test_all_function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import math\n",
    "import json\n",
    "\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "from nets import blaze_palm\n",
    "\n",
    "from utils.data_generator import DataGenerator\n",
    "from utils.encoder import center_to_corner, corner_to_center, match\n",
    "from utils.anchors_generator import AnchorsGenerator\n",
    "from utils.loss_function import SSDLoss, smooth_l1_loss, focal_loss_sigmoid\n",
    "from utils.anchor_config import AnchorsConfig\n",
    "from utils.decoder import preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = blaze_palm.build_blaze_palm_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 128, 128, 32) 896         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 128, 128, 32) 0           conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d (DepthwiseConv (None, 128, 128, 32) 320         activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 128, 128, 32) 0           conv2d_1[0][0]                   \n",
      "                                                                 activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 128, 128, 32) 0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_1 (DepthwiseCo (None, 128, 128, 32) 320         activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 128, 128, 32) 0           conv2d_2[0][0]                   \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 128, 128, 32) 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_2 (DepthwiseCo (None, 128, 128, 32) 320         activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 128, 128, 32) 0           conv2d_3[0][0]                   \n",
      "                                                                 activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 128, 128, 32) 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_3 (DepthwiseCo (None, 128, 128, 32) 320         activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 128, 128, 32) 0           conv2d_4[0][0]                   \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 128, 128, 32) 0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_4 (DepthwiseCo (None, 128, 128, 32) 320         activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 128, 128, 32) 0           conv2d_5[0][0]                   \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 128, 128, 32) 0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_5 (DepthwiseCo (None, 128, 128, 32) 320         activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 128, 128, 32) 0           conv2d_6[0][0]                   \n",
      "                                                                 activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 128, 128, 32) 0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_6 (DepthwiseCo (None, 128, 128, 32) 320         activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 128, 128, 32) 1056        depthwise_conv2d_6[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 128, 128, 32) 0           conv2d_7[0][0]                   \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 128, 128, 32) 0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 64, 64, 32)   0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_7 (DepthwiseCo (None, 64, 64, 32)   320         activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_zeros_like (TensorF [(None, 64, 64, 32)] 0           max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 64, 64)   2112        depthwise_conv2d_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_concat (TensorFlowO [(None, 64, 64, 64)] 0           max_pooling2d[0][0]              \n",
      "                                                                 tf_op_layer_zeros_like[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 64, 64, 64)   0           conv2d_8[0][0]                   \n",
      "                                                                 tf_op_layer_concat[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 64, 64, 64)   0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_8 (DepthwiseCo (None, 64, 64, 64)   640         activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 64, 64, 64)   4160        depthwise_conv2d_8[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 64, 64, 64)   0           conv2d_9[0][0]                   \n",
      "                                                                 activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 64, 64, 64)   0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_9 (DepthwiseCo (None, 64, 64, 64)   640         activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 64, 64, 64)   4160        depthwise_conv2d_9[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 64, 64, 64)   0           conv2d_10[0][0]                  \n",
      "                                                                 activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 64, 64, 64)   0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_10 (DepthwiseC (None, 64, 64, 64)   640         activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 64, 64, 64)   4160        depthwise_conv2d_10[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 64, 64, 64)   0           conv2d_11[0][0]                  \n",
