deepmtl-for-power.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms as T\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "import glob\n",
    "import json\n",
    "import seaborn as sns\n",
    "from torchviz import make_dot, make_dot_from_trace\n",
    "from skimage import io, transform\n",
    "from torch import tensor\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from IPython.display import clear_output\n",
    "import sys\n",
    "sys.path.insert(0, '/home/caitao/Project/dl-localization')\n",
    "from input_output import Default\n",
    "from utility import Utility\n",
    "from torch._six import container_abcs, string_classes, int_classes\n",
    "import re\n",
    "from scipy.ndimage import gaussian_filter\n",
    "np_str_obj_array_pattern = re.compile(r'[SaUO]')\n",
    "default_collate_err_msg_format = (\n",
    "    \"default_collate: batch must contain tensors, numpy arrays, numbers, \"\n",
    "    \"dicts or lists; found {}\")\n",
    "\n",
    "# plt.rcParams['font.size'] = 60\n",
    "# # plt.rcParams['font.weight'] = 'bold'\n",
    "# # plt.rcParams['axes.labelweight'] = 'bold'\n",
    "# plt.rcParams['lines.linewidth'] = 10\n",
    "# plt.rcParams['lines.markersize'] = 15\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Common modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UniformNormalize:\n",
    "    '''Set a uniform threshold accross all samples\n",
    "    '''\n",
    "    def __init__(self, noise_floor):\n",
    "        self.noise_floor = noise_floor\n",
    "\n",
    "    def __call__(self, matrix):\n",
    "        matrix -= self.noise_floor\n",
    "        matrix /= (-self.noise_floor/2)\n",
    "        return matrix.astype(np.float32)\n",
    "    \n",
    "class Metrics:\n",
    "    '''Evaluation metrics\n",
    "    '''\n",
    "    @staticmethod\n",
    "    def localization_error_image_continuous(pred_batch, pred_ntx, truth_batch, index, grid_len, peak_threshold, debug=False):\n",
    "        '''Continuous -- for single TX\n",
    "           euclidian error when modeling the output representation is a matrix (image)\n",
    "           both pred and truth are batches, typically a batch of 32\n",
    "           now both prediction and truth are continuous numbers\n",
    "        Args:\n",
    "            pred_batch:  numpy.ndarray -- size=(N, 1, 100, 100)\n",
    "            truth_batch: numpy.ndarray -- size=(N, num_tx, 2)\n",
    "        Return:\n",
    "            pred_locs -- list<np.ndarray>\n",
    "            errors    -- list<list>\n",
    "            misses    -- list\n",
    "            false     -- list\n",
    "        '''\n",
    "        def float_target(pred_matrix, pred_peaks):\n",
    "            new_pred_peaks = []\n",
    "            for pred_x, pred_y in pred_peaks:\n",
    "                sum_weight = 0\n",
    "                neighbor = []\n",
    "                for d in [(0, 0), (-1, 0), (0, 1), (1, 0), (0, -1)]:\n",
    "                    nxt = (pred_x + d[0], pred_y + d[1])\n",
    "                    if 0 <= nxt[0] < grid_len and 0 <= nxt[1] < grid_len:\n",
    "                        neighbor.append(((nxt[0] + 0.5, nxt[1] + 0.5), pred[nxt[0]][nxt[1]]))\n",
    "                        sum_weight += pred_matrix[nxt[0]][nxt[1]]\n",
    "                pred_x, pred_y = 0, 0\n",
    "                for n in neighbor:\n",
    "                    loc = n[0]\n",
    "                    w   = n[1]\n",
    "                    pred_x += loc[0] / sum_weight * w\n",
    "                    pred_y += loc[1] / sum_weight * w\n",
    "                new_pred_peaks.append((pred_x, pred_y))\n",
    "            return new_pred_peaks\n",
    "\n",
    "        pred_locs = []\n",
    "        errors = []\n",
    "        misses = []\n",
    "        falses = []\n",
    "        for i, pred, pred_n, truth, indx in zip(range(len(pred_batch)), pred_batch, pred_ntx, truth_batch, index):\n",
    "            # 1: get the multiple predicted locations\n",
    "            pred = pred[0]           # there is only one channel\n",
    "            pred_peaks, _ = Utility.detect_peak(pred, np.round(pred_n, 0), peak_threshold)         # get the predictions  TIME: 23 milliseconds\n",
    "            pred_peaks = float_target(pred, pred_peaks)\n",
    "            # 2: do a matching and get the error\n",
    "            radius_threshold = Default.grid_length * Default.error_threshold\n",
    "            error, miss, false = Utility.compute_error(pred_peaks, truth, radius_threshold, False)\n",
    "            errors.append(error)\n",
    "            misses.append(miss)\n",
    "            falses.append(false)\n",
    "            if debug:\n",
    "                print(i, indx, 'pred', [(round(loc[0], 2), round(loc[1], 2)) for loc in pred_peaks], '; truth', \\\n",
    "                      [(round(loc[0], 2), round(loc[1], 2)) for loc in truth], ' ; error', error, ' ; miss', miss, ' ; false', false)\n",
    "        return errors, misses, falses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_padding(batch, max_len):\n",
    "    \"\"\"add zeros to elements that are not maximum length\"\"\"\n",
    "    for i in range(len(batch)):\n",
    "        diff = max_len - len(batch[i])\n",
    "        if diff > 0:                      # padding\n",
    "            zeros = torch.zeros(diff, 2)\n",
    "            padded = torch.cat((batch[i], zeros), 0)\n",
    "            batch[i] = padded\n",
    "\n",
    "\n",
    "def my_collate(batch):\n",
    "    \"\"\"Puts each data field into a tensor with outer dimension batch size\"\"\"\n",
    "    elem = batch[0]\n",
    "    elem_type = type(elem)\n",
    "    if isinstance(elem, torch.Tensor):\n",
    "        max_len = len(max(batch, key=len))\n",
