{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ef8dbcc",
   "metadata": {},
   "source": [
    "Training of DnCNN for Denoising\n",
    "===============================\n",
    "\n",
    "This example demonstrates the training and application of the DnCNN model\n",
    "from <cite data-cite=\"zhang-2017-dncnn\"/> to denoise images that have been corrupted\n",
    "with additive Gaussian noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f9933e4",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:18:21.655752Z",
     "iopub.status.busy": "2024-10-13T19:18:21.655130Z",
     "iopub.status.idle": "2024-10-13T19:18:27.458528Z",
     "shell.execute_reply": "2024-10-13T19:18:27.457312Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform:  gpu\n"
     ]
    }
   ],
   "source": [
    "# This scico project Jupyter notebook has been automatically modified\n",
    "# to install the dependencies required for running it on Google Colab.\n",
    "# If you encounter any problems in running it, please open an issue at\n",
    "#   https://github.com/lanl/scico-data/issues\n",
    "\n",
    "!pip install 'scico[examples] @ git+https://github.com/lanl/scico'\n",
    "\n",
    "# isort: off\n",
    "import os\n",
    "from time import time\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Set an arbitrary processor count (only applies if GPU is not available).\n",
    "os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n",
    "\n",
    "import jax\n",
    "\n",
    "try:\n",
    "    from jax.extend.backend import get_backend  # introduced in jax 0.4.33\n",
    "except ImportError:\n",
    "    from jax.lib.xla_bridge import get_backend\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "from scico import flax as sflax\n",
    "from scico import metric, plot\n",
    "from scico.flax.examples import load_image_data\n",
    "plot.config_notebook_plotting()\n",
    "\n",
    "\n",
    "platform = get_backend().platform\n",
    "print(\"Platform: \", platform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275c181d",
   "metadata": {},
   "source": [
    "Read data from cache or generate if not available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99583c34",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:18:27.463053Z",
     "iopub.status.busy": "2024-10-13T19:18:27.462757Z",
     "iopub.status.idle": "2024-10-13T19:19:04.528301Z",
     "shell.execute_reply": "2024-10-13T19:19:04.527190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading BSR_bsds500.tgz from https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download complete\n",
      "Extracting content from tar file BSR_bsds500.tgz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 400 images for training\n",
      "Read 100 images for testing\n",
      "Saving as ~/.cache/scico/examples/data/BSDS/bsds500.npz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Storing data in path: ~/.cache/scico/examples/data\n",
      "Set --training-- size: 104000\n",
      "Set --testing -- size: 4160\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data range -- images --  Min:  0.00  Max:  1.00\n",
      "Data range -- labels --  Min:  0.00  Max:  1.00\n"
     ]
    }
   ],
   "source": [
    "size = 40  # patch size\n",
    "train_nimg = 400  # number of training images\n",
    "test_nimg = 16  # number of testing images\n",
    "nimg = train_nimg + test_nimg\n",
    "gray = True  # use gray scale images\n",
    "data_mode = \"dn\"  # Denoising problem\n",
    "noise_level = 0.1  # Standard deviation of noise\n",
    "noise_range = False  # Use fixed noise level\n",
    "stride = 23  # Stride to sample multiple patches from each image\n",
    "\n",
    "train_ds, test_ds = load_image_data(\n",
    "    train_nimg,\n",
    "    test_nimg,\n",
    "    size,\n",
    "    gray,\n",
    "    data_mode,\n",
    "    verbose=True,\n",
    "    noise_level=noise_level,\n",
    "    noise_range=noise_range,\n",
    "    stride=stride,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae3186a",
   "metadata": {},
   "source": [
    "Define configuration dictionary for model and training loop.\n",
