Merge pull request #746 from effigies/fix/resources

oesteban · web-flow · commit fe8ef5d68119 · 2017-10-26T11:55:22.000-07:00
[RTM] Resource annotation
diff --git a/fmriprep/interfaces/fmap.py b/fmriprep/interfaces/fmap.py
@@ -27,7 +27,7 @@ class FieldEnhanceInputSpec(BaseInterfaceInputSpec):
     bspline_smooth = traits.Bool(True, usedefault=True, desc='run 3D bspline smoother')
     mask_erode = traits.Int(1, usedefault=True, desc='mask erosion iterations')
     despike_threshold = traits.Float(0.2, usedefault=True, desc='mask erosion iterations')
-    njobs = traits.Int(1, usedefault=True, nohash=True, desc='number of jobs')
+    num_threads = traits.Int(1, usedefault=True, nohash=True, desc='number of jobs')
 
 
 class FieldEnhanceOutputSpec(TraitedSpec):
@@ -86,7 +86,7 @@ def _run_interface(self, runtime):
 
             # Fit BSplines (coarse)
             bspobj = fbsp.BSplineFieldmap(datanii, weights=mask,
-                                          njobs=self.inputs.njobs)
+                                          njobs=self.inputs.num_threads)
             bspobj.fit()
             smoothed1 = bspobj.get_smoothed()
 
@@ -112,7 +112,7 @@ def _run_interface(self, runtime):
                     datanii.affine, datanii.header)
 
                 bspobj2 = fbsp.BSplineFieldmap(diffmapnii, knots_zooms=[24., 24., 4.],
-                                               njobs=self.inputs.njobs)
+                                               njobs=self.inputs.num_threads)
                 bspobj2.fit()
                 smoothed2 = bspobj2.get_smoothed().get_data()
 
diff --git a/fmriprep/workflows/anatomical.py b/fmriprep/workflows/anatomical.py
@@ -216,7 +216,6 @@ def init_anat_preproc_wf(skull_strip_template, output_spaces, template, debug,
         RobustMNINormalizationRPT(
             float=True,
             generate_report=True,
-            num_threads=omp_nthreads,
             flavor='testing' if debug else 'precise',
         ),
         name='t1_2_mni',
@@ -427,10 +426,9 @@ def init_skullstrip_ants_wf(skull_strip_template, debug, omp_nthreads, name='sku
     outputnode = pe.Node(niu.IdentityInterface(
         fields=['bias_corrected', 'out_file', 'out_mask', 'out_report']), name='outputnode')
 
-    t1_skull_strip = pe.Node(BrainExtractionRPT(
-        dimension=3, use_floatingpoint_precision=1,
-        debug=debug, generate_report=True,
-        num_threads=omp_nthreads, keep_temporary_files=1),
+    t1_skull_strip = pe.Node(
+        BrainExtractionRPT(dimension=3, use_floatingpoint_precision=1, debug=debug,
+                           generate_report=True, keep_temporary_files=1),
         name='t1_skull_strip', n_procs=omp_nthreads)
 
     t1_skull_strip.inputs.brain_template = brain_template
@@ -537,12 +535,8 @@ def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
                            run_without_submitting=True)
 
     autorecon1 = pe.Node(
-        fs.ReconAll(
-            directive='autorecon1',
-            flags='-noskullstrip',
-            openmp=omp_nthreads),
-        name='autorecon1',
-        n_procs=omp_nthreads)
+        fs.ReconAll(directive='autorecon1', flags='-noskullstrip', openmp=omp_nthreads),
+        name='autorecon1', n_procs=omp_nthreads, mem_gb=32)
     autorecon1.interface._can_resume = False
 
     skull_strip_extern = pe.Node(FSInjectBrainExtracted(), name='skull_strip_extern',
@@ -679,20 +673,20 @@ def init_autorecon_resume_wf(omp_nthreads, name='autorecon_resume_wf'):
                    '-nohyporelabel', '-noaparc2aseg', '-noapas2aseg',
                    '-nosegstats', '-nowmparc', '-nobalabels'],
             openmp=omp_nthreads),
-        iterfield='hemi', n_procs=omp_nthreads,
+        iterfield='hemi', n_procs=omp_nthreads, mem_gb=32,
         name='autorecon_surfs')
     autorecon_surfs.inputs.hemi = ['lh', 'rh']
 
     autorecon3 = pe.MapNode(
         fs.ReconAll(directive='autorecon3', openmp=omp_nthreads),
-        iterfield='hemi', n_procs=omp_nthreads,
+        iterfield='hemi', n_procs=omp_nthreads, mem_gb=32,
         name='autorecon3')
     autorecon3.inputs.hemi = ['lh', 'rh']
 
