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import torch
import torch.nn as nn
class EuroSATCNN(nn.Module):
def __init__(self, num_classes, img_height=64, img_width=64):
super(EuroSATCNN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(13, 128, kernel_size=4, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(128, 64, kernel_size=4, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(64, 32, kernel_size=4, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(32, 16, kernel_size=4, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
)
with torch.no_grad():
dummy_input = torch.randn(1, 13, img_height, img_width)
out = self.features(dummy_input)
fc1_input_size = out.view(1, -1).shape[1]
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(fc1_input_size, 64),
nn.ReLU(),
nn.Linear(64, num_classes)
)
def forward(self, x):
x = self.features(x)
x = self.classifier(x)
return x |