DIT_GAN_balance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "928407b7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extension horovod.torch has not been built: /usr/local/lib/python3.8/dist-packages/horovod/torch/mpi_lib/_mpi_lib.cpython-38-x86_64-linux-gnu.so not found\n",
      "If this is not expected, reinstall Horovod with HOROVOD_WITH_PYTORCH=1 to debug the build error.\n",
      "Warning! MPI libs are missing, but python applications are still avaiable.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import torch.nn.functional as F\n",
    "from timm.models.layers import DropPath, to_2tuple, trunc_normal_\n",
    "from timm.models.registry import register_model\n",
    "import math\n",
    "import pydicom\n",
    "import numpy as np\n",
    "import torch, os\n",
    "import torch.nn.functional as F\n",
    "from torch import nn, einsum\n",
    "from torch.utils.data import Dataset\n",
    "from einops import rearrange, repeat\n",
    "from einops.layers.torch import Rearrange\n",
    "from PIL import Image\n",
    "import glob\n",
    "from torchsummary import summary\n",
    "from torchvision.transforms import Compose, Resize, ToTensor\n",
    "\n",
    "from einops import rearrange, reduce, repeat\n",
    "from einops.layers.torch import Rearrange, Reduce\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9e51f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(x):\n",
    "    return (x - x.mean()) / (x.max() - x.min())\n",
    "#dataset for cbct dicom\n",
    "def load_dcm(image_path):\n",
    "#     image_data=[]\n",
    "#     for i in range(len(image_path)):\n",
    "    ds=pydicom.dcmread(image_path)\n",
    "    ww=400\n",
    "    wc=50\n",
    "    slope = ds.RescaleSlope\n",
    "    intercept = ds.RescaleIntercept\n",
    "\n",
    "    ymin = 0\n",
    "    ymax = 255\n",
    "\n",
    "    pixel_array = ds.pixel_array * slope   intercept\n",
    "\n",
    "    # linear exact array\n",
    "    linear_exact_array = np.zeros(pixel_array.shape)\n",
    "\n",
    "    linear_exact_array_less_idx = pixel_array <= (wc - ww/2)\n",
    "    linear_exact_array_large_idx = pixel_array > (wc   ww/2)\n",
    "    linear_exact_array = ((pixel_array - wc)/ww   0.5) * (ymax - ymin)   ymin\n",
    "    linear_exact_array[linear_exact_array_less_idx] = ymin\n",
    "    linear_exact_array[linear_exact_array_large_idx] = ymax\n",
    "    linear_exact_array = linear_exact_array.astype(np.uint8)        \n",
    "    #add channel\n",
    "\n",
    "    ct=np.expand_dims(linear_exact_array,axis=0)\n",
    "    ct = np.transpose(ct, (2, 1, 0))\n",
    "    ct = np.clip(ct, -200, 2000)\n",
    "    # ct=normalize(ct)        \n",
    "    #because ndarray does not support uint16 , must change to float\n",
    "    ct=ct.astype(float)\n",
    "\n",
    "    return ct #image_data\n",
    "# def load_dcm(image_path):\n",
    "#     ds = pydicom.dcmread(image_path)\n",
    "\n",
    "#     slope = ds.RescaleSlope\n",
    "#     intercept = ds.RescaleIntercept\n",
    "\n",
    "#     ymin = 0\n",
    "#     ymax = 255\n",
    "\n",
    "#     pixel_array = ds.pixel_array * slope   intercept\n",
    "    \n",
    "#     ww = 400\n",
    "#     wl = 50\n",
    "#     # Calculate windowed image\n",
    "#     windowed_image = np.clip(((pixel_array - (wl - ww / 2)) / ww   0.5) * (ymax - ymin)   ymin, ymin, ymax)\n",
    "#     windowed_image = windowed_image.astype(np.uint8)\n",
    "\n",
    "#     # Add channel dimension\n",
    "#     ct = np.expand_dims(windowed_image, axis=0)\n",
    "\n",
    "#     # Transpose dimensions\n",
    "#     ct = np.transpose(ct, (2, 1, 0))\n",
    "\n",
    "#     # Clip values to a specific range, e.g., -200 to 2000\n",
    "#     ct = np.clip(ct, -200, 2000)\n",
    "\n",
    "#     # Convert to float\n",
    "#     ct = ct.astype(float)\n",
    "\n",
    "#     return ct\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Load data from .npy file\n",
    "data = np.load('./result.npy')\n",
    "\n",
    "# Create an empty numpy array to store the loaded data\n",
    "data_load = np.empty((len(data), 2), dtype=object)\n",
    "\n",
    "# Load each pair of data and store it in the numpy array\n",
    "for i, pair in enumerate(data):\n",
    "    data_load[i, 0] = load_dcm(pair[0])\n",
    "    data_load[i, 1] = load_dcm(pair[1])\n",
    "    \n",
    "# print(len(data_load))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba06d6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset,DataLoader\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "# import cv2\n",
    "import random\n",
    "\n",
    "class ImageDatasetLoader(Dataset):\n",
    "    def __init__(self, mode='train', transformers=None, test_indices=[17000,17010,17020,17030,17040,17060,17070,17080,17090,17100,\n",
    "                                                                      18000,18100,18200,18300,18400,18500,18600,18700,18800,18900, \n",
    "                                                                      19500,19050,19150,19250,19350,19450,19550,19650,19750,19850,\n",
    "                                                                      19950,20050,20150,20250,20350,20450,20550,20650,20750,20850,\n",
    "                                                                      20950,22030,22130,22230,22330,22430,22530,22630,22730,22830,\n",
    "                                                                      23070,23170,23270,23370,23470,23570,23670,23770,23870,23970,\n",
    "                                                                     ]):\n",
    "        self.mode = mode\n",
    "        self.transform = transformers\n",
    "        \n",
    "        # Assuming you have the complete dataset in the 'data_load' variable\n",
    "        total_samples = len(data_load)\n",
    "        \n",
    "        if mode == 'train':\n",
    "            self.files_CBCT = data_load[0:8000, 0]\n",
    "            self.files_sim = data_load[0:8000, 1]\n",
    "        elif mode == 'train1':\n",
    "            self.files_CBCT = data_load[0:8000, 0]\n",
    "            self.files_sim = data_load[0:8000, 1]\n",
    "        elif mode == 'test':\n",
    "            if test_indices is None:\n",
    "                # Randomly select 60 samples for the test set\n",
    "                test_indices = random.sample(range(17000, total_samples), 60)\n",
    "            self.files_CBCT = data_load[test_indices, 0]\n",
    "            self.files_sim = data_load[test_indices, 1]\n",
    "        elif mode == 'fid':\n",
    "            self.files_CBCT = data_load[21704:24704, 0]\n",
    "            self.files_sim = data_load[21704:24704, 1]\n",
    "        elif mode == 'evl':\n",
    "            self.files_CBCT = data_load[24000:24704, 0]\n",
    "            self.files_sim = data_load[24000:24704, 1]\n",
    "        # elif mode == 'evl':\n",
    "        #     if test_indices is None:\n",
    "        #         # Randomly select 60 samples for the test set\n",
    "        #         test_indices = random.sample(range(17000, total_samples), 60)\n",
    "        #     self.files_CBCT = data_load[test_indices, 0]\n",
    "        #     self.files_sim = data_load[test_indices, 1]\n",
    "\n",
    "        self.cbctnorm = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "        ])\n",
    "        self.simctnorm = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "        ])\n",
    "        self.alltransform = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "        ])\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.files_sim)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        file_A = self.files_CBCT[index]\n",
    "        file_B = self.files_sim[index]\n",
    "        if self.transform is not None:\n",
    "            if self.mode == 'train': \n",
    "                img_A = self.alltransform(file_A)\n",
    "                img_B = self.alltransform(file_B)\n",
    "                file_cat = torch.cat((img_A, img_B, img_B), dim=0)\n",
    "                img_cat = self.transform(file_cat)\n",
    "                img_A, img_B, xx = torch.split(img_cat, [1, 1, 1], dim=0)\n",
    "                img_A = self.cbctnorm(file_A)\n",
    "                img_B = self.simctnorm(file_B)\n",
    "            else:\n",
    "                img_A = self.cbctnorm(file_A)\n",
    "                img_B = self.simctnorm(file_B)\n",
    "        return img_A, img_B\n",
    "\n",
    "import torchvision.transforms as transforms\n",
    "import torch, os, sys\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.optim import Adam,AdamW\n",
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "# Define the custom transform\n",
    "class NumpyToPIL(object):\n",
    "    def __call__(self, img):\n",
    "        return Image.fromarray(np.uint8(img))\n",
    "# Define your transforms\n",
    "data_transform = transforms.Compose([\n",
    "    # transforms.ToPILImage(),\n",
    "    # transforms.CenterCrop(size=(350, 350)),  # crop the center part of the image with size 300x300\n",
    "    # transforms.Resize(size=(384, 384)),  # resize the cropped image to 384x384\n",
    "    transforms.RandomHorizontalFlip(),  # randomly flip the image horizontally with a probability of 0.5\n",
    "    transforms.RandomVerticalFlip(),  # randomly flip the image vertically with a probability of 0.5\n",
    "    transforms.ColorJitter(brightness=0.1, contrast=0.2),  # randomly adjust brightness, contrast, saturation, and hue\n",
    "    transforms.RandomRotation(degrees=15),  # randomly rotate the image by up to 15 degrees\n",
    "    # transforms.RandomCrop(size=(300, /300)),  # randomly crop the image to size 300x300\n",
    "    # transforms.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),  # randomly apply Gaussian blur\n",
    "    # transforms.ToTensor(),  # convert the image to a PyTorch tensor\n",
    "    # transforms.Normalize(0.5, 0.5, 0.5)\n",
    "])\n",
    "transforms_ = transforms.Compose([\n",
    "    # transforms.ToTensor(),\n",
    "    # transforms.Normalize(0.5, 0.5, 0.5)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d70341f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d0102c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n",
    "import torch, os, sys\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.optim import Adam,AdamW\n",
    "from tqdm import tqdm\n",
    "transforms_ = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(0.5, 0.5, 0.5)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f95c87f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import argparse\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision.utils import make_grid, save_image\n",
    "\n",
    "from tqdm import tqdm\n",
    "from copy import deepcopy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d53a3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, stride=1):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        self.relu = nn.SiLU(inplace=True)\n",
    "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "        self.drop = nn.Dropout(0.2)\n",
    "        self.downsample = None\n",
    "        if stride != 1 or in_channels != out_channels:\n",
    "            self.downsample = nn.Sequential(\n",
    "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_channels)\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.drop(out)\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "        out  = residual\n",
    "        out = self.relu(out)\n",
    "        out = self.drop(out)\n",
    "        return out\n",
    "    \n",
    "#next generation residual block\n",
    "class rSoftMax(nn.Module):\n",
    "    def __init__(self, radix, cardinality):\n",
    "        super().__init__()\n",
    "        assert radix > 0\n",
    "        self.radix = radix\n",
    "        self.cardinality = cardinality\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch = x.size(0)\n",
    "        if self.radix > 1:\n",
    "            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)\n",
