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odak.learn.lensless

spec_track

Bases: Module

The learned holography model used in the paper, Ziyang Chen and Mustafa Dogan and Josef Spjut and Kaan Akşit. "SpecTrack: Learned Multi-Rotation Tracking via Speckle Imaging." In SIGGRAPH Asia 2024 Posters (SA Posters '24).

Parameters:

  • reduction (str, default: 'sum' ) – 
        Reduction used for torch.nn.MSELoss and torch.nn.L1Loss. The default is 'sum'.
  • device – 
        Device to run the model on. Default is CPU.
Source code in odak/learn/lensless/models.py 
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class spec_track(nn.Module):
    """
    The learned holography model used in the paper, Ziyang Chen and Mustafa Dogan and Josef Spjut and Kaan Akşit. "SpecTrack: Learned Multi-Rotation Tracking via Speckle Imaging." In SIGGRAPH Asia 2024 Posters (SA Posters '24).

    Parameters
    ----------
    reduction : str
                Reduction used for torch.nn.MSELoss and torch.nn.L1Loss. The default is 'sum'.
    device    : torch.device
                Device to run the model on. Default is CPU.
    """
    def __init__(
                 self,
                 reduction = 'sum',
                 device = torch.device('cpu')
                ):
        super(spec_track, self).__init__()
        self.device = device
        self.init_layers()
        self.reduction = reduction
        self.l2 = torch.nn.MSELoss(reduction = self.reduction)
        self.l1 = torch.nn.L1Loss(reduction = self.reduction)
        self.train_history = []
        self.validation_history = []


    def init_layers(self):
        """
        Initialize the layers of the network.
        """
        # Convolutional layers with batch normalization and pooling
        self.network = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(5, 32, kernel_size=3, padding=1)),
            ('bn1', nn.BatchNorm2d(32)),
            ('relu1', nn.ReLU()),
            ('pool1', nn.MaxPool2d(kernel_size=3)),

            ('conv2', nn.Conv2d(32, 64, kernel_size=5, padding=1)),
            ('bn2', nn.BatchNorm2d(64)),
            ('relu2', nn.ReLU()),
            ('pool2', nn.MaxPool2d(kernel_size=3)),

            ('conv3', nn.Conv2d(64, 128, kernel_size=7, padding=1)),
            ('bn3', nn.BatchNorm2d(128)),
            ('relu3', nn.ReLU()),
            ('pool3', nn.MaxPool2d(kernel_size=3)),

            ('flatten', nn.Flatten()),

            ('fc1', nn.Linear(6400, 2048)),
            ('fc_bn1', nn.BatchNorm1d(2048)),
            ('relu_fc1', nn.ReLU()),

            ('fc2', nn.Linear(2048, 1024)),
            ('fc_bn2', nn.BatchNorm1d(1024)),
            ('relu_fc2', nn.ReLU()),

            ('fc3', nn.Linear(1024, 512)),
            ('fc_bn3', nn.BatchNorm1d(512)),
            ('relu_fc3', nn.ReLU()),

            ('fc4', nn.Linear(512, 128)),
            ('fc_bn4', nn.BatchNorm1d(128)),
            ('relu_fc4', nn.ReLU()),

            ('fc5', nn.Linear(128, 3))
        ])).to(self.device)


    def forward(self, x):
        """
        Forward pass of the network.

        Parameters
        ----------
        x : torch.Tensor
            Input tensor.

        Returns
        -------
        torch.Tensor
            Output tensor.
        """
        return self.network(x)


    def evaluate(self, input_data, ground_truth, weights = [100., 1.]):
        """
        Evaluate the model's performance.

        Parameters
        ----------
        input_data    : torch.Tensor
                        Predicted data from the model.
        ground_truth  : torch.Tensor
                        Ground truth data.
        weights       : list
                        Weights for L2 and L1 losses. Default is [100., 1.].

