'loss': ['compound_mssim'], 'multicoil': [True], 'refine_smaps': [True], } params = [ dict(dcomp=[True], normalize_image=[False], **base_params), # dict(dcomp=[False], normalize_image=[True], **base_params), ] run_ids = [ 'ncpdnet_sense___rfs_radial_compound_mssim_dcomp_1611913984', 'ncpdnet_sense___rfs_spiral_compound_mssim_dcomp_1611913984', ] eval_results = eval_grid( 'nc_pdnet_mc', # train_fun, eval_fun, params, run_ids=run_ids, # n_gpus_train=1, # timeout_train=25, # n_gpus_eval=1, # n_samples_eval=100, # checkpoints_train=3, n_samples=100, n_gpus=3, ) print(eval_results)
dict(use_mixed_precision=[False], model=['unet'], **base_params), ] run_ids = [ 'ncpdnet_3d___i6_radial_stacks_mse_dcomp_1612291359', 'ncpdnet_3d___i6_spiral_stacks_mse_dcomp_1612291359', 'vnet_3d___radial_stacks_mse_dcomp_1612291357', 'vnet_3d___spiral_stacks_mse_dcomp_1612291357', ] # eval_results = train_eval_grid( eval_results = eval_grid( '3d_nc_eval', # train_ncnet_multinet, evaluate_nc_multinet, params, run_ids=run_ids, # n_gpus_train=1, # timeout_train=40, # n_gpus_eval=1, # n_samples_eval=100, timeout=20, n_gpus=1, params_to_ignore=['use_mixed_precision', 'scale_factor'], # checkpoints_train=7, # resume_checkpoint=4, # resume_run_run_ids=run_ids, project='fastmri4', ) print(eval_results)
from grappa.deep.model import DeepKSpaceFiller from grappa.evaluate.deep_evaluation import test_model from jean_zay.submitit.general_submissions import train_eval_grid, eval_grid job_name = 'd_grappa_eval' metrics = dict() param_grid = { 'model_fun': [DeepKSpaceFiller], 'model_kwargs': [{ 'n_dense': 2 }, { 'n_dense': 3 }], 'distance_from_center_feat': [True, False], 'n_epochs': [1000], 'lr': [1e-3], 'instance_normalisation': [True, False], 'kernel_learning': [True, False], } eval_grid( job_name, test_model, param_grid, n_samples=2, timeout=2, n_gpus=1, )
base_params = { 'n_epochs': [100], 'af': [4], 'acq_type': ['radial', 'spiral'], 'loss': ['compound_mssim'], 'multicoil': [True], 'refine_smaps': [True], 'brain': [True], } params = [ dict(dcomp=[True], normalize_image=[False], **base_params), ] run_ids = [ 'ncpdnet_sense___rfs_radial_compound_mssim_dcomp_1611913984', 'ncpdnet_sense___rfs_spiral_compound_mssim_dcomp_1611913984', ] eval_results = eval_grid( 'nc_pdnet_mc_brain', eval_fun, params, run_ids=run_ids, n_samples=250, n_gpus=3, timeout=20, project='fastmri4', ) print(eval_results)
from jean_zay.submitit.general_submissions import train_eval_grid, eval_grid base_params = { 'n_epochs': [100], 'af': [4], 'acq_type': ['radial', 'spiral'], 'loss': ['compound_mssim'], 'multicoil': [True], 'refine_smaps': [True], } params = [ dict(dcomp=[True], normalize_image=[False], **base_params), ] run_ids = [ 'ncpdnet_sense___rfs_spiral_compound_mssim_dcomp_1611913984', 'ncpdnet_sense___rfs_radial_compound_mssim_dcomp_1611913984', ] eval_results = eval_grid( 'nc_pdnet_mc_rev', eval_fun, params, run_ids=run_ids, n_samples=100, n_gpus=3, project='fastmri4', ) print(eval_results)
job_name = 'shine_classifier_cifar_large_contract' n_gpus = 1 base_params = dict( model_size='LARGE', dataset='cifar', n_gpus=n_gpus, check_contract=True, n_iter=500, seed=0, ) parameters = [] parameters += [ base_params, dict(shine=True, **base_params), dict(fpn=True, **base_params), ] res_all = eval_grid( job_name, evaluate_classifier, parameters, to_grid=False, timeout=20, n_gpus=n_gpus, project='shine', params_to_ignore=['n_epochs'], torch=True, no_force_32=True, )
'n_epochs': [100], 'af': [4], 'acq_type': ['radial', 'spiral'], 'loss': ['compound_mssim'], } params = [ dict(dcomp=[True], normalize_image=[False], **base_params), # dict(dcomp=[False], normalize_image=[True], **base_params), ] run_ids = [ 'ncpdnet_singlecoil___radial_compound_mssim_dcomp_1610872636', 'ncpdnet_singlecoil___spiral_compound_mssim_dcomp_1610911070', ] eval_results = eval_grid( 'nc_pdnet', # train_fun, eval_fun, params, run_ids=run_ids, # n_gpus_train=1, # timeout_train=100, # n_gpus_eval=1, # n_samples_eval=100, n_samples=100, n_gpus=1, ) print(eval_results)
from fastmri_recon.evaluate.scripts.nc_dip_eval import evaluate_dip_nc as eval_fun from jean_zay.submitit.general_submissions import eval_grid base_params = { 'af': [4], 'acq_type': ['radial', 'spiral'], 'model_kwargs': [{}], } eval_results = eval_grid( 'nc_pdnet', eval_fun, base_params, run_ids=None, n_samples=100, n_gpus=1, ) print(eval_results)
'MWCNN_medium_1603197894', 'MWCNN_small_1603197894', 'U-net_big_1603197894', 'U-net_medium_1603197894', 'U-net_medium-ca_1603197894', 'U-net_small_1603197894', ] eval_results = eval_grid( run_ids, 'denoise', # train_denoiser, evaluate_xpdnet_denoising, parameter_grid, # n_samples_eval=500, # timeout_train=20, # n_gpus_train=1, # timeout_eval=4, # n_gpus_eval=1, n_samples=200, timeout=10, n_gpus=1, to_grid=False, noise_std=1, # just for eval ) df_results = pd.DataFrame(columns='model_name model_size psnr ssim'.split()) for (name, model_size, _, _, _, _, _), eval_res in zip(model_specs, eval_results): df_results = df_results.append(dict( model_name=name,
noise_config_eval = dict( noise_input=True, fixed_noise=True, noise_power_spec=noise_level / 255, ) eval_results = eval_grid( job_name, # train, evaluate, parameter_grid, run_ids=run_ids, # n_samples_eval=20, # timeout_train=20, # n_gpus_train=1, # timeout_eval=4, # n_gpus_eval=1, n_samples=n_samples_eval, timeout=4, n_gpus=1, to_grid=False, patch_size=patch_size_eval, # just for eval batch_size=batch_size_eval, # just for eval noise_config=noise_config_eval, # just for eval mode='bsd68', # just for eval project='soft_thresholding', ) eval_res.append(eval_results) eval_res.append(eval_results_50) data_for_df = [] for eval_results, noise_level in zip(eval_res, noise_levels):