plant_disease_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "drive.mount(\"/content/drive\", force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4z_Z5JH2NqjT",
        "outputId": "cd4d7d5c-6c6f-4fdc-9e80-8dceaf2d96fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
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        "id": "UpbnBaFPrdUY",
        "outputId": "2212d990-1a89-45e9-f5fd-564ee6bed456",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.7/dist-packages (1.5.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install torchsummary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
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        "tags": [],
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      "source": [
        "import os                       # for working with files\n",
        "import numpy as np              # for numerical computationss\n",
        "import pandas as pd             # for working with dataframes\n",
        "import torch                    # Pytorch module \n",
        "import matplotlib.pyplot as plt # for plotting informations on graph and images using tensors\n",
        "import torch.nn as nn           # for creating  neural networks\n",
        "from torch.utils.data import DataLoader # for dataloaders \n",
        "from PIL import Image           # for checking images\n",
        "import torch.nn.functional as F # for functions for calculating loss\n",
        "import torchvision.transforms as transforms   # for transforming images into tensors \n",
        "from torchvision.utils import make_grid       # for data checking\n",
        "from torchvision.datasets import ImageFolder  # for working with classes and images\n",
        "from torchsummary import summary              # for getting the summary of our model\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "id": "bTKtXg1CrdUd"
      },
      "outputs": [],
      "source": [
        "data_dir = r\"/content/drive/MyDrive/Rice and Wheat\"\n",
        "train_dir = data_dir + \"/train\"\n",
        "valid_dir = data_dir + \"/valid\"\n",
        "diseases = os.listdir(train_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "id": "qoCROxmurdUd",
        "outputId": "64c879bb-e5c7-45ec-ed3e-189b3cfc657c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Wheat___Septoria', 'Wheat___Healthy', 'Rice___Leaf_Smut', 'Rice___Bacterial_Leaf_Blight', 'Rice___Brown_Spot', 'Wheat___Stripe_Rust']\n"
          ]
        }
      ],
      "source": [
        "# printing the disease names\n",
        "print(diseases)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.064118,
          "end_time": "2020-12-15T06:53:05.416780",
          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "id": "ic_NnuQfrdUm"
      },
      "source": [
        "##  Data Preparation for training \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:53:05.548188Z",
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          "end_time": "2020-12-15T06:54:23.286647",
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          "status": "completed"
        },
        "tags": [],
        "id": "yHYexKMordUm"
      },
      "outputs": [],
      "source": [
        "# datasets for validation and training\n",
        "train = ImageFolder(train_dir, transform=transforms.ToTensor())\n",
        "valid = ImageFolder(valid_dir, transform=transforms.ToTensor()) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.064216,
          "end_time": "2020-12-15T06:54:23.673604",
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          "start_time": "2020-12-15T06:54:23.609388",
          "status": "completed"
        },
        "tags": [],
        "id": "o1b75oE7rdUo"
      },
      "source": [
        "#### Image shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:23.808409Z",
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          "shell.execute_reply": "2020-12-15T06:54:23.881570Z"
        },
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          "duration": 0.144539,
          "end_time": "2020-12-15T06:54:23.882452",
          "exception": false,
          "start_time": "2020-12-15T06:54:23.737913",
          "status": "completed"
        },
        "tags": [],
        "id": "STQmEXdSrdUo",
        "outputId": "3a876dd8-e9a7-4cd6-e8ac-cc65e8dc8e17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 256, 256]) 0\n"
          ]
        }
      ],
      "source": [
        "img, label = train[0]\n",
        "print(img.shape, label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:25.648270Z",
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        "tags": [],
        "id": "_hu_NgiLrdUt",
        "outputId": "1a6075c4-7fb9-4a46-9ea3-74b707102c86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f1425b255f0>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# Setting the seed value\n",
        "torch.manual_seed(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:25.813296Z",
