training_v2.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hoang\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\hoang\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=EfficientNet_B0_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_B0_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Dropout(p=0.2, inplace=True)\n",
      "  (1): Linear(in_features=1280, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from model import *\n",
    "from torchsummary import summary\n",
    "from torchvision.models import efficientnet_b0\n",
    "from torch.utils.data import DataLoader\n",
    "from datasets import *\n",
    "from torch.optim import lr_scheduler\n",
    "from torchvision import transforms, datasets\n",
    "import time\n",
    "from torchvision.transforms import InterpolationMode"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hoang\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\hoang\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=EfficientNet_B0_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_B0_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 32, 112, 112]             864\n",
      "       BatchNorm2d-2         [-1, 32, 112, 112]              64\n",
      "              SiLU-3         [-1, 32, 112, 112]               0\n",
      "            Conv2d-4         [-1, 32, 112, 112]             288\n",
      "       BatchNorm2d-5         [-1, 32, 112, 112]              64\n",
      "              SiLU-6         [-1, 32, 112, 112]               0\n",
      " AdaptiveAvgPool2d-7             [-1, 32, 1, 1]               0\n",
      "            Conv2d-8              [-1, 8, 1, 1]             264\n",
      "              SiLU-9              [-1, 8, 1, 1]               0\n",
      "           Conv2d-10             [-1, 32, 1, 1]             288\n",
      "          Sigmoid-11             [-1, 32, 1, 1]               0\n",
      "SqueezeExcitation-12         [-1, 32, 112, 112]               0\n",
      "           Conv2d-13         [-1, 16, 112, 112]             512\n",
      "      BatchNorm2d-14         [-1, 16, 112, 112]              32\n",
      "           MBConv-15         [-1, 16, 112, 112]               0\n",
      "           Conv2d-16         [-1, 96, 112, 112]           1,536\n",
      "      BatchNorm2d-17         [-1, 96, 112, 112]             192\n",
      "             SiLU-18         [-1, 96, 112, 112]               0\n",
      "           Conv2d-19           [-1, 96, 56, 56]             864\n",
      "      BatchNorm2d-20           [-1, 96, 56, 56]             192\n",
      "             SiLU-21           [-1, 96, 56, 56]               0\n",
      "AdaptiveAvgPool2d-22             [-1, 96, 1, 1]               0\n",
      "           Conv2d-23              [-1, 4, 1, 1]             388\n",
      "             SiLU-24              [-1, 4, 1, 1]               0\n",
      "           Conv2d-25             [-1, 96, 1, 1]             480\n",
      "          Sigmoid-26             [-1, 96, 1, 1]               0\n",
      "SqueezeExcitation-27           [-1, 96, 56, 56]               0\n",
      "           Conv2d-28           [-1, 24, 56, 56]           2,304\n",
      "      BatchNorm2d-29           [-1, 24, 56, 56]              48\n",
      "           MBConv-30           [-1, 24, 56, 56]               0\n",
      "           Conv2d-31          [-1, 144, 56, 56]           3,456\n",
      "      BatchNorm2d-32          [-1, 144, 56, 56]             288\n",
      "             SiLU-33          [-1, 144, 56, 56]               0\n",
      "           Conv2d-34          [-1, 144, 56, 56]           1,296\n",
      "      BatchNorm2d-35          [-1, 144, 56, 56]             288\n",
      "             SiLU-36          [-1, 144, 56, 56]               0\n",
      "AdaptiveAvgPool2d-37            [-1, 144, 1, 1]               0\n",
      "           Conv2d-38              [-1, 6, 1, 1]             870\n",
      "             SiLU-39              [-1, 6, 1, 1]               0\n",
      "           Conv2d-40            [-1, 144, 1, 1]           1,008\n",
      "          Sigmoid-41            [-1, 144, 1, 1]               0\n",
      "SqueezeExcitation-42          [-1, 144, 56, 56]               0\n",
      "           Conv2d-43           [-1, 24, 56, 56]           3,456\n",
      "      BatchNorm2d-44           [-1, 24, 56, 56]              48\n",
