Comparision_AlexNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8994e6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import models\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from PIL import Image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45d6dafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AlexNet',\n",
       " 'AlexNet_Weights',\n",
       " 'ConvNeXt',\n",
       " 'ConvNeXt_Base_Weights',\n",
       " 'ConvNeXt_Large_Weights',\n",
       " 'ConvNeXt_Small_Weights',\n",
       " 'ConvNeXt_Tiny_Weights',\n",
       " 'DenseNet',\n",
       " 'DenseNet121_Weights',\n",
       " 'DenseNet161_Weights',\n",
       " 'DenseNet169_Weights',\n",
       " 'DenseNet201_Weights',\n",
       " 'EfficientNet',\n",
       " 'EfficientNet_B0_Weights',\n",
       " 'EfficientNet_B1_Weights',\n",
       " 'EfficientNet_B2_Weights',\n",
       " 'EfficientNet_B3_Weights',\n",
       " 'EfficientNet_B4_Weights',\n",
       " 'EfficientNet_B5_Weights',\n",
       " 'EfficientNet_B6_Weights',\n",
       " 'EfficientNet_B7_Weights',\n",
       " 'EfficientNet_V2_L_Weights',\n",
       " 'EfficientNet_V2_M_Weights',\n",
       " 'EfficientNet_V2_S_Weights',\n",
       " 'GoogLeNet',\n",
       " 'GoogLeNetOutputs',\n",
       " 'GoogLeNet_Weights',\n",
       " 'Inception3',\n",
       " 'InceptionOutputs',\n",
       " 'Inception_V3_Weights',\n",
       " 'MNASNet',\n",
       " 'MNASNet0_5_Weights',\n",
       " 'MNASNet0_75_Weights',\n",
       " 'MNASNet1_0_Weights',\n",
       " 'MNASNet1_3_Weights',\n",
       " 'MaxVit',\n",
       " 'MaxVit_T_Weights',\n",
       " 'MobileNetV2',\n",
       " 'MobileNetV3',\n",
       " 'MobileNet_V2_Weights',\n",
       " 'MobileNet_V3_Large_Weights',\n",
       " 'MobileNet_V3_Small_Weights',\n",
       " 'RegNet',\n",
       " 'RegNet_X_16GF_Weights',\n",
       " 'RegNet_X_1_6GF_Weights',\n",
       " 'RegNet_X_32GF_Weights',\n",
       " 'RegNet_X_3_2GF_Weights',\n",
       " 'RegNet_X_400MF_Weights',\n",
       " 'RegNet_X_800MF_Weights',\n",
       " 'RegNet_X_8GF_Weights',\n",
       " 'RegNet_Y_128GF_Weights',\n",
       " 'RegNet_Y_16GF_Weights',\n",
       " 'RegNet_Y_1_6GF_Weights',\n",
       " 'RegNet_Y_32GF_Weights',\n",
       " 'RegNet_Y_3_2GF_Weights',\n",
       " 'RegNet_Y_400MF_Weights',\n",
       " 'RegNet_Y_800MF_Weights',\n",
       " 'RegNet_Y_8GF_Weights',\n",
       " 'ResNeXt101_32X8D_Weights',\n",
       " 'ResNeXt101_64X4D_Weights',\n",
       " 'ResNeXt50_32X4D_Weights',\n",
       " 'ResNet',\n",
       " 'ResNet101_Weights',\n",
       " 'ResNet152_Weights',\n",
       " 'ResNet18_Weights',\n",
       " 'ResNet34_Weights',\n",
       " 'ResNet50_Weights',\n",
       " 'ShuffleNetV2',\n",
       " 'ShuffleNet_V2_X0_5_Weights',\n",
       " 'ShuffleNet_V2_X1_0_Weights',\n",
       " 'ShuffleNet_V2_X1_5_Weights',\n",
       " 'ShuffleNet_V2_X2_0_Weights',\n",
       " 'SqueezeNet',\n",
       " 'SqueezeNet1_0_Weights',\n",
       " 'SqueezeNet1_1_Weights',\n",
       " 'SwinTransformer',\n",
       " 'Swin_B_Weights',\n",
       " 'Swin_S_Weights',\n",
       " 'Swin_T_Weights',\n",
       " 'Swin_V2_B_Weights',\n",
       " 'Swin_V2_S_Weights',\n",
       " 'Swin_V2_T_Weights',\n",
       " 'VGG',\n",
       " 'VGG11_BN_Weights',\n",
       " 'VGG11_Weights',\n",
       " 'VGG13_BN_Weights',\n",
       " 'VGG13_Weights',\n",
       " 'VGG16_BN_Weights',\n",
       " 'VGG16_Weights',\n",
       " 'VGG19_BN_Weights',\n",
       " 'VGG19_Weights',\n",
       " 'ViT_B_16_Weights',\n",
       " 'ViT_B_32_Weights',\n",
       " 'ViT_H_14_Weights',\n",
       " 'ViT_L_16_Weights',\n",
       " 'ViT_L_32_Weights',\n",
       " 'VisionTransformer',\n",
       " 'Wide_ResNet101_2_Weights',\n",
       " 'Wide_ResNet50_2_Weights',\n",
       " '_GoogLeNetOutputs',\n",
       " '_InceptionOutputs',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '_api',\n",
       " '_meta',\n",
       " '_utils',\n",
       " 'alexnet',\n",
       " 'convnext',\n",
       " 'convnext_base',\n",
       " 'convnext_large',\n",
       " 'convnext_small',\n",
       " 'convnext_tiny',\n",
       " 'densenet',\n",
       " 'densenet121',\n",
       " 'densenet161',\n",
       " 'densenet169',\n",
       " 'densenet201',\n",
       " 'detection',\n",
       " 'efficientnet',\n",
       " 'efficientnet_b0',\n",
       " 'efficientnet_b1',\n",
       " 'efficientnet_b2',\n",
       " 'efficientnet_b3',\n",
       " 'efficientnet_b4',\n",
       " 'efficientnet_b5',\n",
       " 'efficientnet_b6',\n",
       " 'efficientnet_b7',\n",
       " 'efficientnet_v2_l',\n",
       " 'efficientnet_v2_m',\n",
       " 'efficientnet_v2_s',\n",
       " 'get_model',\n",
       " 'get_model_builder',\n",
       " 'get_model_weights',\n",
       " 'get_weight',\n",
       " 'googlenet',\n",
       " 'inception',\n",
       " 'inception_v3',\n",
       " 'list_models',\n",
       " 'maxvit',\n",
       " 'maxvit_t',\n",
       " 'mnasnet',\n",
       " 'mnasnet0_5',\n",
       " 'mnasnet0_75',\n",
       " 'mnasnet1_0',\n",
       " 'mnasnet1_3',\n",
       " 'mobilenet',\n",
       " 'mobilenet_v2',\n",
       " 'mobilenet_v3_large',\n",
       " 'mobilenet_v3_small',\n",
