mxnet/02 - Classify images with pre-trained models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying images with pre-trained Apache MXNet models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's download three image classification models from the Apache MXNet [model zoo](http://mxnet.io/model_zoo/).\n",
    "* **VGG-16**: the 2014 classification winner at the [ImageNet Large Scale Visual Recognition Challenge](http://image-net.org/challenges/LSVRC).\n",
    "* **Inception v3**, an evolution of GoogleNet, the 2014 winner for object detection.\n",
    "* **ResNet-152**, the 2015 winner in multiple categories.\n",
    "\n",
    "Why would we want to try multiple models? Why don't we simply pick the one with the best accuracy? As we will see later on, even though these models have been trained on the same data set and optimized for maximum accuracy, they do behave slightly differently on **specific images**: maybe one of the models them will actually do a better job at solving your business problem. **Prediction speed** can vary a lot as well and that's an important factor for many applications.\n",
    "\n",
    "For each model, we need to download two files:\n",
    "* the **symbol** file containing the JSON definition of the neural network: layers, connections, activation functions, etc.\n",
    "* the **weights** file storing values for all neuron weights, a.k.a. parameters, learned by the network during the training phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!wget http://data.dmlc.ml/models/imagenet/vgg/vgg16-symbol.json -O vgg16-symbol.json\n",
    "!wget http://data.dmlc.ml/models/imagenet/vgg/vgg16-0000.params -O vgg16-0000.params\n",
    "!wget http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json -O Inception-BN-symbol.json\n",
    "!wget http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-0126.params -O Inception-BN-0000.params\n",
    "!wget http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-symbol.json -O resnet-152-symbol.json\n",
    "!wget http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-0000.params -O resnet-152-0000.params\n",
    "!wget http://data.dmlc.ml/models/imagenet/synset.txt -O synset.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the first lines of VGG-16 symbol file. We can see the definition of the input layer ('data'), the input weights and the biases for the first convolution layer. A convolution operation is defined ('conv1_1') as well as a Rectified Linear Unit activation function ('relu1_1')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!head -48 vgg16-symbol.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three models have been pre-trained on the ImageNet data set which includes over 1.2 million pictures of objects and animals sorted in 1,000 categories. We can view these categories in the synset.txt file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n01440764 tench, Tinca tinca\r\n",
      "n01443537 goldfish, Carassius auratus\r\n",
      "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\r\n",
      "n01491361 tiger shark, Galeocerdo cuvieri\r\n",
      "n01494475 hammerhead, hammerhead shark\r\n",
      "n01496331 electric ray, crampfish, numbfish, torpedo\r\n",
      "n01498041 stingray\r\n",
      "n01514668 cock\r\n",
      "n01514859 hen\r\n",
      "n01518878 ostrich, Struthio camelus\r\n"
     ]
    }
   ],
   "source": [
    "!head -10 synset.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MXNet version: 0.12.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/urllib3/contrib/pyopenssl.py:46: DeprecationWarning: OpenSSL.rand is deprecated - you should use os.urandom instead\n",
      "  import OpenSSL.SSL\n"
     ]
    }
   ],
   "source": [
    "import mxnet as mx\n",
    "import numpy as np\n",
    "import cv2,sys,time\n",
    "from collections import namedtuple\n",
    "from IPython.core.display import Image, display\n",
    "\n",
    "print(\"MXNet version: %s\" % mx.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now, let's load a model.\n",
    "\n",
    "First, we have to load the **weights** and **model description** from file. MXNet calls this a **checkpoint**: indeed, it's good practice to save weights after each training epoch. Once training is complete, we can look at the training log and pick the weights for the best epoch, i.e. the one with the highest validation accuracy: it's quite likely it won't be the very last one!\n",
