playground.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PlantSegmentation 🌱 + 🔬 - AI4Science: Accelerating experimental loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "from torchvision.transforms import ToTensor\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from dataset import PlantDataset \n",
    "from utils import (\n",
    "    display_masks, \n",
    "    extract_ids_from_name, \n",
    "    extract_tag_from_name,\n",
    "    upscale_logits, # quizas deberian ir al modulo del modelo?\n",
    "    flatten_logits, # quizas deberian ir al modulo del modelo\n",
    "    set_seed,\n",
    "    )\n",
    "\n",
    "from evaluation import compute_intersection_over_union\n",
    "\n",
    "from transformers import (\n",
    "    SegformerForSemanticSegmentation,\n",
    "    SegformerFeatureExtractor\n",
    ")\n",
    "\n",
    "from matplotlib import cm\n",
    "from PIL import Image \n",
    "from tqdm import tqdm\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `PlantDataset` 🌱: navegar por imágenes y máscaras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import PlantDataset, extract_ids_from_name\n",
    "\n",
    "label2id = {'normal': 0, 'normal_cut': 1, 'noise': 2}\n",
    "dataset = PlantDataset('data', 'cwt', 'data_inventary.csv', label2id=label2id)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos verificar si `CellDataset` navega correctamente por el directorio\n",
    "y carga los archivos correctos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.Image.Image image mode=RGB size=2503x1895>,\n",
       " 'masks': ['data/cwt/original_labeled/11/task-11-annotation-53-by-1-tag-normal_cut-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-47-by-1-tag-noise-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-50-by-1-tag-noise-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-51-by-1-tag-noise-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-49-by-1-tag-noise-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-52-by-1-tag-normal_cut-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-46-by-1-tag-normal-0.png',\n",
       "  'data/cwt/original_labeled/11/task-11-annotation-48-by-1-tag-noise-0.png'],\n",
       " 'labels': [1, 2, 2, 2, 2, 1, 0, 2]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image corresponding to idx 4 has the file name 'cwt1_13.jpg'\n",
      "\n",
      "The name of the folder where the masks are stored is 5, and there are 7 masks:\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-24-by-1-tag-normal_cut-0.png\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-22-by-1-tag-noise-0.png\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-21-by-1-tag-noise-0.png\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-23-by-1-tag-normal-0.png\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-27-by-1-tag-noise-0.png\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-25-by-1-tag-noise-0.png\n",
      " -> data/cwt/original_labeled/5/task-5-annotation-26-by-1-tag-noise-0.png\n",
      "The labels of the masks are [1, 2, 2, 0, 2, 2, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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bppFq32BxV2Yz2fi61DI6dntnbCWYGwIOQfSk6KFWeo9m+6N72i1yb2OO3uAtXgNMLMl9/7f/w+OP/ynAm92/W7E0OhotOqFZ8MulXyDPZWPNNfPfHdLzMNy//+4/+49Hv/nvyO7bh+2hA0GMtnFPZliq50S0JRu1JcxdS+7wo9173/2r//bHl08ktu3SY0OgYjVc5/pV7XpzDkTdr5Qk+kqgPRB6m8GJL5oADPZP/+3o6EXk/9nFkygKVzGZmg9xbGXJiGhx1GXV3Orb+I4QrRATqBTiV13XC27/8upZFIWrfC5QJdDtskqZGYJImI4nVCiyvnQP6fjrws+qoPpe3N6QZxM0lyjYdXeORe8n3u0kDMVqKd7ygTU153AuHkWb7IYLrqFqT84UPrqKcbOaYbS9BFqNgQka7uw7wn0eTGmFi+d5lMZDldYNdFc3eztV9sZQKWZPOr2j3cMJcMthx2Qzd0dny+w3yn7lu56rKAm6u3MzJeAkPikxmCAGu493j+D0ZAQUXGA3M0ublitHiMYL1xkmSrlJuOOjJqxii6SYAQCiw9HxaW84iwK52M0KBS8JurMGUEsds7y7XLFSnv8/H2215Zy1qTs6TCCI3/jm97/5ze+P+wPQ0mIoCgbtYMz8psyGOz7aGujy8H5/cj4SLmSF86Wq/HVl+6lzcBv4WlyRNtMZFac9FE0FQxKf/uR/vs8YTsaKE7scZP2Bzc6hoej1Oz7aPPKe+fjf49uLqYwiDuOgh8SxrGWQMfdMAvmwNunffXWimfhRgljW/hayTxXxN5410okvnZ7v9pzeENQDGA4gJThKSlCcP00ISTFVOnAEQOAQUYgIi9wRi9t2TfDXtbPnKk4D7YZphkYbGee2iLMq/VwQE0Dg8/37Z6I3IoLroL9HMoI3hpRJnQwIIdgdiNERDQ8G7kA6vT6xF87Cqxd0e8FSlt52ejOg5rNwmzG91YuqK3SrgZ6sH6Z+Ztnn4kYOwKAb7+asv+tGodMbnb73rdvZzc0v/07IuWIqABaCeyPn8deGh48fe+P7kxuKAgfRJZxPj9/zJDC5EBwlTgECIARHEbqJFYhB3X53+ugasRTKABg7N6/POHhNdLh7/LuB/9CbITlFJkiHZH/3o/sf/Xp/9P70+tv+5OHsVnoTMfO+Hk4fMw+PHnFvKAWpHkRWPna/kaG1gObuM73lamgGnDJuCGBwFPqzMJoK5/5g9/Hk4q/G5yRlYt5Tb/fkwUcf9HYeezefCPzg5uLi6swPIxJ4Z7BzdO/dcPee651GN8TBDBwBkiUjl+ihsh+g8wvXFI0aglbfRmTZDEEAYRicsu/2hxfCnUW3V76HmJhIyJ29d08//H3CqTf+6yj80eWzyWwMBgEc8ZNp+Lx/firck17/s+N3PN/H9BLBDYURGIw5oZOSqGzd7qo2FbOGaDQfoVf+SqdjbWyRTw0UP858tovjgHs5dnZPdve+4l2fkZhyBCEYxP39h48+/iaJQ+/mB9Prv7h+IcNApOhARuPzIAhHo93HveHtcP9ksPOL2WQ6uYZ/yxwRz20pguYzpCZsw/zf3V1eAXbrK0BSAEJ4JJ6dvn9vZ/frv/jff3vwgAMfszEzPz75yr8Wg0Hg/Y/J+O+un1E+9pmZouB8cjEJbg9HR4c7h4+F+/7O7pPB6JNgGtxcUjBBFKfWr01zNdhMS2iORvMX9Mzo8g62ve5CySXEomMjIgKBhMDg4MMHXzuVweHLn0W9neP9028N9yLf+2J09C9l+O3xq/8ejH90+5rk3LevycfJ4XQWTcPgbDp+1t/5cLB7ONr/zuD0xfDo6cyT3pW8OeMoAOwINffFB4t3KqBBTaP8m7Z1K+4wCZbCkka13mnlkzoEgAS7QvT3Rt/8V6d79x9cPf/Y9z90+470p0F4iejLvdMTopGUfx76P3/1WfD8U8gQJklNILAAi35/ePDO7uFwcHA5ujf+4LveD/5rdP3SNhY0k1wJAmgyY1lTYOY7WV+MGgSqvh2TqyPYHZ189Ntf2X/0Tug9CINfgoajQzk5exrcOkQfeFdHgvzhyW+O9p9748tXnwKA8YNeAJOEiGaBfP0Lb+wORs7sFlEoZ5MKAjujQEPO8891jEyJaA00qmEtSMTidsCmq/rYEcVzCYBAbv/eb/z7r7//a/dfff6hP5mJ3pSCF7evX169fHL57Hi4/ztSHhOu2Pncd09AV/sP+Oo5ICn+/JKWmzLFP4hIsvQ59P3pFb34GVgSUl+jivUGE8GnR9TBFWqJjxKJru3O9SGlhzHT8fsP3/3V35tehbtHt68+H108/SfSIxbfkfz5YHTt9nwZXRB+MH72Vbj/yN0ZHH7lk8APb1+D2cjTCPOjAckMkiBAMmdcgEwAEbqWaaIKGuKj5lv5iMPQKk7TG+GZYkavP/r2Fz8aeDd48JE3vX4R3IZRMANJZubo//re80i+Nzr887Mvfka93zp69B1yosPHPwk8hFOSBEiWUenkJck28ldP2HUQRMid8s83UtVbIpu4VbISjVrbbttNapWQmhDC8PLZNwbD3b37nxD9r4svvrh8GsgozgzukCOlPBfiRzsjfzb9OfD1Xv+93ePfGgxe7D+4GL9iZnAAKViGFGnmMHHUF86vZIokMsy1iyI9Bc5+IGV1uz6fu36r0OR5zJzNEM2vMIGE883j9w9P3v3hxfPZ+ZdhNON5PvrlIjBJASGBe8Oj33v40Tv90XUUPg39cX/35/70+uqZ711H/lRIJkgoYfZNHiZ1eNXuaLRRJKJQPa93CMJ1WUbK6XyKVSxeiYTzrdMPfufg0ddn1wPHudl/+KfDg8nzT26CycSbXM0mN1EoZcjRDCxJNsoRO7xqq+mjeU20w0NtA8rwlxFJEUOGkdYtH7+08Bc5LD+5fuEfv+PtPHQDfxrMLv1nD8LgQciHovdwZ/elcH7cH/2YA+/sS/heA0ESHVsv7bdQ7vyjDaBI4M5lMQpcSOliAMAIg5l//uWT+x//LPRevvxMBtNPmXYc932Bfbf/a8Kh/s7n++/Nxlfwp29eJI92Jl0svL6VB2u3BbsYXd+Icbr0eCvCpNYFxiSaDmAhw+ls+koGN8yBPyV/EjD7RH8L/qnTe2dnd9Yb3HhTeNfAahPbMQ5qgAsAjmDM3SGNN9A5Am0OyfZLvoCRRzYAqqCM+q9bZiH5kJxxfBEZALMD+GHws5tLur0WKPdGbS+UHTuHICKCQ3BZl2uvBGqWh3WnADZmlCCu4uutlKuCiSA0BYglR0vxEw9fn1rQKliOmQEmpoh5RuTP9QNeAhDMJCOWaxHx3bkdpcmlwyvFdHUDVMTIGkAjokDhuHoQ0XyvCedIskQoswF6KYPMvleWN547KPHiLgkAjAiICNCmLS2uoKywgT/VuOhn5NMMZhlWrlNfVy7IMP/BqqpVpsmrsAyBiUa9nT5FMgohpXbnaWswJzPrIP3Zw13KfiKCgJDMbLZD77AiEt975s4T+qPj937tdw9O3/Gmfy9vSIaWcQ7dEdPNIh5XIusB4bLTg+gJ0W9GcCYqmvYTJTG2JWlZhpUm3a77BZU8Y2MZ7b37jX/+7re+N/Pf92cuCQmRCcg3cNBqnLI5y6GF7RHTKJhjTzIjzt/iOHAcEo4QTj7XXpPYEhItwopzkz7cJyfwXxw+/IeTrzw/OP2e4x70h5xWv6oSxDoIKFNnC1qEwNzeIGaQDIkjAEQOiR6cHsi1uoNv2JclW3YTSkWG81XySMSFl3frVupIStYL5+L5p9c//JOfXD3/sygYRuG3907h9lFGEwZ1M/t8e3yiKub+GpaSELIDAJARk8DijOSNO8soRf3h1hG7ytsOiWHo//312fnkDDtH7/V2Ph7u/zSciUpXPdeMdvqgquxi+VCGkJJZMuZuP2ZmgpXfVNXPMn9gBdfpmrTVhBdqHaWWbRa7Qi0lbKaYJBwPD91ebzy9uL149sn4bCDD7/V39gdD6dh1yR4dNgMWl7AB1WZa/hxJRJIlEzEQEUdABEirS555o2H+ZIXpqELZhJXTRlfdS7qy9o4etRgB5I6+sXckZeTfXDjBLLx6+tfe1RGJP9i9Nxodk9OrmdZR5zjrsh9gLotM+Z4YYOYwQBRyFCIMIVdLedWWdFrTdRTjaja20pLo8Wjvq+R+GQXT2RgccRR+MX79JzL8trPz/dOPew8/4nSmp1XQAZWhBOo49fYQLQ6eYpIu4U9NpcG3eUvPqgt6smJbcUGZbkJhRU1xIyKQcL/VHz4T4vVkLEI/PvgTof/T67M/In42GD0++Qi9Edj2/hyQEwupv6vU0zTM86aPe9KUy/1xhxQWMSSrRHUlVM7Ebr+/29/7P8KNppfxFbk4Wn8aeH9z9sTxboRwyJ/IbffVATBaXdqfmogfbeoeluWNYbW52u1afngyX2z1zwfE1SRpH4nc/uj9w8eyv3MVzuB7sbN60Q0gnMnLlxH056LFDWwle1ljHue3EKXs08xik4PQ4c69f7F39DGJT4TrB1eIghwpxrV0Lwy3LTSVN8+4xRu56lTD31615kycYZFibXGQm+StLSKsuIB0+o8fffQr4ewSzkvHiSbX0LlC62gUHf/Yi1H2EpFcpGDHHR9dH2wIi4ie94d/ylHfn/FU0mzC8yQkqRffHg6qj7bR3gutfHvEMkakET7aDhbNxafEpZlz8rChUSEcHoxA4lcOH74aDC+ffyZn42YM1SK9uSN8tAzqaYiATlFlIeSbYEE2gPWxMCJiluzfuhyx2w+iiGUIquxg77JDfhUk41Ji89Sfmc37rea8GK9klLy7uiaqDahL/mnZrnUknpmJLieQqe+MHLfvhz6FAVV3GmgCR4g1PW8LjeyZ8u+KlAqpt0dDqodK69QfjnrCEd5Ecle/YlMFDV6tidFQDgj73V9k41c6IqrHHrQqWj4IJsM1i/ppHHLZvSUFPfdkZy/iKJyNm5TZ6Z7PleMtUUZjJJbTG/V9pu5rZpkeEmFnsNvvj54HQRBMFxEpzVPStsi95A7sGr4hVnWDrrKhDTfv7FcizWOWD8vyVDaC5W1m0TsZnR6AgsklRUFp9602oUHP7nBIXgxaABmbKV+u1X5tGu3zmMUMk7OzPxwevI5m09tzVM4WsXoH2oZ9q+pINTRqOxE1eKc2stgStcOp1BqSnmS6lv3kl7GfK/REnVvp0s7uke/uPJneBv4U3N5uWamhFVbBvtU1yPptRptMRQklYeoN/ZMPbgizmzNaox92m6BVx2vRqPaWRaXyVWHIJWG/rQu4oyklU9GRd/Xh5JoQbn//eDCacSj9m6LqKu8fswRYW3hac6G0mqjc9fPR1amzNJpkZVOAk+TwVauyoNe8EQCAHWdv92DkjaPXT1hGddho3im2OddSU1qKNv/InawH2jWbGGCBXm+4N7p3Fk5ncVBz0e57+1SA/IBr+fAz4R1aud/Ini5112cOEWo0qvrkbV6veDUl6xAFWBA77s7RO7I39C6ekj9pJsVIaVB2Jjy8k9DqDHd8dANw+nv9w8ehlNH1K5bR2+Xqq44VaNQyrrl25fabvp5Ly/yKjW5qMcZsJCgBwjk8uD90epeT19Hspr7EKeXo2uCS1V14Nh1rtJU7Prp2pJM6id7w8GjveBJOg/OnLIPV7zV278gl9pM01q0q+mii/FlejlsFHVGbSnmVXZgzJ5dI+8P7R4+J+uPzL3h6LWoczue6xEXdy2uo7cwqgcFEhV9LhxCIIrLenXd8dI1YBpMzQMRO//jg0bG7c3v5ZXj+Jclwi6z2ih5QLnBVMMglZ4dE35CMCKgXm2d/XXgNm5Xmsdh298vr2fhFKp02g1VFEAkWjnPyfn/3+Gx28+r6GSwiSGoic4WwIVt+dS8YETEDMmA/gHDNS6luia24czfv7uJbM9uERY5s0P5J7/DhDdg7eyVnU3tJ98YgddM1ipBTBoq2gR2Ntn7TLfM0JtPiL8yk0VpCitI6iAiQwqGDk9H+A/jeZHzGNxe0SviIOchayzVbvq5oAe1VpExmzISVbgUf3crjljh7KztiuPdA7N/vMa4vn/LVGWS4jcNpGVZxT5sJMSzyWXaJB9hgzhIcMdg7GR2c9MDT8Qs5PoMMFq6Zuihin+rf68uXsU7MoxoAWNpMd3u9HpbRd8Id7J/e37t/xZF/+dKfXJEM4nnN02jRZfxVcp5tHbRp1Knk23BFeQTmFRSoPlu1d0tQ7yYxCXaod/DgaP+hJ4PpxbNwekFMWsdK0RPlOYM0E5uyIA0rtW3LEXdXjRTbLv9o16c7uYzj7h0/OHzUQ3R28SS6vSTJVN9I0j/eXv5aVdWxo1Ht8SsVZ+2yqbDqr8QNJ980d77W0ObfqhsMj+99ddftv756zdOL+LHMJ9qwXKoqoQtEBGqbd9ZQr9PpnEpet+ajnTsU7iJiW36wf//dndHl9Nq7eUl52lxgbXeU2+avVQeSuRBiUIFiGPXR0pyM2mhFG6hBjUlbmeQL9rXVgE3qHpti6htzW77/eP++Q3R+/WJxHbnRUNrO3z+ujfxthRhaGm1aqi4rLpZZtCgw/+fK61pjg2nL2NCEcuhI/dHhcP+ldxVOr0SzLM2qJ9uqpCbIXC9jZp2s38xG3ebJJSIhALDAcLg3cPoX43NEwdr2+vbBXmfdxJ27BBk2oPUw56KV67u751UV/1qpsrJuxAGT7A5OTr/qgW9vLhantw2hdQbZ7CHOKl+26LrvaWXDopmltUkkSMzi9KPDf/OfZ/fekbPrrRYM6IZvK7auuk6jXQcBgiAIEsTs7AwvPvjHT6a3FK3BtMlInryJiTfKolKCS/K5xmslOCjfdt2LvrFCSXoLgkMAKGIiwnDfOf04OvsM07Fc8fuA2YZy4U5aGsUWzrCClME0/54KNUOjZS0X+OTRqCFvROWDbzuGlISTLZzngkmCiSK5hkjv4rs6bU3jKjDlgzG+tR2xeatjTdqV6n8mEENC4i55kxaWBKp+rQEamymj8eSzV1bK15wun03lw0QQJf1uWbta7cZtfOKJ9SS5XcLARLGt+qg2yCZ5aLCZGh5tPiXamnIWv4XY6pSxiQ6QicDXxeHnb2jUGHh+H1vu7KIjqPYtrYqaqE3JdW/Frd7q+XRDKrrie9pSNlBKGfZ0/BYiPy3L8PA4ymEhZgVgdamAWEdHDaVMWaZW3Aiazv2yrLj9c6YO2/UJEqvIPD+JAWPLR6VDLNZCQySc7h935XHHHetB9UDpyVT5bEMMC+Lg+JZY7ABUn5OGAVfZxwQmx0FvQL2dOCZDw886fHGsPRXFJsFlhycqQd5oNs9hXMCCRuNKQslSrtI/pS5QvIdIsHBw9JAPT+DurFx527hjpVWRYZCJvq6dSV5+Q8x+8xnOiy0hOD7dJhJMAkS0/4hOP8TuCe+MkDDqqqcma1MoNw9L7th5DoriLa2aSqkzkYUm0NI5E5GAEOwOiENmKSVDOGJ4SI++Ll2X+iN2HCzykdzhzYNZyicOUa0TXUejVa9t2NwIY0nHH/S++4fy/Ivokz+jw0fO8Xv86GuSJZ19AYCicOmPrXElOhVyUTe2uIOXMdTJL1qIbbgvHtNgwh21aqjWG9Xieb0Q4ivfwu4xO31x+IhOP5A3r+RkTP0h94f04pynE/05d6MXJLY1pYLWPE1ADMST16XdlYFydGQgUxWJi6o1GnWYw2h6jXuPaXAghUOTS9ycs+PSxVP5+guKfF7dJIthuJC8JQRacS8ROT2O/DV2qGloyVQdtfq39j5Tzp1kKWeLwVLy9SsxGAlvQq+fYnLF/RG5Lt2+4ic/Jn+GONpS68yK/7O0impIvcwFlU3IzYzBqyHQ4rETzZnUWrWUxh1t+QoTxqk6UNHafSZiKV/8jF//A8YvmT3sDMXogLwpnv6UvWvGGqItN4d6UZK1eTwzyyDY9qspCfJX70XDm69IcyLCbCy/+CH5UyHhDPbFy1/Iz/5CXr0AS+TFfNU4wC5hXRqFaU5W+FiwZePrV5My5/UJ2tJHpRRMfPZL3julMJSvPuVP/hKTK5Ih5BvFRDeDbm3SatDaT2lHqTtscv+p6TTSrhwiko6g/kj0hjy7ReRDSkiZ2jSZWzvlLq313JHoGGPOIp9Ab0tswSKUBZe0RaMAIOaH/kwElmBkvU3doFGCqLrqrRJK572hKqpeY8qb9muzmbTzKJmlZMkkZXytQv+KNiux+iRfrKgXpbOjO0etQW3bzsnyqJFpTPukqgW5Mbs+jfauiHSNdLofzZ25sGGPfPl6g81TtqtJYVcPtSsxJN8zn/JZJgyzRCs6qGnPlA1nLvvWf8l7rfVn2sqfziN9w2kLcuncIUHXZEJVGIz3fDHVYaz4nlYMTSh4t+ROz4onWDYdLrKrOm68x9Dq6A2DARdCUBSC5PoO/e33WMJfW9JHt+7LdF1Aq2orAyREf4jeSLYWv1EA1VpK311er4rDDAYtHDrmttZxU9k+R26e467Gvao5pJS25m+tcA+nAojAoZQRDQYiEuzdxJJ2I+Zd3vHUYtzT9qtTNaC7UIZOyhVmf4ooTG5CtEagRf6p9DlTT3eRqM3MCx3P+ZZjpSnuaDiw0KJqGHWryS82fKZaxMXu7PrKaPE8qaV2Fs3xRph8acxXZ/LmVWIY5m+SGB42C139Jj+G6gG1UnlbppjNcNGS6/aFNDoXSevqtkZcVntflbAFXWzkbKJ2fGCRQ958JJGqcPnD4vuE3VOEmoD5klOJD5+oLPdiJ2AOmWmtG3OsEq2cgmLlN1Bbh5HJkpchuRbv3KXR0CqWcWLDNzrtNQEbK0cQyIEM9N2zqb8IW3HQ0CjytGHio42xhI6iweUnCGcditE2yLG1wDrf0xuqAAHWNqyWVVPuYoaUCP3yCqszRb0z/81FRu7H6Ixdv/Vo6uZ1HnMv99tzqpyR3p3xj66SsKlK5uVVkc/gUgn5kGpt5uvkP5rHePAa90CHoNUtO0Oj68cGdeu3Vq1cHQvfUxeSziXmc42ebEpds04bwTLtSWjE5H8L0G6+pzvcoQrU47oWYvM0zRceFXYPFHtZV8mwUHrjxfJhl7Cm4D3tgdMG9NHt8rluVWfbQ4MEmk+ewwts8F5od1E49fVSRSe2+bKBLcha3xoywaNFt5/vaHSJNyDhx/qwJsle9E/1rkjnaXQ9PoeSPMIsoCaVru0Trco183wX3fC6tK6hqc11nkbXg7IZz53p0IZj1N8qJEx0Q/poVa5Ai7fahIb/MeKbg2v1xuvTYr7hKmz66lJyuR4EASV+tGUWUWHGO8a8Oh0ItpHTrNUbzSSwXnDQ+Y9AfC+UEfsBV2ysHNXvb7DxIyHtWTmWCfo2+omPjeyfokYtHahF6aUYTMTEhJQPv4Wvcze9fp3javUG2OYt3EZRLzNe0eup7zPx3Cagu9i8O9SGmVPaEG7+Wkjun8xWNtOadPY6XvFNeGG0/qBmYZ7hbvieEhRK5wIPfNVq89gm31OHFmqB7Q26q6cmGXjnivlHDYUVWd+yPlS1OSau8VaD0DXdgEJs/5nJRlFvdxW9tUqISalSu018NEZTrKtz9tY2oGjSVlmUzPfT8tVuA40m987iWxPpMJmiqBkUTGgSU1NaEmjl7GBLzHk1EGnFeqoWbpVG7UeYGsnyrbTdl9ZQ87p8aXPbq02uG5bX1g1cIFOmlLPkoayjNm/eelDqcs/s1Hzh+a/xsNUndo0maVuKSpqrah4t+PyZDdlaUnlsmGF9JpJJa1WSrdu6jBat+kftt2b+nrWGsyqUp/xChonLRH+9MSqpSQoXWzlQJmHhOedS4kt52tO1qWXyD2vPdrf0UUO4q/o8mSMzKedlivoks66bkfsNMdH8fjMMPHml3i610Uqb3fzdolEs5i6/WdXnRW+pfyTQ+pzfPE3UsFFhtJ0zr5s986X1FL1Yiq30PWmnQyW4UqabebHBzd24ktDsnlEnx1yvdoa1MkoV3+vQkZhTPpQ0Q+ntbJdmps5g5mEGWtUKacuJ0p9ZeYOhiqY809XSQEcIQ6CT3mxDlxImoWqWpRUWCfr5Ci3+tpyKTc5YXZdt0VbMPDH7VbSvmLH65m9J1ldaVINWXokuNeUXVBg3ofpd3xJuqp3bjHvOppJKLdoX1qIlGi2lOfWJ1rsxrydtBGjV0yIkNS6VqmJ9N/VWkZt2I1jBFaDObd7FYbAp1zFe+zqXNLqReS8SPYU0p3tu0CnNtGtjtOZVi+0F6ZzH+UmAnWtzFYIp2gPaOls9Z1oR6uGnOtcZFxVyZQygXPKWUv+fjYNwHe82VaHWGQfjdCWzZGObGpDhJpbbvtNx+NkFUKSzSlsZJ5SBfM2Tolq4GSeA+qu+b3ajaF9YGYZsGKa2EnvbtKhkVXNiUWcxH+2I60GFgefFDraku0XOlKRwvnIbAlLXsoMGVr5XeVGe4WQqjzTMj3nqMiWJSMrGkvq2G/fEpk99WNVQrMsTERRdtsgOWNEG6iBdZhFLG6cXa0Zxd5cCJy0N8gRdOj8FKuNapoVARKLkuyKNNSYccvpMbtnBR2FPDDKo6J+pDhjtKlgwxaI+dApExHORkiQpT2svFkanSsH5wlrtX33IzE0xUSJBwmlHHyV2+iRcUMhhWLMKnYTNslUAuQOkTDG9GWuxdVKVpL1g1UezXhDA0XKeKyp/S9Ff+mLVTVukgOWXLIZkiUi25sOXzJEifMyFs9zR0uZgpUzexElqyJOsJZUtl415XtGmCVSz5LVOOtSxFA2qyCFgj1JhmKuZgaJvgzcLJiaQcIiIZYi6EtPsJCoy5xNUNZg09gSnooA3TqD2MMyV+S21WCUniQ0sK/n/8igJoiDa/aAAAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=225x225>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx=4\n",
    "img, masks, labels = dataset[idx].values()\n",
    "print(f\"The image corresponding to idx {idx} has the file name '{dataset.images[idx]}'\\n\")\n",
    "name_original_img = dataset.images[idx]\n",
