train.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c913c5b-a9cd-4929-b28a-f6bc05633796",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from utils import save_checkpoint, load_checkpoint, save_some_examples\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import config\n",
    "from dataset import Satellite2Map_Data\n",
    "from Models import Generator,Discriminator\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8af5313-dcda-448d-afdb-51f020a98492",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.backends.cudnn.benchmark = True\n",
    "Gen_loss = []\n",
    "Dis_loss = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a412c67a-ce84-4f13-8d3f-f23c92961516",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(netG, netD, train_dl, OptimizerG, OptimizerD, L1_Loss, BCE_Loss):\n",
    "    loop = tqdm(train_dl)\n",
    "    for idx, (x,y) in enumerate(loop):\n",
    "        x = x.cuda()\n",
    "        y = y.cuda()\n",
    "        ############## Train Discriminator ##############\n",
    "        #with torch.cuda.amp.autocast():\n",
    "        y_fake = netG(x)\n",
    "        D_real = netD(x,y)\n",
    "        D_real_loss = BCE_Loss(D_real, torch.ones_like(D_real))\n",
    "        D_fake = netD(x,y_fake.detach())\n",
    "        D_fake_loss = BCE_Loss(D_fake, torch.zeros_like(D_fake))\n",
    "        D_loss = (D_real_loss + D_fake_loss)/2\n",
    "        \n",
    "        netD.zero_grad()\n",
    "        Dis_loss.append(D_loss.item())\n",
    "        D_loss.backward()\n",
    "        #D_Scaler.scale(D_loss).backward()\n",
    "        OptimizerD.step()\n",
    "        #D_Scaler.step(OptimizerD)\n",
    "        #D_Scaler.update()\n",
    "        \n",
    "        ############## Train Generator ##############\n",
    "        #with torch.cuda.amp.autocast():\n",
    "        D_fake = netD(x, y_fake)\n",
    "        G_fake_loss = BCE_Loss(D_fake, torch.ones_like(D_fake))\n",
    "        L1 = L1_Loss(y_fake,y) * config.L1_LAMBDA\n",
    "        G_loss = G_fake_loss + L1\n",
    "        \n",
    "        OptimizerG.zero_grad()\n",
    "        Gen_loss.append(G_loss.item())\n",
    "        G_loss.backward()\n",
    "        #G_Scaler.scale(G_loss).backward()\n",
    "        #G_Scaler.step(OptimizerG)\n",
    "        OptimizerG.step()\n",
    "        #G_Scaler.update()\n",
    "        \n",
    "        if idx % 10 == 0:\n",
    "            loop.set_postfix(\n",
    "                D_real=torch.sigmoid(D_real).mean().item(),\n",
    "                D_fake=torch.sigmoid(D_fake).mean().item(),\n",
    "            )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe4b7406-cf41-4779-afc6-bb0424104374",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    netD = Discriminator(in_channels=3).cuda()\n",
    "    netG = Generator(in_channels=3).cuda()\n",
    "    OptimizerD = torch.optim.Adam(netD.parameters(),lr=config.LEARNING_RATE,betas=(config.BETA1,0.999))\n",
    "    OptimizerG = torch.optim.Adam(netG.parameters(),lr=config.LEARNING_RATE,betas=(config.BETA1,0.999))\n",
    "    BCE_Loss = nn.BCEWithLogitsLoss()\n",
    "    L1_Loss = nn.L1Loss()\n",
    "    \n",
    "    if config.LOAD_MODEL:\n",
    "        load_checkpoint(\n",
    "            config.CHECKPOINT_GEN,netG,OptimizerG,config.LEARNING_RATE\n",
    "        )\n",
    "        load_checkpoint(\n",
    "            config.CHECKPOINT_DISC,netD,OptimizerD,config.LEARNING_RATE\n",
    "        )\n",
    "        \n",
    "    train_dataset = Satellite2Map_Data(root=config.TRAIN_DIR)\n",
    "    train_dl = DataLoader(train_dataset,batch_size=config.BATCH_SIZE,shuffle=True,num_workers=config.NUM_WORKERS,pin_memory=True)\n",
    "    # G_Scaler = torch.cuda.amp.GradScaler()\n",
    "    # D_Scaler = torch.cuda.amp.GradScaler()\n",
    "    val_dataset = Satellite2Map_Data(root=config.VAL_DIR)\n",
    "    val_dl = DataLoader(val_dataset,batch_size=config.BATCH_SIZE,shuffle=True,num_workers=config.NUM_WORKERS,pin_memory=True)\n",
    "    \n",
    "    \n",
    "    for epoch in range(config.NUM_EPOCHS):\n",
    "        train(\n",
    "            netG, netD, train_dl, OptimizerG, OptimizerD, L1_Loss, BCE_Loss\n",
    "        )\n",
    "        #Generator_loss.append(g_loss.item())\n",
    "        #Discriminator_loss.append(d_loss.item())\n",
    "        if config.SAVE_MODEL and epoch%50==0:\n",
    "            save_checkpoint(netG, OptimizerG, filename=config.CHECKPOINT_GEN)\n",
    "            save_checkpoint(netD, OptimizerD, filename=config.CHECKPOINT_DISC)\n",
    "        if epoch%2==0:\n",
    "            save_some_examples(netG, val_dl, epoch, folder=\"evaluation\")"
   ]
  },
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   "execution_count": 5,
   "id": "07357e41-47ba-4e3d-bb1c-6ebffeebcfc0",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> Saving checkpoint\n",
      "=> Saving checkpoint\n"
     ]
    },
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      "=> Saving checkpoint\n"
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      "=> Saving checkpoint\n",
      "=> Saving checkpoint\n"
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      "=> Saving checkpoint\n"
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Generator and Discriminator Loss During Training\")\n",
    "plt.plot(Gen_loss,label=\"Generator\")\n",
    "plt.plot(Dis_loss,label=\"Discriminator\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}