DeepSDFStruct.deep_sdf.training#

DeepSDF Model Training#

This module implements the complete training pipeline for DeepSDF neural networks. It provides loss functions, learning rate schedules, training loops, and checkpoint management.

Key Features#

Loss Functions

  • ClampedL1Loss: L1 loss with value clamping for stability

  • Support for custom loss functions

Learning Rate Schedules

  • ConstantLearningRateSchedule: Fixed learning rate

  • StepLearningRateSchedule: Step decay schedule

  • WarmupLearningRateSchedule: Warmup followed by decay

Training Loop

  • Multi-epoch training with validation

  • Automatic checkpointing and model saving

  • Loss tracking and visualization

  • Support for distributed training

  • Resume from checkpoint capability

Experiment Management

  • MLflow integration for experiment tracking

  • Automatic logging of hyperparameters

  • Training curve visualization

  • Model versioning

The training process follows the DeepSDF paper methodology with extensions for lattice structures and microstructured materials.

Examples

Train a DeepSDF model:

from DeepSDFStruct.deep_sdf.training import train_deep_sdf

specs = {
    'NetworkSpecs': {...},
    'TrainSpecs': {
        'NumEpochs': 2000,
        'LearningRateSchedule': {...}
    }
}

train_deep_sdf(experiment_dir, specs)

Functions

append_parameter_magnitudes(param_mag_log, model)

clip_logs(loss_log, lr_log, timing_log, ...)

create_interpolated_meshes_from_latent(...)

Interpolate between latent vectors and export reconstructed meshes.

get_learning_rate_schedules(specs)

get_mean_latent_vector_magnitude(latent_vectors)

get_spec_with_default(specs, key, default)

load_logs(experiment_directory)

reconstruct_meshs_from_latent(...[, ...])

save_latent_code_data_map(...)

Save mapping between latent indices and source training .npz files.

save_latent_vectors(experiment_directory, ...)

save_logs(experiment_directory, loss_log, ...)

save_model(experiment_directory, filename, ...)

save_optimizer(experiment_directory, ...)

train_deep_sdf(experiment_directory, data_source)

Classes

ConstantLearningRateSchedule(value)

LearningRateSchedule()

StepLearningRateSchedule(initial, interval, ...)

WarmupLearningRateSchedule(initial, ...)

class DeepSDFStruct.deep_sdf.training.ConstantLearningRateSchedule(value)#

Bases: DeepSDFStruct.deep_sdf.training.LearningRateSchedule

get_learning_rate(epoch)#
class DeepSDFStruct.deep_sdf.training.LearningRateSchedule#

Bases: object

get_learning_rate(epoch)#
class DeepSDFStruct.deep_sdf.training.StepLearningRateSchedule(initial, interval, factor)#

Bases: DeepSDFStruct.deep_sdf.training.LearningRateSchedule

get_learning_rate(epoch)#
class DeepSDFStruct.deep_sdf.training.WarmupLearningRateSchedule(initial, warmed_up, length)#

Bases: DeepSDFStruct.deep_sdf.training.LearningRateSchedule

get_learning_rate(epoch)#
DeepSDFStruct.deep_sdf.training.append_parameter_magnitudes(param_mag_log, model)#
DeepSDFStruct.deep_sdf.training.clip_logs(loss_log, lr_log, timing_log, lat_mag_log, param_mag_log, epoch)#
DeepSDFStruct.deep_sdf.training.create_interpolated_meshes_from_latent(experiment_directory, indices, steps, checkpoint='latest', max_batch=32, filetype='ply', device='cpu')#

Interpolate between latent vectors and export reconstructed meshes.

This function loads a trained DeepSDF model and its latent vectors, then interpolates between consecutive latent codes specified in indices. At each interpolation step, a 3D surface mesh is reconstructed and exported to disk in the requested format.

Parameters:

  • experiment_directory (str | PathLike) – Path to the experiment directory containing checkpoints and latent vectors.

  • checkpoint (str, optional) – Which checkpoint to load. Defaults to “latest”.

  • max_batch (int, optional) – Maximum batch size for inference. Defaults to 32.

  • filetype (str, optional) – File extension for exported meshes (e.g., “ply”, “obj”). Defaults to “ply”.

  • indices (list[int], optional) – Sequence of latent vector indices between which interpolation should be performed. Defaults to [1, 2, 3, 4, 5, 6, 7, 8].

  • steps (int, optional) – Number of interpolation steps (including endpoints). Defaults to 11.

Return type:

None

Example

>>> create_interpolated_meshes_from_latent(
...     experiment_directory="experiments/run1",
...     [1, 2, 3, 4, 5, 6, 7, 8],
...     11,
...     checkpoint="latest",
...     max_batch=32,
...     filetype="ply",
... )
DeepSDFStruct.deep_sdf.training.get_learning_rate_schedules(specs)#
DeepSDFStruct.deep_sdf.training.get_mean_latent_vector_magnitude(latent_vectors)#
DeepSDFStruct.deep_sdf.training.get_spec_with_default(specs, key, default)#
DeepSDFStruct.deep_sdf.training.load_logs(experiment_directory)#
DeepSDFStruct.deep_sdf.training.reconstruct_meshs_from_latent(experiment_directory, checkpoint='latest', max_batch=32, filetype='ply', device='cpu')#
DeepSDFStruct.deep_sdf.training.save_latent_code_data_map(experiment_directory, data_source, npz_filenames)#

Save mapping between latent indices and source training .npz files.

Parameters:

  • experiment_directory (str) – Path to the experiment directory where training artifacts are stored.

  • data_source (str) – Root directory of the dataset used for training.

  • npz_filenames (list[str]) – Relative .npz paths in dataset split order. The order corresponds directly to latent embedding indices.

DeepSDFStruct.deep_sdf.training.save_latent_vectors(experiment_directory, filename, latent_vec, epoch)#
DeepSDFStruct.deep_sdf.training.save_logs(experiment_directory, loss_log, lr_log, timing_log, lat_mag_log, param_mag_log, epoch)#
DeepSDFStruct.deep_sdf.training.save_model(experiment_directory, filename, decoder, epoch)#
DeepSDFStruct.deep_sdf.training.save_optimizer(experiment_directory, filename, optimizer, epoch)#
DeepSDFStruct.deep_sdf.training.train_deep_sdf(experiment_directory, data_source, continue_from=None, batch_split=1, device=None)#