    "id_original_img, id_after_split = extract_ids_from_name(name_original_img)\n",
    "mask_idx = dataset.idx_table[(dataset.idx_table['id orginal img'] == id_original_img) & \n",
    "                             (dataset.idx_table['id after split'] == id_after_split)]['id']._values[0]\n",
    "\n",
    "print(f\"The name of the folder where the masks are stored is {mask_idx}, and there are {len(masks)} masks:\")\n",
    "for mn in masks:\n",
    "    print(f\" -> {mn}\")\n",
    "print(f\"The labels of the masks are {labels}\")\n",
    "img.resize((225, 225))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos visualizar las máscaras asociadas a esta imagen con la\n",
    "función de utilidad `display_masks`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1575x225>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: analizar el orden de como se stackean las imagenes de la lista de direcciones en masks\n",
    "# a la PIL.Image en display_masks()\n",
    "from utils import display_masks\n",
    "display_masks(masks)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De izquierda a derecha, cada máscara identifica las siguientes instancias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal_cut\n",
      "noise\n",
      "noise\n",
      "normal\n",
      "noise\n",
      "noise\n",
      "noise\n"
     ]
    }
   ],
   "source": [
    "for idx in labels:\n",
    "    print(dataset._id2label[idx])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La clase `PlantDataset` contiene dos atributos con los diccionarios para\n",
    "mapear la clase a un entero (i.e. `_label2id`) y viceversa (`_id2label`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dataset.get_number_of_masks());\n",
    "plt.title('Distribución del número de máscaras por imagen en cwt');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay una imagen con 13 máscaras asociadas..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=250x250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=2925x225>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_13masks = dataset.get_number_of_masks().index(13)\n",
    "\n",
    "display(dataset[idx_13masks]['image'].resize((250, 250)))\n",
    "\n",
    "display_masks(dataset[idx_13masks]['masks'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver cuantas máscaras hay por clase con el método `dataset.get_masks_per_labels()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['noise', 'normal', 'normal_cut'], dtype='<U10'),\n",
       " array([119,  99,  98]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.get_masks_per_labels()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collate Function + DataLoader: bonito, bonito, pero quiero tensores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un canal para cada etiqueta, un entero para cada instancia"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora nos enfocaremos en construir el _target_ de una observación. Necesitamos\n",
    "crear un tensor a partir de todas las posibles máscaras y etiquetas asociadas\n",
    "a una imagen, dentro del cual se identifican instancias particulares a una clase\n",
    "dentro de la misma matriz. Tendremos 3 matrices, 1 por cada clase, y todas\n",
    "apiladas en una dimensión adicional en el mismo tensor. Esto será el _target_\n",
    "para cada imagen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/cwt/original_labeled/5/task-5-annotation-24-by-1-tag-normal_cut-0.png',\n",
       " 'data/cwt/original_labeled/5/task-5-annotation-22-by-1-tag-noise-0.png',\n",
       " 'data/cwt/original_labeled/5/task-5-annotation-21-by-1-tag-noise-0.png',\n",
       " 'data/cwt/original_labeled/5/task-5-annotation-23-by-1-tag-normal-0.png',\n",
       " 'data/cwt/original_labeled/5/task-5-annotation-27-by-1-tag-noise-0.png',\n",
       " 'data/cwt/original_labeled/5/task-5-annotation-25-by-1-tag-noise-0.png',\n",
       " 'data/cwt/original_labeled/5/task-5-annotation-26-by-1-tag-noise-0.png']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dado una imagen leer en tensores todas las etiquetas\n",
    "idx=4\n",
    "img, masks, labels = dataset[idx].values()\n",
    "masks"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usando un solo ejemplo, leemos sus máscaras y las transformamos a\n",
    "tensores de PyTorch. Agrupamos todos los tensores asociados a una etiqueta,\n",
    "tenemos 3 distintas, por lo tanto terminamos con 3 tensores que agrupan\n",
    "en un canal todas las máscaras con la misma etiqueta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 17, 17]), torch.Size([1, 17, 17]), torch.Size([5, 17, 17]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision.transforms import ToTensor\n",
    "\n",
    "# obtengamos la información para un ejemplo\n",
    "idx=4\n",
    "img, masks, labels = dataset[idx].values()\n",
    "\n",
    "# crear id de instancias\n",
    "instance_idxs = torch.tensor([l+1 for l in range(len(labels))], dtype=torch.long)\n",
    "tfms = ToTensor()\n",
    "\n",
    "# leer las mascaras por etiquetas, transformarlas a tensores, y concatenarlos en una dimensión\n",
    "# adicional. Creamos un tensor por cada etiqueta\n",
    "# TODO: cómo hacerlo todo una pasada?\n",
    "R=(17,17)\n",
    "m0 = torch.cat([torch.where(tfms(Image.open(m).resize(R)) > 0.0, 1.0, 0.0) * instance_idxs[l] for l, m in enumerate(masks) if labels[l] == 0])\n",
    "m1 = torch.cat([torch.where(tfms(Image.open(m).resize(R)) > 0.0, 1.0, 0.0) * instance_idxs[l] for l, m in enumerate(masks) if labels[l] == 1])\n",
    "m2 = torch.cat([torch.where(tfms(Image.open(m).resize(R)) > 0.0, 1.0, 0.0) * instance_idxs[l] for l, m in enumerate(masks) if labels[l] == 2])\n",
    "m0.shape, m1.shape, m2.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notemos que la etiqueta asociada al índice 2 (i.e. `noise`) tiene 5 máscaras\n",
    "asociadas, las otras dos etiquetas solo una. Necesitamos ahora fundir cada\n",
    "uno de estos tensores con 3 dimensiones `(mask_id, height, width)` a \n",
    "`(height, width)` donde en cada pixel haya un número asociado a la instancia.\n",
    "\n",
    "Partamos con el tensor que agrupa las máscaras `noise` y tiene 5 instancais\n",
    "asociadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id de instancia: tensor([1, 2, 3, 4, 5, 6, 7])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([0., 2., 3., 5., 6., 7.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# revisamos sus ids de instancia\n",
    "print('Id de instancia:', instance_idxs)\n",
    "m2.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordemos que el valor 0 esta reservado para la etiqueta de ausencia (no label).\n",
    "Así que las instancias parten desde el 1, ..., a tantas máscaras tenga asociada\n",
    "la observación. No tienen porque tener un orden asociado según la clase.\n",
    "\n",
    "Ahora podemos colapsar el canal que tiene cada mascara en una sola, sumando\n",
    "todas las matrices a lo largo de este canal, element por elemento. Asumiendo\n",
    "que no tenemos ninguna instancia asolapada, cada pixel debería tener un id\n",
    "identificando a la máscara particular que pertenece. El resto de los valores\n",
    "será 0 que significa que no hay ningúna clase que identificamos en ese pixel."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordemos que en nuestro ejemplo reducimos la resolución de las máscaras a\n",
    "10x10 para lograr visualizarlas completa. Estas son las máscaras asociadas\n",
    "a la etiqueta 2 que es `noise`. Originalmente habían 5 de 7 máscaras en\n",
    "esta observación que se identifican a esta etiqueta. Sin embargo, en su \n",
    "versión matricial solo vemos 4 enteros distintos, esto es porque uno \n",
    "se perdió por la reducción de tamaño de imagen. \n",
    "\n",
    "Veámos las clases de cada máscara y los ids para identificar a cada una \n",
    "de ellas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 4., 4., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 4., 4., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 4., 4., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 4., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 4., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 4., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m0.sum(dim=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si vemos la imagen a clasificar, es posible identificar con los\n",
    "enteros de la matriz de arriba las zonas donde se clasificaron \n",
    "como ruído. Por la resolución, hay un par de puntos que son díficiles\n",
    "de ver, pero las instancias representadas por 3 y 2 son fáciles de mapear\n",
    "de la matriz a la imagen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=450x450>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.resize((450,450))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este procedimiento se debe realizar por cada clase, luego debemos\n",
    "apilar 3 matrices como la de arriba, en un solo tensor que tendrá\n",
    "las dimensiones `(Batch, Clase, Altura, Ancho)`, y serán nuestras etiquetas\n",
    "asociadas a la observación. \n",
    "\n",
    "A continuación una representación visual de lo que estamos haciendo pero\n",
    "con más clases (Fuente: [Jemery Jordan Blog](https://www.jeremyjordan.me/semantic-segmentation/#:~:text=The%20most%20commonly%20used%20loss,one%2Dhot%20encoded%20target%20vector))."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Source: Fuente Jeremy Jordan Blog](https://www.jeremyjordan.me/content/images/2018/05/Screen-Shot-2018-05-16-at-9.36.00-PM.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target(masks, labels, tfms, size=(250, 250), num_classes=4):\n",
    "    \"\"\" \n",
    "        Recibe una lista de máscaras y una lista de etiquetas, retorna\n",
    "        un tensor de dimensiones (n_classes, height, widht). En cada clase,\n",
    "        cualquier entero distinto a 0 representa a uns instancia particular\n",
    "        de la clase. El entero 0 se reserva para background/no-clase.\n",
    "    \"\"\"\n",
    "    # creamos tensor para almacenar máscaras por clase en cada canal (dim 1)\n",
    "    # NOTA: la última es ausencia o ninguna detecctión \n",
    "    out = torch.zeros((1, num_classes, size[0], size[1]))\n",
    "\n",
    "    # si no hay máscaras, retornamos el tensor\n",
    "    if len(masks) == 0:\n",
    "        return out\n",
    "\n",
    "    # crear un entero que represente cada instancia de las mascaras asociadas\n",
    "    # a la observación. No siguen un orden necesario tipo todas las de la clase 0\n",
    "    # parten al principio, se encuentran según el orden de los labels. El entero\n",
    "    # 0 se reserva como background/no-clase\n",
    "    instance_idxs = torch.tensor([l+1 for l in range(len(labels))], dtype=torch.long)\n",
    "\n",
    "    # iteramos sobre cada clase para procesar las máscaras asociadas a estas,\n",
    "    # agregar un identificador de instancias y colapsar en una sola matriz\n",
    "    # Supuesto: no hay clase sobrelapadas. Si hay, se debe hacer un proceso\n",
    "    # adicional para ver que entero se asigna al pixel correspondiente\n",
    "    for l in list(set(labels)):\n",
    "        x = torch.cat([torch.where(tfms(Image.open(m).resize(size)) > 0.0, 1.0, 0.0) * instance_idxs[i] for i, m in enumerate(masks) if labels[i] == l])\n",
    "        out[0, l, :, :] = x.sum(dim=0)\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 4, 10, 10])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 4., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 4., 4., 4., 4., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 4., 4., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 4., 4., 4., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 4., 4., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],\n",
       "\n",
       "         [[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],\n",
       "\n",
       "         [[3., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [3., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 2., 2., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 2., 2., 0., 0., 0., 0., 6., 0., 0.],\n",
       "          [0., 0., 0., 0., 5., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],\n",
       "\n",
       "         [[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx=4\n",
    "img, masks, labels = dataset[idx].values()\n",
    "tensor_mask = get_target(masks, labels, tfms, size=((10, 10)))\n",
    "print(tensor_mask.shape)\n",
    "tensor_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=250x250>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.resize((250, 250))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Targets binarios"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta sección se crea etiquetas para detectar una clase o no. Es decir,\n",
    "de las $X$ potenciales clases, solo nos interesa detectar una de ellas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_binary_target(masks, labels, tfms, size=(250, 250)):\n",
    "    \"\"\" \n",
    "        Recibe una lista de máscaras y una lista de etiquetas, retorna\n",
    "        un tensor de dimensiones (1, height, width). La clase target_id\n",
    "        se identificar con el entero 1, el resto es representado por\n",
    "        el entero 0 (es decir background o noise)\n",
    "\n",
    "        label 0 -> \"normal\" (ver dataset._id2label con mapping id - etiqueta)\n",
    "    \"\"\"\n",
    "    target_id = 0\n",
    "    out = torch.zeros((1, 1, size[0], size[1]))\n",
    "\n",
    "    # si no hay máscaras o la clase a detectar no tiene máscara, retornamos el tensor\n",
    "    if len(masks) == 0 or target_id not in (labels):\n",
    "        return out\n",
    "    \n",
    "    target_masks_idx = np.where(np.array(labels) == target_id)[0]\n",
    "\n",
    "    target_masks=[]\n",
    "    for idx in target_masks_idx:\n",
    "        target_masks.append(torch.where(tfms(Image.open(masks[idx]).resize(size)) > 0.0, 1.0, 0.0))\n",
    "    out[0,0,:,:] = torch.where(torch.cat(target_masks).sum(dim=0) > 0.0, 1.0, 0.0)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/cwt/original_labeled/13/task-13-annotation-60-by-1-tag-normal-0.png',\n",
