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cnn_training.py

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+import torch
+import numpy as np
+import datetime
+import argh
+import time
+import os
+import json
+from jsmin import jsmin
+
+from functions.alicorn_data_loader import alicorn_data_loader
+from functions.train import train
+from functions.test import test
+from functions.make_cnn import make_cnn
+from functions.set_seed import set_seed
+from functions.plot_intermediate import plot_intermediate
+from functions.create_logger import create_logger
+
+
+# to disable logging output from Tensorflow
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+from torch.utils.tensorboard import SummaryWriter
+
+
+def main(
+    idx_conv_out_channels_list: int = 0,
+    idx_conv_kernel_sizes: int = 0,
+    idx_conv_stride_sizes: int = 0,
+    seed_counter: int = 0,
+) -> None:
+    config_filenname = "config.json"
+    with open(config_filenname, "r") as file_handle:
+        config = json.loads(jsmin(file_handle.read()))
+
+    logger = create_logger(
+        save_logging_messages=bool(config["save_logging_messages"]),
+        display_logging_messages=bool(config["display_logging_messages"]),
+    )
+
+    # network settings:
+    conv_out_channels_list: list[list[int]] = config["conv_out_channels_list"]
+    conv_kernel_sizes: list[list[int]] = config["conv_kernel_sizes"]
+    conv_stride_sizes: list[int] = config["conv_stride_sizes"]
+
+    num_pfinkel: list = np.arange(
+        int(config["num_pfinkel_start"]),
+        int(config["num_pfinkel_stop"]),
+        int(config["num_pfinkel_step"]),
+    ).tolist()
+
+    run_network(
+        out_channels=conv_out_channels_list[int(idx_conv_out_channels_list)],
+        kernel_size=conv_kernel_sizes[int(idx_conv_kernel_sizes)],
+        stride=conv_stride_sizes[int(idx_conv_stride_sizes)],
+        activation_function=str(config["activation_function"]),
+        train_first_layer=bool(config["train_first_layer"]),
+        seed_counter=seed_counter,
+        minimum_learning_rate=float(config["minimum_learning_rate"]),
+        conv_0_kernel_size=int(config["conv_0_kernel_size"]),
+        mp_1_kernel_size=int(config["mp_1_kernel_size"]),
+        mp_1_stride=int(config["mp_1_stride"]),
+        batch_size_train=int(config["batch_size_train"]),
+        batch_size_test=int(config["batch_size_test"]),
+        learning_rate=float(config["learning_rate"]),
+        max_epochs=int(config["max_epochs"]),
+        save_model=bool(config["save_model"]),
+        stimuli_per_pfinkel=int(config["stimuli_per_pfinkel"]),
+        num_pfinkel=num_pfinkel,
+        logger=logger,
+        save_ever_x_epochs=int(config["save_ever_x_epochs"]),
+        scheduler_patience=int(config["scheduler_patience"]),
+        condition=str(config["condition"]),
+        data_path=str(config["data_path"]),
+        pooling_type=str(config["pooling_type"]),
+        conv_0_enable_softmax=bool(config["conv_0_enable_softmax"]),
+        scale_data=int(config["scale_data"]),
+        use_scheduler=bool(config["use_scheduler"]),
+        use_adam=bool(config["use_adam"]),
+        use_plot_intermediate=bool(config["use_plot_intermediate"]),
+        leak_relu_negative_slope=float(config["leak_relu_negative_slope"]),
+        scheduler_verbose=bool(config["scheduler_verbose"]),
+        scheduler_factor=float(config["scheduler_factor"]),
+        precision_100_percent=int(config["precision_100_percent"]),
+        scheduler_threshold=float(config["scheduler_threshold"]),
+    )
+
+
+def run_network(
+    out_channels: list[int],
+    kernel_size: list[int],
+    num_pfinkel: list,
+    logger,
+    stride: int,
+    activation_function: str,
+    train_first_layer: bool,
+    seed_counter: int,
+    minimum_learning_rate: float,
+    conv_0_kernel_size: int,
+    mp_1_kernel_size: int,
+    mp_1_stride: int,
+    scheduler_patience: int,
+    batch_size_train: int,
+    batch_size_test: int,
+    learning_rate: float,
+    max_epochs: int,
+    save_model: bool,
+    stimuli_per_pfinkel: int,
+    save_ever_x_epochs: int,
+    condition: str,
+    data_path: str,
+    pooling_type: str,
+    conv_0_enable_softmax: bool,
+    scale_data: float,
+    use_scheduler: bool,
+    use_adam: bool,
+    use_plot_intermediate: bool,
+    leak_relu_negative_slope: float,
+    scheduler_verbose: bool,
+    scheduler_factor: float,
+    precision_100_percent: int,
+    scheduler_threshold: float,
+) -> None:
+    # define device:
+    device_str: str = "cuda:0" if torch.cuda.is_available() else "cpu"
+    logger.info(f"Using {device_str} device")
+    device: torch.device = torch.device(device_str)
