third_stage_preparation_with_threshold_plot_8ensemble_with best_submission_xxx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cb07693",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import cv2\n",
    "\n",
    "from tqdm import tqdm_notebook, tnrange\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "from keras.layers.core import Lambda\n",
    "from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint,ReduceLROnPlateau\n",
    "from keras import backend as K\n",
    "#!pip install segmentation-models\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a32ff0",
   "metadata": {},
   "source": [
    "### added 100 gigabytes of memory limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c95bab1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"stage1/X_train\", \"rb\") as f:\n",
    "    X_train=pickle.load( f)\n",
    "with open(\"stage1/X_val\", \"rb\") as f:\n",
    "    X_val=pickle.load( f)\n",
    "with open(\"stage1/y_train\", \"rb\") as f:\n",
    "    y_train=pickle.load( f)\n",
    "with open(\"stage1/y_val\", \"rb\") as f:\n",
    "    y_val=pickle.load(f)\n",
    "with open(\"preds_test_best_submission\", \"rb\") as f:\n",
    "    preds_test=pickle.load(f)    \n",
    "    \n",
    "    \n",
    "\n",
    "#with open(\"stage1/X_test\", \"rb\") as f:\n",
    "    #X_test=pickle.load(f)    \n",
    "#preds_test=preds_test/8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91c1ed78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12b89364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-b4647d3bd7b7>:13: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n"
     ]
    },
    {
     "data": {
      "application/json": {
       "ascii": false,
       "bar_format": null,
       "colour": null,
       "elapsed": 0.033606767654418945,
       "initial": 0,
       "n": 0,
       "ncols": null,
       "nrows": null,
       "postfix": null,
       "prefix": "",
       "rate": null,
       "total": 18000,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc83c41ba3294ee7ad17d66e8c47a667",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/18000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "im_width = 128\n",
    "im_height = 128\n",
    "im_chan = 1\n",
    "test_ids=[]\n",
    "path_test = r'C:\\\\Users\\diego\\OneDrive\\Desktop\\DS\\seismic_segmentation\\test'\n",
    "for i in os.listdir(os.path.join(path_test,\"images\")):\n",
    "    test_ids.append(i)    \n",
    "# Get and resize test images\n",
    "X_test = np.zeros((len(test_ids), im_height, im_width, im_chan), dtype=np.uint8)\n",
    "sizes_test = []\n",
    "print('Getting and resizing test images ... ')\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n",
    "    path = path_test\n",
    "    img = load_img(path + '/images/' + id_)\n",
    "    x = img_to_array(img)[:,:,1]\n",
    "    sizes_test.append([x.shape[0], x.shape[1]])\n",
    "    x = resize(x, (128, 128, 1), mode='constant', preserve_range=True)\n",
    "    X_test[n] = x\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ad397ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b73463b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[1;32m<ipython-input-14-2ef1d12485f0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mconfident_X_test1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mconfident_X_test1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfident_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1900\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-14-2ef1d12485f0>\u001b[0m in \u001b[0;36mconfident_images\u001b[1;34m(X_test, preds_test, threshold)\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfidence\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mconfident_X_test1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, string)\u001b[0m\n\u001b[0;32m    403\u001b[0m             \u001b[1;31m# only touch the buffer in the IO thread to avoid races\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 404\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    405\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_child\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mschedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[1;31m# wake event thread (message content is ignored)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_event_pipe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\zmq\\sugar\\socket.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m    490\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSocket\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\zmq\\backend\\cython\\checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "'''\n",
    "def confident_images(X_test, preds_test,threshold=1900):\n",
    "    #X_test=X_test.tolist()\n",
    "    #preds_test=preds_test.tolist()\n",
    "    confident_X_test1=[]\n",
    "    confident_y_test1=[]\n",
    "    def BinaryCrossEntropy(y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "        term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "        term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "        return -np.mean(term_0+term_1, axis=0)\n",
    "    for i,elem in enumerate(preds_test):\n",
    "        confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                      ,preds_test[i].flatten())\n",
    "        if confidence>=threshold:\n",
    "            confident_X_test1.append(X_test[i].tolist())\n",
    "            confident_y_test1.append(preds_test[i].tolist())\n",
    "            print(confidence)\n",
    "    return confident_X_test1,confident_y_test1\n",
    "confident_X_test1,confident_y_test1=confident_images(X_test, preds_test,threshold=1900)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "224ce275",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        [[[0.15367094],\n",
       "          [0.08783691],\n",
       "          [0.07364161],\n",
       "          ...,\n",
       "          [0.06103715],\n",
       "          [0.08479187],\n",
       "          [0.17096094]],\n",
       " \n",
       "         [[0.07741006],\n",
       "          [0.03338425],\n",
       "          [0.03070321],\n",
       "          ...,\n",
       "          [0.02785635],\n",
       "          [0.03399269],\n",
       "          [0.08751913]],\n",