       " 'data/cwt/original_labeled/13/task-13-annotation-59-by-1-tag-normal-0.png']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img, masks, labels = dataset[12].values()\n",
    "masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=250x250>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.resize(size=(250, 250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 250, 250])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = get_binary_target(masks, labels, ToTensor(), size=(250, 250))\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 249.5, 249.5, -0.5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(out.numpy()[0,0,:,:])\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple test para ver si se agregan las mascaras correctamente en un solo canal\n",
    "assert (out[0,0,:,:] == torch.where(ToTensor()(Image.open(masks[0]).resize((250, 250))) + ToTensor()(Image.open(masks[1]).resize((250, 250))) > 0.0, 1.0, 0.0)).sum().item() == 250*250, \"Todos los pixeles deben ser iguales\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collate function y DataLoaders"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sabemos como obtener los _targets_ a partir de las máscaras asociadas a una observación.\n",
    "Ahora, aplicaremos la transformación a la imagen del input."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero, las imágenes como _input_ se le aplican una serie de\n",
    "transformaciones para adaptarlo al modelo SegFormer. Estas transformaciones\n",
    "se pueden instanciar con `SegformerFeatureExtractor()`. Toma una imagen\n",
    "en forma `PIL.Image` y entrega una lista con un array que puede ser\n",
    "adaptado a numpy, torch, jax, etcétera."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Qué pasa con las máscaras?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alkzar/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/transformers/models/segformer/feature_extraction_segformer.py:28: FutureWarning: The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use SegformerImageProcessor instead.\n",
      "  warnings.warn(\n",
      "/home/alkzar/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/transformers/models/segformer/image_processing_segformer.py:99: FutureWarning: The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use `do_reduce_labels` instead.\n",
      "  warnings.warn(\n",
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.batch_norm.running_mean', 'decode_head.batch_norm.weight', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.batch_norm.bias', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.linear_c.3.proj.bias', 'decode_head.batch_norm.running_var', 'decode_head.classifier.bias', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.3.proj.weight', 'decode_head.classifier.weight', 'decode_head.linear_fuse.weight', 'decode_head.linear_c.2.proj.weight', 'decode_head.linear_c.2.proj.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import SegformerForSemanticSegmentation, SegformerFeatureExtractor\n",
    "    \n",
    "feature_extractor = SegformerFeatureExtractor.from_pretrained(\"nvidia/mit-b0\")\n",
    "model = SegformerForSemanticSegmentation.from_pretrained(\"nvidia/mit-b0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a collate function to strack the masks as channels in the same tensor\n",
    "def collate_fn(batch, target_fn=get_target):\n",
    "    \"\"\"\n",
    "        Collate function to stack the masks as channels in the same tensor.\n",
    "        get_target: function to get the target tensor from the masks and labels\n",
    "            could be multi-labeling or binary.\n",
    "    \"\"\" \n",
    "    # Acá se pueden agregar todas las transformacaiones adicionales de preproceso\n",
    "    # que se requieran para las máscaras. Lo única esencial es pasar una PIL.Image\n",
    "    # a tensor\n",
    "    tfms = ToTensor()\n",
    "    images = torch.cat([feature_extractor(example['image'], return_tensors='pt')['pixel_values'] for example in batch])\n",
    "    masks = [example['masks'] for example in batch]\n",
    "    labels = [example['labels'] for example in batch]\n",
    "    targets = torch.cat([target_fn(x[0], x[1], tfms, size=(512,512)) for x in zip(masks, labels)])\n",
    "\n",
    "    # transformar a 1 cuando haya un entero distinto a 0 (semantic segmentation)\n",
    "    targets = torch.where(targets > 0.0, 1.0, 0.0)\n",
    "    return images, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 3, 512, 512]), torch.Size([2, 4, 512, 512]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Create a DataLoader object to stacking each mask as a channel in the same tensor\n",
    "# and to shuffle the dataset\n",
    "dataloader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=0,\n",
    "                        collate_fn=collate_fn)\n",
    "\n",
    "xb, yb = next(iter(dataloader))\n",
    "xb.shape, yb.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SegFormer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check I: understanding the output models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset._id2label[3] = 'non-detection'\n",
    "dataset._label2id['non-detection'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.batch_norm.running_mean', 'decode_head.batch_norm.weight', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.batch_norm.bias', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.linear_c.3.proj.bias', 'decode_head.batch_norm.running_var', 'decode_head.classifier.bias', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.3.proj.weight', 'decode_head.classifier.weight', 'decode_head.linear_fuse.weight', 'decode_head.linear_c.2.proj.weight', 'decode_head.linear_c.2.proj.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 3, 512, 512]), torch.Size([2, 4, 512, 512]))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_seed()  # for reproducibility\n",
    "\n",
    "# initialize model\n",
    "dataset._id2label[3] = 'non-detection'\n",
    "dataset._label2id['non-detection'] = 3\n",
    "\n",
    "model = SegformerForSemanticSegmentation.from_pretrained(\n",
    "    \"nvidia/mit-b0\",\n",
    "    id2label=dataset._id2label,\n",
    "    label2id=dataset._label2id\n",
    ")\n",
    "\n",
    "# model to device\n",
    "model=model.to(device)\n",
    "\n",
    "lr = 1e-4 \n",
    "n_epochs = 1000\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
    "\n",
    "xb=xb.to(device)\n",
    "yb=yb.to(device)\n",
    "xb.shape, yb.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos el output que nos entrega un modelo al pasarle un batch con dos ejemplos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 128, 128])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()\n",
    "out = model(xb)['logits']\n",
    "out.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Escalamos los logits a la resolución de entrada de la imagen, utilizamos `upscale_logits`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 512, 512])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = upscale_logits(out)\n",
    "out.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Necesitamos clasificar cada pixel de la imagen en alguna de las 3 clases posibles (+1 que es no detección). Utilizamos una función `softmax(out, dim=1)` en la profundidad de cada pixel para crear una distribución multinomial que nos entregue la probabilidad de que cada pixel pertenezca a la clase 0, 1, 2 y 3.\n",
    "\n",
    "Esto sería equivalente a `argmax(out, dim=1)` donde `dim=1` es la dimensión donde tenemos nuestras etiquetas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 512, 512])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_labels = torch.softmax(out, dim=1).detach().cpu()\n",
    "pred_labels.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces, para el primer pixel las predicciones respecto a los 4 outputs son practicamente uniformes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.2387, 0.2538, 0.2585, 0.2490])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_labels[0,:,0,0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cómo comparar la predicción de cada pixel usando `nn.CrossEntropyLoss`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 4, 512, 512]), torch.Size([2, 4, 512, 512]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out.shape, yb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 1048576]), torch.Size([2, 1048576]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out.view(2, -1).shape, yb.view(2, -1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.4154, device='cuda:0', grad_fn=<NllLoss2DBackward>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "model.eval()\n",
    "loss_fn(\n",
    "    out,\n",
    "    torch.argmax(yb, dim=1).to(torch.long)\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizar `ignore_index` para ignorar la clase `non-detection` en el calculo de la función de costo.\n",
    "\n",
    "Ver documentación de [`nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss).\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computar la función de costo con `nn.CrossEntropyLoss`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.4154, device='cuda:0', grad_fn=<NllLoss2DBackward>)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss(ignore_index=3)\n",
    "\n",
    "loss_fn(\n",
    "    out,\n",
    "    torch.argmax(yb, dim=1).to(torch.long)\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computar función de costo con single-mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.bias', 'classifier.weight']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.3.proj.weight', 'decode_head.linear_c.0.proj.bias', 'decode_head.batch_norm.bias', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.3.proj.bias', 'decode_head.linear_fuse.weight', 'decode_head.batch_norm.weight', 'decode_head.linear_c.1.proj.weight', 'decode_head.batch_norm.running_mean', 'decode_head.linear_c.2.proj.weight', 'decode_head.batch_norm.running_var', 'decode_head.classifier.bias', 'decode_head.linear_c.2.proj.bias', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 3, 512, 512]), torch.Size([2, 1, 512, 512]))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_seed()  # for reproducibility\n",
    "\n",
    "stoi = {'normal': 1, 'non-detection': 0}\n",
    "itos = {1: 'normal', 0: 'non-detection'}\n",
    "\n",
    "model = SegformerForSemanticSegmentation.from_pretrained(\n",
    "    \"nvidia/mit-b0\",\n",
    "    id2label=stoi,\n",
    "    label2id=itos,\n",
    ")\n",
    "\n",
    "# model to device\n",
    "model=model.to(device)\n",
    "\n",
    "lr = 1e-4 \n",
    "n_epochs = 300\n",
    "loss_fn = nn.CrossEntropyLoss(ignore_index=0)\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
    "\n",
    "dataloader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=0,\n",
    "                        collate_fn = \n",
    "                        lambda x: collate_fn(x, target_fn=get_binary_target))\n",
    "\n",
    "xb, yb = next(iter(dataloader))\n",
    "\n",
    "xb=xb.to(device)\n",
    "yb=yb.to(device)\n",
    "xb.shape, yb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2, 128, 128])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()\n",
    "out = model(xb)['logits']\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1, 512, 512])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.6803, device='cuda:0', grad_fn=<NllLoss2DBackward>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss(ignore_index=0)\n",
    "\n",
    "loss_fn(upscale_logits(out),            # (2, 2, 512, 512)\n",
    "        yb.squeeze(1).to(torch.long)    # (2, 512, 512)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.6803, device='cuda:0', grad_fn=<NllLoss2DBackward>)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn(\n",
    "        upscale_logits(out).view(2, 2, -1),   # (2, 2, 512*512=262144)\n",
    "        yb.view(2, -1).to(torch.long)         # (2, 512*512=262144)\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check II: overfit a mini-batch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.batch_norm.running_mean', 'decode_head.batch_norm.weight', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.batch_norm.bias', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.linear_c.3.proj.bias', 'decode_head.batch_norm.running_var', 'decode_head.classifier.bias', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.3.proj.weight', 'decode_head.classifier.weight', 'decode_head.linear_fuse.weight', 'decode_head.linear_c.2.proj.weight', 'decode_head.linear_c.2.proj.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "set_seed()  # for reproducibility\n",
    "\n",
    "model = SegformerForSemanticSegmentation.from_pretrained(\n",
    "    \"nvidia/mit-b0\",\n",
    "    id2label=dataset._id2label,\n",
    "    label2id=dataset._label2id\n",
    ")\n",
    "\n",
    "model=model.to(device)\n",
    "\n",
    "lr = 1e-4 \n",
    "n_epochs = 600\n",
    "loss_fn = nn.CrossEntropyLoss(ignore_index=3)\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
    "\n",
    "# move tensors into the current device\n",
    "xb=xb.to(device)\n",
