neo_cmp_outer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from PIL import Image\n",
    "\n",
    "from network.styler import Unet\n",
    "from loss.loss import CLIPLoss\n",
    "from utils.func import get_features,vgg_normalize\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "lr1 = 0.0001\n",
    "lr2 = 0.0003\n",
    "# model = Unet(device)\n",
    "model = Unet().to(device)\n",
    "cliploss = CLIPLoss(device)\n",
    "# mseloss = torch.nn.MSELoss()\n",
    "vgg = torchvision.models.vgg19(pretrained=True).features.to(device)\n",
    "for x in vgg.parameters():\n",
    "    x.requires_grad = False\n",
    "\n",
    "\n",
    "topil = transforms.ToPILImage()\n",
    "topic = transforms.ToTensor()\n",
    "\n",
    "dir_lambda = 500\n",
    "content_lambda = 150\n",
    "patch_lambda = 9000\n",
    "norm_lambda = 0.002\n",
    "gol_lambda = 300"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "loss_li = [0]*200"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def train(iteration1, iteration2, pic, source, target, path):\n",
    "    input = pic\n",
    "\n",
    "    # opt = optim.Adam(model.parameters(), lr=lr1)\n",
    "    # for i in range(iteration1):\n",
    "    #     opt.zero_grad()\n",
    "    #     neo_pic = model(input)\n",
    "    #     loss = mseloss(pic, neo_pic) * 1\n",
    "    #     loss.backward()\n",
    "    #     opt.step()\n",
    "    #     pil = topil(neo_pic.squeeze(0).cpu())\n",
    "    #     print(\"iter:\", i + 1, \"loss:\", loss.item())\n",
    "    #     if ((i + 1) % 50) == 0:\n",
    "    #         pil.save(f\"./pic1/{(i + 1) // 50}.jpg\")\n",
    "    #\n",
    "    #\n",
    "    # torch.save(model,'unet.pth')\n",
    "\n",
    "\n",
    "    # model = torch.load('unet.pth')\n",
    "\n",
    "    pic_f = get_features(vgg_normalize(pic),vgg)\n",
    "\n",
    "    opt = optim.Adam(model.parameters(), lr=lr2)\n",
    "    sch = torch.optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.5)\n",
    "    for i in range(iteration2):\n",
    "\n",
    "\n",
    "        opt.zero_grad()\n",
    "        neo_pic = model(input)\n",
    "\n",
    "        dir_loss = 0\n",
    "        dir_loss += cliploss.forward_dir(pic, source, neo_pic, target)\n",
    "\n",
    "        gol_loss = 0\n",
    "        # gol_loss += cliploss.forward_gol(pic, source, neo_pic, target)\n",
    "\n",
    "        content_loss = 0\n",
    "        neo_pic_f = get_features(vgg_normalize(neo_pic), vgg)\n",
    "        content_loss += torch.mean((pic_f['conv4_2'] - neo_pic_f['conv4_2']) ** 2)\n",
    "        content_loss += torch.mean((pic_f['conv5_2'] - neo_pic_f['conv5_2']) ** 2)\n",
    "\n",
    "        patch_loss = 0\n",
    "        patch_loss += cliploss.forward_patch(pic, source, neo_pic, target)\n",
    "\n",
    "        norm_loss = 0\n",
    "        norm_loss += cliploss.forward_prior(pic, source, neo_pic, target)\n",
    "\n",
    "        loss = dir_loss * dir_lambda + \\\n",
    "               content_loss * content_lambda + \\\n",
    "               patch_loss * patch_lambda + \\\n",
    "               norm_loss * norm_lambda + \\\n",
    "               gol_loss * gol_lambda\n",
    "\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "        sch.step()\n",
    "\n",
    "        loss_li[i] = loss.item()\n",
    "        print(\"iter:\", i + 1, \"loss:\", loss.item())\n",
    "\n",
    "    with torch.no_grad():\n",
    "        neo_pic = model(input)\n",
    "        pil = topil(neo_pic.squeeze(0).cpu())\n",
    "        pil = transforms.Resize(size=ori_size, interpolation=Image.BICUBIC)(pil)\n",
    "        pil.save(f\"mid.jpg\")\n",
    "\n",
    "        # pil = topil(neo_pic.squeeze(0).cpu())\n",
    "        # if ((i + 1) % 10) == 0:\n",
    "        #     pil.save(f\"./pic2/{(i + 1) // 10}.jpg\")\n",
