Add paddle.no_grad in predict()

paddlehub/module/cv_module.py

@@ -92,6 +92,7 @@ class ImageClassifierModule(RunModule, ImageServing):
             results(list[dict]) : The prediction result of each input image
         '''
         self.eval()
+        with paddle.no_grad():
             res = []
             total_num = len(images)
             loop_num = int(np.ceil(total_num / batch_size))
@@ -223,6 +224,7 @@ class ImageColorizeModule(RunModule, ImageServing):
             res(list[dict]) : The prediction result of each input image
         '''
         self.eval()
+        with paddle.no_grad():
             lab2rgb = T.LAB2RGB()
             res = []
             total_num = len(images)
@@ -393,6 +395,7 @@ class Yolov3Module(RunModule, ImageServing):
             labels(np.ndarray): Predict labels.
         '''
         self.eval()
+        with paddle.no_grad():
             boxes = []
             scores = []
             self.downsample = 32
@@ -521,6 +524,7 @@ class StyleTransferModule(RunModule, ImageServing):
             output(list[np.ndarray]) : The style transformed images with bgr mode.
         '''
         self.eval()
+        with paddle.no_grad():
             style = paddle.to_tensor(self.transform(style).astype('float32'))
             style = style.unsqueeze(0)
 
@@ -655,6 +659,7 @@ class ImageSegmentationModule(ImageServing, RunModule):
             output(list[np.ndarray]) : The segmentation mask.
         '''
         self.eval()
+        with paddle.no_grad():
             result = []
 
             total_num = len(images)