        Returns
        -------
        torch.Tensor
            Combined weighted loss.
        """
        loss = weights[0] * self.l2(input_data, ground_truth) + weights[1] * self.l1(input_data, ground_truth)
        return loss


    def fit(self, trainloader, testloader, number_of_epochs=100, learning_rate=1e-5, weight_decay=1e-5, directory='./output'):
        """
        Train the model.

        Parameters
        ----------
        trainloader      : torch.utils.data.DataLoader
                           Training data loader.
        testloader       : torch.utils.data.DataLoader
                           Testing data loader.
        number_of_epochs : int
                           Number of epochs to train for. Default is 100.
        learning_rate    : float
                           Learning rate for the optimizer. Default is 1e-5.
        weight_decay     : float
                           Weight decay for the optimizer. Default is 1e-5.
        directory        : str
                           Directory to save the model weights. Default is './output'.
        """
        makedirs(directory, exist_ok=True)
        makedirs(join(directory, "log"), exist_ok=True)

        self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate, weight_decay=weight_decay)
        best_val_loss = float('inf')

        for epoch in range(number_of_epochs):
            # Training phase
            self.train()
            train_loss = 0.0
            train_batches = 0
            train_pbar = tqdm(trainloader, desc=f"Epoch {epoch+1}/{number_of_epochs} [Train]", leave=False, dynamic_ncols=True)

            for batch, labels in train_pbar:
                self.optimizer.zero_grad()
                batch, labels = batch.to(self.device), labels.to(self.device)
                predicts = torch.squeeze(self.forward(batch))
                loss = self.evaluate(predicts, labels)
                loss.backward()
                self.optimizer.step()

                train_loss += loss.item()
                train_batches += 1
                train_pbar.set_postfix({'Loss': f"{loss.item():.4f}"})

            avg_train_loss = train_loss / train_batches
            self.train_history.append(avg_train_loss)

            # Validation phase
            self.eval()
            val_loss = 0.0
            val_batches = 0
            val_pbar = tqdm(testloader, desc=f"Epoch {epoch+1}/{number_of_epochs} [Val]", leave=False, dynamic_ncols=True)

            with torch.no_grad():
                for batch, labels in val_pbar:
                    batch, labels = batch.to(self.device), labels.to(self.device)
                    predicts = torch.squeeze(self.forward(batch), dim=1)
                    loss = self.evaluate(predicts, labels)

                    val_loss += loss.item()
                    val_batches += 1
                    val_pbar.set_postfix({'Loss': f"{loss.item():.4f}"})

            avg_val_loss = val_loss / val_batches
            self.validation_history.append(avg_val_loss)

            # Print epoch summary
            print(f"Epoch {epoch+1}/{number_of_epochs} - Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}")

            # Save best model
            if avg_val_loss < best_val_loss:
                best_val_loss = avg_val_loss
                self.save_weights(join(directory, f"best_model_epoch_{epoch+1}.pt"))
                print(f"Best model saved at epoch {epoch+1}")

        # Save training history
        torch.save(self.train_history, join(directory, "log", "train_log.pt"))
        torch.save(self.validation_history, join(directory, "log", "validation_log.pt"))
        print("Training completed. History saved.")


    def save_weights(self, filename = './weights.pt'):
        """
        Save the current weights of the network to a file.

        Parameters
        ----------
        filename : str
                   Path to save the weights. Default is './weights.pt'.
        """
        torch.save(self.network.state_dict(), os.path.expanduser(filename))


    def load_weights(self, filename = './weights.pt'):
        """
        Load weights for the network from a file.

        Parameters
        ----------
        filename : str
                   Path to load the weights from. Default is './weights.pt'.
        """
        self.network.load_state_dict(torch.load(os.path.expanduser(filename), weights_only = True))
        self.network.eval()

evaluate(input_data, ground_truth, weights=[100.0, 1.0])

Evaluate the model's performance.

