import torch
import torch.nn as nn
class VGG(nn.Module):
def __init__(self, depth, num_classes):
super(VGG, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 1)),
nn.Conv2d(256, 512, kernel_size=(3, 3), padding=(0, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=(3, 3), padding=(0, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 1)),
nn.Conv2d(512, 512, kernel_size=(3, 3), padding=(0, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=(3, 3), padding=(0, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 1)),
)
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
self._initialize_weights()
self.depth = depth
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