E2E_framework_WGAN_SSPA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a060dfa1-4be6-4214-a85d-ca8ae31e6631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x73fd4e70fc10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch.nn import functional as F\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm\n",
    "import math\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "import pickle\n",
    "import csv\n",
    "import sys\n",
    "\n",
    "sys.path.append('src/')\n",
    "\n",
    "# Own libraries\n",
    "import utils\n",
    "import trainer_WGAN as trainer\n",
    "import model_WGAN\n",
    "import WGAN\n",
    "import models\n",
    "\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c97ad605-5e1c-46f8-8f97-2f363762dcc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "\n",
    "#writer = SummaryWriter('runs/SSPA_WGAN_bigger_model')\n",
    "\n",
    "# Encoding parameters\n",
    "\n",
    "n = 8 #7\n",
    "M = 64 #16\n",
    "k = math.log2(M)\n",
    "TRAINING_SNR = 9\n",
    "rate = k/n\n",
    "noise_std = utils.EbNo_to_noise(TRAINING_SNR, rate)\n",
    "save_every = 1000\n",
    "\n",
    "\n",
    "#GAN parameters\n",
    "g_input_size = 2*n      # Random noise dimension coming into generator, per output vector\n",
    "g_hidden_size = 256 # Generator complexity\n",
    "g_output_size = n     # Size of generated output vector\n",
    "\n",
    "d_input_size = 2*n    # Minibatch size - cardinality of distributions\n",
    "d_hidden_size = 256    # Discriminator complexity\n",
    "d_output_size = 1     # Single dimension for 'real' vs. 'fake' classification\n",
    "\n",
    "batch_size = 5000\n",
    "num_epochs = 20000\n",
    "\n",
    "G = WGAN.Generator(input_size=g_input_size, hidden_size=g_hidden_size, output_size=g_output_size)\n",
    "D = WGAN.Discriminator(input_size=d_input_size, hidden_size=d_hidden_size, output_size=d_output_size)\n",
    "G = G.to(device)\n",
    "D = D.to(device)\n",
    "\n",
    "writer_G = SummaryWriter('runs/SSPA_WGAN_G')\n",
    "writer_D = SummaryWriter('runs/SSPA_WGAN_D')\n",
    "#example_data_g = torch.rand(size=(1,n)).to(device)\n",
    "#writer_G.add_graph(G, example_data_g)\n",
    "#writer_D.add_graph(D, (G(example_data_g), example_data_g))\n",
    "\n",
    "d_lr = 1e-4\n",
    "g_lr = 1e-4\n",
    "\n",
    "channel_sspa = model_WGAN.Channel_SSPA_only(noise_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47d835bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of parameters (WGAN model Generator): 72200\n",
      "# of parameters (WGAN model Critic): 70401\n"
     ]
    }
   ],
   "source": [
    "total_params_g = sum(p.numel() for p in G.parameters())\n",
    "print('# of parameters (WGAN model Generator): ' + str(total_params_g))\n",
    "total_params_d = sum(p.numel() for p in D.parameters())\n",
    "print('# of parameters (WGAN model Critic): ' + str(total_params_d))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5f8a74-0c00-4804-89c3-e4dec24cd93e",
   "metadata": {},
   "source": [
    "# Training GAN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b75c382f-7ca9-4064-bed6-7c478eb88cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discriminator(\n",
       "  (map1): Linear(in_features=16, out_features=256, bias=True)\n",
       "  (map2): Linear(in_features=256, out_features=256, bias=True)\n",
