denoising_images.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "denoising_images.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMG9PjYiyJdl5NNSvomUfxE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Davidportlouis/denoising_images/blob/master/denoising_images.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aoUQSMLsHfHr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "import torch\n",
        "import numpy as np\n",
        "from torch import nn,optim\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB4ERwgaK1Dm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_batch(img_batch,labels):\n",
        "  fig = plt.figure(figsize=(20,4))\n",
        "  for idx in range(len(img_batch)):\n",
        "    ax = fig.add_subplot(2,len(img_batch)/2,idx+1,xticks=[],yticks=[])\n",
        "    ax.imshow(img_batch[idx].numpy().squeeze(),cmap=\"gray\")\n",
        "    ax.set_title(f\"Class: {classes[labels[idx].item()]}\")\n",
        "\n",
        "transform = transforms.ToTensor()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pW6plnrTO4xH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = datasets.FashionMNIST(root=\"./\",train=True,transform=transform,download=True)\n",
        "test_data = datasets.FashionMNIST(root=\"./\",train=False,transform=transform,download=True)\n",
        "trainloader = DataLoader(train_data,batch_size=20,shuffle=True)\n",
        "testloader = DataLoader(test_data,batch_size=20,shuffle=True)\n",
        "classes = train_data.classes"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhpfa5qPGlup",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "9512fd44-3702-43be-d91d-7c0480e7e913"
      },
      "source": [
        "train_imgs,train_labels = next(iter(trainloader))\n",
        "plot_batch(train_imgs,train_labels)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjAce_mHT1Te",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "d657d098-1d4c-4841-d90a-718b766aaab4"
      },
      "source": [
        "class ConvDenoiser(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(ConvDenoiser,self).__init__()\n",
        "    ## Encoder layers\n",
        "    self.conv_1 = nn.Conv2d(1,64,3,padding=1)\n",
        "    self.conv_2 = nn.Conv2d(64,32,3,padding=1)\n",
        "    self.conv_3 = nn.Conv2d(32,16,3,padding=1)\n",
        "    self.conv_4 = nn.Conv2d(16,8,3,padding=1)\n",
        "    self.pool = nn.MaxPool2d(2,2)\n",
        "\n",
        "    ## Decoder layers\n",
        "    self.tconv_1 = nn.ConvTranspose2d(8,8,3,stride=2)\n",
        "    self.tconv_2 = nn.ConvTranspose2d(8,16,3,stride=2)\n",
        "    self.tconv_3 = nn.ConvTranspose2d(16,32,2,stride=2)\n",
        "    self.tconv_4 = nn.ConvTranspose2d(32,64,2,stride=2)\n",
        "    self.out = nn.Conv2d(64,1,3,padding=1)\n",
        "\n",
        "  def forward(self,x):\n",
        "    x = F.relu(self.conv_1(x))\n",
        "    x = self.pool(x)\n",
        "    x = F.relu(self.conv_2(x))\n",
        "    x = self.pool(x)\n",
        "    x = F.relu(self.conv_3(x))\n",
        "    x = self.pool(x)\n",
        "    x = F.relu(self.conv_4(x))\n",
        "    x = self.pool(x)\n",
        "    x = F.relu(self.tconv_1(x))\n",
        "    x = F.relu(self.tconv_2(x))\n",
        "    x = F.relu(self.tconv_3(x))\n",
        "    x = F.relu(self.tconv_4(x))\n",
        "    x = torch.sigmoid(self.out(x))\n",
        "    return x\n",
        "\n",
        "model = ConvDenoiser()\n",
        "print(model)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ConvDenoiser(\n",
            "  (conv_1): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_2): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_3): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv_4): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (tconv_1): ConvTranspose2d(8, 8, kernel_size=(3, 3), stride=(2, 2))\n",
            "  (tconv_2): ConvTranspose2d(8, 16, kernel_size=(3, 3), stride=(2, 2))\n",
            "  (tconv_3): ConvTranspose2d(16, 32, kernel_size=(2, 2), stride=(2, 2))\n",
            "  (tconv_4): ConvTranspose2d(32, 64, kernel_size=(2, 2), stride=(2, 2))\n",
            "  (out): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zu2-lulWGxl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(model,trainloader,criterion,optimizer,epochs=10):\n",
