encoders_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "encoders_project.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "eJPmdaY6XTmI",
        "colab_type": "code",
        "outputId": "627acf03-8251-4021-aba5-612be2023d42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "cell_type": "code",
      "source": [
        "!wget http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz -O data.tgz && tar -xf data.tgz && rm -rf data.tgz && mv lfw-deepfunneled dataset\n",
        "!wget http://www.cs.columbia.edu/CAVE/databases/pubfig/download/lfw_attributes.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-21 07:53:23--  http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz\n",
            "Resolving vis-www.cs.umass.edu (vis-www.cs.umass.edu)... 128.119.244.95\n",
            "Connecting to vis-www.cs.umass.edu (vis-www.cs.umass.edu)|128.119.244.95|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 108761145 (104M) [application/x-gzip]\n",
            "Saving to: ‘data.tgz’\n",
            "\n",
            "data.tgz            100%[===================>] 103.72M  30.7MB/s    in 4.7s    \n",
            "\n",
            "2019-02-21 07:53:33 (22.0 MB/s) - ‘data.tgz’ saved [108761145/108761145]\n",
            "\n",
            "--2019-02-21 07:53:36--  http://www.cs.columbia.edu/CAVE/databases/pubfig/download/lfw_attributes.txt\n",
            "Resolving www.cs.columbia.edu (www.cs.columbia.edu)... 128.59.11.206\n",
            "Connecting to www.cs.columbia.edu (www.cs.columbia.edu)|128.59.11.206|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 14879205 (14M) [text/plain]\n",
            "Saving to: ‘lfw_attributes.txt’\n",
            "\n",
            "lfw_attributes.txt  100%[===================>]  14.19M  6.08MB/s    in 2.3s    \n",
            "\n",
            "2019-02-21 07:53:39 (6.08 MB/s) - ‘lfw_attributes.txt’ saved [14879205/14879205]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BOVjpsIPXb-I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torchvision.transforms as T\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.nn import Module\n",
        "import torch.nn as Layers\n",
        "import torch.nn.functional as F\n",
        "from torch import optim as Opt\n",
        "from skimage import io\n",
        "from torchvision import models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y8p7b_xhwwrp",
        "colab_type": "code",
        "outputId": "c67188e4-78de-4a2b-d602-104ee0bc06b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Kh7Shx30OjS7",
        "colab_type": "code",
        "outputId": "10e9a433-74ca-4144-fecd-d5c95e720aeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rJcBFv8vYuAj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"lfw_attributes.txt\", sep='\\t', skiprows=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nS2MvqqLb00G",
        "colab_type": "code",
        "outputId": "86dd7b84-9729-4d80-b6e6-a733477c514f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        }
      },
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>#</th>\n",
              "      <th>person</th>\n",
              "      <th>imagenum</th>\n",
              "      <th>Male</th>\n",
              "      <th>Asian</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Baby</th>\n",
              "      <th>Child</th>\n",
              "      <th>Youth</th>\n",
              "      <th>...</th>\n",
              "      <th>Pale Skin</th>\n",
              "      <th>5 o' Clock Shadow</th>\n",
              "      <th>Strong Nose-Mouth Lines</th>\n",
              "      <th>Wearing Lipstick</th>\n",
              "      <th>Flushed Face</th>\n",
              "      <th>High Cheekbones</th>\n",
              "      <th>Brown Eyes</th>\n",
              "      <th>Wearing Earrings</th>\n",
              "      <th>Wearing Necktie</th>\n",
              "      <th>Wearing Necklace</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aaron Eckhart</td>\n",
              "      <td>1</td>\n",
              "      <td>1.568346</td>\n",
              "      <td>-1.889043</td>\n",
              "      <td>1.737203</td>\n",
              "      <td>-0.929729</td>\n",
              "      <td>-1.471799</td>\n",
              "      <td>-0.195580</td>\n",
              "      <td>-0.835609</td>\n",
              "      <td>-0.351468</td>\n",
              "      <td>...</td>\n",
              "      <td>1.166118</td>\n",
              "      <td>-1.164916</td>\n",
              "      <td>-1.139990</td>\n",
              "      <td>-2.371746</td>\n",
              "      <td>-1.299932</td>\n",
              "      <td>-0.414682</td>\n",
              "      <td>-1.144902</td>\n",
              "      <td>0.694007</td>\n",
              "      <td>-0.826609</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aaron Guiel</td>\n",
              "      <td>1</td>\n",
              "      <td>0.169851</td>\n",
              "      <td>-0.982408</td>\n",
              "      <td>0.422709</td>\n",
              "      <td>-1.282184</td>\n",
              "      <td>-1.360060</td>\n",
              "      <td>-0.867002</td>\n",
