TrainingSony_Dark_vision-Pyramid-SSIM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import os, time\n",
    "import numpy as np\n",
    "import glob\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import random\n",
    "import sys\n",
    "from optparse import OptionParser\n",
    "import numpy as np\n",
    "from torch import optim\n",
    "from PIL import Image\n",
    "from torch.autograd import Function, Variable\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset\n",
    "# import cv2\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import rawpy\n",
    "%matplotlib inline\n",
    "\n",
    "input_dir = './Dataset/Sony/short/'\n",
    "gt_dir = './Dataset/Sony/long/'\n",
    "\n",
    "result_dir = './results_SSIM/'\n",
    "model_save_path = './results_SSIM/net_weights/'\n",
    "if os.path.isdir(model_save_path) is not True:\n",
    "    os.makedirs(model_save_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    deviceTag = torch.device('cuda')\n",
    "else:\n",
    "    deviceTag = torch.device('cpu')\n",
    "print(deviceTag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get train IDs\n",
    "train_fns = glob.glob(gt_dir + '0*.ARW')\n",
    "train_ids = [int(os.path.basename(train_fn)[0:5]) for train_fn in train_fns]\n",
    "\n",
    "ps = 512  # patch size for training\n",
    "save_freq = 500\n",
    "\n",
    "DEBUG = 0\n",
    "if DEBUG == 1:\n",
    "    save_freq = 2\n",
    "    train_ids = train_ids[0:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UNET MODULES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class conv_lrelu(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch):\n",
    "        super(conv_lrelu, self).__init__()\n",
    "        self.conv = nn.Sequential(nn.Conv2d(in_ch,out_ch,3, padding = 1),nn.LeakyReLU())\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        return x\n",
    "    \n",
    "class down(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch):\n",
    "        super(down, self).__init__()\n",
    "        self.conv1 = conv_lrelu(in_ch,out_ch)\n",
    "        self.conv2 = conv_lrelu(out_ch,out_ch)\n",
    "        self.down =  nn.MaxPool2d((2,2))\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.down(x)\n",
    "        return x\n",
    "    \n",
    "\n",
    "class up(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch):\n",
    "        super(up, self).__init__()       \n",
    "        self.up =  nn.UpsamplingBilinear2d(scale_factor = 2)\n",
    "        self.conv1 = conv_lrelu(in_ch,out_ch) \n",
    "        self.conv2 = conv_lrelu(out_ch,out_ch) \n",
    "\n",
    "    def forward(self, x1, x2):\n",
    "        x1 = self.up(x1)\n",
    "        x = torch.cat([x2, x1], dim=1)\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        return x\n",
    "\n",
    "class UNet(nn.Module):\n",
    "    def __init__(self, in_ch = 4, CH_PER_SCALE = [32,64,128,256,512], out_ch = 12):\n",
    "        super(UNet, self).__init__()\n",
    "        self.inc = conv_lrelu(in_ch, CH_PER_SCALE[0])\n",
    "        self.inc2 = conv_lrelu(CH_PER_SCALE[0], CH_PER_SCALE[0])\n",
    "        self.down1 = down(CH_PER_SCALE[0], CH_PER_SCALE[1]) \n",
    "        self.down2 = down(CH_PER_SCALE[1],CH_PER_SCALE[2])\n",
    "        self.down3 = down(CH_PER_SCALE[2],CH_PER_SCALE[3])                \n",
    "        self.down4 = down(CH_PER_SCALE[3],CH_PER_SCALE[4])                \n",
    "        self.up1 = up(CH_PER_SCALE[4]+CH_PER_SCALE[3],CH_PER_SCALE[3])\n",
    "        self.up2 = up(CH_PER_SCALE[3]+CH_PER_SCALE[2],CH_PER_SCALE[2])\n",
    "        self.up3 = up(CH_PER_SCALE[2]+CH_PER_SCALE[1],CH_PER_SCALE[1])\n",
    "        self.up4 = up(CH_PER_SCALE[1]+CH_PER_SCALE[0],CH_PER_SCALE[0])\n",
    "        self.outc = nn.Conv2d(CH_PER_SCALE[0], out_ch, 1, padding = 0)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x0 = self.inc(x)\n",
    "        x0 = self.inc2(x0)\n",
    "        x1 = self.down1(x0)\n",
    "        x2 = self.down2(x1)\n",
    "        x3 = self.down3(x2)\n",
    "        x4 = self.down4(x3)\n",
    "        x3_up = self.up1(x4,x3)\n",
    "        x2_up = self.up2(x3_up,x2)\n",
    "        x1_up = self.up3(x2_up,x1)\n",
