From fc51349ef735a69faae8026ba6a13fa81749f8e0 Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Mon, 5 May 2025 15:08:59 +0200
Subject: [PATCH] fix : normalisation before classification label
---
image_ref/main.py | 6 ++----
image_ref/model.py | 5 +++--
2 files changed, 5 insertions(+), 6 deletions(-)
diff --git a/image_ref/main.py b/image_ref/main.py
index 2b53852..1c3299b 100644
--- a/image_ref/main.py
+++ b/image_ref/main.py
@@ -214,7 +214,6 @@ 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()
@@ -234,13 +233,12 @@ def make_prediction_duo(model, data, f_name, f_name2):
imana = imana.cuda()
img_ref = img_ref.cuda()
label = label.cuda()
- output = model(imaer, imana, img_ref)
- confidence = soft_max(output)
+ confidence = model(imaer, imana, img_ref)
print(label)
print(confidence)
confidence_pred_list[specie].append(confidence[:, 0].data.cpu().numpy())
# Mono class output (only most postive paire)
- output = torch.argmax(output[:, 0])
+ output = torch.argmax(confidence[:, 0])
label = torch.argmin(label)
y_pred.append(output.tolist())
y_true.append(label.tolist()) # Save Truth
diff --git a/image_ref/model.py b/image_ref/model.py
index 5302e3b..0f702fb 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)
@@ -295,4 +295,5 @@ 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)
- return self.predictor(out)
\ No newline at end of file
+ out = self.predictor(out)
+ return self.soft_max(out)
\ No newline at end of file
--
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