       " \n",
       "         [[0.06354789],\n",
       "          [0.02425988],\n",
       "          [0.02707133],\n",
       "          ...,\n",
       "          [0.0278316 ],\n",
       "          [0.03434233],\n",
       "          [0.07382488]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.05518735],\n",
       "          [0.02231076],\n",
       "          [0.02006663],\n",
       "          ...,\n",
       "          [0.02592955],\n",
       "          [0.02889075],\n",
       "          [0.07457046]],\n",
       " \n",
       "         [[0.08089829],\n",
       "          [0.03146153],\n",
       "          [0.02213553],\n",
       "          ...,\n",
       "          [0.03089258],\n",
       "          [0.03338139],\n",
       "          [0.09061517]],\n",
       " \n",
       "         [[0.17896758],\n",
       "          [0.08038741],\n",
       "          [0.05743518],\n",
       "          ...,\n",
       "          [0.06872702],\n",
       "          [0.07999385],\n",
       "          [0.15787953]]],\n",
       " \n",
       " \n",
       "        ...,\n",
       " \n",
       " \n",
       "        [[[0.13235444],\n",
       "          [0.07057228],\n",
       "          [0.060172  ],\n",
       "          ...,\n",
       "          [0.04447305],\n",
       "          [0.05879083],\n",
       "          [0.13362062]],\n",
       " \n",
       "         [[0.06211763],\n",
       "          [0.02361135],\n",
       "          [0.02220295],\n",
       "          ...,\n",
       "          [0.01245202],\n",
       "          [0.01507732],\n",
       "          [0.05751184]],\n",
       " \n",
       "         [[0.04765025],\n",
       "          [0.01463218],\n",
       "          [0.01539921],\n",
       "          ...,\n",
       "          [0.00911013],\n",
       "          [0.00996229],\n",
       "          [0.04098514]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.85304364],\n",
       "          [0.93790572],\n",
       "          [0.96507642],\n",
       "          ...,\n",
       "          [0.03089425],\n",
       "          [0.03501814],\n",
       "          [0.0837126 ]],\n",
       " \n",
       "         [[0.77512523],\n",
       "          [0.90510564],\n",
       "          [0.93986892],\n",
       "          ...,\n",
       "          [0.03416885],\n",
       "          [0.03936854],\n",
       "          [0.0994188 ]],\n",
       " \n",
       "         [[0.67650454],\n",
       "          [0.81940272],\n",
       "          [0.87394334],\n",
       "          ...,\n",
       "          [0.07624412],\n",
       "          [0.08464613],\n",
       "          [0.16449532]]],\n",
       " \n",
       " \n",
       "        [[[0.26771497],\n",
       "          [0.22003955],\n",
       "          [0.25479421],\n",
       "          ...,\n",
       "          [0.50226959],\n",
       "          [0.44642373],\n",
       "          [0.42821242]],\n",
       " \n",
       "         [[0.20424077],\n",
       "          [0.17397954],\n",
       "          [0.22728165],\n",
       "          ...,\n",
       "          [0.54734733],\n",
       "          [0.47839942],\n",
       "          [0.4340199 ]],\n",
       " \n",
       "         [[0.22190968],\n",
       "          [0.18649495],\n",
       "          [0.22862356],\n",
       "          ...,\n",
       "          [0.58542186],\n",
       "          [0.5465465 ],\n",
       "          [0.49851387]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.06358859],\n",
       "          [0.03699582],\n",
       "          [0.02784219],\n",
       "          ...,\n",
       "          [0.10572692],\n",
       "          [0.10180446],\n",
       "          [0.1443044 ]],\n",
       " \n",
       "         [[0.08718732],\n",
       "          [0.03517069],\n",
       "          [0.0252047 ],\n",
       "          ...,\n",
       "          [0.09101118],\n",
       "          [0.08937257],\n",
       "          [0.14590965]],\n",
       " \n",
       "         [[0.20037591],\n",
       "          [0.09927584],\n",
       "          [0.07033972],\n",
       "          ...,\n",
       "          [0.14839024],\n",
       "          [0.14054031],\n",
       "          [0.20147124]]],\n",
       " \n",
       " \n",
       "        [[[0.70231529],\n",
       "          [0.81086766],\n",
       "          [0.8924841 ],\n",
       "          ...,\n",
       "          [0.18260552],\n",
       "          [0.19398985],\n",
       "          [0.26261623]],\n",
       " \n",
       "         [[0.80648885],\n",
       "          [0.90793924],\n",
       "          [0.95400635],\n",
       "          ...,\n",
       "          [0.13750979],\n",
       "          [0.13645199],\n",
       "          [0.19874132]],\n",
       " \n",
       "         [[0.87626957],\n",
       "          [0.9435351 ],\n",
       "          [0.9709351 ],\n",
       "          ...,\n",
       "          [0.12900424],\n",
       "          [0.1471703 ],\n",
       "          [0.2014595 ]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.08290443],\n",
       "          [0.04029538],\n",
       "          [0.03774004],\n",
       "          ...,\n",
       "          [0.01499378],\n",
       "          [0.01875022],\n",
       "          [0.05920242]],\n",
       " \n",
       "         [[0.11679711],\n",
       "          [0.04906406],\n",
       "          [0.03333148],\n",
       "          ...,\n",
       "          [0.01888432],\n",
       "          [0.02549528],\n",
       "          [0.07821638]],\n",
       " \n",
       "         [[0.21490501],\n",
       "          [0.1093847 ],\n",
       "          [0.08141428],\n",
       "          ...,\n",
       "          [0.05189187],\n",
       "          [0.06436097],\n",
       "          [0.14080294]]]]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true= np.where(preds_test >= 0.500, 1, 0)\n",
    "y_true,preds_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83e01180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 128, 128, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cf2dec68",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresholds=[y for y in range (950,2000,30)]\n",
    "#thresholds=[1900,2000]\n",