    "        min_len = len(min(batch, key=len))\n",
    "        if max_len != min_len:\n",
    "            my_padding(batch, max_len)\n",
    "        out = None\n",
    "        if torch.utils.data.get_worker_info() is not None:\n",
    "            # If we're in a background process, concatenate directly into a\n",
    "            # shared memory tensor to avoid an extra copy\n",
    "            numel = sum([x.numel() for x in batch])\n",
    "            storage = elem.storage()._new_shared(numel)\n",
    "            out = elem.new(storage)\n",
    "        return torch.stack(batch, 0, out=out)\n",
    "    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\n",
    "            and elem_type.__name__ != 'string_':\n",
    "        if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':\n",
    "            # array of string classes and object\n",
    "            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:\n",
    "                raise TypeError(default_collate_err_msg_format.format(elem.dtype))\n",
    "\n",
    "            return my_collate([torch.as_tensor(b) for b in batch])\n",
    "        elif elem.shape == ():  # scalars\n",
    "            return torch.as_tensor(batch)\n",
    "    elif isinstance(elem, float):\n",
    "        return torch.tensor(batch, dtype=torch.float64)\n",
    "    elif isinstance(elem, int_classes):\n",
    "        return torch.tensor(batch)\n",
    "    elif isinstance(elem, string_classes):\n",
    "        return batch\n",
    "    elif isinstance(elem, container_abcs.Mapping):\n",
    "        return {key: my_collate([d[key] for d in batch]) for key in elem}\n",
    "    elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple\n",
    "        return elem_type(*(my_collate(samples) for samples in zip(*batch)))\n",
    "    elif isinstance(elem, container_abcs.Sequence):\n",
    "        # check to make sure that the elements in batch have consistent size\n",
    "        it = iter(batch)\n",
    "        elem_size = len(next(it))\n",
    "        if not all(len(elem) == elem_size for elem in it):\n",
    "            raise RuntimeError('each element in list of batch should be of equal size')\n",
    "        transposed = zip(*batch)\n",
    "        return [my_collate(samples) for samples in transposed]\n",
    "\n",
    "    raise TypeError(default_collate_err_msg_format.format(elem_type))\n",
    "\n",
    "\n",
    "def my_uncollate(y_num, y_float):\n",
    "    \"\"\"this is for uncollating the target_float\"\"\"\n",
    "    y_float_tmp = []\n",
    "    for ntx, y_f in zip(y_num, y_float):\n",
    "        y_float_tmp.append(y_f[:ntx])\n",
    "    return np.array(y_float_tmp, dtype=object)\n",
    "\n",
    "\n",
    "def get_sen_num(idx, root_dir='data/205test', sample_per_label=5):\n",
    "    folder = int(idx / sample_per_label)\n",
    "    folder = format(folder, '06d')\n",
    "    json_name = str(idx % sample_per_label) + '.json'\n",
    "    json_path = os.path.join(root_dir, folder, json_name)\n",
    "    with open(json_path, 'r') as f:\n",
    "        json_dict = json.loads(f.read())\n",
    "    return len(json_dict['sensor_data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset & Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matrix shape: torch.Size([1, 100, 100])\n",
      "matrix shape: tensor([[[0., 0., 0.,  ..., 0., 0., 0.],\n",
      "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "         ...,\n",
      "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "         [0., 0., 0.,  ..., 0., 0., 0.]]])\n",
      "target: [[[0. 0. 0. ... 0. 0. 0.]\n",
      "  [0. 0. 0. ... 0. 0. 0.]\n",
      "  [0. 0. 0. ... 0. 0. 0.]\n",
      "  ...\n",
      "  [0. 0. 0. ... 0. 0. 0.]\n",
      "  [0. 0. 0. ... 0. 0. 0.]\n",
      "  [0. 0. 0. ... 0. 0. 0.]]]\n",
      "target float: [[22.247738 37.7787  ]\n",
      " [24.536934 23.045094]\n",
      " [23.43694  57.95972 ]\n",
      " [27.604172 73.78899 ]\n",
      " [41.88352  49.199535]\n",
      " [38.15534  43.336834]\n",
      " [27.21706  57.622578]\n",
      " [50.848633 32.910103]\n",
      " [37.225933 53.44451 ]\n",
      " [46.512    31.36058 ]]\n",
      "length: 64000\n",
      "---\n",
      "\n",
      "matrix type: torch.float32\n",
      "target type: float32\n",
      "target float: [[34.54623  65.708595]\n",
      " [40.354164 38.00436 ]\n",
      " [47.26604  49.20305 ]\n",
      " [63.003532 10.165834]\n",
      " [20.328283 14.921877]\n",
      " [29.685894 81.67642 ]\n",
      " [72.957596 86.33103 ]]\n",
      "target num: 7\n",
      "32000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class SensorInputDatasetTranslation(Dataset):\n",
    "    '''Sensor reading input dataset -- for multi TX\n",
    "       Output is image, model as a image segmentation problem\n",
    "    '''\n",
    "    def __init__(self, root_dir: str, transform=None):\n",
    "        '''\n",
    "        Args:\n",
    "            root_dir:  directory with all the images\n",
    "            labels:    labels of images\n",
    "            transform: optional transform to be applied on a sample\n",
    "        '''\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "        self.length = len(os.listdir(self.root_dir))\n",
    "        self.sample_per_label = self.get_sample_per_label()\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.length * self.sample_per_label\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        folder = int(idx / self.sample_per_label)\n",
    "        folder = format(folder, '06d')\n",
    "        matrix_name = str(idx % self.sample_per_label) + '.npy'\n",
    "        matrix_path = os.path.join(self.root_dir, folder, matrix_name)\n",
