convert_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.compat.v2.keras import backend as K\n",
    "K.set_learning_phase(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0506 13:33:36.293589 140350688552768 hdf5_format.py:224] No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model = load_model('./baseline_vgg_imagenet.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Tensor 'predictions/Softmax:0' shape=(None, 1000) dtype=float32>]\n",
      "[<tf.Tensor 'input_1:0' shape=(None, 224, 224, 3) dtype=float32>]\n"
     ]
    }
   ],
   "source": [
    "print(model.outputs)\n",
    "print(model.inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0506 13:33:54.144106 140350688552768 deprecation.py:323] From /home/cody/miniconda3/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n"
     ]
    }
   ],
   "source": [
    "tf.keras.experimental.export_saved_model(model, './models/save_model_baseline_imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow.python.keras.api._v2.keras.backend' has no attribute 'get_session'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-87c6a354ba7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m frozen_graph = freeze_session(K.get_session(),\n\u001b[0m\u001b[1;32m     35\u001b[0m                               output_names=[out.op.name for out in model.outputs])\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow.python.keras.api._v2.keras.backend' has no attribute 'get_session'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):\n",
    "    \"\"\"\n",
    "    Freezes the state of a session into a pruned computation graph.\n",
    "\n",
    "    Creates a new computation graph where variable nodes are replaced by\n",
    "    constants taking their current value in the session. The new graph will be\n",
    "    pruned so subgraphs that are not necessary to compute the requested\n",
    "    outputs are removed.\n",
    "    @param session The TensorFlow session to be frozen.\n",
    "    @param keep_var_names A list of variable names that should not be frozen,\n",
    "                          or None to freeze all the variables in the graph.\n",
    "    @param output_names Names of the relevant graph outputs.\n",
    "    @param clear_devices Remove the device directives from the graph for better portability.\n",
    "    @return The frozen graph definition.\n",
    "    \"\"\"\n",
    "    from tensorflow.python.framework.graph_util import convert_variables_to_constants\n",
    "    graph = session.graph\n",
    "    with graph.as_default():\n",
    "        freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))\n",
    "        output_names = output_names or []\n",
    "        output_names += [v.op.name for v in tf.global_variables()]\n",
    "        # Graph -> GraphDef ProtoBuf\n",
    "        input_graph_def = graph.as_graph_def()\n",
    "        if clear_devices:\n",
    "            for node in input_graph_def.node:\n",
    "                node.device = \"\"\n",
    "        frozen_graph = convert_variables_to_constants(session, input_graph_def,\n",
    "                                                      output_names, freeze_var_names)\n",
    "        return frozen_graph\n",
    "\n",
    "\n",
    "frozen_graph = freeze_session(K.get_session(),\n",
    "                              output_names=[out.op.name for out in model.outputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorrt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-0c428996a76e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Import TensorFlow and TensorRT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorrt\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtrt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m# Inference with TF-TRT `SavedModel` workflow:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorrt'"
     ]
    }
   ],
   "source": [
    "# Import TensorFlow and TensorRT\n",
    "import tensorflow as tf\n",
    "import tensorrt as trt\n",
    "# Inference with TF-TRT `SavedModel` workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    with tf.Session() as sess:\n",
    "        # Create a TensorRT inference graph from a SavedModel:\n",
    "        trt_graph = trt.create_inference_graph(\n",
    "            input_graph_def=None,\n",
    "            outputs=None,\n",
    "            input_saved_model_dir=”,\n",
    "            input_saved_model_tags=[”your_saved_model_tags”],\n",
    "            max_batch_size=your_batch_size,\n",
    "            max_workspace_size_bytes=max_GPU_mem_size_for_TRT,\n",
    "            precision_mode=”your_precision_mode”) \n",
    "        # Import the TensorRT graph into a new graph and run:\n",
    "        output_node = tf.import_graph_def(\n",
    "            trt_graph,\n",
    "            return_elements=[“your_outputs”])\n",
    "       sess.run(output_node)"
   ]
  }
 ],
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