              "      <td>-0.452293</td>\n",
              "      <td>-0.197521</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.397680</td>\n",
              "      <td>0.874160</td>\n",
              "      <td>-0.945431</td>\n",
              "      <td>-0.268649</td>\n",
              "      <td>-0.006244</td>\n",
              "      <td>-0.030406</td>\n",
              "      <td>-0.480128</td>\n",
              "      <td>0.666760</td>\n",
              "      <td>-0.496559</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aaron Patterson</td>\n",
              "      <td>1</td>\n",
              "      <td>0.997749</td>\n",
              "      <td>-1.364195</td>\n",
              "      <td>-0.157377</td>\n",
              "      <td>-0.756447</td>\n",
              "      <td>-1.891825</td>\n",
              "      <td>-0.871526</td>\n",
              "      <td>-0.862893</td>\n",
              "      <td>0.031445</td>\n",
              "      <td>...</td>\n",
              "      <td>1.884745</td>\n",
              "      <td>-0.999765</td>\n",
              "      <td>-1.359858</td>\n",
              "      <td>-1.912108</td>\n",
              "      <td>-1.095634</td>\n",
              "      <td>0.915126</td>\n",
              "      <td>-0.572332</td>\n",
              "      <td>0.144262</td>\n",
              "      <td>-0.841231</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aaron Peirsol</td>\n",
              "      <td>1</td>\n",
              "      <td>1.122719</td>\n",
              "      <td>-1.997799</td>\n",
              "      <td>1.916144</td>\n",
              "      <td>-2.514214</td>\n",
              "      <td>-2.580071</td>\n",
              "      <td>-1.404239</td>\n",
              "      <td>0.057551</td>\n",
              "      <td>0.000196</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.176089</td>\n",
              "      <td>1.108125</td>\n",
              "      <td>-1.600944</td>\n",
              "      <td>-3.264613</td>\n",
              "      <td>0.813418</td>\n",
              "      <td>0.308631</td>\n",
              "      <td>-0.848693</td>\n",
              "      <td>0.475941</td>\n",
              "      <td>-0.447025</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aaron Peirsol</td>\n",
              "      <td>2</td>\n",
              "      <td>1.078214</td>\n",
              "      <td>-2.008098</td>\n",
              "      <td>1.676211</td>\n",
              "      <td>-2.278056</td>\n",
              "      <td>-2.651845</td>\n",
              "      <td>-1.348408</td>\n",
              "      <td>0.649089</td>\n",
              "      <td>0.017656</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.955283</td>\n",
              "      <td>0.119113</td>\n",
              "      <td>-1.128176</td>\n",
              "      <td>-3.161048</td>\n",
              "      <td>0.082680</td>\n",
              "      <td>-0.439614</td>\n",
              "      <td>-0.359859</td>\n",
              "      <td>-0.760774</td>\n",
              "      <td>-0.410152</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 76 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 #  person  imagenum      Male     Asian     White     Black  \\\n",
              "0    Aaron Eckhart       1  1.568346 -1.889043  1.737203 -0.929729 -1.471799   \n",
              "1      Aaron Guiel       1  0.169851 -0.982408  0.422709 -1.282184 -1.360060   \n",
              "2  Aaron Patterson       1  0.997749 -1.364195 -0.157377 -0.756447 -1.891825   \n",
              "3    Aaron Peirsol       1  1.122719 -1.997799  1.916144 -2.514214 -2.580071   \n",
              "4    Aaron Peirsol       2  1.078214 -2.008098  1.676211 -2.278056 -2.651845   \n",
              "\n",
              "       Baby     Child     Youth        ...         Pale Skin  \\\n",
              "0 -0.195580 -0.835609 -0.351468        ...          1.166118   \n",
              "1 -0.867002 -0.452293 -0.197521        ...         -0.397680   \n",
              "2 -0.871526 -0.862893  0.031445        ...          1.884745   \n",
              "3 -1.404239  0.057551  0.000196        ...         -0.176089   \n",
              "4 -1.348408  0.649089  0.017656        ...         -0.955283   \n",
              "\n",
              "   5 o' Clock Shadow  Strong Nose-Mouth Lines  Wearing Lipstick  Flushed Face  \\\n",
              "0          -1.164916                -1.139990         -2.371746     -1.299932   \n",
              "1           0.874160                -0.945431         -0.268649     -0.006244   \n",
              "2          -0.999765                -1.359858         -1.912108     -1.095634   \n",
              "3           1.108125                -1.600944         -3.264613      0.813418   \n",
              "4           0.119113                -1.128176         -3.161048      0.082680   \n",
              "\n",
              "   High Cheekbones  Brown Eyes  Wearing Earrings  Wearing Necktie  \\\n",
              "0        -0.414682   -1.144902          0.694007        -0.826609   \n",
              "1        -0.030406   -0.480128          0.666760        -0.496559   \n",
