Dataset Generator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import ObjectsDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ObjectsDataset('/Users/yehyaa/Downloads/dataset/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterator = ds.batch_training_triplets(batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_batch = next(iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.features import Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "net = Features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.3885908e-02 2.5596880e+03 2.5597720e+03]\n",
      " [2.4244505e-01 1.2487957e+04 1.2488199e+04]\n",
      " [0.0000000e+00 1.3230007e+03 1.3230007e+03]\n",
      " [0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      " [0.0000000e+00 7.4776923e+02 7.4776923e+02]\n",
      " [0.0000000e+00 2.5459193e+02 2.5459193e+02]\n",
      " [0.0000000e+00 4.7085938e+03 4.7085938e+03]\n",
      " [0.0000000e+00 0.0000000e+00 0.0000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "#initialize\n",
    "init = tf.initialize_all_variables()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    anchor_feats, puller_feats, pusher_feats, loss = net.evaluate_triplet(\n",
    "        first_batch['anchor'],\n",
    "        first_batch['puller'],\n",
    "        first_batch['pusher'],\n",
    "        session=sess\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2200.2551  3431.3726   748.17786    0.      1398.9423   244.8122\n",
      " 6138.2363     0.     ]\n"
     ]
    }
   ],
   "source": [
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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