    "        out = self.up4(x1_up,x0)\n",
    "        out = self.outc(out)\n",
    "#         out = F.pixel_shuffle(out,2) ## Paper final step rearranges 12 channes to 3 RGB channels\n",
    "#         out = F.hardtanh(out, min_val=0, max_val=1) #Clamp the top and bottom to 0,1 since pixels can only be in this value\n",
    "        return out\n",
    "    \n",
    "class PRIDNet(nn.Module):\n",
    "    def __init__(self, in_ch = 4, out_ch = 12):\n",
    "        super(PRIDNet, self).__init__()\n",
    "        self.feature_extraction = nn.Sequential(conv_lrelu(in_ch, 32), *[conv_lrelu(32, 32) for i in range(3)])\n",
    "        self.unet0 = UNet(in_ch = 32, out_ch = 12)\n",
    "        self.unet1 = UNet(in_ch = 32, out_ch = 12)\n",
    "        self.unet2 = UNet(in_ch = 32, out_ch = 12)\n",
    "        self.unet3 = UNet(in_ch = 32, out_ch = 12)\n",
    "        self.unet4 = UNet(in_ch = 32, out_ch = 12)\n",
    "        self.avgpool1 = nn.AvgPool2d((2,2))\n",
    "        self.avgpool2 = nn.AvgPool2d((4,4))\n",
    "        self.avgpool3 = nn.AvgPool2d((8,8))\n",
    "        self.avgpool4 = nn.AvgPool2d((16,16))\n",
    "        self.up4 =  nn.UpsamplingBilinear2d(scale_factor = 16)\n",
    "        self.up3 =  nn.UpsamplingBilinear2d(scale_factor = 8)\n",
    "        self.up2 =  nn.UpsamplingBilinear2d(scale_factor = 4)\n",
    "        self.up1 =  nn.UpsamplingBilinear2d(scale_factor = 2)\n",
    "        self.out =  nn.Conv2d(32+12*5, out_ch, 1, padding = 0)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x_feat = self.feature_extraction(x)\n",
    "        x0 = self.unet0(x_feat)\n",
    "        x1 = self.up1(self.unet1(self.avgpool1(x_feat)))\n",
    "        x2 = self.up2(self.unet2(self.avgpool2(x_feat)))\n",
    "        x3 = self.up3(self.unet3(self.avgpool3(x_feat)))\n",
    "        x4 = self.up4(self.unet4(self.avgpool4(x_feat)))\n",
    "        x_unet_all = torch.cat([x_feat,x0,x1,x2,x3,x4], axis = 1)\n",
    "        out = self.out(x_unet_all)\n",
    "        \n",
    "        out = F.pixel_shuffle(out,2) ## Paper final step rearranges 12 channes to 3 RGB channels\n",
    "        out = F.hardtanh(out, min_val=0, max_val=1) #Clamp the top and bottom to 0,1 since pixels can only be in this value\n",
    "        return out\n",
    "    \n",
    "    def load_my_state_dict(self, state_dict):\n",
    "        own_state = self.state_dict()\n",
    "        for name, param in state_dict.items():\n",
    "            if name not in own_state:\n",
    "                 continue\n",
    "            #if isinstance(param, self.Parameter):\n",
    "            else:\n",
    "                # backwards compatibility for serialized parameters\n",
    "                param = param.data\n",
    "            own_state[name].copy_(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Functions for packing raw and saving images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pack_raw(raw):\n",
    "    # pack Bayer image to 4 channels\n",
    "    im = raw.raw_image_visible.astype(np.float32)\n",
    "    im = np.maximum(im - 512, 0) / (16383 - 512)  # subtract the black level\n",
    "\n",
    "    im = np.expand_dims(im, axis=2)\n",
    "    img_shape = im.shape\n",
    "    H = img_shape[0]\n",
    "    W = img_shape[1]\n",
    "\n",
    "    out = np.concatenate((im[0:H:2, 0:W:2, :],\n",
    "                          im[0:H:2, 1:W:2, :],\n",
    "                          im[1:H:2, 1:W:2, :],\n",
    "                          im[1:H:2, 0:W:2, :]), axis=2)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raw data takes long time to load. Keep them in memory after loaded.\n",
    "gt_images = [None] * 6000\n",
    "input_images = {}\n",
    "input_images['300'] = [None] * len(train_ids)\n",
    "input_images['250'] = [None] * len(train_ids)\n",
    "input_images['100'] = [None] * len(train_ids)\n",
    "\n",
    "g_loss = np.zeros((5000, 1))\n",
    "\n",
    "allfolders = glob.glob(result_dir + '*0')\n",
    "\n",
    "for folder in allfolders:\n",
    "    lastepoch = np.maximum(epochs, int(folder[-4:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_img(input_raw_img, model, ratio):\n",
