Delete vgg16.py

vgg16.py

@@ -1,72 +0,0 @@
-import torch.nn as nn
-import torchvision.models as models
-
-""" VGG_16 Architecture
-VGG(
-  (features): Sequential(
-    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (1): ReLU(inplace=True)
-    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (3): ReLU(inplace=True)
-    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (6): ReLU(inplace=True)
-    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (8): ReLU(inplace=True)
-    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (11): ReLU(inplace=True)
-    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (13): ReLU(inplace=True)
-    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (15): ReLU(inplace=True)
-    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (18): ReLU(inplace=True)
-    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (20): ReLU(inplace=True)
-    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (22): ReLU(inplace=True)
-    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (25): ReLU(inplace=True)
-    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (27): ReLU(inplace=True)
-    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-    (29): ReLU(inplace=True)
-    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-  )
-  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
-  (classifier): Sequential(
-    (0): Linear(in_features=25088, out_features=4096, bias=True)
-    (1): ReLU(inplace=True)
-    (2): Dropout(p=0.5, inplace=False)
-    (3): Linear(in_features=4096, out_features=4096, bias=True)
-    (4): ReLU(inplace=True)
-    (5): Dropout(p=0.5, inplace=False)
-    (6): Linear(in_features=4096, out_features=1000, bias=True)
-  )
-)
-"""
-
-class VGG_16(nn.Module):
-    def __init__(self):
-        super(VGG_16, self).__init__()
-        self.model = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features[:30]
-        
-        for i, _ in enumerate(self.model):
-            if i in [4, 9, 16, 23]:
-                self.model[i] = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
-                
-    def forward(self, x):
-        features = []
-        for i, layer in enumerate(self.model):
-            x = layer(x)
-            if i in [0, 5, 10, 17, 24]:
-                features.append(x)
-        return features
-    
-
-if __name__ == '__main__':
-    model = VGG_16()
-    print(model)