Parameters:

  • input_data – 
            Predicted data from the model.
  • ground_truth – 
            Ground truth data.
  • weights – 
            Weights for L2 and L1 losses. Default is [100., 1.].

Returns:

  • Tensor –

    Combined weighted loss.

Source code in odak/learn/lensless/models.py 
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def evaluate(self, input_data, ground_truth, weights = [100., 1.]):
    """
    Evaluate the model's performance.

    Parameters
    ----------
    input_data    : torch.Tensor
                    Predicted data from the model.
    ground_truth  : torch.Tensor
                    Ground truth data.
    weights       : list
                    Weights for L2 and L1 losses. Default is [100., 1.].

    Returns
    -------
    torch.Tensor
        Combined weighted loss.
    """
    loss = weights[0] * self.l2(input_data, ground_truth) + weights[1] * self.l1(input_data, ground_truth)
    return loss

fit(trainloader, testloader, number_of_epochs=100, learning_rate=1e-05, weight_decay=1e-05, directory='./output')

Train the model.

Parameters:

  • trainloader – 
               Training data loader.
  • testloader – 
               Testing data loader.
  • number_of_epochs (int, default: 100 ) – 
               Number of epochs to train for. Default is 100.
  • learning_rate – 
               Learning rate for the optimizer. Default is 1e-5.
  • weight_decay – 
               Weight decay for the optimizer. Default is 1e-5.
  • directory – 
               Directory to save the model weights. Default is './output'.
Source code in odak/learn/lensless/models.py 
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def fit(self, trainloader, testloader, number_of_epochs=100, learning_rate=1e-5, weight_decay=1e-5, directory='./output'):
    """
    Train the model.

    Parameters
    ----------
    trainloader      : torch.utils.data.DataLoader
                       Training data loader.
    testloader       : torch.utils.data.DataLoader
                       Testing data loader.
    number_of_epochs : int
                       Number of epochs to train for. Default is 100.
    learning_rate    : float
                       Learning rate for the optimizer. Default is 1e-5.
    weight_decay     : float
                       Weight decay for the optimizer. Default is 1e-5.
    directory        : str
                       Directory to save the model weights. Default is './output'.
    """
    makedirs(directory, exist_ok=True)
    makedirs(join(directory, "log"), exist_ok=True)

    self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate, weight_decay=weight_decay)
    best_val_loss = float('inf')

    for epoch in range(number_of_epochs):
        # Training phase
        self.train()
        train_loss = 0.0
        train_batches = 0
        train_pbar = tqdm(trainloader, desc=f"Epoch {epoch+1}/{number_of_epochs} [Train]", leave=False, dynamic_ncols=True)

        for batch, labels in train_pbar:
            self.optimizer.zero_grad()
            batch, labels = batch.to(self.device), labels.to(self.device)
            predicts = torch.squeeze(self.forward(batch))
            loss = self.evaluate(predicts, labels)
            loss.backward()
            self.optimizer.step()

            train_loss += loss.item()
            train_batches += 1
            train_pbar.set_postfix({'Loss': f"{loss.item():.4f}"})

        avg_train_loss = train_loss / train_batches
        self.train_history.append(avg_train_loss)

        # Validation phase
        self.eval()
        val_loss = 0.0
        val_batches = 0
        val_pbar = tqdm(testloader, desc=f"Epoch {epoch+1}/{number_of_epochs} [Val]", leave=False, dynamic_ncols=True)

        with torch.no_grad():
            for batch, labels in val_pbar:
                batch, labels = batch.to(self.device), labels.to(self.device)
                predicts = torch.squeeze(self.forward(batch), dim=1)
                loss = self.evaluate(predicts, labels)

                val_loss += loss.item()
                val_batches += 1
                val_pbar.set_postfix({'Loss': f"{loss.item():.4f}"})

        avg_val_loss = val_loss / val_batches
        self.validation_history.append(avg_val_loss)

        # Print epoch summary
        print(f"Epoch {epoch+1}/{number_of_epochs} - Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}")

        # Save best model
        if avg_val_loss < best_val_loss:
            best_val_loss = avg_val_loss
            self.save_weights(join(directory, f"best_model_epoch_{epoch+1}.pt"))
            print(f"Best model saved at epoch {epoch+1}")

    # Save training history
    torch.save(self.train_history, join(directory, "log", "train_log.pt"))
    torch.save(self.validation_history, join(directory, "log", "validation_log.pt"))
    print("Training completed. History saved.")

forward(x)

Forward pass of the network.