    "yb=yb.to(device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicciones de máscaras con capacidad inicial del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADGCAYAAABxXlODAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYHklEQVR4nO2dffAeVXXHv+f3S0wgCchLICQkQWW0wESgDKatUNIOZQjlpbSWQTMpBpGX2g51sFgKtogolukwhsEOtVZsQCCMbRhC60ihJlGJVasUodQWYpIfhEBKCC+JxN/L6R/3PsnmybPvd3fv3f1+ZjJ5nt/us3vvued+99xz7+6KqoIQQkg9DDVdAEII6RIUXUIIqRGKLiGE1AhFlxBCaoSiSwghNULRJYSQGumE6IrIGhG5rOlyAICIbBSRM+3nPxeRLxc8ztMisshl2VyQVicR+bCIfKfOMvWd/yoReUlE3hSRw+z/72yqPFkRkdNF5Kc1nk9F5Ni6zleUaH9K2e8YW6dJdZQrCS9E1xruFyJyeN/ff2wNdUxDRasUVf2cqqZeDETkqyJyc99vT1DVNZUVriDROtXt6GkdUEQmA7gNwFmqOl1VX7H/b6igLItE5HlXx1PVb6vqe1wdL4pPQUkX8EJ0LT8D8MHeFxFZAODA5oqTjg9XTZKLIwFMBfB00wUh3cUn0b0bwB9Evl8CYEV0BxH5bRv9vi4iIyJyY2TbVBG5R0ReEZEdIvIDETmy/yQicpSIPCkifzqoEDZauk5E/ktEXhWRu0Rkqt22SESeF5FPishWAHeJyJCI/JmIPGfP/YCIHBo53lIR2WS3Xd93rhtF5J7I99NE5HFb/hE7FL8cwBIA19qh8OpIOXtpiiki8gUR2WL/fUFEpvSV+RoReVlEXhSRZZFznmPr+oaIvCAin4ixyyYROcV+XmIj2BPs94+IyIMD6rTO/r/Dlv1XI8f7a2vfn4nI4sjfZ4vIQyKyXUSeFZGPRrbtE/FHo0kRuRvAPACr7bmu7Sv/uwH0huc7ROTf7N/3DKPt8b8oIv9s7fHvIvKuyDF+SUT+1ZbtpyJyUYytpgH4BoDZtixv2nrFlt9+3ygin7D++ZqIrOz3vSz72u3X2rbeIiKXSUy6QEQ+C+B0AHfYct4R2XymiPyv9ccviohEfnepiDxj2/CbIjI/xha90c4y69OvisiVInKqLfuO6DnF9KcbrL+9LCIrROTgyPak/pTYF71BVRv/B2AjgDNhOsVxAIYBPA9gPgAFcIzdbxGABTAXi/cCeAnA79htVwBYDRMdDwM4BcBBdtsaAJcBeAeA/wFweUpZngIwF8ChAL4L4ObI+ccA/BWAKQAOAHA1gO8BONr+7W8B3Gf3Px7AmwB+3W67zf7+TLv9RgD32M/zAbwBE+1PBnAYgJPstq/2ytBvM/v5JluGIwDMBPA4gM/0lfkme9xzAOwCcIjd/iKA0+3nQwD8coxdVgC4xn7+EoDnAFwV2fbxAXU6xrbfpMhxPgxgFMBHbTtdBWALALHb1wH4G5iI9CQA2wD85iA72Lo9P8gmMXUYVB4FcGzk+K8AeB+ASQC+BuB+u20agBEAy+y2kwH8H4DjY861T9lylP/7AGbD+N4zAK4ssO/ZALYCOAGmP9wTreeAsq4BcFnf3xTAwwDeDnMx2wbgbLvtAgDPwvTVSQBuAPB4is3vtG16FoC3ADwI469zALwM4Ay7/6X22O8EMB3APwG4O2N/SuqL+7V9U/98inSBvdHub8E40QvRjaq6RlV/oqoTqvokgPsAnGE3j8II1bGqOq6q/6Gqr0d+fjyAbwH4S1X9Uko57lDVEVXdDuCziKQ9AEzYY+xW1Z8DuBLA9ar6vKruhhGdD4hJPXwAwMOqus5u+5T9/SA+BOBRVb1PVUfV5BufSClnjyUAblLVl1V1G4BPA1ga2T5qt4+q6r/AOO57ItuOF5GDVPVVVf1RzDnWYq+tTwdwS+T7GXZ7Vjap6t+p6jiAfwBwFIAjRWQugPcD+KSqvmXr/2XsOwKqmlWq+n1VHYMR3ZPs388FsFFV71LVMVX9MYB/BPD7js9/u6pusb63OnL+PPteBOAuVX1aVXfB+GQRPq+qO1R1M0zf6R3/SgC3qOoz1k6fA3BSXLRr+Yxt00cA7IQRw5dV9QUA34a5iAHGl29T1Q2q+iaA6wBcnLE/JfVFb/BRdD8EEw2t6N8oIgtF5Fsisk1EXoMx8uGR334TwP12SHWrmImTHktgRPzrGcoxEvm8CSaa6LFNVd+KfJ8PYJUdJu2AuViMw+QPZ0ePpao7YSKpQcyFiR6LMNuWM67Mr9jO0WMXTBQBAL8HE/1uEpG10RRAH2sBnC4iR8FEqA8AeL+YSc6DATyRo7xbex+sKMCWZzaA7ar6Rl9d5uQ4dlm2Rj5H7TQfwMJeO9u2XgJglojMi6QR3qzo/Hn23cfv+j67KMt8AMsjdtgOQJDcTi9FPv98wPdo2ft9eRKy9aekvugNXomuqm6CmVA7B2ZY0c+9AB4CMFdVD4YZsoj97aiqflpVjwfwazCRSTRCuhFmOHiviAynFGVu5PM8mOHvnmL27TsCYLGqvj3yb6q9gr8YPZaIHAgTjQ9iBMC7YralPQpuC4zDxZU5FlX9gapeADPUexBGTAft9yxMx/tjAOvsKGIrgMsBfEdVB0XweR9htwXAoSIyI/K3edg74tmJfSdXZ5U8Xx5GAKzta+fpqnqVqm62n6erak88BpUlrfyueBFmiN1jbtyOlrx2GwFwRZ8tDlDVx3MeZxCDfHkMRqTT+lNSX/QGr0TX8hGYHN7OAdtmwERCb4nI+2CiYgCAiPyGiCywgvo6zLA5KgSjMEPBaQBWiEhS3T8mIkfbJPz1AFYm7HsngM/2hlYiMlNELrDbvg7gXDETZG+DyavGnfdrMBMXF4nIJDFrSE+y216CyXHFcR+AG+y5DwfwFzB5vERE5G1iJsUOVtVRGLvFpT8AE+3+EfamEtb0fe9nmz1epnWwqjoCk4++RczE6Hth/KFXlycAnCMih4rILAB/0neINDuV4WEA77YTOZPtv1NF5LiY/V8CcFh0Egjp5XfFAwCWichxVpg+lbJ/XrvdCeA62TuRerCIuEqz3Afg4yLyDhGZDpO6WGlHamn9KakveoN3oquqz6nqD2M2/yGAm0TkDRhhiUZls2Aa5XWYYcVamJRD9Ni/APC7MMONryQI770AHgGwAWbIf3PMfgCwHCb6fsSW63sAFtrzPQ3gY/Z4LwJ4FWaCcD9s3uwcANfADNeeAHCi3fz3MHnXHWJXCfRxM4AfAngSwE8A/CilzFGWAtgoIq/DpGuWJOy7FubCty7me3+ddsHkxL9ry/4rGcrzQZhJjy0AVsHkzx+12+4G8J8wk0iPYP+L4S0wF58dErMKoyg25XEWgItt2bZi74TqoP3/G0ZANtjyzM5Qfldl/QaA22HysM/C+CQA7I75yXKY3OerInJ7huOvgqn7/dZvngKwOPlXmfkKjJ3WwYx634IZXWXpT7F90Sd6M8bEIiIbYWZyH03bl5AQsNH4UwCm9OX2SQN4F+kSQsojIheKWb99CExUupqC6wcUXULayRUw61+fg5nBv6rZ4pAeTC8QQkiNMNIlhJAaoegSQkiNJN4et3v3bh0aoi4T/5iYmAB9k/jIxMQEpkyZInHbE0V3aGgIkyal37YsEnt8QpyjqhgbG8Pw8HCi79EvSROMjo4mbmeoQAghNVJadBlNEB+hXxJfKSW6dGxCCMlHYdGl4BJCSH4KPdyXgkt8hb5JfCe36NKpiS/0fJE+SUKCqxdI0FBwSWjkinTp4ISQ0Oh/vkzTOpYquk0XkBBCiuLjA728ekumCwYZmRcOQrqDj0IbpRWim2bk3vY6xLfOcxHSNXwX1CwEKbpFDR/9XRFRzHNeVaXwOqANncxHBuU5s9ratV831cZN5XqDEN0qGiUtImVnL0ea/fJ0clKOLHbOG1AUxYd2jzt/kUCpSCDnvehW3UBVHr+L0W5We5axe9OdNiR8s5Vv5eknS59NEu0seC+6JBx871B1k2SPOucXSDlc25E3RxDSAFWPsCi4xenZrio7MtIlxBF5O6jrSJhCGwbeR7pdy4mS8PAhsmz6/G2jSnt6L7pA2MLLztBefBDbXjlIOAQhukDYwtsVRKQz7VTHqposok7BDY+gcrq9Dk1HI03hy0J+Ei5BiW4Pii+pk6b9rOnzE7cEKbo9QrmbrOxNEr49mo6QJon6v299PQvB5HTz0MsttlWcfJnAiaMtdvfdzl0l9DZppehGaYsAEEL2ErLwtl50fcHFk9FCIcQyk/AI1c+CzulmxYcnGwHZnrXrQzkJIdXRCdH1jbLCypRJ9fDiR6qCohsQIYhtG8SqDXUg/sKcLiERKLikajoT6fbndfujRna28tCGhKTTGdEFkofnvMuNsO1JHXRKdLMQ+t0upBhs67AIOUhiTpeUJvQ7t0Iue9cJYXK5H4puAm2+ldgVFCxSN6H3SaYXSCHaIrZtqUeXGSTCRV6NPgi+I60hfLmjrUm6Xn/iB1kF1FU0XEXfZ3ohI6EPaYoSer6WtIs2+CIj3Rx0JeLtQh1JuOR5PvUgX246gKLo5iTkpSpJtK0+pL1kvbEp60sO0kTYdbBF0S1IlqtlKEIWSjkJAbJHuln9OosIuxReim6FNBkV9z9GksJK2kSWx6S6PrYr4Q1GdF0tAWmCKnPBg67S0b+1UWxdtX8bbeOSEOYwqizfoIh60B2ref3Re9H1vdGz4tqB447VFnsR4jtFL/7eLhlLWqoUqrCUidBCi+5J2PT8jX7nHi9FN1RRzQKduBxMLdRDyOk83/FSdLPATkOKQt/JR5ftVUXdvRPdLjcwiYerMOqHdyNWg3eiS8ggXHV+igjJS6ufvdCFDtGFOvoKbU+K4tJ3vBLdvHS5E3V5cqPL7U6aw5XfBS26oUGxaA7anrigVXeksVPkJ4Q7hghpG2WfXOaN6JJ4upxKcAEvTKRq8vgY0wuEEFIjFN0aYcRKCPEivdDV4R9FuHq66lvEXyoTXTr7YCi0hHSbStILFFxCCBmMc9Gl4NYLI+dkaB/iG05Fl4JLCCHJOBNdCm5zMJojJByCXjJGsdlLl2yRp64MBohvOBFdOrYfdEl4CQmVoCNdsj9tFl4RaXX9SDfw4uaIIrDzxTPoNdEhw7YmbYKRLiFkDxxNVE9wokunyAftRbLAd9DVh7fpBQqFW0LuVL0yF/EJPnM4G1Eb0V7V4kR0izg2RbUZqhIhihsh2ag90qXYNk9WgRwUHUd/29+WVUfTFHXSBmoVXQquPySJ56B9k74nHdsHfCsP6TbORDfJsSm2flJVu1DgCInH6eqFQZ2YgksIIXtxnl6gyJKqKRJJM8VAfCG4dbrEf3y98PpaLrJ3PXkX1pV7u06XEBIergQz5HXlaVB0iRNC6RxpaYZBouFD3XwQoSYi0DamhSoR3f51naS9tKlD+OirWZbr1dEGPtrGN7LayLnotqkTkni60s4hRJa+Rueu8D3azXtB4jvSSOfoQtTWlUmppiliX+Z0SW7aenH1qV4uxdKHfHBbcNEuFF2SC3bc6mF06h8u24Si2yD9AsbOVh++RX91tL3vudEkmip7Fe1S+c0RoTZy1QyyC23VTXixzUbddqrqfLwjrWZUNVFcKbykSkKfXAu57D2cpRcoFvtCe5AsNCUieYfrquqN4NWRaqiyrk4iXQrMvpS1B+1J6iCvsKSN0uok5Iid6QVCGqRpESsiXL6JbwjHjFJadH0xvu/kaUhfr+BsaxLFF39wFfXWFT1zyVgFhLw0h5BQiQpm1v7XRIBTSnQpLIPhcrDB+LY2FvDjAtnUJFXT9a4SX0eLAHO6pAFCngSpiroEsJePdXW+Ngt3VTC9QGoh7f15eTovBbsYFEg/qCXSZWOTNJoUUl+eyMV+0g0Y6VZA0Qiu/7ddo878atyD9su0nQt8ugmBVENtOV1exbPRRjvlXS6Xtr9rG8Udj+JHqqBUpOvDzG/VMFL1D9qYhAyXjHlGGwWlyJA5aX/XfuebzZliqJ9BPlVVG9SW0+2qE+VZm9pVGzVB3SkMH+jCyDQvaU/8q6JPciItBVcTK2niS8FtHl8Eib7Qbii6NcMO5Se+CG7VtP3NwXloqt68I410Hl9Ep6m1wj6sUfaVKnyDoksqhx06DIq0U6ht2+SFlqLrkFAdkDQPfac7FM7p+jIkIyRkQhbbkMveJIx0CWkIH0Ura5l8LHsoUHRJJto6smlrvcqQJKicdCsPl4wR0gC+C5fv5QsZii6pHF9va2WU6wdJ7VCV3zR5d15h0c1TaB87nEvaXj9CXJNVO9r4wClGujkIoUGrwEVE4Gu0S+qnygizd2xXvlaFz/LNEYSQ1uH6XXAu4fN0SW0w2m0f/f3fx6e3xT22sSn94pIxQmqmDReeuCgylCAsmoaIa4+q2qkW0W2Dk3WVul6N0yVCt0Fa+X0d1vcT9568quFEGiEN0IVUS/+klu9CXFd7UHRJLL53kjL4ULekmXZVxYYNG/DYY49h06ZN+2ybNm0azj77bJx44okYHh5utJxZf9/2C0weJMn5RkdHdfLkyakHSXNgGjw8qhSlsv6gqhgbG8OkSZNKCYFP9Ndj/fr1uPjiizEyMjKwrDNnzsTq1auxcOHCSss1aKLMN9uVoQptGh0dxeTJk2MPzEiX7EfVnYqRTzorV67E5s2bY7fv3LkTW7ZsyXXMvDczhTxR5jO1iC47WTh0oVOFUMeZM2fGbhsaGsIll1yCc889N/U4Reoagn1ckXfJmwtKr17oUgMRd9Bvklm8eDGmTZu2z99EBEcccQSuvvpq3HrrrUhL/dHG+anjpgqmF8ge6u6kHAHFs2DBApx//vlYuXIlVBWLFi3CsmXLsGjRIsyZMwdDQ/HxEsXWDa5vKe5ReiItTwOzg/lLUx21iE+UnUjzUZQG1WP79u1YtWoVpk6divPOOw8zZszw8o6vLpDHz9Im0ii6OajqyucDTXbWvPbsiugC+X3Ox7q1jbS2SBNd3gZcgFDuuMlKm+oSInxTQ1iU7S8U3Yxw+Ux11GlHn9rMtaD6VLe2U8bWnEgrCSeD3NA1O3aprk0yPj6OzZs3Y3x8HLNmzcL06dOdHbuoz9Ya6bb1StzWetVN1Q+39qGdqkoX+FA331BVrFixAqeccgpOPvlkLF26FNu3b2+6WEwvZCGLQ9Pp/cWXtqkquvWlfr6xa9cuLF++HDt27MDOnTvx0EMPYf369U0Xq37RpYOQJFz6hy/RbZW0vX5lGB8fx2uvvbbnu6piYmLC2fGLXkQbiXTb6ihtrVdodEFsAfpbGsPDwzjooIP2fJ8xYwbmzZvn5NhlRi2NpRe60jFIfvLcihnd13efch3Fk2QOPPBAnHrqqXu+T58+HUcffXTp45ZNEzWe06XzkCSS/CNE33H1ZmWSTu9ZFb3Pp5122n7PsyhyzLKUXjLm4vmaXVsu5Bu+PyPV57LVDW2Rj0svvRQHHHAA5syZgwsvvBBTp04tfCxnr3Wv4yHmuQrkmfjmrZtv5c9KSJ1ZVTE+Po7h4eFg7Q0Uu/2ZFMPFK4OytldwtwGH7lihl5/UR55cNP2qWVxe3J2Irutogw5WP86GTnbxf8gRaFPEiTD7QznKRrmufdnZbcC+5wVJOkXbMM4p+/9O/8gH7eWGOlIKeXCaXmCEEz552i9ve9M/SEhU5auV5HTZscImS/uVaWP6B+kylU2kdbVjtaXeac94rfL4ZC+0U/vwbvUC8YdBHZ4iUC/M6zZHVbav9Hm6nFwLH4osIW5JFd3+dzQNElHnb8v0qKPnuXD4VG5CiJ9kTi8kLeBOEqW8QkTh6g5sa+I7VYzUa3ldT9bFyaF2wlDLTfzFxW2rxE+ciW6Wh9aEKk6hlpsQ4h9OVy/wqkxIeXiR9wvXusYlY6RRKDD7E+3ktE/7cC66jHYJKQ/7kV+4bA9GuoR4iu+vH+oartqiEtGlo6QTynu9CCF7cdFXK1syVsdNFCGStta5izbinYskJMq+XqzW9EKXO1aet9uSZLp4YSJ+UaafMqdbA3kbqIvCyzdOkNAo2k9ruSMtCt/8m40u2ymt3l28KBE/KeKLjUS67DTZoJ0IaR+NpRc4a0/K0tWRAAmb2tMLhLgkTnh5Qa8PvoA0HxTdGuATo+onKgS0ezWkvQWadh8MVy8QQiqB6Z/BpIpulYZjoxBCukamSLeK9ZNdE9wiQ62u2YiQLpArp+tq0oJiQuqEtxm7J2sfpu33J1F0JyYmMDo6WldZCMnMxMQExsbGmi4GIfsxMTGRuF14FSKEkPrg6gVCCKkRii4hhNQIRZcQQmqEoksIITVC0SWEkBqh6BJCSI38P6p7isA83CakAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "\n",
    "grid=torchvision.utils.make_grid(\n",
    "    torch.argmax(torch.softmax(upscale_logits(model(xb)[\"logits\"]), dim=1), dim=1).unsqueeze(1).detach().cpu(),\n",
    "    nrow=2,\n",
    "    pad_value=8)\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy()) * 255);\n",
    "plt.axis(\"off\");\n",
    "plt.title(f\"Mask predictions without fine-tuning the model\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Memorizar las máscaras del _mini-batch_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 1/600 [00:00<03:50,  2.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 0: 1.4154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 101/600 [00:37<03:05,  2.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 100: 0.0312\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|███▎      | 201/600 [01:14<02:28,  2.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 200: 0.0093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 301/600 [01:52<01:51,  2.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 300: 0.0060\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 401/600 [02:29<01:13,  2.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 400: 0.0046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████▎ | 501/600 [03:06<00:36,  2.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 500: 0.0045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 600/600 [03:43<00:00,  2.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 223.1400125026703 seconds ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# compute running time\n",
    "start_time = time.time()\n",
    "\n",
    "set_seed()  # for reproducibility\n",
    "\n",
    "# the target will be constant (just one batch)\n",
    "#target=flatten_logits(torch.where(yb > 0.0, 1.0, 0.0))\n",
    "lossi=np.zeros(n_epochs)\n",
    "\n",
    "for idx in tqdm(range(n_epochs)):\n",
    "\n",
    "  # Perform a forward pass\n",
    "  logits = model(xb)[\"logits\"]\n",
    "\n",
    "  # Clean the gradients  \n",
    "  optimizer.zero_grad()\n",
    "\n",
    "  # compute the loss with class indices (faster way)\n",
    "  loss = loss_fn(upscale_logits(logits),\n",
    "                 torch.argmax(yb, dim=1).to(torch.long))\n",
    "\n",
    "  #loss = loss_fn(upscale_logits(logits),\n",
    "  #               yb.squeeze(1).to(torch.long)\n",
    "  #)\n",
    "\n",
    "  # store the current loss value\n",
    "  cur_loss = loss.item()\n",
    "  lossi[idx] = cur_loss\n",
    "\n",
    "  # Backward prop\n",
    "  loss.backward()\n",
    "\n",
    "  # Update the parameters\n",
    "  optimizer.step()\n",
    "\n",
    "  if idx%100== 0:\n",
    "    print(f\" -- Loss at epoch {idx}: {cur_loss:.4f}\")\n",
    "\n",
    "# compute running time\n",
    "end_time = time.time()\n",
    "print(f\"--- {end_time - start_time} seconds ---\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspeccionemos la curva de pérdida..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x331.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4.6));\n",
    "plt.plot(lossi, color=\"darkcyan\");\n",
    "plt.xlabel(\"# epochs\");\n",
    "plt.ylabel(\"Loss\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora veamos las máscaras luego de memorizarlas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title\n",
    "out=torch.argmax(torch.softmax(upscale_logits(model(xb)[\"logits\"]), dim=1), dim=1).unsqueeze(1).detach().cpu()\n",
    "\n",
    "grid=torchvision.utils.make_grid(\n",
    "    out,\n",
    "    nrow=2,\n",
    "    pad_value=8)\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy()) * 255, cmap='Greys');\n",
    "plt.axis(\"off\");\n",
    "plt.title(f\"After {n_epochs} update steps\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparemos con las autenticas máscaras..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=torchvision.utils.make_grid(\n",
    "    #yb.unsqueeze(1).detach(),\n",
    "    yb.max(dim=1)[0].unsqueeze(1).detach().cpu(),\n",
    "    nrow=2,\n",
    "    padding=8,\n",
    "    pad_value=8\n",
    "    )\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).cpu().numpy()) * 255)\n",
    "plt.axis(\"off\");\n",
    "plt.title(\"Original masks\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No es perfecto, pero la red no solo propaga la información sino que es\n",
    "capaz de memorizar algunos máscaras. Antes de probar con más iteraciones o\n",
    "una mayor tasa de aprendizaje, hay que ver como visualizar las distintas clases.\n",
    "\n",
    "Lo de arriba es una imagen 1/0 o detección/no-detección. Sin embargo, dentro\n",
    "de detección tenemos más categorias como `normal`, `normal_cut`, y `noise`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = torchvision.utils.make_grid(out.detach().cpu(), nrow=2, padding=8, pad_value=4)\n",
    "\n",
    "#plt.imshow(np.uint8(grid.permute(1,2,0).numpy() * 120), cmap='viridis');\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy() * 150))\n",
    "plt.axis('off');\n",
    "plt.title('Predicted masks by class');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: cada clase mapearla a un color RGB a partir de un entero de clase a\n",
    "# una tupla RGB proveniente de una paleta como cividis\n",
    "viridis = cm.get_cmap('cividis', torch.max(out.detach().cpu()).item()+1)\n",
    "grid = torchvision.utils.make_grid(out.detach().cpu(), nrow=2, padding=2, \n",
    "                                   pad_value=torch.max(out.detach().cpu()).item()+1)\n",
    "#post_grid = (grid.permute(1,2,0).numpy() * 255).astype('float')\n",
    "post_grid = (grid.permute(1,2,0).numpy() * 255).astype('float')\n",
    "# por cada clase remplazar entero a RGB, viridis(integer) -> (R,G,B)\n",
    "for v in np.unique(post_grid):\n",
    "    for i in range(post_grid.shape[2]):\n",
    "        #print(f\"viridis({v})[{i}]: {viridis(v)[i]}\")\n",
    "        post_grid[:,:,i] = np.ma.array(post_grid[:,:], \n",
    "                                       mask=np.where(post_grid[:,:] == v, 1, 0)).filled(\n",
    "                                                viridis(v)[i])[:,:,i]\n",
    "plt.imshow(post_grid);\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>cividis</strong> </div><div class=\"cmap\"><img alt=\"cividis colormap\" title=\"cividis\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#00224eff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00224eff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#fee838ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #fee838ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x7ff2ec3ccf40>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.get_cmap('cividis', grid.max()+1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overfit a single mini-batch with one label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.bias', 'classifier.weight']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.linear_c.3.proj.bias', 'decode_head.batch_norm.bias', 'decode_head.linear_c.2.proj.weight', 'decode_head.classifier.bias', 'decode_head.linear_c.2.proj.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.batch_norm.running_mean', 'decode_head.classifier.weight', 'decode_head.linear_fuse.weight', 'decode_head.batch_norm.weight', 'decode_head.linear_c.0.proj.bias', 'decode_head.batch_norm.running_var', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.3.proj.weight', 'decode_head.linear_c.1.proj.bias', 'decode_head.batch_norm.num_batches_tracked']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 3, 512, 512]), torch.Size([2, 1, 512, 512]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_seed(42313988)  # for reproducibility\n",