    "nimages=[]\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "        term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "        term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "        return -np.mean(term_0+term_1, axis=0)\n",
    "for j in thresholds:\n",
    "    k=0\n",
    "    for i,elem in enumerate(preds_test):\n",
    "        confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                      ,y_true[i].flatten())\n",
    "        #print(confidence)\n",
    "        if confidence>=j:         \n",
    "            k+=1\n",
    "            #print(k)\n",
    "    nimages.append(k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6920bf3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#nimages\n",
    "\n",
    "plt.title('Number of images by threshold',fontsize=14,y=1.03 )\n",
    "plt.grid( True )    \n",
    "plt.plot(nimages,thresholds)\n",
    "plt.xlabel('Number of Images',labelpad=7,fontsize=10)    \n",
    "plt.ylabel('Thresholds',labelpad=7,fontsize=10)   \n",
    "#plt.gca().set_ylim(0,1)\n",
    "plt.savefig(os.path.join('plots/','Number_of_images_by_threshold_8_ensemble.png'), dpi=500, bbox_inches='tight',facecolor=(1,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ae13b933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{950: 17510,\n",
       " 980: 17495,\n",
       " 1010: 17483,\n",
       " 1040: 17464,\n",
       " 1070: 17441,\n",
       " 1100: 17410,\n",
       " 1130: 17388,\n",
       " 1160: 17358,\n",
       " 1190: 17331,\n",
       " 1220: 17302,\n",
       " 1250: 17245,\n",
       " 1280: 17194,\n",
       " 1310: 17121,\n",
       " 1340: 17035,\n",
       " 1370: 16954,\n",
       " 1400: 16863,\n",
       " 1430: 16769,\n",
       " 1460: 16660,\n",
       " 1490: 16525,\n",
       " 1520: 16376,\n",
       " 1550: 16195,\n",
       " 1580: 16009,\n",
       " 1610: 15791,\n",
       " 1640: 15553,\n",
       " 1670: 15246,\n",
       " 1700: 14843,\n",
       " 1730: 14438,\n",
       " 1760: 13955,\n",
       " 1790: 13374,\n",
       " 1820: 12702,\n",
       " 1850: 11972,\n",
       " 1880: 11154,\n",
       " 1910: 10274,\n",
       " 1940: 9188,\n",
       " 1970: 7880}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds_images = {thresholds[i]: nimages[i] for i in range(len(nimages))}\n",
    "thresholds_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d3e4d7ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1472.6359\n",
      "1634.9846\n",
      "1308.5106\n",
      "1522.6438\n",
      "1462.9843\n",
      "1964.0485\n",
      "1922.512\n",
      "1368.7544\n",
      "1903.526\n",
      "1593.7562\n",
      "1644.2834\n",
      "1805.2688\n",
      "2010.0925\n",
      "1636.3228\n",
      "1350.6238\n",
      "1786.3478\n",
      "1920.6299\n",
      "1337.1305\n",
      "1544.1599\n",
      "1665.7168\n",
      "1902.6768\n",
      "1681.6045\n",
      "1927.9142\n",
      "1459.2766\n",
      "1736.9176\n",
      "1848.068\n",
      "1661.8872\n",
      "1402.6268\n",
      "1602.0759\n",
      "1535.445\n",
      "1873.4133\n",
      "1895.5964\n",
      "1947.8939\n",
      "1298.9797\n",
      "1796.1855\n",
      "1744.6359\n",
      "1382.9524\n",
      "1823.7787\n",
      "1899.9674\n",
      "1520.8308\n",
      "1696.0917\n",
      "1725.5372\n",
      "1282.567\n",
      "1790.5936\n",
      "1692.255\n",
      "1854.1204\n",
      "1438.7705\n",
      "1355.1785\n",
      "2065.3633\n",
      "1689.6796\n",
      "1894.7637\n",
      "1647.4738\n",
      "1758.6621\n",
      "2166.0366\n",
      "1848.3523\n",
      "1338.6316\n",
      "1936.2699\n",
      "1881.621\n",
      "1497.3165\n",
      "1366.8746\n",
      "1562.4729\n",
      "1408.8239\n",
      "1964.1313\n",
      "2134.8474\n",
      "1642.0396\n",
      "2248.5688\n",
      "2057.1152\n",
      "1499.8087\n",
      "2188.3584\n",
      "1505.8694\n",
      "1767.2942\n",
      "1421.3064\n",
      "2377.1108\n",
      "1985.593\n",
      "1488.9026\n",
      "1272.2872\n",
      "2197.579\n",
      "1472.5928\n",
      "1854.7081\n",
      "1755.1312\n",
      "1996.3522\n",
      "1569.9331\n",
      "1844.1974\n",
      "1266.2795\n",
      "2277.9531\n",
      "1466.4436\n",
      "1998.0907\n",
      "1992.3853\n",
      "1517.6035\n",
      "1424.9762\n",
      "1641.6306\n",
      "1451.6113\n",
      "1266.1382\n",
      "1784.699\n",
      "1483.4814\n",
      "1808.8999\n",
      "1554.3936\n",
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     ]
    }
   ],
   "source": [
    "#confident_X_test1=np.array([])\n",
    "confident_y_test1=np.array([])\n",
    "threshold=1250\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "    term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "    return -np.mean(term_0+term_1, axis=0)\n",
    "for i,elem in enumerate(preds_test):\n",
    "    confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                  ,preds_test[i].flatten())\n",
    "    if confidence>=threshold:\n",
    "        #confident_X_test1=np.append(confident_X_test1,X_test[i])\n",
    "        confident_y_test1=np.append(confident_y_test1,preds_test[i])\n",
    "        print(confidence)\n",
    "#confident_X_test1,confident_y_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "bd90363b",
   "metadata": {},
   "outputs": [],
   "source": [
    "confident_X_test1=np.array([])\n",
    "#confident_y_test1=np.array([])\n",
    "threshold=1250\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "    term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "    return -np.mean(term_0+term_1, axis=0)\n",
    "for i,elem in enumerate(preds_test):\n",
    "    confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                  ,preds_test[i].flatten())\n",
    "    if confidence>=threshold:\n",
    "        confident_X_test1=np.append(confident_X_test1,X_test[i])\n",
    "        #confident_y_test1=np.append(confident_y_test1,preds_test[i])\n",
    "        #print(confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "17192e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#confident_X_test1_1250=pred_test.reshape((-1,128,128,1))\n",
    "#confident_y_test1_1250=confident_y_test1.reshape((-1,128,128,1))\n",
    "#with open(\"confident_X_test1_1250\", \"wb\") as f:\n",
    "  #  pickle.dump(confident_X_test1_1250, f)\n",
