Predicting_Result.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Result\n",
    "\n",
    "- Plug in the trained model and predict the result\n",
    "- Keep the backbone and batch size same.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /kaggle/input/kaggle-efficientnet-repo/efficientnet-1.0.0-py3-none-any.whl\r\n",
      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /opt/conda/lib/python3.6/site-packages (from efficientnet==1.0.0) (1.0.8)\r\n",
      "Requirement already satisfied: scikit-image in /opt/conda/lib/python3.6/site-packages (from efficientnet==1.0.0) (0.16.2)\r\n",
      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet==1.0.0) (1.18.1)\r\n",
      "Requirement already satisfied: h5py in /opt/conda/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet==1.0.0) (2.10.0)\r\n",
      "Requirement already satisfied: networkx>=2.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet==1.0.0) (2.4)\r\n",
      "Requirement already satisfied: scipy>=0.19.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet==1.0.0) (1.4.1)\r\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet==1.0.0) (3.0.3)\r\n",
      "Requirement already satisfied: pillow>=4.3.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet==1.0.0) (5.4.1)\r\n",
      "Requirement already satisfied: PyWavelets>=0.4.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet==1.0.0) (1.1.1)\r\n",
      "Requirement already satisfied: imageio>=2.3.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet==1.0.0) (2.6.1)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->efficientnet==1.0.0) (1.14.0)\r\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.6/site-packages (from networkx>=2.0->scikit-image->efficientnet==1.0.0) (4.4.1)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0) (0.10.0)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0) (1.1.0)\r\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0) (2.4.6)\r\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0) (2.8.1)\r\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0) (45.2.0.post20200210)\r\n",
      "Installing collected packages: efficientnet\r\n",
      "Successfully installed efficientnet-1.0.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install ../input/kaggle-efficientnet-repo/efficientnet-1.0.0-py3-none-any.whl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../input/bengali-cnn-1/model-0000.h5\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../input/bengali-cnn-1/model-0000.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using backbone efficientnet-b3 and weights None\n",
      "Loading weights ../input/bengali-cnn-1/model-0000.h5\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "imgs (InputLayer)            [(None, 160, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "tf_op_layer_sub (TensorFlowO [(None, 160, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "tf_op_layer_truediv (TensorF [(None, 160, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "efficientnet-b3 (Model)      (None, 5, 8, 1536)        10783528  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1292)              1985804   \n",
      "=================================================================\n",
      "Total params: 12,769,332\n",
      "Trainable params: 12,682,036\n",
      "Non-trainable params: 87,296\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a9cb4099b8e4152bd1060ef85d3a8fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Done wrote to submission.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np  # noqa\n",
    "import pandas as pd\n",
    "import argparse\n",
    "import tensorflow as tf\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from tensorflow.keras import layers as L\n",