    "\n",
    "stoi = {'normal': 1, 'non-detection': 0}\n",
    "itos = {1: 'normal', 0: 'non-detection'}\n",
    "\n",
    "model = SegformerForSemanticSegmentation.from_pretrained(\n",
    "    \"nvidia/mit-b0\",\n",
    "    id2label=stoi,\n",
    "    label2id=itos,\n",
    ")\n",
    "\n",
    "# model to device\n",
    "model=model.to(device)\n",
    "\n",
    "lr = 1e-4 \n",
    "n_epochs = 10\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
    "\n",
    "dataloader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=0,\n",
    "                        collate_fn = \n",
    "                        lambda x: collate_fn(x, target_fn=get_binary_target))\n",
    "\n",
    "xb, yb = next(iter(dataloader))\n",
    "\n",
    "xb=xb.to(device)\n",
    "yb=yb.to(device)\n",
    "xb.shape, yb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 1.95 GiB (GPU 0; 3.95 GiB total capacity; 2.75 GiB already allocated; 619.50 MiB free; 2.77 GiB reserved in total by PyTorch)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb Cell 94\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m model\u001b[39m.\u001b[39meval()\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m grid\u001b[39m=\u001b[39mtorchvision\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mmake_grid(\n\u001b[0;32m----> <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     torch\u001b[39m.\u001b[39margmax(torch\u001b[39m.\u001b[39;49msoftmax(upscale_logits(model(xb)[\u001b[39m\"\u001b[39;49m\u001b[39mlogits\u001b[39;49m\u001b[39m\"\u001b[39;49m]), dim\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m), dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39munsqueeze(\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mdetach()\u001b[39m.\u001b[39mcpu(),\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     nrow\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     padding\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     pad_value\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m plt\u001b[39m.\u001b[39mimshow(np\u001b[39m.\u001b[39muint8(grid\u001b[39m.\u001b[39mpermute(\u001b[39m1\u001b[39m,\u001b[39m2\u001b[39m,\u001b[39m0\u001b[39m)\u001b[39m.\u001b[39mnumpy()) \u001b[39m*\u001b[39m \u001b[39m255\u001b[39m);\n\u001b[1;32m     <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y254sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m plt\u001b[39m.\u001b[39maxis(\u001b[39m\"\u001b[39m\u001b[39moff\u001b[39m\u001b[39m\"\u001b[39m);\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 1.95 GiB (GPU 0; 3.95 GiB total capacity; 2.75 GiB already allocated; 619.50 MiB free; 2.77 GiB reserved in total by PyTorch)"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "\n",
    "grid=torchvision.utils.make_grid(\n",
    "    torch.argmax(torch.softmax(upscale_logits(model(xb)[\"logits\"]), dim=1), dim=1).unsqueeze(1).detach().cpu(),\n",
    "    nrow=2,\n",
    "    padding=8,\n",
    "    pad_value=8)\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy()) * 255);\n",
    "plt.axis(\"off\");\n",
    "plt.title(f\"Mask predictions without fine-tuning the model\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 3.95 GiB total capacity; 3.33 GiB already allocated; 19.50 MiB free; 3.35 GiB reserved in total by PyTorch)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb Cell 95\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m lossi\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mzeros(n_epochs)\n\u001b[1;32m     <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m tqdm(\u001b[39mrange\u001b[39m(n_epochs)):\n\u001b[1;32m     <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \n\u001b[1;32m     <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m   \u001b[39m# Perform a forward pass\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m   logits \u001b[39m=\u001b[39m model(xb)[\u001b[39m\"\u001b[39m\u001b[39mlogits\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m     <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m   \u001b[39m# Clean the gradients  \u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y253sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m   optimizer\u001b[39m.\u001b[39mzero_grad()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/torch/nn/modules/module.py:727\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_slow_forward(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    726\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 727\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mforward(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    728\u001b[0m \u001b[39mfor\u001b[39;00m hook \u001b[39min\u001b[39;00m itertools\u001b[39m.\u001b[39mchain(\n\u001b[1;32m    729\u001b[0m         _global_forward_hooks\u001b[39m.\u001b[39mvalues(),\n\u001b[1;32m    730\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks\u001b[39m.\u001b[39mvalues()):\n\u001b[1;32m    731\u001b[0m     hook_result \u001b[39m=\u001b[39m hook(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, result)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/transformers/models/segformer/modeling_segformer.py:802\u001b[0m, in \u001b[0;36mSegformerForSemanticSegmentation.forward\u001b[0;34m(self, pixel_values, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    793\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msegformer(\n\u001b[1;32m    794\u001b[0m     pixel_values,\n\u001b[1;32m    795\u001b[0m     output_attentions\u001b[39m=\u001b[39moutput_attentions,\n\u001b[1;32m    796\u001b[0m     output_hidden_states\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,  \u001b[39m# we need the intermediate hidden states\u001b[39;00m\n\u001b[1;32m    797\u001b[0m     return_dict\u001b[39m=\u001b[39mreturn_dict,\n\u001b[1;32m    798\u001b[0m )\n\u001b[1;32m    800\u001b[0m encoder_hidden_states \u001b[39m=\u001b[39m outputs\u001b[39m.\u001b[39mhidden_states \u001b[39mif\u001b[39;00m return_dict \u001b[39melse\u001b[39;00m outputs[\u001b[39m1\u001b[39m]\n\u001b[0;32m--> 802\u001b[0m logits \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdecode_head(encoder_hidden_states)\n\u001b[1;32m    804\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    805\u001b[0m \u001b[39mif\u001b[39;00m labels \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    806\u001b[0m     \u001b[39m# upsample logits to the images' original size\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/torch/nn/modules/module.py:727\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_slow_forward(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    726\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 727\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mforward(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    728\u001b[0m \u001b[39mfor\u001b[39;00m hook \u001b[39min\u001b[39;00m itertools\u001b[39m.\u001b[39mchain(\n\u001b[1;32m    729\u001b[0m         _global_forward_hooks\u001b[39m.\u001b[39mvalues(),\n\u001b[1;32m    730\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks\u001b[39m.\u001b[39mvalues()):\n\u001b[1;32m    731\u001b[0m     hook_result \u001b[39m=\u001b[39m hook(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, result)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/transformers/models/segformer/modeling_segformer.py:723\u001b[0m, in \u001b[0;36mSegformerDecodeHead.forward\u001b[0;34m(self, encoder_hidden_states)\u001b[0m\n\u001b[1;32m    721\u001b[0m     encoder_hidden_state \u001b[39m=\u001b[39m encoder_hidden_state\u001b[39m.\u001b[39mreshape(batch_size, \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, height, width)\n\u001b[1;32m    722\u001b[0m     \u001b[39m# upsample\u001b[39;00m\n\u001b[0;32m--> 723\u001b[0m     encoder_hidden_state \u001b[39m=\u001b[39m nn\u001b[39m.\u001b[39;49mfunctional\u001b[39m.\u001b[39;49minterpolate(\n\u001b[1;32m    724\u001b[0m         encoder_hidden_state, size\u001b[39m=\u001b[39;49mencoder_hidden_states[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49msize()[\u001b[39m2\u001b[39;49m:], mode\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mbilinear\u001b[39;49m\u001b[39m\"\u001b[39;49m, align_corners\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m    725\u001b[0m     )\n\u001b[1;32m    726\u001b[0m     all_hidden_states \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (encoder_hidden_state,)\n\u001b[1;32m    728\u001b[0m hidden_states \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlinear_fuse(torch\u001b[39m.\u001b[39mcat(all_hidden_states[::\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m], dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m))\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.5/envs/eda/lib/python3.8/site-packages/torch/nn/functional.py:3151\u001b[0m, in \u001b[0;36minterpolate\u001b[0;34m(input, size, scale_factor, mode, align_corners, recompute_scale_factor)\u001b[0m\n\u001b[1;32m   3149\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39minput\u001b[39m\u001b[39m.\u001b[39mdim() \u001b[39m==\u001b[39m \u001b[39m4\u001b[39m \u001b[39mand\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mbilinear\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m   3150\u001b[0m     \u001b[39massert\u001b[39;00m align_corners \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 3151\u001b[0m     \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39;49m_C\u001b[39m.\u001b[39;49m_nn\u001b[39m.\u001b[39;49mupsample_bilinear2d(\u001b[39minput\u001b[39;49m, output_size, align_corners, scale_factors)\n\u001b[1;32m   3152\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39minput\u001b[39m\u001b[39m.\u001b[39mdim() \u001b[39m==\u001b[39m \u001b[39m5\u001b[39m \u001b[39mand\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtrilinear\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m   3153\u001b[0m     \u001b[39massert\u001b[39;00m align_corners \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 3.95 GiB total capacity; 3.33 GiB already allocated; 19.50 MiB free; 3.35 GiB reserved in total by PyTorch)"
     ]
    }
   ],
   "source": [
    "# compute running time\n",
    "start_time = time.time()\n",
    "\n",
    "set_seed()  # for reproducibility\n",
    "\n",
    "# the target will be constant (just one batch)\n",
    "#target=flatten_logits(torch.where(yb > 0.0, 1.0, 0.0))\n",
    "lossi=np.zeros(n_epochs)\n",
    "\n",
    "for idx in tqdm(range(n_epochs)):\n",
    "\n",
    "  # Perform a forward pass\n",
    "  logits = model(xb)[\"logits\"]\n",
    "\n",
    "  # Clean the gradients  \n",
    "  optimizer.zero_grad()\n",
    "\n",
    "  # Compute the loss with class probabilities (slower way)\n",
    "  #loss = loss_fn(upscale_logits(logits), yb)\n",
    "\n",
    "  # compute the loss with class indices (faster way)\n",
    "  #loss = loss_fn(upscale_logits(logits),\n",
    "  #               torch.argmax(yb, dim=1).to(torch.long))\n",
    "\n",
    "  loss = loss_fn(upscale_logits(logits),\n",
    "                 yb.squeeze(1).to(torch.long)\n",
    "  )\n",
    "\n",
    "  # store the current loss value\n",
    "  cur_loss = loss.item()\n",
    "  lossi[idx] = cur_loss\n",
    "\n",
    "  # Backward prop\n",
    "  loss.backward()\n",
    "\n",
    "  # Update the parameters\n",
    "  optimizer.step()\n",
    "\n",
    "  if idx%5== 0:\n",
    "    print(f\" -- Loss at epoch {idx}: {cur_loss:.4f}\")\n",
    "\n",
    "# compute running time\n",
    "end_time = time.time()\n",
    "print(f\"--- {end_time - start_time} seconds ---\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lossi' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb Cell 96\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y255sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m15\u001b[39m, \u001b[39m4.6\u001b[39m));\n\u001b[0;32m----> <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y255sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(lossi, color\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mdarkcyan\u001b[39m\u001b[39m\"\u001b[39m);\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y255sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m\"\u001b[39m\u001b[39m# epochs\u001b[39m\u001b[39m\"\u001b[39m);\n\u001b[1;32m      <a href='vscode-notebook-cell:/mnt/C992-E9FE/dev/plant-segmentation/playground.ipynb#Y255sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m\"\u001b[39m\u001b[39mLoss\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'lossi' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x331.2 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4.6));\n",
    "plt.plot(lossi, color=\"darkcyan\");\n",
    "plt.xlabel(\"# epochs\");\n",
    "plt.ylabel(\"Loss\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "\n",
    "grid=torchvision.utils.make_grid(\n",
    "    torch.argmax(torch.softmax(upscale_logits(model(xb)[\"logits\"]), dim=1), dim=1).unsqueeze(1).detach().cpu(),\n",
    "    nrow=2,\n",
    "    padding=8,\n",
    "    pad_value=8)\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy()) * 255);\n",
    "plt.axis(\"off\");\n",
    "plt.title(f\"Mask predictions without fine-tuning the model\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title\n",
    "out=torch.argmax(torch.softmax(upscale_logits(model(xb)[\"logits\"]), dim=1), dim=1).unsqueeze(1).detach().cpu()\n",
    "\n",
    "grid=torchvision.utils.make_grid(\n",
    "    out,\n",
    "    nrow=2,\n",
    "    padding=8,\n",
    "    pad_value=8)\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy()) * 255, cmap='Greys');\n",
    "plt.axis(\"off\");\n",
    "plt.title(f\"After {n_epochs} update steps\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=torchvision.utils.make_grid(\n",
