Added VGG implementation (#2)

Author: burntcarrot

CNNs/vgg.py

@@ -0,0 +1,134 @@
+"""
+VGG Implementation in PyTorch
+
+Paper Link: https://arxiv.org/pdf/1409.1556.pdf
+"""
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+
+model_configs = {
+    "VGG11": [
+        64,
+        "pooling",
+        128,
+        "pooling",
+        256,
+        256,
+        "pooling",
+        512,
+        512,
+        "pooling",
+        512,
+        512,
+        "pooling",
+    ],
+    "VGG13": [
+        64,
+        64,
+        "pooling",
+        128,
+        128,
+        "pooling",
+        256,
+        256,
+        "pooling",
+        512,
+        512,
+        "pooling",
+        512,
+        512,
+        "pooling",
+    ],
+    "VGG16": [
+        64,
+        64,
+        "pooling",
+        128,
+        128,
+        "pooling",
+        256,
+        256,
+        256,
+        "pooling",
+        512,
+        512,
+        512,
+        "pooling",
+        512,
+        512,
+        512,
+        "pooling",
+    ],
+    "VGG19": [
+        64,
+        64,
+        "pooling",
+        128,
+        128,
+        "pooling",
+        256,
+        256,
+        256,
+        256,
+        "pooling",
+        512,
+        512,
+        512,
+        512,
+        "pooling",
+        512,
+        512,
+        512,
+        512,
+        "pooling",
+    ],
+}
+
+
+class VGG(nn.Module):
+    def __init__(self, model_name):
+        super(VGG, self).__init__()
+        self.features = self._make_layers(model_configs[model_name])
+        self.classifier = nn.Linear(512, 10)
+
+    def forward(self, x):
+        out = self.features(x)
+        out = out.view(out.size(0), -1)
+        out = self.classifier(out)
+        return out
+
+    # Main model creation function
+    def _make_layers(self, model_configs):
+
+        layers = []
+        in_channels = 3
+
+        for x in model_configs:
+            if x == "pooling":
+                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+            else:
+                layers += [
+                    nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
+                    nn.BatchNorm2d(x),
+                    nn.ReLU(inplace=True),
+                ]
+                in_channels = x
+
+        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
+
+        # Unpack layers for adding to the final Sequential container
+        return nn.Sequential(*layers)
+
+
+# VGG16 creation using generic class
+def VGG16():
+    return VGG("VGG16")
+
+
+# VGG19 creation using generic class
+def VGG19():
+    return VGG("VGG19")
+