tsp/train.ipynb

{
 "cells": [
  {
   "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, load_val_dataset\n",
    "\n",
    "torch.manual_seed(1234)\n",
    "\n",
    "lr = 3e-4\n",
    "EPS = 1e-10\n",
    "T=5\n",
    "device = 'cuda:0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_instance(model, optimizer, pyg_data, distances, n_ants):\n",
    "    model.train()\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = model.reshape(pyg_data, heu_vec) + EPS\n",
    "    \n",
    "    aco = ACO(\n",
    "        n_ants=n_ants,\n",
    "        heuristic=heu_mat,\n",
    "        distances=distances,\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(model, pyg_data, distances, n_ants):\n",
    "    model.eval()\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = model.reshape(pyg_data, heu_vec) + EPS\n",
    "    aco = ACO(\n",
    "        n_ants=n_ants,\n",
    "        heuristic=heu_mat,\n",
    "        distances=distances,\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.lowest_cost\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",
    "                k_sparse, \n",
    "                epoch, \n",
    "                steps_per_epoch, \n",
    "                net, \n",
    "                optimizer\n",
    "                ):\n",
    "    for _ in range(steps_per_epoch):\n",
    "        instance = torch.rand(size=(n_node, 2), device=device)\n",
    "        data, distances = gen_pyg_data(instance, k_sparse=k_sparse)\n",
    "        train_instance(net, optimizer, data, distances, n_ants)\n",
    "\n",
    "\n",
    "@torch.no_grad()\n",
    "def validation(n_ants, epoch, net, val_dataset, animator=None):\n",
    "    sum_bl, sum_sample_best, sum_aco_best = 0, 0, 0\n",
    "    \n",
    "    for data, distances in val_dataset:\n",
    "        bl, sample_best, aco_best = infer_instance(net, data, distances, n_ants)\n",
    "        sum_bl += bl; sum_sample_best += sample_best; sum_aco_best += aco_best\n",
    "    \n",
    "    n_val = len(val_dataset)\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",
    "    \n",
    "    return avg_bl, avg_sample_best, avg_aco_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(n_node, k_sparse, n_ants, steps_per_epoch, epochs):\n",
    "    net = Net().to(device)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr)\n",
    "    val_list = load_val_dataset(n_node, k_sparse, device)\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_ants, -1, net, val_list, animator)\n",
    "    val_results = [(avg_bl, avg_best, avg_aco_best)]\n",
    "    \n",
    "    sum_time = 0\n",
    "    for epoch in range(0, epochs):\n",
    "        start = time.time()\n",
    "        train_epoch(n_node, n_ants, k_sparse, epoch, steps_per_epoch, net, optimizer)\n",
    "        sum_time += time.time() - start\n",
    "        avg_bl, avg_sample_best, avg_aco_best = validation(n_ants, epoch, net, val_list, animator)\n",
    "        val_results.append((avg_bl, avg_sample_best, avg_aco_best))\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",
    "        \n",
    "    torch.save(net.state_dict(), f'../pretrained/tsp/tsp{n_node}.pt')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for TSP20: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 68.66173219680786\n",
      "epoch -1: (7.389515957832336, 6.151585960388184, 5.653606867790222)\n",
      "epoch 0: (4.983130354881286, 4.283209140300751, 3.962330186367035)\n",
      "epoch 1: (4.476540601253509, 3.951026213169098, 3.8490072774887083)\n",
      "epoch 2: (4.351146795749664, 3.9194301557540894, 3.838328719139099)\n",
      "epoch 3: (4.365202698707581, 3.90883412361145, 3.827320861816406)\n",
      "epoch 4: (4.38464241027832, 3.9300931763648985, 3.84234961271286)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_node, n_ants = 20, 20\n",
    "k_sparse = 10\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, k_sparse, n_ants, steps_per_epoch, epochs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for TSP100: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 120.20243453979492\n",
      "epoch -1: (21.443779048919676, 19.615448265075685, 18.82631748199463)\n",
      "epoch 0: (13.097439441680908, 11.753785648345946, 10.807051305770875)\n",
      "epoch 1: (10.070752849578858, 9.330580339431762, 8.833520278930663)\n",
      "epoch 2: (9.818960437774658, 9.07780930519104, 8.626941604614258)\n",
      "epoch 3: (9.721839113235474, 8.991541881561279, 8.594109416007996)\n",
      "epoch 4: (9.711450233459473, 9.026623315811158, 8.63921733379364)\n"
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      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
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   ],
   "source": [
    "n_node = 100\n",
    "n_ants = 20\n",
    "k_sparse = 20\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, k_sparse, n_ants, steps_per_epoch, epochs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for TSP500: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 472.1851406097412\n",
      "epoch -1: (69.15739501953125, 65.84911876678467, 64.88369365692138)\n",
      "epoch 0: (27.6236510848999, 26.075670528411866, 24.09028289794922)\n",
      "epoch 1: (23.90336416244507, 22.48627794265747, 21.41227710723877)\n",
      "epoch 2: (23.06234348297119, 21.618331356048586, 20.543487224578858)\n",
      "epoch 3: (22.123531036376953, 20.900113105773926, 19.917785358428954)\n",
      "epoch 4: (21.94193012237549, 20.73219964981079, 19.791852359771728)\n"
     ]
    },
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      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_node = 500\n",
    "n_ants = 50\n",
    "k_sparse = 50\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, k_sparse, n_ants, steps_per_epoch, epochs)"
   ]
  }
 ],
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