Small restructuring and less repetitions

bob/bio/base/script/verify.py

@@ -206,7 +206,7 @@ def add_jobs(args, submitter):
                   dependencies = score_deps[group])
           concat_deps[group].extend([job_ids['score-%s-B'%group], job_ids['score-%s-C'%group], job_ids['score-%s-D'%group], job_ids['score-%s-Z'%group]])
     else:
-      concat_deps[group] = []
+      concat_deps[group] = deps[:]
 
     # concatenate results
     if not args.skip_concatenation:
@@ -309,48 +309,26 @@ def execute(args):
 
   # enroll the models
   elif args.sub_task == 'enroll':
-    if args.model_type == 'N':
-      tools.enroll(
-          args.algorithm,
-          args.extractor,
-          args.zt_norm,
-          indices = tools.indices(fs.model_ids(args.group), None if args.grid is None else args.grid.number_of_enrollment_jobs),
-          groups = [args.group],
-          types = ['N'],
-          allow_missing_files = args.allow_missing_files,
-          force = args.force)
-
-    else:
-      tools.enroll(
-          args.algorithm,
-          args.extractor,
-          args.zt_norm,
-          indices = tools.indices(fs.t_model_ids(args.group), None if args.grid is None else args.grid.number_of_enrollment_jobs),
-          groups = [args.group],
-          types = ['T'],
-          allow_missing_files = args.allow_missing_files,
-          force = args.force)
+    model_ids = fs.model_ids(args.group) if args.model_type == 'N' else fs.t_model_ids(args.group)
+    tools.enroll(
+        args.algorithm,
+        args.extractor,
+        args.zt_norm,
+        indices = tools.indices(model_ids, None if args.grid is None else args.grid.number_of_enrollment_jobs),
+        groups = [args.group],
+        types = [args.model_type],
+        allow_missing_files = args.allow_missing_files,
+        force = args.force)
 
   # compute scores
   elif args.sub_task == 'compute-scores':
-    if args.score_type in ['A', 'B']:
-      tools.compute_scores(
-          args.algorithm,
-          args.extractor,
-          args.zt_norm,
-          indices = tools.indices(fs.model_ids(args.group), None if args.grid is None else args.grid.number_of_scoring_jobs),
-          groups = [args.group],
-          types = [args.score_type],
-          force = args.force,
-          allow_missing_files = args.allow_missing_files,
-          write_compressed = args.write_compressed_score_files)
-
-    elif args.score_type in ['C', 'D']:
+    if args.score_type != 'Z':
+      model_ids = fs.model_ids(args.group) if args.score_type in ('A', 'B') else fs.t_model_ids(args.group)
       tools.compute_scores(
           args.algorithm,
           args.extractor,
           args.zt_norm,
-          indices = tools.indices(fs.t_model_ids(args.group), None if args.grid is None else args.grid.number_of_scoring_jobs),
+          indices = tools.indices(model_ids, None if args.grid is None else args.grid.number_of_scoring_jobs),
           groups = [args.group],
           types = [args.score_type],
           force = args.force,

bob/bio/base/tools/scoring.py

@@ -13,11 +13,13 @@ from .FileSelector import FileSelector
 from .extractor import read_features
 from .. import utils
 
-def _scores(algorithm, reader, model, probes, allow_missing_files):
+def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
   """Compute scores for the given model and a list of probes.
   """
   # the file selector object
   fs = FileSelector.instance()
+  # get probe files
+  probes = fs.get_paths(probe_objects, 'projected' if algorithm.performs_projection else 'extracted')
   # the scores to be computed; initialized with NaN
   scores = numpy.ones((1,len(probes)), numpy.float64) * numpy.nan
 
@@ -135,10 +137,8 @@ def _scores_a(algorithm, reader, model_ids, group, compute_zt_norm, force, write
         model = None
       else:
         model = algorithm.read_model(model_file)
-      # get the probe files
-      current_probe_files = fs.get_paths(current_probe_objects, 'projected' if algorithm.performs_projection else 'extracted')
       # compute scores
-      a = _scores(algorithm, reader, model, current_probe_files, allow_missing_files)
+      a = _scores(algorithm, reader, model, current_probe_objects, allow_missing_files)
 
       if compute_zt_norm:
         # write A matrix only when you want to compute zt norm afterwards
@@ -155,7 +155,6 @@ def _scores_b(algorithm, reader, model_ids, group, force, allow_missing_files):
 
   # probe files:
   z_probe_objects = fs.z_probe_objects(group)
-  z_probe_files = fs.get_paths(z_probe_objects, 'projected' if algorithm.performs_projection else 'extracted')
 
   logger.info("- Scoring: computing score matrix B for group '%s'", group)
 
@@ -171,7 +170,7 @@ def _scores_b(algorithm, reader, model_ids, group, force, allow_missing_files):
         model = None
       else:
         model = algorithm.read_model(model_file)
-      b = _scores(algorithm, reader, model, z_probe_files, allow_missing_files)
+      b = _scores(algorithm, reader, model, z_probe_objects, allow_missing_files)
       bob.io.base.save(b, score_file, True)
 
 def _scores_c(algorithm, reader, t_model_ids, group, force, allow_missing_files):
@@ -181,7 +180,6 @@ def _scores_c(algorithm, reader, t_model_ids, group, force, allow_missing_files)
 
   # probe files:
   probe_objects = fs.probe_objects(group)
-  probe_files = fs.get_paths(probe_objects, 'projected' if algorithm.performs_projection else 'extracted')
 
   logger.info("- Scoring: computing score matrix C for group '%s'", group)
 
@@ -197,7 +195,7 @@ def _scores_c(algorithm, reader, t_model_ids, group, force, allow_missing_files)
         t_model = None
       else:
         t_model = algorithm.read_model(t_model_file)
-      c = _scores(algorithm, reader, t_model, probe_files, allow_missing_files)
+      c = _scores(algorithm, reader, t_model, probe_objects, allow_missing_files)
       bob.io.base.save(c, score_file, True)
 
 def _scores_d(algorithm, reader, t_model_ids, group, force, allow_missing_files):
@@ -207,7 +205,6 @@ def _scores_d(algorithm, reader, t_model_ids, group, force, allow_missing_files)
 
   # probe files:
   z_probe_objects = fs.z_probe_objects(group)
-  z_probe_files = fs.get_paths(z_probe_objects, 'projected' if algorithm.performs_projection else 'extracted')
 
   logger.info("- Scoring: computing score matrix D for group '%s'", group)
 
@@ -227,7 +224,7 @@ def _scores_d(algorithm, reader, t_model_ids, group, force, allow_missing_files)
         t_model = None
       else:
         t_model = algorithm.read_model(t_model_file)
-      d = _scores(algorithm, reader, t_model, z_probe_files, allow_missing_files)
+      d = _scores(algorithm, reader, t_model, z_probe_objects, allow_missing_files)
       bob.io.base.save(d, score_file, True)
 
       t_client_id = [fs.client_id(t_model_id, group, True)]