Freeze_TF_Graph.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of frozen_graph.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hN6VL9aznhRF"
      },
      "source": [
        "!sudo apt-get purge cuda\n",
        "!sudo apt-get purge libcudnn6\n",
        "!sudo apt-get purge libcudnn6-dev\n",
        "\n",
        "!wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb\n",
        "!wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb\n",
        "!wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb\n",
        "!wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl2_2.1.4-1+cuda9.0_amd64.deb\n",
        "!wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl-dev_2.1.4-1+cuda9.0_amd64.deb\n",
        "!sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb\n",
        "!sudo apt-get update\n",
        "!sudo apt-get install cuda=9.0.176-1\n",
        "!sudo apt-get install libcudnn7-dev\n",
        "!sudo apt-get install libnccl-dev"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwVo5xQ_gPbj"
      },
      "source": [
        "!pip show tensorflow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuZbERWGDV1U"
      },
      "source": [
        "import cv2\n",
        "import os\n",
        "import tempfile\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sn\n",
        "import sklearn\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.preprocessing import image\n",
        "from time import time\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from google.colab import files\n",
        "from zipfile import ZipFile\n",
        "from PIL import Image\n",
        "from numpy import asarray\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ziv1KM0ga8Qt"
      },
      "source": [
        "cp -r /tmp/frozen_graph/frozen_resnet502.pb /content/drive/MyDrive/frozen_graph/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tl5NyBoAC971",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "372923c0-4308-478a-d219-5e152ae3caee"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0jzIBNyDYy1"
      },
      "source": [
        "model = tf.keras.models.load_model('/content/drive/My Drive/resnet50avg.h5', compile=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Wqi2IueYtnz"
      },
      "source": [
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.models import Sequential\n",
        "def get_compiled_model():\n",
        "    base_model = ResNet50(weights = \"imagenet\", include_top=False, input_shape=(256,256,3))\n",
        "    base_model.trainable = True\n",
        "    for layer in base_model.layers[:35]:\n",
        "        layer.trainable = False\n",
        "    model = Sequential([\n",
        "        base_model,\n",
        "        tf.keras.layers.MaxPool2D(),\n",
        "        tf.keras.layers.MaxPool2D(),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(5, activation='softmax')\n",
        "    ])\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElZslyFgYvjn",
        "outputId": "ef021f68-a8b8-4248-e3dc-32d628ed5139"
      },
      "source": [
        "model = get_compiled_model()\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
            "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resnet50 (Model)             (None, 8, 8, 2048)        23587712  \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 4, 4, 2048)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 2, 2, 2048)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 5)                 40965     \n",
            "=================================================================\n",
            "Total params: 23,628,677\n",
            "Trainable params: 23,365,893\n",