              "2         0.915126   -0.572332          0.144262        -0.841231   \n",
              "3         0.308631   -0.848693          0.475941        -0.447025   \n",
              "4        -0.439614   -0.359859         -0.760774        -0.410152   \n",
              "\n",
              "   Wearing Necklace  \n",
              "0               NaN  \n",
              "1               NaN  \n",
              "2               NaN  \n",
              "3               NaN  \n",
              "4               NaN  \n",
              "\n",
              "[5 rows x 76 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "JHDtb13fb02u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "smiling = data[['#', 'person', 'Smiling']]\n",
        "smiling.columns = ['person', 'number', 'smiling']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Tzh993Sxb06Q",
        "colab_type": "code",
        "outputId": "a3a6572e-dda0-4df7-fb6c-445e1f616c76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "cell_type": "code",
      "source": [
        "smiling.person = smiling.person.str.replace(' ', '_')\n",
        "smiling[\"photo\"] = smiling.iloc[:, :2].apply(lambda x: \"{}_{}.jpg\".format(x[0], str(x[1]).zfill(4)), axis=1)\n",
        "smiling.iloc[np.argsort(smiling.smiling)[:10], :]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:3643: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  self[name] = value\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>person</th>\n",
              "      <th>number</th>\n",
              "      <th>smiling</th>\n",
              "      <th>photo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12609</th>\n",
              "      <td>Vanessa_Incontrada</td>\n",
              "      <td>4</td>\n",
              "      <td>-2.953572</td>\n",
              "      <td>Vanessa_Incontrada_0004.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1045</th>\n",
              "      <td>Azra_Akin</td>\n",
              "      <td>3</td>\n",
              "      <td>-2.898542</td>\n",
              "      <td>Azra_Akin_0003.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9005</th>\n",
              "      <td>Michelle_Rodriguez</td>\n",
              "      <td>2</td>\n",
              "      <td>-2.888022</td>\n",
              "      <td>Michelle_Rodriguez_0002.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13034</th>\n",
              "      <td>Yekaterina_Guseva</td>\n",
              "      <td>1</td>\n",
              "      <td>-2.872781</td>\n",
              "      <td>Yekaterina_Guseva_0001.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5624</th>\n",
              "      <td>Jaime_Pressly</td>\n",
              "      <td>1</td>\n",
              "      <td>-2.860385</td>\n",
              "      <td>Jaime_Pressly_0001.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4681</th>\n",
              "      <td>Gloria_Macapagal_Arroyo</td>\n",
              "      <td>38</td>\n",
              "      <td>-2.836526</td>\n",
              "      <td>Gloria_Macapagal_Arroyo_0038.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9755</th>\n",
              "      <td>Paris_Hilton</td>\n",
              "      <td>1</td>\n",
              "      <td>-2.818490</td>\n",
              "      <td>Paris_Hilton_0001.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1955</th>\n",
              "      <td>Cheryl_James</td>\n",
              "      <td>1</td>\n",
              "      <td>-2.817712</td>\n",
              "      <td>Cheryl_James_0001.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6151</th>\n",
              "      <td>Jennifer_Lopez</td>\n",
              "      <td>13</td>\n",
              "      <td>-2.807535</td>\n",
              "      <td>Jennifer_Lopez_0013.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>983</th>\n",
              "      <td>Ashlea_Talbot</td>\n",
              "      <td>1</td>\n",
              "      <td>-2.790396</td>\n",
              "      <td>Ashlea_Talbot_0001.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        person  number   smiling  \\\n",
              "12609       Vanessa_Incontrada       4 -2.953572   \n",
              "1045                 Azra_Akin       3 -2.898542   \n",
              "9005        Michelle_Rodriguez       2 -2.888022   \n",
              "13034        Yekaterina_Guseva       1 -2.872781   \n",
              "5624             Jaime_Pressly       1 -2.860385   \n",
              "4681   Gloria_Macapagal_Arroyo      38 -2.836526   \n",
              "9755              Paris_Hilton       1 -2.818490   \n",
              "1955              Cheryl_James       1 -2.817712   \n",
              "6151            Jennifer_Lopez      13 -2.807535   \n",
              "983              Ashlea_Talbot       1 -2.790396   \n",
              "\n",
              "                                  photo  \n",
              "12609       Vanessa_Incontrada_0004.jpg  \n",
              "1045                 Azra_Akin_0003.jpg  \n",
              "9005        Michelle_Rodriguez_0002.jpg  \n",
              "13034        Yekaterina_Guseva_0001.jpg  \n",