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          "status": "completed"
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        "tags": [],
        "id": "OXYc4Q85rdUu"
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      "outputs": [],
      "source": [
        "# setting the batch size\n",
        "batch_size = 32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:26.144973Z",
          "iopub.status.busy": "2020-12-15T06:54:26.143969Z",
          "iopub.status.idle": "2020-12-15T06:54:26.146484Z",
          "shell.execute_reply": "2020-12-15T06:54:26.147104Z"
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          "duration": 0.097126,
          "end_time": "2020-12-15T06:54:26.147260",
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          "start_time": "2020-12-15T06:54:26.050134",
          "status": "completed"
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        "tags": [],
        "id": "9JodfdP4rdUu"
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      "outputs": [],
      "source": [
        "# DataLoaders for training and validation\n",
        "train_dl = DataLoader(train, batch_size, shuffle=True, num_workers=2, pin_memory=True)\n",
        "valid_dl = DataLoader(valid, batch_size, num_workers=2, pin_memory=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:26.459616Z",
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          "shell.execute_reply": "2020-12-15T06:54:26.461924Z"
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          "duration": 0.08434,
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          "status": "completed"
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        "tags": [],
        "id": "etDSJl4mrdUv"
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      "outputs": [],
      "source": [
        "# helper function to show a batch of training instances\n",
        "def show_batch(data):\n",
        "    for images, labels in data:\n",
        "        fig, ax = plt.subplots(figsize=(30, 30))\n",
        "        ax.set_xticks([]); ax.set_yticks([])\n",
        "        ax.imshow(make_grid(images, nrow=8).permute(1, 2, 0))\n",
        "        break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.148317,
          "end_time": "2020-12-15T06:54:33.881587",
          "exception": false,
          "start_time": "2020-12-15T06:54:33.733270",
          "status": "completed"
        },
        "tags": [],
        "id": "nzBfhY5MrdUw"
      },
      "source": [
        "#Modelling "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htJwMTabRmk7",
        "outputId": "5ed3912e-811a-4f60-dc25-e8272362599a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Jan 22 10:41:48 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.46       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   50C    P0    28W /  70W |  12306MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "execution": {
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        "id": "KjySUk83rdUw"
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      "outputs": [],
      "source": [
        "# for moving data into GPU (if available)\n",
        "def get_default_device():\n",
        "    \"\"\"Pick GPU if available, else CPU\"\"\"\n",
        "    if torch.cuda.is_available:\n",
        "        return torch.device(\"cuda\")\n",
        "    else:\n",
        "        return torch.device(\"cpu\")\n",
        "\n",
        "# for moving data to device (CPU or GPU)\n",
        "def to_device(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)\n",
        "\n",
        "# for loading in the device (GPU if available else CPU)\n",
        "class DeviceDataLoader():\n",
        "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "        \n",
        "    def __iter__(self):\n",
        "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
        "        for b in self.dl:\n",
        "            yield to_device(b, self.device)\n",
        "        \n",
        "    def __len__(self):\n",
        "        \"\"\"Number of batches\"\"\"\n",
        "        return len(self.dl)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "duration": 0.145958,
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          "status": "completed"
        },
        "tags": [],
        "id": "txMKWwcordUw"
      },
      "source": [
        "#### Some helper functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:35.382785Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "TclfDlfmrdUx",
        "outputId": "e366dca8-5b70-4ee1-f925-f3a5e8a27c01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "device = get_default_device()\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Np4uAmy9Rz_v",
        "outputId": "d51692c1-1f3e-4102-a173-0fe6085b1905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:35.989565Z",
          "iopub.status.busy": "2020-12-15T06:54:35.988272Z",
