E2E_framework_DDM_AWGN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d79849a",
   "metadata": {
    "id": "3d79849a"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/azad-academy/denoising-diffusion-model/blob/main/diffusion_model_demo.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa72d80",
   "metadata": {
    "id": "6671430e"
   },
   "source": [
    "## Learning End-to-End Channel Coding with Diffusion Models\n",
    " \n",
    "This notebook file is a framework to learn the E2E channel coding with a generated channel by using diffusion models. For diffusion models, we use two denoising algorithms, DDPM and DDIM. They are evaluated by the generative performance, sampling speed, and the E2E error probability of the channel codes trained with them. \n",
    "To train the E2E framework, we first train the channel generative model with Gaussian signals as channel input. And then, the encoder and decoder models are trained with a fake channel sampled by the generative model. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2ddcaa7",
   "metadata": {
    "executionInfo": {
     "elapsed": 1182,
     "status": "ok",
     "timestamp": 1669225000074,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "e2ddcaa7"
   },
   "outputs": [],
   "source": [
    "# Set the path\n",
    "GDRIVE_MOUNT = False # In case you use Google Colab with G-Drive\n",
    "PLOT_FIGURES = True \n",
    "\n",
    "if GDRIVE_MOUNT: \n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive/', force_remount=True)\n",
    "    path = '/content/drive/MyDrive/Colab Notebooks' # The path to the source codes\n",
    "else: \n",
    "    path = !pwd\n",
    "    path = path[0] # The path with the lbraries\n",
    "\n",
    "import sys\n",
    "sys.path.append(path+'/src')\n",
    "\n",
    "# Import packages\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from torch.utils.data.dataloader import DataLoader\n",
    "from torch.nn import functional as F\n",
    "import pickle\n",
    "import csv\n",
    "from tqdm import tqdm\n",
    "from utils import *\n",
    "from utils_plot import *\n",
    "import trainer as trainer\n",
    "\n",
    "# Import modules from own libraries\n",
    "import models\n",
    "from channel_models import *\n",
    "from ema import EMA\n",
    "\n",
    "# Set the device \n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc9b434",
   "metadata": {},
   "source": [
    "## Choose channel model and diffusion model type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4650b2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_model_str = 'AWGN' #available options: AWGN, Rayleigh, SSPA\n",
    "denoising_alg = 'DDPM' #available options: DDPM, DDIM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b92ba0",
   "metadata": {},
   "source": [
    "## Channel model settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3ebbf23",
   "metadata": {
    "executionInfo": {
     "elapsed": 2418,
     "status": "ok",
     "timestamp": 1669225002490,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "d3ebbf23"
   },
   "outputs": [],
   "source": [
    "# Channel Model\n",
    "M = 16\n",
    "k = math.log2(M)\n",
    "n = 7  # Block length\n",
    "rate = k/n \n",
    "\n",
    "\n",
    "if channel_model_str == 'AWGN':\n",
    "    channel_model = ch_AWGN\n",
    "elif channel_model_str == 'Rayleigh':\n",
    "    channel_model = ch_Rayleigh_AWGN\n",
    "elif channel_model_str == 'SSPA':\n",
    "    channel_model = ch_SSPA\n",
    "else:\n",
    "    raise Exception(\"Unrecognized channel model. Possible channel models: AWGN, Rayleigh, SSPA.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76ef1704",
   "metadata": {},
   "source": [
    "## Deep Learning Settings - model parameters, training and testing hyperparameters, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f85d303e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and save models before and after training, respectively. \n",
    "LOADMODEL_gen = False\n",
    "LOADMODEL_AE = False\n",
    "\n",
    "SAVEMODEL_gen = False \n",
    "SAVEMODEL_AE = False\n",
    "\n",
    "# Framework \n",
    "TRAINING_EbN0 = 5 # decibel\n",
    "noise_std = EbNo_to_noise(TRAINING_EbN0, rate)\n",
    "\n",
    "# Autoencoder (AE) Model (channel coding encoder & decoder)\n",
    "### Models \n",
    "encoder = models.Encoder_1h(M, n, n).to(device)# _1h means taking one hot encoded messages. _n means normalizing the output.\n",
    "decoder = models.Decoder_1h(M, n).to(device)\n",
    "optimizer_AE = torch.optim.NAdam\n",