              "5624             Jaime_Pressly_0001.jpg  \n",
              "4681   Gloria_Macapagal_Arroyo_0038.jpg  \n",
              "9755              Paris_Hilton_0001.jpg  \n",
              "1955              Cheryl_James_0001.jpg  \n",
              "6151            Jennifer_Lopez_0013.jpg  \n",
              "983              Ashlea_Talbot_0001.jpg  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "lSSyFXBnb09T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def show_im(person, photo_path):\n",
        "  img = cv2.imread(f\"dataset/{person}/{photo_path}\")\n",
        "  print(img.shape)\n",
        "  return \n",
        "  plt.imshow(img[..., [2, 1, 0]])\n",
        "  plt.show();"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PzLISyh3b1Ac",
        "colab_type": "code",
        "outputId": "8f6bcd5b-f44e-44e8-d66a-2f36b71f7e76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "for i in range(3):\n",
        "  num_rand = np.random.randint(0, data.shape[0])\n",
        "  photo = smiling.loc[num_rand, [\"person\", \"photo\"]]\n",
        "  show_im(*photo)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 250, 3)\n",
            "(250, 250, 3)\n",
            "(250, 250, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ysE4nDTQb1DN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Images(Dataset):\n",
        "  def __init__(self, root_dir, data, transforms=None):\n",
        "    self.data = data.reset_index().drop(\"index\", axis=1)\n",
        "    self.transforms = transforms\n",
        "    self.root_dir = root_dir\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "  \n",
        "  def __getitem__(self, idx):\n",
        "    img_name = self.root_dir + \"/\".join(self.data.loc[idx, [\"person\", \"photo\"]])\n",
        "    img = io.imread(img_name) / 255\n",
        "    img = cv2.resize(img, (48, 48))\n",
        "    \n",
        "    if self.transforms:\n",
        "      img = self.transforms(img)\n",
        "      \n",
        "    return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zt8Ey-y9b1GE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "transform = T.Compose([\n",
        "#     ToPILImage(),\n",
        "#     Resize((28, 28)),\n",
        "    T.ToTensor(),\n",
        "#     Resize(28),\n",
        "#     T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MrQjXOAbb1I-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train, test = train_test_split(smiling, test_size=0.01)\n",
        "# train = smiling[:40]\n",
        "train = Images(\"dataset/\", train, transform)\n",
        "# test = Images(\"dataset/\", test, transform)\n",
        "train = DataLoader(train, batch_size=32, shuffle=True, num_workers=4)\n",
        "# test = DataLoader(test, batch_size=32, shuffle=True, num_workers=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R3Uskqh2b1Lf",
        "colab_type": "code",
        "outputId": "401b0ee0-b87a-4d59-efc5-9f1bd50f63fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "for i in train:\n",
        "  print(i.shape)\n",
        "  break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([32, 3, 64, 64])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gISDInmit-6W",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "incept = models.Inception3()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QsqxFSnNt__2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "incept.modules"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bLgv_nFIuAPG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class BasicConv2d(Module):\n",
        "  def __init__(self, D_in, D_out, kernel_size=3, stride=1, padding=1):\n",
        "    super().__init__()\n",
        "    self.conv = Layers.Conv2d(D_in, D_out, kernel_size=kernel_size, stride=stride, padding=padding)\n",
        "    self.bn = Layers.BatchNorm2d(D_out, eps=0.001)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    x = self.bn(self.conv(x))\n",
        "    \n",
        "    return x\n",
        "\n",
        "class AutoEnc_a_forward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(3, 64)\n",
        "    self.conv_2 = BasicConv2d(64, 192)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = F.max_pool2d(self.conv_1(x), 2) # 64 32 32\n",
        "    x = F.max_pool2d(self.conv_2(x), 2) # 192 16 16\n",
        "    \n",
        "    return x # 192 16 16\n",
        "\n",
        "class AutoEnc_b_forward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(192, 480)\n",
        "    self.conv_2 = BasicConv2d(480, 256)\n",
        "    self.conv_3 = BasicConv2d(256, 64)\n",
        "    self.conv_4 = BasicConv2d(64, 32)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = self.conv_2(self.conv_1(x))\n",
        "    x = self.conv_4(self.conv_3(x))\n",
        "    \n",
        "    return F.max_pool2d(x, 2) # 32 8 8\n",
        "\n",
        "class AutoEnc_c_forward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(32, 192)\n",
        "    self.conv_2 = BasicConv2d(192, 480)\n",
        "    self.conv_3 = BasicConv2d(480, 224)\n",
        "    self.conv_4 = BasicConv2d(224, 32)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = self.conv_2(self.conv_1(x))\n",