    "        target_name = str(idx % self.sample_per_label) + '.target.npy'\n",
    "        target_img, target_float = self.get_translation_target(folder, target_name)\n",
    "        matrix = np.load(matrix_path)\n",
    "        if self.transform:\n",
    "            matrix = self.transform(matrix)\n",
    "        target_num = len(target_float)\n",
    "        sample = {'matrix': matrix, 'target': target_img, 'target_float': target_float, 'target_num': target_num, 'index': idx}\n",
    "        return sample\n",
    "\n",
    "    def get_sample_per_label(self):\n",
    "        folder = glob.glob(os.path.join(self.root_dir, '*'))[0]\n",
    "        samples = glob.glob(os.path.join(folder, '*.npy'))\n",
    "        targets = glob.glob(os.path.join(folder, '*.target.npy'))\n",
    "        powers = glob.glob(os.path.join(folder, '*.power.npy'))\n",
    "        return len(samples) - len(targets) - len(powers)\n",
    "\n",
    "    def gaussian(self, b_x, b_y, x, y):\n",
    "        '''2D gaussian\n",
    "        '''\n",
    "        a, c = 10, 0.9\n",
    "        dist = math.sqrt((x - b_x) ** 2 + (y - b_y) ** 2)\n",
    "        return a * np.exp(- dist ** 2 / (2 * c ** 2))\n",
    "\n",
    "    def get_translation_target(self, folder: str, target_name: str):\n",
    "        '''try guassian peak\n",
    "        Args:\n",
    "            folder      -- eg. 000001\n",
    "            target_name -- eg. 0.target.npy\n",
    "        Return:\n",
    "            np.ndarray, n = 2, the pixels surrounding TX will be assigned some values\n",
    "        '''\n",
    "        location = np.load(os.path.join(self.root_dir, folder, target_name))\n",
    "        num_tx = len(location)\n",
    "        grid = np.zeros((Default.grid_length, Default.grid_length))\n",
    "        for i in range(num_tx):\n",
    "            x, y = location[i][0], location[i][1]\n",
    "            target_float = (x, y)\n",
    "            x, y = int(x), int(y)\n",
    "            neighbor = []\n",
    "            sum_weight = 0\n",
    "            for i in [-2, -1, 0, 1, 2]:\n",
    "                for j in [-2, -1, 0, 1, 2]:\n",
    "                    nxt = (x + i, y + j)\n",
    "                    if 0 <= nxt[0] < Default.grid_length and 0 <= nxt[1] < Default.grid_length:\n",
    "                        val = self.gaussian(target_float[0], target_float[1], nxt[0]+0.5, nxt[1]+0.5)\n",
    "                        grid[nxt[0]][nxt[1]] = max(val, grid[nxt[0]][nxt[1]])\n",
    "        grid = np.expand_dims(grid, 0)\n",
    "        return grid.astype(np.float32), location.astype(np.float32)\n",
    "\n",
    "    def save_object_detection_image(self, output_dir, pred_matrix, idx):\n",
    "        '''Take a 2D matrix (one channel image) as input, output as an image with three channels (by duplication)\n",
    "        Args:\n",
    "            pred_matrix -- np.ndarray, n=2\n",
    "        '''\n",
    "        img = np.stack((pred_matrix, pred_matrix, pred_matrix), axis=2)\n",
    "        filename = format(idx, '06d')\n",
    "        path = os.path.join(output_dir, filename)\n",
    "        np.save(path, img.astype(np.float32))\n",
    "        return filename\n",
    "\n",
    "    def save_object_detection_label(self, output_dir, idx, grid_len, pred_matrix, peak_threashold=0.3, debug=False):\n",
    "        filename = format(idx, '06d') + '.txt'\n",
    "        path = os.path.join(output_dir, filename)\n",
    "        with open(path, 'w') as f:\n",
    "            sample = self.__getitem__(idx)\n",
    "            ground_truth = sample['target_float']\n",
    "            for x, y in ground_truth:\n",
    "                if debug:\n",
    "                    print(x, y)\n",
    "                if pred_matrix[int(x)][int(y)] > peak_threashold:\n",
    "                    f.write('0 {} {} {} {}\\n'.format(y / grid_len, x / grid_len, 5 / grid_len, 5 / grid_len))\n",
    "                else:\n",
    "                    print(f'index = {idx} no peak at ({x}, {y}), value is {pred_matrix[int(x)][int(y)]}')\n",
    "\n",
    "                    \n",
    "tf = T.Compose([\n",
    "     UniformNormalize(Default.noise_floor),                 # TUNE: Uniform normalization is better than the above minmax normalization\n",
    "     T.ToTensor()])\n",
    "\n",
    "# training\n",
    "i = 9876\n",
    "root_dir = './data/805train'\n",
    "sensor_input_dataset = SensorInputDatasetTranslation(root_dir=root_dir, transform=tf)\n",
    "sensor_input_dataloader = DataLoader(sensor_input_dataset, batch_size=32, shuffle=True, num_workers=3, collate_fn=my_collate)\n",
    "print('matrix shape:', sensor_input_dataset[i]['matrix'].shape)\n",
    "print('matrix shape:', sensor_input_dataset[i]['matrix'])\n",
    "print('target:', sensor_input_dataset[i]['target'])\n",
    "print('target float:', sensor_input_dataset[i]['target_float'])\n",
    "print('length:', sensor_input_dataset.__len__())\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(25, 20))\n",
    "sns.heatmap(sensor_input_dataset[i]['target'][0], annot=False)\n",
    "\n",
    "\n",
    "print('---\\n')\n",
    "# testing\n",
    "root_dir = './data/805test'\n",
    "sensor_input_test_dataset = SensorInputDatasetTranslation(root_dir=root_dir, transform=tf)\n",
    "sensor_input_test_dataloader = DataLoader(sensor_input_test_dataset, batch_size=32, shuffle=True, num_workers=3, collate_fn=my_collate)\n",
    "print('matrix type:', sensor_input_test_dataset[i]['matrix'].dtype)\n",
    "print('target type:', sensor_input_test_dataset[i]['target'].dtype)\n",
    "print('target float:', sensor_input_test_dataset[i]['target_float'])\n",
    "print('target num:', sensor_input_test_dataset[i]['target_num'])\n",
