Fix batch execution

.github/workflows/regressiontests.yml

@@ -58,8 +58,8 @@ jobs:
 
       - name: Run SuperDSM
         run: |
-            python -m "superdsm.batch" examples --task-dir "${{ matrix.taskdir }}"
-            python -m "superdsm.batch" examples --task-dir "${{ matrix.taskdir }}" --run
+            python -m "superdsm" examples --task-dir "examples/${{ matrix.taskdir }}"
+            python -m "superdsm" examples --task-dir "examples/${{ matrix.taskdir }}" --run
         env:
           SUPERDSM_INTERMEDIATE_OUTPUT: false
           SUPERDSM_NUM_CPUS: 20

examples/README.md

@@ -1,5 +1,5 @@
 Run the script `load_data.py` to download the example image data from the web. 
 
-Run `python -m 'superdsm.batch' .` within this directory to process the examples.
+Run `python -m 'superdsm' .` within this directory to process the examples.
 
 You can add your own data to this directory, and then process it in the same way.

superdsm/pipeline.py

@@ -41,7 +41,8 @@ def configure(self, base_config: repype.config.Config, input_id: InputID, *args,
         scale = base_config.get('scale', None)
         if scale is None:
             if img is None:
-                img_filepath = self.resolve('input', input_id)
+                img_filepath = self.resolve('inputs', input_id)
+                assert img_filepath is not None, f'Scopes: {self.scopes}'
                 img = superdsm.io.imread(img_filepath)
             scale = _estimate_scale(img, num_radii=10, thresholds=[0.01])[0]
             base_config['scale'] = scale

superdsm/task.py

@@ -1,5 +1,7 @@
 import csv
+import os
 
+import ray
 import repype.config
 import repype.stage
 import repype.status
@@ -10,10 +12,10 @@
     PipelineData,
 )
 
+import superdsm.globalenergymin
+import superdsm.io
 import superdsm.pipeline
 import superdsm.render
-import superdsm.io
-import superdsm.globalenergymin
 
 
 def _write_performance_report(task_path, performance_path, data, overall_performance):
@@ -36,10 +38,13 @@ def _write_performance_report(task_path, performance_path, data, overall_perform
 class Task(repype.task.Task):
 
     def create_pipeline(self, *args, **kwargs) -> Pipeline:
-        return superdsm.pipeline.Pipeline(*args, **kwargs)
+        scopes = self.full_spec.get('scopes', dict())
+        scopes = {key: self.resolve_path(value) for key, value in scopes.items()}
+        return superdsm.pipeline.Pipeline(*args, scopes=scopes, **kwargs)
 
     def run(self, *args, **kwargs) -> repype.task.TaskData:
-        data = super().run()
+        ray.init(num_cpus=int(os.environ.get('SUPERDSM_NUM_CPUS', 2)), log_to_driver=False, logging_level='error')
+        data = super().run(*args, **kwargs)
 
         # Aggregate performance report
         performance = superdsm.globalenergymin.PerformanceReport()