ptyrad.reconstruction

Reconstruction and hypertune workflows for ptychographic reconstructions

Classes

IndicesDataset(indices)	The Dataset class used specifically for the multiGPU mode for DDP
PtyRADSolver(params[, device, seed, acc, logger])	A wrapper class to perform ptychographic reconstruction or hyperparameter tuning.

Functions

compute_loss(batch, model, model_instance, ...)	Compute the model output and loss, with optional support for accelerate's autocast.
compute_optuna_error(model, indices, metric)	Helper function to compute the current error for Optuna
create_optimizer(optimizer_params, ...[, ...])
create_optuna_pruner(pruner_params[, verbose])
create_optuna_sampler(sampler_params[, verbose])
get_optuna_suggest(trial, suggest, name, kwargs)
loss_logger(batch_losses, niter, iter_t[, ...])	Logs and summarizes the loss values for an iteration during the ptychographic reconstruction.
make_batches(indices, pos, batch_size[, ...])	Make batches from input indices
optuna_objective(trial, params, init, ...[, ...])	Objective function for Optuna hyperparameter tuning in ptychographic reconstruction.
parse_torch_compile_configs(configs)	Convert user-facing CompilerConfigs to dict suitable for torch.compile
prepare_recon(model, init, params)	Prepares the indices, batches, and output path for ptychographic reconstruction.
recon_loop(model, init, params, optimizer, ...)	Executes the iterative optimization loop for ptychographic reconstruction.
recon_step(batches, grad_accumulation, ...)	Performs one iteration (or step) of the ptychographic reconstruction in the optimization loop.
select_scan_indices(N_scan_slow, N_scan_fast)
toggle_grad_requires(model, niter[, verbose])	Toggle requires_grad based on start and end iteration for each optimizable tensor.