    "#with open(\"confident_y_test1_1250\", \"wb\") as f:\n",
    "   # pickle.dump(confident_y_test1_1250, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41b48af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#[confident_y_test1_1750 >= 0.500]= True\n",
    "#confident_y_test1_1750[confident_y_test1_1750 < 0.500]= False\n",
    "pseudolabels_ensemble = np.where(preds_test >= 0.500, True, False)\n",
    "#pseudolabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b7c786ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#confident_X_test1_1250=confident_X_test1_1250.astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23cae706",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install albumentations\n",
    "from albumentations import (HorizontalFlip, ShiftScaleRotate, RandomContrast, RandomBrightness, Compose,Rotate)\n",
    "import albumentations as A\n",
    "import cv2\n",
    "\n",
    "transform = A.Compose([\n",
    "        HorizontalFlip(p=0.5),\n",
    "        #Rotate(limit=15,p=0.5)\n",
    "        #RandomBrightness(p=0.2, limit=0.2),\n",
    "        #RandomContrast(p=0.1, limit=0.2),\n",
    "        #ShiftScaleRotate(shift_limit=0.07, scale_limit=0.1, rotate_limit=15, p=0.5)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e677543f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train_round2=np.concatenate((X_train,X_val,X_test))\n",
    "y_train_round2=np.concatenate((y_train,y_val,pseudolabels_ensemble))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train2,X_val2,y_train2,y_val2 = train_test_split(X_train_round2,y_train_round2, test_size=0.1)\n",
    "\n",
    "\n",
    "transformed2 = transform(image=np.squeeze(X_train2), \n",
    "                        mask=np.squeeze(y_train2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a5e0fa02",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"X_train2\", \"wb\") as f:\n",
    "    pickle.dump(X_train2, f)\n",
    "with open(\"X_val2\", \"wb\") as f:\n",
    "    pickle.dump(X_val2, f)\n",
    "with open(\"y_train2\", \"wb\") as f:\n",
    "    pickle.dump(y_train2, f)\n",
    "with open(\"y_val2\", \"wb\") as f:\n",
    "    pickle.dump(y_val2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b4f60d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11355, 128, 128, 1)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.concatenate((X_train,X_val,confident_X_test1_1700)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f92f3465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11355, 128, 128, 1)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.concatenate((y_train,y_val,pseudolabels_1700)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "890d76b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install segmentation-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "062eb72f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Intersection_over_Union(y_true, y_pred, threshold=0.5):\n",
    "    y_pred = K.squeeze(tf.cast(y_pred > threshold,tf.int32), -1)\n",
    "    y_true = K.cast(y_true[..., 0], K.floatx())\n",
    "    y_pred = K.cast(y_pred, K.floatx())\n",
    "    truth_areas = K.sum(y_true, axis=[1, 2])\n",
    "    pred_areas = K.sum(y_pred, axis=[1, 2])\n",
    "    intersection = K.sum(y_true * y_pred, axis=[1, 2])\n",
    "    union = K.clip(truth_areas + pred_areas - intersection, 1e-9, 128 * 128)\n",
    "    check = K.map_fn(lambda x: K.equal(x, 0), truth_areas + pred_areas, dtype=tf.bool) #images with no predicted and true salt bodies\n",
    "    p = intersection / union #ratio for each image\n",
    "    iou = K.switch(check, p + 1., p) # put 1 for images where no salt bodies were found \n",
    "\n",
    "    prec = K.map_fn(lambda x: K.mean(K.greater(x, np.arange(0.5, 1.0, 0.05))), iou, dtype=tf.float32) \n",
    "    #does each image have a iou greater than threshold? if so, the image iou is used to calculate the mean\n",
    "    prec_iou = K.mean(prec)\n",
    "    return prec_iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6bd951dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(None, None, None, 1)]   0         \n",
      "_________________________________________________________________\n",
      "tf.math.truediv_2 (TFOpLambd (None, None, None, 1)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, None, None, 3)     6         \n",
      "_________________________________________________________________\n",
      "model_5 (Functional)         (None, None, None, 1)     24456154  \n",
      "=================================================================\n",
      "Total params: 24,456,160\n",
      "Trainable params: 24,438,810\n",
      "Non-trainable params: 17,350\n",
      "_________________________________________________________________\n",
      "Epoch 1/25\n",
      "1320/1320 [==============================] - 156s 113ms/step - loss: 0.1980 - Intersection_over_Union: 0.6259 - val_loss: 0.1266 - val_Intersection_over_Union: 0.7828\n",
      "Epoch 2/25\n",
      "1320/1320 [==============================] - 151s 115ms/step - loss: 0.1003 - Intersection_over_Union: 0.7997 - val_loss: 0.0751 - val_Intersection_over_Union: 0.8323\n",
      "Epoch 3/25\n",
      "1320/1320 [==============================] - 153s 116ms/step - loss: 0.0743 - Intersection_over_Union: 0.8345 - val_loss: 0.0702 - val_Intersection_over_Union: 0.8593\n",
      "Epoch 4/25\n",
      "1320/1320 [==============================] - 153s 116ms/step - loss: 0.0618 - Intersection_over_Union: 0.8526 - val_loss: 0.0644 - val_Intersection_over_Union: 0.8639\n",
      "Epoch 5/25\n",
      "1320/1320 [==============================] - 154s 117ms/step - loss: 0.0528 - Intersection_over_Union: 0.8637 - val_loss: 0.0662 - val_Intersection_over_Union: 0.8748\n",
      "Epoch 6/25\n",
      "1320/1320 [==============================] - 155s 117ms/step - loss: 0.0471 - Intersection_over_Union: 0.8753 - val_loss: 0.0599 - val_Intersection_over_Union: 0.8678\n",
      "Epoch 7/25\n",
      "1320/1320 [==============================] - 156s 118ms/step - loss: 0.0390 - Intersection_over_Union: 0.8859 - val_loss: 0.0547 - val_Intersection_over_Union: 0.8810\n",
      "Epoch 8/25\n",
      "1320/1320 [==============================] - 157s 119ms/step - loss: 0.0376 - Intersection_over_Union: 0.8883 - val_loss: 0.0568 - val_Intersection_over_Union: 0.8803\n",