    "import efficientnet.tfkeras as efn\n",
    "\n",
    "\n",
    "def normalize(image):\n",
    "  # https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/main.py#L325-L326\n",
    "  # https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_builder.py#L31-L32\n",
    "  image -= tf.constant([0.485 * 255, 0.456 * 255, 0.406 * 255])  # RGB\n",
    "  image /= tf.constant([0.229 * 255, 0.224 * 255, 0.225 * 255])  # RGB\n",
    "  return image\n",
    "\n",
    "\n",
    "def get_model(input_size, backbone='efficientnet-b0', weights='imagenet', tta=False):\n",
    "  print(f'Using backbone {backbone} and weights {weights}')\n",
    "  x = L.Input(shape=input_size, name='imgs', dtype='float32')\n",
    "  y = normalize(x)\n",
    "  if backbone.startswith('efficientnet'):\n",
    "    model_fn = getattr(efn, f'EfficientNetB{backbone[-1]}')\n",
    "\n",
    "  y = model_fn(input_shape=input_size, weights=weights, include_top=False)(y)\n",
    "  y = L.GlobalAveragePooling2D()(y)\n",
    "  y = L.Dropout(0.2)(y)\n",
    "  # 1292 of 1295 are present\n",
    "  y = L.Dense(1292, activation='softmax')(y)\n",
    "  model = tf.keras.Model(x, y)\n",
    "\n",
    "  if tta:\n",
    "    assert False, 'This does not make sense yet'\n",
    "    x_flip = tf.reverse(x, [2])  # 'NHWC'\n",
    "    y_tta = tf.add(model(x), model(x_flip)) / 2.0\n",
    "    tta_model = tf.keras.Model(x, y_tta)\n",
    "    return model, tta_model\n",
    "\n",
    "  return model\n",
    "\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "\n",
    "def normalize_image(img, org_width, org_height, new_width, new_height):\n",
    "  # Invert\n",
    "  img = 255 - img\n",
    "  # Normalize\n",
    "  img = (img * (255.0 / img.max())).astype(np.uint8)\n",
    "  # Reshape\n",
    "  img = img.reshape(org_height, org_width)\n",
    "  image_resized = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_LINEAR)\n",
    "  return image_resized\n",
    "\n",
    "\n",
    "def dump_images(args, org_width, org_height, new_width, new_height):\n",
    "  labels = pd.read_csv(args.labels)\n",
    "  iids = labels['image_id']\n",
    "  root = labels['grapheme_root']\n",
    "  vowel = labels['vowel_diacritic']\n",
    "  consonant = labels['consonant_diacritic']\n",
    "  labels = {a: (b, c, d) for a, b, c, d in zip(iids, root, vowel, consonant)}\n",
    "  tuples = sorted(set(labels.values()))\n",
    "  tuples_to_int = {v: k for k, v in enumerate(tuples)}\n",
    "  print(f'Got {len(tuples)} unique combinations')\n",
    "  for i in tqdm(range(0, 4)):\n",
    "    df = pd.read_parquet(args.data_template % i)\n",
    "    image_ids = df['image_id'].values\n",
    "    df = df.drop(['image_id'], axis=1)\n",
    "    for image_id, index in tqdm(zip(image_ids, range(df.shape[0])), total=df.shape[0]):\n",
    "      normalized = normalize_image(df.loc[df.index[index]].values,\n",
    "          org_width, org_height, new_width, new_height)\n",
    "      r, v, c = labels[image_id]\n",
    "      tuple_int = tuples_to_int[(r, v, c)]\n",
    "      # e.g: 'Train_300_rt_29_vl_5_ct_0_ti_179.png'\n",
    "      out_fn = os.path.join(args.image_dir, f'{image_id}_rt_{r}_vl_{v}_ct_{c}_ti_{tuple_int}.png')\n",
    "      cv2.imwrite(out_fn, normalized)\n",
    "\n",
    "\n",
    "def decode_predictions(y_pred, inv_tuple_map):\n",
    "  # return predictions as tuple (root, vowel, consonant)\n",
    "  y_argmax = np.argmax(y_pred, -1)\n",
    "  decoded = []\n",
    "  for yy in y_argmax:\n",
    "    decoded.append(inv_tuple_map[int(yy)])\n",
    "  return decoded\n",
    "\n",
    "\n",
    "def decode_predictions_v2(y_pred, inv_tuple_map):\n",
    "  # return predictions as tuple (root / 168, vowel / 11, consonant / 7) & ti 1292\n",
    "  rr = np.zeros((len(y_pred), 168), dtype=np.float32)\n",