      "                                                                 activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 64, 64, 64)   0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_11 (DepthwiseC (None, 64, 64, 64)   640         activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 64, 64, 64)   4160        depthwise_conv2d_11[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 64, 64, 64)   0           conv2d_12[0][0]                  \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 64, 64, 64)   0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_12 (DepthwiseC (None, 64, 64, 64)   640         activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 64, 64, 64)   4160        depthwise_conv2d_12[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 64, 64, 64)   0           conv2d_13[0][0]                  \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 64, 64, 64)   0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_13 (DepthwiseC (None, 64, 64, 64)   640         activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 64, 64, 64)   4160        depthwise_conv2d_13[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 64, 64, 64)   0           conv2d_14[0][0]                  \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 64, 64, 64)   0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_14 (DepthwiseC (None, 64, 64, 64)   640         activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 64, 64)   4160        depthwise_conv2d_14[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 64, 64, 64)   0           conv2d_15[0][0]                  \n",
      "                                                                 activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 64, 64, 64)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 64)   0           activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_15 (DepthwiseC (None, 32, 32, 64)   640         activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_zeros_like_1 (Tenso [(None, 32, 32, 64)] 0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 32, 32, 128)  8320        depthwise_conv2d_15[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_concat_1 (TensorFlo [(None, 32, 32, 128) 0           max_pooling2d_1[0][0]            \n",
      "                                                                 tf_op_layer_zeros_like_1[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 32, 32, 128)  0           conv2d_16[0][0]                  \n",
      "                                                                 tf_op_layer_concat_1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 32, 32, 128)  0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_16 (DepthwiseC (None, 32, 32, 128)  1280        activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_16[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 32, 32, 128)  0           conv2d_17[0][0]                  \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 32, 32, 128)  0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_17 (DepthwiseC (None, 32, 32, 128)  1280        activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_17[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 32, 32, 128)  0           conv2d_18[0][0]                  \n",
      "                                                                 activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 32, 32, 128)  0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_18 (DepthwiseC (None, 32, 32, 128)  1280        activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_18[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 32, 32, 128)  0           conv2d_19[0][0]                  \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 32, 32, 128)  0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_19 (DepthwiseC (None, 32, 32, 128)  1280        activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_19[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 32, 32, 128)  0           conv2d_20[0][0]                  \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 32, 32, 128)  0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_20 (DepthwiseC (None, 32, 32, 128)  1280        activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_20[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 32, 32, 128)  0           conv2d_21[0][0]                  \n",
      "                                                                 activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 32, 32, 128)  0           add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_21 (DepthwiseC (None, 32, 32, 128)  1280        activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_21[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 32, 32, 128)  0           conv2d_22[0][0]                  \n",
      "                                                                 activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 32, 32, 128)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_22 (DepthwiseC (None, 32, 32, 128)  1280        activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_22[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, 32, 32, 128)  0           conv2d_23[0][0]                  \n",
      "                                                                 activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 32, 32, 128)  0           add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 128)  0           activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_23 (DepthwiseC (None, 16, 16, 128)  1280        activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_zeros_like_2 (Tenso [(None, 16, 16, 128) 0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 16, 16, 256)  33024       depthwise_conv2d_23[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_concat_2 (TensorFlo [(None, 16, 16, 256) 0           max_pooling2d_2[0][0]            \n",
      "                                                                 tf_op_layer_zeros_like_2[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, 16, 16, 256)  0           conv2d_24[0][0]                  \n",
