retrieval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the networks for this task could be pretrained on the classification task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    # General\n",
    "    'batchsize': 256,\n",
    "    'shuffle': True,\n",
    "    # Augmentations\n",
    "    'flip': False,\n",
    "    'ascale': True, 'as_min': 0.6667, 'as_max': 1.5,\n",
    "    'rotate': False, 'r_positions': 12, 'test_pos': None,\n",
    "    'translate': True, 't_rate': 0.1,\n",
    "    # Point clouds and kd-trees generation\n",
    "    'steps': 10, # also control the depth of the network\n",
    "    'dim': 3,\n",
    "    'lim': 1,\n",
    "    'det': False,\n",
    "    'gamma': 10.,\n",
    "    # NN options\n",
    "    'input_features': 'all', # 'all' for point coordinates, 'no' for feeding 1's as point features\n",
    "    'n_f': [16, \n",
    "            32,  32,  \n",
    "            64,  64,  \n",
    "            128, 128, \n",
    "            256, 256, \n",
    "            512, 128], # representation sizes\n",
    "    'n_groups': 32,  # -//-\n",
    "    'min_cov': 1e-6, # parameters of histogram loss\n",
    "    'bin_num': 256,  # -//-\n",
    "    'l2': 1e-4,\n",
    "    'lr': 1e-4,\n",
    "    'n_ens': 16\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py as h5\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path2data = \"./shapenet_normal.h5\"\n",
    "hf = h5.File(path2data, 'r')\n",
    "train_vertices = np.array(hf.get('train_vertices'))\n",
    "train_faces = hf.get('train_faces')\n",
    "train_nFaces = np.array(hf.get('train_nFaces'))\n",
    "train_names = np.array(hf.get('train_names'))\n",
    "train_labels = np.array(hf.get('train_labels'))\n",
    "\n",
    "val_vertices = np.array(hf.get('val_vertices'))\n",
    "val_faces = hf.get('val_faces')\n",
    "val_nFaces = np.array(hf.get('val_nFaces'))\n",
    "val_names = np.array(hf.get('val_names'))\n",
    "val_labels = np.array(hf.get('val_labels'))\n",
    "\n",
    "test_vertices = np.array(hf.get('test_vertices'))\n",
    "test_faces = hf.get('test_faces')\n",
    "test_nFaces = np.array(hf.get('test_nFaces'))\n",
    "test_names = np.array(hf.get('test_names'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
      " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
      "\n",
      "Using gpu device 0: GeForce GTX 970 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 5110)\n"
     ]
    }
   ],
   "source": [
    "import theano.sandbox.cuda\n",
    "theano.sandbox.cuda.use('gpu0')\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "\n",
    "from lasagne.layers import InputLayer, ReshapeLayer, NonlinearityLayer, ExpressionLayer\n",
    "from lasagne.layers import ElemwiseSumLayer, ElemwiseMergeLayer\n",
    "from lasagne.layers import DenseLayer\n",
    "from lasagne.layers.dnn import BatchNormDNNLayer\n",
    "from lasagne.nonlinearities import rectify, softmax\n",
    "\n",
    "from lasagne.layers import get_output, get_all_params\n",
    "from lasagne.regularization import regularize_network_params, l2\n",
    "from lasagne.objectives import categorical_crossentropy, categorical_accuracy\n",
    "from lasagne.updates import adam\n",
    "\n",
    "from lib.nn.layers import SharedDotLayer, SPTNormReshapeLayer\n",
    "from lib.nn.utils import dump_weights, load_weights, calc_hist_vals_vector, hist_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clouds = T.tensor3(dtype='float32')\n",
    "norms = [T.tensor3(dtype='float32') for step in xrange(config['steps'])]\n",
    "target = T.vector(dtype='int32')\n",
    "\n",
    "KDNet = {}\n",
    "if config['input_features'] == 'no':\n",
    "    KDNet['input'] = InputLayer((None, 1, 2**config['steps']), input_var=clouds)\n",
    "else:\n",
    "    KDNet['input'] = InputLayer((None, 3, 2**config['steps']), input_var=clouds)\n",
    "for i in xrange(config['steps']):\n",
    "    KDNet['norm{}_r'.format(i+1)] = InputLayer((None, 3, 2**(config['steps']-1-i)), input_var=norms[i])\n",