    "    ## Process image(s) using the given model\n",
    "    # input_raw_img: numpy array, dimension: (Batch,Height,Width,Channel)\n",
    "    # ratio: numpy array, dimension: (Batch,)\n",
    "    model.eval();\n",
    "    model.to(deviceTag)\n",
    "    ratio = ratio.reshape(ratio.shape[0],1,1,1)\n",
    "    input_raw_img = np.transpose(input_raw_img, [0,3,1,2]).astype('float32')*ratio\n",
    "    input_tensor = torch.from_numpy(input_raw_img.copy()).float().to(deviceTag)\n",
    "    with torch.no_grad():\n",
    "        output_tensor = model(input_tensor)\n",
    "    output_img = output_tensor.cpu().numpy()\n",
    "    output_img = np.transpose(output_img, [0,2,3,1])\n",
    "    \n",
    "    return output_img\n",
    "    \n",
    "def validate(model, input_list, gt_list, block_size = None, batch_size = 8):\n",
    "    assert len(input_list) == len(gt_list)\n",
    "    \n",
    "    model.eval();\n",
    "    PSNR_list = []\n",
    "    \n",
    "    for i in range(len(input_list)//batch_size):\n",
    "        if i%10 == 0:\n",
    "            print(i)\n",
    "        input_raw_img_batch = []\n",
    "        gt_img_batch = []\n",
    "        ratio_batch = []\n",
    "        for b in range(batch_size):\n",
    "            if i*batch_size+b < len(input_list):\n",
    "                in_path = input_list[i*batch_size+b]\n",
    "                gt_path = gt_list[i*batch_size+b]\n",
    "            else:\n",
    "                break\n",
    "            in_fn = os.path.basename(in_path)\n",
    "            gt_fn = os.path.basename(gt_path)\n",
    "            in_exposure = float(in_fn[9:-5])\n",
    "            gt_exposure = float(gt_fn[9:-5])\n",
    "            ratio = min(gt_exposure / in_exposure, 300)\n",
    "        \n",
    "            raw = rawpy.imread(in_path)\n",
    "            input_raw_img = pack_raw(raw)\n",
    "            \n",
    "            gt_raw = rawpy.imread(gt_path)\n",
    "            gt_img = gt_raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)\n",
    "            gt_img = np.float32(gt_img / 65535.0)\n",
    "            \n",
    "            if block_size is not None:\n",
    "                i_cut, j_cut = np.random.randint(0,input_raw_img.shape[0]-block_size), np.random.randint(0,input_raw_img.shape[1]-block_size)\n",
    "                gt_img = gt_img[i_cut*2:i_cut*2+block_size*2, j_cut*2:j_cut*2+block_size*2, :]\n",
    "                input_raw_img = input_raw_img[i_cut:i_cut+block_size, j_cut:j_cut+block_size, :]\n",
    "            \n",
    "            ratio_batch.append(ratio)\n",
    "            input_raw_img_batch.append(input_raw_img)\n",
    "            gt_img_batch.append(gt_img)\n",
    "        \n",
    "        input_raw_img_batch = np.array(input_raw_img_batch)\n",
    "        ratio_batch = np.array(ratio_batch)\n",
    "        gt_img_batch = np.array(gt_img_batch)\n",
    "        \n",
    "        output_img_batch = process_img(input_raw_img_batch, model, ratio_batch)\n",
    "        plt.figure()\n",
    "        plt.imshow(gt_img_batch[0,:,:,:])\n",
    "        plt.title(\"Ground Truth\")\n",
    "        plt.figure()\n",
    "        plt.imshow(output_img_batch[0,:,:,:])\n",
    "        plt.title(\"Predicted patch\")\n",
    "        MSE = np.mean((output_img_batch.reshape(output_img_batch.shape[0],-1) - gt_img_batch.reshape(gt_img_batch.shape[0],-1))**2, axis = 1)\n",
    "        PSNR_batch = 10*np.log10(1/MSE)\n",
    "        PSNR_list.append(list(PSNR_batch))\n",
    "    \n",
    "    Val_PSNR = np.mean(PSNR_list)\n",
    "        \n",
    "    return Val_PSNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PRIDNet()\n",
    "model = model.cuda()\n",
    "model = model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting epoch 1/4000.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-d3612b83f9ad>:20: DeprecationWarning: This function is deprecated. Please call randint(0, 0 + 1) instead\n",
      "  in_path = in_files[np.random.random_integers(0, len(in_files) - 1)]\n",
      "<ipython-input-10-d3612b83f9ad>:20: DeprecationWarning: This function is deprecated. Please call randint(0, 2 + 1) instead\n",