    "\n",
    "Parameters have been selected for demonstration purposes and relatively\n",
    "short training. The depth of the model has been reduced to 6, instead of\n",
    "the 17 of the original model. The suggested settings can be found in the\n",
    "original paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29c9e712",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:19:04.533250Z",
     "iopub.status.busy": "2024-10-13T19:19:04.533019Z",
     "iopub.status.idle": "2024-10-13T19:19:04.537683Z",
     "shell.execute_reply": "2024-10-13T19:19:04.537038Z"
    }
   },
   "outputs": [],
   "source": [
    "# model configuration\n",
    "model_conf = {\n",
    "    \"depth\": 6,\n",
    "    \"num_filters\": 64,\n",
    "}\n",
    "# training configuration\n",
    "train_conf: sflax.ConfigDict = {\n",
    "    \"seed\": 0,\n",
    "    \"opt_type\": \"ADAM\",\n",
    "    \"batch_size\": 128,\n",
    "    \"num_epochs\": 50,\n",
    "    \"base_learning_rate\": 1e-3,\n",
    "    \"warmup_epochs\": 0,\n",
    "    \"log_every_steps\": 5000,\n",
    "    \"log\": True,\n",
    "    \"checkpointing\": True,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f4b062",
   "metadata": {},
   "source": [
    "Construct DnCNN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "698211aa",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:19:04.540966Z",
     "iopub.status.busy": "2024-10-13T19:19:04.540708Z",
     "iopub.status.idle": "2024-10-13T19:19:04.544584Z",
     "shell.execute_reply": "2024-10-13T19:19:04.543946Z"
    }
   },
   "outputs": [],
   "source": [
    "channels = train_ds[\"image\"].shape[-1]\n",
    "model = sflax.DnCNNNet(\n",
    "    depth=model_conf[\"depth\"],\n",
    "    channels=channels,\n",
    "    num_filters=model_conf[\"num_filters\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0904735b",
   "metadata": {},
   "source": [
    "Run training loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65a35cf2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:19:04.547748Z",
     "iopub.status.busy": "2024-10-13T19:19:04.547412Z",
     "iopub.status.idle": "2024-10-13T19:24:53.686448Z",
     "shell.execute_reply": "2024-10-13T19:24:53.685420Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "JAX local devices: [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)]\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channels: 1   training signals: 104000   testing signals: 4160   signal size: 40\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network Structure:\n",
      "+---------------------------------+----------------+--------+-----------+--------+\n",
      "| Name                            | Shape          | Size   | Mean      | Std    |\n",
      "+---------------------------------+----------------+--------+-----------+--------+\n",
      "| ConvBNBlock_0/BatchNorm_0/bias  | (64,)          | 64     | 0.0       | 0.0    |\n",
      "| ConvBNBlock_0/BatchNorm_0/scale | (64,)          | 64     | 1.0       | 0.0    |\n",
      "| ConvBNBlock_0/Conv_0/kernel     | (3, 3, 64, 64) | 36,864 | -0.000522 | 0.0589 |\n",
      "| ConvBNBlock_1/BatchNorm_0/bias  | (64,)          | 64     | 0.0       | 0.0    |\n",
      "| ConvBNBlock_1/BatchNorm_0/scale | (64,)          | 64     | 1.0       | 0.0    |\n",
      "| ConvBNBlock_1/Conv_0/kernel     | (3, 3, 64, 64) | 36,864 | 0.000178  | 0.0589 |\n",
      "| ConvBNBlock_2/BatchNorm_0/bias  | (64,)          | 64     | 0.0       | 0.0    |\n",
      "| ConvBNBlock_2/BatchNorm_0/scale | (64,)          | 64     | 1.0       | 0.0    |\n",
      "| ConvBNBlock_2/Conv_0/kernel     | (3, 3, 64, 64) | 36,864 | 5.46e-05  | 0.0588 |\n",
      "| ConvBNBlock_3/BatchNorm_0/bias  | (64,)          | 64     | 0.0       | 0.0    |\n",
      "| ConvBNBlock_3/BatchNorm_0/scale | (64,)          | 64     | 1.0       | 0.0    |\n",
      "| ConvBNBlock_3/Conv_0/kernel     | (3, 3, 64, 64) | 36,864 | -9.56e-05 | 0.0592 |\n",
      "| conv_end/kernel                 | (3, 3, 64, 1)  | 576    | -0.00121  | 0.0605 |\n",
      "| conv_start/kernel               | (3, 3, 1, 64)  | 576    | 0.0155    | 0.457  |\n",
