ACGPN_and_SieveNet.ipynb

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  "metadata": {
    "colab": {
      "name": "ACGPN_and_SieveNet.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "7WJtP2PfBcPN",
        "veedN5N8BYOl"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CCSQIBMAYYZ"
      },
      "source": [
        "For inferencing ACGPN and SieveNet! \r\n",
        "\r\n",
        "ACGPN repo: https://github.com/switchablenorms/DeepFashion_Try_On\r\n",
        "\r\n",
        "SieveNet repo: https://github.com/levindabhi/SieveNet\r\n",
        "\r\n",
        "This notebook is hard coded for inferencing one image at a time.\r\n",
        "\r\n",
        "Notebook by [Levin Dabhi](https://levindabhi.github.io/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WJtP2PfBcPN"
      },
      "source": [
        "# ACGPN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVm5QFBMDBbT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc38a0b2-6041-4ce3-a67a-ba6740ee3989"
      },
      "source": [
        "!git clone https://github.com/levindabhi/ACGPN.git\n",
        "%cd ACGPN"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ACGPN'...\n",
            "remote: Enumerating objects: 148, done.\u001b[K\n",
            "remote: Counting objects: 100% (148/148), done.\u001b[K\n",
            "remote: Compressing objects: 100% (126/126), done.\u001b[K\n",
            "remote: Total 148 (delta 53), reused 84 (delta 17), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (148/148), 169.84 KiB | 692.00 KiB/s, done.\n",
            "Resolving deltas: 100% (53/53), done.\n",
            "/content/ACGPN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cnk7syY0rPKp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79818be7-0731-40be-ebb2-a277f610dedc"
      },
      "source": [
        "!pip install ninja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ninja\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/de/393468f2a37fc2c1dc3a06afc37775e27fde2d16845424141d4da62c686d/ninja-1.10.0.post2-py3-none-manylinux1_x86_64.whl (107kB)\n",
            "\r\u001b[K     |███                             | 10kB 20.8MB/s eta 0:00:01\r\u001b[K     |██████                          | 20kB 22.3MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 30kB 12.2MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 40kB 9.7MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 51kB 9.1MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 61kB 9.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 71kB 8.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 81kB 9.1MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 92kB 8.8MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 102kB 8.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 112kB 8.5MB/s \n",
            "\u001b[?25hInstalling collected packages: ninja\n",
            "Successfully installed ninja-1.10.0.post2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQwI--uhoH6R"
      },
      "source": [
        "import gdown\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import IPython\n",
        "import gdown\n",
        "import os\n",
        "import sys\n",
        "\n",
        "from predict_pose import generate_pose_keypoints"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwQ5zfh5oMe1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ec4825fb-9dc2-4e2c-afd3-a371cfd5b477"
      },
      "source": [
        "'''\n",
        "gdown.download('https://drive.google.com/uc?id=1tE7hcVFm8Td8kRh5iYRBSDFdvZIkbUIR', 'Data_preprocessing/data.zip', quiet=False)\n",
        "%cd Data_preprocessing\n",
        "!unzip data\n",
        "%cd ..\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\ngdown.download('https://drive.google.com/uc?id=1tE7hcVFm8Td8kRh5iYRBSDFdvZIkbUIR', 'Data_preprocessing/data.zip', quiet=False)\\n%cd Data_preprocessing\\n!unzip data\\n%cd ..\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86Ll7LIW1kGw"
      },
      "source": [
        "!mkdir Data_preprocessing/test_color\n",
        "!mkdir Data_preprocessing/test_colormask\n",
        "!mkdir Data_preprocessing/test_edge\n",
