From 74e2780680e61b9b15d7d663d9b24c3fbb2a537b Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Tue, 1 Apr 2025 15:13:36 +0200
Subject: [PATCH] fix : dataloader batched
---
image_ref/main.py | 12 ++++++++++--
1 file changed, 10 insertions(+), 2 deletions(-)
diff --git a/image_ref/main.py b/image_ref/main.py
index e2dbdeb..9cd4c7b 100644
--- a/image_ref/main.py
+++ b/image_ref/main.py
@@ -110,22 +110,26 @@ def run_duo(args):
plt.savefig('output/training_plot_contrastive_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model))
#load and evaluate best model
load_model(model, args.save_path)
- make_prediction_duo(model,data_test, 'output/confusion_matrix_contractive_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model))
+ make_prediction_duo(model,data_test, 'output/confusion_matrix_contractive_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model))
def make_prediction_duo(model, data, f_name):
+ n_class = len(data[0][2])
+ confidence_pred_list = [[] for i in range(n_class)]
y_pred = []
y_true = []
# iterate over test data
for imaer,imana,img_ref, label in data:
label = label.long()
+ specie = torch.argmax(label)
+
if torch.cuda.is_available():
imaer = imaer.cuda()
imana = imana.cuda()
img_ref = img_ref.cuda()
label = label.cuda()
output = model(imaer,imana,img_ref)
-
+ confidence_pred_list[specie].append(output.data.cpu().numpy())
output = (torch.max(torch.exp(output), 1)[1]).data.cpu().numpy()
y_pred.extend(output)
@@ -135,6 +139,10 @@ def make_prediction_duo(model, data, f_name):
# Build confusion matrix
cf_matrix = confusion_matrix(y_true, y_pred)
+ confidence_matrix = np.zeros((n_class,n_class))
+ for i in range(n_class):
+ confidence_matrix[i]=np.mean(confidence_pred_list[i],axis=0)
+
df_cm = pd.DataFrame(cf_matrix / np.sum(cf_matrix, axis=1)[:, None], index=[i for i in range(2)],
columns=['True','False'])
print('Saving Confusion Matrix')
--
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