      "  in_path = in_files[np.random.random_integers(0, len(in_files) - 1)]\n",
      "<ipython-input-10-d3612b83f9ad>:20: DeprecationWarning: This function is deprecated. Please call randint(0, 1 + 1) instead\n",
      "  in_path = in_files[np.random.random_integers(0, len(in_files) - 1)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.7763022989034652\n",
      "Starting epoch 2/4000.\n",
      "Epoch finished ! Loss: 0.6982577428221702\n",
      "Starting epoch 3/4000.\n",
      "Epoch finished ! Loss: 0.6785474762320518\n",
      "Starting epoch 4/4000.\n",
      "Epoch finished ! Loss: 0.5824663564562798\n",
      "Starting epoch 5/4000.\n",
      "Epoch finished ! Loss: 0.5399887874722481\n",
      "Starting epoch 6/4000.\n",
      "Epoch finished ! Loss: 0.5064837828278541\n",
      "Starting epoch 7/4000.\n",
      "Epoch finished ! Loss: 0.49664628356695173\n",
      "Starting epoch 8/4000.\n",
      "Epoch finished ! Loss: 0.4947374314069748\n",
      "Starting epoch 9/4000.\n",
      "Epoch finished ! Loss: 0.4861320167779922\n",
      "Starting epoch 10/4000.\n",
      "Epoch finished ! Loss: 0.479812479019165\n",
      "Starting epoch 11/4000.\n",
      "Epoch finished ! Loss: 0.46261010468006136\n",
      "Starting epoch 12/4000.\n",
      "Epoch finished ! Loss: 0.3993742436170578\n",
      "Starting epoch 13/4000.\n",
      "Epoch finished ! Loss: 0.3443560630083084\n",
      "Starting epoch 14/4000.\n",
      "Epoch finished ! Loss: 0.33322286456823347\n",
      "Starting epoch 15/4000.\n",
      "Epoch finished ! Loss: 0.3272545367479324\n",
      "Starting epoch 16/4000.\n",
      "Epoch finished ! Loss: 0.3175528973340988\n",
      "Starting epoch 17/4000.\n",
      "Epoch finished ! Loss: 0.3247975006699562\n",
      "Starting epoch 18/4000.\n",
      "Epoch finished ! Loss: 0.303954541683197\n",
      "Starting epoch 19/4000.\n",
      "Epoch finished ! Loss: 0.31453131437301635\n",
      "Starting epoch 20/4000.\n",
      "Epoch finished ! Loss: 0.31147550195455553\n",
      "Starting epoch 21/4000.\n",
      "Epoch finished ! Loss: 0.3097573101520538\n",
      "Starting epoch 22/4000.\n",
      "Epoch finished ! Loss: 0.312240406870842\n",
      "Starting epoch 23/4000.\n",
      "Epoch finished ! Loss: 0.3150433287024498\n",
      "Starting epoch 24/4000.\n",
      "Epoch finished ! Loss: 0.3082579761743546\n",
      "Starting epoch 25/4000.\n",
      "Epoch finished ! Loss: 0.29746133983135226\n",
      "Starting epoch 26/4000.\n",
      "Epoch finished ! Loss: 0.307849557697773\n",
      "Starting epoch 27/4000.\n",
      "Epoch finished ! Loss: 0.30447901487350465\n",
      "Starting epoch 28/4000.\n",
      "Epoch finished ! Loss: 0.30214022397994994\n",
      "Starting epoch 29/4000.\n",
      "Epoch finished ! Loss: 0.3073967397212982\n",
      "Starting epoch 30/4000.\n",
      "Epoch finished ! Loss: 0.30736598670482634\n",
      "Starting epoch 31/4000.\n",
      "Epoch finished ! Loss: 0.30612863749265673\n",
      "Starting epoch 32/4000.\n",
      "Epoch finished ! Loss: 0.2966527670621872\n",
      "Starting epoch 33/4000.\n",
      "Epoch finished ! Loss: 0.29119897335767747\n",
      "Starting epoch 34/4000.\n",
      "Epoch finished ! Loss: 0.306718048453331\n",
      "Starting epoch 35/4000.\n",
      "Epoch finished ! Loss: 0.299887977540493\n",
      "Starting epoch 36/4000.\n",
      "Epoch finished ! Loss: 0.31240352988243103\n",
      "Starting epoch 37/4000.\n",
      "Epoch finished ! Loss: 0.2995546251535416\n",
      "Starting epoch 38/4000.\n",
      "Epoch finished ! Loss: 0.3054358199238777\n",
      "Starting epoch 39/4000.\n",
      "Epoch finished ! Loss: 0.302694508433342\n",
      "Starting epoch 40/4000.\n",
      "Epoch finished ! Loss: 0.3008403807878494\n",
      "Starting epoch 41/4000.\n",
      "Epoch finished ! Loss: 0.2935501843690872\n",
      "Starting epoch 42/4000.\n",
      "Epoch finished ! Loss: 0.3066807761788368\n",
      "Starting epoch 43/4000.\n",
      "Epoch finished ! Loss: 0.3030719503760338\n",
      "Starting epoch 44/4000.\n",
      "Epoch finished ! Loss: 0.30090234577655794\n",
      "Starting epoch 45/4000.\n",
      "Epoch finished ! Loss: 0.29686217904090884\n",