        "!mkdir Data_preprocessing/test_img\n",
        "!mkdir Data_preprocessing/test_label\n",
        "!mkdir Data_preprocessing/test_mask\n",
        "!mkdir Data_preprocessing/test_pose\n",
        "!mkdir inputs\n",
        "!mkdir inputs/img\n",
        "!mkdir inputs/cloth"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8hYM6XqCnxC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ed66fb7-5d87-4308-8551-cef8edb558a3"
      },
      "source": [
        "%cd pose\n",
        "!gdown --id 1hOHMFHEjhoJuLEQY0Ndurn5hfiA9mwko\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ACGPN/pose\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1hOHMFHEjhoJuLEQY0Ndurn5hfiA9mwko\n",
            "To: /content/ACGPN/pose/pose_iter_440000.caffemodel\n",
            "209MB [00:02, 71.6MB/s]\n",
            "/content/ACGPN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbGDB31KrKHV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61dbf0d0-3ffe-48b8-ebac-e343ce7986c0"
      },
      "source": [
        "!git clone https://github.com/levindabhi/Self-Correction-Human-Parsing-for-ACGPN.git\n",
        "!git clone https://github.com/levindabhi/U-2-Net.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Self-Correction-Human-Parsing-for-ACGPN'...\n",
            "remote: Enumerating objects: 12, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 752 (delta 6), reused 0 (delta 0), pack-reused 740\u001b[K\n",
            "Receiving objects: 100% (752/752), 3.80 MiB | 6.81 MiB/s, done.\n",
            "Resolving deltas: 100% (171/171), done.\n",
            "Cloning into 'U-2-Net'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 282 (delta 0), reused 0 (delta 0), pack-reused 279\u001b[K\n",
            "Receiving objects: 100% (282/282), 8.27 MiB | 32.43 MiB/s, done.\n",
            "Resolving deltas: 100% (121/121), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hSJI347rZtQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "outputId": "38e91766-0e18-458b-f199-44c4a6110cc8"
      },
      "source": [
        "#for segmentation mask generation\n",
        "url = 'https://drive.google.com/uc?id=1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH'\n",
        "output = 'lip_final.pth'\n",
        "gdown.download(url, output, quiet=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH\n",
            "To: /content/ACGPN/lip_final.pth\n",
            "267MB [00:02, 116MB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'lip_final.pth'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzy8poZT6pcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0bc436a-a863-4f80-dc5b-7ddfd4b0e35d"
      },
      "source": [
        "%cd U-2-Net\n",
        "!mkdir saved_models\n",
        "!mkdir saved_models/u2net\n",
        "!mkdir saved_models/u2netp\n",
        "!gdown --id 1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy -O saved_models/u2netp/u2netp.pth\n",
        "!gdown --id 1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ -O saved_models/u2net/u2net.pth\n",
        "import u2net_load\n",
        "import u2net_run\n",
        "u2net = u2net_load.model(model_name = 'u2netp')\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ACGPN/U-2-Net\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy\n",
            "To: /content/ACGPN/U-2-Net/saved_models/u2netp/u2netp.pth\n",
            "4.68MB [00:00, 41.2MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ\n",
            "To: /content/ACGPN/U-2-Net/saved_models/u2net/u2net.pth\n",
            "176MB [00:01, 103MB/s]\n",
            "...load U2NEP---4.7 MB\n",
            "/content/ACGPN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1VknOqswSTW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bab7bcd7-cedd-4b01-c294-4ea50ad0d9f7"
      },
      "source": [
        "!mkdir checkpoints\n",
        "gdown.download('https://drive.google.com/uc?id=1UWT6esQIU_d4tUm8cjxDKMhB8joQbrFx',output='checkpoints/ACGPN_checkpoints.zip', quiet=False)\n",
        "%cd checkpoints\n",
        "!unzip ACGPN_checkpoints\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1UWT6esQIU_d4tUm8cjxDKMhB8joQbrFx\n",
