GoogleLeNet.ipynb

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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transfroms\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GoogleNet(nn.Module):\n",
    "    def __init__(self,in_channels=3,num_classes=1000):\n",
    "        super(GoogleNet,self).__init__()\n",
    "\n",
    "        self.conv1 = conv_block(in_channels=in_channels,out_channels=64,kernel_size=(7,7),stride=(2,2),padding=(3,3))\n",
    "        self.maxpool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1) \n",
    "        self.conv2 = conv_block(64,192,kernel_size=3,stride=1,padding=1)\n",
    "        self.maxpool2 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)\n",
    "\n",
    "        self.inception3a = Inception_Block(192, 64, 96, 128, 16, 32, 32)\n",
    "        self.inception3b = Inception_Block(256, 128, 128, 192, 32, 96, 64)\n",
    "        self.maxpool3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)\n",
    "        self.inception4a = Inception_Block(480, 192, 96, 208, 16 ,48, 64)\n",
    "        self.inception4b = Inception_Block(512, 160, 112, 224, 24 ,64, 64)\n",
    "        self.inception4c = Inception_Block(512, 128, 128, 256, 24 ,64, 64)\n",
    "        self.inception4d = Inception_Block(512, 112, 144, 288, 32 ,64, 64)\n",
    "        self.inception4e = Inception_Block(528, 256, 160, 320, 32 ,128, 128)\n",
    "        self.maxpool4 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)\n",
    "\n",
    "        self.inception5a = Inception_Block(832, 256, 160, 320, 32 ,128, 128)\n",
    "        self.inception5b = Inception_Block(832, 384, 192, 384, 48 ,128, 128)\n",
    "        \n",
    "        self.avgpool = nn.AvgPool2d(kernel_size=7,stride=1)\n",
    "        self.dropout = nn.Dropout(p=0.4)\n",
    "        self.fc1 = nn.Linear(1024,1000)\n",
    "\n",
    "    def forward(self,x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.maxpool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.maxpool2(x)\n",
    "\n",
    "        x = self.inception3a(x)\n",
    "        x = self.inception3b(x)\n",
    "        x = self.maxpool3(x)\n",
    "\n",
    "        x = self.inception4a(x)\n",
    "        x = self.inception4b(x)\n",
    "        x = self.inception4c(x)\n",
    "        x = self.inception4d(x)\n",
    "        x = self.inception4e(x)\n",
    "        x = self.maxpool4(x)\n",
    "\n",
    "        x = self.inception5a(x)\n",
    "        x = self.inception5b(x)\n",
    "        x = self.avgpool(x)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc1(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class Inception_Block(nn.Module):\n",
    "    def __init__(self,in_channels,out_1x1,red_3x3,out_3x3,red_5x5,out_5x5,out_1x1_pool):\n",
    "        super(Inception_Block,self).__init__()\n",
    "        self.branch1 = conv_block(in_channels,out_1x1,kernel_size=1)\n",
    "        self.branch2 = nn.Sequential(\n",
    "            conv_block(in_channels,red_3x3,kernel_size=1),\n",
    "            conv_block(red_3x3,out_3x3,kernel_size=3,stride=1,padding=1)\n",
    "        )\n",
    "        self.branch3 = nn.Sequential(\n",
    "            conv_block(in_channels,red_5x5,kernel_size=1),\n",
    "            conv_block(red_5x5,out_5x5,kernel_size=5,stride=1,padding=2)\n",
    "        ) \n",
    "        self.branch4 = nn.Sequential(\n",
    "            nn.MaxPool2d(kernel_size=3,stride=1,padding=1),\n",
    "            conv_block(in_channels,out_1x1_pool,kernel_size=1)\n",
    "        ) \n",
    "    \n",
    "    def forward(self,x):\n",
    "        return torch.cat([self.branch1(x),self.branch2(x),self.branch3(x),self.branch4(x)],1)\n",
    "\n",
    "\n",
    "class conv_block(nn.Module):\n",
    "    def __init__(self,in_channels,out_channels,**kwargs):\n",
    "        super(conv_block,self).__init__()\n",
    "        self.relu = nn.ReLU()\n",
    "        self.conv = nn.Conv2d(in_channels=in_channels,out_channels=out_channels,**kwargs)\n",
    "        self.batchnorm = nn.BatchNorm2d(out_channels)\n",
    "    \n",
    "    def forward(self,x):\n",
    "        return self.relu(self.batchnorm(self.conv(x)))"
   ]
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     "text": [
      "torch.Size([3, 1000])\n"
     ]
    }
   ],
   "source": [
    "x = torch.randn(3,3,224,224)\n",
    "model = GoogleNet()\n",
    "print(model(x).shape)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
  }
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