      "Starting epoch 46/4000.\n",
      "Epoch finished ! Loss: 0.30187241584062574\n",
      "Starting epoch 47/4000.\n",
      "Epoch finished ! Loss: 0.2960881978273392\n",
      "Starting epoch 48/4000.\n",
      "Epoch finished ! Loss: 0.29452774077653887\n",
      "Starting epoch 49/4000.\n",
      "Epoch finished ! Loss: 0.29660201966762545\n",
      "Starting epoch 50/4000.\n",
      "Epoch finished ! Loss: 0.2881389781832695\n",
      "Starting epoch 51/4000.\n",
      "Epoch finished ! Loss: 0.2868896067142487\n",
      "Starting epoch 52/4000.\n",
      "Epoch finished ! Loss: 0.29384503811597823\n",
      "Starting epoch 53/4000.\n",
      "Epoch finished ! Loss: 0.2906388431787491\n",
      "Starting epoch 54/4000.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.2118067115545273\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.20614922940731048\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.20193127691745758\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.194363072514534\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18993605077266693\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.1853237956762314\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.184169040620327\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18801751881837844\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18845455199480057\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18376102447509765\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18229379802942275\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18044889718294144\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.17376600950956345\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.18126543313264848\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.1753830075263977\n",
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      "Epoch finished ! Loss: 0.1710707113146782\n",
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      "Epoch finished ! Loss: 0.17261173576116562\n",
      "Starting epoch 2612/4000.\n",
      "Epoch finished ! Loss: 0.17675460278987884\n",
      "Starting epoch 2613/4000.\n",
      "Epoch finished ! Loss: 0.17451515346765517\n",
      "Starting epoch 2614/4000.\n",
      "Epoch finished ! Loss: 0.1767221823334694\n",
      "Starting epoch 2615/4000.\n",
      "Epoch finished ! Loss: 0.1704955980181694\n",
      "Starting epoch 2616/4000.\n",
      "Epoch finished ! Loss: 0.17675715684890747\n",
      "Starting epoch 2617/4000.\n",
      "Epoch finished ! Loss: 0.1798758715391159\n",
      "Starting epoch 2618/4000.\n",
      "Epoch finished ! Loss: 0.17506814897060394\n",
      "Starting epoch 2619/4000.\n",
      "Epoch finished ! Loss: 0.16937980353832244\n",
      "Starting epoch 2620/4000.\n",
      "Epoch finished ! Loss: 0.17098818868398666\n",
      "Starting epoch 2621/4000.\n",
      "Epoch finished ! Loss: 0.17375686317682265\n",
      "Starting epoch 2622/4000.\n",
      "Epoch finished ! Loss: 0.17498528212308884\n",
      "Starting epoch 2623/4000.\n",
      "Epoch finished ! Loss: 0.17831121236085892\n",
      "Starting epoch 2624/4000.\n",
      "Epoch finished ! Loss: 0.1743251219391823\n",
      "Starting epoch 2625/4000.\n",
      "Epoch finished ! Loss: 0.16682856678962707\n",
      "Starting epoch 2626/4000.\n",
      "Epoch finished ! Loss: 0.17555604875087738\n",
      "Starting epoch 2627/4000.\n",
      "Epoch finished ! Loss: 0.17737092226743698\n",
      "Starting epoch 2628/4000.\n",
      "Epoch finished ! Loss: 0.17224177569150925\n",
      "Starting epoch 2629/4000.\n",
      "Epoch finished ! Loss: 0.17388359010219573\n",
      "Starting epoch 2630/4000.\n",
      "Epoch finished ! Loss: 0.1773112565279007\n",
      "Starting epoch 2631/4000.\n",
      "Epoch finished ! Loss: 0.1751851975917816\n",
      "Starting epoch 2632/4000.\n",
      "Epoch finished ! Loss: 0.17484520077705384\n",
      "Starting epoch 2633/4000.\n",
      "Epoch finished ! Loss: 0.1712157517671585\n",
      "Starting epoch 2634/4000.\n",
      "Epoch finished ! Loss: 0.17058925181627274\n",
      "Starting epoch 2635/4000.\n",
      "Epoch finished ! Loss: 0.16970552057027816\n",
