-4017-c76d-6abf3a901563"
      },
      "source": [
        "print(next(model.parameters()).device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mDV7mXwIKZgO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a82ce674-d2c2-4b51-8776-d4d637b80fea"
      },
      "source": [
        "model.embeddings['armor'].weight.device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "riv0S_jbKZgQ",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "/gdrive/My Drive/dl_projects/CSGOAI/epoch=39-val_model2_loss=0.000.ckpt' test on 16 17 acc=0.697  \n",
        "/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata/epoch=7-val_loss=0.529-val_acc=0.700.ckpt' test on 16 17 acc=0.6969 loss0.5319\n",
        "/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata/epoch=3-val_loss=0.526-val_acc=0.702.ckpt  test on 16 17 acc=0.6977 loss0.5293\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSgli6lfAFJb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "e863f9b6-1bbb-42a0-99cf-e902b33b2722"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Aug 27 16:53:08 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 450.57       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   54C    P8    10W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPq8PvvUdL9y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9705623-88bb-4aba-c099-bf1251d84ff6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxPfDJ2AtZNU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b9fbff86-08bd-404e-d26f-4d77ada95838"
      },
      "source": [
        "\n",
        "!pip install pytorch-lightning\n",
        "!pip install wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pytorch-lightning\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/af/2f10c8ee22d7a05fe8c9be58ad5c55b71ab4dd895b44f0156bfd5535a708/pytorch_lightning-0.9.0-py3-none-any.whl (408kB)\n",
            "\u001b[K     |████████████████████████████████| 409kB 5.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (20.4)\n",
            "Requirement already satisfied: numpy>=1.16.4 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (1.18.5)\n",
            "Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (1.6.0+cu101)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning) (4.41.1)\n",
            "Collecting PyYAML>=5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/c2/b80047c7ac2478f9501676c988a5411ed5572f35d1beff9cae07d321512c/PyYAML-5.3.1.tar.gz (269kB)\n",
            "\u001b[K     |████████████████████████████████| 276kB 19.4MB/s \n",
            "\u001b[?25hCollecting tensorboard==2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/54/f5/d75a6f7935e4a4870d85770bc9976b12e7024fbceb83a1a6bc50e6deb7c4/tensorboard-2.2.0-py3-none-any.whl (2.8MB)\n",
            "\u001b[K     |████████████████████████████████| 2.8MB 23.1MB/s \n",
            "\u001b[?25hCollecting future>=0.17.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz (829kB)\n",
            "\u001b[K     |████████████████████████████████| 829kB 54.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->pytorch-lightning) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->pytorch-lightning) (1.15.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard==2.2.0->pytorch-lightning) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard==2.2.0->pytorch-lightning) (0.4.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard==2.2.0->pytorch-lightning) (1.7.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard==2.2.0->pytorch-lightning) (49.6.0)\n",
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.2.0->pytorch-lightning) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard==2.2.0->pytorch-lightning) (1.7.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.2.0->pytorch-lightning) (4.6)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.2.0->pytorch-lightning) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.2.0->pytorch-lightning) (4.1.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard==2.2.0->pytorch-lightning) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard==2.2.0->pytorch-lightning) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard==2.2.0->pytorch-lightning) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard==2.2.0->pytorch-lightning) (2020.6.20)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.2.0->pytorch-lightning) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard==2.2.0->pytorch-lightning) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard==2.2.0->pytorch-lightning) (0.4.8)\n",
            "Building wheels for collected packages: PyYAML, future\n",
            "  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyYAML: filename=PyYAML-5.3.1-cp36-cp36m-linux_x86_64.whl size=44619 sha256=862d1a2fb56c179eabf7a6f4f3d160283e3d9d6991487de2746cd25c64007832\n",
            "  Stored in directory: /root/.cache/pip/wheels/a7/c1/ea/cf5bd31012e735dc1dfea3131a2d5eae7978b251083d6247bd\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-cp36-none-any.whl size=491057 sha256=20dfaf37fcb836f5af34d8f2fe8b23d5445f46d83a5c0e87ed70a3c699dc5406\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n",
            "Successfully built PyYAML future\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorboard<3,>=2.3.0, but you'll have tensorboard 2.2.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: PyYAML, tensorboard, future, pytorch-lightning\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "Successfully installed PyYAML-5.3.1 future-0.18.2 pytorch-lightning-0.9.0 tensorboard-2.2.0\n",
            "Collecting wandb\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/65/14/e7988204e4d4c9a349e73362399263b1c17f2b4d8a753864444f9eac1c92/wandb-0.9.5-py2.py3-none-any.whl (1.4MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4MB 4.6MB/s \n",
            "\u001b[?25hCollecting watchdog>=0.8.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0e/06/121302598a4fc01aca942d937f4a2c33430b7181137b35758913a8db10ad/watchdog-0.10.3.tar.gz (94kB)\n",
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            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (1.15.0)\n",
            "Collecting gql==0.2.0\n",
            "  Downloading https://files.pythonhosted.org/packages/c4/6f/cf9a3056045518f06184e804bae89390eb706168349daa9dff8ac609962a/gql-0.2.0.tar.gz\n",
            "Requirement already satisfied: requests>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (2.23.0)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from wandb) (5.3.1)\n",
            "Collecting docker-pycreds>=0.4.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f5/e8/f6bd1eee09314e7e6dee49cbe2c5e22314ccdb38db16c9fc72d2fa80d054/docker_pycreds-0.4.0-py2.py3-none-any.whl\n",
            "Collecting sentry-sdk>=0.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8f/0f/e6ae366e926589878f2b1e41485473ee0368e5b1b62fd0f8c0bc8311eb75/sentry_sdk-0.17.0-py2.py3-none-any.whl (115kB)\n",