+    torch.set_default_dtype(torch.float32)
+
+    # -------------------------------------------------------------------
+    logger.info("-==- START -==-")
+
+    train_accuracy: list[float] = []
+    train_losses: list[float] = []
+    train_loss: list[float] = []
+    test_accuracy: list[float] = []
+    test_losses: list[float] = []
+
+    # prepare data:
+
+    logger.info(num_pfinkel)
+    logger.info(condition)
+
+    logger.info("Loading training data")
+    data_train = alicorn_data_loader(
+        num_pfinkel=num_pfinkel,
+        load_stimuli_per_pfinkel=stimuli_per_pfinkel,
+        condition=condition,
+        logger=logger,
+        data_path=data_path,
+    )
+
+    logger.info("Loading test data")
+    data_test = alicorn_data_loader(
+        num_pfinkel=num_pfinkel,
+        load_stimuli_per_pfinkel=stimuli_per_pfinkel,
+        condition=condition,
+        logger=logger,
+        data_path=data_path,
+    )
+
+    logger.info("Loading done!")
+
+    # data loader
+    loader_train = torch.utils.data.DataLoader(
+        data_train, shuffle=True, batch_size=batch_size_train
+    )
+    loader_test = torch.utils.data.DataLoader(
+        data_test, shuffle=False, batch_size=batch_size_test
+    )
+
+    previous_test_acc: float = -1
+
+    # set seed for reproducibility
+    set_seed(seed=int(seed_counter), logger=logger)
+
+    # number conv layer:
+    if train_first_layer:
+        num_conv_layers = len(out_channels)
+    else:
+        num_conv_layers = len(out_channels) if len(out_channels) >= 2 else 1
+
+    # determine num conv layers
+    model_name = (
+        f"ArghCNN__MPk3s2_numConvLayers{num_conv_layers}"
+        f"_outChannels{out_channels}_kernelSize{kernel_size}_"
+        f"{activation_function}_stride{stride}_"
+        f"trainFirstConvLayer{train_first_layer}_"
+        f"seed{seed_counter}_{condition}_MPk3s2"
+    )
+    current = datetime.datetime.now().strftime("%d%m-%H%M")
+
+    # new tb session
+    os.makedirs("tb_runs", exist_ok=True)
+    path: str = os.path.join("tb_runs", f"{model_name}")
+    tb = SummaryWriter(path)
+
+    # --------------------------------------------------------------------------
+
+    # print network configuration:
+    logger.info("----------------------------------------------------")
+    logger.info(f"Number conv layers: {num_conv_layers}")
+    logger.info(f"Output channels: {out_channels}")
+    logger.info(f"Kernel sizes: {kernel_size}")
+    logger.info(f"Stride: {stride}")
+    logger.info(f"Activation function: {activation_function}")
+    logger.info(f"Training conv 0: {train_first_layer}")
+    logger.info(f"Seed: {seed_counter}")
+    logger.info(f"LR-scheduler patience: {scheduler_patience}")
+    logger.info(f"Pooling layer kernel: {mp_1_kernel_size}, stride: {mp_1_stride}")
+
+    # define model:
+    model = make_cnn(
+        conv_out_channels_list=out_channels,
+        conv_kernel_size=kernel_size,
+        conv_stride_size=stride,
+        conv_activation_function=activation_function,
+        train_conv_0=train_first_layer,
+        conv_0_kernel_size=conv_0_kernel_size,
+        mp_1_kernel_size=mp_1_kernel_size,
+        mp_1_stride=mp_1_stride,
+        logger=logger,
+        pooling_type=pooling_type,
+        conv_0_enable_softmax=conv_0_enable_softmax,
+        l_relu_negative_slope=leak_relu_negative_slope,
+    ).to(device)
+
+    logger.info(model)
+
+    old_params: dict = {}
+    for name, param in model.named_parameters():
+        old_params[name] = param.data.detach().cpu().clone()
+
+    # pararmeters for training:
+    param_list: list = []
+
+    for i in range(0, len(model)):
+        if (not train_first_layer) and (i == 0):
+            pass
+        else:
+            for name, param in model[i].named_parameters():
+                logger.info(f"Learning parameter: layer: {i} name: {name}")
+                param_list.append(param)
+
+    for name, param in model.named_parameters():
+        assert (
+            torch.isfinite(param.data).sum().cpu()
+            == torch.tensor(param.data.size()).prod()
+        ), name
+
+    # optimizer and learning rate scheduler
+    if use_adam:
+        optimizer = torch.optim.Adam(param_list, lr=learning_rate)
+    else:
+        optimizer = torch.optim.SGD(param_list, lr=learning_rate)  # type: ignore
+
+    if use_scheduler:
+        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+            optimizer,
+            patience=scheduler_patience,
+            eps=minimum_learning_rate / 10,
+            verbose=scheduler_verbose,
+            factor=scheduler_factor,
+            threshold=scheduler_threshold,
+        )