      "+---------------------------------+----------------+--------+-----------+--------+\n",
      "Total weights: 149,120\n",
      "\n",
      "Batch Normalization:\n",
      "+--------------------------------+-------+------+------+-----+\n",
      "| Name                           | Shape | Size | Mean | Std |\n",
      "+--------------------------------+-------+------+------+-----+\n",
      "| ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64   | 0.0  | 0.0 |\n",
      "| ConvBNBlock_0/BatchNorm_0/var  | (64,) | 64   | 1.0  | 0.0 |\n",
      "| ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64   | 0.0  | 0.0 |\n",
      "| ConvBNBlock_1/BatchNorm_0/var  | (64,) | 64   | 1.0  | 0.0 |\n",
      "| ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64   | 0.0  | 0.0 |\n",
      "| ConvBNBlock_2/BatchNorm_0/var  | (64,) | 64   | 1.0  | 0.0 |\n",
      "| ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64   | 0.0  | 0.0 |\n",
      "| ConvBNBlock_3/BatchNorm_0/var  | (64,) | 64   | 1.0  | 0.0 |\n",
      "+--------------------------------+-------+------+------+-----+\n",
      "Total weights: 512\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial compilation, which might take some time ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial compilation completed.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  Time      Train_LR  Train_Loss  Train_SNR  Eval_Loss  Eval_SNR\n",
      "---------------------------------------------------------------------\n",
      "    6  4.53e+01  0.001000    0.001960      13.19   0.000986     14.43\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   12  8.72e+01  0.001000    0.000939      14.33   0.001067     14.08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   18  1.29e+02  0.001000    0.000875      14.63   0.001289     13.29\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   24  1.70e+02  0.001000    0.000838      14.82   0.000868     14.99\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   30  2.12e+02  0.001000    0.000816      14.93   0.000844     15.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   36  2.54e+02  0.001000    0.000804      15.00   0.001076     14.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   43  2.96e+02  0.001000    0.000797      15.04   0.000827     15.20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   49  3.38e+02  0.001000    0.000792      15.07   0.001254     13.60\n"
     ]
    }
   ],
   "source": [
    "workdir = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"scico\", \"examples\", \"dncnn_out\")\n",
    "train_conf[\"workdir\"] = workdir\n",
    "print(f\"\\nJAX local devices: {jax.local_devices()}\\n\")\n",
    "\n",
    "trainer = sflax.BasicFlaxTrainer(\n",
    "    train_conf,\n",
    "    model,\n",
    "    train_ds,\n",
    "    test_ds,\n",
    ")\n",
    "modvar, stats_object = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c066059",
   "metadata": {},
   "source": [
    "Evaluate on testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8385f33a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:24:53.691303Z",
     "iopub.status.busy": "2024-10-13T19:24:53.690906Z",
     "iopub.status.idle": "2024-10-13T19:24:55.903088Z",
     "shell.execute_reply": "2024-10-13T19:24:55.901765Z"
    }
   },
   "outputs": [],
   "source": [
    "test_patches = 720\n",
    "start_time = time()\n",
    "fmap = sflax.FlaxMap(model, modvar)\n",
    "output = fmap(test_ds[\"image\"][:test_patches])\n",
    "time_eval = time() - start_time\n",
    "output = np.clip(output, a_min=0, a_max=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec657aa4",
   "metadata": {},
   "source": [
    "Evaluate trained model in terms of reconstruction time and data fidelity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24fdeb49",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:24:55.908803Z",
     "iopub.status.busy": "2024-10-13T19:24:55.908302Z",
     "iopub.status.idle": "2024-10-13T19:24:56.723560Z",
     "shell.execute_reply": "2024-10-13T19:24:56.722760Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DnCNNNet training epochs:   50                     time[s]:   343.96\n",