            "\u001b[K     |████████████████████████████████| 122kB 27.9MB/s \n",
            "\u001b[?25hCollecting GitPython>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/1e/a45320cab182bf1c8656107b3d4c042e659742822fc6bff150d769a984dd/GitPython-3.1.7-py3-none-any.whl (158kB)\n",
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            "\u001b[?25hRequirement already satisfied: nvidia-ml-py3>=7.352.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (7.352.0)\n",
            "Collecting shortuuid>=0.5.0\n",
            "  Downloading https://files.pythonhosted.org/packages/25/a6/2ecc1daa6a304e7f1b216f0896b26156b78e7c38e1211e9b798b4716c53d/shortuuid-1.0.1-py3-none-any.whl\n",
            "Collecting configparser>=3.8.1\n",
            "  Downloading https://files.pythonhosted.org/packages/4b/6b/01baa293090240cf0562cc5eccb69c6f5006282127f2b846fad011305c79/configparser-5.0.0-py3-none-any.whl\n",
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            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (7.1.2)\n",
            "Collecting subprocess32>=3.5.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/c8/564be4d12629b912ea431f1a50eb8b3b9d00f1a0b1ceff17f266be190007/subprocess32-3.5.4.tar.gz (97kB)\n",
            "\u001b[K     |████████████████████████████████| 102kB 10.8MB/s \n",
            "\u001b[?25hCollecting pathtools>=0.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n",
            "Collecting graphql-core<2,>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/89/00ad5e07524d8c523b14d70c685e0299a8b0de6d0727e368c41b89b7ed0b/graphql-core-1.1.tar.gz (70kB)\n",
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            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 9.6MB/s \n",
            "\u001b[?25hCollecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/b0/9a/4d409a6234eb940e6a78dfdfc66156e7522262f5f2fecca07dc55915952d/smmap-3.0.4-py2.py3-none-any.whl\n",
            "Building wheels for collected packages: watchdog, gql, subprocess32, pathtools, graphql-core\n",
            "  Building wheel for watchdog (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for watchdog: filename=watchdog-0.10.3-cp36-none-any.whl size=73873 sha256=5db37e8cf4b0bce1d0df76e819715813d2e35f083e2b9543cfad7524aa1f02b2\n",
            "  Stored in directory: /root/.cache/pip/wheels/a8/1d/38/2c19bb311f67cc7b4d07a2ec5ea36ab1a0a0ea50db994a5bc7\n",
            "  Building wheel for gql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gql: filename=gql-0.2.0-cp36-none-any.whl size=7630 sha256=69a4e87a5a954ecb2c5fa5c2cb8441e7d47c263d5c0af34ea0db284522697d66\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/0e/7b/58a8a5268655b3ad74feef5aa97946f0addafb3cbb6bd2da23\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp36-none-any.whl size=6489 sha256=7f988b5684b8a460cc2b76ac5dc9ad113624345add0b1b8078ae70146a3c6f84\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-cp36-none-any.whl size=8785 sha256=14c37d4b06f257e91763fb6db85a724fb2e1d916cf84ea0c8ab394bd2d2deabb\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
            "  Building wheel for graphql-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for graphql-core: filename=graphql_core-1.1-cp36-none-any.whl size=104651 sha256=a68518dd9e466d4ca368b0b7391f41a8d986a97cdce99eeb5910d0045a1b086b\n",
            "  Stored in directory: /root/.cache/pip/wheels/45/99/d7/c424029bb0fe910c63b68dbf2aa20d3283d023042521bcd7d5\n",
            "Successfully built watchdog gql subprocess32 pathtools graphql-core\n",
            "Installing collected packages: pathtools, watchdog, graphql-core, gql, docker-pycreds, sentry-sdk, smmap, gitdb, GitPython, shortuuid, configparser, subprocess32, wandb\n",
            "Successfully installed GitPython-3.1.7 configparser-5.0.0 docker-pycreds-0.4.0 gitdb-4.0.5 gql-0.2.0 graphql-core-1.1 pathtools-0.1.2 sentry-sdk-0.17.0 shortuuid-1.0.1 smmap-3.0.4 subprocess32-3.5.4 wandb-0.9.5 watchdog-0.10.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPhWPVS-dmvh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pt  = '/gdrive/My Drive/dl_projects/CSGOAI'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wt7g-Pc3tuLE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import Dataset,DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "from PIL import Image\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "from argparse import Namespace\n",
        "from typing import *\n",
        "import random\n",
        "from functools import lru_cache\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.core.lightning import LightningModule\n",
        "from pytorch_lightning import Trainer\n",
        "from pytorch_lightning import loggers as pl_loggers\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from pytorch_lightning.metrics import Accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1ho5X9femr1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "from collections import OrderedDict \n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpC13NfZuP_M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBUva_u53qIs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "inventory = ['Deagle', 'Elite', 'FiveSeven', 'Glock', 'Ak47', 'Aug', 'Awp', 'Famas', 'G3sg1', 'GalilAr', 'M249', 'M4a4', 'Mac10', 'P90', 'Mp5sd', 'Ump45', 'Xm1014', 'Bizon', 'Mag7', 'Negev', 'Sawedoff', 'Tec9', 'ZeusX27', 'P2000', 'Mp7', 'Mp9', 'Nova', 'P250', 'Scar20', 'Sg553', 'Ssg08', 'M4a1S', 'UspS', 'Cz75Auto', 'R8Revolver', 'Flashbang', 'HeGrenade', 'SmokeGrenade', 'MolotovGrenade', 'DecoyGrenade', 'IncendiaryGrenade', 'Knife', 'C4']\n",
        "maps = ['de_dust2', 'de_mirage', 'de_nuke', 'de_inferno', 'de_overpass',\n",
        "       'de_vertigo', 'de_train', 'de_cache']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4kkMHzcXp0E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "maps_label = {map:i+1 for i,map in enumerate(maps)}\n",
        "round_status = {'BombPlanted':1, 'FreezeTime':2, 'Normal':3, \"SlackTime\":4}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMS83IpYx4UJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bins = {\n",
        "    'round_time_left':[0, 10, 30, 50, 70, 100,115,4000],\n",
        "    'health':[0,10,20,30,40,50,65,80,90,100],\n",
        "    'money':[0,500,100.,1500,2200,3000,3500,3800,4000,4500,5000,5500,7000,9000,12000,16000],\n",
        "    'clip' : [0,1,2,10,20,30,35,300],\n",
        "    'ammo' :[0,2,30,60,120,300],\n",
        "    'smokes':[0,1,2,3,4,10],\n",
        "    'molotov':[0,1,2,3,4,10],\n",
        "    'armor':[0,10,40,80,100]\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGI0JfZlOlrV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "480ff178-b548-4832-97df-84f9b256cd48"
      },
      "source": [