+
+    # training loop:
+    logger.info("-==- Data and network loader: Done -==-")
+    t_dis0 = time.perf_counter()
+    for epoch in range(1, max_epochs + 1):
+        # train
+        logger.info("-==- Training... -==-")
+        running_loss = train(
+            model=model,
+            loader=loader_train,
+            optimizer=optimizer,
+            epoch=epoch,
+            device=device,
+            tb=tb,
+            test_acc=previous_test_acc,
+            logger=logger,
+            train_accuracy=train_accuracy,
+            train_losses=train_losses,
+            train_loss=train_loss,
+            scale_data=scale_data,
+        )
+
+        # logging:
+        logger.info("")
+
+        logger.info("Check for changes in the weights:")
+        for name, param in model.named_parameters():
+            if isinstance(old_params[name], torch.Tensor) and isinstance(
+                param.data, torch.Tensor
+            ):
+                temp_torch = param.data.detach().cpu().clone()
+                if old_params[name].ndim == temp_torch.ndim:
+                    if old_params[name].size() == temp_torch.size():
+                        abs_diff = torch.abs(old_params[name] - temp_torch).max()
+                        logger.info(f"Parameter {name}: {abs_diff:.3e}")
+
+            old_params[name] = temp_torch
+
+        logger.info("")
+
+        logger.info("-==- Testing... -==-")
+        previous_test_acc = test(  # type: ignore
+            model=model,
+            loader=loader_test,
+            device=device,
+            tb=tb,
+            epoch=epoch,
+            logger=logger,
+            test_accuracy=test_accuracy,
+            test_losses=test_losses,
+            scale_data=scale_data,
+        )
+
+        logger.info(f"Time required: {time.perf_counter()-t_dis0:.2e} sec")
+
+        # save model after every 100th epoch:
+        if save_model and (epoch % save_ever_x_epochs == 0):
+            pt_filename: str = f"{model_name}_{epoch}Epoch_{current}.pt"
+            logger.info("")
+            logger.info(f"Saved model: {pt_filename}")
+            os.makedirs("trained_models", exist_ok=True)
+            torch.save(
+                model,
+                os.path.join(
+                    "trained_models",
+                    pt_filename,
+                ),
+            )
+
+        # check nan
+        for name, param in model.named_parameters():
+            assert (
+                torch.isfinite(param.data).sum().cpu()
+                == torch.tensor(param.data.size()).prod()
+            ), name
+
+        # update scheduler
+        if use_scheduler:
+            if scheduler_verbose and isinstance(scheduler.best, float):
+                logger.info(
+                    "Step LR scheduler: "
+                    f"Loss: {running_loss:.2e} "
+                    f"Best: {scheduler.best:.2e} "
+                    f"Delta: {running_loss-scheduler.best:.2e} "
+                    f"Threshold: {scheduler.threshold:.2e} "
+                    f"Number of bad epochs: {scheduler.num_bad_epochs} "
+                    f"Patience: {scheduler.patience} "
+                )
+            scheduler.step(running_loss)
+
+        # stop learning: lr too small
+        if optimizer.param_groups[0]["lr"] <= minimum_learning_rate:
+            logger.info("Learning rate is too small. Stop training.")
+            break
+
+        # stop learning: done
+        if round(previous_test_acc, precision_100_percent) == 100.0:
+            logger.info("100% test performance reached. Stop training.")
+            break
+
+    if use_plot_intermediate:
+        plot_intermediate(
+            train_accuracy=train_accuracy,
+            test_accuracy=test_accuracy,
+            train_losses=train_losses,
+            test_losses=test_losses,
+            save_name=model_name,
+        )
+
+    os.makedirs("performance_data", exist_ok=True)
+    np.savez(
+        os.path.join("performance_data", f"performances_{model_name}.npz"),
+        train_accuracy=np.array(train_accuracy),
+        test_accuracy=np.array(test_accuracy),
+        train_losses=np.array(train_losses),
+        test_losses=np.array(test_losses),
+    )
+
+    # end TB session:
+    tb.close()
+
+    # print model name:
+    logger.info("")
+    logger.info(f"Saved model: {model_name}_{epoch}Epoch_{current}")
+    if save_model:
+        os.makedirs("trained_models", exist_ok=True)
+        torch.save(
+            model,
+            os.path.join(
+                "trained_models",
+                f"{model_name}_{epoch}Epoch_{current}.pt",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)