    "\n",
    "# Diffusion Model\n",
    "### Model Parameters\n",
    "N_hl = 110 # Number of neurons in the hidden layer\n",
    "num_steps = 100 # T\n",
    "skip = 1 # Set this as 1 if DDPM. If DDIM, choose between 1 to T.\n",
    "if denoising_alg == 'DDPM': \n",
    "    traj = range(0,num_steps)\n",
    "elif denoising_alg == 'DDIM':\n",
    "    traj = range(skip-1, num_steps, skip)\n",
    "\n",
    "### Noise scheduling    \n",
    "# constant beta, no scheduling. \n",
    "#betas = torch.tensor([5e-2] * num_steps).to(device) \n",
    "# cosine scheduling\n",
    "betas = make_beta_schedule(schedule='cosine-zf', n_timesteps=num_steps).to(device) \n",
    "# sigmoid scheduling\n",
    "#betas = make_beta_schedule(schedule='sigmoid', n_timesteps=num_steps, start=5e-3, end=5e-2).to(device) \n",
    "\n",
    "pred_type = 'v' # Available options: 'epsilon', v.\n",
    "\n",
    "channel_gen = models.ConditionalModel_w_Condition(num_steps, 0, n, N_hl).to(device) # The condition includes the channel input.\n",
    "optimizer_gen = optim.Adam\n",
    "ema = EMA(0.9) # Create EMA model (Exponential Moving Average)\n",
    "ema.register(channel_gen)\n",
    "\n",
    "# Test Setting\n",
    "ebn0_range = [*range(0,8+1)]\n",
    "dataset_size_test = 1000000\n",
    "batch_size_test = 100\n",
    "max_epoch_test = 30\n",
    "\n",
    "\n",
    "# File tag for saving and loading\n",
    "file_tag = f'_{channel_model_str}_M{M}N{n}_T{num_steps}'\n",
    "\n",
    "model_tag_load_gen = file_tag +'.pt' \n",
    "model_tag_load_AE = file_tag +'.pt'\n",
    "model_tag_save_gen = file_tag +'.pt'\n",
    "model_tag_save_AE = file_tag +'.pt' \n",
    "\n",
    "path_state_dict = path + '/state_dict'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cfa3a89a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1669225002492,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "cfa3a89a",
    "outputId": "5b00852e-d64d-458a-b0e0-69dbc0f27146"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of parameters (diffusion model): 59847\n",
      "# of parameters (encoder): 663\n",
      "# of parameters (decoder): 672\n"
     ]
    }
   ],
   "source": [
    "total_params_gen = sum(p.numel() for p in channel_gen.parameters() if p.requires_grad)\n",
    "print('# of parameters (diffusion model): ' + str(total_params_gen))\n",
    "total_params_enc = sum(p.numel() for p in encoder.parameters())\n",
    "print('# of parameters (encoder): ' + str(total_params_enc))\n",
    "total_params_dec = sum(p.numel() for p in decoder.parameters())\n",
    "print('# of parameters (decoder): ' + str(total_params_dec))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e26210",
   "metadata": {},
   "source": [
    "## Defining variables and functions for diffusion models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0dd5e45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating constants\n",
    "alphas = 1 - betas\n",
    "alphas_prod = torch.cumprod(alphas, 0)\n",
    "alphas_prod_p = torch.cat([torch.tensor([1]).float().to(device), alphas_prod[:-1]], 0)\n",
    " \n",
    "alphas_bar_sqrt = torch.sqrt(alphas_prod)\n",
    "one_minus_alphas_bar_sqrt = torch.sqrt(1 - alphas_prod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f881271",
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1669225002491,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "3f881271"
   },
   "outputs": [],
   "source": [
    "def q_x(x_0, t, noise=None):\n",
    "    shape = x_0.shape\n",
    "    if noise is None:\n",
    "        noise = torch.randn_like(x_0)\n",
    "    alphas_t = extract(alphas_bar_sqrt, t, shape)\n",
    "    alphas_1_m_t = extract(one_minus_alphas_bar_sqrt, t, shape)\n",
    "    return (alphas_t * x_0 + alphas_1_m_t * noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3b411ad",
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1669225002491,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "e3b411ad"
   },
   "outputs": [],
   "source": [
    "posterior_mean_coef_1 = (betas * torch.sqrt(alphas_prod_p) / (1 - alphas_prod))\n",
    "posterior_mean_coef_2 = ((1 - alphas_prod_p) * torch.sqrt(alphas) / (1 - alphas_prod))\n",
    "posterior_variance = betas * (1 - alphas_prod_p) / (1 - alphas_prod)\n",
    "posterior_log_variance_clipped = torch.log(torch.cat((posterior_variance[1].view(1, 1), posterior_variance[1:].view(-1, 1)), 0)).view(-1)\n",
    "\n",
    "def q_posterior_mean_variance(x_0, x_t, t):\n",
    "    shape = x_0.shape\n",
    "    coef_1 = extract(posterior_mean_coef_1, t, shape)\n",
