Capsule Tutorial(WIP).ipynb

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    "Capsule Network\n",
    "================\n",
    "**Author**: `Jinjing Zhou`\n",
    "\n",
    "This tutorial explains how to use DGL library and its language to implement the [capsule network](http://arxiv.org/abs/1710.09829) proposed by Geoffrey Hinton and his team. The algorithm aims to provide a better alternative to current neural network structures. By using DGL library, users can implement the algorithm in a more intuitive way."
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    "## Model Overview\n",
    "\n",
    "### Introduction\n",
    "Capsule Network is \n",
    "\n",
    "### What's a capsule?\n",
    "> A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part.    \n",
    "\n",
    "Generally Speaking, the idea of capsule is to encode all the information about the features in a vector form, by substituting scalars in traditional neural network with vectors. And use the norm of the vector to represents the meaning of original scalars. \n",
    "![figure_1](./capsule_f1.png)\n",
    "\n",
    "### Dynamic Routing Algorithm\n",
    "<img src=\"./capsule_f2.png\" style=\"height:300px;\"/>"
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    "## Model Implementations\n",
    "\n",
    "### 1. Consider capsule routing  as a graph structure\n",
    "\n",
    "We can consider each capsule as a node in a graph, and connect the nodes between layers.\n",
    "<img src=\"./capsule_f3.png\" style=\"height:200px;\"/>"
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    "def construct_graph(self):\n",
    "    g = dgl.DGLGraph()\n",
    "    g.add_nodes(self.in_channel + self.num_unit)\n",
    "    self.in_channel_nodes = list(range(self.in_channel))\n",
    "    self.capsule_nodes = list(range(self.in_channel, self.in_channel + self.num_unit))\n",
    "    u, v = [], []\n",
    "    for i in self.in_channel_nodes:\n",
    "        for j in self.capsule_nodes:\n",
    "            u.append(i)\n",
    "            v.append(j)\n",
    "    g.add_edges(u, v)\n",
    "    return g"
   ]
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   "source": [
    "### 2. Pre-compute $\\hat{u}_{j|i}$, initialize $b_{ij}$ and store them as edge attribute\n",
    "<img src=\"./capsule_f4.png\" style=\"height:200px;\"/>"
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   "source": [
    "x = x.transpose(1, 2)\n",
    "x = torch.stack([x] * self.num_unit, dim=2).unsqueeze(4)\n",
    "W = self.weight.expand(self.batch_size, *self.weight.shape)\n",
    "u_hat = torch.matmul(W, x).permute(1, 2, 0, 3, 4).squeeze().contiguous()\n",
    "self.g.set_e_repr({'b_ij': edge_features.view(-1)})\n",
    "self.g.set_e_repr({'u_hat': u_hat.view(-1, self.batch_size, self.unit_size)})"
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   "source": [
    "### 3. Initialize node features"
   ]
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   "source": [
    "node_features = torch.zeros(self.in_channel + self.num_unit, self.batch_size, self.unit_size).to(device)\n",
    "self.g.set_n_repr({'h': node_features})"
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   "source": [
    "### 4. Write message passing functions"
   ]
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   "source": [
    "@staticmethod\n",
    "def capsule_msg(src, edge):\n",
    "    return {'b_ij': edge['b_ij'], 'h': src['h'], 'u_hat': edge['u_hat']}\n",
    "\n",
    "@staticmethod\n",
    "def capsule_reduce(node, msg):\n",
    "    b_ij_c, u_hat = msg['b_ij'], msg['u_hat']\n",
    "    # line 4\n",
    "    c_i = F.softmax(b_ij_c, dim=0)\n",
    "    # line 5\n",
    "    s_j = (c_i.unsqueeze(2).unsqueeze(3) * u_hat).sum(dim=1)\n",
    "    return {'h': s_j}\n",
    "\n",
    "def capsule_update(self, msg):\n",
    "    # line 6\n",
    "    v_j = self.squash(msg['h'])\n",
    "    return {'h': v_j}\n",
    "\n",
    "def update_edge(self, u, v, edge):\n",
    "    # line 7\n",
    "    return {'b_ij': edge['b_ij'] + (v['h'] * edge['u_hat']).mean(dim=1).sum(dim=1)}\n"
   ]
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   "source": [
    "### 4. Executing algorithm"
   ]
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   "source": [
    "for i in range(self.num_routing):\n",
    "    self.g.update_all(self.capsule_msg, self.capsule_reduce, self.capsule_update)\n",
    "    self.g.update_edge(edge_func=self.update_edge)"
   ]
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   "execution_count": null,
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