DeblurGAN_v2_CustomDatasetipynb.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Colab NoteBook DeblurGANv2 on Custom data\n",
        "Dataset link - https://drive.google.com/file/d/1Mk99lU3zJgBkBpHTl0PigJ3UiSX4yyKB/view?usp=sharing"
      ],
      "metadata": {
        "id": "tgKIVuRXsqdL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset download"
      ],
      "metadata": {
        "id": "STgLU8F-s6nm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Mk99lU3zJgBkBpHTl0PigJ3UiSX4yyKB' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1Mk99lU3zJgBkBpHTl0PigJ3UiSX4yyKB\" -O 'Licenseplate_blur_clear_dataset.zip' && rm -rf /tmp/cookies.txt"
      ],
      "metadata": {
        "id": "SYh_HiHToyEK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fcc7476-65c5-4d9e-8e92-902cf364f831"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-07 05:43:10--  https://docs.google.com/uc?export=download&confirm=t&id=1Mk99lU3zJgBkBpHTl0PigJ3UiSX4yyKB\n",
            "Resolving docs.google.com (docs.google.com)... 142.251.10.139, 142.251.10.100, 142.251.10.113, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.10.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-0c-50-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/cao35sml818ck27bui4f0i2kukblau5c/1683438150000/12164758996825312632/*/1Mk99lU3zJgBkBpHTl0PigJ3UiSX4yyKB?e=download&uuid=3f80690c-fed1-4a32-9066-04918456110c [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-05-07 05:43:10--  https://doc-0c-50-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/cao35sml818ck27bui4f0i2kukblau5c/1683438150000/12164758996825312632/*/1Mk99lU3zJgBkBpHTl0PigJ3UiSX4yyKB?e=download&uuid=3f80690c-fed1-4a32-9066-04918456110c\n",
            "Resolving doc-0c-50-docs.googleusercontent.com (doc-0c-50-docs.googleusercontent.com)... 74.125.24.132, 2404:6800:4003:c03::84\n",
            "Connecting to doc-0c-50-docs.googleusercontent.com (doc-0c-50-docs.googleusercontent.com)|74.125.24.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 125385064 (120M) [application/x-zip-compressed]\n",
            "Saving to: ‘Licenseplate_blur_clear_dataset.zip’\n",
            "\n",
            "Licenseplate_blur_c 100%[===================>] 119.58M   142MB/s    in 0.8s    \n",
            "\n",
            "2023-05-07 05:43:11 (142 MB/s) - ‘Licenseplate_blur_clear_dataset.zip’ saved [125385064/125385064]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip Licenseplate_blur_clear_dataset.zip"
      ],
      "metadata": {
        "id": "YxZsT7jOpRhY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33ec7f66-0e90-496a-adf0-746bdfee8dc3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FI9WRTqOAGb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efac1f97-e71c-4033-8e96-73ed8c4e7ae9"
      },
      "source": [
        "!git clone https://github.com/ajithvallabai/DeblurGANv2_Customdataset.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'DeblurGANv2'...\n",
            "remote: Enumerating objects: 902, done.\u001b[K\n",
            "remote: Counting objects: 100% (274/274), done.\u001b[K\n",
            "remote: Compressing objects: 100% (125/125), done.\u001b[K\n",
            "remote: Total 902 (delta 151), reused 149 (delta 149), pack-reused 628\u001b[K\n",
            "Receiving objects: 100% (902/902), 66.04 MiB | 16.53 MiB/s, done.\n",
            "Resolving deltas: 100% (479/479), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDjfJUR_DnN-",
        "outputId": "9e05e931-930d-470b-b34c-9d378dce5316"
      },
      "source": [
        "cd DeblurGANv2_Customdataset/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DeblurGANv2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQNX7CmxEreo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cd14efc-09ff-4b33-e33b-811c999d2cc3"
      },
      "source": [
        "!pip install glog\n",
