Latent_Code_ResNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Latent Code ResNet.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m1RufaMVC4iN"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/acoadmarmon/resnet18-tensorflow.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-MohRoedq2J8",
        "outputId": "9c3b0091-87d0-4fc3-d502-9cd66e2ffe6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'resnet18-tensorflow'...\n",
            "remote: Enumerating objects: 18, done.\u001b[K\n",
            "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
            "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
            "remote: Total 18 (delta 3), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (18/18), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "latent_cars = np.load('/content/drive/MyDrive/latents_cars.npy')\n",
        "images = np.load('/content/drive/MyDrive/generated_images_cars.npy')"
      ],
      "metadata": {
        "id": "bTI9iKSEC7sF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images = np.transpose(images, [0,2,3,1])"
      ],
      "metadata": {
        "id": "XrD4ib3vGIED"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "cv2_imshow(images[987]*255)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "wciIxNMmEZJV",
        "outputId": "91a3d71e-1de8-4db3-8e0a-e3cbd7634188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F4C496FDDD0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mv \"/content/resnet18-tensorflow/resnet_18.py\" \"/content/resnet_18.py\""
      ],
      "metadata": {
        "id": "jkfBFVeJsD9y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from resnet_18 import ResNet18\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input"
      ],
      "metadata": {
        "id": "hpe6SyktFL3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inp = tf.keras.layers.Input(shape=(128,128,3))\n",
        "preprocess = preprocess_input(inp)\n",
        "res = ResNet18(include_top=False, weights='imagenet', input_shape=(128,128,3))(preprocess)\n",
        "flat = tf.keras.layers.Flatten()(res)\n",
        "fc1  = tf.keras.layers.Dense(2048)(flat)\n",
        "fc2 = tf.keras.layers.Dense(512)(fc1)\n",
        "out = tf.keras.layers.Dense(128)(fc2)"
      ],
      "metadata": {
        "id": "AvRUnwSRJF4T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Model(inputs=preprocess, outputs=out)"
      ],
      "metadata": {
        "id": "xjKX15baKBFb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(images, latent_cars, test_size=0.2)"
      ],
      "metadata": {
        "id": "IYf5x5jJNQO_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))"
      ],
      "metadata": {
        "id": "TCqgNJ4yKJV8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.batch(64).cache().prefetch(tf.data.AUTOTUNE)\n",
        "val_ds = val_ds.batch(64).cache().prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "D9COOQfuLVvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss=\"logcosh\")"
      ],
      "metadata": {
        "id": "5gGCjstjNMgy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_ds, validation_data=val_ds, epochs=7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fpbC3EpN5sC",
        "outputId": "6c8a74a3-8ec1-4a45-fda6-53a481359e7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/7\n",
            "104/104 [==============================] - 662s 6s/step - loss: 1.5821 - val_loss: 25.9243\n",
            "Epoch 2/7\n",
            "104/104 [==============================] - 661s 6s/step - loss: 0.3932 - val_loss: 0.5260\n",
            "Epoch 3/7\n",
            "104/104 [==============================] - 661s 6s/step - loss: 0.3798 - val_loss: 0.3871\n",
            "Epoch 4/7\n",
            "104/104 [==============================] - 666s 6s/step - loss: 0.3736 - val_loss: 0.3874\n",
            "Epoch 5/7\n",
            "104/104 [==============================] - 669s 6s/step - loss: 0.3692 - val_loss: 0.3795\n",
            "Epoch 6/7\n",
            "104/104 [==============================] - 671s 6s/step - loss: 0.3653 - val_loss: 0.3733\n",
            "Epoch 7/7\n",
            "104/104 [==============================] - 676s 7s/step - loss: 0.3619 - val_loss: 0.3729\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f4c4f9bdf90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"/content/drive/MyDrive/saved_net/\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVNNz7KXObtq",
