From 8f15534fbfefe4add5cfcd448b3c742f5a012e40 Mon Sep 17 00:00:00 2001
From: lcalmettes <leo.calmettes@etu.ec-lyon.fr>
Date: Mon, 12 May 2025 17:03:28 +0200
Subject: [PATCH] =?UTF-8?q?=09modifi=C3=A9=C2=A0:=20=20=20=20=20=20=20=20?=
=?UTF-8?q?=20AugmentTests.py=20=09modifi=C3=A9=C2=A0:=20=20=20=20=20=20?=
=?UTF-8?q?=20=20=20config/config.py?=
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---
AugmentTests.py | 53 ++++++++++++++----------------------------------
config/config.py | 2 +-
2 files changed, 16 insertions(+), 39 deletions(-)
diff --git a/AugmentTests.py b/AugmentTests.py
index b71b81e..deecc9a 100644
--- a/AugmentTests.py
+++ b/AugmentTests.py
@@ -12,44 +12,21 @@ if __name__ == "__main__":
args.random_state = random
losses[random], accs[random] = run_duo(args)
records = pd.DataFrame([["Standard",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])
- records.to_csv("output/DataAugment/perfs.csv",index = False)
+ records.to_csv("output/perfs.csv",index = False)
- #On continue avec le random_erasing
- for prob in [k/20 for k in range(1,21)]:
- args.augment_args[0] = prob
- for prop in [k/20 for k in range(1,21)]:
- args.augment_args[3] = prop
- losses = np.zeros(5); accs = np.zeros(5)
- for random in range(5):
- args.random_state = random
- losses[random], accs[random] = run_duo(args)
- records = pd.concat([records,pd.DataFrame([[f"erasing prob{prob} prop{prop}",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
- records.to_csv("output/DataAugment/perfs.csv",index = False)
-
- #Puis le int shift
- for prob in [k/20 for k in range(1,21)]:
- args.augment_args[1] = prob
- for maximum in ([k/10 for k in range(11,20)]+[k for k in range(2,11)]):
- args.augment_args[4] = maximum
- losses = np.zeros(5); accs = np.zeros(5)
- for random in range(5):
- args.random_state = random
- losses[random], accs[random] = run_duo(args)
- records = pd.concat([records,pd.DataFrame([[f"intShift prob{prob} max{maximum}",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
- records.to_csv("output/DataAugment/perfs.csv",index = False)
-
- #Et enfin le rt-shift
- for prob in [k/20 for k in range(1,21)]:
- args.augment_args[2] = prob
- for mean in [5,10,15,20,25,30,40,50,60,70,80,90]:
- args.augment_args[5] = mean
- for std in [mean/k for k in range(1,11)]:
- args.augment_args[6] = std
- losses = np.zeros(5); accs = np.zeros(5)
- for random in range(5):
- args.random_state = random
- losses[random], accs[random] = run_duo(args)
- records = pd.concat([records,pd.DataFrame([[f"rtShift prob{prob} mean{mean} std{std}",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
- records.to_csv("output/DataAugment/perfs.csv",index = False)
+
+ # #Et enfin le rt-shift
+ # for prob in [k/20 for k in range(1,21)]:
+ # args.augment_args[2] = prob
+ # for mean in [5,10,15,20,25,30,40,50,60,70,80,90]:
+ # args.augment_args[5] = mean
+ # for std in [mean/k for k in range(1,11)]:
+ # args.augment_args[6] = std
+ # losses = np.zeros(5); accs = np.zeros(5)
+ # for random in range(5):
+ # args.random_state = random
+ # losses[random], accs[random] = run_duo(args)
+ # records = pd.concat([records,pd.DataFrame([[f"rtShift prob{prob} mean{mean} std{std}",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
+ # records.to_csv("output/DataAugment/perfs.csv",index = False)
\ No newline at end of file
diff --git a/config/config.py b/config/config.py
index 63b5999..ae2861d 100644
--- a/config/config.py
+++ b/config/config.py
@@ -5,7 +5,7 @@ def load_args():
parser = argparse.ArgumentParser()
parser.add_argument('--epoches', type=int, default=20)
parser.add_argument('--eval_inter', type=int, default=1)
- parser.add_argument('--augment_args', nargs = '+', type = float, default = [0,0,0.5,0.1,0.1,0.,7.5])
+ parser.add_argument('--augment_args', nargs = '+', type = float, default = [0,0,0,0.1,0.1,0.,7.5])
parser.add_argument('--noise_threshold', type=int, default=0)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--optim', type = str, default = "Adam")
--
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