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          "status": "completed"
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        "tags": [],
        "id": "s0K-_zbWrdUy"
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      "outputs": [],
      "source": [
        "# Moving data into GPU\n",
        "train_dl = DeviceDataLoader(train_dl, device)\n",
        "valid_dl = DeviceDataLoader(valid_dl, device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.147431,
          "end_time": "2020-12-15T06:54:36.291718",
          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "id": "zAVofrxsrdUy"
      },
      "source": [
        "##Building the model architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:38.140386Z",
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        },
        "tags": [],
        "id": "6ixOU5zzrdU0"
      },
      "outputs": [],
      "source": [
        "class SimpleResidualBlock(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu1 = nn.ReLU()\n",
        "        self.conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu2 = nn.ReLU()\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out = self.conv1(x)\n",
        "        out = self.relu1(out)\n",
        "        out = self.conv2(out)\n",
        "        return self.relu2(out) + x # ReLU can be applied before or after adding the input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:39.367899Z",
          "iopub.status.busy": "2020-12-15T06:54:39.362848Z",
          "iopub.status.idle": "2020-12-15T06:54:39.389963Z",
          "shell.execute_reply": "2020-12-15T06:54:39.391114Z"
        },
        "papermill": {
          "duration": 0.532121,
          "end_time": "2020-12-15T06:54:39.391404",
          "exception": false,
          "start_time": "2020-12-15T06:54:38.859283",
          "status": "completed"
        },
        "tags": [],
        "id": "62-o1PIArdU0"
      },
      "outputs": [],
      "source": [
        "# for calculating the accuracy\n",
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "\n",
        "# base class for the model\n",
        "class ImageClassificationBase(nn.Module):\n",
        "    \n",
        "    def training_step(self, batch):\n",
        "        images, labels = batch\n",
        "        out = self(images)                  # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels) # Calculate loss\n",
        "        return loss\n",
        "    \n",
        "    def validation_step(self, batch):\n",
        "        images, labels = batch\n",
        "        out = self(images)                   # Generate prediction\n",
        "        loss = F.cross_entropy(out, labels)  # Calculate loss\n",
        "        acc = accuracy(out, labels)          # Calculate accuracy\n",
        "        return {\"val_loss\": loss.detach(), \"val_accuracy\": acc}\n",
        "    \n",
        "    def validation_epoch_end(self, outputs):\n",
        "        batch_losses = [x[\"val_loss\"] for x in outputs]\n",
        "        batch_accuracy = [x[\"val_accuracy\"] for x in outputs]\n",
        "        epoch_loss = torch.stack(batch_losses).mean()       # Combine loss  \n",
        "        epoch_accuracy = torch.stack(batch_accuracy).mean()\n",
        "        return {\"val_loss\": epoch_loss, \"val_accuracy\": epoch_accuracy} # Combine accuracies\n",
        "    \n",
        "    def epoch_end(self, epoch, result):\n",
        "        print(\"Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "            epoch, result['lrs'][-1], result['train_loss'], result['val_loss'], result['val_accuracy']))\n",
        "        "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.203515,
          "end_time": "2020-12-15T06:54:39.894829",
          "exception": false,
          "start_time": "2020-12-15T06:54:39.691314",
          "status": "completed"
        },
        "tags": [],
        "id": "fD8LLZP9rdU1"
      },
      "source": [
        "##Defining the final architecture of our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:40.222910Z",
          "iopub.status.busy": "2020-12-15T06:54:40.222083Z",
          "iopub.status.idle": "2020-12-15T06:54:40.226361Z",
          "shell.execute_reply": "2020-12-15T06:54:40.225720Z"
        },
        "papermill": {
          "duration": 0.17469,
          "end_time": "2020-12-15T06:54:40.226460",
          "exception": false,
          "start_time": "2020-12-15T06:54:40.051770",
          "status": "completed"
        },
        "tags": [],
        "id": "a5-fTc9drdU1"
      },
      "outputs": [],
      "source": [
        "# Architecture for training\n",
        "\n",
        "# convolution block with BatchNormalization\n",
        "def ConvBlock(in_channels, out_channels, pool=False):\n",
        "    layers = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n",
        "             nn.BatchNorm2d(out_channels),\n",
        "             nn.ReLU(inplace=True)]\n",
        "    if pool:\n",
        "        layers.append(nn.MaxPool2d(4))\n",