            "Non-trainable params: 262,784\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FeGXqqeMDrnE",
        "outputId": "787cf71e-aa3d-478a-caa0-29c934f7b2f0"
      },
      "source": [
        "tf.keras.backend.set_learning_phase(0)\n",
        "\n",
        "# set up tensorflow saver object\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "# fetch the tensorflow session using the Keras backend\n",
        "tf_session = tf.keras.backend.get_session()\n",
        "# get the tensorflow session graph\n",
        "input_graph_def = tf_session.graph.as_graph_def()\n",
        "\n",
        "\n",
        "# write out tensorflow checkpoint & inference graph for use with freeze_graph script\n",
        "saver.save(tf_session, '/tmp/frozen_graph/InceptionV3.ckpt')\n",
        "tf.train.write_graph(input_graph_def, '/tmp/InceptionV3.h5', '/tmp/frozen_graph/InceptionV3.pb', as_text=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/tmp/frozen_graph/InceptionV3.pb'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oe9FTuh-Liwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8854f076-f317-4edd-f4e5-5f705e69b05c"
      },
      "source": [
        "model.input\n",
        "\n",
        "#output_node_names=\"dense/Softmax\"\n",
        "#output_node_names.split(\",\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'resnet50_input_1:0' shape=(?, 256, 256, 3) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cI0GBElaZGM5",
        "outputId": "d6d33418-a1db-44e1-c118-3cd379962c36"
      },
      "source": [
        "model.output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'dense_1/Softmax:0' shape=(?, 5) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v400ho5RaF-"
      },
      "source": [
        "# freezing Graph\n",
        "use for tf 1.15.2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uc0At0CmRPu7",
        "outputId": "a42749b6-fb04-4989-ade2-0ecc37cee1fa"
      },
      "source": [
        "tf.keras.backend.set_learning_phase(0)\n",
        "saver = tf.train.import_meta_graph('/tmp/frozen_graph/InceptionV3.ckpt.meta', clear_devices=True)\n",
        "\n",
        "tf_session = tf.keras.backend.get_session()\n",
        "input_graph_def = tf_session.graph.as_graph_def()\n",
        "sess = tf.Session()\n",
        "saver.restore(sess,'/tmp/frozen_graph/InceptionV3.ckpt' )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/frozen_graph/InceptionV3.ckpt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BO2WDsZrRRXH",
        "outputId": "c5b5b3db-01ae-466a-ea2d-1ce2066ee6e5"
      },
      "source": [
        "output_node_names=\"dense_1/Softmax\"\n",
        "output_graph_def = tf.graph_util.convert_variables_to_constants(\n",
        "            sess, # The session\n",
        "            input_graph_def, # input_graph_def is useful for retrieving the nodes \n",
        "            output_node_names.split(\",\") \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Froze 320 variables.\n",
            "INFO:tensorflow:Converted 320 variables to const ops.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgg7py1ZRTml"
      },
      "source": [
        "output_graph=\"/tmp/frozen_graph/frozen_graph.pb\"\n",
        "with tf.io.gfile.GFile(output_graph, \"wb\") as f:\n",
        "    f.write(output_graph_def.SerializeToString())\n",
        " \n",
        "sess.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvFB2JH7-y8t"
      },
      "source": [
        "# freezing graph 2.0\n",
        "use for tf 1.12"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Nm-cHQ-FyEf",
        "outputId": "8f089627-f89c-4a90-acd1-ba3c3d578399"
      },
      "source": [