        "!pip install tensorboardX\n",
        "!pip install pretrainedmodels\n",
        "!pip install albumentations==1.2.1\n",
        "!pip install fire"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting glog\n",
            "  Downloading glog-0.3.1-py2.py3-none-any.whl (7.8 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from glog) (1.16.0)\n",
            "Collecting python-gflags>=3.1\n",
            "  Downloading python-gflags-3.1.2.tar.gz (52 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.1/52.1 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: python-gflags\n",
            "  Building wheel for python-gflags (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-gflags: filename=python_gflags-3.1.2-py3-none-any.whl size=57386 sha256=f16a92dbf8b8b0db4e1fe100528111302e202213a201e130f65d259f1f97a9d2\n",
            "  Stored in directory: /root/.cache/pip/wheels/0d/7d/a2/e858f0632653a3c752a807f85a39ca9d7ed75796c62845e66c\n",
            "Successfully built python-gflags\n",
            "Installing collected packages: python-gflags, glog\n",
            "Successfully installed glog-0.3.1 python-gflags-3.1.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorboardX\n",
            "  Downloading tensorboardX-2.6-py2.py3-none-any.whl (114 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorboardX) (1.22.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorboardX) (23.1)\n",
            "Requirement already satisfied: protobuf<4,>=3.8.0 in /usr/local/lib/python3.10/dist-packages (from tensorboardX) (3.20.3)\n",
            "Installing collected packages: tensorboardX\n",
            "Successfully installed tensorboardX-2.6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pretrainedmodels\n",
            "  Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from pretrainedmodels) (2.0.0+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from pretrainedmodels) (0.15.1+cu118)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from pretrainedmodels) (4.65.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from munch->pretrainedmodels) (1.16.0)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->pretrainedmodels) (2.0.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->pretrainedmodels) (1.11.1)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->pretrainedmodels) (1.3.0)\n",
            "Building wheels for collected packages: pretrainedmodels\n",
            "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60962 sha256=770b8e4e12bdc67ec78fbbf625ff3d44b5f26c1d9a3bdd33e695a80f0791fa6d\n",
            "  Stored in directory: /root/.cache/pip/wheels/35/cb/a5/8f534c60142835bfc889f9a482e4a67e0b817032d9c6883b64\n",
            "Successfully built pretrainedmodels\n",
            "Installing collected packages: munch, pretrainedmodels\n",
            "Successfully installed munch-2.5.0 pretrainedmodels-0.7.4\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: albumentations==1.2.1 in /usr/local/lib/python3.10/dist-packages (1.2.1)\n",
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            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from albumentations==1.2.1) (1.10.1)\n",
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            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from qudida>=0.0.4->albumentations==1.2.1) (1.2.2)\n",
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            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (1.4.1)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.2.1) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.2.1) (3.1.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fire\n",
            "  Downloading fire-0.5.0.tar.gz (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.3/88.3 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fire) (1.16.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from fire) (2.3.0)\n",