     # Only generate the report once; should be nothing to do
     recon_report = pe.Node(
         ReconAllRPT(directive='autorecon3', generate_report=True),
-        name='recon_report')
+        name='recon_report', mem_gb=8)
 
     def _dedup(in_list):
         vals = set(in_list)
diff --git a/fmriprep/workflows/bold.py b/fmriprep/workflows/bold.py
@@ -811,7 +811,7 @@ def init_bold_hmc_wf(bold_file_size_gb, omp_nthreads, name='bold_hmc_wf'):
     mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True, save_plots=True),
                       name='mcflirt', mem_gb=bold_file_size_gb * 3)
 
-    fsl2itk = pe.Node(MCFLIRT2ITK(num_threads=omp_nthreads), name='fsl2itk',
+    fsl2itk = pe.Node(MCFLIRT2ITK(), name='fsl2itk',
                       mem_gb=0.05, n_procs=omp_nthreads)
 
     normalize_motion = pe.Node(NormalizeMotionParams(format='FSL'),
@@ -1002,10 +1002,9 @@ def init_bold_reg_wf(freesurfer, use_bbr, bold2t1w_dof, bold_file_size_gb, omp_n
             (inputnode, merge_xforms, [('fieldwarp', 'in2')])
         ])
 
-    bold_to_t1w_transform = pe.Node(MultiApplyTransforms(
-        interpolation="LanczosWindowedSinc", float=True, copy_dtype=True,
-        num_threads=omp_nthreads),
-        name='bold_to_t1w_transform', mem_gb=0.1, n_procs=omp_nthreads)
+    bold_to_t1w_transform = pe.Node(
+        MultiApplyTransforms(interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
+        name='bold_to_t1w_transform', mem_gb=bold_file_size_gb * 3, n_procs=omp_nthreads)
 
     merge = pe.Node(Merge(compress=use_compression), name='merge', mem_gb=bold_file_size_gb * 3)
 
@@ -1105,7 +1104,7 @@ def select_target(subject_id, space):
                            override_reg_subj=True, out_type='gii'),
         iterfield=['source_file', 'target_subject'],
         iterables=('hemi', ['lh', 'rh']),
-        name='sampler')
+        name='sampler', mem_gb=3)
 
     def medial_wall_to_nan(in_file, subjects_dir, target_subject):
         """ Convert values on medial wall to NaNs
@@ -1261,7 +1260,7 @@ def _aslist(in_value):
     mask_mni_tfm = pe.Node(
         ApplyTransforms(interpolation='NearestNeighbor', float=True),
         name='mask_mni_tfm',
-        mem_gb=0.1
+        mem_gb=3
     )
 
     # Write corrected file in the designated output dir
@@ -1285,9 +1284,10 @@ def _aslist(in_value):
         (inputnode, mask_mni_tfm, [('bold_mask', 'input_image')])
     ])
 
-    bold_to_mni_transform = pe.Node(MultiApplyTransforms(
-        interpolation="LanczosWindowedSinc", float=True, num_threads=omp_nthreads,
-        copy_dtype=True), name='bold_to_mni_transform', mem_gb=0.1, n_procs=omp_nthreads)
+    bold_to_mni_transform = pe.Node(
+        MultiApplyTransforms(interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
+        name='bold_to_mni_transform', mem_gb=bold_file_size_gb * 3, n_procs=omp_nthreads)
+
     merge = pe.Node(Merge(compress=use_compression), name='merge',
                     mem_gb=bold_file_size_gb * 3)
 
@@ -1406,9 +1406,9 @@ def init_nonlinear_sdc_wf(bold_file, freesurfer, bold2t1w_dof,
     invert_t1w = pe.Node(InvertT1w(), name='invert_t1w',
                          mem_gb=0.3)
 
-    ref_2_t1 = pe.Node(Registration(from_file=affine_transform, num_threads=omp_nthreads),
+    ref_2_t1 = pe.Node(Registration(from_file=affine_transform),
                        name='ref_2_t1', n_procs=omp_nthreads)
-    t1_2_ref = pe.Node(ApplyTransforms(invert_transform_flags=[True], num_threads=omp_nthreads),
+    t1_2_ref = pe.Node(ApplyTransforms(invert_transform_flags=[True]),
                        name='t1_2_ref', n_procs=omp_nthreads)
 