    "    coef_2 = extract(posterior_mean_coef_2, t, shape)\n",
    "    mean = coef_1 * x_0 + coef_2 * x_t\n",
    "    var = extract(posterior_log_variance_clipped, t, shape)\n",
    "    return mean, var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f6241d3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 254
    },
    "executionInfo": {
     "elapsed": 3344,
     "status": "ok",
     "timestamp": 1669225005830,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "9f6241d3",
    "outputId": "0f7e8ee6-ccb9-4a13-e740-38dbfce068e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diffusion process for message 1 in the first two dimensions.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2800x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjust beta according to the diffusion results. If it needs to be more noisy, increase beta. If it gets noisy too soon, decrease beta.\n"
     ]
    }
   ],
   "source": [
    "# Checking diffusion process \n",
    "\n",
    "if PLOT_FIGURES:\n",
    "    show_diffusion_m(channel_model, q_x, encoder, device, noise_std, M=M, m = 0,num_samples_df=1000\n",
    "                     ,num_steps=num_steps, max_amp = 3)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7031773c",
   "metadata": {
    "id": "7031773c"
   },
   "source": [
    "# Training E2E framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d7129f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pretrained diffusion model if necessary\n",
    "\n",
    "load_class = LoadModels(path_state_dict, LOADMODEL_gen, channel_gen, model_tag_load_gen, LOADMODEL_AE, encoder\n",
    "                        , decoder, model_tag_load_AE)\n",
    "\n",
    "channel_gen, encoder, decoder = load_class.load() # It loads only the models whose LOADMODEL flag is True.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2091966",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 846
    },
    "executionInfo": {
     "elapsed": 305182,
     "status": "ok",
     "timestamp": 1669225310995,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "e2091966",
    "outputId": "a1b81afe-b2c9-4d45-82c0-a8a79ac3c4f6",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 5.63e-01: 100%|███████████████| 2000/2000 [01:41<00:00, 19.61it/s]\n",
      "epoch 2: loss 5.34e-01: 100%|███████████████| 2000/2000 [01:41<00:00, 19.72it/s]\n",
      "epoch 3: loss 5.25e-01: 100%|███████████████| 2000/2000 [01:42<00:00, 19.50it/s]\n",
      "epoch 4: loss 5.25e-01: 100%|███████████████| 2000/2000 [01:40<00:00, 19.93it/s]\n",
      "epoch 5: loss 5.25e-01: 100%|███████████████| 2000/2000 [01:42<00:00, 19.50it/s]\n",
      "epoch 6: loss 5.27e-01: 100%|███████████████| 2000/2000 [01:40<00:00, 19.96it/s]\n",
      "epoch 7: loss 5.19e-01: 100%|███████████████| 2000/2000 [01:42<00:00, 19.46it/s]\n",
      "epoch 8: loss 5.26e-01: 100%|███████████████| 2000/2000 [01:40<00:00, 19.90it/s]\n",
      "epoch 9: loss 5.19e-01: 100%|███████████████| 2000/2000 [01:44<00:00, 19.15it/s]\n",
      "epoch 10: loss 5.24e-01: 100%|██████████████| 2000/2000 [02:00<00:00, 16.61it/s]\n",
      "epoch 11: loss 5.19e-01:   7%|▉              | 131/2000 [00:09<02:20, 13.29it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m t_gen \u001b[38;5;241m=\u001b[39m trainer\u001b[38;5;241m.\u001b[39mTrainer_DDM(channel_gen, ema, device, channel_model, tconf_gen)\n\u001b[1;32m     18\u001b[0m loss_gen \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 19\u001b[0m loss_gen\u001b[38;5;241m.\u001b[39mappend(\u001b[43mt_gen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_PreT\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m PLOT_FIGURES :   \n\u001b[1;32m     23\u001b[0m     show_denoising_m(channel_gen, encoder, device, alphas, betas, alphas_bar_sqrt, one_minus_alphas_bar_sqrt,\n\u001b[1;32m     24\u001b[0m                      M \u001b[38;5;241m=\u001b[39mM, m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, num_samples_dn \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1000\u001b[39m, num_steps \u001b[38;5;241m=\u001b[39m num_steps,max_amp\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m     25\u001b[0m                      PreT\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, denoising_alg \u001b[38;5;241m=\u001b[39m denoising_alg, traj \u001b[38;5;241m=\u001b[39m traj, pred_type\u001b[38;5;241m=\u001b[39mpred_type) \n",