        "    x = self.conv_4(self.conv_3(x))\n",
        "    \n",
        "    return F.max_pool2d(x, 2) # 32 4 4\n",
        "  \n",
        "class AutoEnc_d_forward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(32, 192)\n",
        "    self.conv_2 = BasicConv2d(192, 256)\n",
        "    self.conv_3 = BasicConv2d(256, 480)\n",
        "    self.conv_4 = BasicConv2d(480, 832)\n",
        "    self.conv_5 = BasicConv2d(832, 512)\n",
        "    self.conv_6 = BasicConv2d(512, 1024)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = self.conv_2(self.conv_1(x))\n",
        "    x = self.conv_4(self.conv_3(x))\n",
        "    x = self.conv_6(self.conv_5(x))\n",
        "    \n",
        "    return F.avg_pool2d(x, 4) # 1024 1 1\n",
        "\n",
        "class AutoEnc_d_backward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(1024, 512)\n",
        "    self.conv_2 = BasicConv2d(512, 832)\n",
        "    self.conv_3 = BasicConv2d(832, 480)\n",
        "    self.conv_4 = BasicConv2d(480, 256)\n",
        "    self.conv_5 = BasicConv2d(256, 192)\n",
        "    self.conv_6 = BasicConv2d(192, 32)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = F.interpolate(x, 4) # 1024 4 4\n",
        "    x = self.conv_2(self.conv_1(x))\n",
        "    x = self.conv_4(self.conv_3(x))\n",
        "    x = self.conv_6(self.conv_5(x))\n",
        "    \n",
        "    return x # 32 4 4\n",
        "\n",
        "class AutoEnc_c_backward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(32, 224)\n",
        "    self.conv_2 = BasicConv2d(224, 480)\n",
        "    self.conv_3 = BasicConv2d(480, 192)\n",
        "    self.conv_4 = BasicConv2d(192, 32)\n",
        " \n",
        "  def forward(self, x):\n",
        "    x = F.interpolate(x, 8) # 32 8 8\n",
        "    x = self.conv_2(self.conv_1(x))\n",
        "    x = self.conv_4(self.conv_3(x))\n",
        "    \n",
        "    return x # 32 8 8\n",
        "  \n",
        "class AutoEnc_b_backward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(32, 64)\n",
        "    self.conv_2 = BasicConv2d(64, 256)\n",
        "    self.conv_3 = BasicConv2d(256, 480)\n",
        "    self.conv_4 = BasicConv2d(480, 192)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = F.interpolate(x, 16) # 32 16 16\n",
        "    x = self.conv_2(self.conv_1(x))\n",
        "    x = self.conv_4(self.conv_3(x))\n",
        "    \n",
        "    return x # 192 16 16\n",
        "\n",
        "class AutoEnc_a_backward(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_1 = BasicConv2d(192, 64)\n",
        "    self.conv_2 = BasicConv2d(64, 3)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    x = F.interpolate(x, 32) # 192 32 32\n",
        "    x = self.conv_1(x) # 64 32 32\n",
        "    x = F.interpolate(x, 64) # 64 64 64\n",
        "    x = self.conv_2(x) \n",
        "    \n",
        "    return x # 3 64 64\n",
        "    \n",
        "class AutoEncoder(Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.enc = Layers.Sequential(\n",
        "        AutoEnc_a_forward(), # \n",
        "        AutoEnc_b_forward(),\n",
        "        AutoEnc_c_forward(),\n",
        "        AutoEnc_d_forward()\n",
        "    )\n",
        "    self.fc_forward = Layers.Linear(1024, 100) \n",
        "    self.dec = Layers.Sequential(\n",
        "        AutoEnc_d_backward(),\n",
        "        AutoEnc_c_backward(),\n",
        "        AutoEnc_b_backward(),\n",
        "        AutoEnc_a_backward(),\n",
        "    )\n",
        "    self.fc_backward = Layers.Linear(100, 1024)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    x = self.enc(x)\n",
        "    x = x.view(-1, 1024)\n",
        "    enc = F.relu(self.fc_forward(x))\n",
        "    \n",
        "    x = F.relu(self.fc_backward(enc))\n",
        "    x = x.view(-1, 1024, 1, 1)\n",
        "    dec = self.dec(x)\n",
        "    \n",
        "    return enc, dec"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VGUrUYcnb1Od",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class AutoEncoder(Module):\n",
        "  def __init__(self, D_in, H_1, H_2, H_3, H_4, L_1, L_2):\n",
        "    super().__init__()\n",
        "    self.conv_1 = Layers.Conv2d(D_in, H_1, kernel_size=3, stride=1, padding=1)\n",
        "    self.batch_norm_1 = Layers.BatchNorm2d(H_1, eps=0.01)\n",
        "    self.conv_2 = Layers.Conv2d(H_1, H_2, kernel_size=3, stride=1, padding=1)\n",
        "    self.batch_norm_2 = Layers.BatchNorm2d(H_2, eps=0.01)\n",
        "    self.conv_3 = Layers.Conv2d(H_2, H_3, kernel_size=3, stride=1, padding=1)\n",
        "    self.batch_norm_3 = Layers.BatchNorm2d(H_3, eps=0.01)\n",
        "    self.conv_4 = Layers.Conv2d(H_3, H_4, 3, stride=1, padding=1)\n",
        "    self.batch_norm_4 = Layers.BatchNorm2d(H_4, eps=0.01)\n",
        "    self.linear_1 = Layers.Linear(1024 * 4 * 4, L_1)\n",
        "#     self.batch_norm_5 = Layers.BatchNorm2d(L_1)\n",
        "    self.linear_2 = Layers.Linear(L_1, L_2)\n",
        "    \n",
        "    self.linear_3 = Layers.Linear(L_2, L_1)\n",
        "#     self.batch_norm_5 = Layers.BatchNorm2d(L_1)\n",
