Added jobs for DARTS FEAR experiments.

.vscode/launch.json

@@ -689,7 +689,7 @@
             "request": "launch",
             "program": "${cwd}/scripts/reports/fear_analysis/analysis_create_darts_space_benchmark.py",
             "console": "integratedTerminal",
-            "args": ["--results-dir", "F:\\archaiphilly\\phillytools\\ft_dt_fb96_ftlr0.025_fte10_ct96_ftt0.6",
+            "args": ["--results-dir", "F:\\archaiphilly\\phillytools\\ft_dt_fb96_ftlr0.025_fte10_ct96_ftt0.6_fixed",
             "--out-dir", "F:\\archai_experiment_reports"]
         },
         {

archai/algos/proxynas/freeze_darts_space_experiment_runner.py

@@ -66,6 +66,7 @@ class FreezeDartsSpaceExperimentRunner(ExperimentRunner):
             conf_eval['checkpoint']['filename'] = '$expdir/freeze_checkpoint.pth'
 
         logger.pushd('freeze_evaluate')
+        if conf_eval['trainer']['train_fear']:
             freeze_evaler = FreezeDartsSpaceEvaluater()
             conf_eval_freeze = deepcopy(conf_eval)
             freeze_eval_result = freeze_evaler.evaluate(conf_eval_freeze, model_desc_builder=self.model_desc_builder())

confs/algos/proxynas_darts_space.yaml

@@ -19,12 +19,14 @@ nas:
     trainer:
       use_val: False
       plotsdir: ''
-      epochs: 100
+      epochs: 20
       top1_acc_threshold: 0.60 # after some accuracy we will shift into training only the last 'n' layers
       train_regular: True # if False the full regular training of the architecture will be bypassed
+      train_fear: False
       
     freeze_trainer:
       plotsdir: ''
+      bypass_freeze: False
       identifiers_to_unfreeze: ['logits_op._op', 'cells.7', 'cells.6'] # last few layer names in DARTS space are 'logits_op._op': Linear, 'cells.19': prefix for all cell 19 parameters
       apex:
         _copy: '/common/apex'

scripts/reports/fear_analysis/analysis_create_darts_space_benchmark.py

@@ -70,7 +70,7 @@ def main():
     #     a = parse_a_job(job_dir)
 
     # parallel parsing of yaml logs
-    num_workers = 9
+    num_workers = 8
     with Pool(num_workers) as p:
         a = p.map(parse_a_job, job_dirs)