Bottle_Cap_Detection_MobileNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "WeocNlrd7FT9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WeocNlrd7FT9",
    "outputId": "424c1bb7-50ca-4d1a-a936-65f5a83dcc3e"
   },
   "outputs": [],
   "source": [
    "#!pip install efficientnet_pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "later-timothy",
   "metadata": {
    "id": "later-timothy"
   },
   "outputs": [],
   "source": [
    "import torch, time, copy\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "from torchvision import datasets, models, transforms \n",
    "import os \n",
    "from PIL import Image\n",
    "from efficientnet_pytorch import EfficientNet\n",
    "from tqdm import tqdm\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "musical-elite",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "musical-elite",
    "outputId": "9104cb11-85b0-4873-a581-1631993e9af9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Capped', 'NoCap']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dobri\\Anaconda3\\envs\\HuggingFace Torch\\lib\\site-packages\\torchvision\\transforms\\transforms.py:1200: UserWarning: Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "image_size = (256,256)\n",
    "data_transforms={\n",
    "                        \"train\":    transforms.Compose\n",
    "                                            (\n",
    "                                                [\n",
    "                                                    transforms.Resize(image_size),\n",
    "                                                    transforms.ColorJitter(hue=.05, saturation=.05),\n",
    "                                                    transforms.RandomHorizontalFlip(),\n",
    "                                                    transforms.RandomRotation(20, resample=Image.BILINEAR),\n",
    "                                                    transforms.ToTensor(),\n",
    "                                                    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "                                                ]\n",
    "                                            ),\n",
    "\n",
    "                        \"val\":      transforms.Compose\n",
    "                                            (\n",
    "                                                [\n",
    "                                                    transforms.Resize(image_size),\n",
    "                                                    transforms.ToTensor(),\n",
    "                                                    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "                                                ]\n",
    "                                            )\n",
    "                }\n",
    "\n",
    " \n",
    "data_dir=r\"Dataset/\"\n",
    " \n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir,x),data_transforms[x]) for x in [\"train\", \"val\"]}\n",
    "\n",
    "dataloaders            =   {}\n",
    "dataloaders[\"train\"]   =   torch.utils.data.DataLoader(image_datasets[\"train\"], batch_size=32, shuffle=True, num_workers=2) \n",
    "dataloaders[\"val\"]     =   torch.utils.data.DataLoader(image_datasets[\"val\"], batch_size=32, shuffle=False, num_workers=2) \n",
    "\n",
    "dataset_sizes={x: len(image_datasets[x]) for x in [\"train\",\"val\"]}\n",
    " \n",
    "class_names=image_datasets[\"train\"].classes\n",
    "print(class_names)\n",
    "\n",
    "num_classes=len(class_names)\n",
    "device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    " \n",
    "val = {\"loss\":[],\"acc\":[]}\n",
    "train = {\"loss\":[],\"acc\":[]}\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cognitive-beverage",
   "metadata": {
    "id": "cognitive-beverage"
   },
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer , num_epochs=10):\n",
    "    start_time=time.time()\n",
    " \n",
    "    best_acc= 0.0\n",
    " \n",
    "    for epoch in range(num_epochs):\n",
    "        print(\"epoch{}/{}\".format(epoch,num_epochs-1))\n",
    "        print(\"-\"*10)\n",
    " \n",
    "        for phase in [\"train\", \"val\"]:\n",
    "            if phase ==\"train\":\n",
    "                model.train()\n",
    "            else:\n",
    "                model.eval()\n",
    " \n",
    "            running_loss=0.0\n",
    "            running_corrects=0.0\n",
    " \n",
    "            for inputs,labels in tqdm(dataloaders[phase]):\n",
    "                inputs=inputs.to(device)\n",
    "                labels=labels.to(device)\n",
    " \n",
    "                optimizer.zero_grad()\n",
    " \n",
    "                with torch.set_grad_enabled(phase==\"train\"):\n",
    "                    outputs=model(inputs)\n",
    "                    _,preds=torch.max(outputs,1)\n",
    "                    loss=criterion(outputs,labels)\n",
    " \n",
    "                    if phase == \"train\":\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    " \n",
    "                running_loss+=loss.item() * inputs.size(0)\n",
    "                running_corrects+=  torch.sum(preds==labels.data)\n",
    " \n",
    "            epoch_loss=running_loss/dataset_sizes[phase]\n",
    "            epoch_acc=running_corrects.double()/dataset_sizes[phase]\n",
    " \n",
    "            if phase == \"train\":\n",
    "              train[\"loss\"].append(epoch_loss)\n",
    "              train[\"acc\"].append(epoch_acc.item())\n",
    "            else:\n",
    "              val[\"loss\"].append(epoch_loss)\n",
    "              val[\"acc\"].append(epoch_acc.item())\n",
    " \n",
    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase,epoch_loss,epoch_acc))\n",
    " \n",
    "            if phase == \"val\" and epoch_acc>best_acc:\n",
    "                torch.save(model,\"./models/mobilenet-v2-best.pth\")\n",
    "                best_acc=epoch_acc\n",
    " \n",
    "    time_elapsed=time.time()- start_time\n",
    "    print(\"training completed in {:.0f}m {:.0f}s\".format(time_elapsed//60,time_elapsed%60))\n",
    "    print(\"best val accuracy: {:4f}\".format(best_acc))\n",
    " \n",
    "    return model\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mediterranean-double",
   "metadata": {
    "id": "mediterranean-double"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\dobri/.cache\\torch\\hub\\pytorch_vision_v0.9.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MobileNetV2(\n",
       "  (features): Sequential(\n",
       "    (0): ConvBNActivation(\n",
       "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU6(inplace=True)\n",
       "    )\n",
       "    (1): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
       "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (2): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (3): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
       "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (4): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
       "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
       "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (8): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (9): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (10): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (11): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (12): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (13): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (14): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