      "  StochasticDepth-45           [-1, 24, 56, 56]               0\n",
      "           MBConv-46           [-1, 24, 56, 56]               0\n",
      "           Conv2d-47          [-1, 144, 56, 56]           3,456\n",
      "      BatchNorm2d-48          [-1, 144, 56, 56]             288\n",
      "             SiLU-49          [-1, 144, 56, 56]               0\n",
      "           Conv2d-50          [-1, 144, 28, 28]           3,600\n",
      "      BatchNorm2d-51          [-1, 144, 28, 28]             288\n",
      "             SiLU-52          [-1, 144, 28, 28]               0\n",
      "AdaptiveAvgPool2d-53            [-1, 144, 1, 1]               0\n",
      "           Conv2d-54              [-1, 6, 1, 1]             870\n",
      "             SiLU-55              [-1, 6, 1, 1]               0\n",
      "           Conv2d-56            [-1, 144, 1, 1]           1,008\n",
      "          Sigmoid-57            [-1, 144, 1, 1]               0\n",
      "SqueezeExcitation-58          [-1, 144, 28, 28]               0\n",
      "           Conv2d-59           [-1, 40, 28, 28]           5,760\n",
      "      BatchNorm2d-60           [-1, 40, 28, 28]              80\n",
      "           MBConv-61           [-1, 40, 28, 28]               0\n",
      "           Conv2d-62          [-1, 240, 28, 28]           9,600\n",
      "      BatchNorm2d-63          [-1, 240, 28, 28]             480\n",
      "             SiLU-64          [-1, 240, 28, 28]               0\n",
      "           Conv2d-65          [-1, 240, 28, 28]           6,000\n",
      "      BatchNorm2d-66          [-1, 240, 28, 28]             480\n",
      "             SiLU-67          [-1, 240, 28, 28]               0\n",
      "AdaptiveAvgPool2d-68            [-1, 240, 1, 1]               0\n",
      "           Conv2d-69             [-1, 10, 1, 1]           2,410\n",
      "             SiLU-70             [-1, 10, 1, 1]               0\n",
      "           Conv2d-71            [-1, 240, 1, 1]           2,640\n",
      "          Sigmoid-72            [-1, 240, 1, 1]               0\n",
      "SqueezeExcitation-73          [-1, 240, 28, 28]               0\n",
      "           Conv2d-74           [-1, 40, 28, 28]           9,600\n",
      "      BatchNorm2d-75           [-1, 40, 28, 28]              80\n",
      "  StochasticDepth-76           [-1, 40, 28, 28]               0\n",
      "           MBConv-77           [-1, 40, 28, 28]               0\n",
      "           Conv2d-78          [-1, 240, 28, 28]           9,600\n",
      "      BatchNorm2d-79          [-1, 240, 28, 28]             480\n",
      "             SiLU-80          [-1, 240, 28, 28]               0\n",
      "           Conv2d-81          [-1, 240, 14, 14]           2,160\n",
      "      BatchNorm2d-82          [-1, 240, 14, 14]             480\n",
      "             SiLU-83          [-1, 240, 14, 14]               0\n",
      "AdaptiveAvgPool2d-84            [-1, 240, 1, 1]               0\n",
      "           Conv2d-85             [-1, 10, 1, 1]           2,410\n",
      "             SiLU-86             [-1, 10, 1, 1]               0\n",
      "           Conv2d-87            [-1, 240, 1, 1]           2,640\n",
      "          Sigmoid-88            [-1, 240, 1, 1]               0\n",
      "SqueezeExcitation-89          [-1, 240, 14, 14]               0\n",
      "           Conv2d-90           [-1, 80, 14, 14]          19,200\n",
      "      BatchNorm2d-91           [-1, 80, 14, 14]             160\n",
      "           MBConv-92           [-1, 80, 14, 14]               0\n",
      "           Conv2d-93          [-1, 480, 14, 14]          38,400\n",
      "      BatchNorm2d-94          [-1, 480, 14, 14]             960\n",
      "             SiLU-95          [-1, 480, 14, 14]               0\n",
      "           Conv2d-96          [-1, 480, 14, 14]           4,320\n",
      "      BatchNorm2d-97          [-1, 480, 14, 14]             960\n",
      "             SiLU-98          [-1, 480, 14, 14]               0\n",
      "AdaptiveAvgPool2d-99            [-1, 480, 1, 1]               0\n",
      "          Conv2d-100             [-1, 20, 1, 1]           9,620\n",
      "            SiLU-101             [-1, 20, 1, 1]               0\n",
      "          Conv2d-102            [-1, 480, 1, 1]          10,080\n",
      "         Sigmoid-103            [-1, 480, 1, 1]               0\n",