    "            x = F.softmax(x, dim=1)\n",
    "            x = x.reshape(batch, -1)\n",
    "        else:\n",
    "            x = torch.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "class Splat(nn.Module):\n",
    "    def __init__(self, channels, radix, cardinality, reduction_factor=4):\n",
    "        super(Splat, self).__init__()\n",
    "        self.radix = radix\n",
    "        self.cardinality = cardinality\n",
    "        self.channels = channels\n",
    "        inter_channels = max(channels*radix//reduction_factor, 32)\n",
    "        self.fc1 = nn.Conv2d(channels//radix, inter_channels, 1, groups=cardinality)\n",
    "#         self.bn1 = nn.BatchNorm2d(inter_channels)\n",
    "        self.bn1 = nn.InstanceNorm2d(inter_channels)\n",
    "        self.relu = nn.SiLU(inplace=True)\n",
    "        self.fc2 = nn.Conv2d(inter_channels, channels*radix, 1, groups=cardinality)\n",
    "        self.rsoftmax = rSoftMax(radix, cardinality)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch, rchannel = x.shape[:2]\n",
    "        if self.radix > 1:\n",
    "            splited = torch.split(x, rchannel//self.radix, dim=1)\n",
    "            gap = sum(splited) \n",
    "        else:\n",
    "            gap = x\n",
    "        gap = F.adaptive_avg_pool2d(gap, 1)\n",
    "        gap = self.fc1(gap)\n",
    "\n",
    "        gap = self.bn1(gap)\n",
    "        gap = self.relu(gap)\n",
    "\n",
    "        atten = self.fc2(gap)\n",
    "        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)\n",
    "\n",
    "        if self.radix > 1:\n",
    "            attens = torch.split(atten, rchannel//self.radix, dim=1)\n",
    "            out = sum([att*split for (att, split) in zip(attens, splited)])\n",
    "        else:\n",
    "            out = atten * x\n",
    "        return out.contiguous()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96e13ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ds_conv2d(nn.Module):\n",
    "    def __init__(self, in_planes, out_planes, kernel_size, stride=1, \n",
    "                 dilation=[1,3,5], groups=1, bias=True, \n",
    "                 act_layer='nn.SiLU(True)', init='kaiming'):\n",
    "        super().__init__()\n",
    "        assert in_planes%groups==0\n",
    "        assert kernel_size==3, 'only support kernel size 3 now'\n",
    "        self.in_planes = in_planes\n",
    "        self.out_planes = out_planes\n",
    "        self.kernel_size = kernel_size\n",
    "        self.stride = stride\n",
    "        self.dilation = dilation\n",
    "        self.groups = groups\n",
    "        self.with_bias = bias\n",
    "        \n",
    "        self.weight = nn.Parameter(torch.randn(out_planes, in_planes//groups, kernel_size, kernel_size), requires_grad=True)\n",
    "        if bias:\n",
    "            self.bias = nn.Parameter(torch.Tensor(out_planes))\n",
    "        else:\n",
    "            self.bias = None\n",
    "        self.act = eval(act_layer)\n",
    "        self.init = init\n",
    "        self._initialize_weights()\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        if self.init == 'dirac':\n",
    "            nn.init.dirac_(self.weight, self.groups)\n",
    "        elif self.init == 'kaiming':\n",
    "            nn.init.kaiming_uniform_(self.weight)\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "        if self.with_bias:\n",
    "            if self.init == 'dirac':\n",
    "                nn.init.constant_(self.bias, 0.)\n",
    "            elif self.init == 'kaiming':\n",
    "                bound = self.groups / (self.kernel_size**2 * self.in_planes)\n",
    "                bound = math.sqrt(bound)\n",
    "                nn.init.uniform_(self.bias, -bound, bound)\n",
    "            else:\n",
    "                raise NotImplementedError\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = 0\n",
    "        for dil in self.dilation:\n",
    "            output  = self.act(\n",
    "                F.conv2d(\n",
    "                    x, weight=self.weight, bias=self.bias, stride=self.stride, padding=dil,\n",
    "                    dilation=dil, groups=self.groups,\n",
    "                )\n",
    "            )\n",
    "        return output\n",
    "class AtrousSelfAttention(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, num_heads=2):\n",
    "        super(AtrousSelfAttention, self).__init__()\n",
    "        self.num_heads = num_heads\n",
    "        self.head_dim = out_channels // num_heads\n",
    "        self.query_conv = ds_conv2d(in_channels, out_channels, kernel_size)\n",
    "#         self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n",
    "        self.key_conv = nn.Conv2d(in_channels, out_channels,1)\n",
    "        self.value_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n",
    "#         self.value_conv = nn.Conv2d(in_channels, out_channels, 1)\n",
    " \n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        nn.init.kaiming_uniform_(self.query_conv.weight)\n",
    "        nn.init.kaiming_uniform_(self.key_conv.weight)\n",
    "        nn.init.kaiming_uniform_(self.value_conv.weight)\n",
    "        nn.init.zeros_(self.query_conv.bias)\n",
    "        nn.init.zeros_(self.key_conv.bias)\n",
    "        nn.init.zeros_(self.value_conv.bias)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size, channels, height, width = x.size()\n",
    "        queries = self.query_conv(x).view(batch_size, self.num_heads, self.head_dim, height*width).permute(0, 1, 3, 2)\n",
    "        keys = self.key_conv(x).view(batch_size, self.num_heads, self.head_dim, height*width).permute(0, 1, 2, 3)\n",
    "        values = self.value_conv(x).view(batch_size, self.num_heads, self.head_dim, height*width).permute(0, 1, 3, 2)\n",
    "        energy = torch.matmul(queries, keys)\n",
    "        attention = torch.softmax(energy / self.head_dim**0.5, dim=-1)\n",
    "#         attention = self.dropout(attention)\n",
    "        out = torch.matmul(attention, values)\n",
    "        out = out.permute(0, 1, 3, 2).contiguous().view(batch_size, -1, height, width)\n",
    "        return out\n",
    "class Mlp(nn.Module):\n",
    "    def __init__(self, in_features, hidden_features=None,\n",
    "                 out_features=None, act_layer=nn.GELU,\n",
    "                 drop=0.2, with_depconv=True):\n",
    "        super().__init__()\n",
    "        out_features = out_features or in_features\n",
    "        hidden_features = hidden_features or in_features\n",
    "        self.with_depconv = with_depconv\n",
    "        \n",
    "        if self.with_depconv:\n",
    "            self.fc1 = nn.Conv2d(\n",
    "                in_features, hidden_features, 1, stride=1, padding=0, dilation=1, \n",
    "                groups=1, bias=True,\n",
    "            )\n",
    "            self.depconv = nn.Conv2d(\n",
    "                hidden_features, hidden_features, 3, stride=1, padding=1, dilation=1, \n",
    "                groups=hidden_features, bias=True,\n",
    "            )\n",
    "            self.act = act_layer()\n",
    "            self.fc2 = nn.Conv2d(\n",
    "                hidden_features, out_features, 1, stride=1, padding=0, dilation=1, \n",
    "                groups=1, bias=True,\n",
    "            )\n",
    "        else:\n",
    "            self.fc1 = nn.Linear(in_features, hidden_features)\n",
    "            self.act = act_layer()\n",
    "            self.fc2 = nn.Linear(hidden_features, out_features)\n",
    "        self.drop = nn.Dropout(drop)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        if self.with_depconv:\n",
    "            x = self.fc1(x)\n",
    "            x = self.depconv(x)\n",
    "            x = self.act(x)\n",
    "            x = self.drop(x)\n",
    "            x = self.fc2(x)\n",
    "            x = self.drop(x)\n",
    "            return x\n",
    "        else:\n",
    "            x = self.fc1(x)\n",
    "            x = self.act(x)\n",
    "            x = self.drop(x)\n",
    "            x = self.fc2(x)\n",
    "            x = self.drop(x)\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1eee8ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "\n",
    "class CSA(nn.Module):\n",
    "    def __init__(self, in_dim, out_dim, num_heads, kernel_size=3, padding=1, stride=2,\n",
    "                 qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n",
    "        super().__init__()\n",
    "        self.in_dim = in_dim\n",
    "        self.out_dim = out_dim\n",
    "        head_dim = out_dim // num_heads\n",
    "        self.num_heads = num_heads\n",
    "        self.kernel_size = kernel_size\n",
    "        self.padding = padding\n",
    "        self.stride = stride\n",
    "        self.scale = qk_scale or head_dim**-0.5\n",
    "        \n",
    "        self.attn = nn.Linear(in_dim, kernel_size**4 * num_heads)\n",
    "        self.attn_drop = nn.Dropout(attn_drop)\n",
    "\n",
    "        self.unfold = nn.Unfold(kernel_size=kernel_size, padding=padding, stride=stride)\n",
    "        self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True)\n",
    "        \n",
    "        self.csa_group = 1\n",
    "        assert out_dim % self.csa_group == 0\n",
    "        self.weight = nn.Conv2d(\n",
    "            self.kernel_size*self.kernel_size*out_dim, \n",
    "            self.kernel_size*self.kernel_size*out_dim, \n",
    "            1, \n",
    "            stride=1, padding=0, dilation=1, \n",
    "            groups=self.kernel_size*self.kernel_size*self.csa_group, \n",
    "            bias=qkv_bias,\n",
    "        )\n",
    "        assert qkv_bias == False\n",
    "        fan_out = self.kernel_size*self.kernel_size*self.out_dim\n",
    "        fan_out //= self.csa_group\n",
    "        self.weight.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) # init\n",
    "        \n",
    "        self.proj = nn.Linear(out_dim, out_dim)\n",
    "        self.proj_drop = nn.Dropout(proj_drop)\n",
    "        \n",
    "    def forward(self, x, v=None):\n",
    "        x = x.permute(0, 2, 3, 1)\n",
    "        B, H, W, _ = x.shape\n",
    "        h, w = math.ceil(H / self.stride), math.ceil(W / self.stride)\n",
    "        \n",
    "        attn = self.pool(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)\n",
    "        attn = self.attn(attn).reshape(\n",
    "            B, h * w, self.num_heads, self.kernel_size * self.kernel_size,\n",
    "            self.kernel_size * self.kernel_size).permute(0, 2, 1, 3, 4) # B,H,N,kxk,kxk\n",
    "        attn = attn * self.scale\n",
    "        attn = attn.softmax(dim=-1)\n",
    "        attn = self.attn_drop(attn)\n",
    "        \n",
    "        v = x.permute(0, 3, 1, 2) # B,C,H, W\n",
    "        v = self.unfold(v).reshape(\n",
    "            B, self.out_dim, self.kernel_size*self.kernel_size, h*w\n",
    "        ).permute(0,3,2,1).reshape(B*h*w, self.kernel_size*self.kernel_size*self.out_dim, 1, 1)\n",
    "        v = self.weight(v)\n",
    "        v = v.reshape(B, h*w, self.kernel_size*self.kernel_size, self.num_heads, \n",
    "                      self.out_dim//self.num_heads).permute(0,3,1,2,4).contiguous() # B,H,N,kxk,C/H\n",
    "        \n",
    "        x = (attn @ v).permute(0, 1, 4, 3, 2)\n",
    "        x = x.reshape(B, self.out_dim * self.kernel_size * self.kernel_size, h * w)\n",
    "        x = F.fold(x, output_size=(H, W), kernel_size=self.kernel_size, \n",
    "                   padding=self.padding, stride=self.stride)\n",
    "\n",
    "        x = self.proj(x.permute(0, 2, 3, 1))\n",
    "        x = self.proj_drop(x)\n",
    "        return x.permute(0, 3, 1, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef62f5f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "# class cross_attention(nn.Module):\n",
    "#     def __init__(self, in_channels,num_heads=2, kernel_size=3, dilation=1):\n",
    "#         super(cross_attention, self).__init__()\n",
    "#         out_channels = in_channels\n",