        "len(bins['armor'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y-Nc9aQDQq4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "class CsData(Dataset):\n",
        "    def __init__(self, dfx,dfy):\n",
        "        self.dfx = dfx\n",
        "        self.dfy = dfy\n",
        "        self.dfx['map'] = self.dfx['map'].apply(lambda x: maps_label[x])\n",
        "        self.dfx['round_status'] = self.dfx['round_status'].apply(lambda x: round_status[x])\n",
        "        self.dfx['round_status_time_left_binned']=self.dfx['round_status_time_left'].apply(lambda x: self.encode_time(x))\n",
        "        \n",
        "\n",
        "    def __getitem__(self, id):\n",
        "        row = self.dfx.iloc[id]\n",
        "        outs = self.process_row(row)\n",
        "        #outs = tuple(outs.values())\n",
        "        if len(outs)!= 236:\n",
        "            print(id)\n",
        "        return tuple(outs), torch.FloatTensor([self.dfy.iloc[id]])\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dfx)\n",
        "\n",
        "    def process_row(self, row):\n",
        "        \n",
        "        row_embs = []#OrderedDict()\n",
        "\n",
        "        #map\n",
        "        row_embs.append(torch.LongTensor([row['map']]))\n",
        "\n",
        "        #cts and ts\n",
        "        cts = [p for p in row['alive_players'] if p['team']=='CT']\n",
        "        ts = [p for p in row['alive_players'] if p['team']=='Terrorist']\n",
        "        row_embs.extend(self.encode_alive_players(cts))\n",
        "        row_embs.extend(self.encode_alive_players(ts))\n",
        "\n",
        "        # GRS\n",
        "        \n",
        "        grs = OrderedDict()\n",
        "        grs['molotovs'] = self.label_molotovs(len(row['active_molotovs']))\n",
        "        grs['smokes'] = self.label_smokes(len(row['active_smokes']))\n",
        "        row_embs.extend(grs.values())\n",
        "        \n",
        "        #round\n",
        "        round = OrderedDict()\n",
        "        round['round_status'] = torch.LongTensor([row['round_status']])\n",
        "        round['round_status_time_left_binned']= torch.LongTensor([row['round_status_time_left_binned']])\n",
        "        row_embs.extend(round.values())\n",
        "\n",
        "        #score\n",
        "        row_embs.append(self.encode_score(row['current_score']))\n",
        "\n",
        "\n",
        "        return row_embs\n",
        "\n",
        "    def encode_time(self, time):\n",
        "        bins = [0, 10, 30, 50, 70, 100,115,4000]\n",
        "        return (np.digitize([time],bins)[0])+1\n",
        "\n",
        "    def encode_alive_players(self,alive_pls):\n",
        "        encoding = []\n",
        "        for i,plr in enumerate(alive_pls):\n",
        "            encoding.extend(self.player_encoder(plr))\n",
        "            # bad dta contains more thn 5 in a team\n",
        "            if i==4:\n",
        "                break\n",
        "\n",
        "        for _ in range(len(alive_pls),5):\n",
        "            encoding.extend(self.player_encoder(pad=True))\n",
        "\n",
        "        return encoding\n",
        "\n",
        "    def player_encoder(self, plr=None, pad=False):\n",
        "        embs =[]# OrderedDict()\n",
        "        if not pad:\n",
        "            health = self.label_health(plr['health'])\n",
        "            embs.append( health)\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= self.label_armor(plr['armor'])\n",
        "            embs.append(armor)\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = self.label_money(plr['money'])\n",
        "            embs.append(money)\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = self.label_has_defuser(plr['has_defuser'])\n",
        "            embs.append(has_defuser)\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = self.label_has_helmet(plr['has_helmet'])\n",
        "            embs.append(has_helmet)\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            #embs['inventory']=[]\n",
        "            for i,item in enumerate(plr['inventory']):\n",
        "                embs.extend(self.encode_inventory(item).values())\n",
        "                # max 6 items considered\n",
        "                if i==5:\n",
        "                    break\n",
        "            for _ in range(len(plr['inventory']),6):\n",
        "                embs.extend(self.encode_inventory(pad=True).values())\n",
        "\n",
        "        else:\n",
        "\n",
        "            health = torch.LongTensor([0])\n",
        "            embs.append(health)\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= torch.LongTensor([0])\n",
        "            embs.append(armor)\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = torch.LongTensor([0])\n",
        "            embs.append(money)\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = torch.LongTensor([0])\n",
        "            embs.append(has_defuser)\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = torch.LongTensor([0])\n",
        "            embs.append(has_helmet)\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            #embs['inventory']=[]\n",
        "            for _ in range(6):\n",
        "                embs.extend(self.encode_inventory(pad=True).values())\n",
        "\n",
        "        return embs\n",
        "\n",
        "\n",
        "    def encode_inventory(self, item=None, pad = False):\n",
        "        embs = OrderedDict()\n",
        "        if not pad:\n",
        "            typ = item['item_type']\n",
        "            typ = torch.LongTensor([inventory.index(typ)])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = self.label_clip(item['clip_ammo'])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = self.label_ammo(item['clip_ammo'])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "\n",
        "        else: \n",
        "            typ = torch.LongTensor([0])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = torch.LongTensor([0])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = torch.LongTensor([0])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "        return embs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def encode_score(self, score):\n",
        "        score = torch.tensor(score,dtype = torch.float)/16\n",
        "        return score\n",
        "\n",
        "    def label_health(self, health):\n",
        "        health_bins=[0,10,20,30,40,50,65,80,90,100]\n",
        "        return torch.LongTensor((np.digitize([health],health_bins)))+1\n",
        "\n",
        "    def label_money(self, money):\n",
        "        bins = [0,500,1000,1500,2200,3000,3500,3800,4000,4500,5000,5500,7000,9000,12000,16000]\n",
        "        return torch.LongTensor((np.digitize([money],bins)))+1\n",
