smtwtp/train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\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",
    "    pyg_data, due_time, weights, processing_time = instance_gen(problem_size, device)\n",
    "\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = model.reshape(pyg_data, heu_vec) + EPS\n",
    "    \n",
    "    aco = ACO(\n",
    "        due_time=due_time,\n",
    "        weights=weights,\n",
    "        processing_time=processing_time,\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(model, problem_size, n_ants):\n",
    "    model.eval()\n",
    "    pyg_data, due_time, weights, processing_time = instance_gen(problem_size, device)\n",
    "\n",
    "    heu_vec = model(pyg_data)\n",
    "    heu_mat = model.reshape(pyg_data, heu_vec) + EPS\n",
    "    \n",
    "    aco = ACO(\n",
    "        due_time=due_time,\n",
    "        weights=weights,\n",
    "        processing_time=processing_time,\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",
    "    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/smtwtp/smtwtp{n_node}.pt')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn heuristic for SMTWTP50: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 83.65491104125977\n",
      "epoch -1: (51.138839416503906, 27.220097885131835)\n",
      "epoch 0: (3.4920669136941433, 0.7225811843760311)\n",
      "epoch 1: (0.46120976632155364, 0.21419585101306438)\n",
      "epoch 2: (0.3490694498363882, 0.20601881805516314)\n",
      "epoch 3: (0.25118220870383084, 0.1039320881664753)\n",
      "epoch 4: (0.41925502485551985, 0.25645703631918876)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "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 SMTWTP100: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 141.24576592445374\n",
      "epoch -1: (216.19709671020507, 142.79268447875975)\n",
      "epoch 0: (2.183804383352399, 0.706129934610799)\n",
      "epoch 1: (1.422333369664848, 0.5058790126148961)\n",
      "epoch 2: (0.6543451187683967, 0.3064299412816763)\n",
      "epoch 3: (0.5904533290304244, 0.28556705886963757)\n",
      "epoch 4: (0.8797953298850917, 0.49366521606221797)\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 = 100, 50\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, n_ants, steps_per_epoch, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training duration: 668.4393274784088\n",
      "epoch -1: (5176.2578515625, 4309.775747070313)\n",
      "epoch 0: (398.0970230102539, 247.8647900390625)\n",
      "epoch 1: (9.564925746917725, 3.4632743223011495)\n",
      "epoch 2: (4.792858643531799, 1.500822302699089)\n",
      "epoch 3: (3.2330729561299085, 1.0147837746795267)\n",
      "epoch 4: (3.062594361063093, 1.2463829231821)\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 = 500, 50\n",
    "steps_per_epoch = 128\n",
    "epochs = 5\n",
    "train(n_node, n_ants, steps_per_epoch, epochs)"
   ]
  }
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