GL_delineation_geocoded_complex_customLoss.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GL_delineation_geocoded_complex_customLoss.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KNLgtCArppC",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCrpI0flVW78",
        "colab_type": "text"
      },
      "source": [
        "# Set up run"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k78IROuVLJ8g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "84ef68ef-93f4-417e-e592-85c60fd4b4f1"
      },
      "source": [
        "#-- Set up configurations / parameters\n",
        "retrain = True # retrain previously existing model\n",
        "ninit = 32 #number of channels to start with\n",
        "dropout_frac = 0.2 # dropout fraction\n",
        "n_batch = 15\n",
        "n_epochs = 30\n",
        "n_test = 500\n",
        "ratio = 727 # penalization ratio for GL and non-GL points based on smaller dataaset\n",
        "mod_lbl = 'atrous'\n",
        "if mod_lbl == 'unet':\n",
        "  mod_str = '{0}_{1}init_{2}down_drop{3:.1f}_customLossR{4}'.format(mod_lbl,ninit,ndown,\n",
        "                                                        dropout_frac,ratio)\n",
        "elif mod_lbl in ['atrous','atrous_noPool']:\n",
        "  mod_str = '{0}_{1}init_drop{2:.1f}_customLossR{3}'.format(mod_lbl,ninit,dropout_frac,ratio)\n",
        "else:\n",
        "  print('model label not matching.')\n",
        "print(mod_str)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "atrous_32init_drop0.2_customLossR727\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0iqhtdnvws0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "63076a04-c7a1-4646-ef12-eed04d367764"
      },
      "source": [
        "#-- Import Modules\n",
        "import os\n",
        "import imp\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "from google.colab import drive\n",
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.preprocessing import image\n",
        "from tensorflow.python.client import device_lib\n",
        "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
        "from keras.utils import plot_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sR2mHxpv5NU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d4210681-048a-4c95-8eb0-320bfd2517eb"
      },
      "source": [
        "#-- Mount Google Drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VEY8FLTI-WM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- Directory setup\n",
        "colabdir = '/content/gdrive/My Drive/Colab Notebooks/'\n",
        "output_dir = '/content/gdrive/My Drive/GL_Learning/'\n",
        "ddir = '/content/gdrive/Shared drives/GROUNDING_LINE_TEAM_DRIVE/ML_Yara/geocoded_v1/'\n",
        "train_dir = os.path.join(ddir,'train_n%i.dir'%n_test)\n",
        "test_dir = os.path.join(ddir,'test_n%i.dir'%n_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJkQl4knwzvp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- Get list of images\n",
        "fileList = os.listdir(train_dir)\n",
        "train_list = [f for f in fileList if (f.endswith('.npy') and f.startswith('coco'))]\n",
        "fileList = os.listdir(test_dir)\n",
        "test_list = [f for f in fileList if (f.endswith('.npy') and f.startswith('coco'))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1SZivgc6o2Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "42d22cb7-b5ba-4d0a-b1bb-85ed5f28a4e7"
      },
      "source": [
        "#-- set ID_list to be passed onto data generator class\n",
        "ID_list = [os.path.join(train_dir,f) for f in train_list]\n",
        "N = len(ID_list)\n",
        "print(N)\n",
        "\n",
        "#-- read 1 file to get dimensions\n",
        "im = np.load(ID_list[0])\n",
        "h,wi,ch = im.shape\n",
        "print(h,wi,ch)\n",
        "\n",
        "#-- randomly set aside 10% of data for validation\n",
        "valid_ii = random.sample(range(N),int(0.1*N))\n",
        "train_ii = np.setdiff1d(np.arange(N),valid_ii)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5320\n",
            "512 512 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvcBs7mo0UfG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "f0f35f01-e26d-4a98-9c4f-d8bf9da10661"
      },
      "source": [
        "#-- NOTE: we need to install rasterio so that it can be used by the\n",
        "#--  data generator in the next cell\n",