    "    KDNet['norm{}_l'.format(i+1)] = ExpressionLayer(KDNet['norm{}_r'.format(i+1)], lambda X: -X)\n",
    "\n",
    "    KDNet['norm{}_l_X-'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_l'.format(i+1)], '-', 0, config['n_f'][i+1])\n",
    "    KDNet['norm{}_l_Y-'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_l'.format(i+1)], '-', 1, config['n_f'][i+1])\n",
    "    KDNet['norm{}_l_Z-'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_l'.format(i+1)], '-', 2, config['n_f'][i+1])\n",
    "    KDNet['norm{}_l_X+'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_l'.format(i+1)], '+', 0, config['n_f'][i+1])\n",
    "    KDNet['norm{}_l_Y+'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_l'.format(i+1)], '+', 1, config['n_f'][i+1])\n",
    "    KDNet['norm{}_l_Z+'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_l'.format(i+1)], '+', 2, config['n_f'][i+1])\n",
    "    KDNet['norm{}_r_X-'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_r'.format(i+1)], '-', 0, config['n_f'][i+1])\n",
    "    KDNet['norm{}_r_Y-'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_r'.format(i+1)], '-', 1, config['n_f'][i+1])\n",
    "    KDNet['norm{}_r_Z-'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_r'.format(i+1)], '-', 2, config['n_f'][i+1])\n",
    "    KDNet['norm{}_r_X+'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_r'.format(i+1)], '+', 0, config['n_f'][i+1])\n",
    "    KDNet['norm{}_r_Y+'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_r'.format(i+1)], '+', 1, config['n_f'][i+1])\n",
    "    KDNet['norm{}_r_Z+'.format(i+1)] = SPTNormReshapeLayer(KDNet['norm{}_r'.format(i+1)], '+', 2, config['n_f'][i+1])\n",
    "\n",
    "    KDNet['cloud{}'.format(i+1)] = SharedDotLayer(KDNet['input'], config['n_f'][i]) if i == 0 else \\\n",
    "                                   ElemwiseSumLayer([KDNet['cloud{}_l_X-_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_l_Y-_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_l_Z-_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_l_X+_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_l_Y+_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_l_Z+_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_r_X-_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_r_Y-_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_r_Z-_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_r_X+_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_r_Y+_masked'.format(i)],\n",
    "                                                     KDNet['cloud{}_r_Z+_masked'.format(i)]])\n",
    "    KDNet['cloud{}_bn'.format(i+1)] = BatchNormDNNLayer(KDNet['cloud{}'.format(i+1)])\n",
    "    KDNet['cloud{}_relu'.format(i+1)] = NonlinearityLayer(KDNet['cloud{}_bn'.format(i+1)], rectify)\n",
    "\n",
    "    KDNet['cloud{}_r'.format(i+1)] = ExpressionLayer(KDNet['cloud{}_relu'.format(i+1)],\n",
    "                                                     lambda X: X[:, :, 1::2], \n",
    "                                                     (None, config['n_f'][i], 2**(config['steps']-i-1)))\n",
    "    KDNet['cloud{}_l'.format(i+1)] = ExpressionLayer(KDNet['cloud{}_relu'.format(i+1)],\n",
    "                                                     lambda X: X[:, :, ::2], \n",
    "                                                     (None, config['n_f'][i], 2**(config['steps']-i-1)))\n",
    "\n",
    "    KDNet['cloud{}_l_X-'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_l'.format(i+1)], config['n_f'][i+1])\n",
    "    KDNet['cloud{}_l_Y-'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_l'.format(i+1)], config['n_f'][i+1])\n",
    "    KDNet['cloud{}_l_Z-'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_l'.format(i+1)], config['n_f'][i+1])\n",
    "    KDNet['cloud{}_l_X+'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_l'.format(i+1)], config['n_f'][i+1])\n",
    "    KDNet['cloud{}_l_Y+'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_l'.format(i+1)], config['n_f'][i+1])\n",
    "    KDNet['cloud{}_l_Z+'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_l'.format(i+1)], config['n_f'][i+1])\n",