        "    self.linear_4 = Layers.Linear(L_1, 1024 * 4 * 4)\n",
        "    self.batch_norm_5 = Layers.BatchNorm2d(H_4, eps=0.01)\n",
        "    self.conv_5 = Layers.Conv2d(H_4, H_3, 3, stride=1, padding=1)\n",
        "    self.batch_norm_6 = Layers.BatchNorm2d(H_3, eps=0.01)\n",
        "    self.conv_6 = Layers.Conv2d(H_3, H_2, 3, stride=1, padding=1)\n",
        "    self.batch_norm_7 = Layers.BatchNorm2d(H_2, eps=0.01)\n",
        "    self.conv_7 = Layers.Conv2d(H_2, H_1, 3, stride=1, padding=1)\n",
        "    self.batch_norm_8 = Layers.BatchNorm2d(H_1, eps=0.01)\n",
        "    self.conv_8 = Layers.Conv2d(H_1, D_in, 3, stride=1, padding=1)\n",
        "  \n",
        "  \n",
        "  def forward(self, x):\n",
        "    enc = F.max_pool2d(self.batch_norm_1(self.conv_1(x)), 2) # H_1 64 64\n",
        "    enc = F.max_pool2d(self.batch_norm_2(self.conv_2(enc)), 2) # H_2 32 32\n",
        "    enc = F.max_pool2d(self.batch_norm_3(self.conv_3(enc)), 2) # H_3 16 16\n",
        "    enc = F.max_pool2d(self.batch_norm_4(self.conv_4(enc)), 2) # D_in 8 8\n",
        "#     print(enc.size())\n",
        "    enc = enc.view(-1, 1024 * 4 * 4)\n",
        "#     print(enc.size())\n",
        "    enc = F.relu(self.linear_1(enc))\n",
        "#     enc = self.batch_norm_5(enc)\n",
        "#     enc = F.dropout2d(enc, 0.3)\n",
        "    enc = F.relu(self.linear_2(enc))\n",
        "#     print(enc.size())\n",
        "    dec = F.relu(self.linear_3(enc))\n",
        "#     dec = self.batch_norm_6(dec)\n",
        "#     dec = F.dropout2d(dec, 0.3)\n",
        "    dec = F.relu(self.linear_4(dec))\n",
        "#     print(dec.size())\n",
        "    dec = dec.view(-1, 1024, 4, 4)\n",
        "    dec = self.batch_norm_5(dec)\n",
        "    dec = F.interpolate(self.batch_norm_6(self.conv_5(dec)), 8)#, mode=\"bilinear\", align_corners=True)) # H_3 8 8\n",
        "    dec = F.interpolate(self.batch_norm_7(self.conv_6(dec)), 16)#, mode=\"bilinear\", align_corners=True)) # H_2 16 16\n",
        "    dec = F.interpolate(self.batch_norm_8(self.conv_7(dec)), 32)#, mode=\"bilinear\", align_corners=True)) # H_1 32 32\n",
        "    dec = F.interpolate(self.conv_8(dec), 64)#, mode=\"bilinear\", align_corners=True)) # D_in 64 64\n",
        "    \n",
        "    return enc, dec"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y982_zcwR70a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dump_model(model, path, epoch, optimizer, loss):\n",
        "  import torch\n",
        "  torch.save({\n",
        "            'epoch': epoch,\n",
        "            'model_state_dict': model.state_dict(),\n",
        "            'optimizer_state_dict': optimizer.state_dict(),\n",
        "            'loss': loss,\n",
        "            }, path) # \"gdrive/My Drive/encoder_project/model_checkpoint.model\"\n",
        "D_in, H_1, H_2, H_3, H_4, L_1, L_2 = 3, 128, 256, 512, 1024, 1000, 2000\n",
        "def load_model(path):\n",
        "  import torch\n",
        "  model = AutoEncoder(D_in, H_1, H_2, H_3, H_4, L_1, L_2).cuda().double()\n",
        "  optimizer = Opt.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "  checkpoint = torch.load(path)\n",
        "  model.load_state_dict(checkpoint['model_state_dict'])\n",
        "  optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "  epoch = checkpoint['epoch']\n",
        "  loss = checkpoint['loss']\n",
        "  \n",
        "  return model, optimizer, epoch, loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "etzTbitzb1RS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "try:\n",
        "  model\n",
        "except NameError:\n",
        "  model, optimizer, _, __ = load_model(\"gdrive/My Drive/encoder_project/model_light_checkpoint.model\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-S-yJiR6b1US",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# model = AutoEncoder(D_in, H_1, H_2, H_3, H_4, L_1, L_2).cuda().double()\n",
        "# model = AutoEncoder().cuda().double()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LL_IzNkTb1XS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# def init_weights(m):\n",
        "#     if type(m) == Layers.Linear:\n",
        "#         Layers.init.xavier_uniform_(m.weight)\n",
        "#         m.bias.data.fill_(0.01)\n",
        "#     if type(m) == Layers.Conv2d:\n",
        "#       Layers.init.xavier_uniform_(m.weight)\n",
        "#       m.bias.data.fill_(0.01)\n",
        "        \n",
        "# model.apply(init_weights)\n",
        "        \n",
        "criterion = Layers.MSELoss()\n",
        "# optimizer = Opt.SGD(model.parameters(), lr=0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xCgWJmUbb1c0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def fit(epoch, model, optimizer, data_loader, criterion, phase=\"training\"):\n",
        "  if phase == \"training\":\n",
        "    model.train()\n",
        "  if phase == \"validation\":\n",
        "    model.eval()\n",
        "  running_loss = 0.0\n",
        "  running_correct = 0.0\n",
        "  for batch_idx, data in enumerate(data_loader):\n",
        "    data, target = data.cuda(), data.cuda()\n",