    "#         self.num_heads = num_heads\n",
    "#         self.head_dim = out_channels // num_heads\n",
    "#         self.query_conv = nn.Conv2d(in_channels, out_channels,3,1,1)\n",
    "# #         self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n",
    "#         self.key_conv = nn.Conv2d(in_channels, out_channels,1)\n",
    "#         self.value_conv = nn.Conv2d(in_channels, out_channels, 1)\n",
    "# #         self.value_conv = nn.Conv2d(in_channels, out_channels, 1)\n",
    "\n",
    "#         self.reset_parameters()\n",
    "\n",
    "#     def reset_parameters(self):\n",
    "#         nn.init.kaiming_uniform_(self.query_conv.weight)\n",
    "#         nn.init.kaiming_uniform_(self.key_conv.weight)\n",
    "#         nn.init.kaiming_uniform_(self.value_conv.weight)\n",
    "#         nn.init.zeros_(self.query_conv.bias)\n",
    "#         nn.init.zeros_(self.key_conv.bias)\n",
    "#         nn.init.zeros_(self.value_conv.bias)\n",
    "\n",
    "#     def forward(self, x, k, v):\n",
    "#         batch_size, channels, height, width = x.size()\n",
    "#         queries = self.query_conv(x).view(batch_size, self.num_heads, self.head_dim, height*width).permute(0, 1, 3, 2)\n",
    "#         keys = self.key_conv(k).view(batch_size, self.num_heads, self.head_dim, height*width).permute(0, 1, 2, 3)\n",
    "#         values = self.value_conv(v).view(batch_size, self.num_heads, height*width, self.head_dim)\n",
    "#         energy = torch.matmul(queries, keys)\n",
    "#         attention = torch.softmax(energy / self.head_dim**0.5, dim=-1)\n",
    "#         out = torch.matmul(attention, values)\n",
    "#         out = out.permute(0, 1, 3, 2).contiguous().view(batch_size, -1, height, width)\n",
    "#         return out\n",
    "class cross_attention(nn.Module):\n",
    "    def __init__(self,in_channels) :\n",
    "        super(cross_attention, self).__init__()\n",
    "        self.queryConv =nn.Sequential(nn.Conv2d(in_channels, in_channels,1,1), \n",
    "                                      nn.GroupNorm(in_channels//4,in_channels)) \n",
    "        self.keyConv = nn.Sequential(nn.Conv2d(in_channels,in_channels,1,1), \n",
    "                                     nn.GroupNorm(in_channels//4,in_channels)) \n",
    "        self.psiConv = nn.Sequential(\n",
    "            nn.GroupNorm(in_channels//4,in_channels),\n",
    "            nn.Conv2d(in_channels,1,1,1), \n",
    "            nn.SiLU()) \n",
    "    def forward(self, query, key):\n",
    "        value = key\n",
    "        key = self.keyConv(key)\n",
    "        query = self.queryConv(query)\n",
    "        psi = F.silu(key query)\n",
    "        psi = self.psiConv(psi)\n",
    "        return value*psi\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84913995",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SPADE(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):\n",
    "        super(SPADE, self).__init__()\n",
    "        self.norm = nn.GroupNorm(in_channels//4,in_channels)\n",
    "        self.share = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding)\n",
    "        self.conv_gamma = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding)\n",
    "        self.conv_beta = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding)\n",
    "\n",
    "    def forward(self, x, segmap):\n",
    "        normalized = self.norm(x)\n",
    "        seg_share  = self.share(F.silu(self.norm(segmap)))\n",
    "        gamma = self.conv_gamma(F.silu(self.norm(seg_share)))\n",
    "        #beta = F.silu(self.conv_beta(self.norm(x)))\n",
    "        return (1   gamma) * normalized #  beta\n",
    "\n",
    "class style_encoder(nn.Module):\n",
    "    def __init__(self,in_channels,embed_dim ):\n",
    "        super(style_encoder,self).__init__()\n",
    "        self.embed_dim =embed_dim \n",
    "        self.styleconv1 = nn.Sequential(\n",
    "            nn.Conv2d(1, self.embed_dim, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(self.embed_dim//4,self.embed_dim),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "        self.styleconv2 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim, self.embed_dim, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(self.embed_dim//4,self.embed_dim),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "            \n",
    "        self.styleconv3 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim, self.embed_dim*2, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(self.embed_dim//2,self.embed_dim*2),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "        self.styleconv4 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim*2, self.embed_dim*4, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(self.embed_dim//2,self.embed_dim*4),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "        self.upUpsample = nn.Upsample(scale_factor=2, mode='nearest')\n",
    "        self.styleup4 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim*6,self.embed_dim*2, 3,1,1),\n",
    "            nn.GroupNorm(self.embed_dim//2,self.embed_dim*2),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "        self.styleup3 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim*3,self.embed_dim*1, 3,1,1),\n",
    "            nn.GroupNorm(self.embed_dim//4,self.embed_dim*1),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "        self.styleup2 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim*2,self.embed_dim*1,3,1,1),\n",
    "            nn.GroupNorm(self.embed_dim//4,self.embed_dim*1),\n",
    "            nn.SiLU(inplace=True)\n",
    "        )\n",
    "        \n",
    "    def forward (self,x):\n",
    "        x1 = self.styleconv1(x)\n",
    "        x2 = self.styleconv2(x1)\n",
    "        x3 = self.styleconv3(x2)\n",
    "        x4 = self.styleconv4(x3)\n",
    "        \n",
    "        x5 = self.upUpsample(x4)\n",
    "        x5 = torch.cat((x5,x3),dim=1)\n",
    "        x5 = self.styleup4(x5)\n",
    "        \n",
    "        x6 = self.upUpsample(x5)\n",
    "        x6 = torch.cat((x6,x2),dim=1)\n",
    "        x6 = self.styleup3(x6)\n",
    "        \n",
    "        x7 = self.upUpsample(x6)\n",
    "        x7 = torch.cat((x7,x1),dim=1)\n",
    "        x7 = self.styleup2(x7)\n",
    "        \n",
    "        return x7, x6, x5, x4\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ebbe62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicConv(nn.Module):\n",
    "    def __init__(self, in_channel, out_channel, kernel_size, stride, bias=True, norm=False, relu=True, transpose=False):\n",
    "        super(BasicConv, self).__init__()\n",
    "        if bias and norm:\n",
    "            bias = False\n",
    "\n",
    "        padding = kernel_size // 2\n",
    "        layers = list()\n",
    "        if transpose:\n",
    "            padding = kernel_size // 2 -1\n",
    "            layers.append(nn.ConvTranspose2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias))\n",
    "        else:\n",
    "            layers.append(\n",
    "                nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias))\n",
    "        if norm:\n",
    "            layers.append(nn.BatchNorm2d(out_channel))\n",
    "        if relu:\n",
    "            layers.append(nn.ReLU(inplace=True))\n",
    "        self.main = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.main(x)\n",
    "\n",
    "\n",
    "class ResBlock(nn.Module):\n",
    "    def __init__(self, in_channel, out_channel, norm=False):\n",
    "        super(ResBlock, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            BasicConv(in_channel, out_channel, kernel_size=3, stride=1, norm=norm, relu=True),\n",
    "            BasicConv(out_channel, out_channel, kernel_size=3, stride=1, norm=norm, relu=False)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.main(x)   x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef18cdc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TGenerator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.overlappatch_size=48\n",
    "        self.patch_size = 96\n",
    "        self.num_patch=(384//self.patch_size)\n",
    "        self.num_patches=self.num_patch**2\n",
    "        self.embed_dim = 64\n",
    "        \n",
    "        \n",
    "        self.patches = nn.Sequential(\n",
    "            nn.Conv2d(1, self.embed_dim,  kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(self.embed_dim//4,self.embed_dim),\n",
    "            nn.SiLU(inplace=True),\n",
    "        )\n",
    "        self.down1 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim, self.embed_dim,  kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(self.embed_dim//4,self.embed_dim),\n",
    "            nn.SiLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        \n",
    "        # self.cross_attention1 = cross_attention(in_channels=self.embed_dim, num_heads=2)\n",
    "        self.cross_attention1 = cross_attention(in_channels=self.embed_dim)\n",
    "        self.attn1 = CSA(in_dim=self.embed_dim, out_dim=self.embed_dim, num_heads=2)\n",
    "        self.pos_embed1 = nn.Parameter(torch.zeros(1, self.embed_dim, self.patch_size,self.patch_size))\n",
    "        self.norm1 = nn.GroupNorm(self.embed_dim//4,self.embed_dim)\n",
    "        self.mlp1 = Mlp(self.embed_dim,self.embed_dim*4)\n",
    "        # self.ResidualBlock1 = ResidualBlock(self.embed_dim,self.embed_dim)\n",
    "        \n",
    "        self.down2 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim, self.embed_dim*2,  kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,self.embed_dim*2),\n",
    "            nn.SiLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        # self.cross_attention2 = cross_attention(in_channels=self.embed_dim*2, num_heads=2)\n",
    "        self.cross_attention2 = cross_attention(in_channels=self.embed_dim*2)\n",
    "        self.attn2 = AtrousSelfAttention(self.embed_dim*2, self.embed_dim*2, kernel_size=3, dilation=1, num_heads=4)\n",
    "        self.pos_embed2 = nn.Parameter(torch.zeros(1, self.embed_dim*2, self.patch_size//2, self.patch_size//2))\n",
    "        self.norm2 =nn.GroupNorm(32,self.embed_dim*2)\n",
    "        self.mlp2 = Mlp(self.embed_dim*2,self.embed_dim*2*8)\n",
    "        # self.ResidualBlock2 = ResidualBlock(self.embed_dim*2,self.embed_dim*2)\n",
    "\n",
    "        \n",
    "        self.down3 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim*2, self.embed_dim*4, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,self.embed_dim*4),\n",
    "            nn.SiLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        # self.cross_attention3 = cross_attention(in_channels=self.embed_dim*4, num_heads=2)\n",
    "        self.cross_attention3 = cross_attention(in_channels=self.embed_dim*4)\n",
    "        self.attn3 = AtrousSelfAttention(self.embed_dim*4, self.embed_dim*4, kernel_size=3, dilation=1, num_heads=4)\n",
    "        self.pos_embed3 = nn.Parameter(torch.zeros(1, self.embed_dim*4, self.patch_size//4, self.patch_size//4))\n",
    "        self.norm3 = nn.GroupNorm(32,self.embed_dim*4)\n",
    "        self.mlp3 = Mlp(self.embed_dim*4,self.embed_dim*4*4)\n",
    "        # self.ResidualBlock3 = ResidualBlock(self.embed_dim*4,self.embed_dim*4)\n",
    "\n",
    "        \n",
    "        self.down4 = nn.Sequential(\n",
    "            nn.Conv2d(self.embed_dim*4, self.embed_dim*8,  kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,self.embed_dim*8),\n",
    "            nn.SiLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        # self.cross_attention4 = cross_attention(in_channels=self.embed_dim*1, num_heads=2)\n",
    "        self.cross_attention4 = cross_attention(in_channels=self.embed_dim*1)\n",
    "        self.attn4 = AtrousSelfAttention(self.embed_dim*8, self.embed_dim*8, kernel_size=3, dilation=1, num_heads=4)\n",
    "        self.pos_embed4 = nn.Parameter(torch.zeros(1, self.embed_dim*8, self.patch_size//8, self.patch_size//8))\n",
    "        self.norm4 = nn.GroupNorm(self.embed_dim//2,self.embed_dim*8)\n",