       "  (map3): Linear(in_features=256, out_features=1, bias=True)\n",
       "  (f): ReLU()\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milestone=100\n",
    "\n",
    "G.load_state_dict(torch.load(f'state_dict_WGAN/G_SSPA-{milestone}.pt'))#, map_location=\"cpu\"))\n",
    "G.eval()\n",
    "\n",
    "D.load_state_dict(torch.load(f'state_dict_WGAN/D_SSPA-{milestone}.pt'))#, map_location=\"cpu\"))\n",
    "D.eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b31dfeb-169f-43c3-9d61-bd88442ad557",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 50000: KL Div 1.643e-03 G loss 1.539e-02: 100%|#| 50000/50000 [17:32<00:00\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 50000\n",
    "g_lr=1e-5; d_lr=1e-5\n",
    "epochs = tqdm(range(num_epochs), ascii=True)\n",
    "\n",
    "for epoch in epochs:\n",
    "    kl, g_loss = WGAN.train_WGAN(G, D, batch_size, n, channel_sspa, d_lr, g_lr, is_GP=False)\n",
    "    example_data_g = torch.rand(size=(1,n)).to(device)\n",
    "    writer_G.add_scalar('Loss/G train', g_loss, epoch)\n",
    "    writer_G.add_scalar('KL Div/G train', kl, epoch)\n",
    "    if (epoch+1) % 100 == 0:\n",
    "        epochs.set_description(f\"Epoch {epoch+1}: KL Div {kl:.3e} G loss {-g_loss:.3e}\")\n",
    "    if (epoch+1) % save_every == 0:\n",
    "        torch.save(G.state_dict(), f'state_dict_WGAN/G_SSPA-{(epoch+1)//save_every}.pt')\n",
    "        torch.save(D.state_dict(), f'state_dict_WGAN/D_SSPA-{(epoch+1)//save_every}.pt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b96b9c9-5cf7-4beb-b98c-6b936bc1d884",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1:  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WGAN.plot_GAN_m(G, 10000,M, n, channel_sspa, 1 , device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43096bd1-8797-44d0-aa02-bdebfb25fe63",
   "metadata": {},
   "source": [
    "# Training GAN Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "888a0483-42b5-4490-b7e4-f8a7c942d1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_GAN = models.Encoder_1h( M, n, n/2)\n",
    "decoder_GAN = models.Decoder_1h( M, n)\n",
    "\n",
    "encoder_GAN.load_state_dict(torch.load(f'state_dict_WGAN/enc_GAN_SSPA.pt'))#, map_location=\"cpu\"))\n",
    "encoder_GAN.eval()\n",
    "encoder_GAN = encoder_GAN.to(device)\n",
    "\n",
    "decoder_GAN.load_state_dict(torch.load(f'state_dict_WGAN/dec_GAN_SSPA.pt'))#, map_location=\"cpu\"))\n",
    "decoder_GAN.eval()\n",
    "decoder_GAN = decoder_GAN.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "23194890-8bc3-4913-bb24-de6235227d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.random.randint(0, M, size=10000000) # size = 10000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d58c10c6-1688-48e0-9ef7-393d403f23ff",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 2.66e-02 SER 3.91e-03: 100%|#| 10000/10000 [00:42<00:00, 236.68it/\n",
      "epoch 2: loss 9.70e-04 SER 3.17e-04: 100%|#| 10000/10000 [00:43<00:00, 232.06it/\n",
      "epoch 3: loss 8.29e-04 SER 2.64e-04: 100%|#| 10000/10000 [00:42<00:00, 233.32it/\n",
      "epoch 4: loss 7.69e-04 SER 2.46e-04: 100%|#| 10000/10000 [00:47<00:00, 210.80it/\n",
      "epoch 5: loss 7.14e-04 SER 2.29e-04: 100%|#| 10000/10000 [00:48<00:00, 207.76it/\n",
      "epoch 6: loss 6.52e-04 SER 2.05e-04: 100%|#| 10000/10000 [00:46<00:00, 214.16it/\n",