        "    return nn.Sequential(*layers)\n",
        "\n",
        "\n",
        "# resnet architecture \n",
        "class ResNet9(ImageClassificationBase):\n",
        "    def __init__(self, in_channels, num_diseases):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.conv1 = ConvBlock(in_channels, 64)\n",
        "        self.conv2 = ConvBlock(64, 128, pool=True) # out_dim : 128 x 64 x 64 \n",
        "        self.res1 = nn.Sequential(ConvBlock(128, 128), ConvBlock(128, 128))\n",
        "        \n",
        "        self.conv3 = ConvBlock(128, 256, pool=True) # out_dim : 256 x 16 x 16\n",
        "        self.conv4 = ConvBlock(256, 512, pool=True) # out_dim : 512 x 4 x 44\n",
        "        self.res2 = nn.Sequential(ConvBlock(512, 512), ConvBlock(512, 512))\n",
        "        \n",
        "        self.classifier = nn.Sequential(nn.MaxPool2d(4),\n",
        "                                       nn.Flatten(),\n",
        "                                       nn.Linear(512, num_diseases))\n",
        "        \n",
        "    def forward(self, xb): # xb is the loaded batch\n",
        "        out = self.conv1(xb)\n",
        "        out = self.conv2(out)\n",
        "        out = self.res1(out) + out\n",
        "        out = self.conv3(out)\n",
        "        out = self.conv4(out)\n",
        "        out = self.res2(out) + out\n",
        "        out = self.classifier(out)\n",
        "        return out        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:40.847626Z",
          "iopub.status.busy": "2020-12-15T06:54:40.846635Z",
          "iopub.status.idle": "2020-12-15T06:54:40.918385Z",
          "shell.execute_reply": "2020-12-15T06:54:40.918921Z"
        },
        "papermill": {
          "duration": 0.237477,
          "end_time": "2020-12-15T06:54:40.919048",
          "exception": false,
          "start_time": "2020-12-15T06:54:40.681571",
          "status": "completed"
        },
        "tags": [],
        "id": "Djzm-m-9rdU9",
        "outputId": "e0788779-d302-449c-fcc0-177c95a2204b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet9(\n",
              "  (conv1): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "  )\n",
              "  (conv2): Sequential(\n",
              "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (res1): Sequential(\n",
              "    (0): Sequential(\n",
              "      (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (1): Sequential(\n",
              "      (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (conv3): Sequential(\n",
              "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv4): Sequential(\n",
              "    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (res2): Sequential(\n",
              "    (0): Sequential(\n",
              "      (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (1): Sequential(\n",
              "      (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "    (1): Flatten(start_dim=1, end_dim=-1)\n",
              "    (2): Linear(in_features=512, out_features=6, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "# defining the model and moving it to the GPU\n",
        "model = to_device(ResNet9(3, len(train.classes)), device) \n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:41.526182Z",
          "iopub.status.busy": "2020-12-15T06:54:41.525236Z",
          "iopub.status.idle": "2020-12-15T06:54:42.238107Z",
          "shell.execute_reply": "2020-12-15T06:54:42.237349Z"
        },
        "papermill": {
          "duration": 0.865999,
          "end_time": "2020-12-15T06:54:42.238301",
          "exception": false,
          "start_time": "2020-12-15T06:54:41.372302",
          "status": "completed"
        },
        "tags": [],
        "id": "SZxn-zcWrdU-",
        "outputId": "b2966c05-c5e3-4bd6-ddb2-eb07f07791ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 64, 256, 256]           1,792\n",
            "       BatchNorm2d-2         [-1, 64, 256, 256]             128\n",
            "              ReLU-3         [-1, 64, 256, 256]               0\n",
            "            Conv2d-4        [-1, 128, 256, 256]          73,856\n",
            "       BatchNorm2d-5        [-1, 128, 256, 256]             256\n",
            "              ReLU-6        [-1, 128, 256, 256]               0\n",
            "         MaxPool2d-7          [-1, 128, 64, 64]               0\n",
            "            Conv2d-8          [-1, 128, 64, 64]         147,584\n",
            "       BatchNorm2d-9          [-1, 128, 64, 64]             256\n",
            "             ReLU-10          [-1, 128, 64, 64]               0\n",
            "           Conv2d-11          [-1, 128, 64, 64]         147,584\n",
            "      BatchNorm2d-12          [-1, 128, 64, 64]             256\n",
            "             ReLU-13          [-1, 128, 64, 64]               0\n",
            "           Conv2d-14          [-1, 256, 64, 64]         295,168\n",
