preprocessing3_dtdl_fastset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddf15ada-3fcb-47df-b122-e6bc5f2a609f",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.load(os.path.join(download_dir,f'{fnames[0]}.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b3aea80-9941-4588-b927-974a7c4f415b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import json \n",
    "import numpy as np \n",
    "\n",
    "import vitaldb\n",
    "import torch\n",
    "from torch.nn import functional as F\n",
    "\n",
    "from preprocess import list2specgram, ecgs2specgram, ppgs2specgram\n",
    "# from dataset import get_vital \n",
    "\n",
    "class VitalDataset_fs(torch.utils.data.Dataset):\n",
    "    def __init__(self,root_dir='../data/all_3',ann_path='../data/pd_gy/train.json',sample_rate=300,duration=300*60*5):\n",
    "        \n",
    "        with open(ann_path,'r') as f:\n",
    "            jf = json.load(f)\n",
    "        \n",
    "        self.jf = jf\n",
    "        self.root_dir = root_dir\n",
    "        self.sample_rate = sample_rate\n",
    "        self.duration = duration\n",
    "        \n",
    "    def __getitem__(self,idx):\n",
    "        \n",
    "        item = self.jf[idx]\n",
    "        \n",
    "        fname = item['pt_id_date']\n",
    "        vital = np.load(os.path.join(self.root_dir,f'{fname}.npy'))\n",
    "        \n",
    "        \n",
    "        y = item['nrs_2']\n",
    "        # y = F.one_hot(y,num_classes=2)\n",
    "        \n",
    "        \n",
    "        vital = torch.tensor(vital)\n",
    "        vital = vital.float()\n",
    "        y = torch.tensor(y)\n",
    "        y = y.float()\n",
    "        return vital, y\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.jf)\n",
    "    \n",
    "    def get_npvital(self,idx):\n",
    "        pass\n",
    "    \n",
    "    def get_path(self, item):\n",
    "        \n",
    "        orp = item['or_path'][0].replace('\\\\','/')\n",
    "        orp = os.path.join(self.root_dir, orp)\n",
    "        recp = item['rec_path'][0].replace('\\\\','/')\n",
    "        recp = os.path.join(self.root_dir,recp)\n",
    "        return orp, recp\n",
    "\n",
    "    \n",
    "    def getitem_np(self,idx):\n",
    "        \n",
    "        item = self.jf[idx]\n",
    "        \n",
    "        fname = item['pt_id_date']\n",
    "        vital = np.load(os.path.join(self.root_dir,f'{fname}.npy'))\n",
    "        y = item['nrs_2']\n",
    "        \n",
    "        return vital, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b40b513-e152-4198-a62d-e4d03c7513e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from dataset import VitalDataset\n",
    "trdt  = VitalDataset_fs('../data/all_3','../data/pd_gy/all_3.json') #  './split/train.txt'\n",
    "\n",
    "trdl  = torch.utils.data.DataLoader(trdt, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17f0a085-e971-4ec8-94c4-b69590e4f029",
   "metadata": {},
   "outputs": [],
   "source": [
    "for x,y in trdl:\n",
    "    print(x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02deaaf-7bef-42de-b50f-6f8ec2ca6968",
   "metadata": {},
   "source": [
    "## test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a017058e-5bea-4ae9-bd78-ff2520abba10",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import VitalDataset_fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cd84c38-99f3-4f1a-b808-a75b96ff8a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "trdt  = VitalDataset_fs('../data/all_3','../data/pd_gy/all_3.json') #  './split/train.txt'\n",
    "\n",
    "trdl  = torch.utils.data.DataLoader(trdt, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ade39a6-54a6-4ca9-9e70-bd519349b1ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
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      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([4, 4, 151, 342])\n",
      "torch.Size([3, 4, 151, 342])\n"
     ]
    }
   ],
   "source": [
    "for x,y in trdl:\n",
    "    print(x.shape)"
   ]
  }
 ],
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