yolo_algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "407f8c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import struct\n",
    "import numpy as np\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import Input\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import LeakyReLU\n",
    "from keras.layers import ZeroPadding2D\n",
    "from keras.layers import UpSampling2D\n",
    "from keras.layers import add, concatenate\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04f17a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _conv_block(inp, convs, skip=True):\n",
    "    x = inp\n",
    "    count = 0\n",
    "    for conv in convs:\n",
    "        if count == (len(convs) - 2) and skip:\n",
    "            skip_connection = x\n",
    "        count += 1\n",
    "        if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x)\n",
    "        x = Conv2D(conv['filter'],\n",
    "                conv['kernel'],\n",
    "                strides=conv['stride'],\n",
    "                padding='valid' if conv['stride'] > 1 else 'same',\n",
    "                name='conv_' + str(conv['layer_idx']),\n",
    "                use_bias=False if conv['bnorm'] else True)(x)\n",
    "        if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)\n",
    "        if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)\n",
    "    return add([skip_connection, x]) if skip else x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a04a08eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_yolov3_model():\n",
    "    input_image = Input(shape=(None, None, 3))\n",
    "    # Layer  0 => 4\n",
    "    x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},\n",
    "                                {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},\n",
    "                                {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},\n",
    "                                {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])\n",
    "    # Layer  5 => 8\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},\n",
    "                        {'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},\n",
    "                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])\n",
    "    # Layer  9 => 11\n",
    "    x = _conv_block(x, [{'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},\n",
    "                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])\n",
    "    # Layer 12 => 15\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},\n",
    "                        {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},\n",
    "                        {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])\n",
    "    # Layer 16 => 36\n",
    "    for i in range(7):\n",
    "        x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},\n",
    "                            {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])\n",
    "    skip_36 = x\n",
    "    # Layer 37 => 40\n",
    "    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])\n",
    "    # Layer 41 => 61\n",
    "    for i in range(7):\n",
    "        x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},\n",
    "                            {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])\n",
    "    skip_61 = x\n",
    "    # Layer 62 => 65\n",
    "    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])\n",
    "    # Layer 66 => 74\n",
    "    for i in range(3):\n",
    "        x = _conv_block(x, [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},\n",
    "                            {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])\n",
    "    # Layer 75 => 79\n",
    "    x = _conv_block(x, [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)\n",
    "    # Layer 80 => 82\n",
    "    yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 80},\n",
    "                              {'filter':  255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)\n",
    "    # Layer 83 => 86\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)\n",
    "    x = UpSampling2D(2)(x)\n",
    "    x = concatenate([x, skip_61])\n",
    "    # Layer 87 => 91\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)\n",
    "    # Layer 92 => 94\n",
    "    yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 92},\n",
    "                              {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)\n",
    "    # Layer 95 => 98\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True,   'layer_idx': 96}], skip=False)\n",
    "    x = UpSampling2D(2)(x)\n",
    "    x = concatenate([x, skip_36])\n",
    "    # Layer 99 => 106\n",
    "    yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 99},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 100},\n",
    "                               {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 101},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 102},\n",
    "                               {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 103},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 104},\n",
    "                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)\n",
    "    model = Model(input_image, [yolo_82, yolo_94, yolo_106])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52f08dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class WeightReader:\n",
    "    def __init__(self, weight_file):\n",
    "        with open(weight_file, 'rb') as w_f:\n",
    "            major, = struct.unpack('i', w_f.read(4))\n",
    "            minor, = struct.unpack('i', w_f.read(4))\n",
    "            revision, = struct.unpack('i', w_f.read(4))\n",
    "            if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:\n",
    "                w_f.read(8)\n",
    "            else:\n",
    "                w_f.read(4)\n",
    "            transpose = (major > 1000) or (minor > 1000)\n",
    "            binary = w_f.read()\n",
    "        self.offset = 0\n",
    "        self.all_weights = np.frombuffer(binary, dtype='float32')\n",
    "\n",
    "    def read_bytes(self, size):\n",
    "        self.offset = self.offset + size\n",
    "        return self.all_weights[self.offset-size:self.offset]\n",
    "\n",
    "    def load_weights(self, model):\n",
    "        for i in range(106):\n",
    "            try:\n",
    "                conv_layer = model.get_layer('conv_' + str(i))\n",
    "                print(\"loading weights of convolution #\" + str(i))\n",
    "                if i not in [81, 93, 105]:\n",
    "                    norm_layer = model.get_layer('bnorm_' + str(i))\n",
    "                    size = np.prod(norm_layer.get_weights()[0].shape)\n",
    "                    beta  = self.read_bytes(size) # bias\n",
    "                    gamma = self.read_bytes(size) # scale\n",
    "                    mean  = self.read_bytes(size) # mean\n",
    "                    var   = self.read_bytes(size) # variance\n",
    "                    weights = norm_layer.set_weights([gamma, beta, mean, var])\n",
    "                if len(conv_layer.get_weights()) > 1:\n",
    "                    bias   = self.read_bytes(np.prod(conv_layer.get_weights()[1].shape))\n",
    "                    kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n",
    "                    kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n",
    "                    kernel = kernel.transpose([2,3,1,0])\n",
    "                    conv_layer.set_weights([kernel, bias])\n",
    "                else:\n",
    "                    kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n",
    "                    kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n",
    "                    kernel = kernel.transpose([2,3,1,0])\n",
    "                    conv_layer.set_weights([kernel])\n",
    "            except ValueError:\n",
    "                print(\"no convolution #\" + str(i))\n",
    "    def reset(self):\n",
    "        self.offset = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eab75bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the model\n",
    "model = make_yolov3_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c76d138",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_reader = WeightReader('yolov3.weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c39c016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading weights of convolution #0\n",
      "loading weights of convolution #1\n",
      "loading weights of convolution #2\n",
      "loading weights of convolution #3\n",
      "no convolution #4\n",
      "loading weights of convolution #5\n",
      "loading weights of convolution #6\n",
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      "no convolution #8\n",
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      "no convolution #11\n",
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      "no convolution #15\n",
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      "no convolution #36\n",
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      "no convolution #40\n",
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      "no convolution #82\n",
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      "no convolution #94\n",
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      "no convolution #98\n",
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      "loading weights of convolution #104\n",
      "loading weights of convolution #105\n"
     ]
    }
   ],
   "source": [
    "weight_reader.load_weights(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71a072db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "model.save('model.h5')"
   ]
  }
 ],
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