            "      BatchNorm2d-15          [-1, 256, 64, 64]             512\n",
            "             ReLU-16          [-1, 256, 64, 64]               0\n",
            "        MaxPool2d-17          [-1, 256, 16, 16]               0\n",
            "           Conv2d-18          [-1, 512, 16, 16]       1,180,160\n",
            "      BatchNorm2d-19          [-1, 512, 16, 16]           1,024\n",
            "             ReLU-20          [-1, 512, 16, 16]               0\n",
            "        MaxPool2d-21            [-1, 512, 4, 4]               0\n",
            "           Conv2d-22            [-1, 512, 4, 4]       2,359,808\n",
            "      BatchNorm2d-23            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-24            [-1, 512, 4, 4]               0\n",
            "           Conv2d-25            [-1, 512, 4, 4]       2,359,808\n",
            "      BatchNorm2d-26            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-27            [-1, 512, 4, 4]               0\n",
            "        MaxPool2d-28            [-1, 512, 1, 1]               0\n",
            "          Flatten-29                  [-1, 512]               0\n",
            "           Linear-30                    [-1, 6]           3,078\n",
            "================================================================\n",
            "Total params: 6,573,318\n",
            "Trainable params: 6,573,318\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.75\n",
            "Forward/backward pass size (MB): 343.95\n",
            "Params size (MB): 25.08\n",
            "Estimated Total Size (MB): 369.77\n",
            "----------------------------------------------------------------\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# getting summary of the model\n",
        "INPUT_SHAPE = (3, 256, 256)\n",
        "print(summary(model.cuda(), (INPUT_SHAPE)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.14664,
          "end_time": "2020-12-15T06:54:42.545891",
          "exception": false,
          "start_time": "2020-12-15T06:54:42.399251",
          "status": "completed"
        },
        "tags": [],
        "id": "OeXD0GxtrdU-"
      },
      "source": [
        "#Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:43.464522Z",
          "iopub.status.busy": "2020-12-15T06:54:43.463494Z",
          "iopub.status.idle": "2020-12-15T06:54:43.466732Z",
          "shell.execute_reply": "2020-12-15T06:54:43.466153Z"
        },
        "papermill": {
          "duration": 0.169191,
          "end_time": "2020-12-15T06:54:43.466833",
          "exception": false,
          "start_time": "2020-12-15T06:54:43.297642",
          "status": "completed"
        },
        "tags": [],
        "id": "--XNJRhnrdU_"
      },
      "outputs": [],
      "source": [
        "# for training\n",
        "@torch.no_grad()\n",
        "def evaluate(model, val_loader):\n",
        "    model.eval()\n",
        "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
        "    return model.validation_epoch_end(outputs)\n",
        "\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    for param_group in optimizer.param_groups:\n",
        "        return param_group['lr']\n",
        "    \n",
        "\n",
        "def fit_OneCycle(epochs, max_lr, model, train_loader, val_loader, weight_decay=0,\n",
        "                grad_clip=None, opt_func=torch.optim.SGD):\n",
        "    torch.cuda.empty_cache()\n",
        "    history = []\n",
        "    \n",
        "    optimizer = opt_func(model.parameters(), max_lr, weight_decay=weight_decay)\n",
        "    # scheduler for one cycle learniing rate\n",
        "    sched = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, epochs=epochs, steps_per_epoch=len(train_loader))\n",
        "    \n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "        # Training\n",
        "        model.train()\n",
        "        train_losses = []\n",
        "        lrs = []\n",
        "        for batch in train_loader:\n",
        "            loss = model.training_step(batch)\n",
        "            train_losses.append(loss)\n",
        "            loss.backward()\n",
        "            \n",
        "            # gradient clipping\n",
        "            if grad_clip: \n",
        "                nn.utils.clip_grad_value_(model.parameters(), grad_clip)\n",
        "                \n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "            # recording and updating learning rates\n",
        "            lrs.append(get_lr(optimizer))\n",
        "            sched.step()\n",
        "            \n",
        "    \n",
        "        # validation\n",
        "        result = evaluate(model, val_loader)\n",
        "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "        result['lrs'] = lrs\n",
        "        model.epoch_end(epoch, result)\n",
        "        history.append(result)\n",