        "\n",
        "    def label_clip(self, clip):\n",
        "        bins = [0,1,2,10,20,30,35,300]\n",
        "        return torch.LongTensor((np.digitize([clip],bins)))+1\n",
        "\n",
        "    def label_ammo(self, ammo):\n",
        "        bins = [0,2,30,60,120,300]\n",
        "        return torch.LongTensor((np.digitize([ammo],bins)))+1\n",
        "\n",
        "    def label_smokes(self, num_smokes):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_smokes],bins)))+1\n",
        "\n",
        "    def label_molotovs(self, num_molotov):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_molotov],bins)))+1\n",
        "\n",
        "    def cal_dis(self, pos1, pos2 ):\n",
        "        pos1 = np.array([pos1['x'],pos1['y'],pos1['z']])\n",
        "        pos2 = np.array([pos2['x'],pos2['y'],pos2['z']])\n",
        "        dist = np.linalg.norm(pos1-pos2)\n",
        "        return dist\n",
        "\n",
        "    def label_armor(self, armor):\n",
        "        bins=[0,10,40,80,100]\n",
        "        return torch.LongTensor((np.digitize([armor],bins)))+1\n",
        "\n",
        "    def label_has_helmet(self, has_helmet:bool):\n",
        "        if has_helmet:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "    def label_has_defuser(self, has_defuser:bool):\n",
        "        if has_defuser:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "\n",
        "            "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SJnl_OEDmxm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "class CsData(Dataset):\n",
        "    def __init__(self, dfx,dfy):\n",
        "        self.dfx = dfx\n",
        "        self.dfy = dfy\n",
        "        self.dfx['map'] = self.dfx['map'].apply(lambda x: maps_label[x])\n",
        "        self.dfx['round_status'] = self.dfx['round_status'].apply(lambda x: round_status[x])\n",
        "        self.dfx['round_status_time_left_binned']=self.dfx['round_status_time_left'].apply(lambda x: self.encode_time(x))\n",
        "        \n",
        "\n",
        "    def __getitem__(self, id):\n",
        "        row = self.dfx.iloc[id]\n",
        "        outs = self.process_row(row)\n",
        "        #outs = tuple(outs.values())\n",
        "        return outs, torch.FloatTensor([self.dfy.iloc[id]])\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dfx)\n",
        "\n",
        "    def process_row(self, row):\n",
        "        \n",
        "        row_embs = OrderedDict()\n",
        "\n",
        "        #map\n",
        "        row_embs['map'] = torch.LongTensor([row['map']])\n",
        "\n",
        "        #cts and ts\n",
        "        cts = [p for p in row['alive_players'] if p['team']=='CT']\n",
        "        ts = [p for p in row['alive_players'] if p['team']=='Terrorist']\n",
        "        row_embs['cts'] = self.encode_alive_players(cts)\n",
        "        row_embs['ts'] = self.encode_alive_players(ts)\n",
        "\n",
        "        # GRS\n",
        "        \n",
        "        grs = OrderedDict()\n",
        "        grs['molotovs'] = self.label_molotovs(len(row['active_molotovs']))\n",
        "        grs['smokes'] = self.label_smokes(len(row['active_smokes']))\n",
        "        row_embs['grs'] = grs\n",
        "        \n",
        "        #round\n",
        "        round = OrderedDict()\n",
        "        round['round_status'] = torch.LongTensor([row['round_status']])\n",
        "        round['round_status_time_left_binned']= torch.LongTensor([row['round_status_time_left_binned']])\n",
        "        row_embs['round_info'] = round\n",
        "\n",
        "        #score\n",
        "        row_embs['score'] = self.encode_score(row['current_score'])\n",
        "\n",
        "\n",
        "        return row_embs\n",
        "\n",
        "    def encode_time(self, time):\n",
        "        bins = [0, 10, 30, 50, 70, 100,115,4000]\n",
        "        return (np.digitize([time],bins)[0])+1\n",
        "\n",
        "    def encode_alive_players(self,alive_pls):\n",
        "        encoding = []\n",
        "        for i,plr in enumerate(alive_pls):\n",
        "            encoding.append(self.player_encoder(plr))\n",
        "\n",
        "            # bad dta contains more thn 5 in a team\n",
        "            if i==4:\n",
        "                break\n",
        "\n",
        "        for _ in range(len(alive_pls),5):\n",
        "            encoding.append(self.player_encoder(pad=True))\n",
        "\n",
        "        return encoding\n",
        "\n",
        "    def player_encoder(self, plr=None, pad=False):\n",
        "        embs = OrderedDict()\n",
        "        if not pad:\n",
        "            health = self.label_health(plr['health'])\n",
        "            embs['health_bin'] = health\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= self.label_armor(plr['armor'])\n",
        "            embs['armor'] = armor\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = self.label_money(plr['money'])\n",
        "            embs['money'] = money\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = self.label_has_defuser(plr['has_defuser'])\n",
        "            embs['has_defuser'] = has_defuser\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = self.label_has_helmet(plr['has_helmet'])\n",
        "            embs['has_helmet'] = has_helmet\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            embs['inventory']=[]\n",
        "            for i,item in enumerate(plr['inventory']):\n",
        "                embs['inventory'].append(self.encode_inventory(item))\n",
        "                #considering only 6 items\n",
        "                if i==5:\n",
        "                    break\n",
        "            for _ in range(len(plr['inventory']),6):\n",
        "                embs['inventory'].append(self.encode_inventory(pad=True))\n",
        "\n",
        "        else:\n",
        "\n",
        "            health = torch.LongTensor([0])\n",
        "            embs['health_bin'] = health\n",
        "            #embs.append(self.embeddings['health_bin'](health)[0])\n",
        "        \n",
        "            armor= torch.LongTensor([0])\n",
        "            embs['armor'] = armor\n",
        "            #embs.append(self.embeddings['armor'](armor)[0])\n",
        "                        \n",
        "            money = torch.LongTensor([0])\n",
        "            embs['money'] = money\n",
        "            #embs.append(self.embeddings['money'](money)[0] )\n",
        "                        \n",
        "            has_defuser = torch.LongTensor([0])\n",
        "            embs['has_defuser'] = has_defuser\n",
        "            #embs.append(self.embeddings['has_defuser'](has_defuser)[0])\n",
        "                        \n",