        "    if phase == \"training\":\n",
        "      optimizer.zero_grad()\n",
        "    _, output = model(data)\n",
        "    loss = criterion(output, target)\n",
        "    \n",
        "    running_loss += loss\n",
        "    if phase == \"training\":\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "  \n",
        "  loss = running_loss / len(data_loader.dataset)\n",
        "  print(f\"{epoch}: {phase} loss is {loss}\")\n",
        "  \n",
        "  return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TiTO8Es6b1gA",
        "colab_type": "code",
        "outputId": "a5e2cdcb-b1b4-42c3-d027-f2e3825d1e58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1907
        }
      },
      "cell_type": "code",
      "source": [
        "train_loss = []\n",
        "val_loss = []\n",
        "\n",
        "for epoch in range(_, 400):\n",
        "  epoch_loss = fit(epoch, model, optimizer, train, criterion, phase=\"training\")\n",
        "  dump_model(model, \"gdrive/My Drive/encoder_project/model_light_checkpoint.model\", \n",
        "             epoch, optimizer, epoch_loss)\n",
        "  if epoch % 30 == 0:\n",
        "    for i in train:\n",
        "      img = i\n",
        "      break\n",
        "    img = img[:1]\n",
        "    _ , pred = model(img.cuda())\n",
        "    plt.imshow(np.rot90(pred[0].detach().cpu().numpy().T, k=3))\n",
        "    plt.show();\n",
        "#   test_loss = fit(epoch, model, test, criterion, phase=\"validation\")\n",
        "#   train_loss.append(epoch_loss)\n",
        "#   val_loss.append(val_loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "122: training loss is 0.0006582567285795026\n",
            "123: training loss is 0.0006565410257179052\n",
            "124: training loss is 0.0006559574996580905\n",
            "125: training loss is 0.0006544097384997903\n",
            "126: training loss is 0.0006554175169849274\n",
            "127: training loss is 0.000652015248521907\n",
            "128: training loss is 0.0006517830468452028\n",
            "129: training loss is 0.0006510820804584875\n",
            "130: training loss is 0.000650133866715932\n",
            "131: training loss is 0.0006492871023101033\n",
            "132: training loss is 0.0006481354743760668\n",
            "133: training loss is 0.0006475442088864707\n",
            "134: training loss is 0.0006464678018250889\n",
            "135: training loss is 0.0006467942113896713\n",
            "136: training loss is 0.0006442924348973839\n",
            "137: training loss is 0.0006449412255925471\n",
            "138: training loss is 0.0006445417386787156\n",
            "139: training loss is 0.0006454646118137808\n",
            "140: training loss is 0.0006415563540702887\n",
            "141: training loss is 0.0006423640341512224\n",
            "142: training loss is 0.0006411090347478198\n",
            "143: training loss is 0.0006379889465776752\n",
            "144: training loss is 0.0006378033651014285\n",
            "145: training loss is 0.0006386188268102197\n",
            "146: training loss is 0.0006384213336149618\n",
            "147: training loss is 0.0006370819869334468\n",
            "148: training loss is 0.0006363229360475417\n",
            "149: training loss is 0.0006357211557993703\n",
            "150: training loss is 0.0006341534936336081\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kk2hpacHNN988dDd56NAhNDc3Y9euXaP+7qF/PYTZc2afnx4TEY0D1Qc8r732GrZv344d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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "151: training loss is 0.0006329402791263046\n",
            "152: training loss is 0.000634304434852711\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "Traceback (most recent call last):\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "  File \"/usr/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n",
            "BrokenPipeError: [Errno 32] Broken pipe\n",
            "BrokenPipeError: [Errno 32] Broken pipe\n",
            "  File \"/usr/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-65ecfa7a617f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mepoch_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"training\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   dump_model(model, \"gdrive/My Drive/encoder_project/model_light_checkpoint.model\", \n\u001b[1;32m      7\u001b[0m              epoch, optimizer, epoch_loss)\n",
            "\u001b[0;32m<ipython-input-21-c3a8e1e61711>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epoch, model, optimizer, data_loader, criterion, phase)\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mrunning_correct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mphase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"training\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m       \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "text": [
            "BrokenPipeError: [Errno 32] Broken pipe\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n",