        "        \n",
        "    return history\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:54:44.229384Z",
          "iopub.status.busy": "2020-12-15T06:54:44.228659Z",
          "iopub.status.idle": "2020-12-15T06:56:16.949501Z",
          "shell.execute_reply": "2020-12-15T06:56:16.948857Z"
        },
        "papermill": {
          "duration": 93.028639,
          "end_time": "2020-12-15T06:56:16.949629",
          "exception": false,
          "start_time": "2020-12-15T06:54:43.920990",
          "status": "completed"
        },
        "tags": [],
        "id": "-hAMh5FmrdU_",
        "outputId": "b5a0e5ed-17c3-48d4-91d7-bf89ca33484e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 168 ms, sys: 75.9 ms, total: 244 ms\n",
            "Wall time: 440 ms\n"
          ]
        }
      ],
      "source": [
        "#checking our validation loss and accuracy\n",
        "%%time\n",
        "history = [evaluate(model, valid_dl)]\n",
        "history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:56:17.600661Z",
          "iopub.status.busy": "2020-12-15T06:56:17.598919Z",
          "iopub.status.idle": "2020-12-15T06:56:17.601325Z",
          "shell.execute_reply": "2020-12-15T06:56:17.601787Z"
        },
        "papermill": {
          "duration": 0.162208,
          "end_time": "2020-12-15T06:56:17.601908",
          "exception": false,
          "start_time": "2020-12-15T06:56:17.439700",
          "status": "completed"
        },
        "tags": [],
        "id": "N7CiCprcrdVA"
      },
      "outputs": [],
      "source": [
        "epochs = 9\n",
        "max_lr = 0.01\n",
        "grad_clip = 0.1\n",
        "weight_decay = 1e-4\n",
        "opt_func = torch.optim.Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T06:56:18.447829Z",
          "iopub.status.busy": "2020-12-15T06:56:18.446899Z",
          "iopub.status.idle": "2020-12-15T07:16:12.440007Z",
          "shell.execute_reply": "2020-12-15T07:16:12.440757Z"
        },
        "papermill": {
          "duration": 1194.323015,
          "end_time": "2020-12-15T07:16:12.440924",
          "exception": false,
          "start_time": "2020-12-15T06:56:18.117909",
          "status": "completed"
        },
        "tags": [],
        "id": "PX38BeoWrdVB",
        "outputId": "0f9e452e-3344-4a90-92fb-9ed3d9365c1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [0], last_lr: 0.00308, train_loss: 0.9197, val_loss: 2.4216, val_acc: 0.1875\n",
            "Epoch [1], last_lr: 0.00842, train_loss: 0.5890, val_loss: 2.0020, val_acc: 0.7344\n",
            "Epoch [2], last_lr: 0.00994, train_loss: 0.6868, val_loss: 21.2716, val_acc: 0.1250\n",
            "Epoch [3], last_lr: 0.00899, train_loss: 0.8037, val_loss: 3.7619, val_acc: 0.7031\n",
            "Epoch [4], last_lr: 0.00706, train_loss: 0.4632, val_loss: 1.1386, val_acc: 0.2969\n",
            "Epoch [5], last_lr: 0.00463, train_loss: 0.3724, val_loss: 0.6646, val_acc: 0.8594\n",
            "Epoch [6], last_lr: 0.00229, train_loss: 0.1296, val_loss: 0.5123, val_acc: 0.8281\n",
            "Epoch [7], last_lr: 0.00061, train_loss: 0.0806, val_loss: 0.5101, val_acc: 0.8906\n",
            "Epoch [8], last_lr: 0.00000, train_loss: 0.0562, val_loss: 0.4853, val_acc: 0.8906\n",
            "CPU times: user 32 s, sys: 30.8 s, total: 1min 2s\n",
            "Wall time: 1min 9s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = fit_OneCycle(epochs, max_lr, model, train_dl, valid_dl, \n",
        "                             grad_clip=grad_clip, \n",
        "                             weight_decay=1e-4, \n",
        "                             opt_func=opt_func)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.169551,
          "end_time": "2020-12-15T07:16:13.107269",
          "exception": false,
          "start_time": "2020-12-15T07:16:12.937718",
          "status": "completed"
        },
        "tags": [],
        "id": "bxPa4q1JrdVB"
      },
      "source": [
        "#Plotting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.164374,
          "end_time": "2020-12-15T07:16:13.436479",
          "exception": false,
          "start_time": "2020-12-15T07:16:13.272105",
          "status": "completed"
        },
        "tags": [],
        "id": "ZmBOL4qprdVB"
      },
      "source": [
        "#### Helper functions for plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:13.764368Z",
          "iopub.status.busy": "2020-12-15T07:16:13.763714Z",
          "iopub.status.idle": "2020-12-15T07:16:13.768095Z",
          "shell.execute_reply": "2020-12-15T07:16:13.767310Z"
        },
        "papermill": {
          "duration": 0.17176,
          "end_time": "2020-12-15T07:16:13.768238",
          "exception": false,
          "start_time": "2020-12-15T07:16:13.596478",
          "status": "completed"
        },
        "tags": [],
        "id": "Cp7YgthYrdVB"
      },
      "outputs": [],
      "source": [
        "def plot_accuracies(history):\n",