    "    #yb.unsqueeze(1).detach(),\n",
    "    yb.max(dim=1)[0].unsqueeze(1).detach().cpu(),\n",
    "    nrow=2,\n",
    "    padding=8,\n",
    "    pad_value=8\n",
    "    )\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).cpu().numpy()) * 255)\n",
    "plt.axis(\"off\");\n",
    "plt.title(\"Original masks\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity Check III: compute Intersection Over Union (mIoU) metric"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La intersección sobre la unión de dos conjuntos A y B es una métrica que va de $0\\leq J(A, B) \\leq 1$.\n",
    "\n",
    "Esta métrica de similitud entre dos conjuntos, `preds` y `targets` en  este caso particular, se conoce también como [_Jaccard index_](https://en.wikipedia.org/wiki/Jaccard_index).\n",
    "\n",
    "Más sobre las métricas utilizadas para este _task_, ver página 380,\n",
    "sección Precision vs. recall del libro [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "logits = model(xb)[\"logits\"]\n",
    "target = yb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 4, 128, 128]), torch.Size([2, 4, 512, 512]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = upscale_logits(logits.detach().cpu())\n",
    "target_c = torch.argmax(target.detach().cpu(), dim=1)\n",
    "# normalize the logits and get probabilities\n",
    "preds = torch.argmax(torch.softmax(preds, dim=1), dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 512, 512]), torch.Size([2, 512, 512]))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.shape, target_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.930385410785675"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps=1e-6\n",
    "intersection = torch.logical_and(preds, target_c)\n",
    "union = torch.logical_or(preds, target_c)\n",
    "iou = (torch.div(torch.sum(intersection, dim=(1,2)) + eps, (torch.sum(union, dim=(1,2)) + eps)).sum() / logits.shape[0]).item()\n",
    "iou"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora creamos una función que reciba los logits extraídos del modelo\n",
    "y los targets de cada _batch_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_intersection_over_union(logits, targets, eps=1e-6):\n",
    "    \"\"\" \n",
    "    Compute the intersection over union between the logits and the targets\n",
    "\n",
    "    B: batch size\n",
    "    C: number of classes\n",
    "    H: height of the image\n",
    "    W: width of the image\n",
    "\n",
    "    Args:\n",
    "        logits: torch.tensor with shape (B, C, H, W) containing the logits \n",
    "            return by the model\n",
    "        targets: torch.tensor with shape (B, C, H, W) containing the targets\n",
    "            return by the DataLoader format\n",
    "\n",
    "    Returns: a float with the ratio of intersection over union\n",
    "    \"\"\"\n",
    "    # eliminar grafo de gradiente y pasar a cpu\n",
    "    preds = upscale_logits(logits.detach().cpu())\n",
    "    targets = targets.detach().cpu()\n",
    "\n",
    "    # normalizar logits y obtener probabilidades\n",
    "    preds = torch.argmax(torch.softmax(preds, dim=1), dim=1)\n",
    "\n",
    "    # colapsar los canales del target en uno solo, de multiples one-hot channels\n",
    "    # a un solo canal con valores enteros\n",
    "    targets = torch.argmax(targets, dim=1)\n",
    "\n",
    "    # computar intersección y unión entre conjuntos de prediccion y targets\n",
    "    intersection = torch.logical_and(preds, targets)\n",
    "    union = torch.logical_or(preds, targets)\n",
    "\n",
    "    # computar ratio de intersección sobre la unión \n",
    "    iou = (torch.div(torch.sum(intersection, dim=(1,2)) + eps, \n",
    "                     (torch.sum(union, dim=(1,2)) + eps)).sum() / logits.shape[0]).item()\n",
    "\n",
    "    return iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.930385410785675"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_intersection_over_union(logits, target)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configuración y dínamica básica de entrenamiento del modelo."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**TODO:** \n",
    "\n",
    "* during training all predictions with $\\text{IoU} > \\tau$ with a ground truth, are matched to the ground truth (check slide 86 [here](https://drive.google.com/file/d/1tL4N6Xg4h4IjGR8UCfvUWFZ8OUHIwlOO/view))\n",
    "* Freeze the backbone like ~3-5 epochs to warm the classification head, then unfreeze and train with a lower learning rate than the classification head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.linear_fuse.weight', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.batch_norm.running_var', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.3.proj.weight', 'decode_head.classifier.bias', 'decode_head.batch_norm.weight', 'decode_head.linear_c.3.proj.bias', 'decode_head.batch_norm.running_mean', 'decode_head.classifier.weight', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.2.proj.weight', 'decode_head.linear_c.2.proj.bias', 'decode_head.batch_norm.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.linear_c.1.proj.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "#from evaluation import compute_intersection_over_union\n",
    "from utils import flatten_logits, upscale_logits\n",
    "from contextlib import nullcontext\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "\n",
    "set_seed()  # for reproducibility\n",
    "\n",
    "model = SegformerForSemanticSegmentation.from_pretrained(\n",
    "    \"nvidia/mit-b0\",\n",
    "    id2label=dataset._id2label,\n",
    "    label2id=dataset._label2id\n",
    ")\n",
    "\n",
    "model=model.to(device)\n",
    "\n",
    "n_epochs = 100\n",
    "lr = 0.03\n",
    "beta1 = 0.9\n",
    "beta2 = 0.95 \n",
    "bs=2\n",
    "gradient_accumulation_steps = bs * 4 # bs * 1 = normal, bs * 16 = emulate batch size of 32\n",
    "dtype = 'float16' # 'float32' or 'float16'\n",
    "weight_decay = 1e-1\n",
    "grad_clip = 1.0\n",
    "scheduler__gamma=0.9\n",
    "scheduler__step_size=5\n",
    "\n",
    "torch.backends.cuda.matmul.allow_tf32 = True    # allow tf32 on matmul\n",
    "torch.backends.cudnn.allow_tf32 = True      # allow tf32 on cudnn\n",
    "ptdtype={'float32': torch.float32, 'float16': torch.float16, 'bfloat16': torch.bfloat16}[dtype]\n",
    "ctx = nullcontext() if device=='cpu' else torch.amp.autocast(device_type=device, dtype=ptdtype)\n",
    "#loss_fn = nn.CrossEntropyLoss(\n",
    "#                              #weight=weight_classes, \n",
    "#                              ignore_index=dataset._label2id['non-detection'])\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=(beta1, beta2),\n",
    "                              weight_decay=weight_decay)\n",
    "\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=scheduler__step_size, \n",
    "                                            gamma=scheduler__gamma, verbose=False)\n",
    "scaler = GradScaler()\n",
    "\n",
    "train_dl = DataLoader(dataset, batch_size=bs, shuffle=True, num_workers=0,\n",
    "                        collate_fn=collate_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# freeze all the parameters except the classification head\n",
    "for name, params in model.named_parameters():\n",
    "    if name in ['decode_head.classifier.weight', 'decode_head.classifier.bias']:\n",
    "        params.requires_grad = True\n",
    "    else:\n",
    "        params.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set as 42313988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▍                                        | 1/100 [00:55<1:32:10, 55.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 0: 0.0834\n",
      " -- mIoU at epoch 0: 0.0470\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▊                                        | 2/100 [01:25<1:05:37, 40.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 1: 0.0307\n",
      " -- mIoU at epoch 1: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|█▎                                         | 3/100 [01:56<58:08, 35.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 2: 0.0248\n",
      " -- mIoU at epoch 2: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|█▋                                         | 4/100 [02:23<52:09, 32.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 3: 0.0243\n",
      " -- mIoU at epoch 3: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|██▏                                        | 5/100 [02:50<48:31, 30.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 4: 0.0223\n",
      " -- mIoU at epoch 4: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|██▌                                        | 6/100 [03:18<46:33, 29.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 5: 0.0199\n",
      " -- mIoU at epoch 5: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|███                                        | 7/100 [03:47<45:31, 29.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 6: 0.0174\n",
      " -- mIoU at epoch 6: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|███▍                                       | 8/100 [04:14<44:01, 28.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 7: 0.0150\n",
      " -- mIoU at epoch 7: 0.0571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|███▊                                       | 9/100 [04:41<42:50, 28.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 8: 0.0129\n",
      " -- mIoU at epoch 8: 0.0586\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|████▏                                     | 10/100 [05:09<42:20, 28.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 9: 0.0118\n",
      " -- mIoU at epoch 9: 0.0801\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|████▌                                     | 11/100 [05:39<42:31, 28.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 10: 0.0121\n",
      " -- mIoU at epoch 10: 0.0697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█████                                     | 12/100 [06:09<42:22, 28.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 11: 0.0113\n",
      " -- mIoU at epoch 11: 0.0617\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█████▍                                    | 13/100 [06:38<41:59, 28.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 12: 0.0110\n",
      " -- mIoU at epoch 12: 0.0711\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█████▉                                    | 14/100 [07:06<41:28, 28.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 13: 0.0107\n",
      " -- mIoU at epoch 13: 0.0624\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|██████▎                                   | 15/100 [07:36<41:07, 29.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 14: 0.0105\n",
      " -- mIoU at epoch 14: 0.0673\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|██████▋                                   | 16/100 [08:06<41:01, 29.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 15: 0.0104\n",
      " -- mIoU at epoch 15: 0.0785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|███████▏                                  | 17/100 [08:35<40:24, 29.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 16: 0.0102\n",
      " -- mIoU at epoch 16: 0.0881\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|███████▌                                  | 18/100 [09:02<39:09, 28.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 17: 0.0102\n",
      " -- mIoU at epoch 17: 0.0880\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|███████▉                                  | 19/100 [09:30<38:19, 28.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 18: 0.0098\n",
      " -- mIoU at epoch 18: 0.0874\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|████████▍                                 | 20/100 [09:57<37:28, 28.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 19: 0.0096\n",
      " -- mIoU at epoch 19: 0.0999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|████████▊                                 | 21/100 [10:25<36:42, 27.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 20: 0.0096\n",
      " -- mIoU at epoch 20: 0.0829\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|█████████▏                                | 22/100 [10:53<36:18, 27.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 21: 0.0095\n",
      " -- mIoU at epoch 21: 0.0827\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|█████████▋                                | 23/100 [11:20<35:26, 27.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 22: 0.0093\n",
      " -- mIoU at epoch 22: 0.0845\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██████████                                | 24/100 [11:47<34:47, 27.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 23: 0.0093\n",
      " -- mIoU at epoch 23: 0.0869\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██████████▌                               | 25/100 [12:13<33:58, 27.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 24: 0.0090\n",
      " -- mIoU at epoch 24: 0.1022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██████████▉                               | 26/100 [12:40<33:15, 26.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 25: 0.0090\n",
      " -- mIoU at epoch 25: 0.0883\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|███████████▎                              | 27/100 [13:06<32:46, 26.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 26: 0.0092\n",
      " -- mIoU at epoch 26: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|███████████▊                              | 28/100 [13:33<32:10, 26.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 27: 0.0089\n",
      " -- mIoU at epoch 27: 0.0872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 29%|████████████▏                             | 29/100 [14:00<31:45, 26.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 28: 0.0088\n",
      " -- mIoU at epoch 28: 0.0867\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|████████████▌                             | 30/100 [14:26<31:09, 26.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 29: 0.0090\n",
