{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71c25a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'exit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 55\u001b[0m\n\u001b[1;32m     53\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mmanual_seed(SEED)\n\u001b[1;32m     54\u001b[0m torch\u001b[38;5;241m.\u001b[39mbackends\u001b[38;5;241m.\u001b[39mcudnn\u001b[38;5;241m.\u001b[39mbenchmark \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 55\u001b[0m model, aug \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdiffusion_ver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mVER\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSIZE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTIMESTEP\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdecoder_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/code/DeCLIP/extractor_sd.py:186\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(config_path, seed, diffusion_ver, image_size, num_timesteps, block_indices, decoder_only, encoder_only, resblock_only)\u001b[0m\n\u001b[1;32m    184\u001b[0m dataset_cfg \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mdataloader\u001b[38;5;241m.\u001b[39mtest\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28mprint\u001b[39m(cfg\u001b[38;5;241m.\u001b[39mtrain\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 186\u001b[0m \u001b[43mexit\u001b[49m()\n\u001b[1;32m    187\u001b[0m aug \u001b[38;5;241m=\u001b[39m instantiate(dataset_cfg\u001b[38;5;241m.\u001b[39mmapper)\u001b[38;5;241m.\u001b[39maugmentations\n\u001b[1;32m    188\u001b[0m model \u001b[38;5;241m=\u001b[39m instantiate_odise(cfg\u001b[38;5;241m.\u001b[39mmodel)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from training.utils import build_vfm\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '3'\n",
    "import torch\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "import torch.nn.functional as F\n",
    "from extractor_sd import load_model, process_features_and_mask, get_mask\n",
    "from third_party.utils.utils_correspondence import co_pca, resize, find_nearest_patchs, find_nearest_patchs_replace\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from extractor_dino import ViTExtractor\n",
    "from sklearn.decomposition import PCA as sklearnPCA\n",
    "import math\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.optimize import linear_sum_assignment\n",
    "from torchvision import transforms\n",
    "\n",
    "def preprocess_pil(pil_image):\n",
    "    prep = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n",
    "    ])\n",
    "    prep_img = prep(pil_image)[None, ...]\n",
    "    return prep_img\n",
    "\n",
    "MASK = True\n",
    "VER = \"v1-5\"\n",
    "PCA = False\n",
    "CO_PCA = True\n",
    "PCA_DIMS = [256, 256, 256]\n",
    "SIZE =960\n",
    "EDGE_PAD = False\n",
    "\n",
    "FUSE_DINO = 1\n",
    "ONLY_DINO = 0\n",
    "MODEL_SIZE = 'base'\n",
    "DRAW_DENSE = 1\n",
    "DRAW_SWAP = 1\n",
    "TEXT_INPUT = False\n",
    "SEED = 42\n",
    "TIMESTEP = 100\n",
    "\n",
    "DIST = 'l2' if FUSE_DINO and not ONLY_DINO else 'cos'\n",
    "if ONLY_DINO:\n",
    "    FUSE_DINO = True\n",
    "\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.benchmark = True\n",
    "model, aug = load_model(diffusion_ver=VER, image_size=SIZE, num_timesteps=TIMESTEP,decoder_only=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75082563",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_pair_feature(model, aug, save_path, files, category, mask=False, dist='cos', real_size=960):\n",
    "    img_size = 840\n",
    "    stride = 14 \n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    extractor=build_vfm(\"dinov2-B\").to(device).eval().to(torch.float32)\n",
    "    patch_size = 14\n",
    "    num_patches = int(patch_size / stride * (img_size // patch_size - 1) + 1)\n",
    "    input_text =  None\n",
    "\n",
    "    # Load image 1\n",
    "    img1 = Image.open(files[0]).convert('RGB')\n",
    "    img1_input = resize(img1, real_size, resize=True, to_pil=True, edge=EDGE_PAD)\n",
    "    img1 = resize(img1, img_size, resize=True, to_pil=True, edge=EDGE_PAD)\n",
    "\n",
    "    # Load image 2\n",
    "    img2 = Image.open(files[1]).convert('RGB')\n",
    "    img2_input = resize(img2, real_size, resize=True, to_pil=True, edge=EDGE_PAD)\n",
    "    img2 = resize(img2, img_size, resize=True, to_pil=True, edge=EDGE_PAD)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        features1 = process_features_and_mask(model, aug, img1_input, input_text=input_text, mask=False, raw=True)\n",
