QueueRunnerExample

queuerunner_example.ipynb

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   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Get some dummy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "records_dict = {\n",
    "\"records\": [\n",
    "    \"predator-5.jpg,predator-5.csv\",\n",
    "    \"predator-18.jpg,predator-18.csv\",\n",
    "    \"predator-55.jpg,predator-55.csv\",\n",
    "    \"predator-186.jpg,predator-186.csv\",\n",
    "    \"predator-539.jpg,predator-539.csv\",\n",
    "    \"predator-1976.jpg,predator-1976.csv\",\n",
    "    \"predator-2006.jpg,predator-2006.csv\",\n",
    "    \"predator-2015.jpg,predator-2015.csv\",\n",
    "    \"predator-5477.jpg,predator-5477.csv\",\n",
    "    \"predator-71940.jpg,predator-71940.csv\"\n",
    "  ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "records = records_dict[\"records\"]\n",
    "data = tf.convert_to_tensor(records, dtype=tf.string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create a queue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_queue = tf.train.string_input_producer(data, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Decode the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_location, bb_location = tf.decode_csv(data_queue.dequeue(), [[''], ['']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Shuffle and batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "record =  tf.train.shuffle_batch(\n",
    "    [image_location, bb_location],\n",
    "    batch_size=2,\n",
    "    capacity=20,\n",
    "    min_after_dequeue=16,\n",
    "    num_threads=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Run it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    coord = tf.train.Coordinator()\n",
    "    threads = tf.train.start_queue_runners(coord=coord)\n",
    "\n",
    "    rec = sess.run(record)\n",
    "    \n",
    "    print(rec[0], rec[1])\n",
    "    \n",
    "    coord.request_stop()\n",
    "    coord.join(threads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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