      " -- mIoU at epoch 29: 0.0882\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 31%|█████████████                             | 31/100 [14:53<30:40, 26.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 30: 0.0087\n",
      " -- mIoU at epoch 30: 0.0866\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|█████████████▍                            | 32/100 [15:20<30:12, 26.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 31: 0.0088\n",
      " -- mIoU at epoch 31: 0.0875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|█████████████▊                            | 33/100 [15:46<29:41, 26.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 32: 0.0086\n",
      " -- mIoU at epoch 32: 0.0879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|██████████████▎                           | 34/100 [16:12<29:12, 26.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 33: 0.0086\n",
      " -- mIoU at epoch 33: 0.0885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35%|██████████████▋                           | 35/100 [16:39<28:51, 26.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 34: 0.0085\n",
      " -- mIoU at epoch 34: 0.0885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███████████████                           | 36/100 [17:03<27:33, 25.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 35: 0.0084\n",
      " -- mIoU at epoch 35: 0.0884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███████████████▌                          | 37/100 [17:26<26:13, 24.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 36: 0.0087\n",
      " -- mIoU at epoch 36: 0.0887\n"
     ]
    },
    {
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    {
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     "text": [
      " -- Loss at epoch 37: 0.0086\n",
      " -- mIoU at epoch 37: 0.0886\n"
     ]
    },
    {
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    {
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     "text": [
      " -- Loss at epoch 38: 0.0085\n",
      " -- mIoU at epoch 38: 0.0888\n"
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    },
    {
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    {
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 39: 0.0084\n",
      " -- mIoU at epoch 39: 0.0891\n"
     ]
    },
    {
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    {
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     "text": [
      " -- Loss at epoch 40: 0.0084\n",
      " -- mIoU at epoch 40: 0.0888\n"
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     "text": [
      " -- Loss at epoch 41: 0.0083\n",
      " -- mIoU at epoch 41: 0.0888\n"
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     "text": [
      " -- Loss at epoch 42: 0.0083\n",
      " -- mIoU at epoch 42: 0.0954\n"
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     "text": [
      " -- Loss at epoch 43: 0.0083\n",
      " -- mIoU at epoch 43: 0.0887\n"
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     "text": [
      " -- Loss at epoch 44: 0.0083\n",
      " -- mIoU at epoch 44: 0.0888\n"
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     "text": [
      " -- Loss at epoch 45: 0.0083\n",
      " -- mIoU at epoch 45: 0.0889\n"
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     "text": [
      " -- Loss at epoch 46: 0.0084\n",
      " -- mIoU at epoch 46: 0.0917\n"
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      " -- Loss at epoch 47: 0.0084\n",
      " -- mIoU at epoch 47: 0.0889\n"
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     "text": [
      " -- Loss at epoch 48: 0.0084\n",
      " -- mIoU at epoch 48: 0.0890\n"
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     "text": [
      " -- Loss at epoch 49: 0.0082\n",
      " -- mIoU at epoch 49: 0.0956\n"
     ]
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     "text": [
      " -- Loss at epoch 50: 0.0084\n",
      " -- mIoU at epoch 50: 0.0891\n"
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     "text": [
      " -- Loss at epoch 51: 0.0082\n",
      " -- mIoU at epoch 51: 0.0891\n"
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     "text": [
      " -- Loss at epoch 52: 0.0083\n",
      " -- mIoU at epoch 52: 0.0891\n"
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     "text": [
      " -- Loss at epoch 53: 0.0083\n",
      " -- mIoU at epoch 53: 0.0892\n"
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     "text": [
      " -- Loss at epoch 54: 0.0084\n",
      " -- mIoU at epoch 54: 0.0893\n"
     ]
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    {
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     "text": [
      " -- Loss at epoch 55: 0.0083\n",
      " -- mIoU at epoch 55: 0.0899\n"
     ]
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     "text": [
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     "text": [
      " -- Loss at epoch 56: 0.0083\n",
      " -- mIoU at epoch 56: 0.0892\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      " -- Loss at epoch 57: 0.0084\n",
      " -- mIoU at epoch 57: 0.0896\n"
     ]
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    {
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     "text": [
      " -- Loss at epoch 58: 0.0083\n",
      " -- mIoU at epoch 58: 0.0894\n"
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     "text": [
      " -- Loss at epoch 59: 0.0083\n",
      " -- mIoU at epoch 59: 0.0905\n"
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     "text": [
      " -- Loss at epoch 60: 0.0082\n",
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     "text": [
      " -- Loss at epoch 61: 0.0083\n",
      " -- mIoU at epoch 61: 0.0894\n"
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     "text": [
      " -- Loss at epoch 62: 0.0083\n",
      " -- mIoU at epoch 62: 0.0894\n"
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     "text": [
      " -- Loss at epoch 63: 0.0082\n",
      " -- mIoU at epoch 63: 0.0894\n"
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     "text": [
      " -- Loss at epoch 64: 0.0083\n",
      " -- mIoU at epoch 64: 0.0894\n"
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     "text": [
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     "text": [
      " -- Loss at epoch 65: 0.0084\n",
      " -- mIoU at epoch 65: 0.0894\n"
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     "text": [
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      " -- Loss at epoch 66: 0.0083\n",
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     "text": [
      " -- Loss at epoch 67: 0.0083\n",
      " -- mIoU at epoch 67: 0.0894\n"
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     "text": [
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     "text": [
      " -- Loss at epoch 68: 0.0084\n",
      " -- mIoU at epoch 68: 0.0925\n"
     ]
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     "text": [
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     "text": [
      " -- Loss at epoch 69: 0.0082\n",
      " -- mIoU at epoch 69: 0.0894\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 70: 0.0086\n",
      " -- mIoU at epoch 70: 0.0935\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 71: 0.0082\n",
      " -- mIoU at epoch 71: 0.0895\n"
     ]
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     "text": [
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     "text": [
      " -- Loss at epoch 72: 0.0082\n",
      " -- mIoU at epoch 72: 0.0916\n"
     ]
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     "text": [
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     "text": [
      " -- Loss at epoch 73: 0.0083\n",
      " -- mIoU at epoch 73: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 74: 0.0084\n",
      " -- mIoU at epoch 74: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 75: 0.0084\n",
      " -- mIoU at epoch 75: 0.0898\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 76: 0.0084\n",
      " -- mIoU at epoch 76: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 77: 0.0082\n",
      " -- mIoU at epoch 77: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 78: 0.0083\n",
      " -- mIoU at epoch 78: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 79: 0.0083\n",
      " -- mIoU at epoch 79: 0.0923\n"
     ]
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    {
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 80: 0.0082\n",
      " -- mIoU at epoch 80: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 81: 0.0082\n",
      " -- mIoU at epoch 81: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 82: 0.0082\n",
      " -- mIoU at epoch 82: 0.1038\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 83: 0.0084\n",
      " -- mIoU at epoch 83: 0.0898\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 84: 0.0082\n",
      " -- mIoU at epoch 84: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 85: 0.0082\n",
      " -- mIoU at epoch 85: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 86: 0.0082\n",
      " -- mIoU at epoch 86: 0.1038\n"
     ]
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 87: 0.0083\n",
      " -- mIoU at epoch 87: 0.0895\n"
     ]
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     "text": [
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     "output_type": "stream",
     "text": [
      " -- Loss at epoch 88: 0.0082\n",
      " -- mIoU at epoch 88: 0.0895\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████████████████████████████████▊    | 90/100 [37:47<03:49, 22.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 89: 0.0083\n",
      " -- mIoU at epoch 89: 0.0924\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 91%|██████████████████████████████████████▏   | 91/100 [38:10<03:26, 22.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 90: 0.0083\n",
      " -- mIoU at epoch 90: 0.0906\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|██████████████████████████████████████▋   | 92/100 [38:33<03:03, 22.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 91: 0.0083\n",
      " -- mIoU at epoch 91: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|███████████████████████████████████████   | 93/100 [38:56<02:40, 22.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 92: 0.0083\n",
      " -- mIoU at epoch 92: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|███████████████████████████████████████▍  | 94/100 [39:19<02:17, 22.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 93: 0.0084\n",
      " -- mIoU at epoch 93: 0.0898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|███████████████████████████████████████▉  | 95/100 [39:42<01:55, 23.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 94: 0.0082\n",
      " -- mIoU at epoch 94: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|████████████████████████████████████████▎ | 96/100 [40:05<01:31, 23.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 95: 0.0082\n",
      " -- mIoU at epoch 95: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|████████████████████████████████████████▋ | 97/100 [40:28<01:09, 23.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 96: 0.0082\n",
      " -- mIoU at epoch 96: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████████████████████████████████████▏| 98/100 [40:51<00:45, 22.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 97: 0.0082\n",
      " -- mIoU at epoch 97: 0.0939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████████████████████████████████████▌| 99/100 [41:14<00:22, 22.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 98: 0.0082\n",
      " -- mIoU at epoch 98: 0.0895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [41:37<00:00, 24.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -- Loss at epoch 99: 0.0083\n",
      " -- mIoU at epoch 99: 0.0899\n",
      "--- 2497.665179014206 seconds ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# compute running time\n",
    "start_time = time.time()\n",
    "set_seed()  # for reproducibility\n",
    "lossi=np.zeros(n_epochs)\n",
    "iou=np.zeros(n_epochs)\n",
    "\n",
    "for idx in tqdm(range(n_epochs), ncols=80, leave=True):\n",
    "  cur_loss=0.0 \n",
    "  cur_iou = []\n",
    "  num_batches= 0\n",
    "  for i, batch in tqdm(enumerate(train_dl), ncols=80, leave=False):\n",
    "    xb = batch[0].to(device)\n",
    "    yb = batch[1].to(device)\n",
    "\n",
    "    with ctx:\n",
    "      # Perform a forward pass\n",
    "      logits = model(xb)[\"logits\"]\n",
    "\n",
    "      # Compute the loss\n",
    "      loss = loss_fn(upscale_logits(logits),\n",
    "                     torch.argmax(yb, dim=1).to(torch.long))\n",
    "\n",
    "      # normalize the loss with regards the number of graddient_accumulation_steps \n",
    "      loss = loss / gradient_accumulation_steps\n",
    "\n",
    "    # Backward prop\n",
    "    #loss.backward()\n",
    "    scaler.scale(loss).backward()\n",
    "\n",
    "    # Update the parameters every once we complete gradient_accumulation_steps or\n",
    "    # at the last batch in the dataloadaer\n",
    "    if ((i + 1) % gradient_accumulation_steps == 0) or ((i + 1) == len(train_dl)):\n",
    "      # clip the gradient\n",
    "      if grad_clip != 0.0:\n",
    "        scaler.unscale_(optimizer)\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)\n",
    "\n",
    "      # Scales loss. Calls backward() on scaled loss to create scaled gradients.\n",
    "      scaler.step(optimizer)\n",
    "      scaler.update()\n",
    "      # flush the gradients as soon as we can, no need for this memory anymore\n",
    "      optimizer.zero_grad(set_to_none=True)\n",
    "      scheduler.step()\n",
    "\n",
    "      # update the scale for the next iteration\n",
    "\n",
    "    # store the current loss value, IoU, and increment the number of batches\n",
    "    cur_loss += loss.item()\n",
    "    # require to pass the logits and yb from half to float32 using .float()\n",
    "    # See: https://pytorch.org/docs/stable/amp.html#autocasting\n",
    "    cur_iou.append(compute_intersection_over_union(logits.float(), yb.float()))\n",
    "    num_batches += 1\n",
    "\n",
    "  lossi[idx] = cur_loss / num_batches\n",
    "  iou[idx] = np.mean(cur_iou)\n",
