From affef4b57ce7b70abdd7e767da77b03663fda44c Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Mon, 5 May 2025 15:11:31 +0200
Subject: [PATCH] fix : normalisation before classification label
---
image_ref/main.py | 8 ++++++--
image_ref/model.py | 5 ++---
2 files changed, 8 insertions(+), 5 deletions(-)
diff --git a/image_ref/main.py b/image_ref/main.py
index 1c3299b7..ed7d6d17 100644
--- a/image_ref/main.py
+++ b/image_ref/main.py
@@ -54,6 +54,7 @@ def val_duo(model, data_test, loss_function, epoch, wandb):
losses = 0.
acc = 0.
acc_contrastive = 0.
+ softmax = nn.Softmax(dim=1)
for param in model.parameters():
param.requires_grad = False
@@ -74,7 +75,8 @@ def val_duo(model, data_test, loss_function, epoch, wandb):
label = label.cuda()
label_class = torch.argmin(label).data.cpu().numpy()
pred_logits = model.forward(imaer, imana, img_ref)
- pred_class = torch.argmax(pred_logits[:, 0]).tolist()
+ confidence = softmax(pred_logits)
+ pred_class = torch.argmax(confidence[:, 0]).tolist()
acc_contrastive += (
torch.argmax(pred_logits, dim=1).data.cpu().numpy() == label.data.cpu().numpy()).sum().item()
acc += (pred_class == label_class)
@@ -214,6 +216,7 @@ def make_prediction_duo(model, data, f_name, f_name2):
confidence_pred_list = [[] for i in range(n_class)]
y_pred = []
y_true = []
+ soft_max = nn.Softmax(dim=1)
# iterate over test data
for imaer, imana, img_ref, label in data:
imaer = imaer.float()
@@ -233,7 +236,8 @@ def make_prediction_duo(model, data, f_name, f_name2):
imana = imana.cuda()
img_ref = img_ref.cuda()
label = label.cuda()
- confidence = model(imaer, imana, img_ref)
+ output = model(imaer, imana, img_ref)
+ confidence = soft_max(output)
print(label)
print(confidence)
confidence_pred_list[specie].append(confidence[:, 0].data.cpu().numpy())
diff --git a/image_ref/model.py b/image_ref/model.py
index 0f702fb6..5302e3b1 100644
--- a/image_ref/model.py
+++ b/image_ref/model.py
@@ -287,7 +287,7 @@ class Classification_model_duo_contrastive(nn.Module):
self.im_encoder = resnet34(num_classes=2, in_channels=2)
self.predictor = nn.Linear(in_features=2*2,out_features=2)
- self.soft_max = nn.Softmax(dim=1)
+
def forward(self, input_aer, input_ana, input_ref):
input_ana = torch.concat([input_ana, input_ref], dim=1)
@@ -295,5 +295,4 @@ class Classification_model_duo_contrastive(nn.Module):
out_aer = self.im_encoder(input_aer)
out_ana = self.im_encoder(input_ana)
out = torch.concat([out_aer,out_ana],dim=1)
- out = self.predictor(out)
- return self.soft_max(out)
\ No newline at end of file
+ return self.predictor(out)
\ No newline at end of file
--
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