job_name = 'debug_shine' n_gpus = 1 base_params = dict( model_size='SMALL', dataset='imagenet', n_gpus=n_gpus, n_epochs=100, seed=42, restart_from=48, gradient_correl=False, gradient_ratio=False, n_iter=1000, ) parameters = [ # base_params, # dict(shine=True, **base_params), dict(fpn=True, **base_params), ] executor = get_executor(job_name, timeout_hour=2, n_gpus=n_gpus, project='shine') jobs = [] with executor.batch(): for param in parameters: job = executor.submit(train_classifier, **param) jobs.append(job) [job.result() for job in jobs]
from mdeq_lib.training_scripts.denoise_train import train_mdeq_denoising from jean_zay.submitit.general_submissions import get_executor job_name = 'mdeq_denoise' executor = get_executor(job_name, timeout_hour=6, n_gpus=1, project='mdeq') with executor.batch(): for use_bn in [True, False]: for use_res in [True, False]: for use_new_residual in [True, False]: executor.submit( train_mdeq_denoising, n_val=20, use_res=use_res, use_bn=use_bn, network_size='SMALL', use_new_residual=use_new_residual, grad_clipping=10., )
from fastmri_recon.data.scripts.oasis_tf_records_generation import generate_oasis_tf_records from jean_zay.submitit.general_submissions import get_executor executor = get_executor('oasis_tfrecords', timeout_hour=20, n_gpus=1, project='fastmri') with executor.batch(): for mode in ['train', 'val']: for acq_type in ['radial_stacks', 'spiral_stacks']: executor.submit(generate_oasis_tf_records, acq_type=acq_type, af=4, mode=mode)
'pdnet': { 'radial_stacks': 'ncpdnet_3d___i6_radial_stacks_mse_dcomp_1612291359', 'spiral_stacks': 'ncpdnet_3d___i6_spiral_stacks_mse_dcomp_1612291359', }, 'unet': { 'radial_stacks': 'vnet_3d___radial_stacks_mse_dcomp_1612291357', 'spiral_stacks': 'vnet_3d___spiral_stacks_mse_dcomp_1612291357', }, 'adj-dcomp': { 'radial_stacks': None, 'spiral_stacks': None, }, } executor = get_executor('3dnc_time', timeout_hour=2, n_gpus=1, project='fastmri4') with executor.batch(): for model, run_ids in model_2_run_ids.items(): for acq_type in ['radial_stacks', 'spiral_stacks']: executor.submit( nc_multinet_qualitative_validation, acq_type=acq_type, af=4, model=model, run_id=run_ids[acq_type], three_d=True, refine_smaps=False, dcomp=True, normalize_image=False, n_epochs=8,
from fastmri_recon.models.subclassed_models.denoisers.proposed_params import get_model_specs from jean_zay.submitit.general_submissions import get_executor from jean_zay.submitit.fastmri_reproducible_benchmark.mem_fitting_test import test_works_in_xpdnet_train n_iter_to_try_for_size = { 'medium': range(30, 40), } n_primal = 5 job_name = 'xpdnet_tryouts' executor = get_executor(job_name, timeout_hour=1, n_gpus=1, project='fastmri4') results = {} with executor.batch(): for data_consistency_learning in [True, False]: for model_size_spec, n_iter_to_try in n_iter_to_try_for_size.items(): for model_name, model_size, model_fun, model_kwargs, n_inputs, n_scales, res in get_model_specs( n_primal): if model_size != model_size_spec or model_name != 'MWCNN': continue for n_iter in n_iter_to_try: job = executor.submit( test_works_in_xpdnet_train, model_fun=model_fun, model_kwargs=model_kwargs, n_scales=n_scales, res=res, n_iter=n_iter, multicoil=True,
from fastmri_recon.data.scripts.multicoil_nc_tf_records_generation import generate_multicoil_nc_tf_records from jean_zay.submitit.general_submissions import get_executor executor = get_executor('ncmc_tfrecords', timeout_hour=20, n_gpus=1, project='fastmri4') with executor.batch(): # for mode in ['train', 'val']: for mode in ['val']: for acq_type in ['radial', 'spiral']: executor.submit(generate_multicoil_nc_tf_records, acq_type=acq_type, af=4, mode=mode, brain=True)
from fastmri_recon.evaluate.scripts.nc_eval import evaluate_dcomp from jean_zay.submitit.general_submissions import get_executor executor = get_executor('adjoint_dc', timeout_hour=20, n_gpus=1, project='fastmri') with executor.batch(): for acq_type in ['radial', 'spiral']: executor.submit( evaluate_dcomp, acq_type=acq_type, af=4, n_samples=100, )
from mdeq_lib.training.cls_train import train_classifier from jean_zay.submitit.general_submissions import get_executor job_name = 'shine_classifier_cifar_small_fpn' n_gpus = 4 executor = get_executor( job_name, timeout_hour=1, n_gpus=n_gpus, project='shine', torch=True, no_force_32=True, ) executor.submit( train_classifier, model_size='TINY', dataset='cifar', n_gpus=n_gpus, shine=False, fpn=True, n_epochs=25, )
run_ids = { 4: 'xpdnet_sense__af4_compound_mssim_rf_smb_MWCNNmedium_1606491318', 8: 'xpdnet_sense__af8_compound_mssim_rf_smb_MWCNNmedium_1606491318', } model_name = 'MWCNN' model_size = 'medium' n_primal = 5 model_specs = list(get_model_specs(force_res=False, n_primal=n_primal)) if model_name is not None: model_specs = [ms for ms in model_specs if ms[0] == model_name] if model_size is not None: model_specs = [ms for ms in model_specs if ms[1] == model_size] _, model_size, model_fun, kwargs, _, n_scales, res = model_specs[0] executor = get_executor('postproc_tfrecords', timeout_hour=20, n_gpus=4, project='fastmri') with executor.batch(): for mode in ['train', 'val']: for af in [4, 8]: executor.submit( generate_postproc_tf_records, af=af, mode=mode, model_fun=model_fun, model_kwargs=kwargs, run_id=run_ids[af], brain=False, n_epochs=300, n_iter=10, res=res, n_scales=n_scales,
'pdnet': { 'radial': 'ncpdnet_sense___rfs_radial_compound_mssim_dcomp_1611913984', 'spiral': 'ncpdnet_sense___rfs_spiral_compound_mssim_dcomp_1611913984', }, 'unet': { 'radial': 'unet_mc___radial_compound_mssim_dcomp_1611915508', 'spiral': 'unet_mc___spiral_compound_mssim_dcomp_1611915508', }, 'adj-dcomp': { 'radial': None, 'spiral': None, }, } executor = get_executor('ncmc_quali', timeout_hour=2, n_gpus=1, project='fastmri4') with executor.batch(): for model, run_ids in model_2_run_ids.items(): for acq_type in ['radial', 'spiral']: executor.submit( nc_multinet_qualitative_validation, acq_type=acq_type, af=4, model=model, run_id=run_ids[acq_type], multicoil=True, refine_smaps=True, dcomp=True, normalize_image=False, contrast='CORPD_FBK',
from fastmri_recon.evaluate.scripts.nc_eval import evaluate_dcomp from jean_zay.submitit.general_submissions import get_executor executor = get_executor('adjoint_dc_mc_brain', timeout_hour=20, n_gpus=1, project='fastmri4') with executor.batch(): for acq_type in ['radial', 'spiral']: executor.submit( evaluate_dcomp, acq_type=acq_type, af=4, n_samples=250, multicoil=True, brain=True, )