    "print(sensor_input_test_dataset.__len__())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Dataset for Object Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the img-2-img model\n",
    "device = torch.device('cuda')\n",
    "# path5 = 'model/model1-12.8-net5-norm-32.pt'\n",
    "path5 = 'model/model1.9.27.splat.0.0520.pt'\n",
    "model5_f5_norm = NetTranslation5_norm()\n",
    "model5_f5_norm.load_state_dict(torch.load(path5))\n",
    "model5_f5_norm = model5_f5_norm.to(device)\n",
    "model5_f5_norm.eval()\n",
    "\n",
    "# training\n",
    "root_dir = './data/905train'\n",
    "sensor_input_dataset = SensorInputDatasetTranslation(root_dir=root_dir, transform=tf)\n",
    "sensor_input_dataloader = DataLoader(sensor_input_dataset, batch_size=32, shuffle=False, num_workers=1, collate_fn=my_collate)\n",
    "images_dir = '/home/caitao/Project/PyTorch-YOLOv3/data/deepmtl-905/images/train'\n",
    "labels_dir = '/home/caitao/Project/PyTorch-YOLOv3/data/deepmtl-905/labels/train'\n",
    "list_path  = '/home/caitao/Project/PyTorch-YOLOv3/data/deepmtl-905/train.txt'\n",
    "with open(list_path, 'w') as f:\n",
    "    for t, sample in enumerate(sensor_input_dataloader):\n",
    "        if t % 10 == 9:\n",
    "            print(t, end=' ')\n",
    "        X = sample['matrix'].to(device)\n",
    "        indx = sample['index']\n",
    "        pred_matrix = model5_f5_norm(X)\n",
    "        for idx, matrix in zip(indx, pred_matrix):\n",
    "            idx = idx.data.cpu().numpy()\n",
    "            matrix = matrix.data.cpu().numpy()\n",
    "            image_name = sensor_input_dataset.save_object_detection_image(images_dir, matrix[0], idx)\n",
    "            sensor_input_dataset.save_object_detection_label(labels_dir, idx, Default.grid_length, matrix[0])\n",
    "            path = os.path.join(images_dir, image_name) + '.npy'\n",
    "            f.write(path + '\\n')\n",
    "\n",
    "\n",
    "# testing or valid\n",
    "root_dir = './data/905test'\n",
    "sensor_input_test_dataset = SensorInputDatasetTranslation(root_dir=root_dir, transform=tf)\n",
    "sensor_input_test_dataloader = DataLoader(sensor_input_test_dataset, batch_size=32, shuffle=False, num_workers=1, collate_fn=my_collate)\n",
    "images_dir  = '/home/caitao/Project/PyTorch-YOLOv3/data/deepmtl-905/images/val'\n",
    "labels_dir  = '/home/caitao/Project/PyTorch-YOLOv3/data/deepmtl-905/labels/val'\n",
    "list_path   = '/home/caitao/Project/PyTorch-YOLOv3/data/deepmtl-905/valid.txt'\n",
    "with open(list_path, 'w') as f:\n",
    "    for t, sample in enumerate(sensor_input_test_dataloader):\n",
    "        if t % 10 == 9:\n",
    "            print(t, end=' ')\n",
    "        X = sample['matrix'].to(device)\n",
    "        indx = sample['index']\n",
    "        pred_matrix = model5_f5_norm(X)\n",
    "        for idx, matrix in zip(indx, pred_matrix):\n",
    "            idx = idx.data.cpu().numpy()\n",
    "            matrix = matrix.data.cpu().numpy()\n",
    "            image_name = sensor_input_test_dataset.save_object_detection_image(images_dir, matrix[0], idx)\n",
    "            sensor_input_test_dataset.save_object_detection_label(labels_dir, idx, Default.grid_length, matrix[0])\n",
    "            path = os.path.join(images_dir, image_name) + '.npy'\n",
    "            f.write(path + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'matrix': tensor([[[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "          ...,\n",
       "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "          [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "          [0., 0., 0.,  ..., 0., 0., 0.]]]),\n",
       " 'target': array([[[0.03663718, 0.30959007, 0.7611777 , ..., 0.        ,\n",
       "          0.        , 0.        ],\n",
       "         [0.242177  , 2.0464349 , 5.031494  , ..., 0.        ,\n",
       "          0.        , 0.        ],\n",
       "         [0.4657767 , 3.9358888 , 9.677025  , ..., 0.        ,\n",
       "          0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "          0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "          0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "          0.        , 0.        ]]], dtype=float32),\n",
       " 'target_float': array([[ 2.5297704,  2.7286904],\n",
       "        [30.761032 , 94.10868  ]], dtype=float32),\n",
       " 'target_num': 2,\n",
       " 'index': 1}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor_input_dataset[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(sensor_input_dataset[1]['matrix'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pred_matrix[1][0].data.cpu().numpy(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NetTranslation1: https://nbviewer.jupyter.org/github/caitaozhan/deeplearning-localization/blob/a33c3beb435228c025b85c53ccf9257d5be6ae08/CNN-translation-multi.ipynb, three layers, groupnorm seems not helping\n",
    "\n",
    "NetTranslation2:\n",
    "https://nbviewer.jupyter.org/github/caitaozhan/deeplearning-localization/blob/2f65a0590994cb90b89ab0662ceda58c31253e55/CNN-translation-multi2.ipynb, \n",
    "Concatinating image translation and number of TX\n",
    "\n",
    "NetTranslation3: https://nbviewer.jupyter.org/github/caitaozhan/deeplearning-localization/blob/34a4c8444806b75d89a4b64bc66e122c27c193db/CNN-translation-multi-min-dist-upbound.ipynb, increase filter size from 5 to 7\n",
    "\n",
    "NetTranslation4: remove the \"second CNN\", a pure image-to-image translation\n",
    "\n",
    "NetTranslation5: Add a layer so that it looks symmetric (the BEST)\n",
    "\n",
    "NetTranslation6: incorporate U-Net ideas, i.e. first down sample and then upsample\n",
    "\n",
    "NetTranslation7: incorporate U-Net ideas, i.e. first down sample and then upsample, add skip layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetTranslation5_norm(\n",