Parameters:

  • x (Tensor) –

    Input tensor.

Returns:

  • Tensor –

    Output tensor.

Source code in odak/learn/lensless/models.py 
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def forward(self, x):
    """
    Forward pass of the network.

    Parameters
    ----------
    x : torch.Tensor
        Input tensor.

    Returns
    -------
    torch.Tensor
        Output tensor.
    """
    return self.network(x)

init_layers()

Initialize the layers of the network.

Source code in odak/learn/lensless/models.py 
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def init_layers(self):
    """
    Initialize the layers of the network.
    """
    # Convolutional layers with batch normalization and pooling
    self.network = nn.Sequential(OrderedDict([
        ('conv1', nn.Conv2d(5, 32, kernel_size=3, padding=1)),
        ('bn1', nn.BatchNorm2d(32)),
        ('relu1', nn.ReLU()),
        ('pool1', nn.MaxPool2d(kernel_size=3)),

        ('conv2', nn.Conv2d(32, 64, kernel_size=5, padding=1)),
        ('bn2', nn.BatchNorm2d(64)),
        ('relu2', nn.ReLU()),
        ('pool2', nn.MaxPool2d(kernel_size=3)),

        ('conv3', nn.Conv2d(64, 128, kernel_size=7, padding=1)),
        ('bn3', nn.BatchNorm2d(128)),
        ('relu3', nn.ReLU()),
        ('pool3', nn.MaxPool2d(kernel_size=3)),

        ('flatten', nn.Flatten()),

        ('fc1', nn.Linear(6400, 2048)),
        ('fc_bn1', nn.BatchNorm1d(2048)),
        ('relu_fc1', nn.ReLU()),

        ('fc2', nn.Linear(2048, 1024)),
        ('fc_bn2', nn.BatchNorm1d(1024)),
        ('relu_fc2', nn.ReLU()),

        ('fc3', nn.Linear(1024, 512)),
        ('fc_bn3', nn.BatchNorm1d(512)),
        ('relu_fc3', nn.ReLU()),

        ('fc4', nn.Linear(512, 128)),
        ('fc_bn4', nn.BatchNorm1d(128)),
        ('relu_fc4', nn.ReLU()),

        ('fc5', nn.Linear(128, 3))
    ])).to(self.device)

load_weights(filename='./weights.pt')

Load weights for the network from a file.

Parameters:

  • filename (str, default: './weights.pt' ) – 
       Path to load the weights from. Default is './weights.pt'.
Source code in odak/learn/lensless/models.py 
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def load_weights(self, filename = './weights.pt'):
    """
    Load weights for the network from a file.

    Parameters
    ----------
    filename : str
               Path to load the weights from. Default is './weights.pt'.
    """
    self.network.load_state_dict(torch.load(os.path.expanduser(filename), weights_only = True))
    self.network.eval()

save_weights(filename='./weights.pt')

Save the current weights of the network to a file.

Parameters:

  • filename (str, default: './weights.pt' ) – 
       Path to save the weights. Default is './weights.pt'.
Source code in odak/learn/lensless/models.py 
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def save_weights(self, filename = './weights.pt'):
    """
    Save the current weights of the network to a file.

    Parameters
    ----------
    filename : str
               Path to save the weights. Default is './weights.pt'.
    """
    torch.save(self.network.state_dict(), os.path.expanduser(filename))