      "                                                                 tf_op_layer_concat_2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 16, 16, 256)  0           add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_24 (DepthwiseC (None, 16, 16, 256)  2560        activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_24[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_24 (Add)                    (None, 16, 16, 256)  0           conv2d_25[0][0]                  \n",
      "                                                                 activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 16, 16, 256)  0           add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_25 (DepthwiseC (None, 16, 16, 256)  2560        activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_25[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_25 (Add)                    (None, 16, 16, 256)  0           conv2d_26[0][0]                  \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 16, 16, 256)  0           add_25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_26 (DepthwiseC (None, 16, 16, 256)  2560        activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_26[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_26 (Add)                    (None, 16, 16, 256)  0           conv2d_27[0][0]                  \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 16, 16, 256)  0           add_26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_27 (DepthwiseC (None, 16, 16, 256)  2560        activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_27[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_27 (Add)                    (None, 16, 16, 256)  0           conv2d_28[0][0]                  \n",
      "                                                                 activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 16, 16, 256)  0           add_27[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_28 (DepthwiseC (None, 16, 16, 256)  2560        activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_28[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_28 (Add)                    (None, 16, 16, 256)  0           conv2d_29[0][0]                  \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 16, 16, 256)  0           add_28[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_29 (DepthwiseC (None, 16, 16, 256)  2560        activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_29[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_29 (Add)                    (None, 16, 16, 256)  0           conv2d_30[0][0]                  \n",
      "                                                                 activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 16, 16, 256)  0           add_29[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_30 (DepthwiseC (None, 16, 16, 256)  2560        activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_30[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_30 (Add)                    (None, 16, 16, 256)  0           conv2d_31[0][0]                  \n",
      "                                                                 activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 16, 16, 256)  0           add_30[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_31 (DepthwiseC (None, 8, 8, 256)    2560        activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_31[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 256)    0           activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_31 (Add)                    (None, 8, 8, 256)    0           conv2d_32[0][0]                  \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 8, 8, 256)    0           add_31[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_32 (DepthwiseC (None, 8, 8, 256)    2560        activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_32[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_32 (Add)                    (None, 8, 8, 256)    0           conv2d_33[0][0]                  \n",
      "                                                                 activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 8, 8, 256)    0           add_32[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_33 (DepthwiseC (None, 8, 8, 256)    2560        activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_33[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_33 (Add)                    (None, 8, 8, 256)    0           conv2d_34[0][0]                  \n",
      "                                                                 activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 8, 8, 256)    0           add_33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_34 (DepthwiseC (None, 8, 8, 256)    2560        activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_34[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_34 (Add)                    (None, 8, 8, 256)    0           conv2d_35[0][0]                  \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 8, 8, 256)    0           add_34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_35 (DepthwiseC (None, 8, 8, 256)    2560        activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_35[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_35 (Add)                    (None, 8, 8, 256)    0           conv2d_36[0][0]                  \n",
      "                                                                 activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 8, 8, 256)    0           add_35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_36 (DepthwiseC (None, 8, 8, 256)    2560        activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_36[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_36 (Add)                    (None, 8, 8, 256)    0           conv2d_37[0][0]                  \n",