            "Building wheels for collected packages: fire\n",
            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fire: filename=fire-0.5.0-py2.py3-none-any.whl size=116952 sha256=b401afa9023780793920b35b2d0eb50d3d27b0f7a14f8c8913cd69b418a2db46\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/d4/f7/9404e5db0116bd4d43e5666eaa3e70ab53723e1e3ea40c9a95\n",
            "Successfully built fire\n",
            "Installing collected packages: fire\n",
            "Successfully installed fire-0.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Train\n",
        "Make sure dataset path is correct in config/config.yaml\n"
      ],
      "metadata": {
        "id": "KN0bzzrjvo79"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCigNMqYEnTt",
        "outputId": "ebc2bccd-09eb-458e-b45e-46fe1e73b54b"
      },
      "source": [
        "!python train.py"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W0507 05:46:04.413213 1314 warnings.py:109] /usr/local/lib/python3.10/dist-packages/albumentations/augmentations/dropout/cutout.py:50: FutureWarning: Cutout has been deprecated. Please use CoarseDropout\n",
            "  warnings.warn(\n",
            "\n",
            "I0507 05:46:04.418832 1314 dataset.py:28] Subsampling buckets from 0 to 90.0, total buckets number is 100\n",
            "I0507 05:46:04.424413 1314 dataset.py:71] Dataset has been created with 1640 samples\n",
            "I0507 05:46:04.428673 1314 dataset.py:28] Subsampling buckets from 90.0 to 100, total buckets number is 100\n",
            "I0507 05:46:04.429565 1314 dataset.py:71] Dataset has been created with 54 samples\n",
            "W0507 05:46:04.430942 1314 warnings.py:109] /usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "\n",
            "W0507 05:46:04.431056 1314 warnings.py:109] /usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "\n",
            "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n",
            "100% 548M/548M [00:01<00:00, 296MB/s]\n",
            "Downloading: \"http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth\" to /root/.cache/torch/hub/checkpoints/inceptionresnetv2-520b38e4.pth\n",
            "100% 213M/213M [10:28<00:00, 356kB/s]\n",
            "Epoch 0, lr 0.01:   0% 0/1000 [00:00<?, ?it/s]W0507 05:56:41.126372 1314 warnings.py:109] /usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:3737: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\n",
            "  warnings.warn(\"nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\")\n",
            "\n",
            "W0507 05:56:42.455035 1314 warnings.py:109] /content/DeblurGANv2/models/models.py:28: FutureWarning: `multichannel` is a deprecated argument name for `structural_similarity`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
            "  ssim = SSIM(fake, real, multichannel=True)\n",
            "\n",
            "Epoch 0, lr 0.01: 100% 1000/1000 [05:09<00:00,  3.23it/s, loss=G_loss=0.1772; PSNR=17.8087; SSIM=0.4294]\n",
            "Validation:  54% 54/100 [00:06<00:05,  8.98it/s]\n",
            "G_loss=0.1476; PSNR=17.4395; SSIM=0.4284\n",
            "Epoch 1, lr 0.01: 100% 1000/1000 [05:19<00:00,  3.13it/s, loss=G_loss=0.1205; PSNR=18.8676; SSIM=0.4784]\n",
            "Validation:  54% 54/100 [00:07<00:06,  7.57it/s]\n",
            "G_loss=0.0657; PSNR=19.3493; SSIM=0.5024\n",
            "Epoch 2, lr 0.01: 100% 1000/1000 [05:14<00:00,  3.18it/s, loss=G_loss=0.1760; PSNR=17.6664; SSIM=0.4879]\n",
            "Validation:  54% 54/100 [00:05<00:05,  9.11it/s]\n",
            "G_loss=0.1451; PSNR=18.2504; SSIM=0.4787\n",
            "Epoch 3, lr 0.01: 100% 1000/1000 [05:46<00:00,  2.89it/s, loss=G_loss=-0.0224; PSNR=19.1923; SSIM=0.4895]\n",