    "  vv = np.zeros((len(y_pred), 11), dtype=np.float32)\n",
    "  cc = np.zeros((len(y_pred), 7), dtype=np.float32)\n",
    "\n",
    "  for ti in range(y_pred.shape[1]):\n",
    "    r_index, v_index, c_index = inv_tuple_map[ti]\n",
    "    y_pred_ti = y_pred[:, ti]\n",
    "    rr[:, r_index] += y_pred_ti\n",
    "    vv[:, v_index] += y_pred_ti\n",
    "    cc[:, c_index] += y_pred_ti\n",
    "\n",
    "  rr = rr.argmax(-1)\n",
    "  vv = vv.argmax(-1)\n",
    "  cc = cc.argmax(-1)\n",
    "\n",
    "  decoded = []\n",
    "  for k in range(len(y_pred)):\n",
    "    decoded.append((int(rr[k]), int(vv[k]), int(cc[k])))\n",
    "\n",
    "  return decoded\n",
    "\n",
    "\n",
    "def process_batch(image_id_batch, img_batch, row_id, target, model, inv_tuple_map):\n",
    "  img_batch = np.float32(img_batch)\n",
    "  # deal with single image\n",
    "  if img_batch.ndim != 4:\n",
    "    img_batch = np.expand_dims(img_batch, 0)\n",
    "  y_pred = model.predict(img_batch)\n",
    "  decoded = decode_predictions_v2(y_pred, inv_tuple_map)\n",
    "  for iid, dd in zip(image_id_batch, decoded):\n",
    "    row_id.append(iid + '_grapheme_root')\n",
    "    target.append(dd[0])\n",
    "    row_id.append(iid + '_vowel_diacritic')\n",
    "    target.append(dd[1])\n",
    "    row_id.append(iid + '_consonant_diacritic')\n",
    "    target.append(dd[2])\n",
    "\n",
    "\n",
    "def main():\n",
    "  parser = argparse.ArgumentParser()\n",
    "  parser.add_argument('--seed', type=int, default=123)\n",
    "  parser.add_argument('--input_size', type=str, default='160,256')\n",
    "  parser.add_argument('--batch_size', type=int, default=64)\n",
    "  parser.add_argument('--backbone', type=str, default='efficientnet-b3')\n",
    "  parser.add_argument('--weights', type=str, default='../input/bengali-cnn-1/model-0000.h5')\n",
    "  args, _ = parser.parse_known_args()\n",
    "\n",
    "  org_height = 137\n",
    "  org_width = 236\n",
    "  tuple_map = {(15, 9, 5): 64, (72, 4, 0): 522, (18, 0, 0): 76, (62, 7, 0): 418, (18, 7, 4): 84, (136, 0, 2): 1064, (43, 0, 0): 261, (149, 7, 5): 1184, (165, 7, 0): 1279, (86, 1, 0): 653, (17, 2, 0): 73, (115, 2, 0): 892, (139, 3, 0): 1088, (142, 3, 0): 1117, (23, 10, 0): 149, (150, 1, 0): 1189, (13, 0, 4): 21, (96, 7, 5): 744, (130, 0, 0): 1016, (133, 2, 0): 1035, (60, 1, 0): 407, (81, 4, 4): 615, (64, 4, 0): 437, (107, 0, 0): 801, (115, 9, 2): 907, (125, 1, 0): 993, (25, 2, 0): 155, (153, 2, 5): 1219, (153, 1, 0): 1215, (149, 9, 0): 1185, (103, 1, 1): 776, (148, 9, 0): 1170, (37, 2, 0): 215, (139, 0, 2): 1081, (74, 2, 0): 546, (86, 0, 5): 652, (83, 9, 5): 636, (155, 3, 0): 1232, (133, 4, 4): 1043, (70, 0, 0): 480, (135, 7, 0): 1061, (38, 1, 0): 219, (28, 9, 0): 166, (150, 4, 0): 1195, (81, 5, 4): 617, (59, 2, 0): 397, (108, 2, 0): 835, (69, 3, 0): 478, (140, 7, 0): 1100, (129, 1, 0): 1011, (31, 1, 0): 189, (118, 0, 0): 922, (96, 4, 1): 737, (98, 7, 0): 757, (32, 3, 0): 196, (43, 0, 2): 262, (123, 1, 0): 966, (157, 9, 0): 1241, (72, 6, 2): 529, (89, 2, 5): 675, (8, 0, 0): 12, (94, 0, 0): 709, (113, 0, 4): 857, (72, 0, 4): 507, (163, 1, 0): 1272, (151, 0, 0): 1202, (139, 7, 2): 1092, (79, 8, 0): 598, (144, 7, 0): 1127, (160, 0, 0): 1264, (13, 2, 0): 29, (29, 1, 4): 174, (22, 7, 0): 111, (72, 4, 4): 524, (57, 0, 0): 379, (111, 1, 0): 846, (149, 1, 5): 1176, (127, 4, 0): 1001, (162, 1, 0): 1270, (42, 9, 1): 260, (83, 2, 0): 631, (66, 2, 0): 465, (147, 2, 1): 1142, (97, 1, 0): 752, (53, 1, 0): 327, (120, 0, 0): 939, (167, 7, 0): 1290, (81, 10, 0): 625, (70, 1, 0): 482, (91, 2, 0): 693, (23, 2, 2): 127, (81, 5, 0): 616, (110, 0, 0): 842, (13, 9, 0): 49, (50, 2, 0): 312, (113, 4, 4): 869, (140, 2, 5): 1097, (147, 1, 0): 1136, (64, 6, 2): 443, (66, 0, 