[examples] flickr fine-tuning notebook

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finetune a Pretrained Network with Flickr Style Data\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network, and finetune the last few layers using your custom data.\n",
+    "\n",
+    "The upside of such approach is that, since pre-trained networks are trained on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/jiayq/Research/caffe\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "print os.getcwd()\n",
+    "\n",
+    "import caffe\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2015-03-17 10:51:07--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
+      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
+      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 17858008 (17M) [application/octet-stream]\n",
+      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
+      "\n",
+      "100%[======================================>] 17,858,008   287KB/s   in 55s    \n",
+      "\n",
+      "2015-03-17 10:52:02 (318 KB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
+      "\n",
+      "Unzipping...\n",
+      "Done.\n",
+      "Model already exists.\n",
+      "Downloading 2000 images with 3 workers...\n",
+      "Writing train/val for 1903 successfully downloaded images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "\n",
+    "# You won't need to run this - we should have already created it for you.\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1 --images=2000 --seed=1701 --label=5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's show what is the difference between the finetune network and the original caffe model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1c1\r\n",
+      "< name: \"CaffeNet\"\r\n",
+      "---\r\n",
+      "> name: \"FlickrStyleCaffeNet\"\r\n",
+      "4c4\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "15,26c15,19\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
+      "<     batch_size: 256\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/train.txt\"\r\n",
+      ">     batch_size: 50\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "31c24\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "42,51c35,36\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/test.txt\"\r\n",
+      "53c38,39\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "323a310\r\n",
+      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
+      "360c347\r\n",
+      "<   name: \"fc8\"\r\n",
+      "---\r\n",
+      ">   name: \"fc8_flickr\"\r\n",
+      "363c350,351\r\n",
+      "<   top: \"fc8\"\r\n",
+      "---\r\n",
+      ">   top: \"fc8_flickr\"\r\n",
+      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
+      "365c353\r\n",
+      "<     lr_mult: 1\r\n",
+      "---\r\n",
+      ">     lr_mult: 10\r\n",
+      "369c357\r\n",
+      "<     lr_mult: 2\r\n",
+      "---\r\n",
+      ">     lr_mult: 20\r\n",
+      "373c361\r\n",
+      "<     num_output: 1000\r\n",
+      "---\r\n",
+      ">     num_output: 20\r\n",
+      "384a373,379\r\n",
+      ">   name: \"loss\"\r\n",
+      ">   type: \"SoftmaxWithLoss\"\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      ">   bottom: \"label\"\r\n",
+      ">   top: \"loss\"\r\n",
+      "> }\r\n",
+      "> layer {\r\n",
+      "387c382\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      "393,399d387\r\n",
+      "< }\r\n",
+      "< layer {\r\n",
+      "<   name: \"loss\"\r\n",
+      "<   type: \"SoftmaxWithLoss\"\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "<   bottom: \"label\"\r\n",
+      "<   top: \"loss\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0, loss=3.786610, scratch_loss=3.163587\n",
+      "iter 10, loss=2.556661, scratch_loss=8.774073\n",
+      "iter 20, loss=2.035326, scratch_loss=2.266603\n",
+      "iter 30, loss=1.943101, scratch_loss=1.703273\n",
+      "iter 40, loss=1.982698, scratch_loss=1.831079\n",
+      "iter 50, loss=1.559268, scratch_loss=2.041238\n",
+      "iter 60, loss=1.464433, scratch_loss=1.836157\n",
+      "iter 70, loss=1.481868, scratch_loss=1.705826\n",
+      "iter 80, loss=1.394870, scratch_loss=1.695532\n",
+      "iter 90, loss=1.055422, scratch_loss=1.867379\n",
+      "iter 100, loss=1.407976, scratch_loss=1.881758\n",
+      "iter 110, loss=1.569579, scratch_loss=1.701803\n",
+      "iter 120, loss=0.951682, scratch_loss=1.764299\n",
+      "iter 130, loss=0.905122, scratch_loss=1.879305\n",
+      "iter 140, loss=1.020678, scratch_loss=1.746009\n",
+      "iter 150, loss=0.784985, scratch_loss=1.739624\n",
