File size: 1,263 Bytes
c3f9ef7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import torch
import torch.nn as nn
class MyNetwork(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 5, padding=2),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 5, padding=2),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(256, 10)
)
def forward(self, x):
x = self.model(x)
return x
if __name__ == '__main__':
mynetwork = MyNetwork()
input = torch.ones((64, 3, 32, 32))
output = mynetwork(input)
print(output.shape)
total_params = sum(p.numel() for p in mynetwork.parameters())
print(f"Total params：{total_params}")
print(f"Total params：{total_params / 1000000}M")
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