fix : load model test

image_ref/config.py

@@ -4,20 +4,20 @@ import argparse
 def load_args_contrastive():
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('--epoches', type=int, default=1)
+    parser.add_argument('--epoches', type=int, default=0)
     parser.add_argument('--save_inter', type=int, default=50)
     parser.add_argument('--eval_inter', type=int, default=1)
     parser.add_argument('--noise_threshold', type=int, default=0)
     parser.add_argument('--lr', type=float, default=0.001)
     parser.add_argument('--batch_size', type=int, default=16)
     parser.add_argument('--positive_prop', type=int, default=None)
-    parser.add_argument('--model', type=str, default='ResNet50')
+    parser.add_argument('--model', type=str, default='ResNet18')
     parser.add_argument('--dataset_train_dir', type=str, default='../data/processed_data/npy_image/data_training_contrastive')
     parser.add_argument('--dataset_val_dir', type=str, default='../data/processed_data/npy_image/data_test_contrastive')
     parser.add_argument('--dataset_ref_dir', type=str, default='../image_ref/img_ref')
-    parser.add_argument('--output', type=str, default='output/out_contrastive.csv')
-    parser.add_argument('--save_path', type=str, default='output/best_model_constrastive.pt')
-    parser.add_argument('--pretrain_path', type=str, default='../output/best_model_constrastive.pt')
+    parser.add_argument('--output', type=str, default='../output/out_contrastive.csv')
+    parser.add_argument('--save_path', type=str, default='../output/best_model_constrastive.pt')
+    parser.add_argument('--pretrain_path', type=str, default='../saved_model/baseline_resnet18_contrastive_prop_30.pt')
     args = parser.parse_args()
 
     return args
\ No newline at end of file

image_ref/dataset_ref.py

@@ -114,11 +114,12 @@ def make_dataset_custom(
 
 class ImageFolderDuo(data.Dataset):
     def __init__(self, root, transform=None, target_transform=None,
-                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, positive_prop=None):
+                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, positive_prop=None, ref_transform=None):
         self.root = root
         self.imlist = flist_reader(root)
         self.transform = transform
         self.target_transform = target_transform
+        self.ref_transform = ref_transform
         self.loader = loader
         self.classes = torchvision.datasets.folder.find_classes(root)[0]
         self.ref_dir = ref_dir
@@ -144,7 +145,7 @@ class ImageFolderDuo(data.Dataset):
         if self.transform is not None:
             imgAER = self.transform(imgAER)
             imgANA = self.transform(imgANA)
-            img_ref = self.transform(img_ref)
+            img_ref = self.ref_transform(img_ref)
         contrastive_target = 0 if target == label_ref else 1
         return imgAER, imgANA, img_ref, contrastive_target
 
@@ -169,6 +170,8 @@ def load_data_duo(base_dir_train, base_dir_test, batch_size, shuffle=True, noise
 
     ref_transform = transforms.Compose(
         [transforms.Resize((224, 224)),
+         Threshold_noise(noise_threshold),
+         Log_normalisation(),
          transforms.Normalize(0.5, 0.5)])
     print('Default val transform')
 

image_ref/grad_cam.py

@@ -25,18 +25,18 @@ def compute_class_activation_map():
 
     path_aer ='../data/processed_data/npy_image/data_test_contrastive/Citrobacter freundii/CITFRE17_AER.npy'
     path_ana ='../data/processed_data/npy_image/data_test_contrastive/Citrobacter freundii/CITFRE17_ANA.npy'
-    path_ref ='../image_ref/img_ref/Citrobacter freundii.npy'
-
+    # path_ref ='../image_ref/img_ref/Citrobacter freundii.npy' #positive
+    path_ref = '../image_ref/img_ref/Enterobacter hormaechei.npy' #negative
+    # path_ref = '../image_ref/img_ref/Proteus mirabilis.npy'  # negative
     tensor_aer = npy_loader(path_aer)
     tensor_ana = npy_loader(path_ana)
     tensor_ref = npy_loader(path_ref)
 
     img_ref = np.load(path_ref)
 
-
     tensor_aer = transform(tensor_aer)
     tensor_ana = transform(tensor_ana)
-    tensor_ref = ref_transform(tensor_ref)
+    tensor_ref = transform(tensor_ref)
 
     tensor_aer = torch.unsqueeze(tensor_aer, dim=0)
     tensor_ana = torch.unsqueeze(tensor_ana, dim=0)
@@ -70,6 +70,8 @@ def compute_class_activation_map():
     # Perform the forward pass
     model.eval()  # Set the model to evaluation mode
     output = model(tensor_aer,tensor_ana,tensor_ref)
+
+    print(output)
     pred_class = output.argmax(dim=1).item()
 
     # Zero the gradients
@@ -77,6 +79,7 @@ def compute_class_activation_map():
 
     # Backward pass to compute gradients
     output[:, pred_class].backward()
+    print('Predicted class ',pred_class)
 
     # Compute the weights
     weights = torch.mean(gradients[0], dim=[2, 3])

image_ref/main.py

@@ -81,6 +81,7 @@ def run_duo(args):
     model.double()
     #load weight
     if args.pretrain_path is not None :
+        'Model weight loaded'
         load_model(model,args.pretrain_path)
     #move parameters to GPU
     if torch.cuda.is_available():
@@ -134,12 +135,12 @@ def run_duo(args):
 
 
     plt.show()
-    plt.savefig('output/training_plot_contrastive_{}.png'.format(args.positive_prop))
+    plt.savefig('../output/training_plot_contrastive_{}.png'.format(args.positive_prop))
 
     #load and evaluate best model
     load_model(model, args.save_path)
-    make_prediction_duo(model,data_test_batch, 'output/confusion_matrix_contractive_{}_bis.png'.format(args.positive_prop),
-                        'output/confidence_matrix_contractive_{}_bis.png'.format(args.positive_prop))
+    make_prediction_duo(model,data_test_batch, '../output/confusion_matrix_contractive_{}_bis.png'.format(args.positive_prop),
+                        '../output/confidence_matrix_contractive_{}_bis.png'.format(args.positive_prop))
 
 
 def make_prediction_duo(model, data, f_name, f_name2):
@@ -167,6 +168,7 @@ def make_prediction_duo(model, data, f_name, f_name2):
             img_ref = img_ref.cuda()
             label = label.cuda()
         output = model(imaer,imana,img_ref)
+        print(output)
         confidence = soft_max(output)
         confidence_pred_list[specie].append(confidence[:,0].data.cpu().numpy())
         #Mono class output (only most postive paire)