      "Starting epoch 2636/4000.\n",
      "Epoch finished ! Loss: 0.17388778775930405\n",
      "Starting epoch 2637/4000.\n",
      "Epoch finished ! Loss: 0.17240174114704132\n",
      "Starting epoch 2638/4000.\n",
      "Epoch finished ! Loss: 0.17694337368011476\n",
      "Starting epoch 2639/4000.\n",
      "Epoch finished ! Loss: 0.17220213562250136\n",
      "Starting epoch 2640/4000.\n",
      "Epoch finished ! Loss: 0.17037208527326583\n",
      "Starting epoch 2641/4000.\n",
      "Epoch finished ! Loss: 0.17332399636507034\n",
      "Starting epoch 2642/4000.\n",
      "Epoch finished ! Loss: 0.1771725296974182\n",
      "Starting epoch 2643/4000.\n",
      "Epoch finished ! Loss: 0.1741536170244217\n",
      "Starting epoch 2644/4000.\n",
      "Epoch finished ! Loss: 0.17459920197725295\n",
      "Starting epoch 2645/4000.\n",
      "Epoch finished ! Loss: 0.17500608712434768\n",
      "Starting epoch 2646/4000.\n",
      "Epoch finished ! Loss: 0.18158579468727112\n",
      "Starting epoch 2647/4000.\n",
      "Epoch finished ! Loss: 0.17267506420612336\n",
      "Starting epoch 2648/4000.\n",
      "Epoch finished ! Loss: 0.1708409532904625\n",
      "Starting epoch 2649/4000.\n",
      "Epoch finished ! Loss: 0.1751965969800949\n",
      "Starting epoch 2650/4000.\n",
      "Epoch finished ! Loss: 0.17296697348356246\n",
      "Starting epoch 2651/4000.\n",
      "Epoch finished ! Loss: 0.17968542575836183\n",
      "Starting epoch 2652/4000.\n",
      "Epoch finished ! Loss: 0.17698831260204315\n",
      "Starting epoch 2653/4000.\n",
      "Epoch finished ! Loss: 0.16678424328565597\n",
      "Starting epoch 2654/4000.\n",
      "Epoch finished ! Loss: 0.17279373556375505\n",
      "Starting epoch 2655/4000.\n",
      "Epoch finished ! Loss: 0.16918043345212935\n",
      "Starting epoch 2656/4000.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch finished ! Loss: 0.1753266215324402\n",
      "Starting epoch 2657/4000.\n",
      "Epoch finished ! Loss: 0.1704387202858925\n",
      "Starting epoch 2658/4000.\n",
      "Epoch finished ! Loss: 0.1729739561676979\n",
      "Starting epoch 2659/4000.\n",
      "Epoch finished ! Loss: 0.1681669145822525\n",
      "Starting epoch 2660/4000.\n",
      "Epoch finished ! Loss: 0.16987788528203965\n",
      "Starting epoch 2661/4000.\n",
      "Epoch finished ! Loss: 0.16921505481004714\n",
      "Starting epoch 2662/4000.\n",
      "Epoch finished ! Loss: 0.1737332373857498\n",
      "Starting epoch 2663/4000.\n",
      "Epoch finished ! Loss: 0.179964779317379\n",
      "Starting epoch 2664/4000.\n",
      "Epoch finished ! Loss: 0.17468404471874238\n",
      "Starting epoch 2665/4000.\n",
      "Epoch finished ! Loss: 0.17208441495895385\n",
      "Starting epoch 2666/4000.\n",
      "Epoch finished ! Loss: 0.17543412894010543\n",
      "Starting epoch 2667/4000.\n",
      "Epoch finished ! Loss: 0.17496222704648973\n",
      "Starting epoch 2668/4000.\n",
      "Epoch finished ! Loss: 0.17637979388237\n",
      "Starting epoch 2669/4000.\n",
      "Epoch finished ! Loss: 0.17460937052965164\n",
      "Starting epoch 2670/4000.\n",
      "Epoch finished ! Loss: 0.1740424230694771\n",
      "Starting epoch 2671/4000.\n",
      "Epoch finished ! Loss: 0.1663529634475708\n",
      "Starting epoch 2672/4000.\n",
      "Epoch finished ! Loss: 0.17151722609996795\n",
      "Starting epoch 2673/4000.\n",
      "Epoch finished ! Loss: 0.17414621114730836\n",
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      "Epoch finished ! Loss: 0.16970286816358565\n",
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      "Epoch finished ! Loss: 0.17344742119312287\n",
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      "Epoch finished ! Loss: 0.1736995741724968\n",
      "Starting epoch 2677/4000.\n",
      "Epoch finished ! Loss: 0.1759062647819519\n",
      "Starting epoch 2678/4000.\n",
      "Epoch finished ! Loss: 0.16832707077264786\n",
      "Starting epoch 2679/4000.\n",
      "Epoch finished ! Loss: 0.16972566843032838\n",
      "Starting epoch 2680/4000.\n",
      "Epoch finished ! Loss: 0.17266839742660522\n",
      "Starting epoch 2681/4000.\n",
      "Epoch finished ! Loss: 0.1787150263786316\n",
