convert_tflite.ipynb

{
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   "cell_type": "code",
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   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hui/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/hui/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/hui/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/hui/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/hui/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/hui/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./variables/variables\n",
      "INFO:tensorflow:The given SavedModel MetaGraphDef contains SignatureDefs with the following keys: {'serving_default'}\n",
      "INFO:tensorflow:input tensors info: \n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: image\n",
      "INFO:tensorflow: tensor name: image:0, shape: (1, 224, 224, 3), type: DT_FLOAT\n",
      "INFO:tensorflow:output tensors info: \n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: local_descriptor_map\n",
      "INFO:tensorflow: tensor name: local_descriptor_map:0, shape: (1, 28, 28, 256), type: DT_FLOAT\n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: scores\n",
      "INFO:tensorflow: tensor name: scores:0, shape: (1, 224, 224), type: DT_FLOAT\n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: global_descriptor\n",
      "INFO:tensorflow: tensor name: global_descriptor:0, shape: (1, 4096), type: DT_FLOAT\n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: local_descriptors\n",
      "INFO:tensorflow: tensor name: local_descriptors:0, shape: (1, 28, 28, 256), type: DT_FLOAT\n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: prob_full\n",
      "INFO:tensorflow: tensor name: prob_full:0, shape: (1, 28, 28, 65), type: DT_FLOAT\n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: scores_dense\n",
      "INFO:tensorflow: tensor name: scores_dense:0, shape: (1, 224, 224), type: DT_FLOAT\n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: logits\n",
      "INFO:tensorflow: tensor name: logits:0, shape: (1, 28, 28, 65), type: DT_FLOAT\n",
      "INFO:tensorflow:Restoring parameters from ./variables/variables\n",
      "INFO:tensorflow:Froze 276 variables.\n",
      "INFO:tensorflow:Converted 276 variables to const ops.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "converter = tf.lite.TFLiteConverter.from_saved_model(\"./\")\n",
    "converter.target_ops = [\n",
    "#     tf.lite.OpsSet.TFLITE_BUILTINS,\n",
    "                                       tf.lite.OpsSet.SELECT_TF_OPS]\n",
    "tflite_model = converter.convert()\n",
    "open(\"hfnet.tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Regular TensorFlow ops are not supported by this interpreter. Make sure you invoke the Flex delegate before inference.Node number 0 (Flex) failed to prepare.\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-b9a1548c4973>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minterpreter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterpreter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./hfnet.tflite\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0minterpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallocate_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow/lite/python/interpreter.py\u001b[0m in \u001b[0;36mallocate_tensors\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     71\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mallocate_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAllocateTensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_safe_to_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow/lite/python/interpreter_wrapper/tensorflow_wrap_interpreter_wrapper.py\u001b[0m in \u001b[0;36mAllocateTensors\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mAllocateTensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_tensorflow_wrap_interpreter_wrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterpreterWrapper_AllocateTensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mInvoke\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Regular TensorFlow ops are not supported by this interpreter. Make sure you invoke the Flex delegate before inference.Node number 0 (Flex) failed to prepare.\n"
     ]
    }
   ],
   "source": [
    "interpreter = tf.lite.Interpreter(model_path=\"./hfnet.tflite\")\n",
    "interpreter.allocate_tensors()"
   ]
  }
 ],
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