    "        self.mlp4 = Mlp(self.embed_dim*8,self.embed_dim*8*4)\n",
    "        # self.ResidualBlock4 = ResidualBlock(self.embed_dim*8,self.embed_dim*8)\n",
    "#         self.ResidualBlock4 = Splat(self.embed_dim*8,2,8)\n",
    "\n",
    "        self.upUpsample = nn.Upsample(scale_factor=2, mode='nearest')\n",
    "        \n",
    "        # self.threethree = SPADE(self.embed_dim*4, self.embed_dim*4)\n",
    "        self.con3 = nn.Sequential(\n",
    "            nn.GroupNorm(32,self.embed_dim*8),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*8,self.embed_dim*4, kernel_size=3, padding=1, stride=1),\n",
    "        )\n",
    "        self.con33 = nn.Sequential(\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            nn.GroupNorm(32,self.embed_dim*8),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*8,self.embed_dim*4, kernel_size=1),\n",
    "            # nn.Conv2d(self.embed_dim*8,self.embed_dim*4, kernel_size=1),\n",
    "             nn.Dropout(0.2)\n",
    "        )\n",
    "        # self.con33 = nn.Sequential(\n",
    "        #     nn.GroupNorm(32,self.embed_dim*8),\n",
    "        #     nn.SiLU(True),\n",
    "        #     nn.ConvTranspose2d(self.embed_dim*8, self.embed_dim*4, kernel_size=4, padding=1, stride=2, bias=False),\n",
    "        #     # nn.Conv2d(self.embed_dim*8,self.embed_dim*4, kernel_size=1),\n",
    "        #      nn.Dropout(0.2)\n",
    "        # )\n",
    "            \n",
    "        self.con2 = nn.Sequential(\n",
    "            nn.GroupNorm(32,self.embed_dim*4),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*4,self.embed_dim*2, kernel_size=3, padding=1, stride=1),\n",
    "            \n",
    "        )\n",
    "        self.con22 = nn.Sequential(\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            nn.GroupNorm(32,self.embed_dim*8),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*8, self.embed_dim*2, kernel_size=3, padding=1, stride=1, bias=True),\n",
    "            nn.Dropout(0.2)\n",
    "            \n",
    "        )\n",
    "#         self.con22 = nn.Sequential(\n",
    "#             nn.GroupNorm(32,self.embed_dim*12),\n",
    "#             nn.SiLU(True),\n",
    "#             nn.ConvTranspose2d(self.embed_dim*12, self.embed_dim*2, kernel_size=4, padding=1, stride=2, bias=False),\n",
    "#             nn.Dropout(0.2)\n",
    "            \n",
    "#         )\n",
    "\n",
    "        \n",
    "        #48->96\n",
    "\n",
    "        self.oneone =SPADE(self.embed_dim*1, self.embed_dim*1)\n",
    "        self.con11 = nn.Sequential(\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            nn.GroupNorm(32,self.embed_dim*4),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*4, self.embed_dim*1,kernel_size=3, padding=1, stride=1, bias=True),\n",
    "            nn.Dropout(0.2)\n",
    "        )\n",
    "        # self.con11 = nn.Sequential(\n",
    "        #     nn.GroupNorm(32,self.embed_dim*10),\n",
    "        #     nn.SiLU(True),\n",
    "        #     nn.ConvTranspose2d(self.embed_dim*10, self.embed_dim*1, kernel_size=4, padding=1, stride=2, bias=False),\n",
    "        #     nn.Dropout(0.2)\n",
    "        # )\n",
    "        self.con1 = nn.Sequential(\n",
    "            nn.GroupNorm(32,self.embed_dim*2),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*2,self.embed_dim*1, kernel_size=1),\n",
    "        )\n",
    "        # self.con11 = nn.Conv2d(self.embed_dim*2,self.embed_dim*1, kernel_size=3, stride=1, padding=1)\n",
    "        \n",
    "\n",
    "        self.upup =  SPADE(self.embed_dim*1, self.embed_dim*1)\n",
    "        self.conupup =  nn.Conv2d(self.embed_dim*1,self.embed_dim*1, kernel_size=3, stride=1, padding=1)\n",
    "        self.conupupnorm = nn.GroupNorm(self.embed_dim//4,self.embed_dim)\n",
    "        \n",
    "        # self.transup = nn.Sequential(\n",
    "        #     nn.GroupNorm(32,self.embed_dim*5),\n",
    "        #     nn.SiLU(True),\n",
    "        #     nn.ConvTranspose2d(self.embed_dim*5, self.embed_dim*5, kernel_size=4, padding=1, stride=2, bias=False),\n",
    "        #     nn.Dropout(0.2)\n",
    "        # )\n",
    "        self.transup = nn.Sequential(\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            nn.GroupNorm(32,self.embed_dim*2),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*2, self.embed_dim*5,  kernel_size=3, stride=1, padding=1, bias=True),\n",
    "            nn.Dropout(0.2)\n",
    "        )\n",
    "        \n",
    "        self.conup = nn.Sequential(\n",
    "            nn.GroupNorm(32,self.embed_dim*6),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*6,self.embed_dim*1, kernel_size=1, bias=True),\n",
    "        )\n",
    "        self.transout = nn.Sequential(\n",
    "            nn.Upsample(scale_factor=2, mode='nearest'),\n",
    "            nn.GroupNorm(32,self.embed_dim*2),\n",
    "            nn.SiLU(True),\n",
    "            nn.Conv2d(self.embed_dim*2, self.embed_dim*1,kernel_size=3, stride=1, padding=1, bias=True),\n",
    "            nn.Dropout(0.2)\n",
    "        )\n",
    "        \n",
    "        self.output = nn.Sequential(\n",
    "            nn.SiLU(inplace=True),\n",
    "            nn.Conv2d(self.embed_dim*1, 1, 3,1,1, bias=True),\n",
    "            # nn.Tanh(),\n",
    "        )   \n",
    "        \n",
    "        self.gelu=nn.GELU()\n",
    "        drop_path=0.2\n",
    "        self.drop_path = DropPath(\n",
    "            drop_path) if drop_path > 0. else nn.Identity()\n",
    "        self.transformer_layer = 2\n",
    "        self.style = style_encoder(1,self.embed_dim)\n",
    "\n",
    "        \n",
    "    def forward(self,x,style):\n",
    "        \n",
    "        style1, style2, style3, style4 = self.style(style)\n",
    "        \n",
    "        # x_deblur = self.deblur(x)\n",
    "        #input 1*384*384-> 16*96*96\n",
    "        x1 = self.patches(x)\n",
    "        x2t = self.down1(x1)\n",
    "        #x2r = self.ResidualBlock1(x2t)\n",
    "        x2 = x2t   self.pos_embed1\n",
    "        for i in range(self.transformer_layer):\n",
    "            x2 = x2   self.drop_path(self.attn1(self.norm1(x2)))\n",
    "            x2 = F.silu(x2)\n",
    "        x2 = x2   self.drop_path(self.mlp1(self.norm1(x2)))\n",
    "        \n",
    "        #16*96*96 -> 64*48*48\n",
    "        x3t = self.down2(x2)\n",
    "        #x3r = self.ResidualBlock2(x3t)\n",
    "        x3 = x3t   self.pos_embed2\n",
    "        for i in range(self.transformer_layer):\n",
    "            x3 = x3   self.drop_path(self.attn2(self.norm2(x3)))\n",
    "            x3 = F.silu(x3)\n",
    "        x3 = x3   self.drop_path(self.mlp2(self.norm2(x3)))\n",
    "\n",
    "        \n",
    "        #64*48*48 ->256*24*24\n",
    "        x4t = self.down3(x3)\n",
    "        #x4r = self.ResidualBlock3(x4t)\n",
    "        x4 = x4t   self.pos_embed3\n",
    "        for i in range(self.transformer_layer):\n",
    "            x4 = x4   self.drop_path(self.attn3(self.norm3(x4)))\n",
    "            x4 = F.silu(x4)\n",
    "        x4 = x4   self.drop_path(self.mlp3(self.norm3(x4)))\n",
    "\n",
    "        x5t = self.down4(x4)\n",
    "        #x5r = self.ResidualBlock4(x5t)\n",
    "        x5 = x5t   self.pos_embed4\n",
    "        for i in range(self.transformer_layer):\n",
    "            x5 = x5   self.drop_path(self.attn4(self.norm4(x5)))\n",
    "            x5 = F.silu(x5)\n",
    "        x5 = x5   self.drop_path(self.mlp4(self.norm4(x5)))\n",
    "        \n",
    "        # x6 = self.upUpsample(x5)\n",
    "        x6 = self.con33(x5)\n",
    "        # x4_cross = self.cross_attention3(x6,x4,x4)\n",
    "        x4_cross = self.cross_attention3(style4,x6)\n",
    "        x6_cat = torch.cat((x6,x4_cross),dim=1)\n",
    "        x6_cat1 = self.con3(x6_cat)\n",
    "        # x6_cat_cross = self.cross_attention3(x6_cat1,style4)\n",
    "        x6_cat_cross = self.cross_attention3(x6_cat1,x4)\n",
    "        x6_cat = torch.cat((x6_cat1,x6_cat_cross),dim=1)\n",
    "        # x6_cat = x6_cat1    F.silu(self.norm3(self.con33(x6_cat)))\n",
    "        \n",
    "        # x7 = self.upUpsample(x6_cat)\n",
    "        x7 = self.con22(x6_cat)\n",
    "        x3_cross = self.cross_attention2(style3,x7)\n",
    "        x7_cat = torch.cat((x7,x3_cross),dim=1)\n",
    "        x7_cat1 = self.con2(x7_cat)\n",
    "        # x7_cat_cross = self.cross_attention2(x7_cat1,style3)\n",
    "        x7_cat_cross = self.cross_attention2(x7_cat1,x3)\n",
    "        x7_cat = torch.cat((x7_cat1,x7_cat_cross),dim=1)\n",
    "        # x7_cat = x7_cat1   F.silu(self.norm2(self.con22(x7_cat)))\n",
    "        \n",
    "        # x8 = self.upUpsample(x7_cat)\n",
    "        x8 = self.con11(x7_cat)\n",
    "        # x2_cross = self.cross_attention1(x8,x2,x2)\n",
    "        x2_cross = self.cross_attention1(x8,style2)\n",
    "        x8_cat = torch.cat((x8,x2_cross),dim=1)\n",
    "        x8_cat1 = self.con1(x8_cat)\n",
    "\n",
    "        # x8_cat_cross = self.cross_attention1(x8_cat1,style2)\n",
    "        x8_cat_cross = self.cross_attention1(x8_cat1,x2)\n",
    "        x8_cat = torch.cat((x8_cat1,x8_cat_cross),dim=1)\n",
    "        # x8_cat = x8_cat1   F.silu(self.norm1(self.con11(x8_cat)))\n",
    "        \n",
    "        x9 = self.transup(x8_cat)\n",
    "        x9_cat = torch.cat((x9,style1),dim=1)\n",
    "        x9_cat = self.conup(x9_cat)\n",
    "        # x9_cat_cross = self.cross_attention4(x9_cat,style1)\n",
    "        x9_cat_cross = self.cross_attention4(x9_cat,x1)\n",
    "\n",
    "        x9_cat1 = torch.cat((x9_cat,x9_cat_cross),dim=1)\n",
    "        \n",
    "        x11 = self.transout(x9_cat1)\n",
    "        x11 = self.output(x11)\n",
    "        return x11\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9b9b577c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FPSE discriminator \n",
    "class TDiscriminator(nn.Module):\n",
    "    def __init__(self, input_nc=1, nf=64, label_nc=1):\n",
    "        super(TDiscriminator, self).__init__()\n",
    "        # bottom-up pathway\n",
    "        \n",
    "        self.enc1 = nn.Sequential(\n",
    "            nn.Conv2d(input_nc, nf, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(nf//4,nf),\n",
    "            nn.SiLU(True))\n",
    "        self.enc2 = nn.Sequential(\n",
    "            nn.Conv2d(nf, nf*2, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,nf*2),\n",
    "            nn.SiLU(True))\n",
    "        self.enc3 = nn.Sequential(\n",
    "            nn.Conv2d(nf*2, nf*4, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,nf*4),\n",
    "            nn.SiLU(True))\n",
    "        self.enc4 = nn.Sequential(\n",
    "            nn.Conv2d(nf*4, nf*8, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,nf*8),\n",
    "            nn.SiLU(True))\n",
    "        self.enc5 = nn.Sequential(\n",
    "            nn.Conv2d(nf*8, nf*8, kernel_size=3, stride=2, padding=1),\n",
    "            nn.GroupNorm(32,nf*8),\n",
    "            nn.SiLU(True))\n",
    "\n",
    "        # top-down pathway\n",
    "        self.lat2 = nn.Sequential(\n",
    "            nn.Conv2d(nf*2, nf*4, kernel_size=1),\n",
    "            nn.GroupNorm(32,nf*4),\n",
    "            nn.SiLU(True))\n",
    "        self.lat3 = nn.Sequential(\n",
    "            nn.Conv2d(nf*4, nf*4, kernel_size=1),\n",
    "            nn.GroupNorm(32,nf*4),\n",
    "            nn.SiLU(True))\n",
    "        self.lat4 = nn.Sequential(\n",
    "            nn.Conv2d(nf*8, nf*4, kernel_size=1),\n",
    "            nn.GroupNorm(32,nf*4),\n",
    "            nn.SiLU(True))\n",
    "        self.lat5 = nn.Sequential(\n",
    "            nn.Conv2d(nf*8, nf*4, kernel_size=1),\n",
    "            nn.GroupNorm(32,nf*4),\n",
    "            nn.SiLU(True))\n",
    "        \n",
    "        # upsampling\n",
    "        self.up = nn.Upsample(scale_factor=2, mode='nearest')\n",
    "        \n",
    "        # final layers\n",