            "Validation:  54% 54/100 [00:08<00:06,  6.65it/s]\n",
            "G_loss=0.0498; PSNR=19.0518; SSIM=0.4739\n",
            "Epoch 4, lr 0.01: 100% 1000/1000 [05:46<00:00,  2.89it/s, loss=G_loss=-0.3307; PSNR=19.7789; SSIM=0.5128]\n",
            "Validation:  54% 54/100 [00:06<00:05,  8.93it/s]\n",
            "G_loss=-0.1990; PSNR=19.7699; SSIM=0.5153\n",
            "Epoch 5, lr 0.01: 100% 1000/1000 [06:02<00:00,  2.76it/s, loss=G_loss=-0.2732; PSNR=19.7920; SSIM=0.5232]\n",
            "Validation:  54% 54/100 [00:08<00:06,  6.72it/s]\n",
            "G_loss=-0.1830; PSNR=19.7149; SSIM=0.5235\n",
            "Epoch 6, lr 0.01: 100% 1000/1000 [05:58<00:00,  2.79it/s, loss=G_loss=0.4371; PSNR=20.5560; SSIM=0.5442]\n",
            "Validation:  54% 54/100 [00:08<00:07,  6.47it/s]\n",
            "G_loss=0.3750; PSNR=20.5759; SSIM=0.5478\n",
            "Epoch 7, lr 0.01: 100% 1000/1000 [05:46<00:00,  2.89it/s, loss=G_loss=0.6107; PSNR=20.3565; SSIM=0.5393]\n",
            "Validation:  54% 54/100 [00:08<00:07,  6.49it/s]\n",
            "G_loss=0.6563; PSNR=20.0799; SSIM=0.5323\n",
            "Epoch 8, lr 0.01: 100% 1000/1000 [05:51<00:00,  2.84it/s, loss=G_loss=0.7902; PSNR=20.5617; SSIM=0.5479]\n",
            "Validation:  54% 54/100 [00:06<00:05,  8.05it/s]\n",
            "G_loss=0.7556; PSNR=21.2386; SSIM=0.5698\n",
            "Epoch 9, lr 0.01: 100% 1000/1000 [05:52<00:00,  2.84it/s, loss=G_loss=0.6986; PSNR=20.9802; SSIM=0.5720]\n",
            "Validation:  54% 54/100 [00:07<00:05,  7.69it/s]\n",
            "G_loss=0.7535; PSNR=20.8399; SSIM=0.5590\n",
            "Epoch 10, lr 0.01: 100% 1000/1000 [05:49<00:00,  2.86it/s, loss=G_loss=0.7545; PSNR=21.1726; SSIM=0.5858]\n",
            "Validation:  54% 54/100 [00:05<00:04,  9.70it/s]\n",
            "G_loss=0.8778; PSNR=20.8146; SSIM=0.5637\n",
            "Epoch 11, lr 0.01: 100% 1000/1000 [05:53<00:00,  2.83it/s, loss=G_loss=0.8022; PSNR=21.3371; SSIM=0.5881]\n",
            "Validation:  54% 54/100 [00:07<00:06,  7.49it/s]\n",
            "G_loss=0.7423; PSNR=21.4989; SSIM=0.6115\n",
            "Epoch 12, lr 0.01: 100% 1000/1000 [05:50<00:00,  2.85it/s, loss=G_loss=0.8963; PSNR=21.0088; SSIM=0.5829]\n",
            "Validation:  54% 54/100 [00:05<00:04,  9.46it/s]\n",
            "G_loss=0.8681; PSNR=21.5400; SSIM=0.6035\n",
            "Epoch 13, lr 0.01: 100% 1000/1000 [05:49<00:00,  2.86it/s, loss=G_loss=0.9925; PSNR=21.1204; SSIM=0.5930]\n",
            "Validation:  54% 54/100 [00:07<00:06,  7.44it/s]\n",
            "G_loss=1.0118; PSNR=21.0020; SSIM=0.5792\n",
            "Epoch 14, lr 0.01: 100% 1000/1000 [05:49<00:00,  2.86it/s, loss=G_loss=1.0428; PSNR=21.2601; SSIM=0.5942]\n",
            "Validation:  54% 54/100 [00:07<00:06,  7.34it/s]\n",
            "G_loss=1.0035; PSNR=21.6331; SSIM=0.6085\n",
            "Epoch 15, lr 0.01: 100% 1000/1000 [05:52<00:00,  2.83it/s, loss=G_loss=1.0306; PSNR=21.5562; SSIM=0.6070]\n",
            "Validation:  54% 54/100 [00:08<00:07,  6.04it/s]\n",
            "G_loss=1.0167; PSNR=21.8200; SSIM=0.6154\n",
            "Epoch 16, lr 0.01: 100% 1000/1000 [05:54<00:00,  2.82it/s, loss=G_loss=0.9668; PSNR=21.6635; SSIM=0.6093]\n",
            "Validation:  54% 54/100 [00:07<00:06,  7.21it/s]\n",
            "G_loss=0.9286; PSNR=21.9622; SSIM=0.6300\n",
            "Epoch 17, lr 0.01: 100% 1000/1000 [05:49<00:00,  2.86it/s, loss=G_loss=0.8988; PSNR=21.8772; SSIM=0.6230]\n",
            "Validation:  54% 54/100 [00:06<00:05,  7.79it/s]\n",
            "G_loss=0.8745; PSNR=21.6463; SSIM=0.6170\n",
            "Epoch 18, lr 0.01: 100% 1000/1000 [05:54<00:00,  2.82it/s, loss=G_loss=1.2153; PSNR=21.7668; SSIM=0.6116]\n",
            "Validation:  54% 54/100 [00:09<00:07,  5.83it/s]\n",
            "G_loss=1.2641; PSNR=21.4783; SSIM=0.6158\n",