      "File \u001b[0;32m~/Documents/GitLab/learning-E2E-channel-coding-with-diffusion-models/src/trainer.py:216\u001b[0m, in \u001b[0;36mTrainer_DDM.train_PreT\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    213\u001b[0m loss_batch, batch_BER, batch_SER  \u001b[38;5;241m=\u001b[39m [], [], []\n\u001b[1;32m    214\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(\u001b[38;5;28menumerate\u001b[39m(loader), total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(loader), mininterval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m) \n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m it, x \u001b[38;5;129;01min\u001b[39;00m pbar: \n\u001b[1;32m    217\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    218\u001b[0m     y \u001b[38;5;241m=\u001b[39m channel_model(x, tconf_gen\u001b[38;5;241m.\u001b[39mnoise_std, device) \n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1192\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1196\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1197\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1198\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    631\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    632\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    636\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    637\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:676\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 676\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    677\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_fetcher\u001b[38;5;241m.\u001b[39mfetch(index)  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    678\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:623\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter._next_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_index\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sampler_iter\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/data/sampler.py:257\u001b[0m, in \u001b[0;36mBatchSampler.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    255\u001b[0m batch[idx_in_batch] \u001b[38;5;241m=\u001b[39m idx\n\u001b[1;32m    256\u001b[0m idx_in_batch \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m idx_in_batch \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_size\u001b[49m:\n\u001b[1;32m    258\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m batch\n\u001b[1;32m    259\u001b[0m     idx_in_batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Training Phase 1: Learn the channel by training the diffusion model. \n",
    "# Freeze enc/dec pair and optimize the diffusion model.\n",
    "\n",
    "## Training dataset: Gaussian channel input and channel output. \n",
    "\n",
    "dataset_size_gen = 10**7\n",
    "batch_size_gen = 5000\n",
    "max_epoch_gen = 30 \n",
    "lr_gen = 1e-4\n",
    "\n",
    "tconf_gen = trainer.TrainerConfig_DDM(max_epochs = max_epoch_gen, dataset_size = dataset_size_gen\n",
    "                                      , batch_size = batch_size_gen, noise_std = noise_std\n",
    "                                      , learning_rate = lr_gen, M = M, n = n, rate = rate\n",
    "                                      , num_steps = num_steps, betas = betas, optim_gen = optimizer_gen\n",
    "                                      , pred_type = pred_type)\n",
    "\n",
    "t_gen = trainer.Trainer_DDM(channel_gen, ema, device, channel_model, tconf_gen)\n",
    "t_gen.train_PreT()\n",
    "\n",
    "\n",
    "if PLOT_FIGURES :   \n",
    "    show_denoising_m(channel_gen, encoder, device, alphas, betas, alphas_bar_sqrt, one_minus_alphas_bar_sqrt,\n",
    "                     M =M, m = 0, num_samples_dn = 1000, num_steps = num_steps,max_amp=3,\n",
    "                     PreT=True, denoising_alg = denoising_alg, traj = traj, pred_type=pred_type) \n",
    "\n",
    "print('Training the generative model is completed.')   \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2d61c6-03f7-45cc-9799-7db39f782817",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d909a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the diffusion model if necessary\n",
    "model_tag_load_gen = file_tag +'.pt' \n",
    "model_tag_load_AE = file_tag +'.pt' \n",
    "model_tag_save_gen = file_tag +'.pt'\n",
    "model_tag_save_AE = file_tag +'.pt' \n",
    "\n",
    "path_state_dict = path + '/state_dict'\n",
    "\n",
    "SAVEMODEL_gen = True\n",
    "SAVEMODEL_AE = False\n",
    "save_class = SaveModels(path_state_dict, SAVEMODEL_gen, channel_gen, model_tag_save_gen, SAVEMODEL_AE, encoder\n",
    "                        , decoder, model_tag_save_AE)\n",
    "save_class.model_gen = channel_gen\n",