        "    accuracies = [x['val_accuracy'] for x in history]\n",
        "    plt.plot(accuracies, '-x')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.title('Accuracy vs. No. of epochs');\n",
        "\n",
        "def plot_losses(history):\n",
        "    train_losses = [x.get('train_loss') for x in history]\n",
        "    val_losses = [x['val_loss'] for x in history]\n",
        "    plt.plot(train_losses, '-bx')\n",
        "    plt.plot(val_losses, '-rx')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss')\n",
        "    plt.legend(['Training', 'Validation'])\n",
        "    plt.title('Loss vs. No. of epochs');\n",
        "    \n",
        "def plot_lrs(history):\n",
        "    lrs = np.concatenate([x.get('lrs', []) for x in history])\n",
        "    plt.plot(lrs)\n",
        "    plt.xlabel('Batch no.')\n",
        "    plt.ylabel('Learning rate')\n",
        "    plt.title('Learning Rate vs. Batch no.');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.156803,
          "end_time": "2020-12-15T07:16:14.082237",
          "exception": false,
          "start_time": "2020-12-15T07:16:13.925434",
          "status": "completed"
        },
        "tags": [],
        "id": "kGIgSO3prdVC"
      },
      "source": [
        "## Validation Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:14.408742Z",
          "iopub.status.busy": "2020-12-15T07:16:14.407718Z",
          "iopub.status.idle": "2020-12-15T07:16:14.572814Z",
          "shell.execute_reply": "2020-12-15T07:16:14.573361Z"
        },
        "papermill": {
          "duration": 0.331211,
          "end_time": "2020-12-15T07:16:14.573505",
          "exception": false,
          "start_time": "2020-12-15T07:16:14.242294",
          "status": "completed"
        },
        "tags": [],
        "id": "ZgNWuTtBrdVC",
        "outputId": "dc2dce48-4344-4c08-f4c9-35a60f12ab2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_accuracies(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.163962,
          "end_time": "2020-12-15T07:16:14.905941",
          "exception": false,
          "start_time": "2020-12-15T07:16:14.741979",
          "status": "completed"
        },
        "tags": [],
        "id": "b5zCZ03ardVC"
      },
      "source": [
        "## Validation loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:15.243562Z",
          "iopub.status.busy": "2020-12-15T07:16:15.242443Z",
          "iopub.status.idle": "2020-12-15T07:16:15.434328Z",
          "shell.execute_reply": "2020-12-15T07:16:15.433718Z"
        },
        "papermill": {
          "duration": 0.366892,
          "end_time": "2020-12-15T07:16:15.434461",
          "exception": false,
          "start_time": "2020-12-15T07:16:15.067569",
          "status": "completed"
        },
        "tags": [],
        "id": "5YA2TpFordVD",
        "outputId": "41136605-c2b7-498c-ea78-e62935399b1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_losses(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.163691,
          "end_time": "2020-12-15T07:16:16.647423",
          "exception": false,
          "start_time": "2020-12-15T07:16:16.483732",
          "status": "completed"
        },
        "tags": [],
        "id": "jAAvr7O7rdVD"
      },
      "source": [
        "#Testing model on test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:17.486559Z",
          "iopub.status.busy": "2020-12-15T07:16:17.485301Z",
          "iopub.status.idle": "2020-12-15T07:16:17.501849Z",
          "shell.execute_reply": "2020-12-15T07:16:17.503584Z"
        },
        "papermill": {
          "duration": 0.287752,
          "end_time": "2020-12-15T07:16:17.503784",
          "exception": false,
          "start_time": "2020-12-15T07:16:17.216032",
          "status": "completed"
        },
        "tags": [],
        "id": "F4HYnO33rdVE"
      },
      "outputs": [],
      "source": [
        "test_dir = \"/content/drive/MyDrive/Rice and Wheat/test\"\n",
        "test = ImageFolder(test_dir, transform=transforms.ToTensor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:17.927154Z",
          "iopub.status.busy": "2020-12-15T07:16:17.925594Z",
          "iopub.status.idle": "2020-12-15T07:16:17.930241Z",
          "shell.execute_reply": "2020-12-15T07:16:17.930740Z"
        },
        "papermill": {
          "duration": 0.177962,
          "end_time": "2020-12-15T07:16:17.930865",
          "exception": false,
          "start_time": "2020-12-15T07:16:17.752903",
          "status": "completed"
        },
        "tags": [],
        "id": "s6t_npWdrdVE",
        "outputId": "e5b2ff44-5b82-4511-fc85-45ae01aa7021",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Rice___Bacterial_Leaf_Blight',\n",
              " 'Rice___Brown_Spot',\n",
              " 'Rice___Leaf_Smut',\n",
              " 'Wheat___Healthy',\n",
              " 'Wheat___Septoria',\n",