       " 'mobilenetv2',\n",
       " 'mobilenetv3',\n",
       " 'optical_flow',\n",
       " 'quantization',\n",
       " 'regnet',\n",
       " 'regnet_x_16gf',\n",
       " 'regnet_x_1_6gf',\n",
       " 'regnet_x_32gf',\n",
       " 'regnet_x_3_2gf',\n",
       " 'regnet_x_400mf',\n",
       " 'regnet_x_800mf',\n",
       " 'regnet_x_8gf',\n",
       " 'regnet_y_128gf',\n",
       " 'regnet_y_16gf',\n",
       " 'regnet_y_1_6gf',\n",
       " 'regnet_y_32gf',\n",
       " 'regnet_y_3_2gf',\n",
       " 'regnet_y_400mf',\n",
       " 'regnet_y_800mf',\n",
       " 'regnet_y_8gf',\n",
       " 'resnet',\n",
       " 'resnet101',\n",
       " 'resnet152',\n",
       " 'resnet18',\n",
       " 'resnet34',\n",
       " 'resnet50',\n",
       " 'resnext101_32x8d',\n",
       " 'resnext101_64x4d',\n",
       " 'resnext50_32x4d',\n",
       " 'segmentation',\n",
       " 'shufflenet_v2_x0_5',\n",
       " 'shufflenet_v2_x1_0',\n",
       " 'shufflenet_v2_x1_5',\n",
       " 'shufflenet_v2_x2_0',\n",
       " 'shufflenetv2',\n",
       " 'squeezenet',\n",
       " 'squeezenet1_0',\n",
       " 'squeezenet1_1',\n",
       " 'swin_b',\n",
       " 'swin_s',\n",
       " 'swin_t',\n",
       " 'swin_transformer',\n",
       " 'swin_v2_b',\n",
       " 'swin_v2_s',\n",
       " 'swin_v2_t',\n",
       " 'vgg',\n",
       " 'vgg11',\n",
       " 'vgg11_bn',\n",
       " 'vgg13',\n",
       " 'vgg13_bn',\n",
       " 'vgg16',\n",
       " 'vgg16_bn',\n",
       " 'vgg19',\n",
       " 'vgg19_bn',\n",
       " 'video',\n",
       " 'vision_transformer',\n",
       " 'vit_b_16',\n",
       " 'vit_b_32',\n",
       " 'vit_h_14',\n",
       " 'vit_l_16',\n",
       " 'vit_l_32',\n",
       " 'wide_resnet101_2',\n",
       " 'wide_resnet50_2']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the models available in torchvision\n",
    "dir(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41a2d1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "\n",
    "transform = transforms.Compose([            #[1]\n",
    " transforms.Resize(256),                    #[2]\n",
    " transforms.CenterCrop(224),                #[3]\n",
    " transforms.ToTensor(),                     #[4]\n",
    " transforms.Normalize(                      #[5]\n",
    " mean=[0.485, 0.456, 0.406],                #[6]\n",
    " std=[0.229, 0.224, 0.225]                  #[7]\n",
    " )])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e23a887c",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-11-a1e4944ddce7>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-11-a1e4944ddce7>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    wget https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\u001b[0m\n\u001b[1;37m             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "wget https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fae8f38",
   "metadata": {},
   "source": [
    "## Loading Image and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9913c2f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open(\"./new_mage/Generation_16.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e25fb679",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_t = transform(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aca98cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_t = torch.unsqueeze(img_t, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88cf560c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\umarr\\anaconda3\\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\\umarr\\anaconda3\\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=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "alexnet = models.alexnet(pretrained=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29fd9cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AlexNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
       "  (classifier): Sequential(\n",
       "    (0): Dropout(p=0.5, inplace=False)\n",
       "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
       "    (2): ReLU(inplace=True)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alexnet.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07c8ddf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = alexnet(batch_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37881c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('imagenet_classes.txt') as f:\n",
    "  classes = [line.strip() for line in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d38ded1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
       "  99.63604736328125),\n",
       " ('lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n",
       "  0.1641465425491333),\n",
       " ('indri, indris, Indri indri, Indri brevicaudatus', 0.0280821081250906),\n",
       " ('black-footed ferret, ferret, Mustela nigripes', 0.021337170153856277),\n",
       " ('langur', 0.01537530031055212)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, indices = torch.sort(out, descending=True)\n",
    "percentage = torch.nn.functional.softmax(out, dim=1)[0] * 100\n",
    "[(classes[idx], percentage[idx].item()) for idx in indices[0][:5]]"
   ]
  }
 ],
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