        "            has_helmet = torch.LongTensor([0])\n",
        "            embs['has_helmet'] = has_helmet\n",
        "            #embs.append(self.embeddings['has_helmet'](has_helmet)[0])\n",
        "\n",
        "            embs['inventory']=[]\n",
        "            for _ in range(6):\n",
        "                embs['inventory'].append(self.encode_inventory(pad=True))\n",
        "\n",
        "        return embs\n",
        "\n",
        "\n",
        "    def encode_inventory(self, item=None, pad = False):\n",
        "        embs = OrderedDict()\n",
        "        if not pad:\n",
        "            typ = item['item_type']\n",
        "            typ = torch.LongTensor([inventory.index(typ)])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = self.label_clip(item['clip_ammo'])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = self.label_ammo(item['clip_ammo'])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "\n",
        "        else: \n",
        "            typ = torch.LongTensor([0])\n",
        "            embs['item'] = typ\n",
        "            #embs.append(self.embeddings['item'](typ)[0])\n",
        "\n",
        "            clip = torch.LongTensor([0])\n",
        "            embs['clip'] = clip\n",
        "            #embs.append(self.embeddings['clip'](clip)[0])\n",
        "\n",
        "            ammo = torch.LongTensor([0])\n",
        "            embs['ammo'] = ammo\n",
        "            #embs.append(self.embeddings['ammo'](ammo)[0])\n",
        "        return embs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def encode_score(self, score):\n",
        "        score = torch.tensor(score,dtype = torch.float)/16\n",
        "        return score\n",
        "\n",
        "    def label_health(self, health):\n",
        "        health_bins=[0,10,20,30,40,50,65,80,90,100]\n",
        "        return torch.LongTensor((np.digitize([health],health_bins)))+1\n",
        "\n",
        "    def label_money(self, money):\n",
        "        bins = [0,500,1000,1500,2200,3000,3500,3800,4000,4500,5000,5500,7000,9000,12000,16000]\n",
        "        return torch.LongTensor((np.digitize([money],bins)))+1\n",
        "\n",
        "    def label_clip(self, clip):\n",
        "        bins = [0,1,2,10,20,30,35,300]\n",
        "        return torch.LongTensor((np.digitize([clip],bins)))+1\n",
        "\n",
        "    def label_ammo(self, ammo):\n",
        "        bins = [0,2,30,60,120,300]\n",
        "        return torch.LongTensor((np.digitize([ammo],bins)))+1\n",
        "\n",
        "    def label_smokes(self, num_smokes):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_smokes],bins)))+1\n",
        "\n",
        "    def label_molotovs(self, num_molotov):\n",
        "        bins =[0,1,2,3,4,10]\n",
        "        return torch.LongTensor((np.digitize([num_molotov],bins)))+1\n",
        "\n",
        "    def cal_dis(self, pos1, pos2 ):\n",
        "        pos1 = np.array([pos1['x'],pos1['y'],pos1['z']])\n",
        "        pos2 = np.array([pos2['x'],pos2['y'],pos2['z']])\n",
        "        dist = np.linalg.norm(pos1-pos2)\n",
        "        return dist\n",
        "\n",
        "    def label_armor(self, armor):\n",
        "        bins=[0,10,40,80,100]\n",
        "        return torch.LongTensor((np.digitize([armor],bins)))+1\n",
        "\n",
        "    def label_has_helmet(self, has_helmet:bool):\n",
        "        if has_helmet:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "    def label_has_defuser(self, has_defuser:bool):\n",
        "        if has_defuser:\n",
        "            return torch.LongTensor([2])\n",
        "        else :return torch.LongTensor([1])\n",
        "\n",
        "\n",
        "            "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRLGj-_SBpJS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = dftrain.copy()\n",
        "y= y_train.copy()\n",
        "#x = dfeval.copy()\n",
        "#y= y_eval.copy()\n",
        "data = CsData(x,y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X60AQ3UxaJ4h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "(data[430])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0xgeBIw5e7I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x.iloc[43010]['alive_players'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQ7-rj-yFQOj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6V2WfFUgij-T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dl=DataLoader(data, shuffle=True,\n",
        "                          batch_size= 120,\n",
        "                          num_workers=1,\n",
        "                          drop_last=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBEnNiYP2czO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for batch_idx, batch in enumerate(dl):\n",
        "    continue"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8TV_KFZDx1V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "item_keys = ['item', 'clip', 'ammo']\n",
        "plr_keys = ['health_bin', 'armor', 'money', 'has_defuser', 'has_helmet'] + item_keys*6\n",
        "gr_keys = ['molotovs', 'smokes']\n",
        "round_keys = ['round_status', 'round_status_time_left_binned']\n",
        "keys = ['map'] + plr_keys*10 + gr_keys + round_keys"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBYosATrU3Dc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8674acda-7a5b-4d49-b4dd-92c0b237363c"
      },
      "source": [
        "len(keys)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "235"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYUMlZ3xBhzC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pt  = '/gdrive/My Drive/dl_projects/CSGOAI'\n",
        "def read_dataset(template, start_idx, end_idx):\n",
        "    frames = [ pd.read_json(f) for f in [template.format(i) for i in range(start_idx, end_idx+1)] ]\n",
        "    return pd.concat(frames, ignore_index = True)\n",
        "\n",
        "\"\"\"dftrain = read_dataset(pt+\"/dataset_initial/dataset_{:02}.json\", 9, 14)\n",
        "y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\"\"\"\n",
        "\n",
        "dfeval = read_dataset(pt+\"/dataset_initial/dataset_{:02}.json\", 15, 16)\n",
        "y_eval = dfeval.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0WmyO7I0kyF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFikN-LQtSbQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Submodule(nn.Module):\n",
        "    def __init__(self, hidden1, hidden2, dropout = 0.3):\n",
        "        super(Submodule, self).__init__()\n",
        "        self.lnr1 = nn.Linear(hidden1, hidden1)\n",
        "        self.lnr2 = nn.Linear(hidden1, hidden2)\n",
        "        self.act = nn.LeakyReLU()\n",
        "        self.drop = nn.Dropout(p=dropout)\n",
        "        self.bn = nn.BatchNorm1d(hidden2)\n",
