Merge pull request #8891 from kmindspark:savedmodelcolab

PiperOrigin-RevId: 321899493

research/object_detection/colab_tutorials/inference_from_saved_model_tf2_colab.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "inference_from_saved_model_tf2_colab.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "cT5cdSLPX0ui"
+      },
+      "source": [
+        "# Intro to Object Detection Colab\n",
+        "\n",
+        "Welcome to the object detection colab! This demo will take you through the steps of running an \"out-of-the-box\" detection model in SavedModel format on a collection of images.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "vPs64QA1Zdov"
+      },
+      "source": [
+        "Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OBzb04bdNGM8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!pip install -U --pre tensorflow==\"2.2.0\""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NgSXyvKSNHIl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import os\n",
+        "import pathlib\n",
+        "\n",
+        "# Clone the tensorflow models repository if it doesn't already exist\n",
+        "if \"models\" in pathlib.Path.cwd().parts:\n",
+        "  while \"models\" in pathlib.Path.cwd().parts:\n",
+        "    os.chdir('..')\n",
+        "elif not pathlib.Path('models').exists():\n",
+        "  !git clone --depth 1 https://github.com/tensorflow/models"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rhpPgW7TNLs6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Install the Object Detection API\n",
+        "%%bash\n",
+        "cd models/research/\n",
+        "protoc object_detection/protos/*.proto --python_out=.\n",
+        "cp object_detection/packages/tf2/setup.py .\n",
+        "python -m pip install ."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "yn5_uV1HLvaz",
+        "colab": {}
+      },
+      "source": [
+        "import io\n",
+        "import os\n",
+        "import scipy.misc\n",
+        "import numpy as np\n",
+        "import six\n",
+        "import time\n",
+        "\n",
+        "from six import BytesIO\n",
+        "\n",
+        "import matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "from PIL import Image, ImageDraw, ImageFont\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "from object_detection.utils import visualization_utils as viz_utils\n",
+        "\n",
+        "%matplotlib inline"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "-y9R0Xllefec",
+        "colab": {}
+      },
+      "source": [
+        "def load_image_into_numpy_array(path):\n",
+        "  \"\"\"Load an image from file into a numpy array.\n",
+        "\n",
+        "  Puts image into numpy array to feed into tensorflow graph.\n",
+        "  Note that by convention we put it into a numpy array with shape\n",
+        "  (height, width, channels), where channels=3 for RGB.\n",
+        "\n",
+        "  Args:\n",
+        "    path: a file path (this can be local or on colossus)\n",
+        "\n",
+        "  Returns:\n",
+        "    uint8 numpy array with shape (img_height, img_width, 3)\n",
+        "  \"\"\"\n",
+        "  img_data = tf.io.gfile.GFile(path, 'rb').read()\n",
+        "  image = Image.open(BytesIO(img_data))\n",
+        "  (im_width, im_height) = image.size\n",
+        "  return np.array(image.getdata()).reshape(\n",
+        "      (im_height, im_width, 3)).astype(np.uint8)\n",
+        "\n",
+        "# Load the COCO Label Map\n",
+        "category_index = {\n",
+        "    1: {'id': 1, 'name': 'person'},\n",
+        "    2: {'id': 2, 'name': 'bicycle'},\n",
+        "    3: {'id': 3, 'name': 'car'},\n",
+        "    4: {'id': 4, 'name': 'motorcycle'},\n",
+        "    5: {'id': 5, 'name': 'airplane'},\n",
+        "    6: {'id': 6, 'name': 'bus'},\n",
+        "    7: {'id': 7, 'name': 'train'},\n",
+        "    8: {'id': 8, 'name': 'truck'},\n",
+        "    9: {'id': 9, 'name': 'boat'},\n",
+        "    10: {'id': 10, 'name': 'traffic light'},\n",
+        "    11: {'id': 11, 'name': 'fire hydrant'},\n",
+        "    13: {'id': 13, 'name': 'stop sign'},\n",
+        "    14: {'id': 14, 'name': 'parking meter'},\n",
+        "    15: {'id': 15, 'name': 'bench'},\n",
+        "    16: {'id': 16, 'name': 'bird'},\n",
+        "    17: {'id': 17, 'name': 'cat'},\n",
+        "    18: {'id': 18, 'name': 'dog'},\n",
+        "    19: {'id': 19, 'name': 'horse'},\n",
+        "    20: {'id': 20, 'name': 'sheep'},\n",
+        "    21: {'id': 21, 'name': 'cow'},\n",
+        "    22: {'id': 22, 'name': 'elephant'},\n",
+        "    23: {'id': 23, 'name': 'bear'},\n",
+        "    24: {'id': 24, 'name': 'zebra'},\n",
+        "    25: {'id': 25, 'name': 'giraffe'},\n",
+        "    27: {'id': 27, 'name': 'backpack'},\n",
+        "    28: {'id': 28, 'name': 'umbrella'},\n",
+        "    31: {'id': 31, 'name': 'handbag'},\n",
+        "    32: {'id': 32, 'name': 'tie'},\n",
+        "    33: {'id': 33, 'name': 'suitcase'},\n",
+        "    34: {'id': 34, 'name': 'frisbee'},\n",
+        "    35: {'id': 35, 'name': 'skis'},\n",
+        "    36: {'id': 36, 'name': 'snowboard'},\n",