    "\n",
    "Once loading is complete, we get a *Symbol* object and the weights, a.k.a model parameters. We then create a new *Module* and assign it the input *Symbol*. We could select the *context* where we want to run the model: the default behavior is to use a CPU context. There are two reasons for this:\n",
    "* first, this will allow you to test the notebook even if your machine is not equipped with a GPU :)\n",
    "* second, we're going to predict a single image and we don't have any specific performance requirements. For production applications where you'd want to predict large batches of images with the best possible throughput, a GPU would definitely be the way to go.\n",
    "\n",
    "Then, we bind the input *Symbol* to input data: we have to call it ‘data’ because that’s its name in the **input layer** of the network (remember the first few lines of the JSON file).\n",
    "\n",
    "Finally, we define the **shape** of ‘data’ as 1 x 3 x 224 x 224. ‘224 x 224’ is the image resolution, that’s how the model was trained. ‘3’ is the number of channels : red, green and blue (in this order). ‘1’ is the batch size: we’ll predict one image at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadModel(modelname, gpu=False):\n",
    "        sym, arg_params, aux_params = mx.model.load_checkpoint(modelname, 0)\n",
    "        arg_params['prob_label'] = mx.nd.array([0])\n",
    "        arg_params['softmax_label'] = mx.nd.array([0])\n",
    "        if gpu:\n",
    "            mod = mx.mod.Module(symbol=sym, context=mx.gpu(0))\n",
    "        else:\n",
    "            mod = mx.mod.Module(symbol=sym)\n",
    "        mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])\n",
    "        mod.set_params(arg_params, aux_params)\n",
    "        return mod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to load the 1,000 categories stored in the synset.txt file. We'll need the actual descriptions at prediction time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['n01440764 tench, Tinca tinca', 'n01443537 goldfish, Carassius auratus', 'n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 'n01491361 tiger shark, Galeocerdo cuvieri', 'n01494475 hammerhead, hammerhead shark', 'n01496331 electric ray, crampfish, numbfish, torpedo', 'n01498041 stingray', 'n01514668 cock', 'n01514859 hen', 'n01518878 ostrich, Struthio camelus']\n"
     ]
    }
   ],
   "source": [
    "def loadCategories():\n",
    "        synsetfile = open('synset.txt', 'r')\n",
    "        synsets = []\n",
    "        for l in synsetfile:\n",
    "                synsets.append(l.rstrip())\n",
    "        return synsets\n",
    "    \n",
    "synsets = loadCategories()\n",
    "print(synsets[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's write a function to load an image from file. Remember that the model expects a 4-dimension *NDArray* holding the red, green and blue channels of a single 224 x 224 image. We’re going to use the **OpenCV** library to build this *NDArray* from our input image.\n",
    "\n",
    "Here are the steps:\n",
    "* read the image: this will return a **numpy array** shaped as (image height, image width, 3), with the three channels in **BGR** order (blue, green and red).\n",
    "* convert the image to **RGB**.\n",
    "* resize the image to **224 x 224**.\n",
    "* **reshape** the array from (image height, image width, 3) to (3, image height, image width).\n",
    "* add a **fourth dimension** and build the *NDArray*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepareNDArray(filename):\n",
    "        img = cv2.imread(filename)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        img = cv2.resize(img, (224, 224,))\n",
    "        img = np.swapaxes(img, 0, 2)\n",
    "        img = np.swapaxes(img, 1, 2)\n",
    "        img = img[np.newaxis, :]\n",
    "        array = mx.nd.array(img)\n",
    "        print(array.shape)\n",
    "        return array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take care of prediction. Our parameters are an image, a model, a list of categories and the number of top categories we'd like to return. \n",