        "\n",
        "        \n",
        "    def forward(self, x):\n",
        "        #print(x.shape)\n",
        "        x = self.lnr1(x)\n",
        "        x = self.drop(x)\n",
        "        x = self.act(x)\n",
        "        x = self.lnr2(x)\n",
        "        x = self.bn(x)\n",
        "        return x\n",
        "\n",
        "class FC_model(LightningModule):\n",
        "\n",
        "    def __init__(self,hparams) -> None:\n",
        "        super(FC_model, self).__init__()\n",
        "        self.params = hparams\n",
        "        self.hparams = hparams\n",
        "        self.embeddings =  nn.ModuleDict()\n",
        "        self.embeddings['map'] = nn.Embedding(len(maps)+3, 12,padding_idx =0)\n",
        "        #self.embeddings['map_crc'] = nn.Embedding(25, 2,padding_idx =0)\n",
        "        #self.embeddings['patch_version'] = nn.Embedding(15,2,padding_idx =0)\n",
        "        self.embeddings['round_status_time_left_binned'] = nn.Embedding(len(bins['round_time_left'])+3,24,padding_idx =0)\n",
        "        self.embeddings['round_status'] = nn.Embedding(4+2, 24,padding_idx =0)\n",
        "        self.embeddings['health_bin'] = nn.Embedding(10+4,24,padding_idx =0)\n",
        "        self.embeddings['has_helmet'] = nn.Embedding(2+2,12,padding_idx =0)\n",
        "        self.embeddings['has_defuser'] = nn.Embedding(2+2,4,padding_idx =0)\n",
        "        #self.embeddings['team'] = nn.Embedding(2+2,12,padding_idx =0)\n",
        "        self.embeddings['armor'] = nn.Embedding(len(bins['armor'])+2,12,padding_idx =0)\n",
        "        self.embeddings['item'] = nn.Embedding(len(inventory)+2,12,padding_idx =0)\n",
        "        self.embeddings['money'] = nn.Embedding(len(bins['money'])+2,24,padding_idx =0)\n",
        "        self.embeddings['smokes'] = nn.Embedding(len(bins['smokes'])+2,4,padding_idx =0)\n",
        "        self.embeddings['clip'] = nn.Embedding(len(bins['clip'])+2,4,padding_idx =0)\n",
        "        self.embeddings['ammo'] = nn.Embedding(len(bins['ammo'])+2,2,padding_idx =0)\n",
        "        self.embeddings['molotovs'] = nn.Embedding(len(bins['molotov'])+2,4,padding_idx =0)\n",
        "        \n",
        "\n",
        "        self.map_nn = Submodule(12,64,0.5)\n",
        "        self.ct_nn = Submodule(920, 1024,0.3)\n",
        "        self.t_nn = Submodule(920, 1024,0.3)\n",
        "        self.gr_nn  = Submodule(8,128,0.8)\n",
        "        self.round_nn = Submodule(48,128)\n",
        "        self.score_nn = Submodule(2,128,0.3)\n",
        "\n",
        "        self.drop = nn.Dropout(p=0.1)\n",
        "        self.fc1 = Submodule(128*3+1024*2+64, 3072,0.3)\n",
        "        self.fc2 = Submodule(3072, 1024,0.2)\n",
        "        self.classify  = nn.Linear(1024, 1)\n",
        "        self.loss = nn.BCEWithLogitsLoss()\n",
        "        self.acc = Accuracy(num_classes=2)\n",
        "\n",
        "    def forward(self, input):\n",
        "        input = self.embed_batch(input)\n",
        "        map, ct,t,gr,round, score = input\n",
        "        \"\"\"print(map.shape)\n",
        "        print(ct.shape)\n",
        "        print(t.shape)\n",
        "        print(gr.shape)\n",
        "        print(round.shape)\n",
        "        print(score.shape)\"\"\"\n",
        "        map = self.map_nn(map)\n",
        "        ct = self.ct_nn(ct)\n",
        "        t = self.t_nn(t)\n",
        "        gr = self.gr_nn(gr)\n",
        "        round = self.round_nn(round)\n",
        "        score = self.score_nn(score)\n",
        "        \n",
        "\n",
        "        cat = torch.cat((ct,t,round, map,gr,score), dim =-1)\n",
        "        cat = self.fc1(cat)\n",
        "        cat = self.fc2(cat)\n",
        "        \n",
        "        out = self.drop(cat)\n",
        "        output = self.classify(out)\n",
        "\n",
        "        return output\n",
        "\n",
        "    def embed_input(self, input):\n",
        "        i= 0\n",
        "        map  = self.embeddings[keys[0]](input[0]).squeeze(1)\n",
        "        i=1\n",
        "        ct = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(plr_keys*5,input[1:5*len(plr_keys)+1])], dim=-1)\n",
        "        i+= 5*len(plr_keys)\n",
        "        ts = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(plr_keys*5,input[i:5*len(plr_keys)+i])], dim=-1)\n",
        "        i+= 5*len(plr_keys)\n",
        "        grs = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(gr_keys,input[i:i+len(gr_keys)])], dim=-1)\n",
        "        i+= len(gr_keys)\n",
        "        round = torch.cat([self.embeddings[i](val).squeeze(1) for i,val in zip(round_keys,input[i:i+len(round_keys)])], dim=-1)\n",
        "        i+=len(round_keys)\n",
        "\n",
        "        return map,ct,ts,grs,round, input[-1]\n",
        "\n",
        "    def embed_batch(self,batch):\n",
        "        embs=batch.copy()\n",
        "        for key, val in batch.items():\n",
        "            if key=='map':\n",
        "                \n",
        "                embs[key]=self.embeddings[key](val).squeeze(1)\n",
        "\n",
        "            if key == 'grs':\n",
        "                mol = self.embeddings['molotovs'](val['molotovs']).squeeze(1)\n",
        "                smoke = self.embeddings['smokes'](val['smokes']).squeeze(1)\n",
        "                embs[key] = torch.cat((smoke, mol), dim =-1)\n",
        "\n",
        "            if key == 'round_info':\n",
        "                round_status_time = self.embeddings['round_status_time_left_binned'](val['round_status_time_left_binned']).squeeze(1)\n",
        "                round_status= self.embeddings['round_status'](val['round_status']).squeeze(1)\n",
        "                embs['round_info'] = torch.cat((round_status_time,round_status), dim=-1)\n",
        "\n",
        "            if key in ('cts' or 'ts'):\n",
        "                plr_embs=[]\n",
        "                for pl in val:\n",
        "                    plr_embs.extend(self.embed_player(pl))\n",
        "                embs[key] = torch.cat(plr_embs, dim=-1)\n",
        "\n",
        "        return tuple(embs.values())\n",
        "            \n",
        "\n",
        "    def embed_player(self, plr):\n",
        "        embs = []\n",
        "        for key, val in plr.items():\n",
        "            if key=='inventory':\n",
        "                for item in val:\n",
        "                    embs.extend(self.embed_inventory(item))\n",
        "                continue\n",
        "            embs.append(self.embeddings[key](val).squeeze(1))    \n",
        "\n",
        "        return embs           \n",
        "\n",
        "    def embed_inventory(self, item):\n",
        "        embs = []\n",
