9.8_rcnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.8 区域卷积神经网络（R-CNN）系列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.0a0+6b959ee\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "print(torchvision.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.8.2 Fast R-CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[ 0.,  1.,  2.,  3.],\n",
       "          [ 4.,  5.,  6.,  7.],\n",
       "          [ 8.,  9., 10., 11.],\n",
       "          [12., 13., 14., 15.]]]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rois = torch.tensor([[0, 0, 0, 20, 20], [0, 0, 10, 30, 30]], dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[ 5.,  6.],\n",
       "          [ 9., 10.]]],\n",
       "\n",
       "\n",
       "        [[[ 9., 11.],\n",
       "          [13., 15.]]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torchvision.ops.roi_pool(X, rois, output_size=(2, 2), spatial_scale=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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