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Commit 0f1e7911 authored by 黄璞's avatar 黄璞 Committed by Sergio Guadarrama
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Make slim_walkthrough ipython notebook python3 compatible (#1612) 

* make slim_walkthrough ipython notebook python3 compatible

* make slim_walkthrough ipython notebook python3 compatible
parent 1b2c67af
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slim/slim_walkthrough.ipynb
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...@@ -29,11 +29,14 @@ ...@@ -29,11 +29,14 @@
"## Installation and setup\n", "## Installation and setup\n",
"<a id='Install'></a>\n", "<a id='Install'></a>\n",
"\n", "\n",
"As of 8/28/16, the latest stable release of TF is r0.10, which does not contain the latest version of slim.\n", "Since the stable release of TF 1.0, the latest version of slim has been available as `tf.contrib.slim`.\n",
"To obtain the latest version of TF-Slim, please install the most recent nightly build of TF\n", "To test that your installation is working, execute the following command; it should run without raising any errors.\n",
"as explained [here](https://github.com/tensorflow/models/tree/master/slim#installing-latest-version-of-tf-slim).\n",
"\n", "\n",
"To use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from [here](https://github.com/tensorflow/models/tree/master/slim). Let's suppose you install this into a directory called TF_MODELS. Then you should change directory to TF_MODELS/slim **before** running this notebook, so that these files are in your python path.\n", "```\n",
"python -c \"import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once\"\n",
"```\n",
"\n",
"Although, to use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from [here](https://github.com/tensorflow/models/tree/master/slim). Let's suppose you install this into a directory called TF_MODELS. Then you should change directory to TF_MODELS/slim **before** running this notebook, so that these files are in your python path.\n",
"\n", "\n",
"To check you've got these two steps to work, just execute the cell below. If it complains about unknown modules, restart the notebook after moving to the TF-Slim models directory.\n" "To check you've got these two steps to work, just execute the cell below. If it complains about unknown modules, restart the notebook after moving to the TF-Slim models directory.\n"
] ]
...@@ -42,10 +45,14 @@ ...@@ -42,10 +45,14 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import matplotlib\n", "import matplotlib\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
...@@ -57,7 +64,7 @@ ...@@ -57,7 +64,7 @@
"from datasets import dataset_utils\n", "from datasets import dataset_utils\n",
"\n", "\n",
"# Main slim library\n", "# Main slim library\n",
"slim = tf.contrib.slim" "from tensorflow.contrib import slim"
] ]
}, },
{ {
...@@ -143,7 +150,7 @@ ...@@ -143,7 +150,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -156,15 +163,15 @@ ...@@ -156,15 +163,15 @@
" predictions, end_points = regression_model(inputs)\n", " predictions, end_points = regression_model(inputs)\n",
"\n", "\n",
" # Print name and shape of each tensor.\n", " # Print name and shape of each tensor.\n",
" print \"Layers\"\n", " print(\"Layers\")\n",
" for k, v in end_points.items():\n", " for k, v in end_points.items():\n",
" print 'name = {}, shape = {}'.format(v.name, v.get_shape())\n", " print('name = {}, shape = {}'.format(v.name, v.get_shape()))\n",
"\n", "\n",
" # Print name and shape of parameter nodes (values not yet initialized)\n", " # Print name and shape of parameter nodes (values not yet initialized)\n",
" print \"\\n\"\n", " print(\"\\n\")\n",
" print \"Parameters\"\n", " print(\"Parameters\")\n",
" for v in slim.get_model_variables():\n", " for v in slim.get_model_variables():\n",
" print 'name = {}, shape = {}'.format(v.name, v.get_shape())\n" " print('name = {}, shape = {}'.format(v.name, v.get_shape()))\n"