    "    KDNet['cloud{}_r_X-'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_r'.format(i+1)], config['n_f'][i+1],\n",
    "                                                       W=KDNet['cloud{}_l_X-'.format(i+1)].W, \n",
    "                                                       b=KDNet['cloud{}_l_X-'.format(i+1)].b)\n",
    "    KDNet['cloud{}_r_Y-'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_r'.format(i+1)], config['n_f'][i+1],\n",
    "                                                       W=KDNet['cloud{}_l_Y-'.format(i+1)].W, \n",
    "                                                       b=KDNet['cloud{}_l_Y-'.format(i+1)].b)\n",
    "    KDNet['cloud{}_r_Z-'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_r'.format(i+1)], config['n_f'][i+1],\n",
    "                                                       W=KDNet['cloud{}_l_Z-'.format(i+1)].W, \n",
    "                                                       b=KDNet['cloud{}_l_Z-'.format(i+1)].b)\n",
    "    KDNet['cloud{}_r_X+'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_r'.format(i+1)], config['n_f'][i+1],\n",
    "                                                       W=KDNet['cloud{}_l_X+'.format(i+1)].W,\n",
    "                                                       b=KDNet['cloud{}_l_X+'.format(i+1)].b)\n",
    "    KDNet['cloud{}_r_Y+'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_r'.format(i+1)], config['n_f'][i+1],\n",
    "                                                       W=KDNet['cloud{}_l_Y+'.format(i+1)].W,\n",
    "                                                       b=KDNet['cloud{}_l_Y+'.format(i+1)].b)\n",
    "    KDNet['cloud{}_r_Z+'.format(i+1)] = SharedDotLayer(KDNet['cloud{}_r'.format(i+1)], config['n_f'][i+1],\n",
    "                                                       W=KDNet['cloud{}_l_Z+'.format(i+1)].W,\n",
    "                                                       b=KDNet['cloud{}_l_Z+'.format(i+1)].b)\n",
    "\n",
    "    KDNet['cloud{}_l_X-_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_l_X-'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_l_X-'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_l_Y-_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_l_Y-'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_l_Y-'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_l_Z-_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_l_Z-'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_l_Z-'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_l_X+_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_l_X+'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_l_X+'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_l_Y+_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_l_Y+'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_l_Y+'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_l_Z+_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_l_Z+'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_l_Z+'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_r_X-_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_r_X-'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_r_X-'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_r_Y-_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_r_Y-'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_r_Y-'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_r_Z-_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_r_Z-'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_r_Z-'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_r_X+_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_r_X+'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_r_X+'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_r_Y+_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_r_Y+'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_r_Y+'.format(i+1)]], T.mul)\n",
    "    KDNet['cloud{}_r_Z+_masked'.format(i+1)] = ElemwiseMergeLayer([KDNet['cloud{}_r_Z+'.format(i+1)],\n",