      "  (conv1): Conv2d(1, 8, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (conv2): Conv2d(8, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (conv3): Conv2d(32, 8, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (conv4): Conv2d(8, 1, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (norm1): GroupNorm(4, 8, eps=1e-05, affine=True)\n",
      "  (norm2): GroupNorm(16, 32, eps=1e-05, affine=True)\n",
      "  (norm3): GroupNorm(4, 8, eps=1e-05, affine=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class NetTranslation5_norm(nn.Module):\n",
    "    '''The chosen one!\n",
    "       Image translation. Comparing with version 4, it adds a layer so it is symetric, it also has group normalization\n",
    "       the first CNN is the same as NetTranslation, \n",
    "       the second one uses the output of the first CNN and output the # of Tx\n",
    "       Assuming the input image is 1 x 100 x 100\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        super(NetTranslation5_norm, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1,  8,  5, padding=2)   # TUNE: a larger filter decrease miss, decrease localization error\n",
    "        self.conv2 = nn.Conv2d(8,  32, 5, padding=2)\n",
    "        self.conv3 = nn.Conv2d(32, 8,  5, padding=2)\n",
    "        self.conv4 = nn.Conv2d(8,  1,  5, padding=2)\n",
    "        self.norm1 = nn.GroupNorm(4,  8)\n",
    "        self.norm2 = nn.GroupNorm(16, 32)\n",
    "        self.norm3 = nn.GroupNorm(4,  8)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.norm1(self.conv1(x)))\n",
    "        x = F.relu(self.norm2(self.conv2(x)))\n",
    "        x = F.relu(self.norm3(self.conv3(x)))\n",
    "        y = self.conv4(x)\n",
    "        return y\n",
    "\n",
    "\n",
    "net = NetTranslation5_norm()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_zeros(batch=32, length=100):\n",
    "    '''for the regulation in the loss function\n",
    "    '''\n",
    "    zeros = []\n",
    "    for _ in range(batch):\n",
    "        zero = np.zeros((length, length))\n",
    "        zero = np.expand_dims(zero, 0)\n",
    "        zeros.append(zero)\n",
    "    return torch.as_tensor(zeros).to(torch.device('cuda'))\n",
    "\n",
    "\n",
    "def plot_epoch_stats(train_losses1, train_losses2, train_errors1, train_errors2, test_losses1, test_losses2, test_errors1, test_errors2, test_misses1, test_falses1, test_num_tx):\n",
    "    '''print the error and such of each epoch\n",
    "    '''\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(24, 18))\n",
    "    plt.subplot(5, 2, 1)\n",
    "    plt.title('train loss for image translation')\n",
    "    plt.plot(train_losses1)\n",
    "    plt.subplot(5, 2, 2)\n",
    "    plt.title('train errors for image translation')\n",
    "    train_errors1 = [val for sublist in train_errors1 for val in sublist]  # flatten a 2D matrix\n",
    "    plt.plot(train_errors1)\n",
    "    plt.subplot(5, 2, 3)\n",
    "    plt.title('train loss for num of TX')\n",
    "    plt.plot(train_losses2)\n",
    "    plt.subplot(5, 2, 4)\n",
    "    plt.title('train error for num of TX')\n",
    "    plt.plot(train_errors2)\n",
    "    plt.subplot(5, 2, 5)\n",
    "    plt.title('test loss for image translation')\n",
    "    plt.plot(test_losses1)\n",
    "    plt.subplot(5, 2, 6)\n",
    "    plt.title('test error for image translation')\n",
    "    test_errors1 = [val for sublist in test_errors1 for val in sublist]\n",
    "    plt.plot(test_errors1)\n",
    "    plt.subplot(5, 2, 7)\n",
    "    plt.title('test loss for num of TX')\n",
    "    plt.plot(test_losses2)\n",
    "    plt.subplot(5, 2, 8)\n",
    "    plt.title('test error for num of TX')\n",
    "    plt.plot(test_errors2)\n",
    "    plt.subplot(5, 2, 9)\n",
    "    plt.title('test miss for image translation')\n",
    "    plt.plot(np.array(test_misses1)/np.array(test_num_tx))   # implicitly assuming each sample is \"equal\". Instead it is not. when averaging, samples with higher number of TX should weight more\n",
    "    plt.subplot(5, 2, 10)\n",
    "    plt.title('test false alarm for image translation')\n",
    "    plt.plot(np.array(test_falses1)/np.array(test_num_tx))\n",
    "    plt.show()\n",
    "    print('train loss of image translation  =', np.mean(train_losses1))\n",
    "    print('train loss of num of TX          =', np.mean(train_losses2))\n",
    "    print('test  loss of image translation  =', np.mean(test_losses1))\n",
    "    print('test  loss of num of TX          =', np.mean(test_losses2))\n",
    "    print('train error of image translation =', np.mean(train_errors1))\n",
    "    print('train error of num of TX         =', np.mean(train_errors2))\n",
    "    print('test error of image translation  =', np.mean(test_errors1))\n",
    "    print('test error of num of TX          =', np.mean(test_errors2))\n",
    "    print('test falses of image translation =', np.sum(test_falses1)/np.sum(test_num_tx))\n",
    "    print('test misses of image translation =', np.sum(test_misses1)/np.sum(test_num_tx))\n",
    "    train_losses1_epoch.append((np.mean(train_losses1), np.std(train_losses1)))\n",
    "    train_losses2_epoch.append((np.mean(train_losses2), np.std(train_losses2)))\n",
    "    train_errors1_epoch.append((np.mean(train_errors1), np.std(train_errors1)))\n",
    "    train_errors2_epoch.append((np.mean(train_errors2), np.std(train_errors2)))\n",
    "    test_losses1_epoch.append((np.mean(test_losses1), np.std(test_losses1)))\n",
    "    test_losses2_epoch.append((np.mean(test_losses2), np.std(test_losses2)))\n",
    "    test_errors1_epoch.append((np.mean(test_errors1), np.std(test_errors1)))\n",