config.json

@@ -0,0 +1,52 @@
+{
+    "data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
+    "save_logging_messages": true, // (true), false
+    "display_logging_messages": true, // (true), false
+    "batch_size_train": 250,
+    "batch_size_test": 500,
+    "max_epochs": 2000,
+    "save_model": true,
+    "conv_0_kernel_size": 11,
+    "mp_1_kernel_size": 3,
+    "mp_1_stride": 2,
+    "use_plot_intermediate": false, // true, (false)
+    "stimuli_per_pfinkel": 30000,
+    "num_pfinkel_start": 0,
+    "num_pfinkel_stop": 10,
+    "num_pfinkel_step": 10,
+    "precision_100_percent": 4, // (4)
+    "train_first_layer": true, // true, (false)
+    "save_ever_x_epochs": 10, // (10)
+    "activation_function": "leaky relu", // tanh, relu, (leaky relu), none
+    "leak_relu_negative_slope": 0.1, // (0.1)
+    // LR Scheduler ->
+    "use_scheduler": true, // (true), false
+    "scheduler_verbose": true,
+    "scheduler_factor": 0.1, //(0.1)
+    "scheduler_patience": 10, // (10)
+    "scheduler_threshold": 1e-5, // (1e-4)
+    "minimum_learning_rate": 1e-8,
+    "learning_rate": 0.0001,
+    // <- LR Scheduler
+    "pooling_type": "max", // (max), average, none
+    "conv_0_enable_softmax": false, // true, (false)
+    "use_adam": true, // (true) => adam, false => SGD
+    "condition": "Coignless",
+    "scale_data": 255.0, // (255.0)
+    "conv_out_channels_list": [
+        [
+            32,
+            8,
+            8
+        ]
+    ],
+    "conv_kernel_sizes": [
+        [
+            7,
+            15
+        ]
+    ],
+    "conv_stride_sizes": [
+        1
+    ]
+}