      "epoch 7: loss 6.35e-04 SER 1.99e-04: 100%|#| 10000/10000 [00:48<00:00, 204.54it/\n",
      "epoch 8: loss 6.12e-04 SER 1.89e-04: 100%|#| 10000/10000 [00:49<00:00, 200.58it/\n",
      "epoch 9: loss 5.65e-04 SER 1.80e-04: 100%|#| 10000/10000 [00:46<00:00, 216.46it/\n",
      "epoch 10: loss 5.60e-04 SER 1.74e-04: 100%|#| 10000/10000 [00:48<00:00, 206.82it\n",
      "epoch 11: loss 5.70e-04 SER 1.81e-04: 100%|#| 10000/10000 [00:47<00:00, 209.26it\n",
      "epoch 12: loss 5.42e-04 SER 1.71e-04: 100%|#| 10000/10000 [00:47<00:00, 208.89it\n",
      "epoch 13: loss 5.23e-04 SER 1.62e-04: 100%|#| 10000/10000 [00:47<00:00, 209.02it\n",
      "epoch 14: loss 5.08e-04 SER 1.56e-04: 100%|#| 10000/10000 [00:48<00:00, 207.24it\n",
      "epoch 15: loss 5.23e-04 SER 1.61e-04: 100%|#| 10000/10000 [00:48<00:00, 207.78it\n",
      "epoch 16: loss 4.93e-04 SER 1.46e-04: 100%|#| 10000/10000 [00:48<00:00, 207.01it\n",
      "epoch 17: loss 4.70e-04 SER 1.47e-04: 100%|#| 10000/10000 [00:47<00:00, 210.32it\n",
      "epoch 18: loss 4.81e-04 SER 1.48e-04: 100%|#| 10000/10000 [00:48<00:00, 206.45it\n",
      "epoch 19: loss 4.75e-04 SER 1.53e-04: 100%|#| 10000/10000 [00:48<00:00, 207.40it\n",
      "epoch 20: loss 4.98e-04 SER 1.51e-04: 100%|#| 10000/10000 [00:48<00:00, 208.09it\n",
      "epoch 21: loss 4.75e-04 SER 1.53e-04: 100%|#| 10000/10000 [00:47<00:00, 208.74it\n",
      "epoch 22: loss 4.81e-04 SER 1.49e-04: 100%|#| 10000/10000 [00:48<00:00, 206.28it\n",
      "epoch 23: loss 4.71e-04 SER 1.43e-04: 100%|#| 10000/10000 [00:48<00:00, 206.56it\n",
      "epoch 24: loss 4.77e-04 SER 1.47e-04: 100%|#| 10000/10000 [00:48<00:00, 207.19it\n",
      "epoch 25: loss 4.67e-04 SER 1.46e-04: 100%|#| 10000/10000 [00:47<00:00, 208.67it\n",
      "epoch 26: loss 4.60e-04 SER 1.39e-04: 100%|#| 10000/10000 [00:47<00:00, 209.59it\n",
      "epoch 27: loss 4.43e-04 SER 1.37e-04: 100%|#| 10000/10000 [00:47<00:00, 208.59it\n",
      "epoch 28: loss 4.36e-04 SER 1.33e-04: 100%|#| 10000/10000 [00:47<00:00, 208.95it\n",
      "epoch 29: loss 4.45e-04 SER 1.31e-04: 100%|#| 10000/10000 [00:47<00:00, 210.33it\n",
      "epoch 30: loss 4.42e-04 SER 1.35e-04: 100%|#| 10000/10000 [00:48<00:00, 208.23it\n",
      "epoch 31: loss 4.48e-04 SER 1.38e-04: 100%|#| 10000/10000 [00:48<00:00, 207.75it\n",
      "epoch 32: loss 4.38e-04 SER 1.35e-04: 100%|#| 10000/10000 [00:47<00:00, 209.53it\n",
      "epoch 33: loss 4.59e-04 SER 1.32e-04: 100%|#| 10000/10000 [00:47<00:00, 209.10it\n",
      "epoch 34: loss 4.18e-04 SER 1.28e-04: 100%|#| 10000/10000 [00:45<00:00, 220.23it\n",
      "epoch 35: loss 4.32e-04 SER 1.33e-04: 100%|#| 10000/10000 [00:43<00:00, 227.91it\n",
      "epoch 36: loss 4.31e-04 SER 1.28e-04: 100%|#| 10000/10000 [00:44<00:00, 225.03it\n",
      "epoch 37: loss 4.22e-04 SER 1.28e-04: 100%|#| 10000/10000 [00:44<00:00, 226.71it\n",
      "epoch 38: loss 4.29e-04 SER 1.30e-04: 100%|#| 10000/10000 [00:44<00:00, 226.06it\n",
      "epoch 39: loss 4.40e-04 SER 1.37e-04: 100%|#| 10000/10000 [00:43<00:00, 227.58it\n",
      "epoch 40: loss 4.32e-04 SER 1.33e-04: 100%|#| 10000/10000 [00:44<00:00, 224.53it\n"
     ]
    }
   ],
   "source": [
    "encoder_GAN = models.Encoder_1h( M, n, n/2)\n",
    "decoder_GAN = models.Decoder_1h( M, n)\n",
    "channel = model_WGAN.Channel_GAN(encoder_GAN, decoder_GAN, G).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=40, noise_std = noise_std, batch_size=1000, M=M, rate=rate, learning_rate=1e-3, TRAINING_EbN0=TRAINING_SNR) # epoch = 10, batch_size = 1000\n",
