Darcy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import numpy as np\n",
    "from scipy.io import loadmat, savemat\n",
    "import math\n",
    "import os\n",
    "import h5py\n",
    "\n",
    "from functools import partial\n",
    "from models.models import MWT2d\n",
    "from models.utils import train, test, LpLoss, get_filter, UnitGaussianNormalizer"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "torch.manual_seed(0)\n",
    "np.random.seed(0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "def get_initializer(name):\n",
    "    \n",
    "    if name == 'xavier_normal':\n",
    "        init_ = partial(nn.init.xavier_normal_)\n",
    "    elif name == 'kaiming_uniform':\n",
    "        init_ = partial(nn.init.kaiming_uniform_)\n",
    "    elif name == 'kaiming_normal':\n",
    "        init_ = partial(nn.init.kaiming_normal_)\n",
    "    return init_"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "ntrain = 1000\n",
    "ntest = 200\n",
    "\n",
    "r = 1\n",
    "h = int(((512 - 1)/r) + 1)\n",
    "s = h"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# laod data\n",
    "dataloader = h5py.File('../../data/all_train_test_Darcy.mat','r')\n",
    "\n",
    "a_data = dataloader['a_train']\n",
    "p_data = dataloader['p_train']\n",
    "\n",
    "a_numpy = []\n",
    "for i in range(len(a_data)):\n",
    "    obj = dataloader[a_data[i][0]]\n",
    "    a_numpy.append(obj[:])\n",
    "a_tensor = torch.from_numpy(np.array(a_numpy).astype(np.float32))\n",
    "\n",
    "p_numpy = []\n",
    "for i in range(len(p_data)):\n",
    "    obj = dataloader[p_data[i][0]]\n",
    "    p_numpy.append(obj[:])\n",
    "p_tensor = torch.from_numpy(np.array(p_numpy).astype(np.float32))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "x_train = a_tensor[:ntrain,::r,::r][:,:s,:s]\n",
    "y_train = p_tensor[:ntrain,::r,::r][:,:s,:s]\n",
    "\n",
    "x_test = a_tensor[-ntest:,::r,::r][:,:s,:s]\n",
    "y_test = p_tensor[-ntest:,::r,::r][:,:s,:s]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "x_normalizer = UnitGaussianNormalizer(x_train)\n",
    "x_train = x_normalizer.encode(x_train)\n",
    "x_test = x_normalizer.encode(x_test)\n",
    "\n",
    "y_normalizer = UnitGaussianNormalizer(y_train)\n",
    "y_train = y_normalizer.encode(y_train)\n",
    "\n",
    "grids = []\n",
    "grids.append(np.linspace(0, 1, s))\n",
    "grids.append(np.linspace(0, 1, s))\n",
    "grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T\n",
    "grid = grid.reshape(1,s,s,2)\n",
    "grid = torch.tensor(grid, dtype=torch.float)\n",
    "x_train = torch.cat([x_train.reshape(ntrain,s,s,1), grid.repeat(ntrain,1,1,1)], dim=3)\n",
    "x_test = torch.cat([x_test.reshape(ntest,s,s,1), grid.repeat(ntest,1,1,1)], dim=3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "batch_size = 10\n",
    "train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test), batch_size=batch_size, shuffle=False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "ich = 3\n",
    "initializer = get_initializer('xavier_normal') # xavier_normal, kaiming_normal, kaiming_uniform\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "\n",
    "model = MWT2d(ich, \n",
    "            alpha = 12,\n",
    "            c = 4,\n",
    "            k = 4, \n",
    "            base = 'legendre', # 'chebyshev'\n",
    "            nCZ = 4,\n",
    "            L = 0,\n",
    "            initializer = initializer,\n",
    "            ).to(device)\n",
    "learning_rate = 0.001\n",
    "\n",
    "epochs = 500\n",
    "step_size = 100\n",
    "gamma = 0.5"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)\n",
    "\n",
    "myloss = LpLoss(size_average=False)\n",
    "y_normalizer.cuda()\n",
    "\n",
    "for epoch in range(1, epochs+1):\n",
    "    train_l2 = train(model, train_loader, optimizer, epoch, device,\n",
    "        lossFn = myloss, lr_schedule = scheduler,\n",
    "        post_proc = y_normalizer.decode)\n",
    "    \n",
    "    test_l2 = test(model, test_loader, device, lossFn=myloss, post_proc=y_normalizer.decode)\n",
    "    print(f'epoch: {epoch}, train l2 = {train_l2}, test l2 = {test_l2}')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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