+        "    37: {'id': 37, 'name': 'sports ball'},\n",
+        "    38: {'id': 38, 'name': 'kite'},\n",
+        "    39: {'id': 39, 'name': 'baseball bat'},\n",
+        "    40: {'id': 40, 'name': 'baseball glove'},\n",
+        "    41: {'id': 41, 'name': 'skateboard'},\n",
+        "    42: {'id': 42, 'name': 'surfboard'},\n",
+        "    43: {'id': 43, 'name': 'tennis racket'},\n",
+        "    44: {'id': 44, 'name': 'bottle'},\n",
+        "    46: {'id': 46, 'name': 'wine glass'},\n",
+        "    47: {'id': 47, 'name': 'cup'},\n",
+        "    48: {'id': 48, 'name': 'fork'},\n",
+        "    49: {'id': 49, 'name': 'knife'},\n",
+        "    50: {'id': 50, 'name': 'spoon'},\n",
+        "    51: {'id': 51, 'name': 'bowl'},\n",
+        "    52: {'id': 52, 'name': 'banana'},\n",
+        "    53: {'id': 53, 'name': 'apple'},\n",
+        "    54: {'id': 54, 'name': 'sandwich'},\n",
+        "    55: {'id': 55, 'name': 'orange'},\n",
+        "    56: {'id': 56, 'name': 'broccoli'},\n",
+        "    57: {'id': 57, 'name': 'carrot'},\n",
+        "    58: {'id': 58, 'name': 'hot dog'},\n",
+        "    59: {'id': 59, 'name': 'pizza'},\n",
+        "    60: {'id': 60, 'name': 'donut'},\n",
+        "    61: {'id': 61, 'name': 'cake'},\n",
+        "    62: {'id': 62, 'name': 'chair'},\n",
+        "    63: {'id': 63, 'name': 'couch'},\n",
+        "    64: {'id': 64, 'name': 'potted plant'},\n",
+        "    65: {'id': 65, 'name': 'bed'},\n",
+        "    67: {'id': 67, 'name': 'dining table'},\n",
+        "    70: {'id': 70, 'name': 'toilet'},\n",
+        "    72: {'id': 72, 'name': 'tv'},\n",
+        "    73: {'id': 73, 'name': 'laptop'},\n",
+        "    74: {'id': 74, 'name': 'mouse'},\n",
+        "    75: {'id': 75, 'name': 'remote'},\n",
+        "    76: {'id': 76, 'name': 'keyboard'},\n",
+        "    77: {'id': 77, 'name': 'cell phone'},\n",
+        "    78: {'id': 78, 'name': 'microwave'},\n",
+        "    79: {'id': 79, 'name': 'oven'},\n",
+        "    80: {'id': 80, 'name': 'toaster'},\n",
+        "    81: {'id': 81, 'name': 'sink'},\n",
+        "    82: {'id': 82, 'name': 'refrigerator'},\n",
+        "    84: {'id': 84, 'name': 'book'},\n",
+        "    85: {'id': 85, 'name': 'clock'},\n",
+        "    86: {'id': 86, 'name': 'vase'},\n",
+        "    87: {'id': 87, 'name': 'scissors'},\n",
+        "    88: {'id': 88, 'name': 'teddy bear'},\n",
+        "    89: {'id': 89, 'name': 'hair drier'},\n",
+        "    90: {'id': 90, 'name': 'toothbrush'},\n",
+        "}"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QwcBC2TlPSwg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Download the saved model and put it into models/research/object_detection/test_data/\n",
+        "!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d5_coco17_tpu-32.tar.gz\n",
+        "!tar -xf efficientdet_d5_coco17_tpu-32.tar.gz\n",
+        "!mv efficientdet_d5_coco17_tpu-32/ models/research/object_detection/test_data/"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "Z2p-PmKLYCVU",
+        "colab": {}
+      },
+      "source": [
+        "start_time = time.time()\n",
+        "tf.keras.backend.clear_session()\n",
+        "detect_fn = tf.saved_model.load('models/research/object_detection/test_data/efficientdet_d5_coco17_tpu-32/saved_model/')\n",
+        "end_time = time.time()\n",
+        "elapsed_time = end_time - start_time\n",
+        "print('Elapsed time: ' + str(elapsed_time) + 's')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "vukkhd5-9NSL",
+        "colab": {}
+      },
+      "source": [
+        "import time\n",
+        "\n",
+        "image_dir = 'models/research/object_detection/test_images'\n",
+        "\n",
+        "elapsed = []\n",
+        "for i in range(2):\n",
+        "  image_path = os.path.join(image_dir, 'image' + str(i + 1) + '.jpg')\n",
+        "  image_np = load_image_into_numpy_array(image_path)\n",
+        "  input_tensor = np.expand_dims(image_np, 0)\n",
+        "  start_time = time.time()\n",
+        "  detections = detect_fn(input_tensor)\n",
+        "  end_time = time.time()\n",
+        "  elapsed.append(end_time - start_time)\n",
+        "\n",
+        "  plt.rcParams['figure.figsize'] = [42, 21]\n",
+        "  label_id_offset = 1\n",
+        "  image_np_with_detections = image_np.copy()\n",
+        "  viz_utils.visualize_boxes_and_labels_on_image_array(\n",
+        "        image_np_with_detections,\n",
+        "        detections['detection_boxes'][0].numpy(),\n",
+        "        detections['detection_classes'][0].numpy().astype(np.int32),\n",
+        "        detections['detection_scores'][0].numpy(),\n",
+        "        category_index,\n",
+        "        use_normalized_coordinates=True,\n",
+        "        max_boxes_to_draw=200,\n",
+        "        min_score_thresh=.40,\n",
+        "        agnostic_mode=False)\n",
+        "  plt.subplot(2, 1, i+1)\n",
+        "  plt.imshow(image_np_with_detections)\n",
+        "\n",
+        "mean_elapsed = sum(elapsed) / float(len(elapsed))\n",
+        "print('Elapsed time: ' + str(mean_elapsed) + ' second per image')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file