        "  train_losses = []\n",
        "  last_loss = np.inf\n",
        "  for e in range(epochs):\n",
        "    start = time.time()\n",
        "    train_loss = 0\n",
        "    model.train()\n",
        "    for data in trainloader:\n",
        "      images,_ = data\n",
        "      noisy_imgs = images + 0.5 * torch.randn(*images.shape)\n",
        "      noisy_imgs = np.clip(noisy_imgs,0.,1.)\n",
        "      optimizer.zero_grad()\n",
        "      outputs = model(noisy_imgs)\n",
        "      loss = criterion(outputs,images)\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "      train_loss += loss.item()\n",
        "\n",
        "    train_losses.append(train_loss/len(trainloader))\n",
        "    print(f\"Epoch:{e+1}/{epochs} Trainloss:{train_loss/len(trainloader):.6f} Time:{time.time() - start :.3f}sec\")\n",
        "\n",
        "    if train_loss < last_loss:\n",
        "      torch.save(model.state_dict(),\"ae_weights.pth\")\n",
        "      last_loss = train_loss\n",
        "\n",
        "  return train_losses"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjiDw22faeOW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "b0994037-52a0-4d15-f258-fd342cc40c6c"
      },
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(),lr=0.001)\n",
        "train_losses = train(model,trainloader,criterion,optimizer,10)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch:1/10 Trainloss:0.038378 Time:195.481sec\n",
            "Epoch:2/10 Trainloss:0.027585 Time:195.025sec\n",
            "Epoch:3/10 Trainloss:0.025897 Time:194.614sec\n",
            "Epoch:4/10 Trainloss:0.024940 Time:196.337sec\n",
            "Epoch:5/10 Trainloss:0.024329 Time:199.044sec\n",
            "Epoch:6/10 Trainloss:0.023886 Time:200.092sec\n",
            "Epoch:7/10 Trainloss:0.023539 Time:199.393sec\n",
            "Epoch:8/10 Trainloss:0.023307 Time:199.414sec\n",
            "Epoch:9/10 Trainloss:0.023100 Time:199.858sec\n",
            "Epoch:10/10 Trainloss:0.022926 Time:199.750sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3xd_KmFnTqt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "887d6a48-b507-4f18-a761-9e6b0361ddc3"
      },
      "source": [
        "state_dict = torch.load(\"ae_weights.pth\")\n",
        "model.load_state_dict(state_dict)\n",
        "plt.plot(train_losses,label=\"training loss\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WClgnVmMnoNI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "77f10576-3ad1-468d-9d43-d91b68c27451"
      },
      "source": [
        "test_images, _ = next(iter(testloader))\n",
        "\n",
        "noisy_imgs = test_images + 0.5 * torch.randn(*test_images.shape)\n",
        "noisy_imgs = np.clip(noisy_imgs,0.,1.)\n",
        "\n",
        "outputs = model(noisy_imgs)\n",
        "outputs = outputs.view(outputs.shape[0],1,28,28)\n",
        "outputs = outputs.detach().numpy()\n",
        "\n",
        "fig,ax = plt.subplots(2,10,sharex=True,sharey=True,figsize=(20,5))\n",
        "\n",
        "for noisy_imgs,row in zip([noisy_imgs,outputs],ax):\n",
        "    for img,ax in zip(noisy_imgs,row):\n",
        "        ax.imshow(img.squeeze(),cmap=\"gray\")\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVu-11m6zqTl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "5e525c8f-89ef-4ec4-b1c5-bbf9539756a7"
      },
      "source": [
        "## testing single image samples\n",
        "import cv2\n",
        "\n",
        "noisy_img = cv2.imread(\"test_img.png\",0) # load noisy image in gray scale\n",
        "\n",
        "noisy_img = transform(noisy_img)\n",
        "\n",
        "noisy_img = torch.stack([noisy_img])\n",
        "outputs = model(noisy_img)\n",
        "outputs = outputs.view(outputs.shape[0],1,28,28)\n",
        "outputs = outputs.detach().numpy()\n",
        "fig = plt.figure(figsize=(15,4))\n",
        "ax = fig.add_subplot(1,2,1,xticks=[],yticks=[])\n",
        "ax.imshow(noisy_img.squeeze(),cmap=\"gray\")\n",
        "ax.set_title(\"Noise (raw image)\")\n",
        "ax = fig.add_subplot(1,2,2,xticks=[],yticks=[])\n",
        "ax.imshow(outputs.squeeze(),cmap=\"gray\")\n",
        "ax.set_title(\"Denoised image\")\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}