    "        self.final2 = nn.Sequential(\n",
    "            nn.Conv2d(nf*4, nf*2, kernel_size=3, stride=1, padding=1),\n",
    "            nn.GroupNorm(nf//2,nf*2),\n",
    "            nn.SiLU(True))\n",
    "        self.final3 = nn.Sequential(\n",
    "            nn.Conv2d(nf*4, nf*2, kernel_size=3, stride=1, padding=1),\n",
    "            nn.GroupNorm(nf//2,nf*2),\n",
    "            nn.SiLU(True))\n",
    "        self.final4 = nn.Sequential(\n",
    "            nn.Conv2d(nf*4, nf*2, kernel_size=3, stride=1, padding=1),\n",
    "            nn.GroupNorm(nf//2,nf*2),\n",
    "            nn.SiLU(True))\n",
    "    \n",
    "        # true/false prediction and semantic alignment prediction\n",
    "        self.tf = nn.Conv2d(nf*2, 1, kernel_size=1)\n",
    "        self.seg = nn.Conv2d(nf*2, nf*2, kernel_size=1)\n",
    "        self.embedding = nn.Conv2d(label_nc, nf*2, kernel_size=1)\n",
    "        \n",
    "        self.L1 = nn.L1Loss()\n",
    "\n",
    "    def forward(self, fake_and_real_img, segmap):\n",
    "        # bottom-up pathway\n",
    "        feat11 = self.enc1(fake_and_real_img)  #384->192\n",
    "        feat12 = self.enc2(feat11)   #192->96\n",
    "        feat13 = self.enc3(feat12)   #96->48\n",
    "        feat14 = self.enc4(feat13)  #48->24\n",
    "        feat15 = self.enc5(feat14)   #24->12\n",
    "        # top-down pathway and lateral connections\n",
    "        feat25 = self.lat5(feat15)\n",
    "        feat24 = self.up(feat25)   self.lat4(feat14)  #24\n",
    "        feat23 = self.up(feat24)   self.lat3(feat13)  #48\n",
    "        feat22 = self.up(feat23)   self.lat2(feat12)  #96\n",
    "        # final prediction layers\n",
    "        feat32 = self.final2(feat22)\n",
    "        feat33 = self.final3(feat23)\n",
    "        feat34 = self.final4(feat24)\n",
    "        # Patch-based True/False prediction\n",
    "        pred2 = self.tf(feat32)\n",
    "        pred3 = self.tf(feat33)\n",
    "        pred4 = self.tf(feat34)\n",
    "        seg2 = self.seg(feat32)\n",
    "        seg3 = self.seg(feat33)\n",
    "        seg4 = self.seg(feat34)\n",
    "\n",
    "        # intermediate features for discriminator feature matching loss\n",
    "        feats = [feat12, feat13, feat14, feat15]\n",
    "\n",
    "        # segmentation map embedding\n",
    "        segemb = self.embedding(segmap)\n",
    "        segemb = F.avg_pool2d(segemb, kernel_size=2, stride=2)\n",
    "        segemb2 = F.avg_pool2d(segemb, kernel_size=2, stride=2)\n",
    "        segemb3 = F.avg_pool2d(segemb2, kernel_size=2, stride=2)\n",
    "        segemb4 = F.avg_pool2d(segemb3, kernel_size=2, stride=2)\n",
    "\n",
    "        # semantics embedding discriminator score\n",
    "        pred2  = torch.mul(segemb2, seg2).sum(dim=1, keepdim=True)\n",
    "        pred3  = torch.mul(segemb3, seg3).sum(dim=1, keepdim=True)\n",
    "        pred4  = torch.mul(segemb4, seg4).sum(dim=1, keepdim=True)\n",
    "\n",
    "        # concat results from multiple resolutions\n",
    "        results = [pred2, pred3, pred4]\n",
    "            \n",
    "\n",
    "        return [feats, results]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf35cc2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class GradientVariance(nn.Module):\n",
    "\n",
    "    def __init__(self, patch_size):\n",
    "        super(GradientVariance, self).__init__()\n",
    "        self.patch_size = patch_size\n",
    "        # Sobel kernel for the gradient map calculation\n",
    "        self.kernel_x = torch.FloatTensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0).cuda()\n",
    "        self.kernel_y = torch.FloatTensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]).unsqueeze(0).unsqueeze(0).cuda()\n",
    "        # operation for unfolding image into non overlapping patches\n",
    "        self.unfold = torch.nn.Unfold(kernel_size=(self.patch_size, self.patch_size), stride=self.patch_size)\n",
    "\n",
    "    def forward(self, output, target):\n",
    "        # calculation of the gradient maps of x and y directions\n",
    "        gx_target = F.conv2d(target, self.kernel_x, stride=1, padding=1)\n",
    "        gy_target = F.conv2d(target, self.kernel_y, stride=1, padding=1)\n",
    "        gx_output = F.conv2d(output, self.kernel_x, stride=1, padding=1)\n",
    "        gy_output = F.conv2d(output, self.kernel_y, stride=1, padding=1)\n",
    "\n",
    "        # unfolding image to patches\n",
    "        gx_target_patches = self.unfold(gx_target)\n",
    "        gy_target_patches = self.unfold(gy_target)\n",
    "        gx_output_patches = self.unfold(gx_output)\n",
    "        gy_output_patches = self.unfold(gy_output)\n",
    "\n",
    "        # calculation of variance of each patch\n",
    "        var_target_x = torch.var(gx_target_patches, dim=1)\n",
    "        var_output_x = torch.var(gx_output_patches, dim=1)\n",
    "        var_target_y = torch.var(gy_target_patches, dim=1)\n",
    "        var_output_y = torch.var(gy_output_patches, dim=1)\n",
    "\n",
    "        # loss function as a MSE between variances of patches extracted from gradient maps\n",
    "        gradvar_loss = F.mse_loss(var_target_x, var_output_x)   F.mse_loss(var_target_y, var_output_y)\n",
    "\n",
    "        return gradvar_loss\n",
    "\n",
    "from torchvision.utils import make_grid\n",
    "# from torchmetrics import PeakSignalNoiseRatio,StructuralSimilarityIndexMeasure,MetricCollection\n",
    "Tensor = torch.cuda.FloatTensor if torch.cuda else torch.Tensor\n",
    "def sample_images(real_A, real_B,G_AB,G_BA,epoch,figside=1.5,):\n",
    "    assert real_A.size() == real_B.size(), 'The image size for two domains must be the same'\n",
    "    \n",
    "    G_AB.eval()\n",
    "    G_BA.eval()\n",
    "    \n",
    "    real_A = real_A.type(Tensor)\n",
    "    fake_B = G_AB(real_A).detach()\n",
    "    real_B = real_B.type(Tensor)\n",
    "    fake_A = G_BA(real_B).detach()\n",
    "    psnrA = PeakSignalNoiseRatio(data_range=torch.max(real_A)-torch.min(real_A)).cuda()\n",
    "    psnrB = PeakSignalNoiseRatio(data_range=torch.max(real_B)-torch.min(real_B)).cuda()\n",
    "    ssim = StructuralSimilarityIndexMeasure().cuda()\n",
    "\n",
    "\n",
    "    ans_ssim=ssim(fake_B,real_A)\n",
    "    ans_psnr_toB=psnrB(fake_B,real_B)\n",
    "    ans_psnr_toA=psnrA(fake_B,real_A)\n",
    "    \n",
    "    nrows = real_A.size(0)\n",
    "    real_A = make_grid(real_A, nrow=nrows, normalize=True)\n",
    "    fake_B = make_grid(fake_B, nrow=nrows, normalize=True)\n",
    "    real_B = make_grid(real_B, nrow=nrows, normalize=True)\n",
    "    fake_A = make_grid(fake_A, nrow=nrows, normalize=True)\n",
    "    \n",
    "    image_grid = torch.cat((real_A, fake_B, real_B, fake_A), 1).cpu().permute(1, 2, 0)\n",
    "    \n",
    "    plt.figure(figsize=(figside*nrows, figside*4))\n",
    "    plt.imshow(image_grid)\n",
    "    plt.axis('off')\n",
    "    plt.savefig(f'epoch{epoch}plot.png')\n",
    "    plt.show()\n",
    "    plt.close(image_grid)\n",
    "    return ans_ssim,ans_psnr_toB,ans_psnr_toA\n",
    "\n",
    "from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM\n",
    "class MS_SSIM_Loss2(MS_SSIM):\n",
    "    def forward(self, img1, img2):\n",
    "        return 1*( 1 - super(MS_SSIM_Loss2, self).forward(img1, img2) )\n",
    "MS_SSIM_Loss = MS_SSIM_Loss2(data_range=1.0, size_average=True, channel=1)\n",
    "class CustomSSIMLoss(SSIM):\n",
    "    def forward(self, img1, img2):\n",
    "        return 1 * (1 - super(CustomSSIMLoss, self).forward(img1, img2))\n",
    "\n",
    "SSIM_Loss = CustomSSIMLoss(data_range=1.0, size_average=True, channel=1)\n",
    "def unnormalize(sample_dicom, x):\n",
    "    ds = sample_dicom.copy()\n",
    "    slope = ds.RescaleSlope\n",
    "    intercept = ds.RescaleIntercept\n",
    "    ymin = -200\n",
    "    ymax = 1600\n",
    "    pixel_min = ymin #/ slope   intercept\n",
    "    pixel_max = ymax #/ slope   intercept\n",
    "#     normalized_array = np.clip((x   1) , 0, 2)\n",
    "#     mean = np.mean(x)\n",
    "#     std = np.std(x)\n",
    "#     unnormalized_array = (x * std   mean) * slope   intercept\n",
    "    scaled_array = (x)*6.5-600#(x*0.5 0.5)*100#-500\n",
    "    unnormalized_array = (scaled_array - intercept) / slope \n",
    "    return unnormalized_array\n",
    "\n",
    "\n",
    "def write_dicom(sample_dicom, array, paths, tt):\n",
    "    ds = sample_dicom.copy()\n",
    "    ww = ds.WindowWidth\n",
    "    wc = ds.WindowCenter\n",
    "    array = np.transpose(array, (2, 1, 0))[:, :, 0]\n",
    "    array = unnormalize(sample_dicom, array)\n",
    "    array = np.clip(array, 0, 2 ** 16 - 1)\n",
    "    ds.PixelData = array.astype(np.uint16).tobytes()\n",
    "    return ds.save_as(paths   \"/\"   str(tt)   \".dcm\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "625843db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device 1 Tesla V100-PCIE-32GB\n"
     ]
    }
   ],
   "source": [
    "# all loss function here\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM\n",
    "class MS_SSIM_Loss2(MS_SSIM):\n",
    "    def forward(self, img1, img2):\n",
    "        return 1*( 1 - super(MS_SSIM_Loss2, self).forward(img1, img2) )\n",
    "MS_SSIM_Loss = MS_SSIM_Loss2(data_range=1.0, size_average=True, channel=1)\n",
    "class CustomSSIMLoss(SSIM):\n",
    "    def forward(self, img1, img2):\n",
    "        return 1 * (1 - super(CustomSSIMLoss, self).forward(img1, img2))\n",
    "\n",
    "SSIM_Loss = CustomSSIMLoss(data_range=1.0, size_average=True, channel=1)\n",
    "# import torch.autograd as autograd\n",
    "class GDL(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GDL, self).__init__()\n",
    "\n",
    "    def forward(self, pred, target):\n",
    "        pred_dy = torch.abs(pred[:, :, 1:, :] - pred[:, :, :-1, :])\n",
    "        pred_dx = torch.abs(pred[:, :, :, 1:] - pred[:, :, :, :-1])\n",
    "        target_dy = torch.abs(target[:, :, 1:, :] - target[:, :, :-1, :])\n",
    "        target_dx = torch.abs(target[:, :, :, 1:] - target[:, :, :, :-1])\n",
    "        grad_diff_x = torch.abs(target_dx - pred_dx)\n",
    "        grad_diff_y = torch.abs(target_dy - pred_dy)\n",
    "        return grad_diff_x.mean()   grad_diff_y.mean()\n",
    "\n",
    "class HingeLoss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(HingeLoss, self).__init__()\n",
    "\n",
    "    def forward(self, predictions,seg,real=True):\n",
    "        # Compute the hinge loss using Swish activation\n",
    "        loss = 0\n",
    "        if real ==True:\n",
    "            for pred in seg:\n",
    "                loss  = torch.mean(nn.ReLU(inplace=True)(1 - pred))\n",
    "            for preds in predictions:\n",
    "                loss  = torch.mean(nn.ReLU(inplace=True)(1 - preds))\n",
    "        else:\n",
    "            for pred in seg:\n",
    "                loss  = torch.mean(nn.ReLU(inplace=True)(1   pred))\n",
    "            for preds in predictions:\n",
    "                loss  = torch.mean(nn.ReLU(inplace=True)(1   preds))\n",
    "            \n",
    "        return loss\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0,1'\n",
    "torch.cuda.set_device(1)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "import torchvision\n",
    "print(\"device\", torch.cuda.current_device(), torch.cuda.get_device_name(torch.cuda.current_device()))\n",
    "class Vgg19_out(nn.Module):\n",
    "    def __init__(self, requires_grad=False):\n",
    "        super(Vgg19_out, self).__init__()\n",
    "        self.input=nn.Conv2d(in_channels=1 , out_channels=3, kernel_size=1)\n",
    "        vgg = torchvision.models.vgg19(pretrained=True).to(device) #.cuda()\n",
    "        vgg.eval()\n",
    "        vgg_pretrained_features = vgg.features\n",
    "        #print(vgg_pretrained_features)\n",
    "        self.requires_grad = requires_grad\n",
    "        self.slice1 = torch.nn.Sequential()\n",
    "        self.slice2 = torch.nn.Sequential()\n",