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      "Epoch finished ! Loss: 0.17114801704883575\n",
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      "Epoch finished ! Loss: 0.17607951909303665\n",
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      "Epoch finished ! Loss: 0.17224644720554352\n",
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      "Epoch finished ! Loss: 0.17513174563646317\n",
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      "Epoch finished ! Loss: 0.1744450658559799\n",
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      "Epoch finished ! Loss: 0.1750415235757828\n",
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      "Epoch finished ! Loss: 0.17730536162853242\n",
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      "Epoch finished ! Loss: 0.17319308817386628\n",
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      "Epoch finished ! Loss: 0.17819197624921798\n",
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      "Epoch finished ! Loss: 0.17608466893434524\n",
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      "Epoch finished ! Loss: 0.17597092539072037\n",
      "Starting epoch 2695/4000.\n",
      "Epoch finished ! Loss: 0.1671363651752472\n",
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      "Epoch finished ! Loss: 0.169844089448452\n",
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      "Epoch finished ! Loss: 0.171959525346756\n",
      "Starting epoch 2698/4000.\n",
      "Epoch finished ! Loss: 0.16740640997886658\n",
      "Starting epoch 2699/4000.\n",
      "Epoch finished ! Loss: 0.17683668732643126\n",
      "Starting epoch 2700/4000.\n",
      "Epoch finished ! Loss: 0.1749907210469246\n",
      "Starting epoch 2701/4000.\n",
      "Epoch finished ! Loss: 0.17020283490419388\n",
      "Starting epoch 2702/4000.\n",
      "Epoch finished ! Loss: 0.17547038197517395\n",
      "Starting epoch 2703/4000.\n",
      "Epoch finished ! Loss: 0.17040500789880753\n",
      "Starting epoch 2704/4000.\n",
      "Epoch finished ! Loss: 0.16900457739830016\n",
      "Starting epoch 2705/4000.\n",
      "Epoch finished ! Loss: 0.16931413412094115\n",
      "Starting epoch 2706/4000.\n",
      "Epoch finished ! Loss: 0.17106920778751372\n",
      "Starting epoch 2707/4000.\n",
      "Epoch finished ! Loss: 0.17753157168626785\n",
      "Starting epoch 2708/4000.\n",
      "Epoch finished ! Loss: 0.1752600133419037\n",
      "Starting epoch 2709/4000.\n",
      "Epoch finished ! Loss: 0.17409510612487794\n",
      "Starting epoch 2710/4000.\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 1e-4 \n",
    "batch_num = 4\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=2000, gamma=0.1) #Step Scheduler. \n",
    "criterion = SSIM(data_range=1, size_average=True, channel=3)\n",
    "Start_epoch = 0\n",
    "epochs = 4000\n",
    "TrainingLossData = np.zeros(epochs)\n",
    "\n",
    "for epoch in range(Start_epoch, Start_epoch+epochs):\n",
    "    print('Starting epoch {}/{}.'.format(epoch + 1, epochs))\n",
    "    epoch_loss = 0 ## Set Epoch Loss\n",
    "    count = 0;\n",
    "    batches_processed = 0\n",
    "    ##This version has a batch size of 1. In the future conside increasing batchsize\n",
    "    for ind in np.random.permutation(len(train_ids)):    \n",
    "        # get the path from image id\n",
    "        train_id = train_ids[ind]\n",
    "        in_files = glob.glob(input_dir + '%05d_00*.ARW' % train_id)\n",
    "        in_path = in_files[np.random.random_integers(0, len(in_files) - 1)]\n",
    "        in_fn = os.path.basename(in_path)\n",
    "\n",
    "        gt_files = glob.glob(gt_dir + '%05d_00*.ARW' % train_id)\n",
    "        gt_path = gt_files[0]\n",
    "        gt_fn = os.path.basename(gt_path)\n",
    "        in_exposure = float(in_fn[9:-5])\n",
    "        gt_exposure = float(gt_fn[9:-5])\n",
    "        ratio = min(gt_exposure / in_exposure, 300)\n",
    "\n",
    "        st = time.time()\n",
    "\n",
    "        if input_images[str(ratio)[0:3]][ind] is None:\n",
    "            raw = rawpy.imread(in_path)\n",
