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import torch.nn as nn
class custom_model(nn.Module):
def __init__(self, input_param: int, output_param: int):
super().__init__()
self.layer_block1 = nn.Sequential(
nn.Conv2d(in_channels=input_param, out_channels=32, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(num_features=32),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(num_features=64),
nn.MaxPool2d(kernel_size=2)
)
self.layer_block2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(num_features=128),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(num_features=128),
nn.MaxPool2d(kernel_size=2)
)
self.layer_block3 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(num_features=256),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(num_features=256),
nn.MaxPool2d(kernel_size=2)
)
self.layer_block4 = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=256*12*12, out_features=256),
nn.Dropout(0.5),
nn.Linear(in_features=256, out_features=output_param)
)
def forward(self, x):
x = self.layer_block1(x)
x = self.layer_block2(x)
x = self.layer_block3(x)
x = self.layer_block4(x)
return x
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