    "    test_errors2_epoch.append((np.mean(test_errors2), np.std(test_errors2)))\n",
    "    test_misses1_epoch.append((np.sum(test_misses1)/np.sum(test_num_tx)))\n",
    "    test_falses1_epoch.append((np.mean(test_falses1)/np.sum(test_num_tx)))\n",
    "\n",
    "\n",
    "def plot_epoch_stats_num_tx(train_losses2, train_errors2, test_losses2, test_errors2, test_num_tx):\n",
    "    '''print the error and such of each epoch\n",
    "    '''\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(24, 10))\n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.title('train loss for num of TX')\n",
    "    plt.plot(train_losses2)\n",
    "    plt.subplot(2, 2, 2)\n",
    "    plt.title('train error for num of TX')\n",
    "    plt.plot(train_errors2)\n",
    "    plt.subplot(2, 2, 3)\n",
    "    plt.title('test loss for num of TX')\n",
    "    plt.plot(test_losses2)\n",
    "    plt.subplot(2, 2, 4)\n",
    "    plt.title('test error for num of TX')\n",
    "    plt.plot(test_errors2)\n",
    "    plt.show()\n",
    "    print('train loss of num of TX          =', np.mean(train_losses2))\n",
    "    print('test  loss of num of TX          =', np.mean(test_losses2))\n",
    "    print('train error of num of TX         =', np.mean(train_errors2))\n",
    "    print('test error of num of TX          =', np.mean(test_errors2))\n",
    "    train_losses2_epoch.append((np.mean(train_losses2), np.std(train_losses2)))\n",
    "    train_errors2_epoch.append((np.mean(train_errors2), np.std(train_errors2)))\n",
    "    test_losses2_epoch.append((np.mean(test_losses2), np.std(test_losses2)))\n",
    "    test_errors2_epoch.append((np.mean(test_errors2), np.std(test_errors2)))\n",
    "\n",
    "\n",
    "def plot_epochs(train_losses2, test_losses2, train_errors2, test_errors2):\n",
    "    print('train loss of num of TX   =', np.mean(train_losses2))\n",
    "    print('test  loss of num of TX   =', np.mean(test_losses2))\n",
    "    print('train error of num of TX  =', np.mean(train_errors2))\n",
    "    print('test error of num of TX   =', np.mean(test_errors2))\n",
    "    train_losses2_epoch.append((np.mean(train_losses2)))\n",
    "    train_errors2_epoch.append((np.mean(train_errors2)))\n",
    "    test_losses2_epoch.append((np.mean(test_losses2)))\n",
    "    test_errors2_epoch.append((np.mean(test_errors2)))\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(20, 8))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title('Loss', fontsize=30)\n",
    "    plt.xlabel('Epoch', fontsize=30)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.plot(train_losses2_epoch, label='Train', linewidth=5)\n",
    "    plt.plot(test_losses2_epoch, label='Test', linewidth=5)\n",
    "    plt.legend(fontsize=30)\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.title('Error (1-accuracy)', fontsize=30)\n",
    "    plt.xlabel('Epoch', fontsize=30)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.plot(train_errors2_epoch, label='Train', linewidth=5)\n",
    "    plt.plot(test_errors2_epoch, label='Test', linewidth=5)\n",
    "    plt.legend(fontsize=30)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train only Image Translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss of image translation = 0.05078187603969127\n",
      "test  loss of image translation = 0.0528476535230875\n",
      "train error = 1.024470061349693\n",
      "test error  = 1.0322535238095238\n",
      "train miss = 0.034360189573459717\n",
      "test  miss = 0.06583629893238434\n",
      "train false = 0.02014218009478673\n",
      "test  false = 0.033807829181494664\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%time\n",
    "# image translation problem\n",
    "device     = torch.device('cuda')\n",
    "\n",
    "model1 = NetTranslation5_norm()\n",
    "path1 = 'model/model1.9.27.splat.{}.pt'\n",
    "\n",
    "model1_minloss = 1000\n",
    "model1 = model1.to(device)\n",
    "optimizer1 = optim.Adam(model1.parameters(), lr=0.001)#, weight_decay=0.0001''')\n",
    "mse_loss   = nn.MSELoss()  # criterion is the loss function\n",
    "l1_loss    = nn.L1Loss()\n",
    "scheduler1 = optim.lr_scheduler.StepLR(optimizer1, step_size=10, gamma=0.1)\n",
    "\n",
    "num_epochs = 20\n",
    "train_losses1_epoch, train_errors1_epoch, train_misses1_epoch, train_falses1_epoch = [], [], [], []\n",
    "test_losses1_epoch,  test_errors1_epoch,  test_misses1_epoch,  test_falses1_epoch  = [], [], [], []\n",
    "print_every, error_every = 400, 400\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    print(f'epoch = {epoch}')\n",
    "    train_losses1, train_errors1, train_misses1, train_falses1 = [], [], [], []\n",
    "    test_losses1,  test_errors1,  test_misses1,  test_falses1  = [], [], [], []\n",
    "    train_numtx, test_numtx = 0, 0\n",
    "    model1.train()\n",
    "    for t, sample in enumerate(sensor_input_dataloader):\n",
    "        X = sample['matrix'].to(device)\n",
    "        y = sample['target'].to(device)\n",
    "        y_num   = sample['target_num'].to(device)\n",
    "        y_num2  = np.array(sample['target_num'])\n",
    "        y_float = np.array(sample['target_float'])\n",
    "        y_float = my_uncollate(y_num2, y_float)\n",
    "        indx = sample['index']\n",
    "\n",
    "        pred_matrix = model1(X)              # the model for iamge translation\n",
    "        loss_matrix = mse_loss(pred_matrix, y)\n",
    "#         regulation  = l1_loss(pred_matrix, zeros)\n",
    "        loss1 = loss_matrix # + alpha * regulation\n",
    "        optimizer1.zero_grad()\n",
    "        loss1.backward()\n",
    "        optimizer1.step()\n",
    "\n",
    "        train_losses1.append(loss1.item())\n",
    "        pred_matrix = pred_matrix.data.cpu().numpy()\n",
