siamese .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load siamese_dataloader.py\n",
    "from torch.utils.data import Dataset\n",
    "from PIL import Image\n",
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "def pil_loader(path):\n",
    "    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n",
    "    with open(path, 'rb') as f:\n",
    "        with Image.open(f) as img:\n",
    "            return img.convert('RGB')\n",
    "\n",
    "def accimage_loader(path):\n",
    "    import accimage\n",
    "    try:\n",
    "        return accimage.Image(path)\n",
    "    except IOError:\n",
    "        # Potentially a decoding problem, fall back to PIL.Image\n",
    "        return pil_loader(path)\n",
    "\n",
    "def default_loader(path):\n",
    "    return pil_loader(path)\n",
    "\n",
    "class SiameseData(Dataset):\n",
    "    def __init__(self, root, image_paths0, image_paths1, labels, transform=None, loader=default_loader):\n",
    "        self.image_pairs =zip(image_paths0, image_paths1)\n",
    "        self.labels = labels\n",
    "        self.loader = loader\n",
    "        self.root = root\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        path0, path1 = self.image_pairs[index]\n",
    "        img0 = self.loader(os.path.join(self.root, path0))\n",
    "        img1 = self.loader(os.path.join(self.root, path1))\n",
    "        if self.transform is not None:\n",
    "            img0 = self.transform(img0)\n",
    "            img1 = self.transform(img1)\n",
    "        target = self.labels[index]\n",
    "        return img0, img1, torch.LongTensor(target)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_list0 = open('/tmp/image_list0', 'r').read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_list1 = open('/tmp/image_list1', 'r').read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "targets =  np.random.randint(0, 2, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trans = transforms.Compose([transforms.Scale(160), transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = SiameseData('/home/wenfahu/faces/MTCNN_LFW/', image_list0, image_list1, targets, transform=trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(dataset, batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pin_memory',\n",
       " 'drop_last',\n",
       " 'num_workers',\n",
       " 'batch_size',\n",
       " 'dataset',\n",
       " 'collate_fn',\n",
       " 'sampler']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_loader.__dict__.keys()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}