    "\n",
    "Remember that a *Module* object must feed data to a model in **batches**: the common way to do this is to use a **data iterator**. Here, we’d like to predict a single image, so although we could use a data iterator, it’d probably be overkill. Instead, let's create a named tuple, called *Batch*, which will act as a fake iterator by returning our input *NDArray* when its 'data' attribute is referenced.\n",
    "\n",
    "Once the image has been forwarded, the model outputs an *NDArray* holding **1,000 probabilities**, corresponding to the 1,000 categories it has been trained on: the *NDArray* has only one line since batch size is equal to 1. \n",
    "\n",
    "Let’s turn this into an array with *squeeze()*. Then, using *argsort()*, we create a second array holding the **index** of these probabilities sorted in **descending order**. Finally, we return the top n categories and their description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(filename, model, categories, n):\n",
    "        array = prepareNDArray(filename)\n",
    "        Batch = namedtuple('Batch', ['data'])\n",
    "        t1 = time.time()\n",
    "        model.forward(Batch([array]))\n",
    "        prob = model.get_outputs()[0].asnumpy()\n",
    "        t2 = time.time()\n",
    "        print(\"Predicted in %.2f microseconds\" % (t2-t1))\n",
    "        prob = np.squeeze(prob)\n",
    "        sortedprobindex = np.argsort(prob)[::-1]\n",
    "        \n",
    "        topn = []\n",
    "        for i in sortedprobindex[0:n]:\n",
    "                topn.append((prob[i], categories[i]))\n",
    "        return topn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to put everything together. Let's load all three models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/module/base_module.py:53: UserWarning: \u001b[91mYou created Module with Module(..., label_names=['softmax_label']) but input with name 'softmax_label' is not found in symbol.list_arguments(). Did you mean one of:\n",
      "\tdata\n",
      "\tprob_label\u001b[0m\n",
      "  warnings.warn(msg)\n",
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/module/base_module.py:65: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "def init(modelname, gpu=False):\n",
    "        model = loadModel(modelname,gpu)\n",
    "        categories = loadCategories()\n",
    "        return model, categories\n",
    "\n",
    "vgg16,categories = init(\"vgg16\")\n",
    "resnet152,categories = init(\"resnet-152\")\n",
    "inceptionv3,categories = init(\"Inception-BN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before classifying images, let's take a closer look to some of the VGG-16 **parameters** we just loaded from the '.params' file. First, let's print the names of all **layers**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = vgg16.get_params()\n",
    "\n",
    "layers = []\n",
    "for layer in params[0].keys():\n",
    "    layers.append(layer)\n",
    "    \n",
    "layers.sort()    \n",
    "print(layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each layer, we see two components: the weights and the biases. Count the weights and you'll see that there are **sixteen** layers: thirteen convolutional layers and three fully connected layers. Now you know why this model is called **VGG-16** :)\n",
    "\n",
    "Now let's print the weights for the last fully connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(params[0]['fc8_weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did you notice the **shape** of this matrix? **1000x4096**. This layer contains **1,000 neurons**: each of them will store the **probability** of the image belonging to a specific category. Each neuron is also fully connected to all **4,096 neurons** in the previous layer ('fc7')."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, enough exploring! Now let's use these models to classify our own images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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fR6GFNpN5AOUcg+q/15qD7HMPm8sg+hpRqtbO421ZshaCRWw2QPcZ5qjcx3Aa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\n7smhuopNNvre5TckkZBBVl6EEHFWpOE4yvq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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** VGG16\n",
