From 65c422aa3da21c6503dfb60c59460bd8219b08db Mon Sep 17 00:00:00 2001
From: lcalmettes <leo.calmettes@etu.ec-lyon.fr>
Date: Mon, 12 May 2025 14:48:13 +0200
Subject: [PATCH] =?UTF-8?q?=09modifi=C3=A9=C2=A0:=20=20=20=20=20=20=20=20?=
=?UTF-8?q?=20dataset/dataset.py=20=09modifi=C3=A9=C2=A0:=20=20=20=20=20?=
=?UTF-8?q?=20=20=20=20main.py?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
dataset/dataset.py | 6 +++---
main.py | 4 ++--
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/dataset/dataset.py b/dataset/dataset.py
index 5119a73..e06b3f1 100644
--- a/dataset/dataset.py
+++ b/dataset/dataset.py
@@ -219,9 +219,9 @@ class ImageFolderDuo(data.Dataset):
def load_data_duo(base_dir, batch_size, args, shuffle=True):
train_transform = transforms.Compose(
- [Random_erasing(args.augment_args[0], args.augment_args[3]),
- Random_int_noise(args.augment_args[1], args.augment_args[4]),
- Random_shift_rt(args.augment_args[2], args.augment_args[5], args.augment_args[6]),
+ [#Random_erasing(args.augment_args[0], args.augment_args[3]),
+ #Random_int_noise(args.augment_args[1], args.augment_args[4]),
+ #Random_shift_rt(args.augment_args[2], args.augment_args[5], args.augment_args[6]),
transforms.Resize((224, 224)),
Threshold_noise(args.noise_threshold),
Log_normalisation(),
diff --git a/main.py b/main.py
index 6aa0b89..2cd28d3 100644
--- a/main.py
+++ b/main.py
@@ -254,10 +254,10 @@ def run_duo(args):
plt.plot(train_loss)
plt.plot(train_loss)
- plt.savefig(f'output/training_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+ plt.savefig(f'output/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
#load and evaluate best model
load_model(model, args.save_path)
- make_prediction_duo(model,data_test, f'output/model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+ make_prediction_duo(model,data_test, f'output/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
return best_loss,best_acc
def make_prediction_duo(model, data, f_name):
--
GitLab