            "To: /content/ACGPN/checkpoints/ACGPN_checkpoints.zip\n",
            "524MB [00:04, 122MB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/ACGPN/checkpoints\n",
            "Archive:  ACGPN_checkpoints.zip\n",
            "   creating: label2city/\n",
            "  inflating: label2city/latest_net_G.pth  \n",
            "  inflating: label2city/latest_net_G1.pth  \n",
            "  inflating: label2city/latest_net_G2.pth  \n",
            "  inflating: label2city/latest_net_U.pth  \n",
            "  inflating: label2city/opt.txt      \n",
            "/content/ACGPN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eD-DMczQthkd"
      },
      "source": [
        "### Please upload your cloth image below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAMmTiQ7zibP",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "d29168a0-812c-4087-f34e-8ec0412435a0"
      },
      "source": [
        "%cd inputs/cloth\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "%cd ..\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ACGPN/inputs/cloth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2eab35b7-db2d-416f-a73e-f57716d9862c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2eab35b7-db2d-416f-a73e-f57716d9862c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving 1_1.webp to 1_1.webp\n",
            "Saving 3_1.webp to 3_1.webp\n",
            "Saving 4_1.webp to 4_1.webp\n",
            "Saving 5_1.webp to 5_1.webp\n",
            "Saving 6_1.webp to 6_1.webp\n",
            "/content/ACGPN/inputs\n",
            "/content/ACGPN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQP04cGctrOW"
      },
      "source": [
        "### Please upload your person image below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wmUOR92170v"
      },
      "source": [
        "%cd inputs/img\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "%cd ..\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBXPbfF5XYRB"
      },
      "source": [
        "sorted(os.listdir('inputs/cloth'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tm-9Up6z0Zpg"
      },
      "source": [
        "cloth_name = '000001_1.png'\n",
        "cloth_path = os.path.join('inputs/cloth', sorted(os.listdir('inputs/cloth'))[0])\n",
        "cloth = Image.open(cloth_path)\n",
        "cloth = cloth.resize((192, 256), Image.BICUBIC).convert('RGB')\n",
        "cloth.save(os.path.join('Data_preprocessing/test_color', cloth_name))\n",
        "\n",
        "u2net_run.infer(u2net, 'Data_preprocessing/test_color', 'Data_preprocessing/test_edge')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tj8vmpyXX0tx"
      },
      "source": [
        "os.listdir('inputs/img')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3aHah45D655"
      },
      "source": [
        "import time\n",
        "\n",
        "start_time = time.time()\n",
        "img_name = '000001_0.png'\n",
        "img_path = os.path.join('inputs/img', sorted(os.listdir('inputs/img'))[0])\n",
        "img = Image.open(img_path)\n",
        "img = img.resize((192,256), Image.BICUBIC)\n",
        "\n",
        "img_path = os.path.join('Data_preprocessing/test_img', img_name)\n",
        "img.save(img_path)\n",
        "resize_time = time.time()\n",
        "print('Resized image in {}s'.format(resize_time-start_time))\n",
        "\n",
        "!python3 Self-Correction-Human-Parsing-for-ACGPN/simple_extractor.py --dataset 'lip' --model-restore 'lip_final.pth' --input-dir 'Data_preprocessing/test_img' --output-dir 'Data_preprocessing/test_label'\n",
        "parse_time = time.time()\n",
        "print('Parsing generated in {}s'.format(parse_time-resize_time))\n",
        "\n",
        "pose_path = os.path.join('Data_preprocessing/test_pose', img_name.replace('.png', '_keypoints.json'))\n",
        "generate_pose_keypoints(img_path, pose_path)\n",
        "pose_time = time.time()\n",
        "print('Pose map generated in {}s'.format(pose_time-parse_time))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgMi912KAUNs"
      },
      "source": [
        "!rm -rf Data_preprocessing/test_pairs.txt\n",
        "with open('Data_preprocessing/test_pairs.txt','w') as f:\n",