    "\n",
    "    # neo_pic = model(input)\n",
    "    # pil = topil(neo_pic.squeeze(0).cpu())\n",
    "    # # pil.save(f\"{source}-{target}.jpg\")\n",
    "    # pil.save(path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_25068\\511628581.py:3: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.\n",
      "  pil = transforms.Resize(size=(512, 512), interpolation=Image.BICUBIC)(pil)\n",
      "E:\\Anaconda\\envs\\sth\\lib\\site-packages\\torchvision\\transforms\\transforms.py:257: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pil = Image.open(f\"./source_pic/face5.jpg\")\n",
    "ori_size = pil.size[::-1]\n",
    "pil = transforms.Resize(size=(512, 512), interpolation=Image.BICUBIC)(pil)\n",
    "pic = topic(pil).unsqueeze(0).to(device)\n",
    "# pic = torch.ones(1, 3, 512, 512).to(device)\n",
    "pic.requires_grad = False"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "source = \"photo\"\n",
    "# source = \"black swan\"\n",
    "\n",
    "# target = \"Metal\"\n",
    "# target = \"Neon Light\"\n",
    "# target = \"white swan\"\n",
    "# target = \"Pixar\"\n",
    "# target = \"pop art of night city\"\n",
    "target = \"Van Gogh\"\n",
    "# target = \"the scream by Edvard Munch\"\n",
    "# target = \"Monet\"\n",
    "# target = \"cyberpunk 2077\"\n",
    "# target = \"Chinese Ink and wash painting\"\n",
    "# target = \"fire\"\n",
    "# target = \"woman\"\n",
    "# target = \"man with dark black skin and black hair\"\n",
    "# target = \"gold boat\"\n",
    "# target = \"The great wave off kanagawa by Hokusai\"\n",
    "# target = \"angry peppa pig\"\n",
    "# target = \"Mona Lisa by Leonardo da Vinci\"\n",
    "\n",
    "path = \"result/result3.jpg\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "iter: 200 loss: 6322.4384765625\n",
      "usetime: 94.49838399887085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_25068\\1389563984.py:65: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.\n",
      "  pil = transforms.Resize(size=ori_size, interpolation=Image.BICUBIC)(pil)\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "train(200, 200, pic, source, target, path)\n",
    "end = time.time()\n",
    "usetime = end - start\n",
    "print(f\"usetime: {usetime}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x20991d016a0>]"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [i for i in range(200)]\n",
    "loss_li = [x for x in loss_li]\n",
    "plt.plot(x,loss_li,color=\"red\",marker=\"o\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_25068\\3219620664.py:3: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.\n",
      "  pil = transforms.Resize(size=ori_size, interpolation=Image.BICUBIC)(pil)\n",
      "E:\\Anaconda\\envs\\sth\\lib\\site-packages\\torchvision\\transforms\\transforms.py:257: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "neo_pic = model(pic)\n",
    "pil = topil(neo_pic.squeeze(0).cpu())\n",
    "pil = transforms.Resize(size=ori_size, interpolation=Image.BICUBIC)(pil)\n",
    "pil.save(path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "file_cmp_outer = open(\"styler.txt\", \"r\")\n",
    "read_loss_li = file_cmp_outer.readline()\n",
    "read_loss_li = [float(x) for x in read_loss_li.split()]\n",
    "\n",
    "cur_times = int(file_cmp_outer.readline())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# with open(file = \"styler.txt\", mode = \"w\") as file:\n",
    "#     for i in range(200):\n",
    "#\n",
    "#         file.write(str(read_loss_li[i]+loss_li[i])+\" \")\n",
    "#     file.write(\"\\n\")\n",
    "#     file.write(str(cur_times+1))\n",
    "\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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   "name": "python",
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