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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| class AttentionGate(nn.Module): | |
| def __init__(self, F_g, F_l, F_int): | |
| super(AttentionGate, self).__init__() | |
| self.W_g = nn.Sequential( | |
| nn.Conv2d(F_g, F_int, kernel_size=1, stride=1, padding=0, bias=True), | |
| nn.BatchNorm2d(F_int), | |
| ) | |
| self.W_x = nn.Sequential( | |
| nn.Conv2d(F_l, F_int, kernel_size=1, stride=1, padding=0, bias=True), | |
| nn.BatchNorm2d(F_int), | |
| ) | |
| self.psi = nn.Sequential( | |
| nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True), | |
| nn.BatchNorm2d(1), | |
| nn.Sigmoid(), | |
| ) | |
| self.relu = nn.ReLU(inplace=True) | |
| def forward(self, g, x): | |
| g1 = self.W_g(g) | |
| x1 = self.W_x(x) | |
| psi = self.relu(g1 + x1) | |
| psi = self.psi(psi) | |
| return x * psi | |
| class DoubleConv(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(DoubleConv, self).__init__() | |
| self.conv = nn.Sequential( | |
| nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True), | |
| ) | |
| def forward(self, x): | |
| return self.conv(x) | |
| class AttentionUNet(nn.Module): | |
| def __init__(self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]): | |
| super(AttentionUNet, self).__init__() | |
| self.ups = nn.ModuleList() | |
| self.downs = nn.ModuleList() | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.attention_gates = nn.ModuleList() | |
| # Down part of U-Net | |
| for feature in features: | |
| self.downs.append(DoubleConv(in_channels, feature)) | |
| in_channels = feature | |
| # Up part of U-Net | |
| for feature in reversed(features): | |
| self.ups.append( | |
| nn.ConvTranspose2d( | |
| feature * 2, | |
| feature, | |
| kernel_size=2, | |
| stride=2, | |
| ) | |
| ) | |
| # Attention Gate | |
| self.attention_gates.append( | |
| AttentionGate(F_g=feature, F_l=feature, F_int=feature // 2) | |
| ) | |
| self.ups.append(DoubleConv(feature * 2, feature)) | |
| # Bottleneck | |
| self.bottleneck = DoubleConv(features[-1], features[-1] * 2) | |
| # Final Conv | |
| self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1) | |
| def forward(self, x): | |
| skip_connections = [] | |
| # Encoder path | |
| for down in self.downs: | |
| x = down(x) | |
| skip_connections.append(x) | |
| x = self.pool(x) | |
| x = self.bottleneck(x) | |
| skip_connections = skip_connections[::-1] # Reverse to use from back | |
| # Decoder path | |
| for idx in range(0, len(self.ups), 2): | |
| x = self.ups[idx](x) | |
| skip_connection = skip_connections[idx // 2] | |
| # If sizes don't match | |
| if x.shape != skip_connection.shape: | |
| x = F.interpolate(x, size=skip_connection.shape[2:]) | |
| # Apply attention gate | |
| skip_connection = self.attention_gates[idx // 2](g=x, x=skip_connection) | |
| # Concatenate | |
| concat_skip = torch.cat((skip_connection, x), dim=1) | |
| x = self.ups[idx + 1](concat_skip) | |
| # Final conv | |
| return self.final_conv(x) | |
| def load_model(model_path): | |
| """ | |
| Load the trained model | |
| Args: | |
| model_path: Path to the model weights | |
| Returns: | |
| Loaded model | |
| """ | |
| # Define device | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Initialize model | |
| model = AttentionUNet(in_channels=3, out_channels=1) | |
| # Load model weights | |
| model.load_state_dict(torch.load(model_path, map_location=device)) | |
| # Set model to evaluation mode | |
| model.eval() | |
| return model.to(device) | |