       "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (15): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (16): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (17): InvertedResidual(\n",
       "      (conv): Sequential(\n",
       "        (0): ConvBNActivation(\n",
       "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (1): ConvBNActivation(\n",
       "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU6(inplace=True)\n",
       "        )\n",
       "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (18): ConvBNActivation(\n",
       "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU6(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Dropout(p=0.2, inplace=False)\n",
       "    (1): Linear(in_features=1280, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = torch.hub.load('pytorch/vision:v0.9.0', 'mobilenet_v2', pretrained=True)\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "active-fountain",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "active-fountain",
    "outputId": "1d7fe0f1-fdb9-4240-ca88-487ea24e6335"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch0/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:20<00:00,  1.43it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.8900 Acc: 0.8977\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  2.86it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.1203 Acc: 0.9786\n",
      "epoch1/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.72it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0599 Acc: 0.9860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.28it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0005 Acc: 1.0000\n",
      "epoch2/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.76it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0084 Acc: 0.9968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.31it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0033 Acc: 1.0000\n",
      "epoch3/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:16<00:00,  1.78it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0117 Acc: 0.9946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.26it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0016 Acc: 1.0000\n",
      "epoch4/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.76it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0201 Acc: 0.9946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.35it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0003 Acc: 1.0000\n",
      "epoch5/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:16<00:00,  1.81it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0520 Acc: 0.9860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.29it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0016 Acc: 1.0000\n",
      "epoch6/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.76it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0304 Acc: 0.9935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.35it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0037 Acc: 1.0000\n",
      "epoch7/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:16<00:00,  1.77it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0128 Acc: 0.9935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.35it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0003 Acc: 1.0000\n",
      "epoch8/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:16<00:00,  1.78it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0028 Acc: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.29it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0012 Acc: 1.0000\n",
      "epoch9/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.74it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0067 Acc: 0.9989\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.14it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.1059 Acc: 0.9615\n",
      "epoch10/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.76it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.1153 Acc: 0.9688\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.07it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0571 Acc: 0.9786\n",
      "epoch11/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.71it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0362 Acc: 0.9914\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.31it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.1798 Acc: 0.9530\n",
      "epoch12/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.74it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0668 Acc: 0.9752\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.28it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0260 Acc: 0.9957\n",
      "epoch13/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:17<00:00,  1.74it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0195 Acc: 0.9946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.30it/s]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0016 Acc: 1.0000\n",
      "epoch14/14\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:16<00:00,  1.77it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.0097 Acc: 0.9957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.0005 Acc: 1.0000\n",
      "training completed in 4m 58s\n",
      "best val accuracy: 1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(),lr=1e-3)\n",
    "\n",
    "model_ft = train_model(model, criterion, optimizer, num_epochs=15)\n",
    " \n",
    "torch.save(model_ft,\"./models/mobilenet-v2-last.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "M0ZLJtF7KUA3",
   "metadata": {
    "id": "M0ZLJtF7KUA3"
   },
   "outputs": [],
   "source": [
    "model = torch.load(\"models/mobilenet-v2-best.pth\", map_location = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "MGuQDe2sLBbe",
   "metadata": {
    "id": "MGuQDe2sLBbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MobileNetV2(\n",
      "  (features): Sequential(\n",
      "    (0): ConvBNActivation(\n",
      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU6(inplace=True)\n",
      "    )\n",
      "    (1): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (2): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (3): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (4): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (5): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (6): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (7): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (8): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (9): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (10): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (11): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (12): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (13): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (14): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (15): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (16): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (17): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (18): ConvBNActivation(\n",
      "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU6(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.2, inplace=False)\n",
      "    (1): Linear(in_features=1280, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef9b4004",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Bottle-Cap Detection.ipynb",
   "provenance": []
  },
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}