    "save_class.save_gen()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d937193d",
   "metadata": {},
   "source": [
    "# Check the diffusion models by figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23ed37d2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "executionInfo": {
     "elapsed": 1651,
     "status": "ok",
     "timestamp": 1669225314160,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "23ed37d2",
    "outputId": "ae8a59a6-b37a-4fe1-ff5e-eabd96107925"
   },
   "outputs": [],
   "source": [
    "#denoising_alg = 'DDPM'\n",
    "#skip = 1\n",
    "#traj = range(skip-1, num_steps, skip)\n",
    "\n",
    "if PLOT_FIGURES:\n",
    "    ECDF_histogram_m(encoder, channel_gen, channel_model_str, device, alphas, betas, alphas_bar_sqrt\n",
    "                     , one_minus_alphas_bar_sqrt\n",
    "                     , num_steps, M, 10000,1000, noise_std,max_amp=8, m=1, PreT=True\n",
    "                     , denoising_alg = denoising_alg, traj = traj, pred_type=pred_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2b3cfe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#denoising_alg = 'DDIM' \n",
    "#skip = 1 \n",
    "#traj = range(skip-1, num_steps, skip) # starting from 0 ending with (num_steps-1)\n",
    "\n",
    "if PLOT_FIGURES:\n",
    "    Constellation_InOut_m(encoder, channel_gen, channel_model_str, device, alphas, betas, alphas_bar_sqrt\n",
    "                          , one_minus_alphas_bar_sqrt\n",
    "                          , num_steps, M, 100, noise_std, m=0, max_amp=8, PreT=True\n",
    "                          , denoising_alg = denoising_alg\n",
    "                          , traj = traj, pred_type=pred_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d01b4b-c9b2-4ab6-bbb8-5f4fbc24cd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_denoising_m(channel_gen, encoder, device, alphas, betas, alphas_bar_sqrt\n",
    "                 , one_minus_alphas_bar_sqrt, M = M\n",
    "                 , m = 0, num_samples_dn = 1000, num_steps = num_steps,max_amp=3, PreT=True\n",
    "                 , denoising_alg = denoising_alg, traj = traj, pred_type=pred_type) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9248c2ff",
   "metadata": {},
   "source": [
    "# Train AEs with different sampling algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c341c138",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training Phase 2: train the AE with generated channel outputs. \n",
    "# Freeze the diffusion model and optimize the AE.\n",
    "\n",
    "## Training dataset: uniformly distributed messages, encoded codewords, channel outputs of those codewords.\n",
    "\n",
    "SAVEMODEL_gen = False\n",
    "SAVEMODEL_AE = True\n",
    "\n",
    "lr_AE = 1e-3 # learning rate of the encoder/decoder.\n",
    "dataset_size_AE = 1000000\n",
    "batch_size_AE = 100\n",
    "max_epoch_AE = 10\n",
    "\n",
    "denoising_alg = 'DDIM'\n",
    "skip = 20\n",
    "traj = range(skip-1, num_steps, skip)\n",
    "\n",
    "encoder = models.Encoder_1h(M, n, n).to(device) # _1h means taking one hot encoded messages.\n",
    "decoder = models.Decoder_1h(M, n).to(device)\n",
    "\n",
    "dataset_AE = np.random.randint(M, size=dataset_size_AE)\n",
    "tconf_AE = trainer.TrainerConfig_AE_w_DDM(max_epochs = max_epoch_AE, dataset_size = dataset_size_AE\n",
    "                                          , batch_size = batch_size_AE, noise_std = noise_std\n",
    "                                          , learning_rate = lr_AE, M = M, n=n, rate = rate\n",
    "                                          , num_steps = num_steps, traj = traj, denoising_alg = denoising_alg\n",
    "                                          , optim_AE = optimizer_AE, betas=betas, channel_model = channel_model\n",
    "                                          , pred_type = pred_type)\n",
    "t_AE = trainer.Trainer_AE_w_DDM(encoder, decoder, channel_gen, device, tconf_AE)\n",
    "t_AE.train_PreT()\n",
    "print('Training the AE model is completed.')\n",
    "\n",
    "t_AE.test(channel_model)\n",
    "\n",
    "file_tag_tmp = file_tag + denoising_alg + str(skip)\n",
    "model_tag_save_AE = file_tag_tmp +'.pt' \n",
    "\n",
    "save_class = SaveModels(path_state_dict, SAVEMODEL_gen, channel_gen, model_tag_save_gen, SAVEMODEL_AE, encoder\n",
    "                        , decoder, model_tag_save_AE)\n",
    "save_class.save_AE()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8a4de0",
   "metadata": {},
   "source": [
