argmax_bench.ipynb

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   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch as T\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filters = T.rand(8,300,300,2).cuda()\n",
    "img = np.random.rand(300,300).astype(np.float32)\n",
    "def conv_only(img, w, shape):\n",
    "    fimg = T.zeros(*w.shape[1:]).cuda()\n",
    "    fimg[:,:,0] = T.from_numpy(img).cuda()\n",
    "    conv = T.ifft(T.fft(fimg,2)*w,2)\n",
    "    return conv\n",
    "\n",
    "def find_position(img, w, shape):\n",
    "    fimg = T.zeros(*w.shape[1:]).cuda()\n",
    "    fimg[:,:,0] = T.from_numpy(img).cuda()\n",
    "    conv = T.ifft(T.fft(fimg,2)*w,2)\n",
    "    idx = conv.argmax()\n",
    "    return np.unravel_index(idx, shape)\n",
    "\n",
    "def find_position_2(img, w, shape):\n",
    "    fimg = T.zeros(*w.shape[1:]).cuda()\n",
    "    fimg[:,:,0] = T.from_numpy(img).cuda()\n",
    "    conv = T.ifft(T.fft(fimg,2)*w,2)\n",
    "    conv = conv.cpu().numpy()\n",
    "    idx = np.argmax(conv)\n",
    "    return np.unravel_index(idx, shape)\n",
    "\n",
    "shape = [*filters.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize CuBLAS\n",
    "_ = conv_only(img,filters, shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.17 ms ± 61.2 µs per loop (mean ± std. dev. of 3 runs, 3 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3 -n 3\n",
    "conv_only(img,filters, shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.1 ms ± 828 µs per loop (mean ± std. dev. of 3 runs, 3 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3 -n 3\n",
    "find_position(img,filters, shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.25 ms ± 285 µs per loop (mean ± std. dev. of 3 runs, 3 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3 -n 3\n",
    "find_position_2(img,filters, shape)"
   ]
  }
 ],
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