    "                                                                   KDNet['norm{}_r_Z+'.format(i+1)]], T.mul)\n",
    "\n",
    "KDNet['cloud_fin'] = ElemwiseSumLayer([KDNet['cloud{}_l_X-_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_l_Y-_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_l_Z-_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_l_X+_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_l_Y+_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_l_Z+_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_r_X-_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_r_Y-_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_r_Z-_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_r_X+_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_r_Y+_masked'.format(config['steps'])],\n",
    "                                       KDNet['cloud{}_r_Z+_masked'.format(config['steps'])]])\n",
    "KDNet['desc'] = ReshapeLayer(KDNet['cloud_fin'], (-1, config['n_f'][-1]))\n",
    "KDNet['desc_norm'] = ExpressionLayer(KDNet['desc'], lambda X: X / X.norm(2, axis=1).dimshuffle(0, 'x'))\n",
    "\n",
    "# Loading weights (optional)\n",
    "# load_weights('./models/RT+AS+TR+1e-3_15.pkl', KDNet['desc_norm'])\n",
    "\n",
    "desc_norm = get_output(KDNet['desc_norm'])\n",
    "desc_det = get_output(KDNet['desc_norm'], deterministic=True)\n",
    "\n",
    "weights = get_all_params(KDNet['desc_norm'], trainable=True)\n",
    "l2_pen = regularize_network_params(KDNet['desc_norm'], l2)\n",
    "\n",
    "pos_mask = T.eq(target.dimshuffle('x', 0), target.dimshuffle(0, 'x'))\n",
    "neg_mask = 1. - pos_mask\n",
    "\n",
    "pos_ind0, pos_ind1 = pos_mask.nonzero()\n",
    "neg_ind0, neg_ind1 = neg_mask.nonzero()\n",
    "\n",
    "sim = desc_norm.dot(desc_norm.T)\n",
    "\n",
    "sim_pos = sim[pos_ind0, pos_ind1]\n",
    "sim_neg = sim[neg_ind0, neg_ind1]\n",
    "\n",
    "hist_pos = calc_hist_vals_vector(sim_pos, \n",
    "                                 -1.0, 1.0, \n",
    "                                 (config['batchsize']**2)/config['n_groups'], \n",
    "                                 config['bin_num'], config['min_cov'])\n",
    "hist_pos /= hist_pos.sum()\n",
    "hist_neg = calc_hist_vals_vector(sim_neg, \n",
    "                                 -1.0, 1.0, \n",
    "                                 (config['batchsize']**2)/config['n_groups']*(config['n_groups'] - 1),\n",
    "                                 config['bin_num'], config['min_cov'])\n",
    "hist_neg /= hist_neg.sum()\n",
    "\n",
    "HL = hist_loss(hist_neg, hist_pos, config['bin_num']) + config['l2']*l2_pen\n",
    "\n",
    "lr = theano.shared(np.float32(config['lr']))\n",
    "updates = adam(HL, weights, learning_rate=lr)\n",
    "\n",
    "train_fun = theano.function([clouds] + norms + [target], HL, updates=updates)\n",
    "desc_fun = theano.function([clouds] + norms, desc_det)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Data Iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lib.generators.meshgrid import generate_clouds\n",
    "from lib.trees.kdtrees import KDTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def iterate_minibatches(*arrays, **kwargs):\n",
    "    if kwargs['mode'] == 'train':\n",
    "        _, p = np.unique(arrays[3], return_counts=True)\n",
    "        p = np.float32(p)/p.sum()\n",
    "        labels = np.empty((len(arrays[2]) - 1)/kwargs['batchsize']*kwargs['n_groups'], dtype=np.int32)\n",
    "        for i in xrange((len(arrays[2]) - 1)/kwargs['batchsize']):\n",
    "            labels[kwargs['n_groups']*i:kwargs['n_groups']*(i+1)] = \\\n",
    "                np.random.choice(np.arange(arrays[3].max() + 1),\n",
    "                                 size=kwargs['n_groups'], \n",
    "                                 replace=False, p=p)\n",
    "        indices = np.empty((len(arrays[2]) - 1)/kwargs['batchsize']*kwargs['batchsize'], dtype=np.int32)\n",
    "        for i, label in enumerate(labels):\n",