    "        self.slice3 = torch.nn.Sequential()\n",
    "        self.slice4 = torch.nn.Sequential()\n",
    "        self.slice5 = torch.nn.Sequential()\n",
    "        for x in range(2): #(3):\n",
    "            self.slice1.add_module(str(x), vgg_pretrained_features[x])\n",
    "        for x in range(2, 7): #(3, 7):\n",
    "            self.slice2.add_module(str(x), vgg_pretrained_features[x])\n",
    "        for x in range(7, 12): #(7, 12):\n",
    "            self.slice3.add_module(str(x), vgg_pretrained_features[x])\n",
    "        for x in range(12, 21): #(12, 21):\n",
    "            self.slice4.add_module(str(x), vgg_pretrained_features[x])\n",
    "        for x in range(21, 30):#(21, 30):\n",
    "            self.slice5.add_module(str(x), vgg_pretrained_features[x])\n",
    "        if not self.requires_grad:\n",
    "            for param in self.parameters():\n",
    "                param.requires_grad = False\n",
    "\n",
    " \n",
    "    def forward(self, X):\n",
    "        h=self.input(X)\n",
    "        h_relu1 = self.slice1(h)\n",
    "        h_relu2 = self.slice2(h_relu1)\n",
    "        h_relu3 = self.slice3(h_relu2)\n",
    "        h_relu4 = self.slice4(h_relu3)\n",
    "        h_relu5 = self.slice5(h_relu4)\n",
    "        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]\n",
    "        return out\n",
    "\n",
    "class Perceptual_loss134(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Perceptual_loss134, self).__init__()\n",
    "        self.input=nn.Conv2d(in_channels=1 , out_channels=3, kernel_size=1)\n",
    "        self.vgg = Vgg19_out().to(device)\n",
    "        \n",
    "        self.L1 = nn.L1Loss()\n",
    "        self.mse = nn.MSELoss()\n",
    "        self.weights = [1.0/32, 1.0/4, 1.0/2, 1.0/1, 1.0/32]\n",
    "        # self.weights = [1.0/2.6, 1.0/16, 1.0/2, 1.0/1, 1.0]    \n",
    "    def forward(self, x, y):\n",
    "        x_vgg, y_vgg = self.vgg(x), self.vgg(y)\n",
    "        loss = 0\n",
    "        # for i in range(len(x_vgg)-2):\n",
    "        #     loss  = self.weights[i] * self.mse(x_vgg[i], y_vgg[i].detach())    \n",
    "        for i in range(len(x_vgg)):\n",
    "            loss  = self.weights[i] * self.L1(x_vgg[i], y_vgg[i].detach())        \n",
    "        return loss\n",
    "class Perceptual_loss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Perceptual_loss, self).__init__()\n",
    "        self.input=nn.Conv2d(in_channels=1 , out_channels=3, kernel_size=1)\n",
    "        self.vgg = Vgg19_out().to(device)\n",
    "        \n",
    "        self.L1 = nn.L1Loss()\n",
    "        self.mse = nn.MSELoss()\n",
    "        self.MS_SSIM_Loss = MS_SSIM_Loss2(data_range=1.0, size_average=True, channel=1)\n",
    "        #self.weights = [1.0/2.6, 1.0/16, 1.0/3.7, 1.0/5.6, 1.0]\n",
    "        self.weights = [1.0/15, 1.0/9, 1.0/4, 1.0/3, 1.0/1]    \n",
    "    def forward(self, x, y):\n",
    "        x_vgg, y_vgg = self.vgg(x), self.vgg(y)\n",
    "        loss = 0\n",
    "        # loss  = self.weights[2] * self.L1(x_vgg[2], y_vgg[2].detach())\n",
    "        # loss  = self.weights[4] * self.L1(x_vgg[4], y_vgg[4].detach())\n",
    "        # loss  = self.weights[3] * self.mse(x_vgg[3], y_vgg[3].detach())\n",
    "        for i in range(len(x_vgg)):\n",
    "            loss  = self.weights[i] * self.L1(x_vgg[i], y_vgg[i].detach()) \n",
    "        # for i in range(len(x_vgg)-1):\n",
    "        #     loss  = self.weights[i] * self.MS_SSIM_Loss(x_vgg[i], y_vgg[i].detach())\n",
    "        \n",
    "        return loss\n",
    "\n",
    "\n",
    "# from torchmetrics.image.fid import FrechetInceptionDistance\n",
    "# import torch.nn.functional as F\n",
    "# import numpy as np\n",
    "\n",
    "# def calculate_fid_score(generator, testloader, device=device):\n",
    "#     fid = FrechetInceptionDistance(feature=2048)#.to(device)\n",
    "#     gen_features = []\n",
    "#     real_features = []\n",
    "#     generated_list = None  # Initialize generated_list variable\n",
    "#     images_list = None  # Initialize images_list variable\n",
    "#     generator.eval()\n",
    "#     with torch.no_grad():\n",
    "#         for idx, (x_img, y_img) in enumerate(testloader):\n",
    "#             y_img1 = y_img.type(torch.cuda.FloatTensor).to(device)\n",
    "#             x_img1 = x_img.type(torch.cuda.FloatTensor).to(device)\n",
    "#             generated_images = generator(y_img1,x_img1)\n",
    "#             generated_images = F.interpolate(generated_images, size=(299, 299), mode='bilinear', align_corners=False)\n",
    "#             generated_images_rgb = torch.cat([generated_images] * 3, dim=1)  # Convert grayscale to RGB\n",
    "#             if generated_list is None:\n",
    "#                 generated_list = torch.cat((generated_images_rgb, generated_images_rgb), dim=0)\n",
    "#             generated_list = torch.cat((generated_list, generated_images_rgb), dim=0)\n",
    "\n",
    "#             images = y_img1\n",
    "#             images = F.interpolate(images, size=(299, 299), mode='bilinear', align_corners=False)\n",
    "#             images_rgb = torch.cat([images] * 3, dim=1)  # Convert grayscale to RGB\n",
    "#             if images_list is None:\n",
    "#                 images_list = torch.cat((images_rgb, images_rgb), dim=0)\n",
    "#             images_list = torch.cat((images_list, images_rgb), dim=0)\n",
    "\n",
    "#     generated_list = generated_list.cpu().type(torch.uint8)  # Convert generated_list to torch.uint8\n",
    "#     images_list = images_list.cpu().type(torch.uint8)  # Convert images_list to torch.uint8\n",
    "\n",
    "#     fid.update(images_list, real=True)\n",
    "#     fid.update(generated_list, real=False)\n",
    "#     s= fid.compute()\n",
    "#     return s    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c8ad162d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import time\n",
    "class CycleGANsformer(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0,1'\n",
    "        torch.cuda.set_device(1)\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        print(\"device\", torch.cuda.current_device(), torch.cuda.get_device_name(torch.cuda.current_device()))\n",
    "        self.discX = TDiscriminator().to(device)\n",
    "        self.device=device\n",
    "        self.genX2Y = TGenerator().to(device)\n",
    "        self.state_dict = {\n",
    "            'self.discX': self.discX.state_dict(),\n",
    "            'self.genX2Y': self.genX2Y.state_dict()\n",
    "        }\n",
    "        \n",
    "        self.opt_discX = optim.AdamW(\n",
    "            self.discX.parameters(),\n",
    "            lr=2e-4,\n",
    "            weight_decay=1e-5,\n",
    "        )\n",
    "\n",
    "        self.opt_genX2Y = optim.AdamW(\n",
    "            self.genX2Y.parameters(),\n",
    "            lr=5e-5,\n",
    "            weight_decay=1e-5,\n",
    "        )\n",
    "        self.opt_genX2Y_scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.opt_genX2Y, mode='min', factor=0.5, patience=10, verbose=True)#optim.lr_scheduler.StepLR(self.opt_genX2Y, step_size=20, gamma=0.5)\n",
    "        self.opt_discX_scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.opt_discX, mode='min', factor=0.5, patience=10, verbose=True)\n",
    "        self.L1 = nn.L1Loss()\n",
    "        self.mse = nn.MSELoss()\n",
    "        self.GradientDifferenceLoss = GDL()\n",
    "        self.HingeLoss = HingeLoss()\n",
    "        self.Perceptual_loss = Perceptual_loss134()\n",
    "        self.deblur_percept = Perceptual_loss()\n",
    "        self.batch_size=2\n",
    "    def fit(self, dataset, epochs=100):\n",
    "        loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=1)\n",
    "        # testloader=DataLoader(ImageDatasetLoader(mode=\"test\",transformers=transforms_ ), batch_size=self.batch_size, shuffle=False, num_workers=16, pin_memory=True)\n",
    "        evlloader=DataLoader(ImageDatasetLoader(mode=\"evl\",transformers=transforms_ ), batch_size=self.batch_size, shuffle=False, num_workers=1)\n",
    "\n",
    "        \n",
    "        print(\"Start the training!\")\n",
    "        step = 0\n",
    "        for epoch in range(epochs):\n",
    "            for idx, (x_img, y_img) in enumerate(loader):\n",
    "                step =1\n",
    "                y_img1=y_img.type(torch.cuda.FloatTensor).cuda()\n",
    "                x_img1=x_img.type(torch.cuda.FloatTensor).cuda()\n",
    "                self.genX2Y.train()\n",
    "                self.discX.train()\n",
    "#                 assert fake_x.size() == y_img1.size() , f\"fake_imgs.size(): {fake_x.size()} real_imgs.size(): {y_img1.size()}\"\n",
    "#######################################################################\n",
    "######################################################################                \n",
    "                self.opt_genX2Y.zero_grad()\n",
    "                fake_y1 = self.genX2Y(y_img1, x_img1)\n",
    "                D_Y_real1, segmap1 = self.discX(fake_y1, y_img1)\n",
    "                # D_Y_real1, segmap1 = self.discX(fake_y1[2], y_img1)\n",
    "            \n",
    "                generator_loss = self.HingeLoss(D_Y_real1,segmap1,real = False)\n",
    "                \n",
    "                fake_y2 = self.genX2Y(y_img1, x_img1)\n",
    "                perceptual_loss = self.Perceptual_loss(fake_y2,y_img1)\n",
    "                # perceptual_loss = self.Perceptual_loss(fake_y2[2],y_img1)\n",
    "                \n",
    "                cycle_X = self.genX2Y(y_img1, x_img1)\n",
    "                # GDL_loss = MS_SSIM_Loss(cycle_X,x_img1)*0.84    self.L1(cycle_X,x_img1)*0.16 #  self.deblur_percept(cycle_X,x_img1)*0.7  #  self.L1(cycle_X,x_img1)*0.05\n",
    "                deblur_percept_loss =  self.deblur_percept(cycle_X,x_img1)*1   self.GradientDifferenceLoss(cycle_X,x_img1)*1 #  self.L1(cycle_X,x_img1)*0.16#    MS_SSIM_Loss(cycle_X,x_img1)*0.84    self.GradientDifferenceLoss(cycle_X,x_img1)*0.16 #self.deblur_percept(cycle_X,x_img1)\n",
    "                \n",
    "                G_loss = generator_loss*1   perceptual_loss*0.18   deblur_percept_loss *0.25   #  GDL_loss *5\n",
    "                   \n",
    "                G_loss.backward(retain_graph=True)\n",
    "                self.opt_genX2Y.step()\n",
    "\n",
    "##################################################                \n",
    "                self.opt_discX.zero_grad()\n",
    "                D_X_real,seg_map2 = self.discX(x_img1,y_img1)\n",
    "                # with torch.no_grad():\n",
    "                #     fake_x = self.genX2Y(x_img1,y_img1)\n",
    "                #     fake_x = fake_x.detach()\n",
    "                #     fake_x.requires_grad_()\n",
    "                fake_x = self.genX2Y(y_img1, x_img1)\n",
    "                D_X_fake,seg_map3 = self.discX(fake_x,y_img1)\n",
    "                # D_X_fake,seg_map3 = self.discX(fake_x[2],y_img1)\n",
    "                D_real_loss = self.HingeLoss(D_X_real,seg_map2)\n",
    "                D_fake_loss = self.HingeLoss(D_X_fake,seg_map3,real = False)\n",
    "                D_X_loss = D_real_loss*1   D_fake_loss*1\n",
    "                D_X_loss.backward(retain_graph=True)\n",
    "                self.opt_discX.step()\n",
    "##########################################################################\n",
    "##########################################################################\n",
    "            self.opt_genX2Y_scheduler.step(G_loss)\n",
    "            # self.opt_genX2Y_scheduler.step()\n",
    "            self.opt_discX_scheduler.step(D_X_loss)\n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "            dir_checkpoint =\"./0509lite_checkpoint/\"\n",
    "            torch.save(self.state_dict, dir_checkpoint   f'0509lite_epoch{epoch}.pth')\n",
    "            print(f'[Epoch {epoch 1}/{epochs}]')\n",
    "            print(f'[G loss: {G_loss.item()} | generator_loss: {generator_loss.item()} |perceptual_loss: {perceptual_loss.item()} |deblur_percept_loss: {deblur_percept_loss.item()}]')\n",