        "    f.write('000001_0.png 000001_1.png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIcoP4ll14Ia",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b00ac3ad-47d4-4e02-e51a-4a650917c15a"
      },
      "source": [
        "!python test.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "?\n",
            "------------ Options -------------\n",
            "aspect_ratio: 1.0\n",
            "batchSize: 1\n",
            "checkpoints_dir: ./checkpoints\n",
            "cluster_path: features_clustered_010.npy\n",
            "continue_train: False\n",
            "data_type: 32\n",
            "datapairs: test_pairs.txt\n",
            "dataroot: Data_preprocessing/\n",
            "display_winsize: 512\n",
            "engine: None\n",
            "export_onnx: None\n",
            "fineSize: 512\n",
            "gpu_ids: [0]\n",
            "how_many: 1000\n",
            "input_nc: 3\n",
            "isTrain: False\n",
            "label_nc: 20\n",
            "loadSize: 512\n",
            "max_dataset_size: inf\n",
            "model: pix2pixHD\n",
            "nThreads: 1\n",
            "n_blocks_global: 4\n",
            "n_blocks_local: 3\n",
            "n_downsample_global: 4\n",
            "n_local_enhancers: 1\n",
            "name: label2city\n",
            "netG: global\n",
            "ngf: 64\n",
            "niter_fix_global: 0\n",
            "no_flip: False\n",
            "norm: instance\n",
            "ntest: inf\n",
            "onnx: None\n",
            "output_nc: 3\n",
            "phase: test\n",
            "resize_or_crop: scale_width\n",
            "results_dir: ./results/\n",
            "serial_batches: True\n",
            "tf_log: False\n",
            "use_dropout: False\n",
            "use_encoded_image: False\n",
            "verbose: False\n",
            "which_epoch: latest\n",
            "-------------- End ----------------\n",
            "CustomDatasetDataLoader\n",
            "dataset [AlignedDataset] was created\n",
            "Data_preprocessing/test_label label\n",
            "Data_preprocessing/test_img img\n",
            "Data_preprocessing/test_edge edge\n",
            "Data_preprocessing/test_edge edge\n",
            "Data_preprocessing/test_mask mask\n",
            "Data_preprocessing/test_mask mask\n",
            "Data_preprocessing/test_colormask colormask\n",
            "Data_preprocessing/test_colormask colormask\n",
            "Data_preprocessing/test_color color\n",
            "Data_preprocessing/test_color color\n",
            "# Inference images = 1\n",
            "latest_net_U.pth\n",
            "latest_net_G1.pth\n",
            "latest_net_G2.pth\n",
            "latest_net_G.pth\n",
            "/usr/local/lib/python3.6/dist-packages/torchvision/transforms/transforms.py:280: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.\n",
            "  \"please use transforms.Resize instead.\")\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1628: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
            "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3385: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
            "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n",
            "Saving 000001_0.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5FWfCeyJ8VL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "e1cb363f-7c19-4c03-c7e2-46ffacc8ea62"
      },
      "source": [
        "output_grid = np.concatenate([np.array(Image.open('Data_preprocessing/test_img/000001_0.png')),\n",
        "                np.array(Image.open('Data_preprocessing/test_color/000001_1.png')),\n",
        "                np.array(Image.open('results/test/try-on/000001_0.png'))], axis=1)\n",
        "image_grid = Image.fromarray(output_grid)\n",
        "image_grid"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=576x256 at 0x7FDCC4EFBB38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vz1m-KPtGTSo"
      },
      "source": [
        "### Check results in 'results' folder!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_zzoKdCOTNn"
      },
      "source": [
