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vgg19.py
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vgg19.py
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import torch
class vgg19(torch.nn.Module):
def __init__(self, outputs):
super(vgg19, self).__init__()
self.features = torch.nn.Sequential(
# first block
torch.nn.Conv2d(3, 64, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(64, 64, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.MaxPool2d(kernel_size=2, stride=2),
# second block
torch.nn.Conv2d(64, 128, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(128, 128, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.MaxPool2d(kernel_size=2, stride=2),
# third block
torch.nn.Conv2d(128, 256, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.MaxPool2d(kernel_size=2, stride=2),
# fourth block
torch.nn.Conv2d(256, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.MaxPool2d(kernel_size=2, stride=2),
# fifth block
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),
torch.nn.ReLU(True),
torch.nn.MaxPool2d(kernel_size=2, stride=2),
# tail
torch.nn.AdaptiveAvgPool2d((7, 7)))
self.classifier = torch.nn.Sequential(
torch.nn.Linear(512*7*7, 4096),
torch.nn.ReLU(True),
torch.nn.Dropout(0.5),
torch.nn.Linear(4096, 4096),
torch.nn.ReLU(True),
torch.nn.Dropout(0.5),
torch.nn.Linear(4096, outputs))
def forward(self, x):
return self.classifier(torch.flatten(self.features(x), 0))