        "        for key, val in item.items():\n",
        "            embs.append(self.embeddings[key](val).squeeze(1))\n",
        "\n",
        "        return embs\n",
        "\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        input, target  = batch\n",
        "        #input = self.embed_batch(input)\n",
        "        pred = self(input)\n",
        "        \n",
        "        loss = self.loss(pred, target)\n",
        "        logs = {'train_loss': loss,}\n",
        "        \"\"\"result = pl.TrainResult(minimize=loss,)\n",
        "        result.prediction = pred\n",
        "        result.target = target\n",
        "        result.log('train_loss', loss, prog_bar=True, on_epoch=True)\"\"\"\n",
        "        return {'loss': loss, 'log': logs,\"progress_bar\": logs, \"preds\":pred, \"targets\": target}\n",
        "\n",
        "    \n",
        "    def training_epoch_end(self, outputs):\n",
        "        \n",
        "        \"\"\"y_hat = training_step_outputs.prediction\n",
        "        y_hat = torch.round(torch.sigmoid(y_hat))\n",
        "        y= training_step_outputs.target\"\"\"\n",
        "        #acc = self.acc(y_hat, y)\n",
        "        loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds))\n",
        "        #print(preds)\n",
        "        #print(targets)\n",
        "        acc = self.acc(preds, targets)\n",
        "        logs = {'train_loss': loss,'train_acc':acc}\n",
        "        #training_step_outputs.log('train_acc', acc, on_step = False,prog_bar=True, on_epoch=True)\n",
        "        #training_step_outputs.acc = acc\n",
        "        \n",
        "        return {'loss': loss, 'log': logs,\"progress_bar\": logs, \"acc\":acc}\n",
        "\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        input, target  = batch\n",
        "        #input = self.embed_batch(input)\n",
        "        pred = self(input)\n",
        "\n",
        "        loss = self.loss(pred, target)\n",
        "        logs = {'val_loss': loss,}\n",
        "        \"\"\"result = pl.EvalResult(checkpoint_on=loss)\n",
        "        result.log('val_loss', loss, prog_bar=True, on_epoch=True)\n",
        "        result.prediction = pred\n",
        "        result.target = target\"\"\"\n",
        "\n",
        "        return {'val_loss': loss, 'log': logs,\"progress_bar\": logs, \"preds\":pred, \"targets\": target}\n",
        "    \n",
        "        \"\"\"    \n",
        "        def validation_end(self, outputs: List[dict]):\n",
        "        loss = torch.stack([x[\"val_loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds)\n",
        "        acc = self.acc(preds, targets)\n",
        "        return {\"val_loss\": loss, acc =}\"\"\"\n",
        "\n",
        "    def validation_epoch_end(self, outputs):\n",
        "        \"\"\"y_hat = validation_step_outputs.prediction\n",
        "        y_hat = torch.round(torch.sigmoid(y_hat))\n",
        "        y= validation_step_outputs.target\n",
        "        acc = self.acc(y_hat, y)\n",
        "        \n",
        "        #validation_step_outputs.log('val_acc', acc, on_step = False,prog_bar=True, on_epoch=True)\n",
        "        #validation_step_outputs.acc = acc\"\"\"\n",
        "        loss = torch.stack([x[\"val_loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds))\n",
        "        acc = self.acc(preds, targets)\n",
        "        logs = {'val_loss': loss,'val_acc':acc}\n",
        "\n",
        "        return {\"val_loss\": loss, 'log': logs,\"progress_bar\": logs, \"val_acc\":acc}\n",
        "\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        input, target  = batch\n",
        "        #input = self.embed_batch(input)\n",
        "        pred = self(input)\n",
        "\n",
        "        loss = self.loss(pred, target)\n",
        "        logs = {'test_loss': loss,}\n",
        "        \n",
        "\n",
        "        return {'test_loss': loss, 'log': logs,\"progress_bar\": logs, \"preds\":pred, \"targets\": target}\n",
        "    \n",
        "        \n",
        "\n",
        "    def test_end(self, outputs):\n",
        "        loss = torch.stack([x[\"test_loss\"] for x in outputs]).mean()\n",
        "        preds = torch.stack([x[\"preds\"] for x in outputs])\n",
        "        targets = torch.stack([x[\"targets\"] for x in outputs])\n",
        "        preds = torch.round(torch.sigmoid(preds))\n",
        "        acc = self.acc(preds, targets)\n",
        "        logs = {'test_loss': loss,'test_acc':acc}\n",
        "\n",
        "        return {\"test_loss\": loss, 'log': logs,\"progress_bar\": logs, \"test_acc\":acc}\n",
        "\n",
        "   \n",
        "    \n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optims = []\n",
        "        scheds = []\n",
        "        no_decay = ['bias','LayerNorm.bias', 'LayerNorm.weight']\n",
        "        optimizer_grouped_parameters = [\n",
        "            {'params': [p for n, p in self.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "            {'params': [p for n, p in self.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "        ]\n",
        "        optimizer = optim.Adam(optimizer_grouped_parameters, self.params.lr)\n",
        "\n",
        "        optims.append(optimizer)\n",
        "        scheduler=torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=self.params.milestones, gamma=0.01)\n",
        "        \n",
        "        scheds.append(scheduler)\n",
        "                \n",
        "        return optims\n",
        "\n",
        "    def read_dataset(self, template, start_idx, end_idx):\n",
        "        frames = [ pd.read_json(f) for f in [template.format(i) for i in range(start_idx, end_idx+1)] ]\n",
        "        return pd.concat(frames, ignore_index = True)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        dftrain = self.read_dataset(self.params.root+\"/dataset_initial/dataset_{:02}.json\", 9, 14)\n",
        "        y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\n",
        "        dataset = CsData(dftrain, y_train)\n",
        "        return DataLoader(dataset,\n",
        "                          batch_size= self.params.batch_size,\n",
        "                          num_workers=self.params.num_workers,\n",
        "                          drop_last=True,\n",
        "                          shuffle = True)\n",
        "        \n",
        "    def val_dataloader(self):\n",
        "        dftrain = self.read_dataset(self.params.root+\"/dataset_initial/dataset_{:02}.json\", 15, 16)\n",
        "        y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\n",
        "        dataset = CsData(dftrain, y_train)\n",
        "        return DataLoader(dataset,\n",
        "                          batch_size= self.params.val_batch_size,\n",
