{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "g_nWetWWd_ns",
      "metadata": {
        "id": "g_nWetWWd_ns"
      },
      "source": [
        "##### Copyright 2021 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2pHVBk_seED1",
      "metadata": {
        "cellView": "form",
        "id": "2pHVBk_seED1"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "M7vSdG6sAIQn",
      "metadata": {
        "id": "M7vSdG6sAIQn"
      },
      "source": [
        "# TensorFlow Lite のシグネチャ"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fwc5GKHBASdc",
      "metadata": {
        "id": "fwc5GKHBASdc"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>     <a target=\"_blank\" href=\"https://www.tensorflow.org/lite/guide/signatures\">     <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">     TensorFlow.org で表示</a> </td>\n",
        "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/lite/guide/signatures.ipynb\">     <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">     Google Colab で実行</a> </td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/lite/guide/signatures.ipynb\">     <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">     GitHubでソースを表示</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/lite/guide/signatures.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ee074e4",
      "metadata": {
        "id": "9ee074e4"
      },
      "source": [
        "TensorFlow Lite では、TensorFlow モデルの入出力仕様を TensorFlow Lite モデルに変換できます。入出力仕様は「シグネチャ」と呼ばれます。署名は、SavedModel の構築時または具体的な関数の作成時に指定できます。\n",
        "\n",
        "TensorFlow Lite のシグネチャには次の機能があります。\n",
        "\n",
        "- TensorFlow モデルのシグネチャを守ることで、変換された TensorFlow Lite モデルの入出力を指定する。\n",
        "- 1 つの TensorFlow Lite モデルで複数の入力点をサポートできる。\n",
        "\n",
        "シグネチャには次の 3 つの要素があります。\n",
        "\n",
        "- 入力: シグネチャの入力名から入力テンソルへの入力のマッピング。\n",
        "- 出力: シグネチャの出力名から出力テンソルへの出力のマッピング。\n",
        "- シグネチャキー: グラフの入力点を識別する名前。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "UaWdLA3fQDK2",
      "metadata": {
        "id": "UaWdLA3fQDK2"
      },
      "source": [
        "## MNIST モデルをビルドする"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9j4MGqyKQEo4",
      "metadata": {
        "id": "9j4MGqyKQEo4"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "FN2N6hPEP-Ay",
      "metadata": {
        "id": "FN2N6hPEP-Ay"
      },
      "source": [
        "## サンプル モデル\n",
        "\n",
        "エンコードとデコードといった 2 つのタスクが TensorFlow モデルとして存在するとします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8577c80",
      "metadata": {
        "id": "d8577c80"
      },
      "outputs": [],
      "source": [
        "class Model(tf.Module):\n",
        "\n",
        "  @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])\n",
        "  def encode(self, x):\n",
        "    result = tf.strings.as_string(x)\n",
        "    return {\n",
        "         \"encoded_result\": result\n",
        "    }\n",
        "\n",
        "  @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)])\n",
        "  def decode(self, x):\n",
        "    result = tf.strings.to_number(x)\n",
        "    return {\n",
        "         \"decoded_result\": result\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c814c6e",
      "metadata": {
        "id": "9c814c6e"
      },
      "source": [
        "シグネチャという点では、上記の TensorFlow モデルは次のように要約することができます。\n",
        "\n",
        "- シグネチャ\n",
        "\n",
        "    - キー: encode\n",
        "    - 入力: {\"x\"}\n",
        "    - 出力: {\"encoded_result\"}\n",
        "\n",
        "- シグネチャ\n",
        "\n",
        "    - キー: decode\n",
        "    - 入力: {\"x\"}\n",
        "    - 出力: {\"decoded_result\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c4099f20",
      "metadata": {
        "id": "c4099f20"
      },
      "source": [
        "## シグネチャを使用したモデルの変換\n",
        "\n",
        "TensorFlow Lite コンバータ API は、上記のシグネチャ情報を変換された TensorFlow Lite モデルに渡します。\n",
        "\n",
        "この変換機能は、TensorFlow バージョン 2.7.0 以降のすべてのコンバータ API で提供されています。使用例を参照してください。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Qv0WwFQkQgnO",
      "metadata": {
        "id": "Qv0WwFQkQgnO"
      },
      "source": [
        "### 保存されたモデルから変換"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96c8fc79",
      "metadata": {
        "id": "96c8fc79"
      },
      "outputs": [],
      "source": [
        "model = Model()\n",
        "\n",
        "# Save the model\n",
        "SAVED_MODEL_PATH = 'content/saved_models/coding'\n",
        "\n",
        "tf.saved_model.save(\n",
        "    model, SAVED_MODEL_PATH,\n",
        "    signatures={\n",
        "      'encode': model.encode.get_concrete_function(),\n",
        "      'decode': model.decode.get_concrete_function()\n",
        "    })\n",
        "\n",
        "# Convert the saved model using TFLiteConverter\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(SAVED_MODEL_PATH)\n",
        "converter.target_spec.supported_ops = [\n",
        "    tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.\n",
        "    tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.\n",
        "]\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Print the signatures from the converted model\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "signatures = interpreter.get_signature_list()\n",
        "print(signatures)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5baa9f17",
      "metadata": {
        "id": "5baa9f17"
      },
      "source": [
        "### Keras モデルから変換"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "71f29229",
      "metadata": {
        "id": "71f29229"
      },
      "outputs": [],
      "source": [
        "# Generate a Keras model.\n",
        "keras_model = tf.keras.Sequential(\n",
        "    [\n",
        "        tf.keras.layers.Dense(2, input_dim=4, activation='relu', name='x'),\n",
        "        tf.keras.layers.Dense(1, activation='relu', name='output'),\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Convert the keras model using TFLiteConverter.\n",