    "            input_images[str(ratio)[0:3]][ind] = np.expand_dims(pack_raw(raw), axis=0) * ratio\n",
    "\n",
    "            gt_raw = rawpy.imread(gt_path)\n",
    "            im = gt_raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)\n",
    "            gt_images[ind] = np.expand_dims(np.float32(im / 65535.0), axis=0)\n",
    "\n",
    "        # crop\n",
    "        H = input_images[str(ratio)[0:3]][ind].shape[1]\n",
    "        W = input_images[str(ratio)[0:3]][ind].shape[2]\n",
    "\n",
    "        xx = np.random.randint(0, W - ps)\n",
    "        yy = np.random.randint(0, H - ps)\n",
    "        input_patch = input_images[str(ratio)[0:3]][ind][:, yy:yy + ps, xx:xx + ps, :]\n",
    "        gt_patch = gt_images[ind][:, yy * 2:yy * 2 + ps * 2, xx * 2:xx * 2 + ps * 2, :]\n",
    "\n",
    "        if np.random.randint(2, size=1)[0] == 1:  # random flip\n",
    "            input_patch = np.flip(input_patch, axis=1)\n",
    "            gt_patch = np.flip(gt_patch, axis=1)\n",
    "        if np.random.randint(2, size=1)[0] == 1:\n",
    "            input_patch = np.flip(input_patch, axis=2)\n",
    "            gt_patch = np.flip(gt_patch, axis=2)\n",
    "        if np.random.randint(2, size=1)[0] == 1:  # random transpose\n",
    "            input_patch = np.transpose(input_patch, (0, 2, 1, 3))\n",
    "            gt_patch = np.transpose(gt_patch, (0, 2, 1, 3))\n",
    "        #(1, 512, 512, 4)\n",
    "        #(1, 1024, 1024, 3)\n",
    "        input_patch = np.transpose(input_patch, (0,3,1,2))\n",
    "        input_patch = torch.from_numpy(input_patch.copy()).cuda()\n",
    "        gt_patch = np.transpose(gt_patch, (0,3,1,2))\n",
    "        gt_patch = torch.from_numpy(gt_patch.copy()).cuda()\n",
    "        ##Batch concatenation\n",
    "        if count%(batch_num)==0:\n",
    "            input_patch_all = input_patch\n",
    "            gt_patch_all = gt_patch\n",
    "        else:\n",
    "            input_patch_all = torch.cat([input_patch_all, input_patch], dim=0)\n",
    "            gt_patch_all = torch.cat([gt_patch_all, gt_patch], dim=0)\n",
    "        ##Every N batches we ship it back \n",
    "        if count%(batch_num)==batch_num-1:\n",
    "            #print(input_patch_all.shape)\n",
    "            img_pred = model.forward(input_patch_all)\n",
    "            loss = 1 - criterion(img_pred, gt_patch_all)\n",
    "            epoch_loss += loss.item()\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            batches_processed += 1\n",
    "        count = count +1\n",
    "    scheduler.step()\n",
    "    print('Epoch finished ! Loss: {}'.format(epoch_loss / batches_processed))\n",
    "    if epoch == 0:\n",
    "        trainF= open(result_dir+\"TrainingEpoch.txt\",\"w+\")\n",
    "        trainF.write('Epoch,Train_loss\\n')\n",
    "        trainF.write('{},{}\\n'.format(epoch, epoch_loss / batches_processed))\n",
    "        trainF.close()\n",
    "    else:\n",
    "        trainF= open(result_dir+\"TrainingEpoch.txt\",\"a\")\n",
    "        trainF.write('{},{}\\n'.format(epoch, epoch_loss / batches_processed))\n",
    "        trainF.close()\n",
    "    TrainingLossData[epoch] = epoch_loss / batches_processed ## Save for plotting\n",
    "    ################################################ [TODO] ###################################################\n",
    "    # Perform validation with eval_net() on the validation data\n",
    "    # Save the model after every 10 epochs. This save our Memory on HPC.\n",
    "    ##Save Top results after 95%\n",
    "    if epoch > epochs*0.95:\n",
    "        torch.save(model.state_dict(),model_save_path + 'sony{}.pth'.format(epoch + 1))\n",
    "    if epoch%200 == 0:\n",
    "        torch.save(model.state_dict(),model_save_path + 'sony{}.pth'.format(epoch + 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isdir(model_save_path):\n",
    "            torch.save(model.state_dict(),model_save_path + 'sony{}.pth'.format(epoch + 1))\n",
    "else:\n",
    "    os.makedirs(model_save_path, exist_ok=True)\n",
    "    torch.save(model.state_dict(),model_save_path + 'sony{}.pth'.format(epoch + 1))"
   ]
  }
 ],
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