    "        if t % error_every == 0:\n",
    "            errors, misses, falses = Metrics.localization_error_image_continuous(pred_matrix, y_num2, y_float, indx, Default.grid_length, 1)\n",
    "            train_errors1.extend(errors)\n",
    "            train_misses1.extend(misses)\n",
    "            train_falses1.extend(falses)\n",
    "            train_numtx += y_num2.sum()\n",
    "        if t % print_every == 0:\n",
    "            print(f't = {t}, loss_matrix = {loss_matrix.item()}') #, regulation = {regulation.item()}')\n",
    "\n",
    "    model1.eval()\n",
    "    for t, sample in enumerate(sensor_input_test_dataloader):\n",
    "        X = sample['matrix'].to(device)\n",
    "        y = sample['target'].to(device)\n",
    "        y_num   = sample['target_num'].to(device)\n",
    "        y_num2  = np.array(sample['target_num'])\n",
    "        y_float = np.array(sample['target_float'])\n",
    "        y_float = my_uncollate(y_num2, y_float)\n",
    "        indx = sample['index']\n",
    "\n",
    "        pred_matrix = model1(X)              # the model for iamge translation\n",
    "        loss_matrix = mse_loss(pred_matrix, y)\n",
    "#         regulation  = l1_loss(pred_matrix, zeros)\n",
    "        loss1 = loss_matrix # + alpha*regulation\n",
    "\n",
    "        test_losses1.append(loss1.item())\n",
    "        pred_matrix = pred_matrix.data.cpu().numpy()\n",
    "        if t % error_every == 0:\n",
    "            errors, misses, falses = Metrics.localization_error_image_continuous(pred_matrix, y_num2, y_float, indx, Default.grid_length, 1)\n",
    "            test_errors1.extend(errors)\n",
    "            test_misses1.extend(misses)\n",
    "            test_falses1.extend(falses)\n",
    "            test_numtx += y_num2.sum()\n",
    "\n",
    "    if np.mean(test_losses1) < model1_minloss:\n",
    "        torch.save(model1.state_dict(), path1.format(f'{np.mean(test_losses1):.4}'))\n",
    "        model1_minloss = np.mean(test_losses1)\n",
    "\n",
    "    scheduler1.step()\n",
    "    clear_output(True)\n",
    "    train_losses1_epoch.append(np.mean(train_losses1))\n",
    "    test_losses1_epoch.append(np.mean(test_losses1))\n",
    "    train_errors1 = [val for sublist in train_errors1 for val in sublist]  # flatten a 2D matrix\n",
    "    test_errors1  = [val for sublist in test_errors1 for val in sublist]   # flatten a 2D matrix\n",
    "    train_errors1_epoch.append(np.mean(train_errors1))\n",
    "    test_errors1_epoch.append(np.mean(test_errors1))\n",
    "    train_misses1_epoch.append(np.sum(train_misses1) / train_numtx)\n",
    "    test_misses1_epoch.append(np.sum(test_misses1)   / test_numtx)\n",
    "    train_falses1_epoch.append(np.sum(train_falses1) / train_numtx)\n",
    "    test_falses1_epoch.append(np.sum(test_falses1)   / test_numtx)\n",
    "    print('train loss of image translation =', train_losses1_epoch[-1])\n",
    "    print('test  loss of image translation =', test_losses1_epoch[-1])\n",
    "    print('train error =', train_errors1_epoch[-1])\n",
    "    print('test error  =', test_errors1_epoch[-1])\n",
    "    print('train miss =', train_misses1_epoch[-1])\n",
    "    print('test  miss =', test_misses1_epoch[-1])\n",
    "    print('train false =', train_falses1_epoch[-1])\n",
    "    print('test  false =', test_falses1_epoch[-1])\n",
    "    plt.figure(figsize=(30, 8))\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.title('Loss', fontsize=30)\n",
    "    plt.xlabel('Epoch', fontsize=30)\n",
    "    plt.plot(train_losses1_epoch, label='Train', linewidth=5)\n",
    "    plt.plot(test_losses1_epoch, label='Test', linewidth=5)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.legend(fontsize=30)\n",
    "    plt.subplot(1, 3, 2)\n",
    "    plt.title('Localize Error', fontsize=30)\n",
    "    plt.xlabel('Epoch', fontsize=30)\n",
    "    plt.plot(train_errors1_epoch, label='Train', linewidth=5)\n",
    "    plt.plot(test_errors1_epoch, label='Test', linewidth=5)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.legend(fontsize=30)\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.title('Miss/False', fontsize=30)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.plot(train_misses1_epoch, label='Train Miss',  linewidth=5, linestyle='-', color='#1f77b4')\n",
    "    plt.plot(test_misses1_epoch,  label='Test  Miss',  linewidth=5, linestyle='-', color='#ff7f0e')\n",
    "    plt.plot(train_falses1_epoch, label='Train False', linewidth=5, linestyle=':', color='#1f77b4')\n",
    "    plt.plot(test_falses1_epoch,  label='Test  False', linewidth=5, linestyle=':', color='#ff7f0e')\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.legend(fontsize=30)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myplot(X, pred, pred_n, y, indx, i):\n",
    "    indx = int(indx)\n",
    "    fig, axs = plt.subplots(1, 3, figsize=(30, 8))\n",
    "    sns.heatmap(X[0], cmap='gray', annot=False, ax=axs[0])\n",
    "    sns.heatmap(pred[0], cmap='gray', annot=False, ax=axs[1])\n",
    "    sns.heatmap(y[0], cmap='gray', annot=False, ax=axs[2])\n",
    "    sen_num = get_sen_num(indx)\n",
    "    axs[0].set_title(f'{indx} - {i}. # Sen = {sen_num}')\n",
    "    axs[1].set_title(f'# of predict TX {pred_n:.2f}')\n",
    "    fig.tight_layout()\n",
    "\n",
    "def myplot3(pred, y, y_float):\n",
    "    '''\n",
    "    Args:\n",
    "        pred    -- array like -- 2d dimension pred matrix\n",
    "        y       -- array like -- 2d dimension true matrix\n",
    "        y_float -- array like -- (num_tx, 2), second dimension 2 means a pair of (x, y)\n",
    "    '''\n",
    "    print(y_float)\n",
    "    fig, axs = plt.subplots(1, len(y_float), figsize=(25, 5))\n",