      "SqueezeExcitation-104          [-1, 480, 14, 14]               0\n",
      "          Conv2d-105           [-1, 80, 14, 14]          38,400\n",
      "     BatchNorm2d-106           [-1, 80, 14, 14]             160\n",
      " StochasticDepth-107           [-1, 80, 14, 14]               0\n",
      "          MBConv-108           [-1, 80, 14, 14]               0\n",
      "          Conv2d-109          [-1, 480, 14, 14]          38,400\n",
      "     BatchNorm2d-110          [-1, 480, 14, 14]             960\n",
      "            SiLU-111          [-1, 480, 14, 14]               0\n",
      "          Conv2d-112          [-1, 480, 14, 14]           4,320\n",
      "     BatchNorm2d-113          [-1, 480, 14, 14]             960\n",
      "            SiLU-114          [-1, 480, 14, 14]               0\n",
      "AdaptiveAvgPool2d-115            [-1, 480, 1, 1]               0\n",
      "          Conv2d-116             [-1, 20, 1, 1]           9,620\n",
      "            SiLU-117             [-1, 20, 1, 1]               0\n",
      "          Conv2d-118            [-1, 480, 1, 1]          10,080\n",
      "         Sigmoid-119            [-1, 480, 1, 1]               0\n",
      "SqueezeExcitation-120          [-1, 480, 14, 14]               0\n",
      "          Conv2d-121           [-1, 80, 14, 14]          38,400\n",
      "     BatchNorm2d-122           [-1, 80, 14, 14]             160\n",
      " StochasticDepth-123           [-1, 80, 14, 14]               0\n",
      "          MBConv-124           [-1, 80, 14, 14]               0\n",
      "          Conv2d-125          [-1, 480, 14, 14]          38,400\n",
      "     BatchNorm2d-126          [-1, 480, 14, 14]             960\n",
      "            SiLU-127          [-1, 480, 14, 14]               0\n",
      "          Conv2d-128          [-1, 480, 14, 14]          12,000\n",
      "     BatchNorm2d-129          [-1, 480, 14, 14]             960\n",
      "            SiLU-130          [-1, 480, 14, 14]               0\n",
      "AdaptiveAvgPool2d-131            [-1, 480, 1, 1]               0\n",
      "          Conv2d-132             [-1, 20, 1, 1]           9,620\n",
      "            SiLU-133             [-1, 20, 1, 1]               0\n",
      "          Conv2d-134            [-1, 480, 1, 1]          10,080\n",
      "         Sigmoid-135            [-1, 480, 1, 1]               0\n",
      "SqueezeExcitation-136          [-1, 480, 14, 14]               0\n",
      "          Conv2d-137          [-1, 112, 14, 14]          53,760\n",
      "     BatchNorm2d-138          [-1, 112, 14, 14]             224\n",
      "          MBConv-139          [-1, 112, 14, 14]               0\n",
      "          Conv2d-140          [-1, 672, 14, 14]          75,264\n",
      "     BatchNorm2d-141          [-1, 672, 14, 14]           1,344\n",
      "            SiLU-142          [-1, 672, 14, 14]               0\n",
      "          Conv2d-143          [-1, 672, 14, 14]          16,800\n",
      "     BatchNorm2d-144          [-1, 672, 14, 14]           1,344\n",
      "            SiLU-145          [-1, 672, 14, 14]               0\n",
      "AdaptiveAvgPool2d-146            [-1, 672, 1, 1]               0\n",
      "          Conv2d-147             [-1, 28, 1, 1]          18,844\n",
      "            SiLU-148             [-1, 28, 1, 1]               0\n",
      "          Conv2d-149            [-1, 672, 1, 1]          19,488\n",
      "         Sigmoid-150            [-1, 672, 1, 1]               0\n",
      "SqueezeExcitation-151          [-1, 672, 14, 14]               0\n",
      "          Conv2d-152          [-1, 112, 14, 14]          75,264\n",
      "     BatchNorm2d-153          [-1, 112, 14, 14]             224\n",
      " StochasticDepth-154          [-1, 112, 14, 14]               0\n",
      "          MBConv-155          [-1, 112, 14, 14]               0\n",
      "          Conv2d-156          [-1, 672, 14, 14]          75,264\n",
      "     BatchNorm2d-157          [-1, 672, 14, 14]           1,344\n",
      "            SiLU-158          [-1, 672, 14, 14]               0\n",
      "          Conv2d-159          [-1, 672, 14, 14]          16,800\n",
      "     BatchNorm2d-160          [-1, 672, 14, 14]           1,344\n",
      "            SiLU-161          [-1, 672, 14, 14]               0\n",
