unet.ipynb

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   "source": [
    "- Difference: Padded Convolutuions (Not valid Convlutions)\n",
    "- \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn \n",
    "import torchvision.transforms.functional as TF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DoubleConv(nn.Module): \n",
    "    def __init__(self,in_channels,out_channels): \n",
    "        super(DoubleConv, self).__init__()\n",
    "        #Why are we doing double conv, when in the paper we have triple conv? \n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv2d(in_channels,out_channels,kernel_size=3,stride=1,padding=1,bias=False),\n",
    "            #What does the batchnorm does again? \n",
    "            nn.BatchNorm2d(out_channels), \n",
    "            #How is ReLU helpful here? \n",
    "            nn.ReLU(inplace=True),\n",
    "            ## Doing the same thing here as well\n",
    "            nn.Conv2d(out_channels,out_channels,3,1,1,bias=False),\n",
    "            nn.BatchNorm2d(out_channels), \n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        \n",
    "    def forward(self,x): \n",
    "        return self.conv(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UNET(nn.Module): \n",
    "    def __init__(self,in_channels=3,out_channels=1,features=[64,128,256,512]): \n",
    "        super(UNET,self).__init__()\n",
    "        self.downs = nn.ModuleList()\n",
    "        self.ups = nn.ModuleList()\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2,stride=2)\n",
    "\n",
    "        #Downsampling part of UNET\n",
    "        for feature in features: \n",
    "            self.downs.append(DoubleConv(in_channels,feature))\n",
    "            in_channels = feature\n",
    "\n",
    "\n",
    "        #Upsampling part of UNET: \n",
    "        for feature in reversed(features):\n",
    "            self.ups.append(\n",
    "                nn.ConvTranspose2d(\n",
    "                    feature*2, feature, kernel_size=2,stride=2\n",
    "                )\n",
    "            ) \n",
    "            self.ups.append(DoubleConv(feature*2, feature))\n",
    "\n",
    "        self.bottleneck = DoubleConv(features[-1],features[-1]*2)\n",
    "        self.final_conv = nn.Conv2d(features[0], out_channels,kernel_size=1)\n",
    "             \n",
    "    def forward(self,x): \n",
    "        skip_connections = []\n",
    "        for down in self.downs: \n",
    "            x = down(x)\n",
    "            skip_connections.append(x)\n",
    "            x = self.pool(x)\n",
    "\n",
    "        x = self.bottleneck(x)\n",
    "        skip_connections = skip_connections[::-1]\n",
    "\n",
    "        for idx in range(0,len(self.ups),2): \n",
    "            x = self.ups[idx](x)\n",
    "            skip_connection = skip_connections[idx//2]\n",
    "            \n",
    "\n",
    "            if x.shape != skip_connection.shape:\n",
    "                x = TF.resize(x,size=skip_connection.shape[2:])\n",
    "                \n",
    "            concat_skip = torch.cat((skip_connection,x),dim=1)\n",
    "            x = self.ups[idx+1](concat_skip)\n",
    "\n",
    "        return self.final_conv(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 1, 161, 161])\n",
      "torch.Size([3, 1, 161, 161])\n",
      "torch.Size([3, 1, 161, 161])\n"
     ]
    }
   ],
   "source": [
    "def test(): \n",
    "    x = torch.randn((3,1,161,161))\n",
    "    print(x.shape)\n",
    "    model = UNET(in_channels=1,out_channels=1)\n",
    "    preds = model(x)\n",
    "    print(preds.shape)\n",
    "    print(x.shape)\n",
    "    assert preds.shape == x.shape\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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