SLIVERnet_v2_tests.ipynb

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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing SLIVER-Net code"
   ]
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   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"  # GPU ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   },
   "outputs": [],
   "source": [
    "# sample imput\n",
    "import torch\n",
    "x = torch.zeros(1, 3, 97*224, 224).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512, 679, 7])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sliver_net.model import load_backbone\n",
    "backbone = load_backbone(\"kermany\").cuda()\n",
    "backbone(x).shape\n",
    "# model = SliverNet2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sliver_net.model import SliverNet2 \n",
    "model = SliverNet2(backbone, n_out=2).cuda()\n",
    "model(x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "start_time": "2021-03-30T19:54:45.826785Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 512, 120, 8])\n",
      "torch.Size([5, 2])\n"
     ]
    }
   ],
   "source": [
    "# change input size, keep model the same\n",
    "x = torch.zeros(5, 3, 15*256, 256).cuda()\n",
    "print(backbone(x).shape)\n",
    "print(model(x).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T19:53:55.999315Z",
     "start_time": "2021-03-30T19:53:55.936618Z"
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   },
   "source": [
    "# Testing SLIVER-Net features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heat maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T20:01:47.975098Z",
     "start_time": "2021-03-30T20:01:47.931779Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 120, 8])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_maps = model.feature_maps(x)\n",
    "feature_maps.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Max Slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T20:01:51.630647Z",
     "start_time": "2021-03-30T20:01:51.578675Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 32])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.max_slices(x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-30T19:57:17.101816Z",
     "start_time": "2021-03-30T19:57:17.063844Z"
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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