        "!pip install rasterio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting rasterio\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c7/81/13321f88f582a00705c5f348724728e8999136e19d6e7c56f7e6ac9bb7f9/rasterio-1.1.3-cp36-cp36m-manylinux1_x86_64.whl (18.1MB)\n",
            "\u001b[K     |████████████████████████████████| 18.1MB 195kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from rasterio) (1.18.2)\n",
            "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from rasterio) (7.1.1)\n",
            "Collecting affine\n",
            "  Downloading https://files.pythonhosted.org/packages/ac/a6/1a39a1ede71210e3ddaf623982b06ecfc5c5c03741ae659073159184cd3e/affine-2.3.0-py2.py3-none-any.whl\n",
            "Collecting cligj>=0.5\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
            "Collecting click-plugins\n",
            "  Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.6/dist-packages (from rasterio) (19.3.0)\n",
            "Collecting snuggs>=1.4.1\n",
            "  Downloading https://files.pythonhosted.org/packages/cc/0e/d27d6e806d6c0d1a2cfdc5d1f088e42339a0a54a09c3343f7f81ec8947ea/snuggs-1.4.7-py3-none-any.whl\n",
            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.6/dist-packages (from snuggs>=1.4.1->rasterio) (2.4.7)\n",
            "Installing collected packages: affine, cligj, click-plugins, snuggs, rasterio\n",
            "Successfully installed affine-2.3.0 click-plugins-1.1.1 cligj-0.5.0 rasterio-1.1.3 snuggs-1.4.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O85or-6Jz5ln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- setup generator\n",
        "data_generation = imp.load_source('data_generation',os.path.join(colabdir,'data_generation.py'))\n",
        "from data_generation import DataGenerator\n",
        "training_generator = DataGenerator(list(np.array(ID_list)[train_ii]),batch_size=n_batch, dim=(h,wi), \n",
        "\tn_channels=ch, shuffle=True)\n",
        "validation_generator = DataGenerator(list(np.array(ID_list)[valid_ii]),batch_size=n_batch, dim=(h,wi), \n",
        "\tn_channels=ch, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tL1rcBCZw_9S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- Import model\n",
        "mod_module = imp.load_source('nn_model',os.path.join(colabdir,'nn_model.py'))\n",
        "#-- set up model\n",
        "if mod_lbl == 'unet':\n",
        "  print('loading unet model')\n",
        "  model = mod_module.unet_model_double_dropout(height=h,width=wi,channels=ch, \n",
        "                                        n_init=ninit,n_layers=ndown,\n",
        "                                        drop=dropout_frac)\n",
        "elif mod_lbl == 'atrous':\n",
        "  print(\"loading atrous model\")\n",
        "  model = mod_module.nn_model_atrous_double_dropout(height=h,width=wi,\n",
        "                                                channels=ch,\n",
        "                                                n_filts=ninit,\n",
        "                                                drop=dropout_frac)\n",
        "elif mod_lbl == 'atrous_noPool':\n",
        "  print(\"loading atrous_noPool model\")\n",
        "  model = mod_module.nn_model_atrous_noPool(height=h,width=wi,\n",
        "                                                channels=ch,\n",
        "                                                n_filts=ninit,\n",
        "                                                drop=dropout_frac)\n",
        "else:\n",
        "  print('Model label not correct.')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvAoTa0haL_3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- define custom loss function\n",
        "def customLoss(yTrue,yPred):\n",
        "  return -1*K.mean(ratio*(yTrue*K.log(yPred+1e-32)) + ((1. - yTrue)*K.log(1-yPred+1e-32)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmT7qCY39VzM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- compile imported model\n",
        "model.compile(loss=customLoss,optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHk2_b1cIbIf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "892dc10c-e8b9-4b1b-8977-90409665df85"
      },
      "source": [
        "#-- checkpoint file\n",
        "chk_file = os.path.join(output_dir,'{0}_weights.h5'.format(mod_str))\n",
        "\n",
        "#-- if file exists, read model from file\n",
        "if os.path.isfile(chk_file):\n",
        "  print('Check point exists; loading model from file.')\n",
