train_unet.ipynb

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  {
   "cell_type": "markdown",
   "id": "377f2cc7",
   "metadata": {},
   "source": [
    "# Train set AVG PSNR= 25.2899\n",
    "\n",
    "# Test set AVG PSNR=26.9565\n",
    "\n",
    "# A: Full Dose,  B: Quarter Dose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1718a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import itertools\n",
    "from model import UNetGenerator,get_norm_layer,init_weight\n",
    "from utils import LambdaLR\n",
    "from torchvision import transforms\n",
    "import os\n",
    "import torch.nn as nn\n",
    "from tqdm.auto import tqdm\n",
    "from dataset import makeDataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torchmetrics import PeakSignalNoiseRatio\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e32c335",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(tensor,zero2one=False):\n",
    "    shape=tensor.shape\n",
    "    tensor_norm=tensor.reshape([shape[0],shape[1],-1])\n",
    "    tensor_max=tensor_norm.max(dim=-1,keepdim=True)[0]\n",
    "    tensor_min=tensor_norm.min(dim=-1,keepdim=True)[0]\n",
    "    tensor_norm=((tensor_norm-tensor_min)/(tensor_max-tensor_min))\n",
    "    if zero2one:\n",
    "        tensor_norm=tensor_norm.reshape(shape)\n",
    "    else:\n",
    "        tensor_norm=((tensor_norm-0.5)*2.0).reshape(shape)\n",
    "    return tensor_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a39f9cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_type='batch'\n",
    "epochs=80\n",
    "lr=0.0002\n",
    "ngf=64\n",
    "use_droplayer=False\n",
    "batch_size=8\n",
    "norm_layer=get_norm_layer(norm_type)\n",
    "DEVICE='cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65c4fc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "denoising_Unet=UNetGenerator(in_c=1,out_c=1,ngf=ngf,norm_layer=norm_layer,use_drop=use_droplayer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71223069",
   "metadata": {},
   "outputs": [],
   "source": [
    "denoising_Unet.to(DEVICE)\n",
    "denoising_Unet.apply(init_weight)\n",
    "if torch.cuda.device_count() >1:\n",
    "    print('training with {} GPUs'.format(torch.cuda.device_count()))\n",
    "    denoising_Unet=torch.nn.DataParallel(denoising_Unet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48c28d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "psnr=PeakSignalNoiseRatio(data_range=1,dim=[-1,-2,-3])\n",
    "writer=SummaryWriter()\n",
    "trainTransforms=transforms.Compose([\n",
    "    transforms.RandomCrop((256,256)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82cd9898",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDataset=makeDataset(trainTransforms,alignB=True,sameTransformB=True)\n",
    "validDataset=makeDataset(transform=None,mode='test',alignB=True,sameTransformB=True)\n",
    "\n",
    "trainLoader=DataLoader(trainDataset,batch_size=batch_size,shuffle=True)\n",
    "validLoader=DataLoader(validDataset,batch_size=batch_size,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f74d5b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn=nn.MSELoss()\n",
    "optimizer=torch.optim.Adam(denoising_Unet.parameters(),lr=lr,betas=(0.5,0.999))\n",
    "lr_schedular=torch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambda=LambdaLR(epochs).step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "475da1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('./final_result_denoising_unet',exist_ok=True)\n",
    "history={'loss':[], 'PSNR':[]}"
   ]
  },
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   "execution_count": 20,
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   "source": [
    "trainstep=(len(trainLoader.dataset)//batch_size)+1 \n",
    "outtertqdm=tqdm(range(epochs))\n",
    "best_psnr=0\n",
    "for epoch in outtertqdm:\n",
    "    innertqdm=tqdm(range(trainstep),leave=False)\n",
    "    dataiter=iter(trainLoader)\n",
    "    denoising_Unet.train()\n",
    "    \n",
    "    stepcnt=0\n",
    "    stepValidcnt=0\n",
    "    total_loss=0\n",
    "    total_PSNR=0\n",
    "    \n",
    "    for step in innertqdm:\n",
    "        stepcnt+=1\n",
    "        fd, qd=next(dataiter)\n",
    "        fd, qd=(fd.to(DEVICE), qd.to(DEVICE))\n",
    "        fd_predicted=denoising_Unet(qd)\n",
    "        loss=loss_fn(fd_predicted, fd)\n",
    "        denoising_Unet.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss+=loss\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        denoising_Unet.eval()\n",
    "        for (fd, qd) in validLoader:\n",
    "            fd, qd=(fd.to(DEVICE), qd.to(DEVICE))\n",
    "            fd_predicted=denoising_Unet(qd)\n",
    "            \n",
    "            fd_predicted=normalize(fd_predicted,zero2one=True)\n",
    "            fd=normalize(fd,zero2one=True)\n",
    "            total_PSNR+=psnr(fd_predicted, fd)\n",
    "            stepValidcnt+=1\n",
    "    \n",
    "    avgLoss=total_loss.cpu().detach().numpy()/stepcnt\n",
    "    avgPSNR=total_PSNR.cpu().detach().numpy()/stepValidcnt\n",
    "    \n",
    "    history['loss'].append(avgLoss)\n",
    "    history['PSNR'].append(avgPSNR)\n",
    "    \n",
    "    writer.add_scalar('loss', avgLoss, epoch)\n",
    "    writer.add_scalar('PSNR', avgPSNR, epoch)\n",
    "    \n",
    "    lr_schedular.step()\n",
    "    \n",
    "    outtertqdm.set_postfix({'PSNR':'{:.03f}'.format(avgPSNR),'PSNR_Diff':'{:.03f}'.format(avgPSNR-26.9565)})\n",
    "    with open('./final_result_denoising_unet/history.pkl','wb') as f:\n",
    "            pickle.dump(history,f)\n",
    "    if best_psnr<avgPSNR:\n",
    "        best_psnr=avgPSNR\n",
    "        torch.save(denoising_Unet.state_dict(),'./final_result_denoising_unet/denoising_unet.pt')\n",
    "writer.flush()\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "369f53b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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