functions/alicorn_data_loader.py

@@ -0,0 +1,105 @@
+import torch
+import numpy as np
+import os
+
+
+@torch.no_grad()
+def alicorn_data_loader(
+    num_pfinkel: list[int] | None,
+    load_stimuli_per_pfinkel: int,
+    condition: str,
+    logger,
+    data_path: str,
+) -> torch.utils.data.TensorDataset:
+    """
+    - num_pfinkel: list of the angles that should be loaded (ranging from
+    0-90). If None: all pfinkels loaded
+    - stimuli_per_pfinkel: defines amount of stimuli per path angle but
+    for label 0 and label 1 seperatly (e.g., stimuli_per_pfinkel = 1000:
+    1000 stimuli = label 1, 1000 stimuli = label 0)
+    """
+    filename: str | None = None
+    if condition == "Angular":
+        filename = "angular_angle"
+    elif condition == "Coignless":
+        filename = "base_angle"
+    elif condition == "Natural":
+        filename = "corner_angle"
+    else:
+        filename = None
+    assert filename is not None
+    filepaths: str = os.path.join(data_path, f"{condition}")
+
+    stimuli_per_pfinkel: int = 100000
+
+    # ----------------------------
+
+    # for angles and batches
+    if num_pfinkel is None:
+        angle: list[int] = np.arange(0, 100, 10).tolist()
+    else:
+        angle = num_pfinkel
+
+    assert isinstance(angle, list)
+
+    batch: list[int] = np.arange(1, 11, 1).tolist()
+
+    if load_stimuli_per_pfinkel <= (stimuli_per_pfinkel // len(batch)):
+        num_img_per_pfinkel: int = load_stimuli_per_pfinkel
+        num_batches: int = 1
+    else:
+        # handle case where more than 10,000 stimuli per pfinkel needed
+        num_batches = load_stimuli_per_pfinkel // (stimuli_per_pfinkel // len(batch))
+        num_img_per_pfinkel = load_stimuli_per_pfinkel // num_batches
+
+    logger.info(f"{num_batches} batches")
+    logger.info(f"{num_img_per_pfinkel} stimuli per pfinkel.")
+
+    # initialize data and label tensors:
+    num_stimuli: int = len(angle) * num_batches * num_img_per_pfinkel * 2
+    data_tensor: torch.Tensor = torch.empty(
+        (num_stimuli, 200, 200), dtype=torch.uint8, device=torch.device("cpu")
+    )
+    label_tensor: torch.Tensor = torch.empty(
+        (num_stimuli), dtype=torch.int64, device=torch.device("cpu")
+    )
+
+    logger.info(f"data tensor shape: {data_tensor.shape}")
+    logger.info(f"label tensor shape: {label_tensor.shape}")
+
+    # append data
+    idx: int = 0
+    for i in range(len(angle)):
+        for j in range(num_batches):
+            # load contour
+            temp_filename: str = (
+                f"{filename}_{angle[i]:03}_b{batch[j]:03}_n10000_RENDERED.npz"
+            )
+            contour_filename: str = os.path.join(filepaths, temp_filename)
+            c_data = np.load(contour_filename)
+            data_tensor[idx : idx + num_img_per_pfinkel, ...] = torch.tensor(
+                c_data["gaborfield"][:num_img_per_pfinkel, ...],
+                dtype=torch.uint8,
+                device=torch.device("cpu"),
+            )
+            label_tensor[idx : idx + num_img_per_pfinkel] = int(1)
+            idx += num_img_per_pfinkel
+
+    # next append distractor stimuli
+    for i in range(len(angle)):
+        for j in range(num_batches):
+            # load distractor
+            temp_filename = (
+                f"{filename}_{angle[i]:03}_dist_b{batch[j]:03}_n10000_RENDERED.npz"
+            )
+            distractor_filename: str = os.path.join(filepaths, temp_filename)
+            nc_data = np.load(distractor_filename)
+            data_tensor[idx : idx + num_img_per_pfinkel, ...] = torch.tensor(
+                nc_data["gaborfield"][:num_img_per_pfinkel, ...],
+                dtype=torch.uint8,
+                device=torch.device("cpu"),
+            )
+            label_tensor[idx : idx + num_img_per_pfinkel] = int(0)
+            idx += num_img_per_pfinkel
+
+    return torch.utils.data.TensorDataset(label_tensor, data_tensor.unsqueeze(1))

functions/create_logger.py

@@ -0,0 +1,35 @@
+import logging
+import datetime
+import os
+
+
+def create_logger(save_logging_messages: bool, display_logging_messages: bool):
+    now = datetime.datetime.now()
+    dt_string_filename = now.strftime("%Y_%m_%d_%H_%M_%S")
+
+    logger = logging.getLogger("MyLittleLogger")
+    logger.setLevel(logging.DEBUG)
+
+    if save_logging_messages:
+        time_format = "%b %-d %Y %H:%M:%S"
+        logformat = "%(asctime)s %(message)s"
+        file_formatter = logging.Formatter(fmt=logformat, datefmt=time_format)
+        os.makedirs("logs", exist_ok=True)
+        file_handler = logging.FileHandler(
+            os.path.join("logs", f"log_{dt_string_filename}.txt")
+        )
+        file_handler.setLevel(logging.INFO)
+        file_handler.setFormatter(file_formatter)
+        logger.addHandler(file_handler)
+
+    if display_logging_messages:
+        time_format = "%H:%M:%S"
+        logformat = "%(asctime)s %(message)s"
+        stream_formatter = logging.Formatter(fmt=logformat, datefmt=time_format)
+
+        stream_handler = logging.StreamHandler()
+        stream_handler.setLevel(logging.INFO)
+        stream_handler.setFormatter(stream_formatter)
+        logger.addHandler(stream_handler)
+
+    return logger