        "# Keras model converter API uses the default signature automatically.\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Print the signatures from the converted model\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "\n",
        "signatures = interpreter.get_signature_list()\n",
        "print(signatures)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4d30f85",
      "metadata": {
        "id": "e4d30f85"
      },
      "source": [
        "### Concrete 関数から変換"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9e8a742",
      "metadata": {
        "id": "c9e8a742"
      },
      "outputs": [],
      "source": [
        "model = Model()\n",
        "\n",
        "# Convert the concrete functions using TFLiteConverter\n",
        "converter = tf.lite.TFLiteConverter.from_concrete_functions(\n",
        "    [model.encode.get_concrete_function(),\n",
        "     model.decode.get_concrete_function()], model)\n",
        "converter.target_spec.supported_ops = [\n",
        "    tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.\n",
        "    tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.\n",
        "]\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Print the signatures from the converted model\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "signatures = interpreter.get_signature_list()\n",
        "print(signatures)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b5e85934",
      "metadata": {
        "id": "b5e85934"
      },
      "source": [
        "## シグネチャの実行\n",
        "\n",
        "TensorFlow の推論 API は、シグネチャに基づく実行をサポートします。\n",
        "\n",
        "- シグネチャで指定された入出力の名前を使用して、入出力テンソルにアクセスします。\n",
        "- シグネチャキーで指定されたグラフの各入力点を個別に実行します。\n",
        "- SavedModel の初期化手順をサポートします。\n",
        "\n",
        "Java、C++、Python 言語バインディングは現在使用できます。次のセクションの例を参照してください。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ZRBMFciMQmiB",
      "metadata": {
        "id": "ZRBMFciMQmiB"
      },
      "source": [
        "### Java"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04c5a4fc",
      "metadata": {
        "id": "04c5a4fc"
      },
      "source": [
        "```\n",
        "try (Interpreter interpreter = new Interpreter(file_of_tensorflowlite_model)) {\n",
        "  // Run encoding signature.\n",
        "  Map&lt;String, Object&gt; inputs = new HashMap&lt;&gt;();\n",
        "  inputs.put(\"x\", input);\n",
        "  Map&lt;String, Object&gt; outputs = new HashMap&lt;&gt;();\n",
        "  outputs.put(\"encoded_result\", encoded_result);\n",
        "  interpreter.runSignature(inputs, outputs, \"encode\");\n",
        "\n",
        "  // Run decoding signature.\n",
        "  Map&lt;String, Object&gt; inputs = new HashMap&lt;&gt;();\n",
        "  inputs.put(\"x\", encoded_result);\n",
        "  Map&lt;String, Object&gt; outputs = new HashMap&lt;&gt;();\n",
        "  outputs.put(\"decoded_result\", decoded_result);\n",
        "  interpreter.runSignature(inputs, outputs, \"decode\");\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ba86c64",
      "metadata": {
        "id": "5ba86c64"
      },
      "source": [
        "### C++"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "397ad6fd",
      "metadata": {
        "id": "397ad6fd"
      },
      "source": [
        "```\n",
        "SignatureRunner* encode_runner =\n",
        "    interpreter->GetSignatureRunner(\"encode\");\n",
        "encode_runner->ResizeInputTensor(\"x\", {100});\n",
        "encode_runner->AllocateTensors();\n",
        "\n",
        "TfLiteTensor* input_tensor = encode_runner->input_tensor(\"x\");\n",
        "float* input = GetTensorData<float>(input_tensor);\n",
        "// Fill `input`.\n",
        "\n",
        "encode_runner->Invoke();\n",
        "\n",
        "const TfLiteTensor* output_tensor = encode_runner->output_tensor(\n",
        "    \"encoded_result\");\n",
        "float* output = GetTensorData<float>(output_tensor);\n",
        "// Access `output`.\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f4c6ad4",
      "metadata": {
        "id": "0f4c6ad4"
      },
      "source": [
        "### Python"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ab7b1963",
      "metadata": {
        "id": "ab7b1963"
      },
      "outputs": [],
      "source": [
        "# Load the TFLite model in TFLite Interpreter\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "\n",
        "# Print the signatures from the converted model\n",
        "signatures = interpreter.get_signature_list()\n",
        "print('Signature:', signatures)\n",
        "\n",
        "# encode and decode are callable with input as arguments.\n",
        "encode = interpreter.get_signature_runner('encode')\n",
        "decode = interpreter.get_signature_runner('decode')\n",
        "\n",
        "# 'encoded' and 'decoded' are dictionaries with all outputs from the inference.\n",
        "input = tf.constant([1, 2, 3], dtype=tf.float32)\n",
        "print('Input:', input)\n",
        "encoded = encode(x=input)\n",
        "print('Encoded result:', encoded)\n",
        "decoded = decode(x=encoded['encoded_result'])\n",
        "print('Decoded result:', decoded)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "81b42e5b",
      "metadata": {
        "id": "81b42e5b"
      },
      "source": [
        "## 既知の制限\n",
        "\n",
        "- TFLite インタープリタはスレッドの安全を保証しないため、同じインタープリタからのシグネチャランナーは同時に実行されません。\n",
        "- C/iOS/Swift のサポートはまだ提供されていません。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3032Iof6QqmJ",
      "metadata": {
        "id": "3032Iof6QqmJ"
      },
      "source": [
        "## 更新\n",
        "\n",
        "- バージョン 2.7\n",
        "    - 複数のシグネチャ機能が実装されました。\n",
        "    - バージョン 2 以降のすべてのコンバータ API は、シグネチャ対応 TensorFlow Lite モデルを生成します。\n",
        "- バージョン 2.5\n",
        "    - シグネチャ機能は、`from_saved_model` コンバータ API から利用できます。"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "signatures.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}