    "weights = filter(lambda p: p.requires_grad, list(encoder_GAN.parameters())+list(decoder_GAN.parameters()))\n",
    "t_gan = trainer.Trainer(channel, dataset, tconf, one_hot=True)\n",
    "\n",
    "t_gan.train(weights=weights, GAN=True, global_epoch=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f605cdb0",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 1.74e-04 SER 5.26e-05: 100%|#| 10000/10000 [00:39<00:00, 251.92it/\n",
      "SNR 9  iter 9900: SER 1.028e-03: 100%|###| 10000/10000 [00:16<00:00, 590.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1: validation SER 1.03e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 2: loss 1.11e-04 SER 3.40e-05: 100%|#| 10000/10000 [00:39<00:00, 253.28it/\n",
      "SNR 9  iter 9900: SER 1.056e-03: 100%|###| 10000/10000 [00:16<00:00, 592.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2: validation SER 1.06e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 3: loss 1.22e-04 SER 3.79e-05: 100%|#| 10000/10000 [00:40<00:00, 246.49it/\n",
      "SNR 9  iter 9900: SER 1.066e-03: 100%|###| 10000/10000 [00:16<00:00, 593.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3: validation SER 1.07e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 4: loss 1.09e-04 SER 3.52e-05: 100%|#| 10000/10000 [00:40<00:00, 247.54it/\n",
      "SNR 9  iter 9900: SER 1.049e-03: 100%|###| 10000/10000 [00:16<00:00, 594.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4: validation SER 1.05e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 5: loss 1.13e-04 SER 3.51e-05: 100%|#| 10000/10000 [00:41<00:00, 243.53it/\n",
      "SNR 9  iter 9900: SER 1.096e-03: 100%|###| 10000/10000 [00:16<00:00, 589.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5: validation SER 1.10e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 6: loss 1.17e-04 SER 3.66e-05: 100%|#| 10000/10000 [00:40<00:00, 248.32it/\n",
      "SNR 9  iter 9900: SER 1.068e-03: 100%|###| 10000/10000 [00:17<00:00, 588.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 6: validation SER 1.07e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 7: loss 1.15e-04 SER 3.76e-05: 100%|#| 10000/10000 [00:40<00:00, 249.89it/\n",
      "SNR 9  iter 9900: SER 1.146e-03: 100%|###| 10000/10000 [00:16<00:00, 592.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 7: validation SER 1.14e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 8: loss 1.14e-04 SER 3.37e-05: 100%|#| 10000/10000 [00:40<00:00, 245.19it/\n",
      "SNR 9  iter 9900: SER 1.107e-03: 100%|###| 10000/10000 [00:16<00:00, 593.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 8: validation SER 1.11e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 9: loss 1.26e-04 SER 3.86e-05: 100%|#| 10000/10000 [00:40<00:00, 246.64it/\n",
      "SNR 9  iter 9900: SER 1.052e-03: 100%|###| 10000/10000 [00:16<00:00, 596.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 9: validation SER 1.05e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 10: loss 1.16e-04 SER 3.52e-05: 100%|#| 10000/10000 [00:40<00:00, 247.01it\n",
      "SNR 9  iter 9900: SER 1.114e-03: 100%|###| 10000/10000 [00:16<00:00, 593.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 10: validation SER 1.11e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 11: loss 1.20e-04 SER 3.55e-05: 100%|#| 10000/10000 [00:40<00:00, 248.84it\n",
