112 combining spatial transforms

examples/transform_speed.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data loading pipeline examples\n",
+    "\n",
+    "The purpose of this notebook is to illustrate reading Nifti files and test speed of different methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MONAI version: 0.0.1\n",
+      "Python version: 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 16:30:03)  [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
+      "Numpy version: 1.18.1\n",
+      "Pytorch version: 1.4.0\n",
+      "Ignite version: 0.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "from glob import glob\n",
+    "import tempfile\n",
+    "\n",
+    "import numpy as np\n",
+    "import nibabel as nib\n",
+    "\n",
+    "\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.multiprocessing import Pool, Process, set_start_method\n",
+    "try:\n",
+    "     set_start_method('spawn')\n",
+    "except RuntimeError:\n",
+    "    pass\n",
+    "\n",
+    "sys.path.append('..') # assumes this is where MONAI is\n",
+    "\n",
+    "import monai\n",
+    "from monai.transforms.compose import Compose\n",
+    "from monai.data.nifti_reader import NiftiDataset\n",
+    "from monai.transforms import (AddChannel, Rescale, ToTensor, \n",
+    "                              UniformRandomPatch, Rotate, RandAffine)\n",
+    "\n",
+    "monai.config.print_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0. Preparing input data (nifti images)\n",
+    "\n",
+    "Create a number of test Nifti files, 3d single channel images with spatial size (256, 256, 256) voxels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tempdir = tempfile.mkdtemp()\n",
+    "\n",
+    "for i in range(5):\n",
+    "    im, seg = monai.data.synthetic.create_test_image_3d(256,256,256)\n",
+    "    \n",
+    "    n = nib.Nifti1Image(im, np.eye(4))\n",
+    "    nib.save(n, os.path.join(tempdir, 'im%i.nii.gz'%i))\n",
+    "    \n",
+    "    n = nib.Nifti1Image(seg, np.eye(4))\n",
+    "    nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz'%i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prepare list of image names and segmentation names\n",
+    "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n",
+    "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Test image loading with minimal preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 1, 256, 256, 256]) torch.Size([3, 1, 256, 256, 256])\n"
+     ]
+    }
+   ],
+   "source": [
+    "imtrans = Compose([\n",
+    "    AddChannel(),\n",
+    "    ToTensor()\n",
+    "])    \n",
+    "\n",
+    "segtrans = Compose([\n",
+    "    AddChannel(),\n",
+    "    ToTensor()\n",
+    "])    \n",
+    "    \n",
+    "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n",
+    "loader = DataLoader(ds, batch_size=3, num_workers=8)\n",
+    "\n",
+    "im, seg = monai.utils.misc.first(loader)\n",
+    "print(im.shape, seg.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.11 s ± 207 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit data = next(iter(loader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Test image-patch loading with CPU multi-processing:\n",
+    "\n",
+    "- rotate (256, 256, 256)-voxel in the plane axes=(1, 2)\n",
+    "- extract random (64, 64, 64) patches\n",
+    "- implemented in MONAI using ` scipy.ndimage.rotate`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 1, 64, 64, 64]) torch.Size([3, 1, 64, 64, 64])\n"
+     ]
+    }
+   ],
+   "source": [
+    "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n",
+    "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))\n",
+    "\n",
+    "imtrans = Compose([\n",
+    "    Rescale(),\n",
+    "    AddChannel(),\n",
+    "    Rotate(angle=45.),\n",
+    "    UniformRandomPatch((64, 64, 64)),\n",
+    "    ToTensor()\n",
+    "])    \n",
+    "\n",
+    "segtrans = Compose([\n",
+    "    AddChannel(),\n",
+    "    Rotate(angle=45.),\n",
+    "    UniformRandomPatch((64, 64, 64)),\n",
+    "    ToTensor()\n",
+    "])    \n",
+    "    \n",
+    "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n",
+    "loader = DataLoader(ds, batch_size=3, num_workers=8, pin_memory=torch.cuda.is_available())\n",
+    "\n",
+    "im, seg = monai.utils.misc.first(loader)\n",
+    "print(im.shape, seg.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10.3 s ± 175 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit -n 3 data = next(iter(loader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(the above results were based on a 2.9 GHz 6-Core Intel Core i9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Test image-patch loading with preprocessing on GPU:\n",
+    "\n",
+    "- random rotate (256, 256, 256)-voxel in the plane axes=(1, 2)\n",
+    "- extract random (64, 64, 64) patches\n",
+    "- implemented in MONAI using native pytorch resampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 1, 64, 64, 64]) torch.Size([3, 1, 64, 64, 64])\n"
+     ]
+    }
+   ],
+   "source": [
+    "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n",
+    "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))\n",
+    "\n",
+    "# same parameter with different interpolation mode for image and segmentation\n",
+    "rand_affine_img = RandAffine(prob=1.0, rotate_range=np.pi/4, translate_range=(96, 96, 96),\n",
+    "                             spatial_size=(64, 64, 64), mode='bilinear',\n",
+    "                             as_tensor_output=True, device=torch.device('cuda:0'))\n",
+    "rand_affine_seg = RandAffine(prob=1.0, rotate_range=np.pi/4, translate_range=(96, 96, 96),\n",
+    "                             spatial_size=(64, 64, 64), mode='nearest',\n",
+    "                             as_tensor_output=True, device=torch.device('cuda:0'))\n",
+    "    \n",
+    "imtrans = Compose([\n",
+    "    Rescale(),\n",
+    "    AddChannel(),\n",
+    "    rand_affine_img,\n",
+    "])    \n",
+    "\n",
+    "segtrans = Compose([\n",
+    "    AddChannel(),\n",
+    "    rand_affine_seg,\n",
+    "])    \n",
+    "    \n",
+    "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n",
+    "loader = DataLoader(ds, batch_size=3, num_workers=0)\n",
+    "\n",
+    "im, seg = monai.utils.misc.first(loader)\n",
+    "\n",
+    "print(im.shape, seg.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.42 s ± 1.72 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit -n 3 data = next(iter(loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TITAN Xp COLLECTORS EDITION\n",
+      "|===========================================================================|\n",
+      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
+      "|===========================================================================|\n",
+      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| Allocated memory      |    6144 KB |  156672 KB |   16680 MB |   16674 MB |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| Active memory         |    6144 KB |  156672 KB |   16680 MB |   16674 MB |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| GPU reserved memory   |  225280 KB |  225280 KB |  225280 KB |       0 B  |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| Non-releasable memory |   14336 KB |   77824 KB |   11219 MB |   11205 MB |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| Allocations           |       2    |      14    |    2222    |    2220    |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| Active allocs         |       2    |      14    |    2222    |    2220    |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| GPU reserved segments |       8    |       8    |       8    |       0    |\n",
+      "|---------------------------------------------------------------------------|\n",
+      "| Non-releasable allocs |       1    |       6    |    1460    |    1459    |\n",
+      "|===========================================================================|\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(torch.cuda.get_device_name(0))\n",
+    "print(torch.cuda.memory_summary(0, abbreviated=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -rf {tempdir}"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}