        "                          num_workers=self.params.num_workers,\n",
        "                          drop_last=True)\n",
        "        \n",
        "    def test_dataloader(self):\n",
        "        dftrain = self.read_dataset(self.params.root+\"/dataset_initial/dataset_{:02}.json\", 16, 17)\n",
        "        y_train = dftrain.pop('round_winner').map(lambda s: 0 if s == \"CT\" else 1)\n",
        "        dataset = CsData(dftrain, y_train)\n",
        "        return DataLoader(dataset,\n",
        "                          batch_size= self.params.val_batch_size,\n",
        "                          num_workers=self.params.num_workers,\n",
        "                          drop_last=True)\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNZR4Xsy_M33",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "9d490f0e-77a5-4b2e-9651-fe0ff14a0542"
      },
      "source": [
        "root = '/gdrive/My Drive/dl_projects/CSGOAI'\n",
        "path = '/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata'\n",
        "path2 = '/gdrive/My Drive/dl_projects/CSGOAI/checkpts'\n",
        "\n",
        "hparams = Namespace(\n",
        "        root=root,\n",
        "        batch_size= 256,\n",
        "        val_batch_size=256,\n",
        "        lr= 1e-3,\n",
        "        epochs=40,\n",
        "        save_top_k=30,\n",
        "        accumulate_grad_batches=1,\n",
        "        num_workers = 4,\n",
        "        num_warmup_steps=100,\n",
        "        milestones=[1,3]\n",
        "      \n",
        "    )\n",
        "#wandb_logger = pl_loggers.TensorBoardLogger(path,name = 'lightning_logs',)\n",
        "wandb_logger = WandbLogger(project='CSGO_AI', save_dir = path)\n",
        "# initialize Model Checkpoint Saver\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    filepath=path+'/{epoch}-{val_loss:.3f}-{val_acc:0.3f}',\n",
        "    save_top_k=hparams.save_top_k,\n",
        "    verbose=True,\n",
        "    monitor=\"val_acc\",\n",
        "    period=1,\n",
        "    mode=\"max\",\n",
        ")\n",
        "model = FC_model(hparams)\n",
        "trainer = Trainer(gpus=1,default_root_dir = path2, max_epochs=hparams.epochs,logger = wandb_logger,checkpoint_callback=checkpoint_callback,log_gpu_memory=True,weights_summary=None)\n",
        "#trainer = Trainer(fast_dev_run=True, gpus=1, auto_scale_batch_size= None)\n",
        "#trainer.fit(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "CUDA_VISIBLE_DEVICES: [0]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Db_1oChW7ODp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYfHJLIxAwho",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.load_from_checkpoint('/gdrive/My Drive/dl_projects/CSGOAI/epoch=39-val_model2_loss=0.000.ckpt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoQ7qmrH80Xp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hparams = Namespace(\n",
        "        root=root,\n",
        "        batch_size= 256,\n",
        "        val_batch_size=256,\n",
        "        lr= 1e-4,\n",
        "        epochs=5,\n",
        "        save_top_k=20,\n",
        "        accumulate_grad_batches=1,\n",
        "        num_workers = 4,\n",
        "        num_warmup_steps=100,\n",
        "        milestones=[1]\n",
        "      \n",
        "    )\n",
        "model.params=hparams"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dj-6EstwcQEO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d75c65b53b1049bab7ef4c9dfc3522e3",
            "140c71577c7c4ae99450f051b6c8af71",
            "0824381c0fd14c518c4f9b446cd79056",
            "32dd95854fa6431392449e1d478846af",
            "fbfc9e4a8c1947df8988b2d874190362",
            "51ace61fb79e4cf7abcddcbee4d287d9",
            "c1b5a95ca3e948fba0d248660dcbba3e",
            "686f3952b6d94319a441b2355a90c2b5",
            "43ad024a645b48b9bc2716e7ed9dd560",
            "54d89bc3b44242569907703615cbd18b",
            "a2e5817d81ab41f0a43f6fa4ba60315c",
            "f44248df08a544a28d76a1bd483c3bfb",
            "012a241f62aa4300aba1d8ae57354940",
            "aa51c20e00494f9e8eb5e1656b2f327d",
            "c1716bb3201f4122918a7b3d49168a2d",
            "3f0c7f92d8044c5891f977c246cf7c3a"
          ]
        },
        "outputId": "53dc9ff3-6b46-472a-e0d8-5eb60de646f6"
      },
      "source": [
        "trainer.fit(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d75c65b53b1049bab7ef4c9dfc3522e3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43ad024a645b48b9bc2716e7ed9dd560",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gwb0yjnIC_sc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "b20252a404e44b628f028297a1442546",
            "2a815880ae9f489c817592b92f3b1ada",
            "341c071811414aaa928c5e9bfc97ed61",
            "65d4f11b3c74463cb4383598aec8db44",
            "9e5ccde2ffe84271a2f177bdb9520ee6",
            "8bb1afbbff424439b3ba6b7c792038cb",
            "5c7548f3fdcc4af18d9293214f94e3bb",
            "ff865cd396a648d2b4f9ef6b32886c98"
          ]
        },
        "outputId": "f561ba64-d675-4ea8-965c-24b5cd496822"
      },
      "source": [
        "model=model.load_from_checkpoint('/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata/epoch=3-val_loss=0.526-val_acc=0.702.ckpt')\n",
        "model.params=hparams\n",
        "trainer.test(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b20252a404e44b628f028297a1442546",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Testing', layout=Layout(flex='2'), max=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
            "{'test_acc': tensor(0.6978, device='cuda:0'),\n",
            " 'test_loss': tensor(0.5293, device='cuda:0')}\n",
            "--------------------------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_acc': 0.6977983117103577, 'test_loss': 0.529349148273468}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0CfDDBPrIsFB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43cf3be9-f381-4017-c76d-6abf3a901563"
      },
      "source": [
        "print(next(model.parameters()).device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9KijHzSpwhd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a82ce674-d2c2-4b51-8776-d4d637b80fea"
      },
      "source": [
        "model.embeddings['armor'].weight.device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kGWqx9Jsryy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "/gdrive/My Drive/dl_projects/CSGOAI/epoch=39-val_model2_loss=0.000.ckpt' test on 16 17 acc=0.697  \n",
        "/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata/epoch=7-val_loss=0.529-val_acc=0.700.ckpt' test on 16 17 acc=0.6969 loss0.5319\n",
        "/gdrive/My Drive/dl_projects/CSGOAI/model1_alldata/epoch=3-val_loss=0.526-val_acc=0.702.ckpt  test on 16 17 acc=0.6977 loss0.5293\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}