demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mmengine.model.utils import revert_sync_batchnorm\n",
    "from mmseg.apis import init_model, inference_model\n",
    "from PIL import Image\n",
    "import torchmetrics\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette_coco_stuff=[[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192],\n",
    "                 [0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64],\n",
    "                 [0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224],\n",
    "                 [0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192],\n",
    "                 [0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],\n",
    "                 [128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128],\n",
    "                 [64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160],\n",
    "                 [0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0],\n",
    "                 [0, 128, 0], [192, 128, 32], [128, 96, 128], [0, 0, 128],\n",
    "                 [64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160],\n",
    "                 [0, 96, 128], [128, 128, 128], [64, 0, 160], [128, 224, 128],\n",
    "                 [128, 128, 64], [192, 0, 32], [128, 96, 0], [128, 0, 192],\n",
    "                 [0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160],\n",
    "                 [64, 96, 0], [0, 128, 192], [0, 128, 160], [192, 224, 0],\n",
    "                 [0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192],\n",
    "                 [0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160],\n",
    "                 [64, 32, 128], [128, 192, 192], [0, 0, 160], [192, 160, 128],\n",
    "                 [128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128],\n",
    "                 [64, 128, 96], [64, 160, 0], [0, 64, 0], [192, 128, 224],\n",
    "                 [64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0],\n",
    "                 [0, 192, 0], [192, 128, 96], [192, 96, 128], [0, 64, 128],\n",
    "                 [64, 0, 96], [64, 224, 128], [128, 64, 0], [192, 0, 224],\n",
    "                 [64, 96, 128], [128, 192, 128], [64, 0, 224], [192, 224, 128],\n",
    "                 [128, 192, 64], [192, 0, 96], [192, 96, 0], [128, 64, 192],\n",
    "                 [0, 128, 96], [0, 224, 0], [64, 64, 64], [128, 128, 224],\n",
    "                 [0, 96, 0], [64, 192, 192], [0, 128, 224], [128, 224, 0],\n",
    "                 [64, 192, 64], [128, 128, 96], [128, 32, 128], [64, 0, 192],\n",
    "                 [0, 64, 96], [0, 160, 128], [192, 0, 64], [128, 64, 224],\n",
    "                 [0, 32, 128], [192, 128, 192], [0, 64, 224], [128, 160, 128],\n",
    "                 [192, 128, 0], [128, 64, 32], [128, 32, 64], [192, 0, 128],\n",
    "                 [64, 192, 32], [0, 160, 64], [64, 0, 0], [192, 192, 160],\n",
    "                 [0, 32, 64], [64, 128, 128], [64, 192, 160], [128, 160, 64],\n",
    "                 [64, 128, 0], [192, 192, 32], [128, 96, 192], [64, 0, 128],\n",
    "                 [64, 64, 32], [0, 224, 192], [192, 0, 0], [192, 64, 160],\n",
    "                 [0, 96, 192], [192, 128, 128], [64, 64, 160], [128, 224, 192],\n",
    "                 [192, 128, 64], [192, 64, 32], [128, 96, 64], [192, 0, 192],\n",
    "                 [0, 192, 32], [64, 224, 64], [64, 0, 64], [128, 192, 160],\n",
    "                 [64, 96, 64], [64, 128, 192], [0, 192, 160], [192, 224, 64],\n",
    "                 [64, 128, 64], [128, 192, 32], [192, 32, 192], [64, 64, 192],\n",
    "                 [0, 64, 32], [64, 160, 192], [192, 64, 64], [128, 64, 160],\n",
    "                 [64, 32, 192], [192, 192, 192], [0, 64, 160], [192, 160, 192],\n",
    "                 [192, 192, 0], [128, 64, 96], [192, 32, 64], [192, 64, 128],\n",
    "                 [64, 192, 96], [64, 160, 64], [64, 64, 0]]\n",
    "classes_coco_stuff = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',\n",
    "            'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\n",
    "            'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',\n",
    "            'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',\n",
    "            'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\n",
    "            'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',\n",
    "            'baseball glove', 'skateboard', 'surfboard', 'tennis racket',\n",
    "            'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',\n",
    "            'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',\n",
    "            'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\n",
    "            'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',\n",
    "            'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',\n",
    "            'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\n",
    "            'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',\n",
    "            'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet',\n",
    "            'cage', 'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile',\n",
    "            'cloth', 'clothes', 'clouds', 'counter', 'cupboard', 'curtain',\n",
    "            'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble',\n",
    "            'floor-other', 'floor-stone', 'floor-tile', 'floor-wood', 'flower',\n",
    "            'fog', 'food-other', 'fruit', 'furniture-other', 'grass', 'gravel',\n",
    "            'ground-other', 'hill', 'house', 'leaves', 'light', 'mat', 'metal',\n",
    "            'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net',\n",
    "            'paper', 'pavement', 'pillow', 'plant-other', 'plastic',\n",
    "            'platform', 'playingfield', 'railing', 'railroad', 'river', 'road',\n",
    "            'rock', 'roof', 'rug', 'salad', 'sand', 'sea', 'shelf',\n",
    "            'sky-other', 'skyscraper', 'snow', 'solid-other', 'stairs',\n",
    "            'stone', 'straw', 'structural-other', 'table', 'tent',\n",
    "            'textile-other', 'towel', 'tree', 'vegetable', 'wall-brick',\n",
    "            'wall-concrete', 'wall-other', 'wall-panel', 'wall-stone',\n",
    "            'wall-tile', 'wall-wood', 'water-other', 'waterdrops',\n",
    "            'window-blind', 'window-other', 'wood']\n",
    "\n",
    "config_coco_stuff = '/mnt/disk2/arda_efe/graduation/ITACLIP/configs/cfg_coco_stuff164k.py'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = init_model(config_coco_stuff, device='cuda')\n",
    "if not torch.cuda.is_available():\n",
    "    model = revert_sync_batchnorm(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "IMG_PATH = 'demo.jpg'\n",
    "result = inference_model(model, IMG_PATH)\n",
    "pred_map = result.pred_sem_seg.data.cpu().numpy().squeeze().astype(np.uint8)\n",
    "pred_map_painted = np.array(palette_coco_stuff)[pred_map].astype(np.uint8)\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_map_painted)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tos",
   "language": "python",
   "name": "python3"
  },
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