] ]
}, },
{ {
...@@ -180,7 +187,7 @@ ...@@ -180,7 +187,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -228,7 +235,7 @@ ...@@ -228,7 +235,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -280,7 +287,7 @@ ...@@ -280,7 +287,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -330,7 +337,7 @@ ...@@ -330,7 +337,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -367,7 +374,7 @@ ...@@ -367,7 +374,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -441,7 +448,7 @@ ...@@ -441,7 +448,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -468,14 +475,14 @@ ...@@ -468,14 +475,14 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from datasets import flowers\n", "from datasets import flowers\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"\n", "\n",
"slim = tf.contrib.slim\n", "from tensorflow.contrib import slim\n",
"\n", "\n",
"with tf.Graph().as_default(): \n", "with tf.Graph().as_default(): \n",
" dataset = flowers.get_split('train', flowers_data_dir)\n", " dataset = flowers.get_split('train', flowers_data_dir)\n",
...@@ -485,7 +492,7 @@ ...@@ -485,7 +492,7 @@
" \n", " \n",
" with tf.Session() as sess: \n", " with tf.Session() as sess: \n",
" with slim.queues.QueueRunners(sess):\n", " with slim.queues.QueueRunners(sess):\n",
" for i in xrange(4):\n", " for i in range(4):\n",
" np_image, np_label = sess.run([image, label])\n", " np_image, np_label = sess.run([image, label])\n",
" height, width, _ = np_image.shape\n", " height, width, _ = np_image.shape\n",
" class_name = name = dataset.labels_to_names[np_label]\n", " class_name = name = dataset.labels_to_names[np_label]\n",
...@@ -547,7 +554,7 @@ ...@@ -547,7 +554,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -599,14 +606,14 @@ ...@@ -599,14 +606,14 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from preprocessing import inception_preprocessing\n", "from preprocessing import inception_preprocessing\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"\n", "\n",
"slim = tf.contrib.slim\n", "from tensorflow.contrib import slim\n",
"\n", "\n",
"\n", "\n",
"def load_batch(dataset, batch_size=32, height=299, width=299, is_training=False):\n", "def load_batch(dataset, batch_size=32, height=299, width=299, is_training=False):\n",
...@@ -651,7 +658,7 @@ ...@@ -651,7 +658,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -706,7 +713,7 @@ ...@@ -706,7 +713,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -771,7 +778,7 @@ ...@@ -771,7 +778,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -802,26 +809,30 @@ ...@@ -802,26 +809,30 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"import urllib2\n", "\n",
"try:\n",
" import urllib2\n",
"except ImportError:\n",
" import urllib.request as urllib\n",
"\n", "\n",
"from datasets import imagenet\n", "from datasets import imagenet\n",
"from nets import inception\n", "from nets import inception\n",
"from preprocessing import inception_preprocessing\n", "from preprocessing import inception_preprocessing\n",
"\n", "\n",
"slim = tf.contrib.slim\n", "from tensorflow.contrib import slim\n",
"\n", "\n",
"image_size = inception.inception_v1.default_image_size\n", "image_size = inception.inception_v1.default_image_size\n",
"\n", "\n",
"with tf.Graph().as_default():\n", "with tf.Graph().as_default():\n",
" url = 'https://upload.wikimedia.org/wikipedia/commons/7/70/EnglishCockerSpaniel_simon.jpg'\n", " url = 'https://upload.wikimedia.org/wikipedia/commons/7/70/EnglishCockerSpaniel_simon.jpg'\n",
" image_string = urllib2.urlopen(url).read()\n", " image_string = urllib.urlopen(url).read()\n",
" image = tf.image.decode_jpeg(image_string, channels=3)\n", " image = tf.image.decode_jpeg(image_string, channels=3)\n",
" processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n", " processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n",
" processed_images = tf.expand_dims(processed_image, 0)\n", " processed_images = tf.expand_dims(processed_image, 0)\n",