              " 'Wheat___Stripe_Rust']"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "test_images = sorted(os.listdir(test_dir)) # since images in test folder are in alphabetical order\n",
        "test_images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:18.277682Z",
          "iopub.status.busy": "2020-12-15T07:16:18.275745Z",
          "iopub.status.idle": "2020-12-15T07:16:18.278376Z",
          "shell.execute_reply": "2020-12-15T07:16:18.278875Z"
        },
        "papermill": {
          "duration": 0.17446,
          "end_time": "2020-12-15T07:16:18.278995",
          "exception": false,
          "start_time": "2020-12-15T07:16:18.104535",
          "status": "completed"
        },
        "tags": [],
        "id": "AhjXLx4IrdVE"
      },
      "outputs": [],
      "source": [
        "def predict_image(img, model):\n",
        "    \"\"\"Converts image to array and return the predicted class\n",
        "        with highest probability\"\"\"\n",
        "    # Convert to a batch of 1\n",
        "    xb = to_device(img.unsqueeze(0), device)\n",
        "    # Get predictions from model\n",
        "    yb = model(xb)\n",
        "    # Pick index with highest probability\n",
        "    _, preds  = torch.max(yb, dim=1)\n",
        "    # Retrieve the class label\n",
        "\n",
        "    return train.classes[preds[0].item()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:18.601686Z",
          "iopub.status.busy": "2020-12-15T07:16:18.600998Z",
          "iopub.status.idle": "2020-12-15T07:16:18.784752Z",
          "shell.execute_reply": "2020-12-15T07:16:18.784113Z"
        },
        "papermill": {
          "duration": 0.348624,
          "end_time": "2020-12-15T07:16:18.784870",
          "exception": false,
          "start_time": "2020-12-15T07:16:18.436246",
          "status": "completed"
        },
        "tags": [],
        "id": "_hxo_x5CrdVF",
        "outputId": "ace061cf-5d67-4c2c-821e-2c47d1db8edd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: Rice___Bacterial_Leaf_Blight , Predicted: Rice___Bacterial_Leaf_Blight\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# predicting first image\n",
        "img, label = test[0]\n",
        "plt.imshow(img.permute(1, 2, 0))\n",
        "print('Label:', test_images[0], ', Predicted:', predict_image(img, model))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:19.123586Z",
          "iopub.status.busy": "2020-12-15T07:16:19.122676Z",
          "iopub.status.idle": "2020-12-15T07:16:19.565853Z",
          "shell.execute_reply": "2020-12-15T07:16:19.566653Z"
        },
        "papermill": {
          "duration": 0.612956,
          "end_time": "2020-12-15T07:16:19.566789",
          "exception": false,
          "start_time": "2020-12-15T07:16:18.953833",
          "status": "completed"
        },
        "tags": [],
        "id": "V0vn8GPFrdVF",
        "outputId": "bd89443a-9251-41c5-a0a9-bf1e3188dfc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: Rice___Bacterial_Leaf_Blight , Predicted: Rice___Bacterial_Leaf_Blight\n",
            "Label: Rice___Brown_Spot , Predicted: Rice___Brown_Spot\n",
            "Label: Rice___Leaf_Smut , Predicted: Wheat___Healthy\n",
            "Label: Wheat___Healthy , Predicted: Wheat___Healthy\n",
            "Label: Wheat___Septoria , Predicted: Wheat___Septoria\n",
            "Label: Wheat___Stripe_Rust , Predicted: Wheat___Stripe_Rust\n"
          ]
        }
      ],
      "source": [
        "# getting all predictions (actual label vs predicted)\n",
        "for i, (img, label) in enumerate(test):\n",
        "    print('Label:', test_images[i], ', Predicted:', predict_image(img, model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.167812,
          "end_time": "2020-12-15T07:16:20.231512",
          "exception": false,
          "start_time": "2020-12-15T07:16:20.063700",
          "status": "completed"
        },
        "tags": [],
        "id": "EEJP_ZcPrdVF"
      },
      "source": [
        "# Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "VqJs_HpCZPSQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-15T07:16:21.236912Z",
          "iopub.status.busy": "2020-12-15T07:16:21.236137Z",
          "iopub.status.idle": "2020-12-15T07:16:21.302803Z",
          "shell.execute_reply": "2020-12-15T07:16:21.302251Z"
        },
        "papermill": {
          "duration": 0.241697,
          "end_time": "2020-12-15T07:16:21.302920",
          "exception": false,
          "start_time": "2020-12-15T07:16:21.061223",
          "status": "completed"
        },
        "tags": [],
        "id": "hRb5HtpordVG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "128c62b8-b414-45ca-e641-2533dd035d4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function BufferedWriter.close>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "pickle_out = open(\"model.pkl\",\"wb\")\n",
        "pickle.dump(model,pickle_out)\n",
        "pickle_out.close"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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