Generating fc7-layer codes from one-hot prob-layer activations.

fc7 code generation.ipynb

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   "cell_type": "code",
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   "metadata": {
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    "colab_type": "code",
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    "id": "i9hkSm1IOZNR"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: 1505280 = 10x3x224x224\n",
      "conv1_1: 32112640 = 10x64x224x224\n",
      "conv1_2: 32112640 = 10x64x224x224\n",
      "pool1: 8028160 = 10x64x112x112\n",
      "conv2_1: 16056320 = 10x128x112x112\n",
      "conv2_2: 16056320 = 10x128x112x112\n",
      "pool2: 4014080 = 10x128x56x56\n",
      "conv3_1: 8028160 = 10x256x56x56\n",
      "conv3_2: 8028160 = 10x256x56x56\n",
      "conv3_3: 8028160 = 10x256x56x56\n",
      "conv3_4: 8028160 = 10x256x56x56\n",
      "pool3: 2007040 = 10x256x28x28\n",
      "conv4_1: 4014080 = 10x512x28x28\n",
      "conv4_2: 4014080 = 10x512x28x28\n",
      "conv4_3: 4014080 = 10x512x28x28\n",
      "conv4_4: 4014080 = 10x512x28x28\n",
      "pool4: 1003520 = 10x512x14x14\n",
      "conv5_1: 1003520 = 10x512x14x14\n",
      "conv5_2: 1003520 = 10x512x14x14\n",
      "conv5_3: 1003520 = 10x512x14x14\n",
      "conv5_4: 1003520 = 10x512x14x14\n",
      "pool5: 250880 = 10x512x7x7\n",
      "fc6: 40960 = 10x4096x1x1\n",
      "fc6_fc6_0_split_0: 40960 = 10x4096x1x1\n",
      "fc6_fc6_0_split_1: 40960 = 10x4096x1x1\n",
      "fc6_fc6_0_split_2: 40960 = 10x4096x1x1\n",
      "relu6: 40960 = 10x4096x1x1\n",
      "drop6: 40960 = 10x4096x1x1\n",
      "fc7: 40960 = 10x4096x1x1\n",
      "relu7: 40960 = 10x4096x1x1\n",
      "drop7: 40960 = 10x4096x1x1\n",
      "fc8: 10000 = 10x1000x1x1\n",
      "prob: 10000 = 10x1000x1x1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from google.protobuf import text_format\n",
    "import caffe\n",
    "\n",
    "# load network\n",
    "# the prototxt has force_backward: true, and fc7 is separated into multiple blobs\n",
    "model_name = 'vgg_ilsvrc_19'\n",
    "model_path = '../caffe/models/' + model_name + '/'\n",
    "net_fn = model_path + 'deploy-expanded.prototxt'\n",
    "param_fn = model_path + 'net.caffemodel'\n",
    "net = caffe.Classifier(net_fn, param_fn)\n",
    "\n",
    "# print blob names and sizes\n",
    "for end in net.blobs.keys():\n",
    "    cur = net.blobs[end]\n",
    "    print end + ': {} = {}x{}x{}x{}'.format(cur.count, cur.num, cur.channels, cur.width, cur.height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def optimize(net,\n",
    "             hot=0,\n",
    "             step_size=.01,\n",
    "             iter_n=100,\n",
    "             mu=.9,\n",
    "             basename='fc7',\n",
    "             start='relu7',\n",
    "             end='prob'):\n",
    "    base = net.blobs[basename]\n",
    "    first = net.blobs[start]\n",
    "    last = net.blobs[end]\n",
    "    base.data[0] = np.random.normal(.5, .1, base.data[0].shape)\n",
    "    base.diff[0] = 0.\n",
    "    velocity = np.zeros_like(base.data[0])\n",
    "    velocity_previous = np.zeros_like(base.data[0])\n",
    "    for i in range(iter_n):\n",
    "        net.forward(start=start, end=end)\n",
    "        target = np.zeros_like(last.data[0])\n",
    "        target.flat[hot] = 1.\n",
    "        error = target - last.data[0]\n",
    "        last.diff[0] = error\n",
    "        net.backward(start=end, end=start)\n",
    "        grad = base.diff[0]\n",
    "        learning_rate = (step_size / np.abs(grad).mean())\n",
    "        velocity_previous = velocity\n",
    "        velocity = mu * velocity + learning_rate * grad\n",
    "        base.data[0] += -mu * velocity_previous + (1 + mu) * velocity\n",
    "        base.data[0] = np.clip(base.data[0], 0, +1)\n",
    "    return base.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [ 1.  0.  1.  0.  0.  0.  0.  0.]\n",
      "out: [  9.91814137e-01   4.00208119e-05   9.94682705e-06   9.07168669e-06\n",
      "   1.13390133e-05   1.70246039e-05   1.00811658e-05   3.26871736e-06]\n"
     ]
    }
   ],
   "source": [
    "print 'in:', optimize(net, hot=0)[0:8]\n",
    "print 'out:', net.blobs['prob'].data[0,0:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (1000 of 1000) |#####################| Elapsed Time: 0:07:12 Time: 0:07:12\n"
     ]
    }
   ],
   "source": [
    "from progressbar import ProgressBar\n",
    "vectors = []\n",
    "pbar = ProgressBar()\n",
    "for i in pbar(range(1000)):\n",
    "    vectors.append(optimize(net, hot=i).copy())\n",
    "np.savetxt('vectors', vectors, fmt='%.2f', delimiter='\\t')"
   ]
  }
 ],
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