      "SNR 9  iter 9900: SER 1.093e-03: 100%|###| 10000/10000 [00:17<00:00, 588.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 11: validation SER 1.09e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 12: loss 1.17e-04 SER 3.57e-05: 100%|#| 10000/10000 [00:40<00:00, 246.18it\n",
      "SNR 9  iter 9900: SER 1.079e-03: 100%|###| 10000/10000 [00:16<00:00, 595.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 12: validation SER 1.08e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 13: loss 1.20e-04 SER 3.81e-05: 100%|#| 10000/10000 [00:40<00:00, 248.77it\n",
      "SNR 9  iter 9900: SER 1.105e-03: 100%|###| 10000/10000 [00:16<00:00, 592.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 13: validation SER 1.11e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 14: loss 1.13e-04 SER 3.55e-05: 100%|#| 10000/10000 [00:40<00:00, 246.52it\n",
      "SNR 9  iter 9900: SER 1.116e-03: 100%|###| 10000/10000 [00:16<00:00, 594.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 14: validation SER 1.12e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 15: loss 1.26e-04 SER 4.00e-05: 100%|#| 10000/10000 [00:40<00:00, 247.18it\n",
      "SNR 9  iter 9900: SER 1.162e-03: 100%|###| 10000/10000 [00:16<00:00, 590.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 15: validation SER 1.16e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 16: loss 1.19e-04 SER 3.50e-05: 100%|#| 10000/10000 [00:40<00:00, 247.48it\n",
      "SNR 9  iter 9900: SER 1.108e-03: 100%|###| 10000/10000 [00:16<00:00, 594.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 16: validation SER 1.11e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 17: loss 1.28e-04 SER 4.00e-05: 100%|#| 10000/10000 [00:40<00:00, 249.43it\n",
      "SNR 9  iter 9900: SER 1.096e-03: 100%|###| 10000/10000 [00:16<00:00, 594.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 17: validation SER 1.10e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 18: loss 1.28e-04 SER 4.03e-05: 100%|#| 10000/10000 [00:40<00:00, 247.07it\n",
      "SNR 9  iter 9900: SER 1.115e-03: 100%|###| 10000/10000 [00:16<00:00, 594.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 18: validation SER 1.12e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 19: loss 1.31e-04 SER 4.23e-05: 100%|#| 10000/10000 [00:40<00:00, 249.27it\n",
      "SNR 9  iter 9900: SER 1.081e-03: 100%|###| 10000/10000 [00:16<00:00, 593.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 19: validation SER 1.08e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 20: loss 1.31e-04 SER 4.10e-05: 100%|#| 10000/10000 [00:40<00:00, 247.56it\n",
      "SNR 9  iter 9900: SER 1.070e-03: 100%|###| 10000/10000 [00:16<00:00, 595.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20: validation SER 1.07e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#model_1 = model_WGAN.Channel_GAN(encoder_GAN, decoder_GAN, G).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=20, noise_std = noise_std, batch_size=1000,\n",
    "                              M=M, rate=rate, learning_rate = 1e-4, TRAINING_EbN0=TRAINING_SNR) # epoch = 10, batch_size = 1000\n",
    "weights = filter(lambda p: p.requires_grad, list(encoder_GAN.parameters())+list(decoder_GAN.parameters()))\n",
    "t_gan = trainer.Trainer_(encoder_GAN, decoder_GAN, G, dataset, tconf, one_hot=True)#, writer=writer)\n",
