optimize test_transform

Author: likyoo

change_detection_pytorch/datasets/LEVIR_CD.py

@@ -2,7 +2,9 @@
 
 import albumentations as A
 from albumentations.pytorch import ToTensorV2
-from change_detection_pytorch.datasets.custom import CustomDataset
+
+from .custom import CustomDataset
+from .transforms.albu import ChunkImage, ToTensorTest
 
 
 class LEVIR_CD_Dataset(CustomDataset):
@@ -27,12 +29,9 @@ def get_default_transform(self):
     def get_test_transform(self):
         """Set the test transformation."""
 
-        from change_detection_pytorch.datasets.transforms.albu import (
-            ChunkImage, ToTensorTest)
         test_transform = A.Compose([
             A.Normalize(),
-            ChunkImage(self.size),
-            ToTensorTest(),
+            ToTensorV2()
         ], additional_targets={'image_2': 'image'})
         return test_transform
 

change_detection_pytorch/datasets/SVCD.py

@@ -29,7 +29,6 @@ def get_test_transform(self):
         """Set the test transformation."""
 
         test_transform = A.Compose([
-            A.Resize(self.size, self.size),
             A.Normalize(),
             ToTensorV2()
         ], additional_targets={'image_2': 'image'})

local_test.py

@@ -12,8 +12,8 @@
     encoder_weights="imagenet",  # use `imagenet` pre-trained weights for encoder initialization
     in_channels=3,  # model input channels (1 for gray-scale images, 3 for RGB, etc.)
     classes=2,  # model output channels (number of classes in your datasets)
-    siam_encoder=True,
-    fusion_form='concat',
+    siam_encoder=True,  # whether to use a siamese encoder
+    fusion_form='concat',  # the form of fusing features from two branches. e.g. concat, sum, diff, or abs_diff.
 )
 
 train_dataset = LEVIR_CD_Dataset('../LEVIR-CD/train',
@@ -86,5 +86,4 @@
         print('Model saved!')
 
 # save results (change maps)
-valid_epoch.infer_vis(valid_loader, slide=True, image_size=1024, window_size=256,
-                      save_dir='./res')
+valid_epoch.infer_vis(valid_loader, save=True, slide=False, save_dir='./res')