functions/make_cnn.py

@@ -0,0 +1,114 @@
+import torch
+import numpy as np
+
+
+def make_cnn(
+    conv_out_channels_list: list[int],
+    conv_kernel_size: list[int],
+    conv_stride_size: int,
+    conv_activation_function: str,
+    train_conv_0: bool,
+    logger,
+    conv_0_kernel_size: int,
+    mp_1_kernel_size: int,
+    mp_1_stride: int,
+    pooling_type: str,
+    conv_0_enable_softmax: bool,
+    l_relu_negative_slope: float,
+) -> torch.nn.Sequential:
+    assert len(conv_out_channels_list) >= 1
+    assert len(conv_out_channels_list) == len(conv_kernel_size) + 1
+
+    cnn = torch.nn.Sequential()
+
+    # Fixed structure
+    cnn.append(
+        torch.nn.Conv2d(
+            in_channels=1,
+            out_channels=conv_out_channels_list[0] if train_conv_0 else 32,
+            kernel_size=conv_0_kernel_size,
+            stride=1,
+            bias=train_conv_0,
+        )
+    )
+
+    if conv_0_enable_softmax:
+        cnn.append(torch.nn.Softmax(dim=1))
+
+    setting_understood: bool = False
+    if conv_activation_function.upper() == str("relu").upper():
+        cnn.append(torch.nn.ReLU())
+        setting_understood = True
+    elif conv_activation_function.upper() == str("leaky relu").upper():
+        cnn.append(torch.nn.LeakyReLU(negative_slope=l_relu_negative_slope))
+        setting_understood = True
+    elif conv_activation_function.upper() == str("tanh").upper():
+        cnn.append(torch.nn.Tanh())
+        setting_understood = True
+    elif conv_activation_function.upper() == str("none").upper():
+        setting_understood = True
+    assert setting_understood
+
+    setting_understood = False
+    if pooling_type.upper() == str("max").upper():
+        cnn.append(torch.nn.MaxPool2d(kernel_size=mp_1_kernel_size, stride=mp_1_stride))
+        setting_understood = True
+    elif pooling_type.upper() == str("average").upper():
+        cnn.append(torch.nn.AvgPool2d(kernel_size=mp_1_kernel_size, stride=mp_1_stride))
+        setting_understood = True
+    elif pooling_type.upper() == str("none").upper():
+        setting_understood = True
+    assert setting_understood
+
+    # Changing structure
+    for i in range(1, len(conv_out_channels_list)):
+        if i == 1 and not train_conv_0:
+            in_channels = 32
+        else:
+            in_channels = conv_out_channels_list[i - 1]
+        cnn.append(
+            torch.nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=conv_out_channels_list[i],
+                kernel_size=conv_kernel_size[i - 1],
+                stride=conv_stride_size,
+                bias=True,
+            )
+        )
+        setting_understood = False
+        if conv_activation_function.upper() == str("relu").upper():
+            cnn.append(torch.nn.ReLU())
+            setting_understood = True
+        elif conv_activation_function.upper() == str("leaky relu").upper():
+            cnn.append(torch.nn.LeakyReLU(negative_slope=l_relu_negative_slope))
+            setting_understood = True
+        elif conv_activation_function.upper() == str("tanh").upper():
+            cnn.append(torch.nn.Tanh())
+            setting_understood = True
+        elif conv_activation_function.upper() == str("none").upper():
+            setting_understood = True
+
+        assert setting_understood
+
+    # Fixed structure
+    # define fully connected layer:
+    cnn.append(torch.nn.Flatten(start_dim=1))
+    cnn.append(torch.nn.LazyLinear(2, bias=True))
+
+    # if conv1 not trained:
+    filename_load_weight_0: str | None = None
+    if train_conv_0 is False and cnn[0]._parameters["weight"].shape[0] == 32:
+        filename_load_weight_0 = "weights_radius10.npy"
+    if train_conv_0 is False and cnn[0]._parameters["weight"].shape[0] == 16:
+        filename_load_weight_0 = "8orient_2phase_weights.npy"
+
+    if filename_load_weight_0 is not None:
+        logger.info(f"Replace weights in CNN 0 with {filename_load_weight_0}")
+        cnn[0]._parameters["weight"] = torch.tensor(
+            np.load(filename_load_weight_0),
+            dtype=cnn[0]._parameters["weight"].dtype,
+            requires_grad=False,
+            device=cnn[0]._parameters["weight"].device,
+        )
+
+    return cnn