    "            print(f'[D loss: { D_X_loss.item()} ]')\n",
    "#             ttest_real_A, ttest_real_B = next(iter(testloader))\n",
    "#             ans_ssim,ans_psnr_toB,ans_psnr_toA=sample_images(ttest_real_A, ttest_real_B,self.genX2Y,self.genY2X)\n",
    "            if (epoch) % 2 == 0:\n",
    "                self.genX2Y.eval()\n",
    "                with torch.no_grad():\n",
    "#                     ans_ssim = 0\n",
    "#                     ans_psnr_toB = 0\n",
    "#                     ans_psnr_toA = 0\n",
    "                    for idx, (x_img, y_img) in enumerate(evlloader):\n",
    "                        y_img1=y_img.type(torch.cuda.FloatTensor).cuda(non_blocking=True)\n",
    "                        x_img1=x_img.type(torch.cuda.FloatTensor).cuda(non_blocking=True)\n",
    "#                         psnrA = PeakSignalNoiseRatio(data_range=torch.max(x_img1)-torch.min(x_img1)).cuda()\n",
    "#                         psnrB = PeakSignalNoiseRatio(data_range=torch.max(y_img1)-torch.min(y_img1)).cuda()\n",
    "#                         ssim = StructuralSimilarityIndexMeasure().cuda()\n",
    "                        fake_B = self.genX2Y(y_img1, x_img1)\n",
    "#                         # fake_B = self.deblur(fake_B)\n",
    "\n",
    "#                         ans_ssim  =ssim(fake_B,x_img1)\n",
    "#                         ans_psnr_toB  =psnrB(fake_B,y_img1)\n",
    "#                         ans_psnr_toA  =psnrA(fake_B,x_img1)\n",
    "\n",
    "                        if idx == len(evlloader)-1:\n",
    "\n",
    "                            nrows = x_img1.size(0)\n",
    "                            real_A = make_grid(x_img1, nrow=nrows, normalize=True)\n",
    "                            fake_B = make_grid(fake_B, nrow=nrows, normalize=True)\n",
    "                            real_B = make_grid(y_img1, nrow=nrows, normalize=True)\n",
    "\n",
    "                            image_grid = torch.cat((real_A, fake_B, real_B), 1).cpu().permute(1, 2, 0)\n",
    "\n",
    "                            plt.figure(figsize=(1.5*nrows, 1.5*3))\n",
    "                            plt.imshow(image_grid)\n",
    "                            plt.axis('off')\n",
    "                            plt.savefig(f'./0509lite_checkpoint/plot/{epoch}.png')\n",
    "                            plt.show()\n",
    "                            plt.close(image_grid)\n",
    "\n",
    "\n",
    "\n",
    "#                     print(f\"Epoch {epoch} | ssim : {ans_ssim/len(evlloader)}\")\n",
    "#                     print(f\"Epoch {epoch} | psnr_toB : {ans_psnr_toB/len(evlloader)}\")\n",
    "#                     print(f\"Epoch {epoch} | psnr_toA : {ans_psnr_toA/len(evlloader)}\") \n",
    "\n",
    "                    \n",
    "                    cbct_count=0\n",
    "                    path=\"./0906/\" str(epoch) \"/\"\n",
    "                    if not os.path.exists(path):\n",
    "                        os.mkdir(path)\n",
    "                    cbct_data=[]\n",
    "                    for i in range(len(data)):\n",
    "                        ds=pydicom.dcmread(data[i][0],force=True)\n",
    "                        cbct_data.append(ds)\n",
    "                    cbctdata=cbct_data[24000:24704]\n",
    "                #     cbct_data=[]\n",
    "                #     for i in range(len(data)):\n",
    "                #         ds=pydicom.dcmread(data[i][0],force=True)\n",
    "                #         cbct_data.append(ds)\n",
    "\n",
    "                #     indices = [17000, 17010, 17020, 17030, 17040, 17060, 17070, 17080, 17090, 17100,\n",
    "                #            18000, 18100, 18200, 18300, 18400, 18500, 18600, 18700, 18800, 18900, \n",
    "                #            19500, 19050, 19150, 19250, 19350, 19450, 19550, 19650, 19750, 19850,\n",
    "                #            19950, 20050, 20150, 20250, 20350, 20450, 20550, 20650, 20750, 20850,\n",
    "                #            20950, 22030, 22130, 22230, 22330, 22430, 22530, 22630, 22730, 22830,\n",
    "                #            23070, 23170, 23270, 23370, 23470, 23570, 23670, 23770, 23870, 23970]\n",
    "\n",
    "                #     cbctdata = [cbct_data[i] for i in indices]\n",
    "\n",
    "                    start_time = time.time()\n",
    "                    for idx, (a_img, b_img) in enumerate(evlloader):\n",
    "\n",
    "                        self.genX2Y.eval()\n",
    "                        a=a_img.type(torch.cuda.FloatTensor)\n",
    "                        b=b_img.type(torch.cuda.FloatTensor)\n",
    "\n",
    "                        fake_B = self.genX2Y(b,a).detach()\n",
    "                        b = fake_B.cpu().numpy()\n",
    "                        c =b.copy()\n",
    "                        for s in range(len(a_img)):\n",
    "                            t=s idx*self.batch_size\n",
    "                            write_dicom(cbctdata[t],c[s],path,t)\n",
    "                    end_time = time.time()\n",
    "                    execution_time = end_time - start_time\n",
    "                    print(\"Execution time:\", execution_time, \"seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87f68735",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device 1 Tesla V100-PCIE-32GB\n",
      "Start the training!\n",
      "[Epoch 1/100]\n",
      "[G loss: 14.75023078918457 | generator_loss: 4.084709167480469 |perceptual_loss: 25.431547164916992 |deblur_percept_loss: 24.35137176513672]\n",
      "[D loss: 7.0142822265625 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 36.36360144615173 seconds\n",
      "[Epoch 2/100]\n",
      "[G loss: 9.962010383605957 | generator_loss: 4.260562419891357 |perceptual_loss: 19.18680191040039 |deblur_percept_loss: 8.991294860839844]\n",
      "[D loss: 7.25730037689209 ]\n",
      "[Epoch 3/100]\n",
      "[G loss: 9.206764221191406 | generator_loss: 3.962916612625122 |perceptual_loss: 19.405860900878906 |deblur_percept_loss: 7.003168106079102]\n",
      "[D loss: 6.4461822509765625 ]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 36.86061143875122 seconds\n",
      "[Epoch 4/100]\n",
      "[G loss: 9.410717964172363 | generator_loss: 3.381783962249756 |perceptual_loss: 23.215709686279297 |deblur_percept_loss: 7.400424480438232]\n",
      "[D loss: 5.317792892456055 ]\n",
      "[Epoch 5/100]\n",
      "[G loss: 16.613582611083984 | generator_loss: 3.2331080436706543 |perceptual_loss: 39.40386962890625 |deblur_percept_loss: 25.151107788085938]\n",
      "[D loss: 4.833775997161865 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.824732065200806 seconds\n",
      "[Epoch 6/100]\n",
      "[G loss: 8.12533950805664 | generator_loss: 3.130704164505005 |perceptual_loss: 21.024005889892578 |deblur_percept_loss: 4.841256141662598]\n",
      "[D loss: 4.490151405334473 ]\n",
      "[Epoch 7/100]\n",
      "[G loss: 7.58461856842041 | generator_loss: 3.126380443572998 |perceptual_loss: 17.801828384399414 |deblur_percept_loss: 5.015636920928955]\n",
      "[D loss: 4.619204521179199 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.9407913684845 seconds\n",
      "[Epoch 8/100]\n",
      "[G loss: 7.816462516784668 | generator_loss: 3.058480739593506 |perceptual_loss: 19.672698974609375 |deblur_percept_loss: 4.867583274841309]\n",
      "[D loss: 4.404048442840576 ]\n",
      "[Epoch 9/100]\n",
      "[G loss: 7.167331695556641 | generator_loss: 3.018310546875 |perceptual_loss: 17.369638442993164 |deblur_percept_loss: 4.089944839477539]\n",
      "[D loss: 4.312523365020752 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.1653892993927 seconds\n",
      "[Epoch 10/100]\n",
      "[G loss: 16.626445770263672 | generator_loss: 2.9950060844421387 |perceptual_loss: 43.883907318115234 |deblur_percept_loss: 22.92934799194336]\n",
      "[D loss: 4.280086517333984 ]\n",
      "[Epoch 11/100]\n",
      "[G loss: 7.579383850097656 | generator_loss: 2.9744138717651367 |perceptual_loss: 19.710657119750977 |deblur_percept_loss: 4.228207588195801]\n",
      "[D loss: 4.233798980712891 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.03378987312317 seconds\n",
      "[Epoch 12/100]\n",
      "[G loss: 6.972687721252441 | generator_loss: 2.9653468132019043 |perceptual_loss: 17.66802215576172 |deblur_percept_loss: 3.30838680267334]\n",
      "[D loss: 4.214041709899902 ]\n",
      "[Epoch 13/100]\n",
      "[G loss: 7.0672383308410645 | generator_loss: 2.9588465690612793 |perceptual_loss: 17.907100677490234 |deblur_percept_loss: 3.540452480316162]\n",
      "[D loss: 4.205097675323486 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.87643885612488 seconds\n",
      "[Epoch 14/100]\n",
      "[G loss: 17.222217559814453 | generator_loss: 2.9574100971221924 |perceptual_loss: 51.043399810791016 |deblur_percept_loss: 20.3079833984375]\n",
      "[D loss: 4.201960563659668 ]\n",
      "[Epoch 15/100]\n",
      "[G loss: 7.923276901245117 | generator_loss: 2.952690362930298 |perceptual_loss: 21.926172256469727 |deblur_percept_loss: 4.09550142288208]\n",
      "[D loss: 4.185028076171875 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.88200616836548 seconds\n",
      "[Epoch 16/100]\n",
      "[G loss: 9.142959863726562 | generator_loss: 2.960783004760742 |perceptual_loss: 27.40296173095703 |deblur_percept_loss: 4.998575210571289]\n",
      "[D loss: 4.189355373382568 ]\n",
      "[Epoch 17/100]\n",
      "[G loss: 7.278201580047607 | generator_loss: 3.0939292907714844 |perceptual_loss: 18.14237403869629 |deblur_percept_loss: 3.6745798587799072]\n",
      "[D loss: 4.601437091827393 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.92990159988403 seconds\n",
      "[Epoch 18/100]\n",
      "[G loss: 8.284876823425293 | generator_loss: 3.028773069381714 |perceptual_loss: 22.515731811523438 |deblur_percept_loss: 4.813086986541748]\n",
      "[D loss: 4.36819314956665 ]\n",
      "[Epoch 19/100]\n",
      "[G loss: 6.988898277282715 | generator_loss: 2.9591116905212402 |perceptual_loss: 17.802289962768555 |deblur_percept_loss: 3.3014979362487793]\n",
      "[D loss: 4.237483501434326 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.35814189910889 seconds\n",
      "[Epoch 20/100]\n",
      "[G loss: 6.8261260986328125 | generator_loss: 2.941352367401123 |perceptual_loss: 17.211448669433863 |deblur_percept_loss: 3.146851062774658]\n",
      "[D loss: 4.183932781219482 ]\n",
      "[Epoch 21/100]\n",
      "[G loss: 6.987188339233398 | generator_loss: 2.935070514678955 |perceptual_loss: 18.636186599731445 |deblur_percept_loss: 2.7904162406921387]\n",
      "[D loss: 4.182559967041016 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.739564180374146 seconds\n",
      "[Epoch 22/100]\n",
      "[G loss: 7.126735687255859 | generator_loss: 2.936114549636841 |perceptual_loss: 18.271486282348633 |deblur_percept_loss: 3.6070139408111572]\n",
      "[D loss: 4.155412673950195 ]\n",
      "[Epoch 23/100]\n",
      "[G loss: 6.719414234161377 | generator_loss: 2.931486326879883 |perceptual_loss: 16.871990203857422 |deblur_percept_loss: 3.003983974456787]\n",
      "[D loss: 4.1658616065979 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.679866790771484 seconds\n",
      "[Epoch 24/100]\n",
      "[G loss: 12.03006362915039 | generator_loss: 2.94058895111084 |perceptual_loss: 39.61692810058863 |deblur_percept_loss: 7.8337082862854]\n",
      "[D loss: 4.199081897735596 ]\n",
      "[Epoch 25/100]\n",
      "[G loss: 8.087943077087402 | generator_loss: 2.9323062896728516 |perceptual_loss: 23.38270378112793 |deblur_percept_loss: 3.7870001792907715]\n",
      "[D loss: 4.141416072845459 ]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.1203076839447 seconds\n",
      "[Epoch 26/100]\n",
      "[G loss: 6.979717254638672 | generator_loss: 2.9313876628875732 |perceptual_loss: 17.691299438476562 |deblur_percept_loss: 3.4555821418762207]\n",
      "[D loss: 4.145094871520996 ]\n",
      "[Epoch 27/100]\n",