      "AdaptiveAvgPool2d-162            [-1, 672, 1, 1]               0\n",
      "          Conv2d-163             [-1, 28, 1, 1]          18,844\n",
      "            SiLU-164             [-1, 28, 1, 1]               0\n",
      "          Conv2d-165            [-1, 672, 1, 1]          19,488\n",
      "         Sigmoid-166            [-1, 672, 1, 1]               0\n",
      "SqueezeExcitation-167          [-1, 672, 14, 14]               0\n",
      "          Conv2d-168          [-1, 112, 14, 14]          75,264\n",
      "     BatchNorm2d-169          [-1, 112, 14, 14]             224\n",
      " StochasticDepth-170          [-1, 112, 14, 14]               0\n",
      "          MBConv-171          [-1, 112, 14, 14]               0\n",
      "          Conv2d-172          [-1, 672, 14, 14]          75,264\n",
      "     BatchNorm2d-173          [-1, 672, 14, 14]           1,344\n",
      "            SiLU-174          [-1, 672, 14, 14]               0\n",
      "          Conv2d-175            [-1, 672, 7, 7]          16,800\n",
      "     BatchNorm2d-176            [-1, 672, 7, 7]           1,344\n",
      "            SiLU-177            [-1, 672, 7, 7]               0\n",
      "AdaptiveAvgPool2d-178            [-1, 672, 1, 1]               0\n",
      "          Conv2d-179             [-1, 28, 1, 1]          18,844\n",
      "            SiLU-180             [-1, 28, 1, 1]               0\n",
      "          Conv2d-181            [-1, 672, 1, 1]          19,488\n",
      "         Sigmoid-182            [-1, 672, 1, 1]               0\n",
      "SqueezeExcitation-183            [-1, 672, 7, 7]               0\n",
      "          Conv2d-184            [-1, 192, 7, 7]         129,024\n",
      "     BatchNorm2d-185            [-1, 192, 7, 7]             384\n",
      "          MBConv-186            [-1, 192, 7, 7]               0\n",
      "          Conv2d-187           [-1, 1152, 7, 7]         221,184\n",
      "     BatchNorm2d-188           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-189           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-190           [-1, 1152, 7, 7]          28,800\n",
      "     BatchNorm2d-191           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-192           [-1, 1152, 7, 7]               0\n",
      "AdaptiveAvgPool2d-193           [-1, 1152, 1, 1]               0\n",
      "          Conv2d-194             [-1, 48, 1, 1]          55,344\n",
      "            SiLU-195             [-1, 48, 1, 1]               0\n",
      "          Conv2d-196           [-1, 1152, 1, 1]          56,448\n",
      "         Sigmoid-197           [-1, 1152, 1, 1]               0\n",
      "SqueezeExcitation-198           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-199            [-1, 192, 7, 7]         221,184\n",
      "     BatchNorm2d-200            [-1, 192, 7, 7]             384\n",
      " StochasticDepth-201            [-1, 192, 7, 7]               0\n",
      "          MBConv-202            [-1, 192, 7, 7]               0\n",
      "          Conv2d-203           [-1, 1152, 7, 7]         221,184\n",
      "     BatchNorm2d-204           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-205           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-206           [-1, 1152, 7, 7]          28,800\n",
      "     BatchNorm2d-207           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-208           [-1, 1152, 7, 7]               0\n",
      "AdaptiveAvgPool2d-209           [-1, 1152, 1, 1]               0\n",
      "          Conv2d-210             [-1, 48, 1, 1]          55,344\n",
      "            SiLU-211             [-1, 48, 1, 1]               0\n",
      "          Conv2d-212           [-1, 1152, 1, 1]          56,448\n",
      "         Sigmoid-213           [-1, 1152, 1, 1]               0\n",
      "SqueezeExcitation-214           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-215            [-1, 192, 7, 7]         221,184\n",
      "     BatchNorm2d-216            [-1, 192, 7, 7]             384\n",
      " StochasticDepth-217            [-1, 192, 7, 7]               0\n",
      "          MBConv-218            [-1, 192, 7, 7]               0\n",
      "          Conv2d-219           [-1, 1152, 7, 7]         221,184\n",
