notebooks/sd-gr-ieee.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c80b952-65b2-4c18-8ac2-fcfc00a76839",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import data as data_class\n",
    "import wifi_label_utils\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "848fdf1a-28d6-444c-bfe5-01a143b9dde1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def annotate(filename, label, avg_window_len, avg_duration=-1, debug=False):\n",
    "    \n",
    "    data_obj = data_class.Data(filename)\n",
    "\n",
    "    # use a seconds worth of data to calculate threshold\n",
    "    if avg_duration > -1:\n",
    "        iq_samples = data_obj.get_samples(n_samples=int(data_obj.metadata[\"global\"][\"core:sample_rate\"]*avg_duration))\n",
    "    else:\n",
    "        iq_samples = data_obj.get_samples()\n",
    "        \n",
    "    avg_pwr = wifi_label_utils.moving_average(iq_samples, avg_window_len)\n",
    "    avg_pwr_db = 10*np.log10(avg_pwr)\n",
    "\n",
    "    \n",
    "    guess_threshold = (np.max(avg_pwr_db) + np.mean(avg_pwr_db))/2\n",
    "    #guess_threshold = 1.05 * np.max(avg_pwr_db)\n",
    "\n",
    "    #MAD estimator\n",
    "\n",
    "    # print(f\"{np.quantile(avg_pwr_db, 0.75)=}\")\n",
    "    def median_absolute_deviation(series):\n",
    "        # print(f\"{np.median(series)=}\")\n",
    "        # print(np.abs(series-np.median(series)))\n",
    "        # print(np.median(np.abs(series - np.median(series))))\n",
    "        mad = 1.4826 * np.median(np.abs(series - np.median(series)))\n",
    "        # print(f\"{mad=}\")\n",
    "        # sci_mad = scipy.stats.median_abs_deviation(series, scale=\"normal\")\n",
    "        # print(f\"{sci_mad=}\")\n",
    "        return np.median(series) + 6*mad\n",
    "\n",
    "    mad = median_absolute_deviation(avg_pwr_db)\n",
    "    guess_threshold = mad\n",
    "    \n",
    "    if debug:\n",
    "        print(f\"{np.max(avg_pwr_db)=}\")\n",
    "        print(f\"{np.mean(avg_pwr_db)=}\")\n",
    "        print(f\"median absolute deviation threshold = {mad}\")\n",
    "        print(f\"{guess_threshold=}\")\n",
    "        print(f\"{len(avg_pwr_db)=}\")\n",
    "        \n",
    "        plt.figure()\n",
    "        #plt.plot(avg_pwr_db[int(0*20480000e-2):int(avg_duration*20.48e6)])\n",
    "        db_plot = avg_pwr_db[int(0*20.48e6):int(avg_duration*20.48e6)]\n",
    "        # db_plot = avg_pwr_db[int(0.945*20.48e6):int(0.95*20.48e6)]\n",
    "        # print(f\"{len(db_plot)=}\")\n",
    "        plt.plot(db_plot)\n",
    "        plt.axhline(y = guess_threshold, color = 'g', linestyle = '-') \n",
    "        plt.axhline(y = np.mean(avg_pwr_db), color = 'r', linestyle = '-') \n",
    "        plt.axhline(y = mad, color = 'b', linestyle = '-') \n",
    "        plt.show()\n",
    "        \n",
    "    #wifi_label_utils.annotate_power_squelch(data_obj, label, guess_threshold, avg_window_len, skip_validate=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efa72fae-a19a-4b8b-b8fb-5355575f76bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                       | 0/24 [00:00<?, ?it/s]/home/ltindall/.pyenv/versions/3.10.9/envs/rfml-dev/lib/python3.10/site-packages/sigmf/sigmffile.py:914: UserWarning: Compliant Dataset `data/gamutrf/gamutrf-sd-gr-ieee-wifi/gain_40/wifi-encoding-6-sendsps-5000000.s16.zst.sigmf-data` exists but \"core:dataset\" is also defined; using `data/gamutrf/gamutrf-sd-gr-ieee-wifi/gain_40/wifi-encoding-6-sendsps-5000000.s16.zst.sigmf-data`\n",
      "  warnings.warn(f'Compliant Dataset `{compliant_data_fn}` exists but '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loaded \n",
      " Data file: data/gamutrf/gamutrf-sd-gr-ieee-wifi/gain_40/wifi-encoding-6-sendsps-5000000.s16.zst.sigmf-data \n",
      " SigMF-Meta file: data/gamutrf/gamutrf-sd-gr-ieee-wifi/gain_40/wifi-encoding-6-sendsps-5000000.s16.zst.sigmf-meta\n",
      "\n",
      "np.max(avg_pwr_db)=-52.79944240229695\n",
      "np.mean(avg_pwr_db)=-63.41654634051632\n",
      "median absolute deviation threshold = -61.74742567596584\n",
      "guess_threshold=-61.74742567596584\n",
      "len(avg_pwr_db)=81919745\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                       | 0/24 [00:13<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "for f in tqdm(glob.glob(\"data/gamutrf/gamutrf-sd-gr-ieee-wifi/gain_40/wifi*.sigmf-meta\")):\n",
    "    annotate(f, label=\"wifi\", avg_window_len=256, avg_duration=4, debug=True)\n",
    "    break\n",
    "    \n",
    "# for f in tqdm(glob.glob(\"data/gamutrf/gamutrf-sd-gr-ieee-wifi/gain_40/anom*.sigmf-meta\")):\n",