      "Epoch 9/25\n",
      "1320/1320 [==============================] - 157s 119ms/step - loss: 0.0327 - Intersection_over_Union: 0.8952 - val_loss: 0.0582 - val_Intersection_over_Union: 0.8736\n",
      "Epoch 10/25\n",
      "1320/1320 [==============================] - 158s 120ms/step - loss: 0.0298 - Intersection_over_Union: 0.9026 - val_loss: 0.0492 - val_Intersection_over_Union: 0.8865\n",
      "Epoch 11/25\n",
      "1320/1320 [==============================] - 157s 119ms/step - loss: 0.0295 - Intersection_over_Union: 0.9048 - val_loss: 0.0536 - val_Intersection_over_Union: 0.8840\n",
      "Epoch 12/25\n",
      "1320/1320 [==============================] - 170s 129ms/step - loss: 0.0263 - Intersection_over_Union: 0.9095 - val_loss: 0.0557 - val_Intersection_over_Union: 0.8832\n",
      "Epoch 13/25\n",
      "1320/1320 [==============================] - 452s 342ms/step - loss: 0.0238 - Intersection_over_Union: 0.9131 - val_loss: 0.0555 - val_Intersection_over_Union: 0.8834\n",
      "Epoch 14/25\n",
      "1320/1320 [==============================] - 456s 346ms/step - loss: 0.0250 - Intersection_over_Union: 0.9145 - val_loss: 0.0766 - val_Intersection_over_Union: 0.8655\n",
      "Epoch 15/25\n",
      "1320/1320 [==============================] - 463s 350ms/step - loss: 0.0218 - Intersection_over_Union: 0.9188 - val_loss: 0.0563 - val_Intersection_over_Union: 0.8800\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00015: early stopping\n"
     ]
    }
   ],
   "source": [
    "# tf.float32\n",
    "#tf.keras.clear_session()\n",
    "tf.random.set_seed(50)\n",
    "#\n",
    "#import keras\n",
    "#from keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "import segmentation_models as sm\n",
    "sm.set_framework('tf.keras')\n",
    "\n",
    "sm.framework()\n",
    "\n",
    "\n",
    "\n",
    "#reduce_lr = ReduceLROnPlateau(factor=0.1, patience=4, min_lr=0.00001, verbose=1)\n",
    "N=1\n",
    "#epochs = 100\n",
    "#batch_size = 32\n",
    "backbone='resnet34'\n",
    "base_model = sm.Unet(backbone, encoder_weights='imagenet')# classes=1, activation='sigmoid')\n",
    "inp = Input(shape=(None, None, N))\n",
    "s = Lambda(lambda x: x / 255)(inp)\n",
    "l1 = tf.keras.layers.Conv2D(3, (1, 1))(s) # map N channels data to 3 channels\n",
    "out = base_model(l1)\n",
    "\n",
    "model2 = Model(inp, out, name=base_model.name)\n",
    "\n",
    "checkpoint=r\"models/checkpoint\"\n",
    "if  os.path.exists(os.path.join(os.getcwd(),checkpoint))==False:\n",
    "    if  os.path.exists(os.path.join(os.getcwd(),'models'))==False:\n",
    "        os.mkdir('models/')\n",
    "    os.mkdir(checkpoint)\n",
    "\n",
    "from tensorflow.keras.optimizers import schedules\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate=0.000054,\n",
    "    decay_steps=5200,\n",
    "    decay_rate=0.91\n",
    "    )\n",
    "\n",
    "\n",
    "opt = Adam(\n",
    "    learning_rate=lr_schedule, # add exponential decay\n",
    "    epsilon=1e-08)\n",
    "\n",
    "\n",
    "model2.compile(optimizer=opt, loss='binary_crossentropy', metrics=[Intersection_over_Union])# metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])\n",
    "\n",
    "# summarize the model\n",
    "\n",
    "model2.summary()\n",
    "\n",
    "earlystopper = tf.keras.callbacks.EarlyStopping(patience=5, verbose=1,restore_best_weights=True\n",
    "                                                ,monitor='val_Intersection_over_Union', mode='max')\n",
    "\n",
    "\n",
    "\n",
    "history2=model2.fit( np.expand_dims(transformed2['image'],axis=3),  np.expand_dims(transformed2['mask'],axis=3),validation_data = (X_val2,y_val2), batch_size=15, epochs=25, \n",
    "                    callbacks=[earlystopper])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "18d96d43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Pillow==9.0.0\n",
      "  Downloading Pillow-9.0.0-cp39-cp39-win_amd64.whl (3.2 MB)\n",
      "Installing collected packages: Pillow\n",
      "  Attempting uninstall: Pillow\n",
      "    Found existing installation: Pillow 9.2.0\n",
      "    Uninstalling Pillow-9.2.0:\n",
      "      Successfully uninstalled Pillow-9.2.0\n",
      "Successfully installed Pillow-9.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install Pillow==9.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edc1c8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 6s 59ms/step - loss: 0.0648 - Intersection_over_Union: 0.8876\n",
      "Accuracy: 88.762087 Loss: 0.064753\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#save the model\n",
    "name='CNN_Unet_onlyflip_exponentialdecay__1loss_3round_ensemble'\n",
    "#os.mkdir('models/')\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/'))==False:\n",
    "    os.mkdir('models/')\n",
    "model.save(os.path.join('models/',name+'.h5'))\n",
    "model.save_weights(os.path.join('models/',name+'_weights.h5'))\n",
    "#plt.gca().set_xlim(0,33)\n",
    "   \n",
    "    \n",
    "if  os.path.exists(os.path.join(os.getcwd(),'plots/'))==False:\n",
    "    os.mkdir('plots/')\n",
    "\n",
    "\n",
    "            \n",
    "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "\n",
    "plt.title('Model Loss and Accuracy',fontsize=14,y=1.03 )\n",
    "plt.grid( True )    \n",
    "\n",
    "plt.ylabel('Loss and Accuracy',labelpad=7,fontsize=10)    \n",
    "plt.xlabel('Epochs',labelpad=7,fontsize=10)   \n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.savefig(os.path.join('plots/',name+'.png'), dpi=500, bbox_inches='tight',facecolor=(1, 1, 1) )\n",
    "\n",
    "#evaluate loss and accuracy\n",
    "loss, accuracy = model.evaluate(X_val2,y_val2)\n",
    "#plot loss and accuracy\n",
    "\n",
    "#plt.gca().set_ylim(0,1)\n",
    "#plt.grid(True)\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/details'))==False:\n",
    "    os.mkdir('models/details')\n",
    "with open(os.path.join('models/details',name+'.txt'), 'w+') as f:\n",
    "    f.write(f'{name} \\n  \\n ')\n",
    "    f.write(f'accuracy : {round(accuracy*100,4)} \\n loss : {round(loss,4)}  \\n ')\n",
    "    f.write(f' \\n optimizer: \\n {model.optimizer.get_config()}')\n",
    "\n",
    "\n",
    "\n",