    "t_gan.train(weights=weights, GAN=True,global_epoch=0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a1cd602d-cf32-4b32-b920-ce86eb9f16f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(encoder_GAN.state_dict(), 'state_dict_WGAN/enc_GAN_SSPA.pt')\n",
    "torch.save(decoder_GAN.state_dict(), 'state_dict_WGAN/dec_GAN_SSPA.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12e35c96-035c-4c24-b3ac-8141e7aeaa06",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#torch.save(channel.state_dict(), path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b37e47-c436-410e-aa26-fdb7468eb636",
   "metadata": {},
   "outputs": [],
   "source": [
    "#channel.load_state_dict(torch.load(path, map_location=device))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc83fc0-eb93-4eef-a2bf-a80e9f726fed",
   "metadata": {},
   "source": [
    "# Training AE Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7db9a9d-916e-4934-980b-70420f64ce7a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 3.17e-02 SER 5.56e-03: 100%|#| 10000/10000 [00:31<00:00, 317.62it/\n",
      "epoch 2: loss 3.20e-03 SER 1.06e-03: 100%|#| 10000/10000 [00:33<00:00, 301.91it/\n",
      "epoch 3: loss 2.89e-03 SER 9.62e-04: 100%|#| 10000/10000 [00:34<00:00, 293.44it/\n",
      "epoch 4: loss 2.75e-03 SER 9.24e-04: 100%|#| 10000/10000 [00:33<00:00, 295.99it/\n",
      "epoch 5: loss 2.65e-03 SER 8.84e-04: 100%|#| 10000/10000 [00:36<00:00, 276.59it/\n",
      "epoch 6: loss 2.50e-03 SER 8.45e-04: 100%|#| 10000/10000 [00:38<00:00, 262.07it/\n",
      "epoch 7: loss 2.47e-03 SER 8.20e-04: 100%|#| 10000/10000 [00:37<00:00, 263.96it/\n",
      "epoch 8: loss 2.39e-03 SER 7.99e-04: 100%|#| 10000/10000 [00:37<00:00, 267.42it/\n",
      "epoch 9: loss 2.30e-03 SER 7.64e-04: 100%|#| 10000/10000 [00:40<00:00, 246.08it/\n",
      "epoch 10: loss 2.31e-03 SER 7.70e-04: 100%|#| 10000/10000 [00:40<00:00, 249.03it\n"
     ]
    }
   ],
   "source": [
    "encoder_AE = models.Encoder_1h(M, n, n/2)\n",
    "decoder_AE = models.Decoder_1h( M, n)\n",
    "model_2 = model_WGAN.Channel_GAN_SSPA(encoder_AE, decoder_AE, channel_sspa).to(device)\n",
    "#model_2 = model_WGAN.Channel(encoder_AE, decoder_AE).to(device)\n",
    "\n",
    "tconf = trainer.TrainerConfig(max_epochs=10, noise_std = noise_std, batch_size=1000, M=M, rate=rate,\n",
    "                              TRAINING_EbN0=TRAINING_SNR)\n",
    "weights = filter(lambda p: p.requires_grad, model_2.parameters())\n",
    "\n",
    "t_ae = trainer.Trainer(model_2, dataset, tconf, one_hot=True)\n",
    "t_ae.train(weights=weights, GAN=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b447c298-6d74-42f5-9ce0-17e6fbf33088",
   "metadata": {},
   "source": [
    "# Testing AE & GAN Encoder under real channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18846fc9-872d-4803-857b-e0bd77ab0014",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.random.randint(0,M, size=10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e9ea3ec-2a53-443c-bd8d-4f0bbdd8da32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 1  iter 9900: SER 4.464e-01: 100%|###| 10000/10000 [00:11<00:00, 860.23it/s]\n",
      "SNR 2  iter 9900: SER 3.465e-01: 100%|###| 10000/10000 [00:11<00:00, 902.17it/s]\n",
      "SNR 3  iter 9900: SER 2.482e-01: 100%|###| 10000/10000 [00:11<00:00, 908.04it/s]\n",