functions/plot_intermediate.py

@@ -0,0 +1,76 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import os
+
+mpl.rcParams["text.usetex"] = True
+mpl.rcParams["font.family"] = "serif"
+
+
+def plot_intermediate(
+    train_accuracy: list[float],
+    test_accuracy: list[float],
+    train_losses: list[float],
+    test_losses: list[float],
+    save_name: str,
+    reduction_factor: int = 1,
+) -> None:
+    assert len(train_accuracy) == len(test_accuracy)
+    assert len(train_accuracy) == len(train_losses)
+    assert len(train_accuracy) == len(test_losses)
+
+    max_epochs: int = len(train_accuracy)
+    # set stepsize
+    x = np.arange(1, max_epochs + 1)
+
+    stepsize = max_epochs // reduction_factor
+
+    # accuracies
+    plt.figure(figsize=[12, 7])
+    plt.subplot(2, 1, 1)
+
+    plt.plot(x, np.array(train_accuracy), label="Train")
+    plt.plot(x, np.array(test_accuracy), label="Test")
+    plt.title("Training and Testing Accuracy", fontsize=18)
+    plt.xlabel("Epoch", fontsize=18)
+    plt.ylabel("Accuracy (\\%)", fontsize=18)
+    plt.legend(fontsize=14)
+    plt.xticks(
+        np.concatenate((np.array([1]), np.arange(stepsize, max_epochs + 1, stepsize))),
+        np.concatenate((np.array([1]), np.arange(stepsize, max_epochs + 1, stepsize))),
+    )
+
+    # Increase tick label font size
+    plt.xticks(fontsize=16)
+    plt.yticks(fontsize=16)
+    plt.grid(True)
+
+    # losses
+    plt.subplot(2, 1, 2)
+    plt.plot(x, np.array(train_losses), label="Train")
+    plt.plot(x, np.array(test_losses), label="Test")
+    plt.title("Training and Testing Losses", fontsize=18)
+    plt.xlabel("Epoch", fontsize=18)
+    plt.ylabel("Loss", fontsize=18)
+    plt.legend(fontsize=14)
+    plt.xticks(
+        np.concatenate((np.array([1]), np.arange(stepsize, max_epochs + 1, stepsize))),
+        np.concatenate((np.array([1]), np.arange(stepsize, max_epochs + 1, stepsize))),
+    )
+
+    # Increase tick label font size
+    plt.xticks(fontsize=16)
+    plt.yticks(fontsize=16)
+    plt.grid(True)
+
+    plt.tight_layout()
+    os.makedirs("performance_plots", exist_ok=True)
+    plt.savefig(
+        os.path.join(
+            "performance_plots",
+            f"performance_{save_name}.pdf",
+        ),
+        dpi=300,
+        bbox_inches="tight",
+    )
+    plt.show()

functions/set_seed.py

@@ -0,0 +1,11 @@
+import torch
+import numpy as np
+
+
+def set_seed(seed: int, logger) -> None:
+    # set seed for all used modules
+    logger.info(f"set seed to {seed}")
+    torch.manual_seed(seed=seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed=seed)
+    np.random.seed(seed=seed)