     # 1) BOLD -> T1; 2) MNI -> T1; 3) ATLAS -> MNI
@@ -1420,8 +1420,7 @@ def init_nonlinear_sdc_wf(bold_file, freesurfer, bold2t1w_dof,
     #
     # ATLAS -> MNI -> T1 -> BOLD
     atlas_2_ref = pe.Node(
-        ApplyTransforms(invert_transform_flags=[True, False, False],
-                        num_threads=omp_nthreads),
+        ApplyTransforms(invert_transform_flags=[True, False, False]),
         name='atlas_2_ref', n_procs=omp_nthreads,
         mem_gb=0.3)
     atlas_2_ref.inputs.input_image = atlas_img
@@ -1437,13 +1436,12 @@ def init_nonlinear_sdc_wf(bold_file, freesurfer, bold2t1w_dof,
 
     restrict = [[int(bold_pe[0] == 'i'), int(bold_pe[0] == 'j'), 0]] * 2
     syn = pe.Node(
-        Registration(from_file=syn_transform, num_threads=omp_nthreads,
-                     restrict_deformation=restrict),
+        Registration(from_file=syn_transform, restrict_deformation=restrict),
         name='syn', n_procs=omp_nthreads)
 
     seg_2_ref = pe.Node(
         ApplyTransforms(interpolation='NearestNeighbor', float=True,
-                        invert_transform_flags=[True], num_threads=omp_nthreads),
+                        invert_transform_flags=[True]),
         name='seg_2_ref', n_procs=omp_nthreads, mem_gb=0.3)
     sel_wm = pe.Node(niu.Function(function=extract_wm), name='sel_wm',
                      mem_gb=DEFAULT_MEMORY_MIN_GB)
diff --git a/fmriprep/workflows/fieldmap/fmap.py b/fmriprep/workflows/fieldmap/fmap.py
@@ -55,9 +55,8 @@ def init_fmap_wf(reportlets_dir, omp_nthreads, fmap_bspline, name='fmap_wf'):
                       name='fmapmrg')
 
     # de-gradient the fields ("bias/illumination artifact")
-    n4_correct = pe.Node(ants.N4BiasFieldCorrection(
-        dimension=3, copy_header=True, num_threads=omp_nthreads),
-        n_procs=omp_nthreads, name='n4_correct')
+    n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
+                         name='n4_correct', n_procs=omp_nthreads)
     bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
                   name='bet')
     ds_fmap_mask = pe.Node(DerivativesDataSink(
@@ -77,9 +76,8 @@ def init_fmap_wf(reportlets_dir, omp_nthreads, fmap_bspline, name='fmap_wf'):
 
     if fmap_bspline:
         # despike_threshold=1.0, mask_erode=1),
-        fmapenh = pe.Node(FieldEnhance(
-            unwrap=False, despike=False, njobs=omp_nthreads),
-            name='fmapenh', mem_gb=4, n_procs=omp_nthreads)
+        fmapenh = pe.Node(FieldEnhance(unwrap=False, despike=False),
+                          name='fmapenh', mem_gb=4, n_procs=omp_nthreads)
 
         workflow.connect([
             (bet, fmapenh, [('mask_file', 'in_mask'),
diff --git a/fmriprep/workflows/fieldmap/phdiff.py b/fmriprep/workflows/fieldmap/phdiff.py
@@ -68,8 +68,8 @@ def _pick1st(inlist):
     magmrg = pe.Node(IntraModalMerge(), name='magmrg')
 
     # de-gradient the fields ("bias/illumination artifact")
-    n4 = pe.Node(ants.N4BiasFieldCorrection(
-        dimension=3, copy_header=True, num_threads=omp_nthreads), name='n4', n_procs=omp_nthreads)
+    n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
+                 name='n4', n_procs=omp_nthreads)
     bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
                   name='bet')
     ds_fmap_mask = pe.Node(DerivativesDataSink(
diff --git a/fmriprep/workflows/fieldmap/unwarp.py b/fmriprep/workflows/fieldmap/unwarp.py
@@ -101,9 +101,8 @@ def init_sdc_unwarp_wf(reportlets_dir, omp_nthreads, fmap_bspline,
         ants_settings = pkgr.resource_filename(
             'fmriprep', 'data/fmap-any_registration_testing.json')
     fmap2ref_reg = pe.Node(
-        ANTSRegistrationRPT(
-            generate_report=True, from_file=ants_settings, output_inverse_warped_image=True,
-            output_warped_image=True, num_threads=omp_nthreads),
+        ANTSRegistrationRPT(generate_report=True, from_file=ants_settings,
+                            output_inverse_warped_image=True, output_warped_image=True),
         name='fmap2ref_reg', n_procs=omp_nthreads)
 