    "#print accuracy    \n",
    "print('Accuracy: %f' % (accuracy*100),'Loss: %f' % (loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bfa03cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_coef(y_true, y_pred, smooth=1.0):\n",
    "    y_true_f = tf.cast(K.flatten(y_true), tf.float32)\n",
    "    y_pred_f = tf.cast(K.flatten(y_pred), tf.float32)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    print((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "\n",
    "\n",
    "def binary_crossentropy1(y, p):\n",
    "    #print(K.mean(K.binary_crossentropy(y, p)), tf.float32)\n",
    "    y=tf.cast(y, tf.float32)\n",
    "    return K.mean(K.binary_crossentropy(y, p))\n",
    "\n",
    "\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    \n",
    "    return 1 - dice_coef(y_true, y_pred)\n",
    "   \n",
    "\n",
    "def dice_coef_loss_bce(y_true, y_pred, dice=0.5, bce=0.5):\n",
    "    #print(binary_crossentropy(y_true, y_pred) * bce + dice_coef_loss(y_true, y_pred) * dice)\n",
    "    return binary_crossentropy1(y_true, y_pred) * bce + dice_coef_loss(y_true, y_pred) * dice\n",
    "\n",
    "def lovasz_grad(gt_sorted):\n",
    "    \"\"\"\n",
    "    Computes gradient of the Lovasz extension w.r.t sorted errors\n",
    "    See Alg. 1 in paper\n",
    "    \"\"\"\n",
    "    gts = tf.reduce_sum(gt_sorted)\n",
    "    intersection = gts - tf.cumsum(gt_sorted)\n",
    "    union = gts + tf.cumsum(1. - gt_sorted)\n",
    "    jaccard = 1. - intersection / union\n",
    "    jaccard = tf.concat((jaccard[0:1], jaccard[1:] - jaccard[:-1]), 0)\n",
    "    return jaccard\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def lovasz_hinge_flat(logits, labels):\n",
    "    \"\"\"\n",
    "    Binary Lovasz hinge loss\n",
    "      logits: [P] Variable, logits at each prediction (between -\\infty and +\\infty)\n",
    "      labels: [P] Tensor, binary ground truth labels (0 or 1)\n",
    "      ignore: label to ignore\n",
    "    \"\"\"\n",
    "\n",
    "    def compute_loss():\n",
    "        labelsf = tf.cast(labels, logits.dtype)\n",
    "        signs = 2. * labelsf - 1.\n",
    "        errors = 1. - logits * tf.stop_gradient(signs)\n",
    "        errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name=\"descending_sort\")\n",
    "        gt_sorted = tf.gather(labelsf, perm)\n",
    "        grad = lovasz_grad(gt_sorted)\n",
    "        # loss = tf.tensordot(tf.nn.relu(errors_sorted), tf.stop_gradient(grad), 1, name=\"loss_non_void\")\n",
    "        # ELU + 1\n",
    "        loss = tf.tensordot(tf.nn.elu(errors_sorted) + 1., tf.stop_gradient(grad), 1, name=\"loss_non_void\")\n",
    "        return loss\n",
    "\n",
    "    # deal with the void prediction case (only void pixels)\n",
    "    loss = tf.cond(tf.equal(tf.shape(logits)[0], 0),\n",
    "                   lambda: tf.reduce_sum(logits) * 0.,\n",
    "                   compute_loss,\n",
    "                   name=\"loss\"\n",
    "                   )\n",
    "    return loss\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def flatten_binary_scores(scores, labels, ignore=None):\n",
    "    \"\"\"\n",
    "    Flattens predictions in the batch (binary case)\n",
    "    Remove labels equal to 'ignore'\n",
    "    \"\"\"\n",
    "    scores = tf.reshape(scores, (-1,))\n",
    "    labels = tf.reshape(labels, (-1,))\n",
    "    if ignore is None:\n",
    "        return scores, labels\n",
    "    valid = tf.not_equal(labels, ignore)\n",
    "    vscores = tf.boolean_mask(scores, valid, name='valid_scores')\n",
    "    vlabels = tf.boolean_mask(labels, valid, name='valid_labels')\n",
    "    return vscores, vlabels\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def lovasz_hinge(logits, labels, per_image=True, ignore=None):\n",
    "    \"\"\"\n",
    "    Binary Lovasz hinge loss\n",
    "      logits: [B, H, W] Variable, logits at each pixel (between -\\infty and +\\infty)\n",
    "      labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)\n",
    "      per_image: compute the loss per image instead of per batch\n",
    "      ignore: void class id\n",
    "    \"\"\"\n",
    "    if per_image:\n",
    "        def treat_image(log_lab):\n",
    "            log, lab = log_lab\n",
    "            log, lab = tf.expand_dims(log, 0), tf.expand_dims(lab, 0)\n",
    "            log, lab = flatten_binary_scores(log, lab, ignore)\n",
    "            return lovasz_hinge_flat(log, lab)\n",
    "\n",
    "        losses = tf.map_fn(treat_image, (logits, labels), dtype=tf.float32)\n",
    "\n",
    "        # Fixed python3\n",
    "        losses.set_shape((None,))\n",
    "\n",
    "        loss = tf.reduce_mean(losses)\n",
    "    else:\n",
    "        loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))\n",
    "    return loss\n",
    "\n",
    "\n",
    "\n",
    "def lovasz_loss(y_true, y_pred):\n",
    "    y_true, y_pred = K.cast(K.squeeze(y_true, -1), 'int32'), K.cast(K.squeeze(y_pred, -1), 'float32')\n",
    "    logits = K.log(y_pred / (1. - y_pred))\n",
    "    loss = lovasz_hinge(logits, y_true, per_image=True, ignore=None)\n",
    "    return loss\n",
    "\n",
    "def make_loss(loss_name):\n",
    "    if loss_name == 'bce_dice':\n",
    "        def loss(y, p):\n",
    "            return dice_coef_loss_bce(y, p, dice=0.5, bce=0.5)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    elif loss_name == 'lovasz':\n",
    "        def loss(y, p):\n",
    "            return lovasz_hinge(p, y, per_image=True, ignore=None)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    else:\n",
    "        ValueError(\"Unknown loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc2102fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(11)\n",
    "\n",
    "from tensorflow.keras.optimizers import schedules\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate=0.00004,\n",
    "    decay_steps=6000,\n",
    "    decay_rate=0.95\n",
    "    )\n",
    "\n",
    "\n",
    "opt = Adam(\n",
    "    learning_rate=lr_schedule, # add exponential decay\n",
    "    epsilon=1e-08)\n",
    "\n",
    "model3.compile(optimizer=opt, loss=make_loss('lovasz'), metrics=[Intersection_over_Union])# metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])\n",
    "\n",