    "#     annotate(f, label=\"anom_wifi\", avg_window_len=256, avg_duration=4, debug=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa7f891f-acf6-49e6-aa11-2be4ac16090a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torchsig\n",
    "\n",
    "import numpy as np\n",
    "import torch.nn.functional as F\n",
    "import torchsig.transforms as ST\n",
    "\n",
    "from datetime import datetime\n",
    "from matplotlib import pyplot as plt\n",
    "from models import CustomNetwork\n",
    "from pathlib import Path\n",
    "from pytorch_lightning import LightningModule, Trainer\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from sigmf_db_dataset import SigMFDB\n",
    "from sigmf_pytorch_dataset import SigMFDataset\n",
    "from sklearn.metrics import classification_report\n",
    "from torch import optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torchsig.datasets.sig53 import Sig53\n",
    "from torchsig.models.iq_models.efficientnet.efficientnet import efficientnet_b4\n",
    "from torchsig.transforms import (\n",
    "    Compose,\n",
    "    IQImbalance,\n",
    "    Normalize,\n",
    "    RandomApply,\n",
    "    RandomFrequencyShift,\n",
    "    RandomPhaseShift,\n",
    "    RandomResample,\n",
    "    RandomTimeShift,\n",
    "    RayleighFadingChannel,\n",
    "    TargetSNR,\n",
    "    ComplexTo2D,\n",
    ")\n",
    "from torchsig.utils.cm_plotter import plot_confusion_matrix\n",
    "from torchsig.utils.dataset import SignalDataset\n",
    "from torchsig.utils.visualize import IQVisualizer, SpectrogramVisualizer, two_channel_to_complex\n",
    "from tqdm import tqdm\n",
    "from typing import List\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e92f3d40-eef9-4005-82e6-c43f76b3fd0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#dataset_path = \"./dev_data/torchsig_test/gamutrf-sd-gr-ieee-wifi\"\n",
    "dataset_path = \"./data/gamutrf/gamutrf-sd-gr-ieee-wifi\"\n",
    "num_iq_samples = 1024\n",
    "idx_to_class = {\n",
    "  0: 'anom_wifi',\n",
    "  1: 'mini2_telem',\n",
    "  2: 'mini2_video',\n",
    "  3: 'wifi6',\n",
    "  4: 'wifi',\n",
    "  5: 'fhss_css'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02c06570-6eef-49a6-86f2-90d816fbed06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class List: ['anom_wifi', 'mini2_telem', 'mini2_video', 'wifi6', 'wifi', 'fhss_css']\n",
      "len(dataset)=471979\n",
      "{'anom_wifi': 229527, 'mini2_telem': 0, 'mini2_video': 0, 'wifi6': 0, 'wifi': 242452, 'fhss_css': 0}\n"
     ]
    }
   ],
   "source": [
    "transform = ST.Compose([\n",
    "    # ST.RandomPhaseShift(phase_offset=(-1, 1)),\n",
    "    ST.Normalize(norm=np.inf),\n",
    "    ST.ComplexTo2D(),\n",
    "])\n",
    "dataset = SigMFDataset( \n",
    "    root=dataset_path, \n",
    "    sample_count= num_iq_samples, \n",
    "    class_list = list(idx_to_class.values()),\n",
    "    transform = transform,\n",
    "    only_first_samples=False,\n",
    ")\n",
    "dataset_class_counts = {class_name:0 for class_name in dataset.class_list}\n",
    "for data,label in dataset:\n",
    "    dataset_class_counts[dataset.class_list[label]] += 1\n",
    "print(f\"{len(dataset)=}\")\n",
    "print(dataset_class_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7216e2c5-9656-4532-8029-e87af6c4bba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloader = DataLoader(\n",
    "    dataset=dataset,\n",
    "    batch_size=180,\n",
    "    num_workers=16,\n",
    "    shuffle=False,\n",
    "    drop_last=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bfdd181a-5468-420a-9ffc-cd0cf19993de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([[[ 0.2169, -0.3254,  0.0000,  ...,  0.2169, -0.2169,  0.1085],\n",
       "          [ 0.3254,  0.1085, -0.1152,  ...,  0.6508, -0.2169, -0.3254]],\n",
       " \n",
       "         [[ 0.0000, -0.0070,  0.0000,  ..., -0.5625, -0.4500, -0.1125],\n",
       "          [ 0.1125,  0.2250, -0.1125,  ..., -0.2320, -0.3375, -0.4500]],\n",
       " \n",
       "         [[ 0.3175,  0.0992,  0.2117,  ..., -0.3175, -0.4234, -0.3242],\n",
       "          [-0.3175, -0.3242, -0.2117,  ...,  0.0000,  0.3109,  0.5292]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[ 0.4339,  0.6508,  0.4271,  ...,  0.4271,  0.3254,  0.3254],\n",
       "          [-0.2169, -0.3322, -0.1085,  ..., -0.1085,  0.1085,  0.0000]],\n",
       " \n",
       "         [[ 0.1118,  0.1118, -0.3424,  ..., -0.3354, -0.1118,  0.2236],\n",
       "          [ 0.0000, -0.2236, -0.4472,  ...,  0.1048, -0.3354, -0.2236]],\n",
       " \n",
       "         [[ 0.0000,  0.0952,  0.0000,  ..., -0.1015,  0.0000,  0.2031],\n",