      "     BatchNorm2d-220           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-221           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-222           [-1, 1152, 7, 7]          28,800\n",
      "     BatchNorm2d-223           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-224           [-1, 1152, 7, 7]               0\n",
      "AdaptiveAvgPool2d-225           [-1, 1152, 1, 1]               0\n",
      "          Conv2d-226             [-1, 48, 1, 1]          55,344\n",
      "            SiLU-227             [-1, 48, 1, 1]               0\n",
      "          Conv2d-228           [-1, 1152, 1, 1]          56,448\n",
      "         Sigmoid-229           [-1, 1152, 1, 1]               0\n",
      "SqueezeExcitation-230           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-231            [-1, 192, 7, 7]         221,184\n",
      "     BatchNorm2d-232            [-1, 192, 7, 7]             384\n",
      " StochasticDepth-233            [-1, 192, 7, 7]               0\n",
      "          MBConv-234            [-1, 192, 7, 7]               0\n",
      "          Conv2d-235           [-1, 1152, 7, 7]         221,184\n",
      "     BatchNorm2d-236           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-237           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-238           [-1, 1152, 7, 7]          10,368\n",
      "     BatchNorm2d-239           [-1, 1152, 7, 7]           2,304\n",
      "            SiLU-240           [-1, 1152, 7, 7]               0\n",
      "AdaptiveAvgPool2d-241           [-1, 1152, 1, 1]               0\n",
      "          Conv2d-242             [-1, 48, 1, 1]          55,344\n",
      "            SiLU-243             [-1, 48, 1, 1]               0\n",
      "          Conv2d-244           [-1, 1152, 1, 1]          56,448\n",
      "         Sigmoid-245           [-1, 1152, 1, 1]               0\n",
      "SqueezeExcitation-246           [-1, 1152, 7, 7]               0\n",
      "          Conv2d-247            [-1, 320, 7, 7]         368,640\n",
      "     BatchNorm2d-248            [-1, 320, 7, 7]             640\n",
      "          MBConv-249            [-1, 320, 7, 7]               0\n",
      "          Conv2d-250           [-1, 1280, 7, 7]         409,600\n",
      "     BatchNorm2d-251           [-1, 1280, 7, 7]           2,560\n",
      "            SiLU-252           [-1, 1280, 7, 7]               0\n",
      "AdaptiveAvgPool2d-253           [-1, 1280, 1, 1]               0\n",
      "         Dropout-254                 [-1, 1280]               0\n",
      "          Linear-255                 [-1, 1000]       1,281,000\n",
      "================================================================\n",
      "Total params: 5,288,548\n",
      "Trainable params: 5,288,548\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 173.65\n",
      "Params size (MB): 20.17\n",
      "Estimated Total Size (MB): 194.40\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "model = efficientnet_b0(pretrained=True)\n",
    "\n",
    "summary(model, (3, 224, 224))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_DIR = \"../../data\"\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "IMG_SIZE = (224, 224)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "batch_size = 4\n",
    "\n",
    "learning_rate = 1e-3\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "splitDataset(train_ratio=0.8, dataset_root=ROOT_DIR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardlize process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating ...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.51551778, 0.43288471, 0.44265668]),\n",
       " array([0.19281362, 0.1960019 , 0.20439348]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def CalcStdVsMean():\n",
    "    listImgs = []\n",
    "    print(\"Calculating ...\")\n",
    "    for folder in os.listdir(ROOT_DIR):\n",
    "        if folder != \"dataset_info.csv\":\n",
    "            for fileName in os.listdir(f\"{ROOT_DIR}/{folder}\"):\n",
    "                img = cv2.imread(f\"{ROOT_DIR}/{folder}/{fileName}\")\n",
    "                listImgs.append(cv2.resize(img, IMG_SIZE, interpolation = cv2.INTER_AREA))\n",
    "    listImgs = np.array(listImgs)\n",
    "    return (listImgs/255.0).mean((0, 1, 2)), (listImgs /255.0).std((0,1,2))\n",
    "\n",
    "MEAN, STD = CalcStdVsMean()\n",
    "\n",
