bpp/train.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch.distributions import Categorical, kl\n",
    "from d2l.torch import Animator\n",
    "\n",
    "from net import Net\n",
    "from aco import ACO\n",
    "from utils import gen_pyg_data, gen_instance\n",
    "\n",
    "torch.manual_seed(1234)\n",
    "\n",
    "lr = 3e-4\n",
    "EPS = 1e-10\n",
    "T = 5\n",
    "device = 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_instance(n, model, optimizer, n_ants):\n",
    "    model.train()\n",
    "    demands = gen_instance(n, device)\n",
    "    pyg_data = gen_pyg_data(demands, device)\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = heu_vec.reshape((n+1, n+1)) + EPS\n",
    "    aco = ACO(\n",
    "        demand=demands,\n",
    "        n_ants=n_ants,\n",
    "        heuristic=heu_mat,\n",
    "        device=device\n",
    "        )\n",
    "    \n",
    "    costs, log_probs = aco.sample()\n",
    "    baseline = costs.mean()\n",
    "    reinforce_loss = torch.sum((costs - baseline) * log_probs.sum(dim=0)) / aco.n_ants\n",
    "    optimizer.zero_grad()\n",
    "    reinforce_loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "def infer_instance(n, model, n_ants):\n",
    "    model.eval()\n",
    "    demands = gen_instance(n, device)\n",
    "    pyg_data = gen_pyg_data(demands, device)\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = heu_vec.reshape((n+1, n+1)) + EPS\n",
    "    aco = ACO(\n",
    "        demand=demands,\n",
    "        n_ants=n_ants,\n",
    "        heuristic=heu_mat,\n",
    "        device=device\n",
    "        )\n",
    "    costs, log_probs = aco.sample()\n",
    "    aco.run(n_iterations=T)\n",
    "    baseline = costs.mean()\n",
    "    best_sample_cost = torch.min(costs)\n",
    "    best_aco_cost = -aco.best_fitness\n",
    "    return baseline.item(), best_sample_cost.item(), best_aco_cost.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(n_node,\n",
    "                n_ants, \n",
    "                steps_per_epoch, \n",
    "                net, \n",
    "                optimizer\n",
    "                ):\n",
    "    for _ in range(steps_per_epoch):\n",
    "        train_instance(n_node, net, optimizer, n_ants)\n",
    "\n",
    "\n",
    "@torch.no_grad()\n",
    "def validation(n_node, n_ants, epoch, net, animator=None):\n",
    "    sum_bl, sum_sample_best, sum_aco_best = 0, 0, 0\n",
    "    n_val = 100\n",
    "    for _ in range(n_val):\n",
    "        bl, sample_best, aco_best = infer_instance(n_node, net, n_ants)\n",
    "        sum_bl += bl; sum_sample_best += sample_best; sum_aco_best += aco_best\n",
    "    avg_bl, avg_sample_best, avg_aco_best = sum_bl/n_val, sum_sample_best/n_val, sum_aco_best/n_val\n",
    "    if animator:\n",
    "        animator.add(epoch+1, (avg_bl, avg_sample_best, avg_aco_best))\n",
    "    return avg_bl, avg_sample_best, avg_aco_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(n_node, n_ants, steps_per_epoch, epochs):\n",
    "    net = Net().to(device)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr)\n",
    "    animator = Animator(xlabel='epoch', xlim=[0, epochs],\n",
    "                        legend=[\"Avg. sample obj.\", \"Best sample obj.\", \"Best ACO obj.\"])\n",
    "    \n",
    "    avg_bl, avg_best, avg_aco_best = validation(n_node, n_ants, -1, net, animator)\n",
    "    val_results = [(avg_bl, avg_best, avg_aco_best)]\n",
    "    \n",
    "    val_best = 1e10\n",
    "    sum_time = 0\n",
    "    for epoch in range(0, epochs):\n",
    "        start = time.time()\n",
    "        train_epoch(n_node, n_ants, steps_per_epoch, net, optimizer)\n",
    "        sum_time += time.time() - start\n",
    "        avg_bl, avg_best, avg_aco_best = validation(n_node, n_ants, epoch, net, animator)\n",
    "        val_results.append((avg_bl, avg_best, avg_aco_best))\n",
    "        if avg_aco_best < val_best:\n",
    "            val_best = avg_aco_best\n",
    "            torch.save(net.state_dict(), f'../pretrained/bpp/bpp{n_node}.pt')\n",
    "        \n",
    "    print('total training duration:', sum_time)\n",
    "    \n",
    "    for epoch in range(-1, epochs):\n",
    "        print(f'epoch {epoch}:', val_results[epoch+1])\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn heuristic for BPP120: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 89.71171736717224\n",
      "epoch -1: (-0.18337705628980777, -0.18589751340897936, -0.18798193825032836)\n",
      "epoch 0: (-0.8963365986822392, -0.9136231126128608, -0.9218189613361958)\n",
      "epoch 1: (-0.9273510126496883, -0.9418272748111812, -0.9468318368315312)\n",
      "epoch 2: (-0.932483892757732, -0.9457785395984915, -0.9507360886221088)\n",
      "epoch 3: (-0.9349467789661189, -0.9478074390612083, -0.9515430420061141)\n",
      "epoch 4: (-0.935247632492922, -0.9472265483895573, -0.9519719415808998)\n"
     ]
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    "n_node, n_ants = 120, 20\n",
    "steps_per_epoch = 64\n",
    "epochs = 5\n",
    "train(n_node, n_ants, steps_per_epoch, epochs)"
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