    "            indices[(kwargs['batchsize']/kwargs['n_groups'])*i:(kwargs['batchsize']/kwargs['n_groups'])*(i+1)] = \\\n",
    "                np.random.choice((arrays[3] == label).nonzero()[0], \n",
    "                                 size=(kwargs['batchsize']/kwargs['n_groups']), \n",
    "                                 replace=False)\n",
    "    elif kwargs['mode'] == 'test':\n",
    "        indices = np.arange(len(arrays[2]) - 1)\n",
    "        if kwargs['shuffle']:\n",
    "            np.random.shuffle(indices)\n",
    "        \n",
    "    for start_idx in xrange(0, len(indices), kwargs['batchsize']):\n",
    "        excerpt = indices[start_idx:start_idx + kwargs['batchsize']]\n",
    "        tmp = generate_clouds(excerpt, kwargs['steps'], arrays[0], arrays[1], arrays[2])\n",
    "        \n",
    "        if kwargs['flip']:\n",
    "            flip = np.random.random(size=(len(tmp), 2, 1))\n",
    "            flip[flip >= 0.5] = 1.\n",
    "            flip[flip < 0.5] = -1.\n",
    "            tmp[:, :2] *= flip\n",
    "        \n",
    "        if kwargs['ascale']:\n",
    "            tmp *= (kwargs['as_min'] + (kwargs['as_max'] - kwargs['as_min'])*np.random.random(size=(len(tmp), kwargs['dim'], 1)))\n",
    "            tmp /= np.fabs(tmp).max(axis=(1, 2), keepdims=True)\n",
    "            \n",
    "        if kwargs['rotate']:\n",
    "            r = np.sqrt((tmp[:, :2]**2).sum(axis=1))\n",
    "            coss = tmp[:, 0]/r\n",
    "            sins = tmp[:, 1]/r\n",
    "            \n",
    "            if kwargs['test_pos'] is not None:\n",
    "                alpha = 2*np.pi*kwargs['test_pos']/kwargs['r_positions']\n",
    "            else:\n",
    "                alpha = 2*np.pi*np.random.randint(0, kwargs['r_positions'], (len(tmp), 1))/kwargs['positions']\n",
    "                \n",
    "            cosr = np.cos(alpha)\n",
    "            sinr = np.sin(alpha)\n",
    "            cos = coss*cosr - sins*sinr\n",
    "            sin = sins*cosr + sinr*coss\n",
    "            tmp[:, 0] = r*cos\n",
    "            tmp[:, 1] = r*sin\n",
    "            \n",
    "        if kwargs['translate']:\n",
    "            mins = tmp.min(axis=2, keepdims=True)\n",
    "            maxs = tmp.max(axis=2, keepdims=True)\n",
    "            rngs = maxs - mins\n",
    "            tmp += kwargs['t_rate']*(np.random.random(size=(len(tmp), kwargs['dim'], 1)) - 0.5)*rngs\n",
    "        \n",
    "        trees_data = KDTrees(tmp, dim=kwargs['dim'], steps=kwargs['steps'], \n",
    "                             lim=kwargs['lim'], det=kwargs['det'], gamma=kwargs['gamma'])\n",
    "            \n",
    "        sortings, normals = trees_data['sortings'], trees_data['normals']\n",
    "        \n",
    "        if kwargs['input_features'] == 'all':\n",
    "            clouds = np.empty((len(excerpt), kwargs['dim'], 2**kwargs['steps']), dtype=np.float32)\n",
    "            for i, srt in enumerate(sortings):\n",
    "                clouds[i] = tmp[i, :, srt].T\n",
    "        elif kwargs['input_features'] == 'no':\n",
    "            clouds = np.ones((len(excerpt), 1, 2**kwargs['steps']), dtype=np.float32)\n",
    "        \n",
    "        if kwargs['mode'] == 'train':\n",
    "            yield [clouds] + normals[::-1] + [arrays[3][excerpt]]\n",
    "        if kwargs['mode'] == 'test':\n",
    "            yield [clouds] + normals[::-1] + [excerpt]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction  function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sim_fun(vertices, faces, nFaces, **kwargs):\n",
    "    sim_sum = np.zeros((len(nFaces)-1, len(nFaces)-1), dtype=np.float32)\n",
    "    for ens in xrange(kwargs['n_ens']):\n",
    "        descs = np.zeros((len(nFaces)-1, kwargs['n_f'][-1]), dtype=np.float32)    \n",
    "        for i, batch in enumerate(iterate_minibatches(vertices, faces, nFaces, **kwargs)):\n",
    "            descs[batch[-1]] = desc_fun(batch[0], \n",
    "                                        batch[1], batch[2], batch[3], batch[4], batch[5], \n",