      "(1, 3, 224, 224)\n",
      "Predicted in 0.63 microseconds\n",
      "[(0.96909326, 'n04536866 violin, fiddle'), (0.026661821, 'n02992211 cello, violoncello'), (0.0017283971, 'n02879718 bow'), (0.00056815607, 'n04517823 vacuum, vacuum cleaner'), (0.00024804618, 'n04090263 rifle')]\n",
      "*** ResNet-152\n",
      "(1, 3, 224, 224)\n",
      "Predicted in 0.33 microseconds\n",
      "[(0.96826923, 'n04536866 violin, fiddle'), (0.028052662, 'n02992211 cello, violoncello'), (0.0008367768, 'n02676566 acoustic guitar'), (0.00070531579, 'n02787622 banjo'), (0.0003902076, 'n02879718 bow')]\n",
      "*** Inception v3\n",
      "(1, 3, 224, 224)\n",
      "Predicted in 0.09 microseconds\n",
      "[(0.82023555, 'n04536866 violin, fiddle'), (0.15484105, 'n02992211 cello, violoncello'), (0.0044540642, 'n02676566 acoustic guitar'), (0.002096354, 'n02879718 bow'), (0.0015099689, 'n03447721 gong, tam-tam')]\n"
     ]
    }
   ],
   "source": [
    "image = \"violin.jpg\"\n",
    "\n",
    "display(Image(filename=image))\n",
    "\n",
    "topn = 5\n",
    "print (\"*** VGG16\")\n",
    "print (predict(image,vgg16,categories,topn))\n",
    "print (\"*** ResNet-152\")\n",
    "print (predict(image,resnet152,categories,topn))\n",
    "print (\"*** Inception v3\")\n",
    "print (predict(image,inceptionv3,categories,topn))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's try again with a **GPU context** this time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/module/base_module.py:53: UserWarning: \u001b[91mYou created Module with Module(..., label_names=['softmax_label']) but input with name 'softmax_label' is not found in symbol.list_arguments(). Did you mean one of:\n",
      "\tdata\n",
      "\tprob_label\u001b[0m\n",
      "  warnings.warn(msg)\n",
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/module/base_module.py:65: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** VGG16\n",
      "(1, 3, 224, 224)\n",
      "Predicted in 0.03 microseconds\n",
      "[(0.96909273, 'n04536866 violin, fiddle'), (0.02666183, 'n02992211 cello, violoncello'), (0.0017283944, 'n02879718 bow'), (0.00056815357, 'n04517823 vacuum, vacuum cleaner'), (0.00024804604, 'n04090263 rifle')]\n",
      "*** ResNet-152\n",
      "(1, 3, 224, 224)\n",
      "Predicted in 0.05 microseconds\n",
      "[(0.96826833, 'n04536866 violin, fiddle'), (0.028052796, 'n02992211 cello, violoncello'), (0.0008367768, 'n02676566 acoustic guitar'), (0.00070531643, 'n02787622 banjo'), (0.00039020577, 'n02879718 bow')]\n",
      "*** Inception v3\n",
      "(1, 3, 224, 224)\n",
      "Predicted in 0.02 microseconds\n",
      "[(0.82023585, 'n04536866 violin, fiddle'), (0.15484066, 'n02992211 cello, violoncello'), (0.0044540279, 'n02676566 acoustic guitar'), (0.0020963568, 'n02879718 bow'), (0.0015099607, 'n03447721 gong, tam-tam')]\n"
     ]
    }
   ],
   "source": [
    "vgg16,categories = init(\"vgg16\", gpu=True)\n",
    "resnet152,categories = init(\"resnet-152\", gpu=True)\n",
    "inceptionv3,categories = init(\"Inception-BN\", gpu=True)\n",
    "\n",
    "print (\"*** VGG16\")\n",
    "print (predict(image,vgg16,categories,topn))\n",
    "print (\"*** ResNet-152\")\n",
    "print (predict(image,resnet152,categories,topn))\n",
    "print (\"*** Inception v3\")\n",
    "print (predict(image,inceptionv3,categories,topn))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***If you get an error about GPU support, either your machine or instance is not equipped with a GPU or you're using a version of MXNet that hasn't been built with GPU support (USE_CUDA=1)***\n",
    "\n",
    "The difference in performance is quite noticeable: between **15x** and **20x**. If we predicted **multiple images** at the same time, the gap would widen even more due to the **massive parallelism** of GPU architectures.\n",
    "\n",
    "Now it's time to try your **own images**. Just copy them in the same folder as this notebook, update the filename in the cell above and run the predict() calls again.\n",
    "\n",
    "Have fun with pre-trained models!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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