    "  print(f\" -- Loss at epoch {idx}: {lossi[idx]:.4f}\")\n",
    "  print(f\" -- mIoU at epoch {idx}: {iou[idx]:.4f}\")\n",
    "\n",
    "# compute running time\n",
    "end_time = time.time()\n",
    "print(f\"--- {end_time - start_time} seconds ---\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lossi, color='darkcyan');\n",
    "plt.title('Loss');\n",
    "plt.xlabel('# epochs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iou, color='darkcyan');\n",
    "plt.title('mean Intersection over Union');\n",
    "plt.xlabel('# epochs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 1, 512, 512])\n",
      "torch.Size([3, 528, 1048])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xb, yb = next(iter(train_dl))\n",
    "xb = xb.to(device)\n",
    "yb = yb.to(device)\n",
    "\n",
    "model.eval()\n",
    "out = model(xb)[\"logits\"]\n",
    "out = torch.argmax(torch.softmax(upscale_logits(out), dim=1), dim=1, keepdim=True).cpu()\n",
    "print(out.shape)\n",
    "grid = torchvision.utils.make_grid(out, nrow=2, padding=8, pad_value=4)\n",
    "print(grid.shape)\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).numpy() * 255), cmap='viridis')\n",
    "plt.axis('off');\n",
    "plt.title('Predicted masks by class');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=torchvision.utils.make_grid(\n",
    "    #yb.unsqueeze(1).detach(),\n",
    "    yb.max(dim=1)[0].unsqueeze(1).detach().cpu(),\n",
    "    nrow=2,\n",
    "    padding=8,\n",
    "    pad_value=8\n",
    "    )\n",
    "\n",
    "plt.imshow(np.uint8(grid.permute(1,2,0).cpu().numpy()) * 255)\n",
    "plt.axis(\"off\");\n",
    "plt.title(\"Original masks\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PlantDataset - Dead batch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import PlantDataset, extract_ids_from_name\n",
    "\n",
    "# create a dataset with the test images\n",
    "label2id = {'dead': 0, 'dead_cut': 1, 'noise': 2}\n",
    "dead_dataset = PlantDataset('data', 'dead', 'data_inventary.csv', label2id=label2id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.Image.Image image mode=RGB size=1677x1271>,\n",
       " 'masks': ['data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-2.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-4.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-1.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-5.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-0.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-2.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead_cut-1.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-1.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-3.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-3.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead_cut-0.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-0.png'],\n",
       " 'labels': [2, 0, 2, 0, 0, 0, 1, 0, 2, 0, 1, 2]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dead_dataset[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1677x1271>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dead_dataset[4]['image']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'dead', 1: 'dead_cut', 2: 'noise'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.Image.Image image mode=RGB size=1677x1271>,\n",
       " 'masks': ['data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-2.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-4.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-1.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-5.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-0.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-2.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead_cut-1.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-1.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-3.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead-3.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-dead_cut-0.png',\n",
       "  'data/dead/original_labeled/5/task-5-annotation-23-by-1-tag-noise-0.png'],\n",
       " 'labels': [2, 0, 2, 0, 0, 0, 1, 0, 2, 0, 1, 2]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dead_dataset._id2label)\n",
    "dead_dataset[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=2700x225>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img, masks, labels = dead_dataset[4].values()\n",
    "display_masks(masks)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algunas comparaciones entre los datasets `cwt` y `dead`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEICAYAAAC6fYRZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYbUlEQVR4nO3de7RkZXnn8e9Dc21EuZ2gXJpWccxCVHS1t+goI14QUExGJzoyQUVbjUaSeAEvGY2XEScquJaJiGLQQLgE8RKYZUCEGEfF6cZWETQoNjcbulFBwESCPPPH+56wrT51TnfV7lPVb76ftXp17Uvt/ex3v/tXu/auUxWZiSSpTdtMugBJ0pZjyEtSwwx5SWqYIS9JDTPkJalhhrwkNWwiIR8Rp0TEn/W0rGURcWdELKnDl0XEK/pY9sB67oyIhwyM2yYiPh8Rx/a4ntMj4j2bOO/aiHjGmOu7f0RcExEHj7OcxRIRyyMiI2LbRVzndhGxJiKOWKx1bk0i4iURcdGk65hWW7LPbkpebImVrgX2Au4Bfg1cBXwaODUz7wXIzFdvxrJekZlfGjZPZl4P3G+8qheWmXOt4z3AJZl52pZe/xb0PuCDmblm0oVMsbcAF2TmhZMuZBpl5pnAmZOuQ3PbUmdDz83ML0XEA4CnAR8GngC8rM+VRMS2mXlPn8vcHJn51kmtuw8RsRNwZWaesojrnOg+21z1HeJtwEcWeb1T2U7TWpfmkZm9/gPWAs8YGPd44F7goDp8OvCe+nhP4ALKgfQz4J8ol5H+pj7nX4A7gTcDy4EEjgWuB77SGbdtXd5llLPTbwK/AD4P7F6nHQLcOKxeYAnwVuBHwB3AamC/Oi2BA+rjB1DenWwArgPeDmxTp70U+CrwAeDnwI+B58zTXo8BrqjrOwc4e7Zt6vQjgTW1fb4GPGq+tu5MOx34S+DCuuzLgYfWab/RZp12e0VnG/4vcFJd77XA79TxNwDrgWM6z92hbu/1wC3AKcBO3TYHjgdurvt1B+Bk4Cf138nADkO2Y0ld9q21jtcO7O8HAKcB64CbKO+ulgxZ1juBvwPOqG3yXeA/Uc7U19dte1Zn/pcBV9d5rwVe1Zk2Z7+t0/YDzq/946fAR+r4hwJfruNupZz97jqwP48HvgP8inISdgL39cergN/tzH8A8I/A7XV55wzZ7tn9vbK29zrgjQP7b879Mdf+m2P5LwW+2hlO4A+Ba2rd767b/jXKMXkusH2dd7fajhsox8sFwL6dZT2YcpzfAXyJ0qfP6Ex/Yl3ubcC3gUMG+vS7KX35DuAiYM95jsWFjrU31n1zO+VY3bHvPrsJfWTevJiznsUI+Tr+euA1c4T8+yihsF3995+BmGtZ3NdZPw3sDOzE3CF/E3BQneczs52ChUP+TZQD/+FAAI8G9pgj5D9NefHYpa7/n4FjOx3+34BX1p39GsqBE3O0yfaUF4k/qdv+gvrc2bZ5DCV8nlCXdUytd4f52rrTxj+lvMBuWzvL2ZsR8vdQQm4JpRNeTznAdgCeRelk96vznwR8Adi9tsnfA+/rtPk9wPvrc3cC3gV8A/gtYIZyQL17yHa8Gvg+JTh3By4d2N+fBT5W9/VvUV7cXzVkWe8E/hV4dm2TT1NehN9W2/+VwI878z+XctAF5R3pL4HHztdva3t9u7bJzsCOwFM6ofzM2g4zlPA6eaAvrqnbOvsi+UJgb8qJz+8DdwEPqtPOqrVv013PPCF/Vq3pkZRQne33Q/fHXPtvE0P+88D9gUdQXrAuAR5CCbirqCcJwB7AfwWWUvrO3wGf6yzr65TA3B54CuVFYvZ43ofSxw+vbfDMOjzT6dM/oryQ71SHTxzSRptyrH2z7ovdKS/+r+67z87XR1ggL6Yh5L8BvG2OkH9X7RAHLLQs7uusD5ljXDfkT+xMPxC4u+64Q5g/5H8AHDVku7LugCV1eQd2pr0KuKzT4X/Ymba0PveBcyzzqQy8AFAOsNm2+SgD4VdrfNp8bd1p4090hg8Hvj9Xm3XarRvy13SmPbLOv1dn3E+BgynBdhf1XUKd9iRqWNY2v5vOWQ/lwDu8M/xsYO2Q7fgynYOJ8gKTlJDeixIgO3Wmvxi4dMiy3glc3Bl+LuVd4uxZ1C512bsOef7ngOPm67d12zd023aeY+X5wLcG+uLLF3jOmtk+Sr3XRefMd8hzZvf3b3fG/W/gtIX2x1z7b47lv5SNQ/7JneHVwPGd4Q/SeXEbWNbBwM/r42WUF5ilnelncF/IH8/AOwvgH7jvBeQy4O2daX8IfHHIejflWDt6oP1OWYQ+++99hAXyYti/xfx0zT6Ut7WD/gL4IXBRRFwbESdswrJu2Izp11Fe9fbchOXuR+nw89mzLu+6gXXs0xm+efZBZv6yPpzrxu3ewE1Z91ZnWbP2B94QEbfN/qs17r1AjRvVQTkL3Zwb1Ld0Hv8LQGYOjrsf5WxjKbC6U+MX6/hZGzLzXzvDe7Nx+w3bpr3ZeH/O2p+yL9Z11v0xytnRpm7XrZn5684wdbuIiEPrp7Wurx8CeAb39aNh/XY/4Lqc47p1ROwVEWdHxE0R8QtKYA32yxsGnvMH9ZM9s9t3UOc5b6a8yH4zIr4XES+fZ7sHl91t84X2x+D+2xSD7TxX3yEilkbExyLiutomXwF2rfdC9gZ+1jmGBrdhf+CFA8fHU4AHdebZ1GNgU461TV3WyH12gT6yUF7MaVFCPiIeRwnBrw5Oy8w7MvMNmfkQ4HnAn0bEobOThyxy2PhZ+3UeL6O8pbmVcsa5tFPXEn4zjG6gvD2fz611efsPrOOmBZ43l3XAPhERA8vq1vPezNy1829pZp41wrq67qr/L+2Me+CIy7qVctA+olPjA/I3P400uL9+wsbt95Mhy1/Hxvtz1g2Us6I9O+u+f2Y+YqQt6YiI7Sln6h8E9s/M5ZRLDgHz9tsbgGVDPi73vyht8cjMvD9w9OzyOv69rSJif+DjwOsolw13Ba7s1HBzZr4yM/emvJv8q4g4YJ7NGmzH2TZfaH8sdLyN4w2Uy6NPqG3y1Do+KPt+94jo9tPuNtxAOZPvHh87Z+aJI9TR57E2Tp+dr48slBdz2qIhXz+DfSTl5sAZmfndOeY5MiIOqIXfTvnY5b118i2U63ib6+iIOLB2jncB59WztX8GdoyIIyJiO8oN0x06z/sE8O6IeFgUj4qIPboLrss5F3hvROxSD8Q/pbzibq6vU96Ovr5+Fvv3KNfQZ30ceHVEPKHWs3OtfZcR1tXdhg2UF6WjI2JJPQNc6MVt2LLurXWeFBGzZyP7RMSz53naWcDbI2ImIvYE/ifD2+9cSvvsGxG7UW5Ezq57HeVm2gdrX9smIh4aEU8bZVsGzN4/uKtu03Mo10qpw8P67TcpB+OJdX/tGBFPrk/bhXJ56PaI2IdyD2g+O1MO+A11nS+jnMnP1vDCiNi3Dv68znvv4EI6/qyeOT+Ccr/lnDp+c/ZH33ahnCTcFhG7A++YnZCZ1wGrgHdGxPYR8STKJbZZZwDPjYhn1368Y0Qc0mmTzdHnsTZOn52vjyyUF3PaUiH/9xFxB+VV623Ahxj+8cmHUe6a30nZiL/KzEvrtPdROt9tEfHGzVj/31CuSd9MuSH1eoDMvJ1yXe4TlJC7i/LJgVkfouygiyg3eE6jHOiD/qg+91rKu5O/BT65GfVR67kb+D3KNc2fUW6snd+ZvopyM/AjlIP4h3XePryS0oF+Srk59rUxlnU8pbZv1LeYX6KcnQ3zHsrB+x3Kje4r6ri5fJxynfXbdb7zB6b/AeWG1FWUNjqP33y7PpLMvIPSb86qy/3vlJvLs+bst/Uk4LmU+zfXU/rX79fn/DnwWMqLwoVzbMtgDVdR3kl8nXLC80jKJ0VmPQ64PCLurLUdl5nXzrPIf6Tsp0uAD2Tm7B8wbc7+6NvJlGPsVsp9uy8OTH8J5T7HT2tN51DOhMnMG4CjKJ+I20DJmzcxQq71fKyN02eH9pGF8mKY2U+xSGpURCynfIpou7nuFWxNIuIcygcI3rHgzAL87hpJUywiHlcvZ2wTEYdRztw/N+GytiqL9v0fkjSCB1IuSexBufT1msz81mRL2rp4uUaSGublGklq2KJertlzzz1z+fLli7lKSdrqrV69+tbMnFl4zo0tasgvX76cVatWLeYqJWmrFxEL/mXrMF6ukaSGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ1bMOQj4pMRsT4iruyM+4uI+H5EfCciPhsRu27RKiVJI9mUM/nTgcMGxl1M+VHuR1G+o/0tPdclSerBgiGfmV9h4Gf7MvOizleWfgMY5Uv6JUlbWB9/8fpy7vuFmY1ExEpgJcCyZQv+UtVUWn7ChRNZ79oTj5jIeiW1Y6wbrxHxNsrPUZ05bJ7MPDUzV2TmipmZkb56QZI0opHP5CPipcCRwKHp9xVL0lQaKeTrL7S8GXhaZv6y35IkSX3ZlI9QnkX5IeGHR8SNEXEs5cdudwEujog1EXHKFq5TkjSCBc/kM/PFc4w+bQvUIknqmX/xKkkNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LDDHlJapghL0kNWzDkI+KTEbE+Iq7sjNs9Ii6OiGvq/7tt2TIlSaPYlDP504HDBsadAFySmQ8DLqnDkqQps2DIZ+ZXgJ8NjD4K+FR9/Cng+f2WJUnqw6jX5PfKzHX18c3AXsNmjIiVEbEqIlZt2LBhxNVJkkYx9o3XzEwg55l+amauyMwVMzMz465OkrQZRg35WyLiQQD1//X9lSRJ6suoIf8F4Jj6+Bjg8/2UI0nq06Z8hPIs4OvAwyPixog4FjgReGZEXAM8ow5LkqbMtgvNkJkvHjLp0J5rkST1zL94laSGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhhrwkNcyQl6SGGfKS1LCxQj4i/iQivhcRV0bEWRGxY1+FSZLGN3LIR8Q+wOuBFZl5ELAEeFFfhUmSxjfu5ZptgZ0iYltgKfCT8UuSJPVl5JDPzJuADwDXA+uA2zPzosH5ImJlRKyKiFUbNmwYvVJJ0mYb53LNbsBRwIOBvYGdI+Lowfky89TMXJGZK2ZmZkavVJK02ca5XPMM4MeZuSEz/w04H/idfsqSJPVhnJC/HnhiRCyNiAAOBa7upyxJUh/GuSZ/OXAecAXw3bqsU3uqS5LUg23HeXJmvgN4R0+1SJJ65l+8SlLDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDRvru2u0ZS0/4cKJrHftiUdMZL2S+ueZvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsPGCvmI2DUizouI70fE1RHxpL4KkySNb9yvGv4w8MXMfEFEbA8s7aEmSVJPRg75iHgA8FTgpQCZeTdwdz9lSZL6MM6Z/IOBDcBfR8SjgdXAcZl5V3emiFgJrARYtmzZyCub1A9oSNLWbJxr8tsCjwU+mpmPAe4CThicKTNPzcwVmbliZmZmjNVJkjbXOCF/I3BjZl5eh8+jhL4kaUqMHPKZeTNwQ0Q8vI46FLiql6okSb0Y99M1fwScWT9Zcy3wsvFLkiT1ZayQz8w1wIp+SpEk9c2/eJWkhhnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSw8b9Fko1aJK/wrX2xCMmtm6pRZ7JS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhhrwkNWzskI+IJRHxrYi4oI+CJEn96eNM/jjg6h6WI0nq2VghHxH7AkcAn+inHElSn8Y9kz8ZeDNw77AZImJlRKyKiFUbNmwYc3WSpM0xcshHxJHA+sxcPd98mXlqZq7IzBUzMzOjrk6SNIJxzuSfDDwvItYCZwNPj4gzeqlKktSLkUM+M9+Smftm5nLgRcCXM/Po3iqTJI3Nz8lLUsO27WMhmXkZcFkfy5Ik9cczeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWG9fHeNpNEtP+HCiax37YlHTGS9WlyeyUtSwwx5SWqYIS9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDVs5JCPiP0i4tKIuCoivhcRx/VZmCRpfON81fA9wBsy84qI2AVYHREXZ+ZVPdUmSRrTyGfymbkuM6+oj+8Argb26aswSdL4evnRkIhYDjwGuHyOaSuBlQDLli3rY3VS7yb1wx2TNMlt9gdLFs/YN14j4n7AZ4A/zsxfDE7PzFMzc0VmrpiZmRl3dZKkzTBWyEfEdpSAPzMzz++nJElSX8b5dE0ApwFXZ+aH+itJktSXcc7knwz8D+DpEbGm/ju8p7okST0Y+cZrZn4ViB5rkST1zL94laSGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LDevllKKkv/xF/oUmL5z/ir2F5Ji9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktSwsUI+Ig6LiB9ExA8j4oS+ipIk9WPkkI+IJcBfAs8BDgReHBEH9lWYJGl845zJPx74YWZem5l3A2cDR/VTliSpD+P8aMg+wA2d4RuBJwzOFBErgZV18M6I+MGI69sTuHXE5y4G6xuP9Y1nq6ov3j/BSobbom045jbvP+oTt/gvQ2XmqcCp4y4nIlZl5ooeStoirG881jce6xvf1lDjKMa5XHMTsF9neN86TpI0JcYJ+f8HPCwiHhwR2wMvAr7QT1mSpD6MfLkmM++JiNcB/wAsAT6Zmd/rrbKNjX3JZwuzvvFY33isb3xbQ42bLTJz0jVIkrYQ/+JVkhpmyEtSw7aKkI+ItRHx3YhYExGrpqCeT0bE+oi4sjNu94i4OCKuqf/vNmX1vTMibqptuCYiDp9gfftFxKURcVVEfC8ijqvjp6IN56lvKtowInaMiG9GxLdrfX9exz84Ii6vXzNyTv1AxDTVd3pE/LjTfgdPor5OnUsi4lsRcUEdnor269tWEfLVf8nMg6fkc6ynA4cNjDsBuCQzHwZcUocn5XQ2rg/gpNqGB2fm/1nkmrruAd6QmQcCTwReW78SY1racFh9MB1t+Cvg6Zn5aOBg4LCIeCLw/lrfAcDPgWOnrD6AN3Xab82E6pt1HHB1Z3ha2q9XW1PIT43M/Arws4HRRwGfqo8/BTx/MWvqGlLf1MjMdZl5RX18B+VA24cpacN56psKWdxZB7er/xJ4OnBeHT/J9htW39SIiH2BI4BP1OFgStqvb1tLyCdwUUSsrl+TMI32ysx19fHNwF6TLGaI10XEd+rlnIldTuqKiOXAY4DLmcI2HKgPpqQN66WGNcB64GLgR8BtmXlPneVGJvjCNFhfZs6233tr+50UETtMqj7gZODNwL11eA+mqP36tLWE/FMy87GUb7x8bUQ8ddIFzSfL51Kn6swF+CjwUMrb53XABydaDRAR9wM+A/xxZv6iO20a2nCO+qamDTPz15l5MOUvzR8P/PakapnLYH0RcRDwFkqdjwN2B46fRG0RcSSwPjNXT2L9i22rCPnMvKn+vx74LKVTT5tbIuJBAPX/9ROu5zdk5i31wLsX+DgTbsOI2I4SoGdm5vl19NS04Vz1TVsb1ppuAy4FngTsGhGzf+A4FV8z0qnvsHoZLDPzV8BfM7n2ezLwvIhYS/n23KcDH2YK268PUx/yEbFzROwy+xh4FnDl/M+aiC8Ax9THxwCfn2AtG5kNz+p3mWAb1uufpwFXZ+aHOpOmog2H1TctbRgRMxGxa328E/BMyn2DS4EX1Nkm2X5z1ff9zgt4UK53T6T9MvMtmblvZi6nfB3LlzPzJUxJ+/Vt6v/iNSIeQjl7h/I1DH+bme+dYElExFnAIZSvJr0FeAfwOeBcYBlwHfDfMnMiNz+H1HcI5TJDAmuBV3Wufy92fU8B/gn4LvddE30r5br3xNtwnvpezBS0YUQ8inJjcAnlRO3czHxXPVbOplwK+RZwdD1rnpb6vgzMAAGsAV7duUE7ERFxCPDGzDxyWtqvb1Mf8pKk0U395RpJ0ugMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktSw/w9fBL7ER9xwjwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dead_dataset.get_number_of_masks());\n",
    "plt.title('Distribución del número de máscaras por imagen en dead');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dataset.get_number_of_masks());\n",
    "plt.title('Distribución del número de máscaras por imagen en cwt');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masks per label in dead:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array(['dead', 'dead_cut', 'noise'], dtype='<U8'), array([163,  99, 364]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Masks per label in dead:\")\n",
    "dead_dataset.get_masks_per_labels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masks per label in cwt:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array(['noise', 'normal', 'normal_cut'], dtype='<U10'),\n",
       " array([119,  99,  98]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Masks per label in cwt:\")\n",
    "dataset.get_masks_per_labels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dead dataset: # de masks: 316, cantidad de obs: 69\n"
     ]
    }
   ],
   "source": [
    "print(f\"Dead dataset: # de masks: {sum(dataset.get_number_of_masks())}, cantidad de obs: {len(dataset)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dead dataset: # de masks: 626, cantidad de obs: 39\n"
     ]
    }
   ],
   "source": [
    "print(f\"Dead dataset: # de masks: {sum(dead_dataset.get_number_of_masks())}, cantidad de obs: {len(dead_dataset)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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