      "[G loss: 7.221766471862793 | generator_loss: 2.9292855262756348 |perceptual_loss: 19.25514793395996 |deblur_percept_loss: 3.3062171936035156]\n",
      "[D loss: 4.148110866546631 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.84308123588562 seconds\n",
      "[Epoch 28/100]\n",
      "[G loss: 7.489511489868164 | generator_loss: 2.9306752681732178 |perceptual_loss: 20.246381759643555 |deblur_percept_loss: 3.657949447631836]\n",
      "[D loss: 4.131345272064209 ]\n",
      "[Epoch 29/100]\n",
      "[G loss: 6.883219242098637 | generator_loss: 2.9274473190307617 |perceptual_loss: 17.94195556640625 |deblur_percept_loss: 2.904879093170166]\n",
      "[D loss: 4.146881103515625 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 49.11769080162048 seconds\n",
      "[Epoch 30/100]\n",
      "[G loss: 6.644652366638184 | generator_loss: 2.926237106323242 |perceptual_loss: 16.875530242919922 |deblur_percept_loss: 2.7232773303985596]\n",
      "[D loss: 4.151108741760254 ]\n",
      "[Epoch 31/100]\n",
      "[G loss: 6.76541805267334 | generator_loss: 2.9284088611602783 |perceptual_loss: 17.029613494873047 |deblur_percept_loss: 3.0867156982421875]\n",
      "[D loss: 4.132191181182861 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.480387687683105 seconds\n",
      "[Epoch 32/100]\n",
      "[G loss: 11.982278823852539 | generator_loss: 2.9526236057281494 |perceptual_loss: 40.612300872802734 |deblur_percept_loss: 6.877762317657471]\n",
      "[D loss: 4.229227542877197 ]\n",
      "[Epoch 33/100]\n",
      "[G loss: 11.348268508911133 | generator_loss: 2.9501051902770996 |perceptual_loss: 36.38800811767578 |deblur_percept_loss: 7.3932881355285645]\n",
      "[D loss: 4.214076042175293 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 34.858471155166626 seconds\n",
      "[Epoch 34/100]\n",
      "[G loss: 6.8418378829956055 | generator_loss: 2.9314494132995605 |perceptual_loss: 17.333866119384766 |deblur_percept_loss: 3.1611719131469727]\n",
      "[D loss: 4.074041366577148 ]\n",
      "[Epoch 35/100]\n",
      "[G loss: 8.211270332336426 | generator_loss: 2.9288697242736816 |perceptual_loss: 24.098346710205078 |deblur_percept_loss: 3.7787909507751465]\n",
      "[D loss: 4.046389579772949 ]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 216x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 35.3977484703064 seconds\n",
      "[Epoch 36/100]\n",
      "[G loss: 8.34442138671875 | generator_loss: 2.9266552925109863 |perceptual_loss: 24.657127380371094 |deblur_percept_loss: 3.9179344177246094]\n",
      "[D loss: 4.038774013519287 ]\n",
      "[Epoch 37/100]\n",
      "[G loss: 9.062639236450195 | generator_loss: 2.92773699760437 |perceptual_loss: 28.510759353637695 |deblur_percept_loss: 4.011858940124512]\n",
      "[D loss: 4.109367370605469 ]\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import ConcatDataset\n",
    "\n",
    "cg = CycleGANsformer()\n",
    "# cg.fit(ImageDatasetLoader(mode=\"train\",transformers=data_transform ))\n",
    "seta = ImageDatasetLoader(mode=\"train1\",transformers=transforms_)\n",
    "setb = ImageDatasetLoader(mode=\"train\",transformers=data_transform)\n",
    "trainset = ConcatDataset([seta, setb])\n",
    "cg.fit(seta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46dced1f",
   "metadata": {},
   "source": [
    "wwwwww"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad83c8e3",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46e60315",
   "metadata": {},
   "outputs": [],
   "source": [
    "# checkpoint_path = './0509lite_checkpoint/0509lite_epoch99.pth'\n",
    "\n",
    "# # Load the checkpoint\n",
    "# checkpoint = torch.load(checkpoint_path)\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0,1,2,3'\n",
    "# torch.cuda.set_device(1)\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# print(\"device\", torch.cuda.current_device(), torch.cuda.get_device_name(torch.cuda.current_device()))\n",
    "# model = TGenerator().to(device)\n",
    "# # Load the model state_dict from the checkpoint\n",
    "# model.load_state_dict(checkpoint['self.genX2Y'])\n",
    "\n",
    "# # Optionally, load other components such as optimizer state_dict or epoch number\n",
    "# # optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "# # epoch = checkpoint['epoch']\n",
    "\n",
    "# # Set the model in evaluation mode\n",
    "# model.eval()\n",
    "# with torch.no_grad():\n",
    "#     fidloader=DataLoader(ImageDatasetLoader(mode=\"fid\",transformers=transforms_ ), batch_size=10, shuffle=False, num_workers=1)\n",
    "#     fretchet_dist=calculate_fid_score(model,fidloader) \n",
    "#     print( f\" fid : {fretchet_dist}\")  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0835c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import time\n",
    "# # def unnormalize(sample_dicom, x):\n",
    "# #     ds = sample_dicom.copy()\n",
    "# #     slope = ds.RescaleSlope\n",
    "# #     intercept = ds.RescaleIntercept\n",
    "# #     ymin = -200\n",
    "# #     ymax = 1600\n",
    "# #     pixel_min = ymin #/ slope   intercept\n",
    "# #     pixel_max = ymax #/ slope   intercept\n",
    "# # #     normalized_array = np.clip((x   1) , 0, 2)\n",
    "# # #     mean = np.mean(x)\n",
    "# # #     std = np.std(x)\n",
    "# # #     unnormalized_array = (x * std   mean) * slope   intercept\n",
    "# #     scaled_array = (x)*5-600#(x*0.5 0.5)*100#-500\n",
    "# #     unnormalized_array = (scaled_array - intercept) / slope \n",
    "# #     return unnormalized_array\n",
    "# def unnormalize(sample_dicom, x):\n",
    "#     ds = sample_dicom.copy()\n",
    "#     slope = ds.RescaleSlope\n",
    "#     intercept = ds.RescaleIntercept\n",
    "#     ymin = -200\n",
    "#     ymax = 1600\n",
    "#     pixel_min = ymin #/ slope   intercept\n",
    "#     pixel_max = ymax #/ slope   intercept\n",
    "\n",
    "#     scaled_array = (x)*6-700\n",
    "#     unnormalized_array = (scaled_array - intercept) / slope \n",
    "#     return unnormalized_array\n",
    "\n",
    "\n",
    "# def write_dicom(sample_dicom, array, paths, tt):\n",
    "#     ds = sample_dicom.copy()\n",
    "#     ww = ds.WindowWidth\n",
    "#     wc = ds.WindowCenter\n",
    "#     array = np.transpose(array, (2, 1, 0))[:, :, 0]\n",
    "#     array = unnormalize(sample_dicom, array)\n",
    "#     array = np.clip(array, 0, 2 ** 16 - 1)\n",
    "#     ds.PixelData = array.astype(np.uint16).tobytes()\n",
    "#     return ds.save_as(paths   \"/\"   str(tt)   \".dcm\")\n",
    "# checkpoint_path = './0429lite_checkpoint/0429lite_epoch99.pth'\n",
    "\n",
    "# # Load the checkpoint\n",
    "# checkpoint = torch.load(checkpoint_path)\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0,1,2,3'\n",
    "# torch.cuda.set_device(1)\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# print(\"device\", torch.cuda.current_device(), torch.cuda.get_device_name(torch.cuda.current_device()))\n",
    "# model = TGenerator().to(device)\n",
    "# # Load the model state_dict from the checkpoint\n",
    "# model.load_state_dict(checkpoint['self.genX2Y'])\n",
    "# model.eval()\n",
    "# with torch.no_grad():\n",
    "#     cbct_count=0\n",
    "#     batch_sizes = 2\n",
    "#     testloader=DataLoader(ImageDatasetLoader(mode=\"test\",transformers=transforms_ ), batch_size=batch_sizes, shuffle=False, num_workers=16, pin_memory=True)\n",
    "#     evlloader=DataLoader(ImageDatasetLoader(mode=\"evl\",transformers=transforms_ ), batch_size=batch_sizes, shuffle=False, num_workers=16, pin_memory=True)\n",
    "\n",
    "#     path=\"./0906/\" str(98) \"/\"\n",
    "#     if not os.path.exists(path):\n",
    "#         os.mkdir(path)\n",
    "#     cbct_data=[]\n",
    "#     for i in range(len(data)):\n",
    "#         ds=pydicom.dcmread(data[i][0],force=True)\n",
    "#         cbct_data.append(ds)\n",
    "#     cbctdata=cbct_data[24000:24704]\n",
    "# #     cbct_data=[]\n",
    "# #     for i in range(len(data)):\n",
    "# #         ds=pydicom.dcmread(data[i][0],force=True)\n",
    "# #         cbct_data.append(ds)\n",
    "\n",
    "# #     indices = [17000, 17010, 17020, 17030, 17040, 17060, 17070, 17080, 17090, 17100,\n",
    "# #            18000, 18100, 18200, 18300, 18400, 18500, 18600, 18700, 18800, 18900, \n",
    "# #            19500, 19050, 19150, 19250, 19350, 19450, 19550, 19650, 19750, 19850,\n",
    "# #            19950, 20050, 20150, 20250, 20350, 20450, 20550, 20650, 20750, 20850,\n",
    "# #            20950, 22030, 22130, 22230, 22330, 22430, 22530, 22630, 22730, 22830,\n",
    "# #            23070, 23170, 23270, 23370, 23470, 23570, 23670, 23770, 23870, 23970]\n",
    "\n",
    "# #     cbctdata = [cbct_data[i] for i in indices]\n",
    "    \n",
    "#     start_time = time.time()\n",
    "#     for idx, (a_img, b_img) in enumerate(evlloader):\n",
    "\n",
    "#         model.eval()\n",
    "#         a=a_img.type(torch.cuda.FloatTensor)\n",
    "#         b=b_img.type(torch.cuda.FloatTensor)\n",
    "\n",
    "#         fake_B = model(b,a).detach()\n",
    "#         b = fake_B.cpu().numpy()\n",
    "#         c =b.copy()\n",
    "#         for s in range(len(a_img)):\n",
    "#             t=s idx*batch_sizes\n",
    "#             write_dicom(cbctdata[t],c[s],path,t)\n",
    "#     end_time = time.time()\n",
    "#     execution_time = end_time - start_time\n",
    "#     print(\"Execution time:\", execution_time, \"seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b789a070",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# from sklearn.linear_model import LinearRegression\n",
    "# # Define the directory where the DICOM files are located\n",
    "# directory = \"./0429/98/\"\n",
    "\n",
    "# # Define the range of DICOM file names you want to load\n",
    "# start_index = 0\n",
    "# end_index = 103\n",
    "\n",
    "# # Initialize a list to store the loaded DICOM datasets\n",
    "# dicom_data = []\n",
    "\n",
    "# # Loop through the range of file names and load each DICOM file\n",
    "# for i in range(start_index, end_index   1):\n",
    "#     filename = os.path.join(directory, f\"{i}.dcm\")\n",
    "#     if os.path.exists(filename):\n",
    "#         ds = load_dcm(filename)\n",
    "#         dicom_data.append(ds)\n",
    "# # Replace these with your actual data\n",
    "# scalar_value = 200\n",
    "# scalar_list = [scalar_value] * len(dicom_data)\n",
    "# x1 = np.array([x   scalar_value for x in dicom_data])\n",
    "# x2 = np.array(data_load[24600:24704, 0]   scalar_value)\n",
    "# scalar_value1 = 180\n",
    "# scalar_list1 = [scalar_value1] * len(dicom_data)\n",
    "# y = np.array(data_load[24600:24704, 1] scalar_list1)\n",
    "\n",
    "# # Reshape y to have a 2-dimensional shape (num_samples, 1)\n",
    "# y = y.reshape(-1, 1)\n",
    "# x1 = x1.reshape(-1, 1)\n",
    "# x2 = x2.reshape(-1, 1)\n",
    "\n",
    "# # Fit linear regression models for y with respect to x1 and x2, setting fit_intercept=False\n",
    "# regressor_x1 = LinearRegression(fit_intercept=False)\n",
    "# regressor_x1.fit(x1, y)\n",
    "\n",
    "# regressor_x2 = LinearRegression(fit_intercept=False)\n",
    "# regressor_x2.fit(x2, y)\n",
    "\n",
    "# # Generate points for the regression lines\n",
    "# x1_line = np.linspace(min(x1), max(x1), 100)\n",
    "# x2_line = np.linspace(min(x2), max(x2), 100)\n",
    "\n",
    "# y_line_x1 = regressor_x1.predict(x1_line.reshape(-1, 1))\n",
    "# y_line_x2 = regressor_x2.predict(x2_line.reshape(-1, 1))\n",
    "# # Plot the linear regression lines\n",
    "# plt.plot(x1_line, y_line_x1, label='Linear Regression (sCT)', color='red')\n",
    "# plt.plot(x2_line, y_line_x2, label='Linear Regression (CBCT)', color='blue')\n",
    "# plt.plot(x1_line, x1_line, label='45-Degree Line', linestyle='dashed', color='green')\n",
    "\n",
    "# plt.xlabel('sCT/CBCT values')\n",
    "# plt.ylabel('planner CT values')\n",
    "# plt.legend()\n",
    "# plt.grid(True)\n",
    "# plt.show()"
   ]
  }
 ],
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