    "                                        batch[6], batch[7], batch[8], batch[9], batch[10])\n",
    "        sim_sum += descs.dot(descs.T)\n",
    "    return sim_sum / kwargs['n_ens']\n",
    "\n",
    "\n",
    "def calc_hist(sim, labels, bin_num=256):\n",
    "    brd = np.linspace(-1., 1., bin_num+1)\n",
    "    pos_mask = labels.reshape(1, -1) == labels.reshape(-1, 1)\n",
    "    neg_mask = np.logical_not(pos_mask)\n",
    "    \n",
    "    pos_hist = np.empty(bin_num, dtype=np.float32)\n",
    "    neg_hist = np.empty(bin_num, dtype=np.float32)\n",
    "    \n",
    "    for i in xrange(bin_num):\n",
    "        left_cond = brd[i] <= sim\n",
    "        right_cond = sim <= brd[i+1]\n",
    "        buf_bin = np.logical_and(left_cond, right_cond)\n",
    "        pos_hist[i] = np.float32(pos_mask*buf_bin).sum()\n",
    "        neg_hist[i] = np.float32(neg_mask*buf_bin).sum()\n",
    "    \n",
    "    pos_hist /= pos_hist.sum()\n",
    "    neg_hist /= neg_hist.sum()\n",
    "    return neg_hist, pos_hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sys import stdout\n",
    "from time import time\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "config['mode'] = 'train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch progress: 35584/35584\t\tLoss: 0.13937\n",
      "Epoch 1 of 50 took 262.979s\n",
      "  training loss (in-iteration):\t\t0.139370\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.13624\n",
      "Epoch 2 of 50 took 260.218s\n",
      "  training loss (in-iteration):\t\t0.136243\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.13350\n",
      "Epoch 3 of 50 took 260.458s\n",
      "  training loss (in-iteration):\t\t0.133502\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.12974\n",
      "Epoch 4 of 50 took 260.046s\n",
      "  training loss (in-iteration):\t\t0.129744\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.12672\n",
      "Epoch 5 of 50 took 261.382s\n",
      "  training loss (in-iteration):\t\t0.126719\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.12341\n",
      "Epoch 6 of 50 took 261.828s\n",
      "  training loss (in-iteration):\t\t0.123410\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.12048\n",
      "Epoch 7 of 50 took 261.168s\n",
      "  training loss (in-iteration):\t\t0.120477\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.11852\n",
      "Epoch 8 of 50 took 260.894s\n",
      "  training loss (in-iteration):\t\t0.118517\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.11600\n",
      "Epoch 9 of 50 took 261.410s\n",
      "  training loss (in-iteration):\t\t0.115999\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.11351\n",
      "Epoch 10 of 50 took 262.133s\n",
      "  training loss (in-iteration):\t\t0.113512\n",
      "  Model saved!\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.11080\n",
      "Epoch 11 of 50 took 262.250s\n",
      "  training loss (in-iteration):\t\t0.110798\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.10910\n",
      "Epoch 12 of 50 took 260.677s\n",
      "  training loss (in-iteration):\t\t0.109104\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.10626\n",
      "Epoch 13 of 50 took 262.574s\n",
      "  training loss (in-iteration):\t\t0.106264\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.10454\n",
      "Epoch 14 of 50 took 264.322s\n",
      "  training loss (in-iteration):\t\t0.104535\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.10315\n",
      "Epoch 15 of 50 took 262.736s\n",
      "  training loss (in-iteration):\t\t0.103153\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.10074\n",
      "Epoch 16 of 50 took 259.802s\n",
      "  training loss (in-iteration):\t\t0.100742\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.09802\n",
      "Epoch 17 of 50 took 261.737s\n",
      "  training loss (in-iteration):\t\t0.098021\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.09578\n",
      "Epoch 18 of 50 took 264.610s\n",
      "  training loss (in-iteration):\t\t0.095785\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.09404\n",