    "# summarize the model\n",
    "model3.summary()\n",
    "#model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint,\n",
    "   # save_weights_only=True,\n",
    "   # monitor='val_loss',\n",
    "    #mode='min',\n",
    "    #save_best_only=True)\n",
    "earlystopper = EarlyStopping(patience=7, verbose=1,restore_best_weights=True\n",
    "                             ,monitor='val_Intersection_over_Union', mode='max' )\n",
    "#tf.cast(X_train, tf.int32)\n",
    "#tf.cast( Y_train, tf.int32)\n",
    "#checkpointer = ModelCheckpoint('model-tgs-salt-1.h5', verbose=1, save_best_only=True)\n",
    "\n",
    "\n",
    "history=model3.fit( np.expand_dims(transformed2['image'],axis=3),  np.expand_dims(transformed2['mask'],axis=3),validation_data = (X_val2,y_val2), batch_size=22, epochs=45, \n",
    "                    callbacks=[earlystopper])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "4405afd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\diego\\AppData\\Roaming\\Python\\Python39\\site-packages\\tensorflow\\python\\keras\\utils\\generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 1s 49ms/step - loss: 0.0942 - Intersection_over_Union: 0.7619\n",
      "Accuracy: 76.191068 Loss: 0.094204\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#save the model\n",
    "name='CNN_Unet_onlyflip_exponentialdecay__2loss_2round'\n",
    "#os.mkdir('models/')\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/'))==False:\n",
    "    os.mkdir('models/')\n",
    "model.save(os.path.join('models/',name+'.h5'))\n",
    "model.save_weights(os.path.join('models/',name+'_weights.h5'))\n",
    "#plt.gca().set_xlim(0,33)\n",
    "   \n",
    "    \n",
    "if  os.path.exists(os.path.join(os.getcwd(),'plots/'))==False:\n",
    "    os.mkdir('plots/')\n",
    "\n",
    "\n",
    "            \n",
    "pd.DataFrame(history2.history).plot(figsize=(8, 5))\n",
    "\n",
    "plt.title('Model Loss and Accuracy',fontsize=14,y=1.03 )\n",
    "plt.grid( True )    \n",
    "\n",
    "plt.ylabel('Loss and Accuracy',labelpad=7,fontsize=10)    \n",
    "plt.xlabel('Epochs',labelpad=7,fontsize=10)   \n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.savefig(os.path.join('plots/',name+'.png'), dpi=500, bbox_inches='tight',facecolor=(1, 1, 1) )\n",
    "\n",
    "#evaluate loss and accuracy\n",
    "loss, accuracy = model.evaluate(X_val2,y_val2)\n",
    "#plot loss and accuracy\n",
    "\n",
    "#plt.gca().set_ylim(0,1)\n",
    "#plt.grid(True)\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/details'))==False:\n",
    "    os.mkdir('models/details')\n",
    "with open(os.path.join('models/details',name+'.txt'), 'w+') as f:\n",
    "    f.write(f'{name} \\n  \\n ')\n",
    "    f.write(f'accuracy : {round(accuracy*100,4)} \\n loss : {round(loss,4)}  \\n ')\n",
    "    f.write(f' \\n optimizer: \\n {model.optimizer.get_config()}')\n",
    "\n",
    "\n",
    "\n",
    "#print accuracy    \n",
    "print('Accuracy: %f' % (accuracy*100),'Loss: %f' % (loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "7955810d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"X_train2\", \"wb\") as f:\n",
    "    pickle.dump(X_train2, f)\n",
    "with open(\"X_val2\", \"wb\") as f:\n",
    "    pickle.dump(X_val2, f)\n",
    "with open(\"y_train2\", \"wb\") as f:\n",
    "    pickle.dump(y_train2, f)\n",
    "with open(\"y_val2\", \"wb\") as f:\n",
    "    pickle.dump(y_val2, f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "00fa0f2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 20s 35ms/step\n"
     ]
    }
   ],
   "source": [
    "preds_test2 = model.predict(X_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae90e28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e5a5ef4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"preds_test2\", \"wb\") as f:\n",
    "    pickle.dump(preds_test2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e3f91f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "confident_y_test2=np.array([])\n",
    "threshold=1600\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "    term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "    return -np.mean(term_0+term_1, axis=0)\n",
    "for i,elem in enumerate(preds_test2):\n",
    "    confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                  ,preds_test[i].flatten())\n",
    "    if confidence>=threshold:\n",
    "        #confident_X_test1=np.append(confident_X_test1,X_test[i])\n",
    "        confident_y_test1=np.append(confident_y_test1,preds_test[i])\n",
    "        #print(confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ba0408c",
   "metadata": {},
   "outputs": [],
   "source": [
    "confident_y_test1=np.array([])\n",
    "threshold=1600\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "    term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "    return -np.mean(term_0+term_1, axis=0)\n",
    "for i,elem in enumerate(preds_test):\n",
    "    confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                  ,preds_test[i].flatten())\n",
    "    if confidence>=threshold:\n",
    "        #confident_X_test1=np.append(confident_X_test1,X_test[i])\n",
    "        confident_y_test1=np.append(confident_y_test1,preds_test[i])\n",
    "        #print(confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9353336",
   "metadata": {},
   "outputs": [],
   "source": [
    "im_width = 128\n",
    "im_height = 128\n",
    "im_chan = 1\n",
    "path_test = r'C:\\\\Users\\diego\\OneDrive\\Desktop\\DS\\seismic_segmentation\\test'\n",
    "test_ids = next(os.walk(os.path.join(path_test,\"images\")))[2]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d833560a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-80c5a58b954f>:6: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n"
     ]
    },
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       "  0%|          | 0/18000 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Get and resize test images\n",
    "X_test = np.zeros((len(test_ids), im_height, im_width, im_chan), dtype=np.uint8)\n",
    "sizes_test = []\n",
    "print('Getting and resizing test images ... ')\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n",
    "    path = path_test\n",
    "    img = load_img(path + '/images/' + id_)\n",