        "outputId": "4af00cd0-c2a4-4b62-9ba3-a417a3088503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/MyDrive/saved_net/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.load_model(\"/content/drive/MyDrive/saved_net\")"
      ],
      "metadata": {
        "id": "gLcvsm2eleaT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.reduce_mean(tf.losses.mean_squared_error(model.predict(images[:1000]), latent_cars[:1000]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZNFc5fcmOe5",
        "outputId": "232c0aed-e40e-4367-b816-ecc49baa3eee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.9703874>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip \"/content/lat images.zip\" -d \"/content/\"\n",
        "! unzip \"/content/old.zip\" -d \"/content/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNKMWbsbQxBx",
        "outputId": "0b99d5c7-569f-4472-8dba-04b59584347a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/lat images.zip\n",
            "   creating: /content/lat images/\n",
            "  inflating: /content/lat images/462.jpg  \n",
            "  inflating: /content/lat images/07.jpg  \n",
            "  inflating: /content/lat images/684.jpg  \n",
            "  inflating: /content/lat images/02.jpg  \n",
            "  inflating: /content/lat images/75.jpg  \n",
            "  inflating: /content/lat images/12.jpg  \n",
            "  inflating: /content/lat images/1325.jpg  \n",
            "  inflating: /content/lat images/798.jpg  \n",
            "  inflating: /content/lat images/764.jpg  \n",
            "  inflating: /content/lat images/2886.jpg  \n",
            "  inflating: /content/lat images/1008.jpg  \n",
            "  inflating: /content/lat images/841.jpg  \n",
            "  inflating: /content/lat images/997.jpg  \n",
            "  inflating: /content/lat images/1493.jpg  \n",
            "  inflating: /content/lat images/2925.jpg  \n",
            "Archive:  /content/old.zip\n",
            "   creating: /content/old/\n",
            "  inflating: /content/old/3.jpg      \n",
            "  inflating: /content/old/5.jpg      \n",
            "  inflating: /content/old/.jpg       \n",
            "  inflating: /content/old/6.jpg      \n",
            "  inflating: /content/old/2.jpg      \n",
            "  inflating: /content/old/1.jpg      \n",
            "  inflating: /content/old/7.jpg      \n",
            "  inflating: /content/old/4.jpg      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "lat_arr = {}\n",
        "old_arr = {}\n",
        "for img_name in os.listdir(\"/content/lat images\"):\n",
        "  img_path = os.path.join(\"/content/lat images/\", img_name)\n",
        "  img = cv2.imread(img_path)\n",
        "  img = cv2.resize(img, (128,128))\n",
        "  img = np.array(img).astype('float')/255\n",
        "  l = model.predict(np.expand_dims(img,0))[0]\n",
        "  lat_arr[img_name.split('.')[0]] = l\n",
        "\n",
        "for img_name in os.listdir(\"/content/old\"):\n",
        "  img_path = os.path.join(\"/content/old/\", img_name)\n",
        "  img = cv2.imread(img_path)\n",
        "  img = cv2.resize(img, (128,128))\n",
        "  img = np.array(img).astype('float')/255\n",
        "  l = model.predict(np.expand_dims(img,0))[0]\n",
        "  old_arr[img_name.split('.')[0]] = l"
      ],
      "metadata": {
        "id": "SH_IeW3BRF6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w_arr = []\n",
        "for i in lat_arr.keys():\n",
        "  for j in old_arr.keys():\n",
        "    l1 = lat_arr[i]\n",
        "    l2 = old_arr[j]\n",
        "    w = (l1+l2)/2\n",
        "    w_arr.append(w)"
      ],
      "metadata": {
        "id": "MlcAroIwSXYM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.array(w_arr).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kx2YQlldKm41",
        "outputId": "82eb9a51-6336-4bd0-ef10-001f6abcdf5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(105, 128)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('lat1.npy',w_arr)"
      ],
      "metadata": {
        "id": "5SdzpwTNTVV1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}