    "MEAN, STD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Folder [100/100]: 100%|██████████| 100/100 [00:12<00:00,  7.89folder/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 100 data of type Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Folder [26/26]: 100%|██████████| 26/26 [00:03<00:00,  7.72folder/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 26 data of type Test\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_transforms = transforms.Compose([\n",
    "    MixupTransform(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Resize(IMG_SIZE,interpolation=InterpolationMode.BICUBIC),\n",
    "    transforms.Normalize(MEAN, STD)\n",
    "])\n",
    "\n",
    "\n",
    "train_dataset = Violence_Drone_Dataset(root_dir=ROOT_DIR,train=True, transform=data_transforms)\n",
    "test_dataset = Violence_Drone_Dataset(root_dir=ROOT_DIR,train=False, transform=data_transforms)\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, shuffle=True, batch_size=batch_size, drop_last=True)\n",
    "test_loader = DataLoader(dataset= test_dataset, shuffle=False, batch_size=batch_size, drop_last=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 0\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 1\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 2\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 3\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 4\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 5\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 6\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 7\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 8\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "EPOCH 9\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0])\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    print(\"EPOCH\",epoch)\n",
    "    for img, label in train_loader:\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "samples = iter(train_loader)\n",
    "\n",
    "imgs, labels = samples.__next__()\n",
    "\n",
    "print(imgs.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.AdamW(model.parameters(),\n",
    "                              lr=learning_rate,\n",
    "                              weight_decay=0.0005)\n",
    "\n",
    "exp_lr_scheduler = lr_scheduler.MultiStepLR(\n",
    "    optimizer,\n",
    "    milestones=[20, 40, 60, 80, 100],\n",
    "    gamma=0.2\n",
    ")\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "train_loss, train_accuracy = [], []\n",
    "val_loss, val_accuracy = [], []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(model, dataloader,epoch, epochs, train=True):\n",
    "    if train:\n",
    "        model.train()\n",
    "    else:\n",
    "        model.eval() \n",
    "    \n",
    "    \n",
    "    running_loss = 0.0\n",
    "    running_correct = 0\n",
    "    n_samples = 0\n",
    "    \n",
    "    print(\"Train\" if train else \"Val\")\n",
    "    \n",
    "    with tqdm(dataloader, unit='batch') as tepoch:\n",
    "        for images, labels in tepoch:    \n",
    "            \n",
    "            tepoch.set_description(f\"Epoch [{epoch}/{epochs}]\")\n",
    "                \n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "\n",
    "            running_loss += loss.item()\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            correct = (preds == labels).sum().item()\n",
    "\n",
    "            running_correct += (preds == labels).sum().item()\n",
    "\n",
    "            n_samples += labels.size(0)\n",
    "\n",
    "            if train:\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            \n",
    "            tepoch.set_postfix(loss=loss.item()/labels.size(0), accuracy=100.*correct/labels.size(0))\n",
    "            \n",
    "        process_loss = running_loss / n_samples\n",
    "        process_acc = 100. * running_correct / n_samples\n",
    "    \n",
    "    return process_loss, process_acc\n",
