From ad42bc9573fc0b2b319a72567822edf0dcbf78a5 Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Tue, 22 Apr 2025 10:17:39 +0200
Subject: [PATCH] add : ray result analysis
---
image_ref/main_ray.py | 37 +++++++++++++++++++++++++++++++++----
main_ray.py | 31 ++++++++++++++++++++++++++++++-
2 files changed, 63 insertions(+), 5 deletions(-)
diff --git a/image_ref/main_ray.py b/image_ref/main_ray.py
index 534d1c8..0d678aa 100644
--- a/image_ref/main_ray.py
+++ b/image_ref/main_ray.py
@@ -275,13 +275,42 @@ def main(args, gpus_per_trial=1):
print("Best trial config: {}".format(best_result.config))
print("Best trial final validation loss: {}".format(
- best_result.metrics["loss"]))
+ best_result.metrics["val loss"]))
print("Best trial final validation accuracy: {}".format(
- best_result.metrics["accuracy"]))
+ best_result.metrics["val acc"]))
test_model(best_result, args)
+
+def analyze_ray_res(storage_path, exp_name,args, gpus_per_trial=1):
+ experiment_path = os.path.join(storage_path, exp_name)
+ print(f"Loading results from {experiment_path}...")
+ trainable = tune.with_resources(
+ tune.with_parameters(train_model, args=args),
+ resources={"cpu": 20, "gpu": gpus_per_trial}
+ )
+ restored_tuner = tune.Tuner.restore(experiment_path, trainable=trainable)
+ results = restored_tuner.get_results()
+
+ num_results = len(results)
+ print("Number of results:", num_results)
+ for i, result in enumerate(results):
+ if result.error:
+ print(f"Trial #{i} had an error:", result.error)
+ continue
+
+ print(
+ f"Trial #{i} finished successfully train loss of :",
+ result.metrics["train loss"], " train acc of :",
+ result.metrics["train cont acc"], " val loss of :",
+ result.metrics["val loss"], " val cont acc of :",
+ result.metrics["val cont acc"], " val acc of :",
+ result.metrics["val acc"]
+ )
+
+
if __name__ == '__main__':
args = load_args_contrastive()
- print(args)
- main(args)
+ analyze_ray_res()
+ # print(args)
+ # main(args)
diff --git a/main_ray.py b/main_ray.py
index 2a32229..a79fb47 100644
--- a/main_ray.py
+++ b/main_ray.py
@@ -244,7 +244,36 @@ essayer avec une loss pondérée ou pas pondérée
test_model(best_result, args)
+def analyze_ray_res(storage_path, exp_name, args, gpus_per_trial=1):
+ experiment_path = os.path.join(storage_path, exp_name)
+ print(f"Loading results from {experiment_path}...")
+ trainable = tune.with_resources(
+ tune.with_parameters(train_model, args=args),
+ resources={"cpu": 20, "gpu": gpus_per_trial}
+ )
+ restored_tuner = tune.Tuner.restore(experiment_path, trainable=trainable)
+ results = restored_tuner.get_results()
+
+ num_results = len(results)
+ print("Number of results:", num_results)
+ for i, result in enumerate(results):
+ if result.error:
+ print(f"Trial #{i} had an error:", result.error)
+ continue
+
+ print(
+ f"Trial #{i} finished successfully train loss of :",
+ result.metrics["train loss"], " train acc of :",
+ result.metrics["train cont acc"], " val loss of :",
+ result.metrics["val loss"], " val cont acc of :",
+ result.metrics["val cont acc"], " val acc of :",
+ result.metrics["val acc"]
+ )
+
+
+
if __name__ == '__main__':
args = load_args()
print(args)
- main(args)
+ analyze_ray_res('/lustre/fswork/projects/rech/bun/ucg81ws/these/pseudo_image/image_ref/ray_results/','base_experiment',args)
+ # main(args)
--
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