+      "iter 160, loss=0.911735, scratch_loss=1.673230\n",
+      "iter 170, loss=0.965255, scratch_loss=1.725484\n",
+      "iter 180, loss=1.028102, scratch_loss=1.676103\n",
+      "iter 190, loss=0.905020, scratch_loss=1.885763\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = np.zeros(niter)\n",
+    "scratch_train_loss = np.zeros(niter)\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "# We create a solver that finetunes from a previously trained network.\n",
+    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "# For reference, we also create a solver that does no finetuning.\n",
+    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "\n",
+    "# We run the solver for niter times, and record the training loss.\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    scratch_solver.step(1)\n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
+    "    if it % 10 == 0:\n",
+    "        print 'iter %d, loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
+    "print 'done'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss produced by the two training procedures respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f39dad72390>,\n",
+       " <matplotlib.lines.Line2D at 0x7f39dad72610>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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+       "WE19PLWP72hd1u9wrI/3GdScEjoYbZ8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIikk//DzX8Jz0M\n",
+       "ra0pAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3a2803d590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f39d50acc90>,\n",
+       " <matplotlib.lines.Line2D at 0x7f39d50acf10>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "8sFfgFQsvTD2fIAe586Qd/z02GbzbWAKYjWsA76Ysawey2RagF8B1wDPxZ7r8PezHgK/FkkkWCYS\n",
+       "PQMp+awz/zcBv0HGGmxALt0AxgIbG7BfzUza8Yt/XyeQPdmyIsfOitD3ke8n6LEsSm9E3G9ELBoo\n",
+       "6ftZD4F/CJhBOFDqteR3nlRCBgCt5vZAJGs+HzmGbzKPv4nwi6EUI+343QpcjnxXpyDf3QcqXq24\n",
+       "jHVuv4LQn9djmY+H2FoLgK84jzfV9zNpoJRSjClI1vwxpIzKHr9hiC+vZZL53Aw8i0yKvBp4C9nH\n",
+       "77+R7+pC4Ly67mnXJ34s3wrcgJTxPo4IkZsP0mOZzVlI65fHCMtMz0e/n4qiKIqiKIqiKIqiKIqi\n",
+       "KIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqidBf+P41gkbjYnj6JAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f39dad22150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for fine-tuning: 0.570000001788\n",
+      "Accuracy for training from scratch: 0.224000000954\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_iters = 10\n",
+    "accuracy = 0\n",
+    "scratch_accuracy = 0\n",
+    "for it in arange(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    scratch_solver.test_nets[0].forward()\n",
+    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "scratch_accuracy /= test_iters\n",
+    "print 'Accuracy for fine-tuning:', accuracy\n",
+    "print 'Accuracy for training from scratch:', scratch_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/finetune_flickr_style/assemble_data.py b/examples/finetune_flickr_style/assemble_data.py
index b4c995e8..09bfa261 100755
--- a/examples/finetune_flickr_style/assemble_data.py
+++ b/examples/finetune_flickr_style/assemble_data.py
@@ -9,6 +9,7 @@ import hashlib
 import argparse
 import numpy as np
 import pandas as pd
+from skimage import io
 import multiprocessing
 
 # Flickr returns a special image if the request is unavailable.
@@ -27,6 +28,7 @@ def download_image(args_tuple):
             urllib.urlretrieve(url, filename)
         with open(filename) as f:
             assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+        test_read_image = io.imread(filename)
         return True
     except KeyboardInterrupt:
         raise Exception()  # multiprocessing doesn't catch keyboard exceptions
@@ -48,6 +50,10 @@ if __name__ == '__main__':
         '-w', '--workers', type=int, default=-1,
         help="num workers used to download images. -x uses (all - x) cores [-1 default]."
     )
+    parser.add_argument(
+        '-l', '--labels', type=int, default=0,
+        help="if set to a positive value, only sample images from the first number of labels."
+    )
 
     args = parser.parse_args()
     np.random.seed(args.seed)
@@ -56,6 +62,8 @@ if __name__ == '__main__':
     csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
     df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
     df = df.iloc[np.random.permutation(df.shape[0])]
+    if args.labels > 0:
+        df = df.loc[df['label'] < args.labels]
     if args.images > 0 and args.images < df.shape[0]:
         df = df.iloc[:args.images]