        "model.input"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'inception_v3_input:0' shape=(?, 256, 256, 3) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5Zh4lPEUn14",
        "outputId": "2cc937ae-3449-4e0f-baf7-866e69b030b4"
      },
      "source": [
        "from tensorflow.python.framework import graph_util\n",
        "from tensorflow.python.framework import graph_io\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras import backend as K\n",
        "import os.path as osp\n",
        "\n",
        "#model = load_model('/content/drive/My Drive/Colab Notebooks/Transfer Learning experiments/best model/resnet50 tf 1.12.hdf5')\n",
        "#nb_classes = 5 # The number of output nodes in the model\n",
        "#prefix_output_node_names_of_final_network = 'output_node'\n",
        "\n",
        "K.set_learning_phase(0)\n",
        "\n",
        "sess = K.get_session()\n",
        "output_fld = '/tmp/frozen_graph'\n",
        "if not os.path.isdir(output_fld):\n",
        "    os.mkdir(output_fld)\n",
        "output_graph_name = '/tmp/frozen_graph/Frozen_ResNetAVG' + '.pb'\n",
        "output_graph_suffix = '_inference'\n",
        "\n",
        "constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(),['dense_1/Softmax'] )\n",
        "graph_io.write_graph(constant_graph, output_fld, output_graph_name, as_text=False)\n",
        "print('saved the constant graph (ready for inference) at: ', osp.join(output_fld, output_graph_name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Froze 320 variables.\n",
            "INFO:tensorflow:Converted 320 variables to const ops.\n",
            "saved the constant graph (ready for inference) at:  /tmp/frozen_graph/Frozen_ResNetAVG.pb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G95rHpaR_ZZ_"
      },
      "source": [
        "# Load model saved\n",
        "see output node name  \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8L-dc5McYsMb",
        "outputId": "fa6a099d-9a57-4aec-a819-503a2efd393e"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.python.platform import gfile\n",
        "GRAPH_PB_PATH = '/tmp/frozen_graph/frozen_graph.pb'\n",
        "with tf.Session() as sess:\n",
        "   print(\"load graph\")\n",
        "   with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:\n",
        "       graph_def = tf.GraphDef()\n",
        "   graph_def.ParseFromString(f.read())\n",
        "   sess.graph.as_default()\n",
        "   tf.import_graph_def(graph_def, name='')\n",
        "   graph_nodes=[n for n in graph_def.node]\n",
        "   names = []\n",
        "   for t in graph_nodes:\n",
        "      names.append(t.name)\n",
        "   print(names)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "load graph\n",
            "['conv1_1/kernel', 'conv1_1/bias', 'bn_conv1_1/gamma', 'bn_conv1_1/beta', 'bn_conv1_1/moving_mean', 'bn_conv1_1/moving_variance', 'res2a_branch2a_1/kernel', 'res2a_branch2a_1/bias', 'bn2a_branch2a_1/gamma', 'bn2a_branch2a_1/beta', 'bn2a_branch2a_1/moving_mean', 'bn2a_branch2a_1/moving_variance', 'res2a_branch2b_1/kernel', 'res2a_branch2b_1/bias', 'bn2a_branch2b_1/gamma', 'bn2a_branch2b_1/beta', 'bn2a_branch2b_1/moving_mean', 'bn2a_branch2b_1/moving_variance', 'res2a_branch2c_1/kernel', 'res2a_branch2c_1/bias', 'bn2a_branch2c_1/gamma', 'bn2a_branch2c_1/beta', 'bn2a_branch2c_1/moving_mean', 'bn2a_branch2c_1/moving_variance', 'res2a_branch1_1/kernel', 'res2a_branch1_1/bias', 'bn2a_branch1_1/gamma', 'bn2a_branch1_1/beta', 'bn2a_branch1_1/moving_mean', 'bn2a_branch1_1/moving_variance', 'res2b_branch2a_1/kernel', 'res2b_branch2a_1/bias', 'bn2b_branch2a_1/gamma', 'bn2b_branch2a_1/beta', 'bn2b_branch2a_1/moving_mean', 