    "    for i in range(len(y_float)):\n",
    "        a, b = y_float[i]\n",
    "        a, b = int(a), int(b)\n",
    "        a1 = a-5 if a >= 5 else 0\n",
    "        a2 = a+5 if a <= Default.grid_length-5 else Default.grid_length\n",
    "        b1 = b-5 if b >= 5 else 0\n",
    "        b2 = b+5 if b <= Default.grid_length-5 else Default.grid_length\n",
    "        print(f'pred {i} ({a1}, {a2}), ({b1}, {b2})')\n",
    "        sns.heatmap(pred[a1:a2,b1:b2], cmap='gray', annot=True, ax=axs[i])\n",
    "        axs[i].set_title(f'pred {i} ({a}, {b})')\n",
    "    fig.tight_layout()\n",
    "    fig, axs = plt.subplots(1, len(y_float), figsize=(25, 5))\n",
    "    for i in range(len(y_float)):\n",
    "        a, b = y_float[i]\n",
    "        a, b = int(a), int(b)\n",
    "        a1 = a-5 if a >= 5 else 0\n",
    "        a2 = a+5 if a <= Default.grid_length-5 else Default.grid_length\n",
    "        b1 = b-5 if b >= 5 else 0\n",
    "        b2 = b+5 if b <= Default.grid_length-5 else Default.grid_length\n",
    "        print(f'y    {i} ({a1}, {a2}), ({b1}, {b2})')\n",
    "        sns.heatmap(y[a1:a2, b1:b2], cmap='gray', annot=True, ax=axs[i])\n",
    "        axs[i].set_title(f'y   {i} ({a}, {b})')\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare different image translation models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target = [[[10.682179 10.390335]\n",
      "  [17.877    13.448158]\n",
      "  [88.825264 42.088196]\n",
      "  [56.434814 17.726103]\n",
      "  [33.700302 13.019561]\n",
      "  [36.812004 13.520625]\n",
      "  [13.840323 59.976845]\n",
      "  [56.90113  58.769547]\n",
      "  [10.289261 57.2016  ]\n",
      "  [59.48299  21.361835]]]\n",
      "net1 loss matrix = 0.11221174150705338\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device('cuda')\n",
    "\n",
    "path1 = 'model/model1.9.26.0.02487.pt'\n",
    "model1 = NetTranslation5_norm()\n",
    "model1.load_state_dict(torch.load(path1))\n",
    "model1 = model1.to(device)\n",
    "model1.eval()\n",
    "\n",
    "criterion1 = nn.MSELoss()\n",
    "\n",
    "idx = 0    # three TX close to each other\n",
    "\n",
    "pred = None\n",
    "target   = None\n",
    "\n",
    "def one_sample2(idx):\n",
    "    '''used for comparing different image translation models\n",
    "    '''\n",
    "    device = torch.device('cuda')\n",
    "    sample = sensor_input_test_dataset[idx]\n",
    "    X = torch.as_tensor(sample['matrix'])\n",
    "    X = X.unsqueeze(0).to(device)            # increase dimension to a batch\n",
    "    y = torch.as_tensor(sample['target'])\n",
    "    y = y.unsqueeze(0).to(device)            # increase dimension to a batch\n",
    "    y_num = torch.as_tensor(sample['target_num'])\n",
    "    y_num = y_num.unsqueeze(0).to(device)    # increase dimension to a batch\n",
    "    y_f = np.expand_dims(sample['target_float'], 0)\n",
    "    indx = np.expand_dims(np.array(sample['index']), 0)\n",
    "    print('target =', y_f)\n",
    "\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(24, 20))\n",
    "    plt.rcParams['font.size'] = 20\n",
    "    # input\n",
    "    sns.heatmap(X[0][0].data.cpu().numpy(), cmap='gray', ax=axs.flat[0])\n",
    "    axs.flat[0].set_title('input matrix')\n",
    "    # target\n",
    "    global target\n",
    "    target = y[0][0].data.cpu().numpy()\n",
    "    sns.heatmap(y[0][0].data.cpu().numpy(), cmap='gray', ax=axs.flat[1])\n",
    "    axs.flat[1].set_title('target')\n",
    "    # net1\n",
    "    global pred\n",
    "    pred_matrix = model1(X)\n",
    "    pred  = pred_matrix\n",
    "    loss_matrix = criterion1(pred_matrix, y)\n",
    "    print('net1 loss matrix =', loss_matrix.item())\n",
    "    pred_matrix = pred_matrix[0][0].data.cpu().numpy()\n",
    "    sns.heatmap(pred_matrix, cmap='gray', ax=axs.flat[2])\n",
    "    axs.flat[2].set_title('net1')\n",
    "#     # net5\n",
    "#     pred_matrix = model5_f5_norm(X)\n",
    "#     loss_matrix = criterion1(pred_matrix, y)\n",
    "#     print('net5 gaussian filter 5 norm loss    =', loss_matrix.item())\n",
    "#     pred_matrix = pred_matrix[0][0].data.cpu().numpy()\n",
    "#     global net5pred\n",
    "#     net5pred = pred_matrix\n",
    "#     sns.heatmap(pred_matrix, cmap='gray', ax=axs.flat[3])\n",
    "#     axs.flat[3].set_title('net5 filter5 norm')\n",
    "\n",
    "#     # net5_\n",
    "#     pred_matrix = model5_f7(X)\n",
    "#     loss_matrix = criterion1(pred_matrix, y)\n",
    "#     print('net5 gaussian (filter7) loss matrix =', loss_matrix.item())\n",
    "#     pred_matrix = pred_matrix[0][0].data.cpu().numpy()\n",
    "#     sns.heatmap(pred_matrix, cmap='gray', ax=axs.flat[4])\n",
    "#     axs.flat[4].set_title('net5 gaussian filter7')\n",
    "\n",
    "#     # net5 filter 5\n",
    "#     pred_matrix = model5_f5(X)\n",
    "#     loss_matrix = criterion1(pred_matrix, y)\n",
    "#     print('net5 gaussian (filter5) loss matrix =', loss_matrix.item())\n",
    "#     pred_matrix = pred_matrix[0][0].data.cpu().numpy()\n",
    "#     sns.heatmap(pred_matrix, cmap='gray', ax=axs.flat[5])\n",
    "#     axs.flat[5].set_title('net5 gaussian filter5')\n",
    "\n",
    "    # net7\n",
    "#     pred_matrix = model7(X)\n",
    "#     loss_matrix = criterion1(pred_matrix, y)\n",
    "#     print('net7 loss matrix =', loss_matrix.item())\n",
    "#     pred_matrix = pred_matrix[0][0].data.cpu().numpy()\n",
    "#     sns.heatmap(pred_matrix, cmap='gray', ax=axs.flat[3])\n",
    "#     axs.flat[3].set_title('net7')\n",
    "    fig.tight_layout()\n",
    "\n",
    "one_sample2(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test for one random batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}