      "Epoch 19 of 50 took 262.173s\n",
      "  training loss (in-iteration):\t\t0.094044\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.09271\n",
      "Epoch 20 of 50 took 262.710s\n",
      "  training loss (in-iteration):\t\t0.092708\n",
      "  Model saved!\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08993\n",
      "Epoch 21 of 50 took 264.399s\n",
      "  training loss (in-iteration):\t\t0.089925\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08912\n",
      "Epoch 22 of 50 took 260.576s\n",
      "  training loss (in-iteration):\t\t0.089121\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08623\n",
      "Epoch 23 of 50 took 262.021s\n",
      "  training loss (in-iteration):\t\t0.086228\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08505\n",
      "Epoch 24 of 50 took 273.646s\n",
      "  training loss (in-iteration):\t\t0.085048\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08367\n",
      "Epoch 25 of 50 took 263.011s\n",
      "  training loss (in-iteration):\t\t0.083666\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08085\n",
      "Epoch 26 of 50 took 263.130s\n",
      "  training loss (in-iteration):\t\t0.080850\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.08046\n",
      "Epoch 27 of 50 took 260.674s\n",
      "  training loss (in-iteration):\t\t0.080464\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07879\n",
      "Epoch 28 of 50 took 261.725s\n",
      "  training loss (in-iteration):\t\t0.078787\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07662\n",
      "Epoch 29 of 50 took 262.336s\n",
      "  training loss (in-iteration):\t\t0.076621\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07590\n",
      "Epoch 30 of 50 took 260.861s\n",
      "  training loss (in-iteration):\t\t0.075899\n",
      "  Model saved!\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07428\n",
      "Epoch 31 of 50 took 264.489s\n",
      "  training loss (in-iteration):\t\t0.074278\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07259\n",
      "Epoch 32 of 50 took 263.513s\n",
      "  training loss (in-iteration):\t\t0.072593\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07217\n",
      "Epoch 33 of 50 took 263.457s\n",
      "  training loss (in-iteration):\t\t0.072167\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.07010\n",
      "Epoch 34 of 50 took 263.391s\n",
      "  training loss (in-iteration):\t\t0.070105\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06930\n",
      "Epoch 35 of 50 took 267.224s\n",
      "  training loss (in-iteration):\t\t0.069296\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06858\n",
      "Epoch 36 of 50 took 268.775s\n",
      "  training loss (in-iteration):\t\t0.068575\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06628\n",
      "Epoch 37 of 50 took 265.300s\n",
      "  training loss (in-iteration):\t\t0.066275\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06564\n",
      "Epoch 38 of 50 took 259.849s\n",
      "  training loss (in-iteration):\t\t0.065637\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06516\n",
      "Epoch 39 of 50 took 259.568s\n",
      "  training loss (in-iteration):\t\t0.065158\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06376\n",
      "Epoch 40 of 50 took 264.789s\n",
      "  training loss (in-iteration):\t\t0.063759\n",
      "  Model saved!\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06245\n",
      "Epoch 41 of 50 took 258.294s\n",
      "  training loss (in-iteration):\t\t0.062455\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06131\n",
      "Epoch 42 of 50 took 265.847s\n",
      "  training loss (in-iteration):\t\t0.061309\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.06041\n",
      "Epoch 43 of 50 took 265.237s\n",
      "  training loss (in-iteration):\t\t0.060410\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05890\n",
      "Epoch 44 of 50 took 271.948s\n",
      "  training loss (in-iteration):\t\t0.058900\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05897\n",