        "  #-- load weights\n",
        "  model.load_weights(chk_file)\n",
        "else:\n",
        "  print('Model does not previously exist.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Check point exists; loading model from file.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN7H0GfWKymx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "outputId": "07f1f0bc-1612-4ba2-f8d3-920fb0c4c5b1"
      },
      "source": [
        "#-- Train the model\n",
        "if (retrain) or (not os.path.isfile(chk_file)):\n",
        "  print('Training model...')\n",
        "  #-- create checkpoint\n",
        "  model_checkpoint = keras.callbacks.ModelCheckpoint(chk_file, monitor='loss',\n",
        "                                                     verbose=1, \n",
        "                                                     save_best_only=True)\n",
        "  lr_callback = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5,\n",
        "                                  verbose=1, mode='auto', min_delta=0.0001, \n",
        "                                  cooldown=0, min_lr=0)\n",
        "  es_callback = EarlyStopping(monitor='val_loss',min_delta=0.0001, patience=5,\n",
        "     verbose=1, mode='auto')\n",
        "  #-- now fit the model\n",
        "  history = model.fit_generator(generator=training_generator,\n",
        "                                validation_data=validation_generator,\n",
        "                                use_multiprocessing=True,\n",
        "                                workers=8, epochs=n_epochs,\n",
        "                                shuffle=True, verbose=1,\n",
        "                                callbacks=[lr_callback,es_callback,model_checkpoint])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "Epoch 1/30\n",
            "319/319 [==============================] - 611s 2s/step - loss: 0.0884 - accuracy: 0.9804 - val_loss: 0.0677 - val_accuracy: 0.9824\n",
            "\n",
            "Epoch 00001: loss improved from inf to 0.08840, saving model to /content/gdrive/My Drive/GL_Learning/atrous_32init_drop0.2_customLossR727_weights.h5\n",
            "Epoch 2/30\n",
            "318/319 [============================>.] - ETA: 1s - loss: 0.0916 - accuracy: 0.9796\n",
            "Epoch 00001: loss improved from inf to 0.08840, saving model to /content/gdrive/My Drive/GL_Learning/atrous_32init_drop0.2_customLossR727_weights.h5\n",
            "319/319 [==============================] - 573s 2s/step - loss: 0.0916 - accuracy: 0.9796 - val_loss: 0.0997 - val_accuracy: 0.9802\n",
            "\n",
            "Epoch 00002: loss did not improve from 0.08840\n",
            "Epoch 3/30\n",
            "319/319 [==============================] - 568s 2s/step - loss: 0.0897 - accuracy: 0.9801 - val_loss: 0.0645 - val_accuracy: 0.9815\n",
            "\n",
            "Epoch 00003: loss did not improve from 0.08840\n",
            "Epoch 4/30\n",
            "319/319 [==============================] - 573s 2s/step - loss: 0.0849 - accuracy: 0.9810 - val_loss: 0.0428 - val_accuracy: 0.9833\n",
            "Epoch 4/30\n",
            "\n",
            "Epoch 00004: loss improved from 0.08840 to 0.08493, saving model to /content/gdrive/My Drive/GL_Learning/atrous_32init_drop0.2_customLossR727_weights.h5\n",
            "Epoch 5/30\n",
            "319/319 [==============================] - 600s 2s/step - loss: 0.0828 - accuracy: 0.9816 - val_loss: 0.0748 - val_accuracy: 0.9816\n",
            "\n",
            "Epoch 00005: loss improved from 0.08493 to 0.08283, saving model to /content/gdrive/My Drive/GL_Learning/atrous_32init_drop0.2_customLossR727_weights.h5\n",
            "Epoch 6/30\n",
            "177/319 [===============>..............] - ETA: 3:51 - loss: 0.0813 - accuracy: 0.9821"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:616: UserWarning: The input 89 could not be retrieved. It could be because a worker has died.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "189/319 [================>.............] - ETA: 3:46 - loss: 0.0817 - accuracy: 0.9821"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py:616: UserWarning: The input 263 could not be retrieved. It could be because a worker has died.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "319/319 [==============================] - 597s 2s/step - loss: 0.0799 - accuracy: 0.9824 - val_loss: 0.0844 - val_accuracy: 0.9796\n",