       "          [ 0.1967,  0.2031,  0.1015,  ..., -0.2031, -0.3046,  0.0000]]],\n",
       "        dtype=torch.float64),\n",
       " tensor([4, 4, 0, 4, 0, 4, 4, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 0, 4, 0, 0,\n",
       "         4, 0, 4, 0, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0,\n",
       "         4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 4, 0, 4, 0, 4, 4, 0, 4, 4, 4,\n",
       "         4, 4, 4, 0, 0, 4, 4, 0, 4, 4, 4, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 0, 4, 4,\n",
       "         4, 4, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 0, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0,\n",
       "         0, 4, 0, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 4, 0,\n",
       "         4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0, 4,\n",
       "         4, 4, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4])]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(iter(dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5ae45e20-b6dd-46d2-99ef-0b6ab2a44588",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.jit.load('weights/checkpoint-v19_torchscript.pt')\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = model.to(device)\n",
    "\n",
    "example_model = CustomNetwork(model, dataloader)\n",
    "\n",
    "example_model = example_model.eval()\n",
    "example_model = example_model.cuda() if torch.cuda.is_available() else example_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83ffdeae-6321-4f7d-b6de-7e3d539c19c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████| 2622/2622 [02:47<00:00, 15.64it/s]\n"
     ]
    }
   ],
   "source": [
    "# Infer results over validation set\n",
    "num_test_examples = len(dataset)\n",
    "y_preds = np.zeros((num_test_examples,))\n",
    "y_true = np.zeros((num_test_examples,))\n",
    "y_true_list = []\n",
    "y_preds_list = []\n",
    "with torch.no_grad():\n",
    "    example_model.eval()\n",
    "    for data, label in tqdm(dataloader):\n",
    "        # Infer\n",
    "        data = data.float()\n",
    "\n",
    "        data = data.cuda() if torch.cuda.is_available() else data\n",
    "        pred_tmp = example_model.predict(data)\n",
    "        pred_tmp = pred_tmp.cpu().numpy() if torch.cuda.is_available() else pred_tmp\n",
    "\n",
    "        y_preds_list.extend(np.argmax(pred_tmp, axis=1).tolist())\n",
    "        y_true_list.extend(label.tolist())\n",
    "        \n",
    "y_preds = y_preds_list\n",
    "y_true = y_true_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eeb2238a-f586-4e81-b89a-2c06b35d35bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ltindall/.pyenv/versions/3.10.9/envs/rfml-dev/lib/python3.10/site-packages/torchsig/utils/cm_plotter.py:31: RuntimeWarning: invalid value encountered in divide\n",
      "  cm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2600x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = np.sum(np.asarray(y_preds)==np.asarray(y_true))/len(y_true)\n",
    "plot_confusion_matrix(\n",
    "    y_true, \n",
    "    y_preds, \n",
    "    classes=dataset.class_list,#np.unique(y_preds),#np.unique(np.concatenate((np.arange(len(dataset.class_list)),np.unique(y_preds)))),\n",
    "    normalize=True,\n",
    "    title=\"Example Modulations Confusion Matrix\\nTotal Accuracy: {:.2f}%\".format(acc*100),\n",
    "    text=True,\n",
    "    rotate_x_text=90,\n",
    "    figsize=(26,20),\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c28527c-e8ed-4031-a80e-07cae05c4d1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8465018-eccc-4c7d-9265-a3f10f4130ee",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'WeightedRandomSampler' object is not an iterator",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_weighted_sampler\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'WeightedRandomSampler' object is not an iterator"
     ]
    }
   ],
   "source": [
    "next(dataset.get_weighted_sampler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d16da1a-e17b-43a3-bdfb-690507c458ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anom_wifi', 'mini2_telem', 'mini2_video', 'wifi6', 'wifi', 'fhss_css']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.class_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "23e71a24-12cb-4177-9eba-a7196acb6388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anom_wifi', 'mini2_telem', 'mini2_video', 'wifi6', 'wifi', 'fhss_css']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(idx_to_class.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7899349-0759-49a5-9a1a-7b921bb1744a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}