      "DnCNNNet testing  SNR: 15.34 dB   PSNR: 27.73 dB   time[s]:     2.20\n"
     ]
    }
   ],
   "source": [
    "snr_eval = metric.snr(test_ds[\"label\"][:test_patches], output)\n",
    "psnr_eval = metric.psnr(test_ds[\"label\"][:test_patches], output)\n",
    "print(\n",
    "    f\"{'DnCNNNet training':18s}{'epochs:':2s}{train_conf['num_epochs']:>5d}\"\n",
    "    f\"{'':21s}{'time[s]:':10s}{trainer.train_time:>7.2f}\"\n",
    ")\n",
    "print(\n",
    "    f\"{'DnCNNNet testing':18s}{'SNR:':5s}{snr_eval:>5.2f}{' dB'}{'':3s}\"\n",
    "    f\"{'PSNR:':6s}{psnr_eval:>5.2f}{' dB'}{'':3s}{'time[s]:':10s}{time_eval:>7.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a574ff",
   "metadata": {},
   "source": [
    "Plot comparison. Note that plots may display unidentifiable image\n",
    "fragments due to the small patch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42b3d472",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:24:56.729062Z",
     "iopub.status.busy": "2024-10-13T19:24:56.728587Z",
     "iopub.status.idle": "2024-10-13T19:24:57.541479Z",
     "shell.execute_reply": "2024-10-13T19:24:57.540679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "indx = np.random.randint(0, high=test_patches)\n",
    "\n",
    "fig, ax = plot.subplots(nrows=1, ncols=3, figsize=(15, 5))\n",
    "plot.imview(test_ds[\"label\"][indx, ..., 0], title=\"Ground truth\", cbar=None, fig=fig, ax=ax[0])\n",
    "plot.imview(\n",
    "    test_ds[\"image\"][indx, ..., 0],\n",
    "    title=\"Noisy: \\nSNR: %.2f (dB), PSNR: %.2f\"\n",
    "    % (\n",
    "        metric.snr(test_ds[\"label\"][indx, ..., 0], test_ds[\"image\"][indx, ..., 0]),\n",
    "        metric.psnr(test_ds[\"label\"][indx, ..., 0], test_ds[\"image\"][indx, ..., 0]),\n",
    "    ),\n",
    "    cbar=None,\n",
    "    fig=fig,\n",
    "    ax=ax[1],\n",
    ")\n",
    "plot.imview(\n",
    "    output[indx, ..., 0],\n",
    "    title=\"DnCNNNet Reconstruction\\nSNR: %.2f (dB), PSNR: %.2f\"\n",
    "    % (\n",
    "        metric.snr(test_ds[\"label\"][indx, ..., 0], output[indx, ..., 0]),\n",
    "        metric.psnr(test_ds[\"label\"][indx, ..., 0], output[indx, ..., 0]),\n",
    "    ),\n",
    "    fig=fig,\n",
    "    ax=ax[2],\n",
    ")\n",
    "divider = make_axes_locatable(ax[2])\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\n",
    "fig.colorbar(ax[2].get_images()[0], cax=cax, label=\"arbitrary units\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fad52ba",
   "metadata": {},
   "source": [
    "Plot convergence statistics. Statistics are generated only if a training\n",
    "cycle was done (i.e. if not reading final epoch results from checkpoint)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "125cb873",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-10-13T19:24:57.548816Z",
     "iopub.status.busy": "2024-10-13T19:24:57.548251Z",
     "iopub.status.idle": "2024-10-13T19:24:58.297216Z",
     "shell.execute_reply": "2024-10-13T19:24:58.296127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if stats_object is not None and len(stats_object.iterations) > 0:\n",
    "    hist = stats_object.history(transpose=True)\n",
    "    fig, ax = plot.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
    "    plot.plot(\n",
    "        np.vstack((hist.Train_Loss, hist.Eval_Loss)).T,\n",
    "        x=hist.Epoch,\n",
    "        ptyp=\"semilogy\",\n",
    "        title=\"Loss function\",\n",
    "        xlbl=\"Epoch\",\n",
    "        ylbl=\"Loss value\",\n",
    "        lgnd=(\"Train\", \"Test\"),\n",
    "        fig=fig,\n",
    "        ax=ax[0],\n",
    "    )\n",
    "    plot.plot(\n",
    "        np.vstack((hist.Train_SNR, hist.Eval_SNR)).T,\n",
    "        x=hist.Epoch,\n",
    "        title=\"Metric\",\n",
    "        xlbl=\"Epoch\",\n",
    "        ylbl=\"SNR (dB)\",\n",
    "        lgnd=(\"Train\", \"Test\"),\n",
    "        fig=fig,\n",
    "        ax=ax[1],\n",
    "    )\n",
    "    fig.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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