notebooks/torchsig_snr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aaf344bf-9e03-44be-acea-34b0f886a2dd",
   "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 scipy import signal as sp\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",
    "    SpectralInversion,\n",
    "    TargetSNR,\n",
    "    ComplexTo2D,\n",
    "    AddNoise,\n",
    "    RandAugment,\n",
    "    RandomMagRescale,\n",
    "    RollOff,\n",
    "    RandomConvolve,\n",
    "    RandomDropSamples,\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": 30,
   "id": "7b69ff14-2015-4fc6-98d3-e0d2aec5cd17",
   "metadata": {},
   "outputs": [],
   "source": [
    "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 # 1024*16# \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": 31,
   "id": "025fa8a9-9e8c-4ee8-8f21-80e914e47a8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class List: ['anom_wifi', 'mini2_telem', 'mini2_video', 'wifi6', 'wifi', 'fhss_css']\n",
      "len(dataset)=42777\n",
      "{'anom_wifi': 20246, 'mini2_telem': 0, 'mini2_video': 0, 'wifi6': 0, 'wifi': 22531, 'fhss_css': 0}\n"
     ]
    }
   ],
   "source": [
    "# transform = ST.Compose([\n",
    "#     ST.Normalize(norm=np.inf),\n",
    "#     ST.ComplexTo2D(),\n",
    "# ])\n",
    "transform = ST.Compose([\n",
    "    #RandomApply(RandomFrequencyShift((-0.16, 0.16)), 0.7),\n",
    "    # AddNoise(((-60, 35)),input_noise_floor_db=-100),\n",
    "    #AddNoise(((50, 100))),\n",
    "\n",
    "    \n",
    "    RandomApply(\n",
    "        RandomTimeShift(\n",
    "            shift=(-int(num_iq_samples / 2), int(num_iq_samples / 2))\n",
    "        ),\n",
    "        0.25,\n",
    "    ),\n",
    "    RandomApply(RandomFrequencyShift(freq_shift=(-0.2, 0.2)), 0.25),\n",
    "    RandomApply(\n",
    "        RandomResample(\n",
    "            rate_ratio=(0.8, 1.2), num_iq_samples=num_iq_samples\n",
    "        ),\n",
    "        0.99,\n",
    "    ),\n",
    "    RandomApply(SpectralInversion(), 0.5),\n",
    "    AddNoise(\n",
    "        noise_power_db=(0, 0), input_noise_floor_db=-100\n",
    "    ),  # Set input noise floor very low because this transform sets the floor\n",
    "    AddNoise(\n",
    "        noise_power_db=(-40, 40), input_noise_floor_db=0\n",
    "    ),  # Then add minimal noise without affecting SNR\n",
    "    RandAugment(\n",
    "        [\n",
    "            RandomApply(\n",
    "                RandomMagRescale(start=(0, 0.9), scale=(-4, 4)), 0.5\n",
    "            ),\n",
    "            RollOff(\n",
    "                low_freq=(0.00, 0.05),\n",
    "                upper_freq=(0.95, 1.00),\n",
    "                low_cut_apply=0.5,\n",
    "                upper_cut_apply=0.5,\n",
    "                order=(6, 20),\n",
    "            ),\n",
    "            RandomConvolve(num_taps=(2, 5), alpha=(0.1, 0.4)),\n",
    "            RayleighFadingChannel((0.001, 0.01)),\n",
    "            RandomDropSamples(\n",
    "                drop_rate=0.01,\n",
    "                size=(1, 1),\n",
    "                fill=[\"ffill\", \"bfill\", \"mean\", \"zero\"],\n",
    "            ),\n",
    "            RandomPhaseShift((-1, 1)),\n",
    "            IQImbalance(\n",
    "                (-3, 3),\n",
    "                (-np.pi * 1.0 / 180.0, np.pi * 1.0 / 180.0),\n",
    "                (-0.1, 0.1),\n",
    "            ),\n",
    "        ],\n",
    "        num_transforms=2,\n",
    "    ),\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=True,\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": 32,
   "id": "768a4eb8-7b43-4c53-b239-4fea3b6d94e4",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1600x1600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_loader = DataLoader(\n",
    "    dataset=dataset,\n",
    "    batch_size=9,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "visualizer = SpectrogramVisualizer(\n",
    "    data_loader=data_loader, \n",
    "    window=sp.windows.tukey(64, 0.25),#sp.windows.hamming(64),\n",
    "    nperseg = 64\n",
    ")\n",
    "\n",
    "for figure in iter(visualizer):\n",
    "    figure.set_size_inches(16, 16)\n",
    "    plt.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092b7d5d-6f5f-48ff-bc1f-3171b53e1486",
   "metadata": {},
   "source": [
    "# No Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b64546c-5dfb-4a24-9f2d-df449d43e783",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform = ST.Compose([\n",
    "#     ST.Normalize(norm=np.inf),\n",
    "#     ST.ComplexTo2D(),\n",
    "# ])\n",
    "transform = ST.Compose([\n",
    "    #RandomApply(RandomFrequencyShift((-0.16, 0.16)), 0.7),\n",
    "    # AddNoise(((-60, 35)),input_noise_floor_db=-100),\n",
    "    #AddNoise(((50, 100))),\n",
    "\n",
    "    # AddNoise(\n",
    "    #     noise_power_db=(0, 0), input_noise_floor_db=-100\n",
    "    # ),  # Set input noise floor very low because this transform sets the floor\n",
    "    # AddNoise(\n",
    "    #     noise_power_db=(-40, -20), input_noise_floor_db=0\n",
    "    # ),  # Then add minimal noise without affecting SNR\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=True,\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": null,
   "id": "4fa5515b-1ea7-4d3b-9816-f8bd611b35f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = DataLoader(\n",
    "    dataset=dataset,\n",
    "    batch_size=9,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "visualizer = SpectrogramVisualizer(\n",
    "    data_loader=data_loader, \n",
    "    window=sp.windows.tukey(256, 0.25),#sp.windows.hamming(64),\n",
    "    nperseg = 256\n",
    ")\n",
    "\n",
    "for figure in iter(visualizer):\n",
    "    figure.set_size_inches(16, 16)\n",
    "    plt.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2b195b6-a9a8-4bbe-ab71-db681418578d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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