            "\n",
            "Epoch 00006: loss improved from 0.08283 to 0.07994, saving model to /content/gdrive/My Drive/GL_Learning/atrous_32init_drop0.2_customLossR727_weights.h5\n",
            "Epoch 7/30\n",
            "319/319 [==============================] - 568s 2s/step - loss: 0.0818 - accuracy: 0.9819 - val_loss: 0.0981 - val_accuracy: 0.9838\n",
            "\n",
            "Epoch 00007: loss did not improve from 0.07994\n",
            "Epoch 8/30\n",
            "318/319 [============================>.] - ETA: 1s - loss: 0.0795 - accuracy: 0.9824Epoch 8/30\n",
            "319/319 [==============================] - 575s 2s/step - loss: 0.0795 - accuracy: 0.9824 - val_loss: 0.0878 - val_accuracy: 0.9823\n",
            "\n",
            "Epoch 00008: loss improved from 0.07994 to 0.07947, saving model to /content/gdrive/My Drive/GL_Learning/atrous_32init_drop0.2_customLossR727_weights.h5\n",
            "Epoch 9/30\n",
            "319/319 [==============================] - 566s 2s/step - loss: 0.0849 - accuracy: 0.9812 - val_loss: 0.0514 - val_accuracy: 0.9817\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "\n",
            "Epoch 00009: loss did not improve from 0.07947\n",
            "Epoch 00009: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvmUAppx4LBm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- save model if it fails\n",
        "#model.save(chk_file,overwrite=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Q_pgfnXQvX4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e5aff1e-b5d6-46ce-95a7-776242af69fe"
      },
      "source": [
        "\"\"\"\n",
        "#-- save history to file\n",
        "  outfile = open(os.path.join(output_dir,\n",
        "                        '{0}_history.txt'\n",
        "                        .format(mod_str)),'w')\n",
        "  outfile.write('Epoch loss\\tval_loss\\tacc\\tval_acc\\n')\n",
        "  for i in range(len(history.history['loss'])):\n",
        "      outfile.write('%i\\t%f\\t%f\\t%f\\t%f\\n'%(i,history.history['loss'][i],history.history['val_loss'][i],\\\n",
        "          history.history['accuracy'][i],history.history['val_accuracy'][i]))\n",
        "  outfile.close()\n",
        "\n",
        "  #-- Make plots for training history\n",
        "  for item,name in zip(['accuracy','loss'],['Accuracy','Loss']):\n",
        "    fig = plt.figure(1,figsize=(8,6))\n",
        "    plt.plot(history.history[item])\n",
        "    plt.plot(history.history['val_%s'%item])\n",
        "    plt.title('Model %s'%name)\n",
        "    plt.ylabel(name)\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.legend(['Training', 'Validation'], loc='upper left')\n",
        "    plt.savefig(os.path.join(output_dir,\n",
        "                        '{0}_history.pdf'\n",
        "                        .format(mod_str)),format='pdf')\n",
        "    plt.close(fig)\n",
        "    \"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n#-- save history to file\\n  outfile = open(os.path.join(output_dir,\\n                        '{0}_history.txt'\\n                        .format(mod_str)),'w')\\n  outfile.write('Epoch loss\\tval_loss\\tacc\\tval_acc\\n')\\n  for i in range(len(history.history['loss'])):\\n      outfile.write('%i\\t%f\\t%f\\t%f\\t%f\\n'%(i,history.history['loss'][i],history.history['val_loss'][i],          history.history['accuracy'][i],history.history['val_accuracy'][i]))\\n  outfile.close()\\n\\n  #-- Make plots for training history\\n  for item,name in zip(['accuracy','loss'],['Accuracy','Loss']):\\n    fig = plt.figure(1,figsize=(8,6))\\n    plt.plot(history.history[item])\\n    plt.plot(history.history['val_%s'%item])\\n    plt.title('Model %s'%name)\\n    plt.ylabel(name)\\n    plt.xlabel('Epochs')\\n    plt.legend(['Training', 'Validation'], loc='upper left')\\n    plt.savefig(os.path.join(output_dir,\\n                        '{0}_history.pdf'\\n                        .format(mod_str)),format='pdf')\\n    plt.close(fig)\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Y34oLYfx4i4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "55ab8b40-e01c-4464-e867-dd663d34429f"
      },
      "source": [
        "#-- plot and save model diagaram\n",
        "plot_model(model,to_file=os.path.join(output_dir,'{0}_diagram.png'.format(mod_str)),show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsbiHUo2Texy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2c0278fb-90be-4811-b313-091328310255"