0): 463, (123, 0, 0): 965, (159, 2, 2): 1252, (79, 2, 4): 588, (88, 3, 0): 667, (53, 0, 0): 323, (124, 1, 0): 981, (143, 7, 0): 1123, (86, 7, 5): 661, (29, 1, 1): 172, (134, 2, 0): 1057, (70, 0, 4): 481, (133, 1, 0): 1031, (71, 2, 5): 495, (156, 2, 0): 1237, (107, 1, 6): 811, (29, 9, 0): 184, (147, 8, 0): 1155, (117, 6, 0): 919, (115, 7, 0): 902, (64, 0, 5): 423, (71, 1, 2): 491, (147, 4, 4): 1147, (123, 5, 0): 972, (44, 1, 0): 285, (89, 0, 0): 669, (94, 9, 0): 714, (133, 3, 0): 1039, (81, 3, 0): 612, (128, 9, 0): 1009, (16, 9, 0): 70, (23, 0, 4): 118, (21, 0, 2): 92, (98, 0, 0): 754, (15, 0, 0): 58, (72, 3, 2): 520, (76, 8, 0): 569, (96, 2, 1): 731, (132, 9, 0): 1026, (146, 7, 0): 1131, (18, 7, 0): 83, (59, 0, 0): 391, (61, 0, 0): 409, (51, 2, 0): 316, (92, 3, 0): 702, (133, 3, 5): 1041, (165, 2, 0): 1276, (81, 0, 2): 605, (115, 5, 0): 899, (112, 1, 4): 851, (89, 7, 0): 679, (147, 6, 0): 1150, (113, 4, 0): 867, (103, 7, 0): 788, (107, 1, 5): 810, (43, 4, 2): 275, (107, 2, 2): 813, (64, 7, 1): 445, (10, 0, 0): 16, (42, 1, 4): 253, (72, 1, 2): 512, (115, 3, 2): 895, (107, 8, 0): 830, (76, 9, 0): 570, (129, 7, 0): 1014, (95, 7, 0): 719, (122, 9, 0): 963, (141, 0, 5): 1103, (92, 1, 0): 699, (107, 4, 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    "  inv_tuple_map = {v: k for k, v in tuple_map.items()}\n",
    "  args.input_size = tuple(int(x) for x in args.input_size.split(','))\n",
    "  np.random.seed(args.seed)\n",
    "  tf.random.set_seed(args.seed)\n",
    "\n",
    "  model = get_model(input_size=args.input_size + (3, ), backbone=args.backbone,\n",
    "      weights=None)\n",
    "\n",
    "  print(f'Loading weights {args.weights}')\n",
    "  model.load_weights(args.weights)\n",
    "  print(model.summary())\n",
    "  row_id, target = [], []\n",
    "  image_id_batch, img_batch = [], []\n",
    "  for i in tqdm(range(4)):\n",
    "    parquet_fn = f'../input/bengaliai-cv19/test_image_data_{i}.parquet'\n",
    "    df = pd.read_parquet(parquet_fn)\n",
    "    image_ids = df['image_id'].values\n",
    "    df = df.drop(['image_id'], axis=1)\n",
    "    for k in range(len(image_ids)):\n",
    "      image_id = image_ids[k]\n",
    "      img = df.iloc[k].values\n",
    "      img = normalize_image(img, org_width, org_height, args.input_size[1], args.input_size[0])\n",
    "      img_batch.append(np.dstack([img] * 3))\n",
    "      image_id_batch.append(image_id)\n",
    "      if len(img_batch) >= args.batch_size:\n",
    "        process_batch(image_id_batch, img_batch, row_id, target, model, inv_tuple_map)\n",
    "        image_id_batch, img_batch = [], []\n",
    "\n",
    "  # process remaining batch\n",
    "  if len(img_batch) > 0:\n",
    "    process_batch(image_id_batch, img_batch, row_id, target, model, inv_tuple_map)\n",
    "    image_id_batch, img_batch = [], []\n",
    "\n",
    "  sub_fn = 'submission.csv'\n",
    "  sub = pd.DataFrame({'row_id': row_id, 'target': target})\n",
    "  sub.to_csv(sub_fn, index=False)\n",
    "  print(f'Done wrote to {sub_fn}')\n",
    "\n",
    "main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row_id,target\r\n",
      "Test_0_grapheme_root,3\r\n",
      "Test_0_vowel_diacritic,0\r\n",
      "Test_0_consonant_diacritic,0\r\n",
      "Test_1_grapheme_root,93\r\n",
      "Test_1_vowel_diacritic,2\r\n",
      "Test_1_consonant_diacritic,0\r\n",
      "Test_2_grapheme_root,19\r\n",
      "Test_2_vowel_diacritic,0\r\n",
      "Test_2_consonant_diacritic,0\r\n"
     ]
    }
   ],
   "source": [
    "!head submission.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37 submission.csv\r\n"
     ]
    }
   ],
   "source": [
    "!wc -l submission.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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