      "                                                                 activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 8, 8, 256)    0           add_36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_37 (DepthwiseC (None, 8, 8, 256)    2560        activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_37[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_37 (Add)                    (None, 8, 8, 256)    0           conv2d_38[0][0]                  \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 8, 8, 256)    0           add_37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_38 (DepthwiseC (None, 8, 8, 256)    2560        activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 8, 8, 256)    65792       depthwise_conv2d_38[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_38 (Add)                    (None, 8, 8, 256)    0           conv2d_39[0][0]                  \n",
      "                                                                 activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 8, 8, 256)    0           add_38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 16, 16, 256)  262400      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 16, 16, 256)  0           conv2d_transpose[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_39 (Add)                    (None, 16, 16, 256)  0           activation_40[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_39 (DepthwiseC (None, 16, 16, 256)  2560        add_39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 16, 16, 256)  65792       depthwise_conv2d_39[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_40 (Add)                    (None, 16, 16, 256)  0           conv2d_40[0][0]                  \n",
      "                                                                 add_39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 16, 16, 256)  0           add_40[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 32, 32, 128)  131200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 32, 32, 128)  0           conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "add_41 (Add)                    (None, 32, 32, 128)  0           activation_42[0][0]              \n",
      "                                                                 activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "depthwise_conv2d_40 (DepthwiseC (None, 32, 32, 128)  1280        add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 32, 32, 128)  16512       depthwise_conv2d_40[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_42 (Add)                    (None, 32, 32, 128)  0           conv2d_41[0][0]                  \n",
      "                                                                 add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 32, 32, 128)  0           add_42[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 32, 32, 2)    258         activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 16, 16, 2)    514         activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 8, 8, 6)      1542        activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 32, 32, 36)   4644        activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 16, 16, 36)   9252        activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 8, 8, 108)    27756       activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, None, 1)      0           conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, None, 1)      0           conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, None, 1)      0           conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_3 (Reshape)             (None, None, 18)     0           conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_4 (Reshape)             (None, None, 18)     0           conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_5 (Reshape)             (None, None, 18)     0           conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, None, 1)      0           reshape[0][0]                    \n",
      "                                                                 reshape_1[0][0]                  \n",
      "                                                                 reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, 18)     0           reshape_3[0][0]                  \n",
      "                                                                 reshape_4[0][0]                  \n",
      "                                                                 reshape_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_concat_3 (TensorFlo [(None, None, 19)]   0           concatenate[0][0]                \n",
      "                                                                 concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 1,763,358\n",
      "Trainable params: 1,763,358\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(None, None, 19)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[-1].output_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Anchor Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SSD Default Anchors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor_config = AnchorsConfig(aspect_ratios=[1, 0.5, 2], fixed_anchor_size=False)\n",
    "anchor_generator = AnchorsGenerator(anchor_config)\n",
    "anchors = anchor_generator.generate()\n",
    "anchors = np.array([[anchor.x_center,anchor.y_center, anchor.w, anchor.h] for anchor in anchors])*256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = 0\n",
    "padding = 0\n",
    "color = ['limegreen', 'gray', 'gray', 'gray']\n",
    "width = [5, 3, 3, 3]\n",
    "style = ['-', '--', '--', '--']\n",
    "alpha = [1, 0.5, 0.5, 0.5]\n",
    "feature_map = {0:8, 4:16, 8:32, 12:32, 16:32}\n",
    "c1, c2, c3, c4 = pd.DataFrame(anchors)[2].unique()[x:x+4]\n",
    "condition = (\n",
    "(pd.DataFrame(anchors)[2] == c1) | \\\n",
    "(pd.DataFrame(anchors)[2] == c2) | \\\n",