    "        features2 = process_features_and_mask(model, aug, img2_input, input_text=input_text, mask=False, raw=True)\n",
    "        processed_features1, processed_features2 = co_pca(features1, features2, PCA_DIMS)\n",
    "        img1_desc = processed_features1.reshape(1, 1, -1, num_patches**2).permute(0,1,3,2)\n",
    "        img2_desc = processed_features2.reshape(1, 1, -1, num_patches**2).permute(0,1,3,2)\n",
    "        img1_batch = preprocess_pil(img1).to(device)\n",
    "        img1_desc_dino = extractor.get_intermediate_layers(img1_batch)[0].unsqueeze(1)\n",
    "        img2_batch = preprocess_pil(img2).to(device)\n",
    "        img2_desc_dino = extractor.get_intermediate_layers(img2_batch)[0].unsqueeze(1)\n",
    "        img1_desc = img1_desc / img1_desc.norm(dim=-1, keepdim=True)\n",
    "        img2_desc = img2_desc / img2_desc.norm(dim=-1, keepdim=True)\n",
    "        img1_desc_dino = img1_desc_dino / img1_desc_dino.norm(dim=-1, keepdim=True)\n",
    "        img2_desc_dino = img2_desc_dino / img2_desc_dino.norm(dim=-1, keepdim=True)\n",
    "        img1_desc = torch.cat((img1_desc, img1_desc_dino), dim=-1)\n",
    "        img2_desc = torch.cat((img2_desc, img2_desc_dino), dim=-1)\n",
    "        if DRAW_DENSE:\n",
    "            mask1 = get_mask(model, aug, img1, category[0])\n",
    "            mask2 = get_mask(model, aug, img2, category[-1])\n",
    "            img1_desc_reshaped = img1_desc.permute(0,1,3,2).reshape(-1, img1_desc.shape[-1], num_patches, num_patches)\n",
    "            img2_desc_reshaped = img2_desc.permute(0,1,3,2).reshape(-1, img2_desc.shape[-1], num_patches, num_patches)\n",
    "            trg_dense_output, src_color_map = find_nearest_patchs(mask2, mask1, img2, img1, img2_desc_reshaped, img1_desc_reshaped, mask=mask)\n",
    "\n",
    "            if not os.path.exists(f'{save_path}/{category[0]}'):\n",
    "                os.makedirs(f'{save_path}/{category[0]}')\n",
    "            fig_colormap, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))\n",
    "            ax1.axis('off')\n",
    "            ax2.axis('off')\n",
    "            ax1.imshow(src_color_map)\n",
    "            ax2.imshow(trg_dense_output)\n",
    "            fig_colormap.savefig(f'{save_path}/{category[0]}_colormap.png')\n",
    "            plt.close(fig_colormap)\n",
    "        \n",
    "        if DRAW_SWAP:\n",
    "            if not DRAW_DENSE:\n",
    "                mask1 = get_mask(model, aug, img1, category[0])\n",
    "                mask2 = get_mask(model, aug, img2, category[-1])\n",
    "\n",
    "            if (ONLY_DINO or not FUSE_DINO) and not DRAW_DENSE:\n",
    "                img1_desc = img1_desc / img1_desc.norm(dim=-1, keepdim=True)\n",
    "                img2_desc = img2_desc / img2_desc.norm(dim=-1, keepdim=True)\n",
    "                \n",
    "            img1_desc_reshaped = img1_desc.permute(0,1,3,2).reshape(-1, img1_desc.shape[-1], num_patches, num_patches)\n",
    "            img2_desc_reshaped = img2_desc.permute(0,1,3,2).reshape(-1, img2_desc.shape[-1], num_patches, num_patches)\n",
    "            trg_dense_output, src_color_map = find_nearest_patchs_replace(mask2, mask1, img2, img1, img2_desc_reshaped, img1_desc_reshaped, mask=mask, resolution=156)\n",
    "            if not os.path.exists(f'{save_path}/{category[0]}'):\n",
    "                os.makedirs(f'{save_path}/{category[0]}')\n",
    "            fig_colormap, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))\n",
    "            ax1.axis('off')\n",
    "            ax2.axis('off')\n",
    "            ax1.imshow(src_color_map)\n",
    "            ax2.imshow(trg_dense_output)\n",
    "            fig_colormap.savefig(f'{save_path}/{category[0]}/swap.png')\n",
    "            plt.close(fig_colormap)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4d1bdc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_images(src_img_path,trg_img_path):\n",
    "\n",
    "    categories = [['dog'], ['dog']]\n",
    "    files = [src_img_path, trg_img_path]\n",
    "    save_path = './my_results_vis' + f'/{trg_img_path.split(\"/\")[-1].split(\".\")[0]}_{src_img_path.split(\"/\")[-1].split(\".\")[0]}'\n",
    "    result = compute_pair_feature(model, aug, save_path, files, mask=MASK, category=categories, dist=DIST)\n",
    "    return result\n",
    "\n",
    "src_img_path = \"demo_images/dog.jpg\"\n",
    "trg_img_path = \"demo_images/whitedog.jpg\"\n",
    "\n",
    "result = process_images(src_img_path, trg_img_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "declip",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}