      "SNR 4  iter 9900: SER 1.604e-01: 100%|###| 10000/10000 [00:11<00:00, 906.33it/s]\n",
      "SNR 5  iter 9900: SER 9.101e-02: 100%|###| 10000/10000 [00:11<00:00, 905.16it/s]\n",
      "SNR 6  iter 9900: SER 4.446e-02: 100%|###| 10000/10000 [00:11<00:00, 906.38it/s]\n",
      "SNR 7  iter 9900: SER 1.798e-02: 100%|###| 10000/10000 [00:11<00:00, 895.43it/s]\n",
      "SNR 8  iter 9900: SER 5.862e-03: 100%|###| 10000/10000 [00:11<00:00, 893.85it/s]\n",
      "SNR 9  iter 9900: SER 1.477e-03: 100%|###| 10000/10000 [00:11<00:00, 904.03it/s]\n",
      "SNR 10  iter 9900: SER 2.970e-04: 100%|##| 10000/10000 [00:11<00:00, 895.41it/s]\n",
      "SNR 10  iter 9900: SER 2.932e-04: 100%|##| 10000/10000 [00:11<00:00, 896.29it/s]\n",
      "SNR 11  iter 9900: SER 4.151e-05: 100%|##| 10000/10000 [00:11<00:00, 883.23it/s]\n",
      "SNR 11  iter 9900: SER 3.989e-05: 100%|##| 10000/10000 [00:11<00:00, 898.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.323e-05: 100%|##| 10000/10000 [00:11<00:00, 902.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 3.888e-05: 100%|##| 10000/10000 [00:11<00:00, 894.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.000e-05: 100%|##| 10000/10000 [00:11<00:00, 894.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.030e-05: 100%|##| 10000/10000 [00:11<00:00, 891.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.292e-05: 100%|##| 10000/10000 [00:11<00:00, 897.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.040e-05: 100%|##| 10000/10000 [00:11<00:00, 867.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.090e-05: 100%|##| 10000/10000 [00:11<00:00, 886.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.202e-05: 100%|##| 10000/10000 [00:11<00:00, 904.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.363e-05: 100%|##| 10000/10000 [00:11<00:00, 906.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.161e-05: 100%|##| 10000/10000 [00:11<00:00, 908.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 4.030e-05: 100%|##| 10000/10000 [00:11<00:00, 908.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.44643542127907276, 0.34643401630818843, 0.24821411151885986, 0.16039860734567046, 0.09100400411114097, 0.04446110201030969, 0.017975500948168337, 0.0058623002505162735, 0.0014787000569049268, 0.00029470001352601685, 4.116154041571113e-05]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "bler_testGAN =[]\n",
    "#ebno_range = [4,5,6,7,8]\n",
    "ebno_range = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
    "\n",
    "model_testGAN = model_WGAN.Channel_SSPA(encoder_GAN, decoder_GAN).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=5, noise_std = noise_std, batch_size=1000, M=M, rate=rate)\n",
    "\n",
    "t_testGAN = trainer.Trainer(model_testGAN, dataset, tconf, one_hot=True)\n",
    "\n",
    "\n",
    "bler_testGAN = t_testGAN.test(snr_range=ebno_range, one_hot=True, erasure_bound=0.7)\n",
    "print(bler_testGAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1270490-ea71-4aca-8f30-7877c7b95355",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 1  iter 9900: SER 4.872e-01: 100%|###| 10000/10000 [00:18<00:00, 541.36it/s]\n",
      "SNR 2  iter 9900: SER 3.891e-01: 100%|###| 10000/10000 [00:17<00:00, 569.22it/s]\n",
      "SNR 3  iter 9900: SER 2.890e-01: 100%|###| 10000/10000 [00:17<00:00, 563.81it/s]\n",
