sop/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 *\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, problem_size, n_ants):\n",
    "    model.train()\n",
    "    distances, adj_mat, prec_cons = training_instance_gen(problem_size, device)\n",
    "    pyg_data = gen_pyg_data(distances, adj_mat, device)\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = model.reshape(pyg_data, heu_vec) + EPS\n",
    "    aco = ACO(\n",
    "        distances=distances,\n",
    "        prec_cons=prec_cons,\n",
    "        n_ants=n_ants,\n",
    "        heuristic=heu_mat,\n",
    "        device=device\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, problem_size, n_ants):\n",
    "    model.eval()\n",
    "    distances, adj_mat, prec_cons = training_instance_gen(problem_size, device)\n",
    "    pyg_data = gen_pyg_data(distances, adj_mat, device)\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = model.reshape(pyg_data, heu_vec) + EPS\n",
    "    aco = ACO(\n",
    "        distances=distances,\n",
    "        prec_cons=prec_cons,\n",
    "        n_ants=n_ants,\n",
    "        heuristic=heu_mat,\n",
    "        device=device\n",
    "        )\n",
    "    costs, log_probs = aco.sample()\n",
    "    baseline = costs.mean()\n",
    "    best_sample_cost = torch.min(costs)\n",
    "    return baseline.item(), best_sample_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(net, optimizer, n_node, n_ants)\n",
    "\n",
    "\n",
    "@torch.no_grad()\n",
    "def validation(p_size, n_ants, epoch, net, animator=None):\n",
    "    sum_bl, sum_sample_best = 0, 0\n",
    "    \n",
    "    n_val = 50\n",
    "    for _ in range(n_val):\n",
    "        bl, sample_best = infer_instance(net, p_size, n_ants)\n",
    "        sum_bl += bl\n",
    "        sum_sample_best += sample_best\n",
    "    \n",
    "    avg_bl = sum_bl/n_val\n",
    "    avg_sample_best = sum_sample_best/n_val\n",
    "    \n",
    "    if animator:\n",
    "        animator.add(epoch+1, (avg_bl, avg_sample_best))\n",
    "    \n",
    "    return avg_bl, avg_sample_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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.\"])\n",
    "    avg_bl, avg_best = validation(n_node, n_ants, -1, net, animator)\n",
    "    val_results = [(avg_bl, avg_best)]\n",
    "    \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_sample_best = avg_bl, avg_best = validation(n_node, n_ants, epoch, net, animator)\n",
    "        val_results.append((avg_bl, avg_sample_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/sop/sop{n_node}.pt')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for SOP20: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 56.51935863494873\n",
      "epoch -1: (18.435310554504394, 16.404797630310057)\n",
      "epoch 0: (15.358814373016358, 13.9423783493042)\n",
      "epoch 1: (14.889442806243897, 13.563144054412842)\n",
      "epoch 2: (14.709210796356201, 13.379206619262696)\n",
      "epoch 3: (14.712836933135986, 13.541581745147704)\n",
      "epoch 4: (14.530024127960205, 13.251674461364747)\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",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, n_ants, steps_per_epoch, epochs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for SOP50:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 108.80767154693604\n",
      "epoch -1: (47.96100746154785, 43.76364929199219)\n",
      "epoch 0: (38.18202507019043, 35.160158996582034)\n",
      "epoch 1: (37.63494926452637, 34.87402584075928)\n",
      "epoch 2: (37.41081840515137, 34.774868392944335)\n",
      "epoch 3: (37.53481315612793, 35.0149898147583)\n",
      "epoch 4: (36.91531826019287, 34.414453659057614)\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 = 50, 50\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, n_ants, steps_per_epoch, epochs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for SOP100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 268.74445605278015\n",
      "epoch -1: (98.51773849487304, 92.5518928527832)\n",
      "epoch 0: (77.45054794311524, 73.40152297973633)\n",
      "epoch 1: (75.55254928588867, 71.57979278564453)\n",
      "epoch 2: (76.46000457763672, 72.64052986145019)\n",
      "epoch 3: (74.63874588012695, 71.1023519897461)\n",
      "epoch 4: (75.36011260986328, 71.55303695678711)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
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     "metadata": {},
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   ],
   "source": [
    "n_node, n_ants = 100, 50\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, n_ants, steps_per_epoch, epochs)"
   ]
  }
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