Modified preprocess and added device

1508.06576/feature_maps.py

@@ -2,6 +2,7 @@ import torch
 from logger import Logger
 LOGGER = Logger().logger()
 LOGGER.info("Started Feature Maps")
+device=torch.device( "cuda" if (torch.cude.is_available()) else 'cpu')
 #Author: @meetdoshi
 class FeatureMaps:
     def __init__(self,arch="vgg19"):

1508.06576/preprocess.py

+from os import device_encoding
 from logger import Logger
-from torch import transforms
+import torch
+import torchvision.transforms as transforms
 from PIL import Image
 import numpy as np
 LOGGER = Logger().logger()
+device=torch.device( "cuda" if (torch.cude.is_available()) else 'cpu')
 #Author: @meetdoshi
 class Preprocessor:
     @staticmethod
@@ -34,7 +37,8 @@ class Preprocessor:
         '''
         loader = transforms.Compose([transforms.ToTensor(),transforms.Resize([224,224]),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],),])
         img = loader(img).unsqueeze(0)
-        return img
+        assert img.shape == (1,3,224,224)
+        return img.to(device,torch.float)
 
 
     @staticmethod
@@ -46,15 +50,12 @@ class Preprocessor:
         '''
         img = Preprocessor.load_image(path)
         img = Preprocessor.reshape_img(img)
-        img = Preprocessor.subtract_mean(img)
+        #img = Preprocessor.subtract_mean(img)
         return img
 
+'''
 if __name__=="__main__":
     prec = Preprocessor()
     img = np.zeros(shape=(4,4,3))
-    print(img.shape)
-    for i in range(img.shape[2]):
-        print(img[:,:,i])
-    img = prec.subtract_mean(img)
-    for i in range(img.shape[2]):
-        print(img[:,:,i])
+    img = prec.process('test/sem8.jpeg')
+'''