            "BrokenPipeError: [Errno 32] Broken pipe\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "wWnmNIxKb1lm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# import torch\n",
        "# inp = torch.randn(32, 48)#.reshape(-1, 45, 45, 3)\n",
        "# inp = inp.view(-1, 3, 4, 4)\n",
        "# inp = inp.view(3, 8, 8)\n",
        "# m = torch.nn.Conv2d(3, 64, 3, stride=1, padding=1)\n",
        "# inp = m(inp)\n",
        "# inp = torch.nn.functional.max_pool2d(inp, 2) # 64 22 22\n",
        "# m = torch.nn.Conv2d(64, 16, 3, stride=1, padding=1) # 16 22 22\n",
        "# inp = torch.nn.functional.max_pool2d(m(inp), 2) # 16 11 11\n",
        "# m = torch.nn.Conv2d(16, 3, 3, stride=1, padding=1) # 3 11 11\n",
        "# inp = torch.nn.functional.max_pool2d(m(inp), 2) # 3 5 5\n",
        "\n",
        "\n",
        "# m = torch.nn.Conv2d(3, 16, 3, stride=1, padding=1)\n",
        "# inp = torch.nn.functional.interpolate(m(inp), 11, mode=\"bilinear\", align_corners=True)\n",
        "# m = torch.nn.Conv2d(16, 64, 3, stride=1, padding=1)\n",
        "# inp = torch.nn.functional.interpolate(m(inp), 22, mode=\"bilinear\", align_corners=True)\n",
        "# m = torch.nn.Conv2d(64, 3, 3, stride=1, padding=1)\n",
        "# inp = torch.nn.functional.interpolate(m(inp), 45, mode=\"bilinear\", align_corners=True)\n",
        "\n",
        "# inp.size()\n",
        "# F.dropout2d()\n",
        "incept = models.Inception3()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Q1J9q_A4b1op",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in train:\n",
        "  img = i\n",
        "  break\n",
        "img = img[:1]\n",
        "# img.size()\n",
        "_ , pred = model(img.cuda())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Aq_MFVH6b1z8",
        "colab_type": "code",
        "outputId": "ba3fc583-017e-4ff5-b241-43bc8ad3f7bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(np.rot90(pred[0].detach().cpu().numpy().T, k=3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f54460f33c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8651XIGvpz6uG+poHVTh+UanvQ12w1/BwLtTkNTwv9QSJxet0b8n9+9tvfh8XX/haYE5/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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cClRPRArb12y",
        "colab_type": "code",
        "outputId": "194dfad4-6260-4357-9f67-ca6c0a00ecbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(np.rot90(img[0].detach().cpu().numpy().T, k=3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f99ca4a0358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wGLyCg/bCZx5/FOrak/zpfttjx9rc/J1t9JnrVqs2y8uuuor3swPj0xLZLBU7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\nrY5B3Zte9/M91++563/T37/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QlLQLJHY6+IoyUUPpQ1uM5+CiC6D8xFNPwXFLr+ZPwKQo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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rEL3xvqbb15v",
        "colab_type": "code",
        "outputId": "5434b470-e795-431c-f3d2-d578afd98c4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "cell_type": "code",
      "source": [
        "   plt.imshow(img.numpy()[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f252f2afb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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olW0QjQvNFZaiKFJww2zmc8XqIiymwKEKVYQMTJXPxJX5Cse5qOj1br3LnUK4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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AX_xP97Tb18r",
        "colab_type": "code",
        "outputId": "15bcd8b8-6ff7-4935-c33a-eef961ca3871",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "img.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 250, 250, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "m-ExmEqzh1pE",
        "colab_type": "code",
        "outputId": "5b3d68da-65f9-45d0-cf95-cfb4440bedd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cB0UqaaniMyQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.save({\n",
        "            'epoch': epoch,\n",
        "            'model_state_dict': model.state_dict(),\n",
        "            'optimizer_state_dict': optimizer.state_dict(),\n",
        "            'loss': epoch_loss,\n",
        "            }, \"gdrive/My Drive/encoder_project/model_checkpoint.model\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Wod80Q4YiM06",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = AutoEncoder().cuda().double()\n",
        "optimizer = Opt.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "checkpoint = torch.load(\"gdrive/My Drive/encoder_project/model_checkpoint.model\")\n",
        "model.load_state_dict(checkpoint['model_state_dict'])\n",
        "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "epoch = checkpoint['epoch']\n",
        "loss = checkpoint['loss']"
      ],
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      "outputs": []
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        "id": "6SvmWmReiM34",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BjY1FiEziM62",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
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    {
      "metadata": {
        "id": "9tGeelx8iNAh",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}