    "# Test AEs of different sampling algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7886d6af-8f81-4cd8-b3de-e68930b0e3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the AE\n",
    "\n",
    "labels = torch.randint(low=0, high=M, size=[dataset_size_test])\n",
    "tconf = trainer.TrainerConfig(max_epochs=max_epoch_test, noise_std = noise_std, batch_size=batch_size_test\n",
    "                              , M=M, rate=rate, pred_type=pred_type)\n",
    "\n",
    "# Load the encoder and decoder if necessary\n",
    "#file_tag_tmp = file_tag + denoising_alg + str(skip)\n",
    "#model_tag_load_AE = file_tag_tmp +'.pt'\n",
    "#LOADMODEL_AE = True\n",
    "#load_class = LoadModels(path_state_dict, SAVEMODEL_gen, channel_gen\n",
    "#                        , model_tag_save_gen, SAVEMODEL_AE, encoder\n",
    "#                        , decoder, model_tag_save_AE)\n",
    "#_, encoder, decoder = load_class.load() # It loads only the models whose LOADMODEL flag is True.\n",
    "\n",
    "\n",
    "if channel_model_str == 'AWGN':\n",
    "    channel_diff_test = models.Channel(encoder, decoder).to(device)\n",
    "if channel_model_str == 'Rayleigh':\n",
    "    channel_diff_test = models.Channel_ray_n(encoder, decoder).to(device)\n",
    "if channel_model_str == 'SSPA':\n",
    "    channel_diff_test = models.Channel_SSPA_n(encoder, decoder).to(device)\n",
    "    \n",
    "t_diff = trainer.Trainer(channel_diff_test, labels, tconf, one_hot=True)\n",
    "SER = t_diff.test(snr_range=ebn0_range, one_hot=True, erasure_bound=0.7) \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b603200f",
   "metadata": {},
   "source": [
    "# Plot figures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637e7d05",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1669225354936,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "d32f8412",
    "outputId": "3c0510b8-9f88-4e46-85aa-41425c630327"
   },
   "outputs": [],
   "source": [
    "from matplotlib.markers import MarkerStyle\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.transforms import Affine2D\n",
    "unfilled_markers = [m for m, func in Line2D.markers.items()\n",
    "                    if func != 'nothing' and m not in Line2D.filled_markers]\n",
    "\n",
    "linestyles = ['solid', 'dotted', 'dashed', 'dashdot', 'solid', 'dotted', 'dashed', 'dashdot']\n",
    "\n",
    "if PLOT_FIGURES:\n",
    "    fig = plt.figure(figsize=(3, 2.5))\n",
    "    # Plot Autoencoder\n",
    "    plt.semilogy(ebn0_range, SER, 'x:', label = denoising_alg)\n",
    "    plt.gca().set_ylim(1e-5, 3e-1)\n",
    "    plt.gca().set_xlim(ebn0_range[0], ebn0_range[-1])\n",
    "    plt.ylabel(\"SER\", rotation=90)\n",
    "    plt.xlabel(\"Eb/No [dB]\")\n",
    "    plt.title(channel_model_str)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.grid(True, which=\"both\")\n",
    "    plt.savefig('figures/EbN0_vs._testSER_sample_'+channel_model_str+'.pdf', bbox_inches='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2efb75a0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1669225310995,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "2efb75a0",
    "outputId": "4c2f8256-a2d9-4a45-f628-e1307e4db8de"
   },
   "outputs": [],
   "source": [
    "if PLOT_FIGURES:            \n",
    "    Constellation_InOut_m(encoder, channel_gen, channel_model_str, device, alphas, betas, alphas_bar_sqrt\n",
    "                            , one_minus_alphas_bar_sqrt, num_steps, M, 100, noise_std, 3, m=4, PreT=True\n",
    "                            , denoising_alg = denoising_alg, traj = traj, pred_type = pred_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9418228",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 431
    },
    "executionInfo": {
     "elapsed": 1536,
     "status": "ok",
     "timestamp": 1669225312512,
     "user": {
      "displayName": "M. Kim",
      "userId": "15172492698916514875"
     },
     "user_tz": -60
    },
    "id": "d9418228",
    "outputId": "5694745e-78e7-459d-8363-a1a1d0ff90f3"
   },
   "outputs": [],
   "source": [
    "if PLOT_FIGURES:\n",
    "    Constellation_Enc(encoder, M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4195b93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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     "file_id": "https://github.com/muahkim/ChGen-CondDiffusion/blob/main/E2E_framework_Rayleigh_AWGN-M16N7.ipynb",
     "timestamp": 1666621806973
    }
   ]
  },
  "gpuClass": "standard",
  "kernelspec": {
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