    "    x = img_to_array(img)[:,:,1]\n",
    "    sizes_test.append([x.shape[0], x.shape[1]])\n",
    "    x = resize(x, (128, 128, 1), mode='constant', preserve_range=True)\n",
    "    X_test[n] = x\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d90286d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 36s 54ms/step\n"
     ]
    }
   ],
   "source": [
    "preds_test_3stage = model.predict(X_test, verbose=1)\n",
    "#with open(\"preds_test2_8_ensemble\", \"wb\") as f:\n",
    "    #pickle.dump(preds_test, f)\n",
    "\n",
    "#preds_test = np.round_(preds_test, decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89def298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 23s 36ms/step\n"
     ]
    }
   ],
   "source": [
    "#preds_test2_5 = model5.predict(X_test, verbose=1)\n",
    "#with open(\"preds_test2_5_ensemble\", \"wb\") as f:\n",
    "  #  pickle.dump(preds_test2_5, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "287d53e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 20s 36ms/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with open(\"preds_test_stage3_ensemble\", \"wb\") as f:\n",
    "    pickle.dump(preds_test_stage3, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb6eb33b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 120s 211ms/step\n"
     ]
    }
   ],
   "source": [
    "preds_test_stage3_2 = model2.predict(X_test, verbose=1)\n",
    "with open(\"preds_test_stage3_2model\", \"wb\") as f:\n",
    "    pickle.dump(preds_test_stage3_2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18441f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "with open('preds_test_8ensemble', \"rb\") as f:\n",
    "    stage1_predictions=pickle.load(f)\n",
    "#with open(\"preds_test2_8_4_ensemble\", \"rb\") as f:\n",
    "    #ensemble_4=pickle.load(f)\n",
    "with open('preds_test2_2_ensemble', \"rb\") as f:\n",
    "    pred1=pickle.load(f)    \n",
    "with open('preds_test2_3_ensemble', \"rb\") as f:\n",
    "    pred2=stage1_predictions=pickle.load(f)\n",
    "with open('preds_test2_8_ensemble', \"rb\") as f:\n",
    "    pred3=pickle.load(f)    \n",
    "\n",
    "results=(preds_test2_4+stage1_predictions+pred1+pred2+pred3+preds_test2_5)/6\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2675dc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('preds_test_best_submission', \"rb\") as f:\n",
    "    pred_ensemble=pickle.load(f) \n",
    "results=(pred_ensemble+preds_test_stage3+preds_test_stage3_2)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c000551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 128, 128, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage1_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c316771",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save best\n",
    "with open(\"preds_test_best_submission\", \"wb\") as f:\n",
    "    pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4adfa53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-6d843828a469>:4: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
      "  for i in tnrange(len(preds_test)):\n"
     ]
    },
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       "  0%|          | 0/18000 [00:00<?, ?it/s]"
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   ],
   "source": [
    "# Create list of upsampled test masks\n",
    "preds_test_upsampled = []\n",
    "preds_test=results\n",
    "for i in tnrange(len(preds_test)):\n",
    "    preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), \n",
    "                                       (sizes_test[i][0], sizes_test[i][1]), \n",
    "                                       mode='constant', preserve_range=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3f83aff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 101)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_test_upsampled[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8617c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-6ceb950ee2b3>:38: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  pred_dict = {fn[:-4]:RLenc(np.round(preds_test_upsampled[i])) for i,fn in tqdm_notebook(enumerate(test_ids))}\n"
     ]
    },
    {
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       "0it [00:00, ?it/s]"
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   ],
   "source": [
    "def RLenc(img, order='F', format=True):\n",
    "    \"\"\"\n",
    "    img is binary mask image, shape (r,c)\n",
    "    order is down-then-right, i.e. Fortran\n",
    "    format determines if the order needs to be preformatted (according to submission rules) or not\n",
    "\n",
    "    returns run length as an array or string (if format is True)\n",
    "    \"\"\"\n",
    "    bytes = img.reshape(img.shape[0] * img.shape[1], order=order)\n",
    "    runs = []  ## list of run lengths\n",
    "    r = 0  ## the current run length\n",
    "    pos = 1  ## count starts from 1 per WK\n",
    "    for c in bytes:\n",
    "        if (c == 0):\n",
    "            if r != 0:\n",
    "                runs.append((pos, r))\n",
    "                pos += r\n",
    "                r = 0\n",
    "            pos += 1\n",
    "        else:\n",
    "            r += 1\n",
    "\n",
    "    # if last run is unsaved (i.e. data ends with 1)\n",
    "    if r != 0:\n",
    "        runs.append((pos, r))\n",
    "        pos += r\n",
    "        r = 0\n",
    "\n",
    "    if format:\n",
    "        z = ''\n",
    "\n",
    "        for rr in runs:\n",
    "            z += '{} {} '.format(rr[0], rr[1])\n",
    "        return z[:-1]\n",
    "    else:\n",
    "        return runs\n",
    "\n",
    "pred_dict = {fn[:-4]:RLenc(np.round(preds_test_upsampled[i])) for i,fn in tqdm_notebook(enumerate(test_ids))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "97bce003",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame.from_dict(pred_dict,orient='index')\n",
    "sub.index.names = ['id']\n",
    "sub.columns = ['rle_mask']\n",
    "sub.to_csv('submission_3stage_withpreviousensemble_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e3cb233",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "711a8a37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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