    "                "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [0/10]: 100%|██████████| 25/25 [01:38<00:00,  3.92s/batch, accuracy=100, loss=0.00119] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [0/10]: 100%|██████████| 7/7 [00:08<00:00,  1.18s/batch, accuracy=100, loss=0.000756]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [1/10]: 100%|██████████| 25/25 [01:31<00:00,  3.67s/batch, accuracy=100, loss=2.15e-6] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [1/10]: 100%|██████████| 7/7 [00:08<00:00,  1.28s/batch, accuracy=100, loss=0.384]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [2/10]: 100%|██████████| 25/25 [01:09<00:00,  2.78s/batch, accuracy=100, loss=8.87e-7]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [2/10]: 100%|██████████| 7/7 [00:04<00:00,  1.48batch/s, accuracy=100, loss=0.17]    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [3/10]: 100%|██████████| 25/25 [00:54<00:00,  2.19s/batch, accuracy=100, loss=2.41e-6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [3/10]: 100%|██████████| 7/7 [00:04<00:00,  1.51batch/s, accuracy=100, loss=0.381]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [4/10]: 100%|██████████| 25/25 [00:55<00:00,  2.21s/batch, accuracy=100, loss=1.87e-6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [4/10]: 100%|██████████| 7/7 [00:05<00:00,  1.39batch/s, accuracy=100, loss=0.369]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [5/10]: 100%|██████████| 25/25 [00:53<00:00,  2.13s/batch, accuracy=100, loss=0.000271]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [5/10]: 100%|██████████| 7/7 [00:04<00:00,  1.53batch/s, accuracy=100, loss=1.36]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [6/10]: 100%|██████████| 25/25 [00:54<00:00,  2.18s/batch, accuracy=100, loss=5.27e-6] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [6/10]: 100%|██████████| 7/7 [00:04<00:00,  1.50batch/s, accuracy=100, loss=0.000174]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [7/10]: 100%|██████████| 25/25 [01:49<00:00,  4.40s/batch, accuracy=100, loss=1.3e-6] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [7/10]: 100%|██████████| 7/7 [00:18<00:00,  2.64s/batch, accuracy=100, loss=0.000175]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [8/10]: 100%|██████████| 25/25 [03:04<00:00,  7.39s/batch, accuracy=100, loss=3.01e-6] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [8/10]: 100%|██████████| 7/7 [00:26<00:00,  3.85s/batch, accuracy=100, loss=0.000131]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [9/10]: 100%|██████████| 25/25 [02:02<00:00,  4.92s/batch, accuracy=100, loss=3.06e-6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [9/10]: 100%|██████████| 7/7 [00:10<00:00,  1.52s/batch, accuracy=100, loss=0.00034]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.540188614527384 minutes\n",
      "Model saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = model.to(device)\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    \n",
    "    train_epoch_loss, train_epoch_acc = fit(model, train_loader, epoch, epochs)\n",
    "    exp_lr_scheduler.step()\n",
    "    \n",
    "    val_epoch_loss, val_epoch_acc = fit(model, test_loader, epoch, epochs, train=False)\n",
    "    \n",
    "    train_loss.append(train_epoch_loss)\n",
    "    train_accuracy.append(train_epoch_acc)\n",
    "    \n",
    "    val_loss.append(val_epoch_loss)\n",
    "    val_accuracy.append(val_epoch_acc)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "print((end-start)/60, 'minutes')\n",
    "\n",
    "torch.save(model.state_dict(), \"last_weights_v2.pth\")\n",
    "print(\"Model saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotting(colors: tuple, labels : tuple, savefigName: str, data: tuple, figsize=(10, 7)):\n",
    "    train, val = data\n",
    "    color_train, color_val = colors\n",
    "    label_train, label_val = labels\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.plot(train, color='green', label=label_train)\n",
    "    plt.plot(val, color='blue', label=label_val)\n",
    "    plt.legend()\n",
    "    plt.savefig(savefigName)\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(colors=('orange', 'red'),\n",
    "         labels=(\"train loss\", \"val loss\"),\n",
    "         savefigName=\"loss.png\",\n",
    "         data=(train_loss, val_loss))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(colors=('green', 'blue'),\n",
    "         labels=(\"train acc\", \"val acc\"),\n",
    "         savefigName=\"acc.png\",\n",
    "         data=(train_accuracy, val_accuracy))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7 (tags/v3.9.7:1016ef3, Aug 30 2021, 20:19:38) [MSC v.1929 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9d2e2287b90f84c05b564773cad156a65fe051f83fa7c81a3ad23c3ed1bf9926"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}