...@@ -902,19 +913,23 @@ ...@@ -902,19 +913,23 @@
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"import urllib2\n", "\n",
"try:\n",
" import urllib2\n",
"except ImportError:\n",
" import urllib.request as urllib\n",
"\n", "\n",
"from datasets import imagenet\n", "from datasets import imagenet\n",
"from nets import vgg\n", "from nets import vgg\n",
"from preprocessing import vgg_preprocessing\n", "from preprocessing import vgg_preprocessing\n",
"\n", "\n",
"slim = tf.contrib.slim\n", "from tensorflow.contrib import slim\n",
"\n", "\n",
"image_size = vgg.vgg_16.default_image_size\n", "image_size = vgg.vgg_16.default_image_size\n",
"\n", "\n",
"with tf.Graph().as_default():\n", "with tf.Graph().as_default():\n",
" url = 'https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg'\n", " url = 'https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg'\n",
" image_string = urllib2.urlopen(url).read()\n", " image_string = urllib.urlopen(url).read()\n",
" image = tf.image.decode_jpeg(image_string, channels=3)\n", " image = tf.image.decode_jpeg(image_string, channels=3)\n",
" processed_image = vgg_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n", " processed_image = vgg_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n",
" processed_images = tf.expand_dims(processed_image, 0)\n", " processed_images = tf.expand_dims(processed_image, 0)\n",
...@@ -960,7 +975,7 @@ ...@@ -960,7 +975,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -972,7 +987,7 @@ ...@@ -972,7 +987,7 @@
"from nets import inception\n", "from nets import inception\n",
"from preprocessing import inception_preprocessing\n", "from preprocessing import inception_preprocessing\n",
"\n", "\n",
"slim = tf.contrib.slim\n", "from tensorflow.contrib import slim\n",
"image_size = inception.inception_v1.default_image_size\n", "image_size = inception.inception_v1.default_image_size\n",
"\n", "\n",
"\n", "\n",
...@@ -1043,7 +1058,7 @@ ...@@ -1043,7 +1058,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": false "collapsed": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -1052,7 +1067,7 @@ ...@@ -1052,7 +1067,7 @@
"from datasets import flowers\n", "from datasets import flowers\n",
"from nets import inception\n", "from nets import inception\n",
"\n", "\n",
"slim = tf.contrib.slim\n", "from tensorflow.contrib import slim\n",
"\n", "\n",
"image_size = inception.inception_v1.default_image_size\n", "image_size = inception.inception_v1.default_image_size\n",
"batch_size = 3\n", "batch_size = 3\n",
...@@ -1080,7 +1095,7 @@ ...@@ -1080,7 +1095,7 @@
" init_fn(sess)\n", " init_fn(sess)\n",
" np_probabilities, np_images_raw, np_labels = sess.run([probabilities, images_raw, labels])\n", " np_probabilities, np_images_raw, np_labels = sess.run([probabilities, images_raw, labels])\n",
" \n", " \n",
" for i in xrange(batch_size): \n", " for i in range(batch_size): \n",
" image = np_images_raw[i, :, :, :]\n", " image = np_images_raw[i, :, :, :]\n",
" true_label = np_labels[i]\n", " true_label = np_labels[i]\n",
" predicted_label = np.argmax(np_probabilities[i, :])\n", " predicted_label = np.argmax(np_probabilities[i, :])\n",
...@@ -1093,27 +1108,36 @@ ...@@ -1093,27 +1108,36 @@
" plt.axis('off')\n", " plt.axis('off')\n",
" plt.show()" " plt.show()"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 2", "display_name": "Python 3",
"language": "python", "language": "python",
"name": "python2" "name": "python3"
}, },
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
"name": "ipython", "name": "ipython",
"version": 2 "version": 3
}, },
"file_extension": ".py", "file_extension": ".py",
"mimetype": "text/x-python", "mimetype": "text/x-python",
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython2", "pygments_lexer": "ipython3",
"version": "2.7.11" "version": "3.6.1"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 0 "nbformat_minor": 1
} }
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