infer.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/home/venom/miniforge3/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/home/venom/miniforge3/lib/python3.10/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
      "  @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "from torchvision import transforms\n",
    "import lightning as L\n",
    "import kornia as K\n",
    "import numpy as np\n",
    "import random\n",
    "import sys\n",
    "\n",
    "PROJECT_ROOT = os.path.abspath(os.path.normpath(\"/home/venom/repo/xray-exp/\"))\n",
    "sys.path.append(PROJECT_ROOT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.model_loader import create_model\n",
    "from scripts.trainer import XrayReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class XrayInferenceDataset(Dataset):\n",
    "\n",
    "    def __init__(self, root_dir, transform=None):\n",
    "        self.root_dir = root_dir\n",
    "        self.file_names = os.listdir(root_dir)\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.file_names)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file_name = self.file_names[idx]\n",
    "        img_path = os.path.join(self.root_dir, file_name)\n",
    "        img = Image.open(img_path)\n",
    "\n",
    "        img = img.convert(\"L\")\n",
    "\n",
    "        if self.transform:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        return img, file_name\n",
    "\n",
    "\n",
    "class XrayDataInference(L.LightningDataModule):\n",
    "    common_seed = 42\n",
    "\n",
    "    @staticmethod\n",
    "    def seed_worker(worker_id):\n",
    "        worker_seed = torch.initial_seed() % 2**32\n",
    "        np.random.seed(worker_seed)\n",
    "        random.seed(worker_seed)\n",
    "\n",
    "    def __init__(self, root_dir, batch_size=32):\n",
    "        super().__init__()\n",
    "        self.root_dir = root_dir\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        torch.manual_seed(self.common_seed)\n",
    "        torch.cuda.manual_seed_all(self.common_seed)\n",
    "        torch.backends.cudnn.deterministic = True\n",
    "\n",
    "        self.transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((224, 224)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "        self.inference_dataset = XrayInferenceDataset(\n",
    "            self.root_dir, transform=self.transform\n",
    "        )\n",
    "\n",
    "    def inference_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.inference_dataset,\n",
    "            batch_size=self.batch_size,\n",
    "            shuffle=False,\n",
    "            num_workers=4,\n",
    "            worker_init_fn=self.seed_worker,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/venom/miniforge3/lib/python3.10/site-packages/timm/models/_factory.py:117: UserWarning: Mapping deprecated model name vit_large_patch16_224_in21k to current vit_large_patch16_224.augreg_in21k.\n",
      "  model = create_fn(\n"
     ]
    }
   ],
   "source": [
    "trainer_config = XrayReg.load_from_checkpoint(\n",
    "    \"/home/venom/repo/xray-exp/xray_regression_noaug/912yp4l6/checkpoints/epoch=99-step=5900.ckpt\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "infer_ds = XrayDataInference(\n",
    "    \"/home/venom/Downloads/CXR AI PNG- FINAL 13-12/\", batch_size=16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run inference against the infer_ds and log to a file (file name run_name)\n",
    "\n",
    "model = trainer_config.model\n",
    "\n",
    "model.eval()\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run inference against the infer_ds and log to a file (file name run_ID)\n",
    "RUN_ID = \"912yp4l6\"\n",
    "\n",
    "with open(f\"/home/venom/repo/xray-exp/inference_results/{RUN_ID}.csv\", \"w\") as f:\n",
    "    f.write(\"file_name,predicted\\n\")\n",
    "    for img, file_name in infer_ds.inference_dataloader():\n",
    "        img = img.cuda()\n",
    "        with torch.no_grad():\n",
    "            pred = model(img)\n",
    "            pred = pred.cpu().numpy()\n",
    "            for i in range(len(pred)):\n",
    "                f.write(f\"{file_name[i]},{pred[i][0]}\\n\")"
   ]
  }
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