     ds_reg = pe.Node(DerivativesDataSink(
@@ -437,8 +436,7 @@ def init_prepare_epi_wf(omp_nthreads, name="prepare_epi_wf"):
     ants_settings = pkgr.resource_filename('fmriprep',
                                            'data/translation_rigid.json')
     fmap2ref_reg = pe.Node(ants.Registration(from_file=ants_settings,
-                                             output_warped_image=True,
-                                             num_threads=omp_nthreads),
+                                             output_warped_image=True),
                            name='fmap2ref_reg', n_procs=omp_nthreads)
 
     workflow = pe.Workflow(name=name)
diff --git a/fmriprep/workflows/util.py b/fmriprep/workflows/util.py
@@ -112,9 +112,8 @@ def init_enhance_and_skullstrip_bold_wf(name='enhance_and_skullstrip_bold_wf',
                                                        'bias_corrected_file',
                                                        'out_report']),
                          name='outputnode')
-    n4_correct = pe.Node(
-        ants.N4BiasFieldCorrection(dimension=3, copy_header=True, num_threads=omp_nthreads),
-        name='n4_correct', n_procs=omp_nthreads)
+    n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
+                         name='n4_correct', n_procs=omp_nthreads)
     skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
                                     name='skullstrip_first_pass')
     unifize = pe.Node(afni.Unifize(t2=True, outputtype='NIFTI_GZ',
@@ -298,8 +297,8 @@ def init_bbreg_wf(use_bbr, bold2t1w_dof, omp_nthreads, name='bbreg_wf'):
 
     mri_coreg = pe.Node(
         MRICoregRPT(dof=bold2t1w_dof, sep=[4], ftol=0.0001, linmintol=0.01,
-                    num_threads=omp_nthreads, generate_report=not use_bbr),
-        name='mri_coreg', n_procs=omp_nthreads)
+                    generate_report=not use_bbr),
+        name='mri_coreg', n_procs=omp_nthreads, mem_gb=32)
 
     lta_concat = pe.Node(ConcatenateLTA(out_file='out.lta'), name='lta_concat')
     # XXX LTA-FSL-ITK may ultimately be able to be replaced with a straightforward
@@ -327,7 +326,7 @@ def init_bbreg_wf(use_bbr, bold2t1w_dof, omp_nthreads, name='bbreg_wf'):
         (lta2fsl_inv, fsl2itk_inv, [('out_fsl', 'transform_file')]),
         (fsl2itk_fwd, outputnode, [('itk_transform', 'itk_bold_to_t1')]),
         (fsl2itk_inv, outputnode, [('itk_transform', 'itk_t1_to_bold')]),
-        ])
+    ])
 
     # Short-circuit workflow building, use initial registration
     if use_bbr is False:
@@ -341,14 +340,14 @@ def init_bbreg_wf(use_bbr, bold2t1w_dof, omp_nthreads, name='bbreg_wf'):
     bbregister = pe.Node(
         BBRegisterRPT(dof=bold2t1w_dof, contrast_type='t2', registered_file=True,
                       out_lta_file=True, generate_report=True),
-        name='bbregister')
+        name='bbregister', mem_gb=12)
 
     workflow.connect([
         (inputnode, bbregister, [('subjects_dir', 'subjects_dir'),
                                  ('subject_id', 'subject_id'),
                                  ('in_file', 'source_file')]),
         (mri_coreg, bbregister, [('out_lta_file', 'init_reg_file')]),
-        ])
+    ])
 
     # Short-circuit workflow building, use boundary-based registration
     if use_bbr is True:
@@ -363,7 +362,7 @@ def init_bbreg_wf(use_bbr, bold2t1w_dof, omp_nthreads, name='bbreg_wf'):
     reports = pe.Node(niu.Merge(2), run_without_submitting=True, name='reports')
 
     lta_ras2ras = pe.MapNode(fs.utils.LTAConvert(out_lta=True), iterfield=['in_lta'],
-                             name='lta_ras2ras')
+                             name='lta_ras2ras', mem_gb=2)
     compare_transforms = pe.Node(niu.Function(function=compare_xforms), name='compare_transforms')
 
     select_transform = pe.Node(niu.Select(), run_without_submitting=True, name='select_transform')
@@ -386,7 +385,7 @@ def init_bbreg_wf(use_bbr, bold2t1w_dof, omp_nthreads, name='bbreg_wf'):
         (reports, select_report, [('out', 'inlist')]),
         (compare_transforms, select_report, [('out', 'index')]),
         (select_report, outputnode, [('out', 'out_report')]),
-        ])
+    ])
 
     return workflow
 