    "(pd.DataFrame(anchors)[2] == c3) | \\\n",
    "(pd.DataFrame(anchors)[2] == c4) |\\\n",
    "(pd.DataFrame(anchors)[3] == c1) | \\\n",
    "(pd.DataFrame(anchors)[3] == c2) | \\\n",
    "(pd.DataFrame(anchors)[3] == c3) | \\\n",
    "(pd.DataFrame(anchors)[3] == c4))\n",
    "show_anchors = np.array(pd.DataFrame(anchors)[condition])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title(\"Anchors on Feature Map %sx%s \"% (256//feature_map[x], 256//feature_map[x]))\n",
    "result_image = np.pad(np.ones((256,256,3))*255, ((padding, padding), (padding, padding), (0, 0)), mode='constant')\n",
    "for show in range(3):\n",
    "    random_int = np.random.randint(len(show_anchors)//4)*4\n",
    "    for i, bbox in enumerate(show_anchors[random_int:random_int+4]):\n",
    "        cx, cy, w, h = bbox\n",
    "        cx += padding\n",
    "        cy += padding\n",
    "        x1, y1, x2, y2 = (cx-w//2, cy-h//2, cx+w//2, cy+h//2)\n",
    "        rect = patches.Rectangle((x1,y1), x2-x1, y2-y1,\n",
    "                                 linewidth=width[i], \n",
    "                                 edgecolor=color[i],\n",
    "                                 linestyle=style[i],\n",
    "                                 alpha=alpha[i],\n",
    "                                 facecolor=\"none\")\n",
    "        plt.gca().add_patch(rect)\n",
    "        plt.scatter(cx, cy, color='gold', s=100)\n",
    "\n",
    "rect = patches.Rectangle((padding, padding), \n",
    "                         result_image.shape[1], \n",
    "                         result_image.shape[0],\n",
    "                         linewidth=0,\n",
    "                         edgecolor=color[i], \n",
    "                         linestyle=style[i], \n",
    "                         facecolor=\"none\")\n",
    "plt.gca().add_patch(rect)\n",
    "plt.xticks(np.arange(padding, 257+padding, feature_map[x]))\n",
    "plt.yticks(np.arange(padding, 257+padding, feature_map[x]))\n",
    "plt.grid(linestyle='--', linewidth=2, c='grey', alpha=0.1)\n",
    "plt.imshow(result_image.astype('int'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BlazePalm Anchors**\n",
    "\n",
    "Anchor Setting:\n",
    "- num_layers: 5\n",
    "- min_scale: 0.1171875\n",
    "- max_scale: 0.75\n",
    "- input_size_height: 256\n",
    "- input_size_width: 256\n",
    "- anchor_offset_x: 0.5\n",
    "- anchor_offset_y: 0.5\n",
    "- strides: (8, 16, 32, 32, 32)\n",
    "- aspect_ratios: 1.0\n",
    "- fixed_anchor_size: true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor_config = AnchorsConfig(fixed_anchor_size=False)\n",
    "anchor_generator = AnchorsGenerator(anchor_config)\n",
    "anchors = anchor_generator.generate()\n",
    "anchors = np.array([[anchor.x_center,anchor.y_center, anchor.w, anchor.h] for anchor in anchors])*256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = 0\n",
    "color = ['limegreen', 'gray', 'gray', 'gray']\n",
    "width = [5, 3, 3, 3]\n",
    "style = ['-', '--', '--', '--']\n",
    "alpha = [1, 0.5, 0.5, 0.5]\n",
    "show_anchors = np.array(pd.DataFrame(anchors))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "result_image = np.ones((256,256,3))*255\n",
    "for show in range(32*32*2+16*16*2+6*29, 32*32*2+16*16*2+6*30, 2):\n",
    "    random_int = np.random.randint(len(show_anchors)//2)*2\n",
    "    random_int = show\n",
    "    for i, bbox in enumerate(show_anchors[random_int:random_int+2]):\n",
    "        cx, cy, w, h = bbox\n",
    "        x1, y1, x2, y2 = (cx-w//2, cy-h//2, cx+w//2, cy+h//2)\n",
    "        rect = patches.Rectangle((x1,y1), x2-x1, y2-y1,\n",
    "                                 linewidth=width[i], \n",
    "                                 edgecolor=color[i],\n",
    "                                 linestyle=style[i],\n",
    "                                 alpha=alpha[i],\n",
    "                                 facecolor=\"none\")\n",
    "        plt.gca().add_patch(rect)\n",
    "        plt.scatter(cx, cy, color='gold', s=100)\n",
    "plt.gca().add_patch(rect)\n",
    "plt.xticks(np.arange(0, 256, 32))\n",
    "plt.yticks(np.arange(256, -1, -32))\n",
    "plt.grid(linestyle='--', linewidth=2, c='grey', alpha=0.1)\n",
    "plt.imshow(result_image.astype('int'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Match and Encode for Annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Jaccard Index ( Intersection Over Union )\n",
    "\n",
    "For bounding boxes `boxA` and `boxB` with area `areaA` and `areaB`, if their `IOU > 0.5` then positive, if thier `IOU < 0.3` then negative\n",
    "\n",
    "$IOU = \\frac{areaA \\cap areaB}{areaA \\cup areaB} = \\frac{areaA \\cap areaB}{areaA + areaB - areaA \\cap areaB}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor_config = AnchorsConfig(fixed_anchor_size=False)\n",
    "anchor_generator = AnchorsGenerator(anchor_config)\n",
    "anchors = anchor_generator.generate()\n",
    "anchors = np.array([[anchor.x_center,anchor.y_center, anchor.w, anchor.h] for anchor in anchors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "anno_path = np.random.choice(glob.glob(os.path.join('dataset', 'annotation', '*.json')))\n",
    "img_path = anno_path.replace('annotation', 'image').replace('.json', '.jpg')\n",
    "with open(anno_path, 'r') as json_file:\n",
    "    annotation = json.load(json_file)\n",
    "for i in range(len(annotation)):\n",
    "    temp_loc = np.zeros((len(annotation), 18))\n",
    "    temp_label = np.zeros((len(annotation), 1))\n",
    "    temp_label[i] = float(annotation[i]['label'])\n",
    "    temp_loc[i] = np.concatenate([annotation[i]['bounding_box'], annotation[i]['key_points']])\n",
    "annotation = [temp_label, temp_loc]\n",
    "conf, loc = match(annotation, anchors, match_threshold=0.5)\n",
    "\n",
    "\n",
    "color = ['limegreen', 'r']\n",