        "**Deleting all images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9b-Fx2TyKbZ9"
      },
      "source": [
        "!rm -rf Data_preprocessing/test_color/*\n",
        "!rm -rf Data_preprocessing/test_colormask/*\n",
        "!rm -rf Data_preprocessing/test_edge/*\n",
        "!rm -rf Data_preprocessing/test_img/*\n",
        "!rm -rf Data_preprocessing/test_label/*\n",
        "!rm -rf Data_preprocessing/test_mask/*\n",
        "!rm -rf Data_preprocessing/test_pose/*\n",
        "!rm -rf inputs/cloth/*\n",
        "!rm -rf inputs/img/*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLbylWRlO9QW"
      },
      "source": [
        "!rm -rf results/*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwcbPJYFW8vB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51646a2f-8fa0-4397-a76f-181c36338e72"
      },
      "source": [
        "%cd /content/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veedN5N8BYOl"
      },
      "source": [
        "# SieveNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_bExJ_fOyf9",
        "outputId": "7c308c5d-02f3-428d-b63e-90eb467d42bb"
      },
      "source": [
        "!git clone https://github.com/levindabhi/SieveNet.git\r\n",
        "%cd SieveNet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'SieveNet'...\n",
            "remote: Enumerating objects: 96, done.\u001b[K\n",
            "remote: Counting objects: 100% (96/96), done.\u001b[K\n",
            "remote: Compressing objects: 100% (94/94), done.\u001b[K\n",
            "remote: Total 96 (delta 34), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (96/96), done.\n",
            "/content/SieveNet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yS5XP4A2O0eE",
        "outputId": "fce0807c-abf7-4dc9-e87f-8a479adb03ca"
      },
      "source": [
        "!mkdir checkpoints\r\n",
        "%cd checkpoints\r\n",
        "\r\n",
        "!mkdir GMM\r\n",
        "%cd GMM\r\n",
        "!gdown --id 1zuL44-GpiXJqADjuKZWrKJzD1UNiwn5r\r\n",
        "%cd ..\r\n",
        "\r\n",
        "!mkdir SEG\r\n",
        "%cd SEG\r\n",
        "!gdown --id 1VtQHs2ynCZYFYre7JsF-TCObUv4X0dON\r\n",
        "%cd ..\r\n",
        "\r\n",
        "!mkdir TOM\r\n",
        "%cd TOM\r\n",
        "!gdown --id 1Er5bnFXu6zbSbRhniywcIZdbY2ceCMIp\r\n",
        "%cd ..\r\n",
        "\r\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/SieveNet/checkpoints\n",
            "/content/SieveNet/checkpoints/GMM\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1zuL44-GpiXJqADjuKZWrKJzD1UNiwn5r\n",
            "To: /content/SieveNet/checkpoints/GMM/gmm_final.pth\n",
            "123MB [00:01, 69.3MB/s]\n",
            "/content/SieveNet/checkpoints\n",
            "/content/SieveNet/checkpoints/SEG\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1VtQHs2ynCZYFYre7JsF-TCObUv4X0dON\n",
            "To: /content/SieveNet/checkpoints/SEG/segm_final.pth\n",
            "117MB [00:01, 59.6MB/s]\n",
            "/content/SieveNet/checkpoints\n",
            "/content/SieveNet/checkpoints/TOM\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Er5bnFXu6zbSbRhniywcIZdbY2ceCMIp\n",
            "To: /content/SieveNet/checkpoints/TOM/tom_final.pth\n",
            "117MB [00:02, 43.1MB/s]\n",
            "/content/SieveNet/checkpoints\n",
            "/content/SieveNet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGf_x8RvO2db",
        "outputId": "be1280b1-35d4-418c-fda9-8f37890c5e43"
      },
      "source": [
        "%cd pose/\r\n",
        "!gdown --id 1hOHMFHEjhoJuLEQY0Ndurn5hfiA9mwko\r\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/SieveNet/pose\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1hOHMFHEjhoJuLEQY0Ndurn5hfiA9mwko\n",
            "To: /content/SieveNet/pose/pose_iter_440000.caffemodel\n",
            "209MB [00:04, 48.9MB/s]\n",
            "/content/SieveNet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcZK6HTXO4Pt"
      },
      "source": [
        "%%writefile config.py\r\n",
        "import os.path as osp\r\n",
        "import os\r\n",
        "\r\n",
        "\r\n",
        "class parser:\r\n",
        "    def __init__(self):\r\n",