      },
      "source": [
        "\"\"\"\n",
        "print('Model is trained. Running on Train data...')\n",
        "#-------------------------------\n",
        "#-- First run on training data\n",
        "#-------------------------------\n",
        "#-- make output directory\n",
        "out_dir = os.path.join(output_dir,'Train_predictions.dir',\n",
        "                       '{0}.dir'.format(mod_str))\n",
        "if (not os.path.isdir(out_dir)):\n",
        "  os.mkdir(out_dir)\n",
        "# Read the images 500 at a time to save memory\n",
        "idx = 0 #-- index count\n",
        "incr = 500 #- reading increment\n",
        "while idx < len(train_list):\n",
        "  in_imgs = np.empty((incr,h,w,ch))\n",
        "  for i,f enumerate(train_list[idx:idx+incr]):\n",
        "    in_imgs[i,] = np.load(os.path.join(train_dir,f)) \n",
        "  out_imgs = model.predict(in_imgs, batch_size=1, verbose=1)\n",
        "  out_imgs = out_imgs.reshape(out_imgs.shape[0],h,wi,out_imgs.shape[2])\n",
        "\n",
        "  #-- save output images\n",
        "  for i,f enumerate(train_list[idx:idx+incr]):\n",
        "    im = image.array_to_img(out_imgs[i]) \n",
        "    im.save(os.path.join(out_dir,f.replace('coco','pred').replace('npy','png')))\n",
        "  \n",
        "  #-- increment index count\n",
        "  idx += incr\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nprint('Model is trained. Running on Train data...')\\n#-------------------------------\\n#-- First run on training data\\n#-------------------------------\\n#-- make output directory\\nout_dir = os.path.join(output_dir,'Train_predictions.dir',\\n                       '{0}.dir'.format(mod_str))\\nif (not os.path.isdir(out_dir)):\\n  os.mkdir(out_dir)\\n# Read the images 500 at a time to save memory\\nidx = 0 #-- index count\\nincr = 500 #- reading increment\\nwhile idx + inc <= len(train_list):\\n  in_imgs = np.empty((incr,h,w,ch))\\n  for i,f enumerate(train_list[idx:idx+incr]):\\n    in_imgs[i,] = np.load(os.path.join(train_dir,f)) \\n  out_imgs = model.predict(in_imgs, batch_size=1, verbose=1)\\n  out_imgs = out_imgs.reshape(out_imgs.shape[0],h,wi,out_imgs.shape[2])\\n\\n  #-- save output images\\n  for i,f enumerate(train_list[idx:idx+incr]):\\n    im = image.array_to_img(out_imgs[i]) \\n    im.save(os.path.join(out_dir,f.replace('coco','pred').replace('npy','png')))\\n  \\n  #-- increment index count\\n  idx += incr\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrLWkMTMAryy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#-- Read test data all at once\n",
        "test_imgs = np.ones((n_test,h,wi,ch))\n",
        "for i,f in enumerate(test_list):\n",
        "    test_imgs[i,] = np.load(os.path.join(test_dir,f))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZciosNGdHKQs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6966f9a1-d9df-45ff-8d6d-2d0ceb95a8ce"
      },
      "source": [
        "#-------------------------------\n",
        "#-- Run on test data\n",
        "#-------------------------------\n",
        "out_imgs = model.predict(test_imgs, batch_size=1, verbose=1)\n",
        "out_imgs = out_imgs.reshape(out_imgs.shape[0],h,wi,out_imgs.shape[2])\n",
        "print(out_imgs.shape)\n",
        "\n",
        "#-- make output directory\n",
        "out_dir = os.path.join(output_dir,'Test_predictions.dir',\n",
        "                       '{0}.dir'.format(mod_str))\n",
        "if (not os.path.isdir(out_dir)):\n",
        "  os.mkdir(out_dir)\n",
        "\n",
        "#-- save output images\n",
        "for i,f in enumerate(test_list):\n",
        "  im = image.array_to_img(out_imgs[i]) \n",
        "  im.save(os.path.join(out_dir,f.replace('coco','pred').replace('npy','png')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "500/500 [==============================] - 9s 18ms/step\n",
            "(500, 512, 512, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTBZrMRlCN-U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd13b06a-c32e-4e5b-9544-231aa3b4b428"
      },
      "source": [
        "print(np.max(out_imgs))\n",
        "print(np.min(out_imgs))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.999708\n",
            "0.0005451155\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u9H27mHWPQl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}