    "width = [3, 3]\n",
    "style = ['-', '--']\n",
    "alpha = [1, 0.2]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.title(\"Match Anchors\")\n",
    "result_image = preprocess(cv2.imread(img_path), normalize=False)[0]\n",
    "for i, bbox in enumerate(anchors[np.where(conf != 0)[0]]*256):\n",
    "    cx, cy, w, h = bbox\n",
    "    x1, y1, x2, y2 = (cx-w//2, cy-h//2, cx+w//2, cy+h//2)    \n",
    "    if i == 0:\n",
    "        rect = patches.Rectangle((x1,y1), x2-x1, y2-y1, lw=width[1], ec=color[1], ls=style[1], alpha=alpha[1], facecolor=\"none\", label='Matched Anchors')\n",
    "    else:\n",
    "        rect = patches.Rectangle((x1,y1), x2-x1, y2-y1, lw=width[1], ec=color[1], ls=style[1], alpha=alpha[1], facecolor=\"none\")\n",
    "    plt.gca().add_patch(rect)\n",
    "    plt.scatter(cx, cy, color=color[1], s=10)\n",
    "\n",
    "for i in range(len(annotation[1])):\n",
    "    x1, y1, x2, y2 = annotation[1][:,:4][i]*256\n",
    "    if i == 0:\n",
    "        rect = patches.Rectangle((x1,y1), x2-x1, y2-y1, lw=width[0], ec=color[0], ls=style[0], alpha=alpha[0], facecolor=\"none\", label='Ground Truth')\n",
    "    else:\n",
    "        rect = patches.Rectangle((x1,y1), x2-x1, y2-y1, lw=width[0], ec=color[0], ls=style[0], alpha=alpha[0], facecolor=\"none\")\n",
    "    plt.gca().add_patch(rect)\n",
    "    plt.scatter((x2+x1)/2, (y2+y1)/2, color=color[0], s=87)\n",
    "\n",
    "plt.xticks(np.arange(0, 257, 32))\n",
    "plt.yticks(np.arange(0, 257, 32))\n",
    "plt.grid(linestyle='--', linewidth=2, c='k', alpha=0.2)\n",
    "plt.legend()\n",
    "plt.imshow(result_image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for anno_path in glob.glob(os.path.join('dataset', 'annotation', '*.json')):\n",
    "#     img_path = anno_path.replace('annotation', 'image').replace('.json', '.jpg')\n",
    "#     with open(anno_path, 'r') as json_file:\n",
    "#         annotation = json.load(json_file)\n",
    "#     for i in range(len(annotation)):\n",
    "#         temp_loc = np.zeros((len(annotation), 18))\n",
    "#         temp_label = np.zeros((len(annotation), 1))\n",
    "#         temp_label[i] = float(annotation[i]['label'])\n",
    "#         temp_loc[i] = np.concatenate([annotation[i]['bounding_box'], annotation[i]['key_points']])\n",
    "#     annotation = [temp_label, temp_loc]\n",
    "#     if len(annotation[0]) == 2:\n",
    "#         print(annotation)\n",
    "# #         os.remove(anno_path)\n",
    "# #         os.remove(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.remove(anno_path)\n",
    "# os.remove(img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Data Generator and Loss Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loss Function**\n",
    "\n",
    "There are 2 parts in loss, classification loss and localication loss\n",
    "\n",
    "$L_{total} = L_{cls} + \\alpha L_{loc}$: $\\alpha=1$ by default\n",
    "\n",
    "$L_{cls}$: could be softmax, focal loss or etc.\n",
    "\n",
    "$L_{loc}$: smooth L1 loss, compute with only positive samples\n",
    "\n",
    "**Hard Negative Mining**\n",
    "\n",
    "Positive samples is relatively much less than negatives samples.\n",
    "\n",
    "In order to handle the data embalanced problem, choose only negative samples with lowest confidence score.\n",
    "\n",
    "The ratio `positive samples:negative samples=1:3` is recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssd_loss = SSDLoss(alpha=1./256.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_generator = DataGenerator(image_dir=os.path.join('dataset', 'image'), \n",
    "                                     annotation_dir=os.path.join('dataset', 'annotation'),\n",
    "                                     batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=4161, shape=(32,), dtype=float32, numpy=\n",
       "array([0.13193965, 0.00592376, 0.00641741, 0.00493647, 0.00296188,\n",
       "       0.00444282, 0.00394917, 0.00296188, 0.00493647, 0.00592376,\n",
       "       0.00394917, 0.00444282, 0.00296188, 0.00592376, 0.00345553,\n",
       "       0.00394917, 0.00444282, 0.00345553, 0.00394917, 0.00394917,\n",
       "       0.00394917, 0.00148094, 0.00444282, 0.00345553, 0.00345553,\n",
       "       0.00098729, 0.00394917, 0.00444282, 0.00394917, 0.00493647,\n",
       "       0.00444282, 0.00197459], dtype=float32)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = train_data_generator.__getitem__(0)\n",
    "y_pred = model.predict(batch[0])\n",
    "y_true = batch[1]\n",
    "ssd_loss.compute_loss(y_true, y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=4171, shape=(32, 2944), dtype=float64, numpy=\n",
       "array([[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.]])>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smooth_l1_loss(y_true[:,:,1:], y_true[:,:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=4198, shape=(32, 2944), dtype=float32, numpy=\n",
       "array([[0.0433217, 0.0433217, 0.0433217, ..., 0.0433217, 0.0433217,\n",
       "        0.0433217],\n",
       "       [0.0433217, 0.0433217, 0.0433217, ..., 0.0433217, 0.0433217,\n",
       "        0.0433217],\n",
       "       [0.0433217, 0.0433217, 0.0433217, ..., 0.0433217, 0.0433217,\n",
       "        0.0433217],\n",
       "       ...,\n",
       "       [0.0433217, 0.0433217, 0.0433217, ..., 0.0433217, 0.0433217,\n",
       "        0.0433217],\n",
       "       [0.0433217, 0.0433217, 0.0433217, ..., 0.0433217, 0.0433217,\n",
       "        0.0433217],\n",
       "       [0.0433217, 0.0433217, 0.0433217, ..., 0.0433217, 0.0433217,\n",
       "        0.0433217]], dtype=float32)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focal_loss_sigmoid(y_true[:,:,0], y_true[:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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