clean : grad_cam

fix : error in E.COLI f_name

image_ref/grad_cam.py

@@ -2,15 +2,12 @@ import numpy as np
 import torch
 import cv2
 from torchvision.transforms import transforms
-
-from image_ref.config import load_args_contrastive
 from image_ref.dataset_ref import Threshold_noise, Log_normalisation, npy_loader
 from image_ref.main import load_model
 from image_ref.model import Classification_model_duo_contrastive
 
 
-def compute_class_activation_map():
-    args = load_args_contrastive()
+def compute_class_activation_map(path_aer, path_ana, path_ref, model_path, model_type='Resnet18'):
 
     transform = transforms.Compose(
         [transforms.Resize((224, 224)),
@@ -22,13 +19,8 @@ def compute_class_activation_map():
         [transforms.Resize((224, 224)),
          transforms.Normalize(0.5, 0.5)])
 
-    model_path = '../saved_model/baseline_resnet18_contrastive_prop_30_bis.pt'
 
-    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' #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)
@@ -44,12 +36,11 @@ def compute_class_activation_map():
     tensor_ref = torch.unsqueeze(tensor_ref, dim=0)
 
 
-    model = Classification_model_duo_contrastive(model=args.model, n_class=2)
+    model = Classification_model_duo_contrastive(model=model_type, n_class=2)
     model.double()
     # load weight
-    if args.pretrain_path is not None:
-        load_model(model, model_path)
-        print('model loaded')
+    load_model(model, model_path)
+    print('model loaded')
 
     # Identify the target layer
     target_layer = model.im_encoder.layer4[-1]
@@ -112,24 +103,11 @@ def compute_class_activation_map():
     return heatmap
 
 if __name__ =='__main__':
-    # compute_class_activation_map()
-
-    transform = transforms.Compose(
-        [transforms.Resize((224, 224)),
-         Threshold_noise(500),
-         Log_normalisation(),
-         transforms.Normalize(0.5, 0.5)])
-
-    ref_transform = transforms.Compose(
-        [transforms.Resize((224, 224)),
-         Threshold_noise(0),
-         Log_normalisation(),
-         transforms.Normalize(0.5, 0.5)
-         ])
-
-    path_ref = '../image_ref/img_ref/Enterobacter hormaechei.npy'  # negative
-    tensor_ref = npy_loader(path_ref)
+    model_path = '../saved_model/baseline_resnet18_contrastive_prop_30_bis.pt'
+    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' #positive
+    # path_ref = '../image_ref/img_ref/Enterobacter hormaechei.npy' #negative
+    path_ref = '../image_ref/img_ref/Proteus mirabilis.npy'  # negative
 
-    ref_base = tensor_ref.squeeze()
-    ref_false = transform(tensor_ref).squeeze()
-    ref_true = ref_transform(tensor_ref).squeeze()
\ No newline at end of file
+    compute_class_activation_map(path_aer, path_ana, path_ref, model_path)