        "        self.dataroot = 'dataset'\r\n",
        "        self.datamode = 'test'                                   #train, test\r\n",
        "        self.stage = 'TOM'                                        #GMM, SEG, TOM\r\n",
        "        self.runmode = self.datamode                              \r\n",
        "        self.name = self.stage   \r\n",
        "        if self.datamode == 'train':\r\n",
        "            self.data_list = 'train_pairs.txt'\r\n",
        "        elif self.datamode == 'test':\r\n",
        "            self.data_list = 'test_pairs.txt'\r\n",
        "        self.fine_width = 192\r\n",
        "        self.fine_height = 256\r\n",
        "        self.radius = 4\r\n",
        "        self.grid_path =  osp.join(self.dataroot, 'grid.png')\r\n",
        "        if self.datamode == 'train':                            #for training keep true, for test keep false\r\n",
        "            self.shuffle = True             \r\n",
        "        else:\r\n",
        "            self.shuffle = False\r\n",
        "        self.batch_size = 16\r\n",
        "        self.workers = 1\r\n",
        "        self.grid_size = 5\r\n",
        "        \r\n",
        "        self.lr = 0.002\r\n",
        "        self.keep_step = 8000\r\n",
        "        self.decay_step = 5500\r\n",
        "        self.previous_step = 0                                  #if you want to resume training from some steps    \r\n",
        "                                                                #set previous_step in as per last updated checkpoints \r\n",
        "        self.save_count = 200\r\n",
        "        self.display_count = 50\r\n",
        "        \r\n",
        "        self.tensorboard_dir = osp.join(os.getcwd(), 'tensorboard')\r\n",
        "        self.checkpoint_dir = osp.join(os.getcwd(), 'checkpoints')\r\n",
        "        self.save_dir = osp.join(os.getcwd(), 'outputs')         #for saving output while infernce\r\n",
        "        if not osp.exists(self.save_dir):\r\n",
        "            os.makedirs(self.save_dir)\r\n",
        "        if self.previous_step == 0:\r\n",
        "            self.checkpoint = ''\r\n",
        "        else:\r\n",
        "            self.checkpoint = osp.join(self.checkpoint_dir, self.name, 'step_%06d.pth' % (self.previous_step))\r\n",
        "\r\n",
        "        #Set your images path here\r\n",
        "        self.input_image_path = 'custom/input/019579_0.jpg'\r\n",
        "        self.input_image_path = '/content/SieveNet/000006_0.jpg'\r\n",
        "        self.cloth_image_path = 'custom/input/017575_1.jpg'\r\n",
        "        self.cloth_image_path = '/content/SieveNet/000530_1.jpg'\r\n",
        "        \r\n",
        "        self.human_parsing_image_path = '/content/SieveNet/000006_0.png'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qm7IagDAO6Pz",
        "outputId": "b720a623-e1dc-4128-af80-a66c1ee6ca04"
      },
      "source": [
        "!python inference.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OpenCV(ocl4dnn): consider to specify kernel configuration cache directory \n",
            "                 via OPENCV_OCL4DNN_CONFIG_PATH parameter.\n",
            "OpenCL program build log: dnn/dummy\n",
            "Status -11: CL_BUILD_PROGRAM_FAILURE\n",
            "-cl-no-subgroup-ifp\n",
            "Error in processing command line: Don't understand command line argument \"-cl-no-subgroup-ifp\"!\n",
            "File saved at /content/SieveNet/000006_0_keypoints.json\n",
            "initialization method [normal]\n",
            "initialization method [normal]\n",
            "initialization method [normal]\n",
            "----checkpoints loaded from path: /content/SieveNet/checkpoints/GMM/gmm_final.pth----\n",
            "----checkpoints loaded from path: /content/SieveNet/checkpoints/SEG/segm_final.pth----\n",
            "----checkpoints loaded from path: /content/SieveNet/checkpoints/TOM/tom_final.pth----\n",
            "Output generated!\n",
            "Output saved at /content/SieveNet/outputs\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}