Test CPU Global Normalization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openvino.inference_engine import IENetwork, IEPlugin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"CPU\"\n",
    "cpu_extension_path = \"/opt/intel/computer_vision_sdk/inference_engine/lib/ubuntu_16.04/intel64/libcpu_extension_avx2.so\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision = \"FP16\"\n",
    "\n",
    "if device == \"CPU\":\n",
    "    precision = \"FP32\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = '''\n",
    "<?xml version=\"1.0\" ?>\n",
    "<net batch=\"1\" name=\"ResNet-50-128\" version=\"3\">\n",
    "\t<layers>\n",
    "\t\t<layer id=\"0\" name=\"data\" precision=\"{precision}\" type=\"Input\">\n",
    "\t\t\t<output>\n",
    "\t\t\t\t<port id=\"0\">\n",
    "\t\t\t\t\t<dim>1</dim>\n",
    "          <dim>2</dim>\n",
    "\t\t\t\t</port>\n",
    "\t\t\t</output>\n",
    "\t\t</layer>\n",
    "\t\t<layer id=\"1\" name=\"feat_normalize\" precision=\"{precision}\" type=\"GRN\">\n",
    "\t\t\t<data bias=\"1e-10\" />\n",
    "\t\t\t<input>\n",
    "\t\t\t\t<port id=\"0\">\n",
    "\t\t\t\t\t<dim>1</dim>\n",
    "\t\t\t\t\t<dim>2</dim>\n",
    "\t\t\t\t</port>\n",
    "\t\t\t</input>\n",
    "\t\t\t<output>\n",
    "\t\t\t\t<port id=\"2\">\n",
    "\t\t\t\t\t<dim>1</dim>\n",
    "\t\t\t\t\t<dim>2</dim>\n",
    "\t\t\t\t</port>\n",
    "\t\t\t</output>\n",
    "\t\t</layer>\n",
    "    </layers>\n",
    "    <edges>\n",
    "\t\t<edge from-layer=\"0\" from-port=\"0\" to-layer=\"1\" to-port=\"0\"/>\n",
    "    </edges>\n",
    "    <meta_data>\n",
    "\t\t<MO_version value=\"1.4.288.16472e37\"/>\n",
    "\t\t<cli_parameters>\n",
    "\t\t\t<batch value=\"1\"/>\n",
    "\t\t\t<data_type value=\"FP32\"/>\n",
    "\t\t\t<unset unset_cli_parameters=\"finegrain_fusing, freeze_placeholder_with_value, input, input_shape, mean_file, mean_file_offsets, model_name, output, scale\"/>\n",
    "\t\t</cli_parameters>\n",
    "\t</meta_data>\n",
    "</net>\n",
    "'''.format(precision=precision)\n",
    "\n",
    "with open('test_normalization.xml', 'w') as f:\n",
    "    f.write(network)\n",
    "\n",
    "with open('test_normalization.bin', 'w') as f:\n",
    "    f.write('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plugin = IEPlugin(device=device)\n",
    "if device == \"CPU\":\n",
    "    plugin.add_cpu_extension(cpu_extension_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = IENetwork(model='test_normalization.xml', weights='test_normalization.bin')\n",
    "exec_net = plugin.load(net)\n",
    "input_blob = next(iter(net.inputs))\n",
    "output_blob = next(iter(net.outputs))\n",
    "del net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalized: True input: [1, 1] output: [0.70710677 0.70710677]\n",
      "normalized: True input: [2, -2] output: [ 0.70710677 -0.70710677]\n",
      "normalized: True input: [1, 0] output: [1. 0.]\n",
      "normalized: True input: [0, 1] output: [0. 1.]\n"
     ]
    }
   ],
   "source": [
    "inputs = [[1,1],[2,-2],[1,0],[0,1]]\n",
    "\n",
    "for k in inputs:\n",
    "    res = exec_net.infer(inputs={input_blob: [k]})\n",
    "    out = res[output_blob][0]\n",
    "    d2 = out[0] * out[0] + out[1] * out[1]\n",
    "    normalized = True\n",
    "    \n",
    "    if d2 < 0.999 or d2 > 1.001:\n",
    "        normalized = False\n",
    "    \n",
    "    print('normalized: {} input: {} output: {}'.format(normalized, k, out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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