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import torch
import torch.nn as nn
import torchvision.models as models
# ORIGINAL ORGINAL NET (from template)
class Net_BINARY(nn.Module):
def __init__(self, n_classes: int) -> None:
super(Net_BINARY, self).__init__()
self.cnn_layers = nn.Sequential(
# Defining a 2D convolution layer
nn.Conv2d(1, 32, kernel_size=4, stride=1),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.ReLU6(inplace=True),
nn.AvgPool2d(kernel_size=3),
torch.nn.Dropout(p=0.5, inplace=True),
# Defining another 2D convolution layer
nn.Conv2d(32, 64, kernel_size=4, stride=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.ReLU6(inplace=True),
nn.AvgPool2d(kernel_size=3),
torch.nn.Dropout(p=0.25, inplace=True),
# Defining another 2D convolution layer
nn.Conv2d(64, 128, kernel_size=3, stride=1),
nn.PReLU(),
nn.BatchNorm2d(128),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=3),
torch.nn.Dropout(p=0.125, inplace=True),
)
self.linear_layers = nn.Sequential(
nn.Linear(1152, 312),
nn.Linear(312, n_classes)
)
# Defining the forward pass
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.cnn_layers(x)
# After our convolutional layers which are 2D, we need to flatten our
# input to be 1 dimensional, as the linear layers require this.
x = x.view(x.size(0), -1)
x = self.linear_layers(x)
return x
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