notebook/convert_HardNet_to_JIT.ipynb

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      "text/plain": [
       "HardNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "    (5): ReLU()\n",
       "    (6): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "    (8): ReLU()\n",
       "    (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "    (11): ReLU()\n",
       "    (12): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (13): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "    (14): ReLU()\n",
       "    (15): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (16): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "    (17): ReLU()\n",
       "    (18): Dropout(p=0.1, inplace=False)\n",
       "    (19): Conv2d(128, 128, kernel_size=(8, 8), stride=(1, 1), bias=False)\n",
       "    (20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class HardNet(nn.Module):\n",
    "    \"\"\"HardNet model definition\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super(HardNet, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=3, padding=1, bias = False),\n",
    "            nn.BatchNorm2d(32, affine=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias = False),\n",
    "            nn.BatchNorm2d(32, affine=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias = False),\n",
    "            nn.BatchNorm2d(64, affine=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias = False),\n",
    "            nn.BatchNorm2d(64, affine=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(64, 128, kernel_size=3, stride=2,padding=1, bias = False),\n",
    "            nn.BatchNorm2d(128, affine=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1, bias = False),\n",
    "            nn.BatchNorm2d(128, affine=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.1),\n",
    "            nn.Conv2d(128, 128, kernel_size=8, bias = False),\n",
    "            nn.BatchNorm2d(128, affine=False),\n",
    "        )\n",
    "\n",
    "    def input_norm(self,x):\n",
    "        std, mean = torch.std_mean(x, dim=[2,3])\n",
    "        return (x - mean.detach()[...,None,None]) / (std.detach()[...,None,None]+1e-7)\n",
    "\n",
    "    def forward(self, input):\n",
    "        x_features = self.features(self.input_norm(input))\n",
    "        x = x_features.view(x_features.size(0), -1)\n",
    "        return F.normalize(x, dim=1, p=2.)\n",
    "\n",
    "model = HardNet()\n",
    "checkpoint = '../pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth'\n",
    "model.load_state_dict(torch.load(checkpoint, map_location=torch.device('cpu'))['state_dict'])\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting to JIT\n",
    "example = torch.rand(1,1,32,32)\n",
    "traced_script_module = torch.jit.trace(model, example)\n",
    "traced_script_module.save(\"HardNetLib+JIT.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0.]], grad_fn=<SubBackward0>)\n"
     ]
    }
   ],
   "source": [
    "#Checking if this works properly\n",
    "inp1 = torch.rand(1, 1, 32, 32)\n",
    "out_jit = traced_script_module(inp1)\n",
    "out_pytorch = model(inp1)\n",
    "print (out_jit - out_pytorch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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