ivis.utils.mod_loss

Functions

batch_worker(batch_indices, x, uu, vv, ww, ...)
beam_worker(i, x, uu, vv, ww, data, sigma, ...)
compute_loss(x, beam, fftbeam, data, uu, vv, ...)
compute_loss_Pool(x, beam, fftbeam, data, ...)	Compute total loss (interferometric + single-dish + regularization), memory-safe on GPU and parallel on CPU if needed.
compute_vis_cuda(x, uua, vva, wwa, vis_real, ...)
format_input_tensor(input_tensor)
objective(x, beam, fftbeam, data, uu, vv, ...)
print_gpu_memory([device])
single_frequency_model(x, data, uu, vv, pb, ...)

ivis.utils.mod_loss.batch_worker(batch_indices, x, uu, vv, ww, data, sigma, pb, idmina, idmaxa, cell_size, device, grid_array)

ivis.utils.mod_loss.beam_worker(i, x, uu, vv, ww, data, sigma, pb, idmina, idmaxa, cell_size, device, grid_array)

ivis.utils.mod_loss.compute_loss(x, beam, fftbeam, data, uu, vv, ww, pb, idmina, idmaxa, device, sigma, fftsd, tapper, lambda_sd, lambda_r, fftkernel, cell_size, grid_array)

ivis.utils.mod_loss.compute_loss_Pool(x, beam, fftbeam, data, uu, vv, ww, pb, idmina, idmaxa, device, sigma, fftsd, tapper, lambda_sd, lambda_r, fftkernel, cell_size, grid_array, beam_workers=4, verbose=False)

Compute total loss (interferometric + single-dish + regularization), memory-safe on GPU and parallel on CPU if needed. Uses per-beam backward passes to avoid memory accumulation.

ivis.utils.mod_loss.compute_vis_cuda(x, uua, vva, wwa, vis_real, vis_imag, sig, pb, cell_size, device, grid)

ivis.utils.mod_loss.format_input_tensor(input_tensor)

ivis.utils.mod_loss.objective(x, beam, fftbeam, data, uu, vv, ww, pb, idmina, idmaxa, device, sigma, fftsd, tapper, lambda_sd, lambda_r, fftkernel, shape, cell_size, grid_array, beam_workers)

ivis.utils.mod_loss.print_gpu_memory(device='cuda:0')

ivis.utils.mod_loss.single_frequency_model(x, data, uu, vv, pb, idmina, idmaxa, device, cell_size, grid_array)