            "Epoch 19, lr 0.01: 100% 1000/1000 [05:46<00:00,  2.89it/s, loss=G_loss=1.1397; PSNR=21.9840; SSIM=0.6315]\n",
            "Validation:  54% 54/100 [00:06<00:05,  8.83it/s]\n",
            "G_loss=1.1241; PSNR=22.2656; SSIM=0.6408\n",
            "Epoch 20, lr 0.01: 100% 1000/1000 [05:45<00:00,  2.89it/s, loss=G_loss=1.0787; PSNR=21.9912; SSIM=0.6219]\n",
            "Validation:  54% 54/100 [00:07<00:06,  7.03it/s]\n",
            "G_loss=1.0939; PSNR=21.4695; SSIM=0.6139\n",
            "Epoch 21, lr 0.01:   6% 57/1000 [00:21<05:52,  2.68it/s, loss=G_loss=1.1088; PSNR=21.9008; SSIM=0.6249]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/DeblurGANv2/train.py\", line 196, in <module>\n",
            "    Fire(main)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/fire/core.py\", line 141, in Fire\n",
            "    component_trace = _Fire(component, args, parsed_flag_args, context, name)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/fire/core.py\", line 475, in _Fire\n",
            "    component, remaining_args = _CallAndUpdateTrace(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/fire/core.py\", line 691, in _CallAndUpdateTrace\n",
            "    component = fn(*varargs, **kwargs)\n",
            "  File \"/content/DeblurGANv2/train.py\", line 192, in main\n",
            "    trainer.train()\n",
            "  File \"/content/DeblurGANv2/train.py\", line 43, in train\n",
            "    self._run_epoch(epoch)\n",
            "  File \"/content/DeblurGANv2/train.py\", line 77, in _run_epoch\n",
            "    self.optimizer_G.step()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py\", line 69, in wrapper\n",
            "    return wrapped(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\", line 280, in wrapper\n",
            "    out = func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\", line 33, in _use_grad\n",
            "    ret = func(self, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/optim/adam.py\", line 141, in step\n",
            "    adam(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/optim/adam.py\", line 281, in adam\n",
            "    func(params,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/optim/adam.py\", line 439, in _multi_tensor_adam\n",
            "    torch._foreach_add_(device_state_steps, 1)\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Copy your models to drive so that no need to train again"
      ],
      "metadata": {
        "id": "xrG_XUQbv1il"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO1ZjgJpSd7I"
      },
      "source": [
        "!mkdir saved_models"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuSvsT-QSnKA"
      },
      "source": [
        "!cp best_fpn.h5 saved_models/"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luydfzV5WIrX"
      },
      "source": [
        "!cp last_fpn.h5 saved_models/"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c_bVgowaXbe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd101b89-3f14-4c48-9d53-706690b0ae85"
      },
      "source": [
        "!zip -r saved_models.zip saved_models"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: saved_models/ (stored 0%)\n",
            "  adding: saved_models/best_fpn.h5 (deflated 7%)\n",
            "  adding: saved_models/last_fpn.h5 (deflated 7%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1r9ugQU2EjTI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bef0e087-7d33-443e-ee3c-e85722cfea62"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp saved_models.zip /content/gdrive/MyDrive/Blogs_dataset/"
      ],
      "metadata": {
        "id": "IX3-fmEqSmD2"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3XQ0XvEStRf"
      },
      "source": [
        "!python predict.py -i ./test_img -o ./submit -m ./saved_models/best_fpn.h5"
      ],
      "execution_count": 13,
      "outputs": []
    }
  ]
}