      "SNR 4  iter 9900: SER 1.957e-01: 100%|###| 10000/10000 [00:17<00:00, 570.47it/s]\n",
      "SNR 5  iter 9900: SER 1.180e-01: 100%|###| 10000/10000 [00:17<00:00, 566.81it/s]\n",
      "SNR 6  iter 9900: SER 6.180e-02: 100%|###| 10000/10000 [00:17<00:00, 571.69it/s]\n",
      "SNR 7  iter 9900: SER 2.714e-02: 100%|###| 10000/10000 [00:17<00:00, 569.30it/s]\n",
      "SNR 8  iter 9900: SER 9.759e-03: 100%|###| 10000/10000 [00:17<00:00, 561.29it/s]\n",
      "SNR 9  iter 9900: SER 2.715e-03: 100%|###| 10000/10000 [00:17<00:00, 570.69it/s]\n",
      "SNR 10  iter 9900: SER 5.914e-04: 100%|##| 10000/10000 [00:17<00:00, 564.98it/s]\n",
      "SNR 11  iter 9900: SER 8.575e-05: 100%|##| 10000/10000 [00:17<00:00, 570.52it/s]\n",
      "SNR 11  iter 9900: SER 8.565e-05: 100%|##| 10000/10000 [00:17<00:00, 568.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 8.777e-05: 100%|##| 10000/10000 [00:17<00:00, 565.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 8.676e-05: 100%|##| 10000/10000 [00:17<00:00, 569.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 8.575e-05: 100%|##| 10000/10000 [00:17<00:00, 569.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 11  iter 9900: SER 9.272e-05: 100%|##| 10000/10000 [00:17<00:00, 569.20it/s]\n"
     ]
    }
   ],
   "source": [
    "bler_testAE =[]\n",
    "ebno_range = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
    "\n",
    "model_testAE = model_WGAN.Channel(encoder_AE, decoder_AE).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=5, noise_std = noise_std, batch_size=1000, M=M, rate=rate)\n",
    "\n",
    "t_testAE = trainer.Trainer(model_testAE, dataset, tconf, one_hot=True)\n",
    "\n",
    "\n",
    "bler_testAE = t_testAE.test(snr_range=ebno_range, one_hot=True, erasure_bound=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "92420f2f-3f1d-45f8-bdb3-39b057b5ba8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "# Plot Autoencoder\n",
    "plt.semilogy(ebno_range, bler_testAE, 'o-')\n",
    "#plt.semilogy(bber_data[0], bber_data[2], 'o-')\n",
    "#plt.semilogy(bber_data[0], vec, 'o-')\n",
    "\n",
    "plt.semilogy(ebno_range, bler_testGAN, '^-')\n",
    "\n",
    "\n",
    "# Plot approx 16 QAM\n",
    "#ebnodbs = np.linspace(5,14,10)\n",
    "#a, _ = utils.MQAM_rayleigh_approx(16,ebnodbs)\n",
    "#plt.plot(ebnodbs, a, '^-');\n",
    "\n",
    "#b = [utils.rayleigh_error(t) for t in ebnodbs]\n",
    "#plt.plot(ebnodbs, b, '^-');\n",
    "\n",
    "#ebnodbs = np.linspace(0,11,22)\n",
    "#uncoded_16QAM_Rayleigh_SER = [0.5501, 0.5040, 0.4564, 0.4082, 0.3606, 0.3147, 0.2713, 0.2313, 0.1951, 0.1631, 0.1352, 0.1112, 0.091, 0.07396, 0.05989, 0.04832, 0.03886, 0.03119, 0.02497, 0.01996, 0.01594, 0.01271]\n",
    "#c = [(1-(1-t)**4) for t in uncoded_16QAM_Rayleigh_SER]\n",
    "#plt.plot(ebnodbs, c, '^-');\n",
    "\n",
    "plt.gca().set_ylim(1e-5, 1)\n",
    "plt.gca().set_xlim(1, 9)\n",
    "plt.ylabel(\"Block Error Rate\", fontsize=18, rotation=90)\n",
    "plt.xlabel(\"Eb/No [dB]\", fontsize=18)\n",
    "plt.legend(['AE', 'WGAN'], prop={'size': 16}, loc='upper right');\n",
    "plt.grid(True, which=\"both\")\n",
    "#plt.savefig('ae-ray-testing.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa74c701-f06c-4c3c-a93a-13fb062a7aea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "875728ce-4076-49f5-9800-b4871654c959",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}