functions/test.py

@@ -0,0 +1,58 @@
+import torch
+import logging
+
+
+@torch.no_grad()
+def test(
+    model: torch.nn.modules.container.Sequential,
+    loader: torch.utils.data.dataloader.DataLoader,
+    device: torch.device,
+    tb,
+    epoch: int,
+    logger: logging.Logger,
+    test_accuracy: list[float],
+    test_losses: list[float],
+    scale_data: float,
+) -> float:
+    test_loss: float = 0.0
+    correct: int = 0
+    pattern_count: float = 0.0
+
+    model.eval()
+
+    for data in loader:
+        label = data[0].to(device)
+        image = data[1].type(dtype=torch.float32).to(device)
+        if scale_data > 0:
+            image /= scale_data
+
+        output = model(image)
+
+        # loss and optimization
+        loss = torch.nn.functional.cross_entropy(output, label, reduction="sum")
+        pattern_count += float(label.shape[0])
+        test_loss += loss.item()
+        prediction = output.argmax(dim=1)
+        correct += prediction.eq(label).sum().item()
+
+    logger.info(
+        (
+            "Test set:"
+            f" Average loss: {test_loss / pattern_count:.3e},"
+            f" Accuracy: {correct}/{pattern_count},"
+            f"({100.0 * correct / pattern_count:.2f}%)"
+        )
+    )
+    logger.info("")
+
+    acc = 100.0 * correct / pattern_count
+    test_losses.append(test_loss / pattern_count)
+    test_accuracy.append(acc)
+
+    # add to tb:
+    tb.add_scalar("Test Loss", (test_loss / pattern_count), epoch)
+    tb.add_scalar("Test Performance", 100.0 * correct / pattern_count, epoch)
+    tb.add_scalar("Test Number Correct", correct, epoch)
+    tb.flush()
+
+    return acc

functions/train.py

@@ -0,0 +1,80 @@
+import torch
+import logging
+
+
+def train(
+    model: torch.nn.modules.container.Sequential,
+    loader: torch.utils.data.dataloader.DataLoader,
+    optimizer: torch.optim.Adam | torch.optim.SGD,
+    epoch: int,
+    device: torch.device,
+    tb,
+    test_acc,
+    logger: logging.Logger,
+    train_accuracy: list[float],
+    train_losses: list[float],
+    train_loss: list[float],
+    scale_data: float,
+) -> float:
+    num_train_pattern: int = 0
+    running_loss: float = 0.0
+    correct: int = 0
+    pattern_count: float = 0.0
+
+    model.train()
+    for data in loader:
+        label = data[0].to(device)
+        image = data[1].type(dtype=torch.float32).to(device)
+        if scale_data > 0:
+            image /= scale_data
+
+        optimizer.zero_grad()
+        output = model(image)
+        loss = torch.nn.functional.cross_entropy(output, label, reduction="sum")
+        loss.backward()
+
+        optimizer.step()
+
+        # for loss and accuracy plotting:
+        num_train_pattern += int(label.shape[0])
+        pattern_count += float(label.shape[0])
+        running_loss += float(loss)
+        train_loss.append(float(loss))
+        prediction = output.argmax(dim=1)
+        correct += prediction.eq(label).sum().item()
+
+        total_number_of_pattern: int = int(len(loader)) * int(label.shape[0])
+
+        # infos:
+        logger.info(
+            (
+                "Train Epoch:"
+                f" {epoch}"
+                f" [{int(pattern_count)}/{total_number_of_pattern}"
+                f" ({100.0 * pattern_count / total_number_of_pattern:.2f}%)],"
+                f" Loss: {float(running_loss) / float(num_train_pattern):.4e},"
+                f" Acc: {(100.0 * correct / num_train_pattern):.2f}"
+                f" Test Acc: {test_acc:.2f}%,"
+                f" LR: {optimizer.param_groups[0]['lr']:.2e}"
+            )
+        )
+
+    acc = 100.0 * correct / num_train_pattern
+    train_accuracy.append(acc)
+
+    epoch_loss = running_loss / pattern_count
+    train_losses.append(epoch_loss)
+
+    # add to tb:
+    tb.add_scalar("Train Loss", loss.item(), epoch)
+    tb.add_scalar("Train Performance", torch.tensor(acc), epoch)
+    tb.add_scalar("Train Number Correct", torch.tensor(correct), epoch)
+
+    # for parameters:
+    for name, param in model.named_parameters():
+        if "weight" in name or "bias" in name:
+            tb.add_histogram(f"{name}", param.data.clone(), epoch)
+
+    tb.flush()
+
+    return epoch_loss