      "Epoch 45 of 50 took 259.976s\n",
      "  training loss (in-iteration):\t\t0.058965\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05771\n",
      "Epoch 46 of 50 took 270.092s\n",
      "  training loss (in-iteration):\t\t0.057712\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05626\n",
      "Epoch 47 of 50 took 275.642s\n",
      "  training loss (in-iteration):\t\t0.056258\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05613\n",
      "Epoch 48 of 50 took 265.240s\n",
      "  training loss (in-iteration):\t\t0.056125\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05607\n",
      "Epoch 49 of 50 took 260.114s\n",
      "  training loss (in-iteration):\t\t0.056072\n",
      "Epoch progress: 35584/35584\t\tLoss: 0.05404\n",
      "Epoch 50 of 50 took 270.797s\n",
      "  training loss (in-iteration):\t\t0.054043\n",
      "  Model saved!\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAH/CAYAAADzBG/zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/g9kMZ930M3dEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7efe6ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAH/CAYAAADzBG/zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7efe6dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAH/CAYAAADzBG/zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhQcdzjnnnMsKDzqcc845lxX/B82N+g2PVSaLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7efde5ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAH/CAYAAADzBG/zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nc845lxcedDjnnHMuLzzocM4551xe/B9TuSduLWBRVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7efd718d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAH/CAYAAADzBG/zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFx50OOeccy4v/g9/4WrKpkoOlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7efce4e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_epochs = 50\n",
    "num_save = 5\n",
    "\n",
    "# lr.set_value(np.float32(1e-4))\n",
    "\n",
    "for epoch in xrange(num_epochs):\n",
    "    train_err = 0.\n",
    "    \n",
    "    start_time = time()\n",
    "    for i, batch in enumerate(iterate_minibatches(train_vertices, train_faces, train_nFaces, train_labels, **config)):\n",
    "        train_err_batch = train_fun(batch[0], \n",
    "                                    batch[1], batch[2], batch[3], batch[4], batch[5], \n",
    "                                    batch[6], batch[7], batch[8], batch[9], batch[10],\n",
    "                                    batch[11])\n",
    "        train_err += train_err_batch*len(batch[0])\n",
    "\n",
    "        stdout.write('\\rEpoch progress: {}/{}\\t\\tLoss: {:.5f}'\n",
    "                     .format(config['batchsize']*(i+1),\n",
    "                     len(train_nFaces)/config['batchsize']*config['batchsize'],\n",
    "                     train_err/(config['batchsize']*(i+1))))\n",
    "        stdout.flush()\n",
    "    stdout.write('\\n')\n",
    "    stdout.flush()\n",
    "        \n",
    "    print(\"Epoch {} of {} took {:.3f}s\".format(epoch + 1, num_epochs, time() - start_time))\n",
    "    print(\"  training loss (in-iteration):\\t\\t{:.6f}\".format(train_err/(config['batchsize']*(i+1))))\n",
    "\n",
    "    if (epoch+1) % (num_epochs/num_save) == 0:\n",
    "        config['mode'] = 'test'\n",
    "        val_sims = sim_fun(val_vertices, val_faces, val_nFaces, **config)\n",
    "        nh, ph = calc_hist(val_sims, val_labels, bin_num=config['bin_num'])\n",
    "        fig = plt.figure(figsize=(6, 6))\n",
    "        plt.plot(np.linspace(-1., 1., config['bin_num']), ph, c='red', figure=fig)\n",
    "        plt.plot(np.linspace(-1., 1., config['bin_num']), nh, c='blue', figure=fig)\n",
    "        fig.show()\n",
    "        config['mode'] = 'train'\n",
    "        \n",
    "        dump_weights('./models/RT+AS+TR_1e-4_10.pkl', KDNet['desc_norm'])\n",
    "        print '  Model saved!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}