'bn2b_branch2a_1/moving_variance', 'res2b_branch2b_1/kernel', 'res2b_branch2b_1/bias', 'bn2b_branch2b_1/gamma', 'bn2b_branch2b_1/beta', 'bn2b_branch2b_1/moving_mean', 'bn2b_branch2b_1/moving_variance', 'res2b_branch2c_1/kernel', 'res2b_branch2c_1/bias', 'bn2b_branch2c_1/gamma', 'bn2b_branch2c_1/beta', 'bn2b_branch2c_1/moving_mean', 'bn2b_branch2c_1/moving_variance', 'res2c_branch2a_1/kernel', 'res2c_branch2a_1/bias', 'bn2c_branch2a_1/gamma', 'bn2c_branch2a_1/beta', 'bn2c_branch2a_1/moving_mean', 'bn2c_branch2a_1/moving_variance', 'res2c_branch2b_1/kernel', 'res2c_branch2b_1/bias', 'bn2c_branch2b_1/gamma', 'bn2c_branch2b_1/beta', 'bn2c_branch2b_1/moving_mean', 'bn2c_branch2b_1/moving_variance', 'res2c_branch2c_1/kernel', 'res2c_branch2c_1/bias', 'bn2c_branch2c_1/gamma', 'bn2c_branch2c_1/beta', 'bn2c_branch2c_1/moving_mean', 'bn2c_branch2c_1/moving_variance', 'res3a_branch2a_1/kernel', 'res3a_branch2a_1/bias', 'bn3a_branch2a_1/gamma', 'bn3a_branch2a_1/beta', 'bn3a_branch2a_1/moving_mean', 'bn3a_branch2a_1/moving_variance', 'res3a_branch2b_1/kernel', 'res3a_branch2b_1/bias', 'bn3a_branch2b_1/gamma', 'bn3a_branch2b_1/beta', 'bn3a_branch2b_1/moving_mean', 'bn3a_branch2b_1/moving_variance', 'res3a_branch2c_1/kernel', 'res3a_branch2c_1/bias', 'bn3a_branch2c_1/gamma', 'bn3a_branch2c_1/beta', 'bn3a_branch2c_1/moving_mean', 'bn3a_branch2c_1/moving_variance', 'res3a_branch1_1/kernel', 'res3a_branch1_1/bias', 'bn3a_branch1_1/gamma', 'bn3a_branch1_1/beta', 'bn3a_branch1_1/moving_mean', 'bn3a_branch1_1/moving_variance', 'res3b_branch2a_1/kernel', 'res3b_branch2a_1/bias', 'bn3b_branch2a_1/gamma', 'bn3b_branch2a_1/beta', 'bn3b_branch2a_1/moving_mean', 'bn3b_branch2a_1/moving_variance', 'res3b_branch2b_1/kernel', 'res3b_branch2b_1/bias', 'bn3b_branch2b_1/gamma', 'bn3b_branch2b_1/beta', 'bn3b_branch2b_1/moving_mean', 'bn3b_branch2b_1/moving_variance', 'res3b_branch2c_1/kernel', 'res3b_branch2c_1/bias', 'bn3b_branch2c_1/gamma', 'bn3b_branch2c_1/beta', 'bn3b_branch2c_1/moving_mean', 'bn3b_branch2c_1/moving_variance', 'res3c_branch2a_1/kernel', 'res3c_branch2a_1/bias', 'bn3c_branch2a_1/gamma', 'bn3c_branch2a_1/beta', 'bn3c_branch2a_1/moving_mean', 'bn3c_branch2a_1/moving_variance', 'res3c_branch2b_1/kernel', 'res3c_branch2b_1/bias', 'bn3c_branch2b_1/gamma', 'bn3c_branch2b_1/beta', 'bn3c_branch2b_1/moving_mean', 'bn3c_branch2b_1/moving_variance', 'res3c_branch2c_1/kernel', 'res3c_branch2c_1/bias', 'bn3c_branch2c_1/gamma', 'bn3c_branch2c_1/beta', 'bn3c_branch2c_1/moving_mean', 'bn3c_branch2c_1/moving_variance', 'res3d_branch2a_1/kernel', 'res3d_branch2a_1/bias', 'bn3d_branch2a_1/gamma', 'bn3d_branch2a_1/beta', 'bn3d_branch2a_1/moving_mean', 'bn3d_branch2a_1/moving_variance', 'res3d_branch2b_1/kernel', 'res3d_branch2b_1/bias', 'bn3d_branch2b_1/gamma', 'bn3d_branch2b_1/beta', 'bn3d_branch2b_1/moving_mean', 'bn3d_branch2b_1/moving_variance', 'res3d_branch2c_1/kernel', 'res3d_branch2c_1/bias', 'bn3d_branch2c_1/gamma', 'bn3d_branch2c_1/beta', 'bn3d_branch2c_1/moving_mean', 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'resnet50_1/bn5c_branch2c/FusedBatchNorm', 'resnet50_1/add_31/add', 'resnet50_1/activation_97/Relu', 'max_pooling2d_4/MaxPool', 'max_pooling2d_5/MaxPool', 'flatten_1/Shape', 'flatten_1/strided_slice/stack', 'flatten_1/strided_slice/stack_1', 'flatten_1/strided_slice/stack_2', 'flatten_1/strided_slice', 'flatten_1/Reshape/shape/1', 'flatten_1/Reshape/shape', 'flatten_1/Reshape', 'dense_1/kernel', 'dense_1/bias', 'dense_1/MatMul/ReadVariableOp', 'dense_1/MatMul', 'dense_1/BiasAdd/ReadVariableOp', 'dense_1/BiasAdd', 'dense_1/Softmax']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oD5Ht4MJK_xS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}