Set train=False in predict

chainercv/links/model/faster_rcnn/faster_rcnn.py

@@ -282,7 +282,8 @@ def predict(self, imgs):
         labels = list()
         scores = list()
         for img, scale in zip(prepared_imgs, scales):
-            with chainer.function.no_backprop_mode():
+            with chainer.using_config('train', False), \
+                    chainer.function.no_backprop_mode():
                 img_var = chainer.Variable(self.xp.asarray(img[None]))
                 H, W = img_var.shape[2:]
                 roi_cls_locs, roi_scores, rois, _ = self.__call__(

chainercv/links/model/feature_predictor.py

@@ -148,7 +148,8 @@ def predict(self, imgs):
         # (B, N, C, H, W) -> (B * N, C, H, W)
         imgs = imgs.reshape(shape)
 
-        with chainer.function.no_backprop_mode():
+        with chainer.using_config('train', False), \
+                chainer.function.no_backprop_mode():
             imgs = chainer.Variable(imgs)
             features = self.extractor(imgs)
 

chainercv/links/model/segnet/segnet_basic.py

@@ -168,7 +168,8 @@ def predict(self, imgs):
         labels = []
         for img in imgs:
             C, H, W = img.shape
-            with chainer.function.no_backprop_mode():
+            with chainer.using_config('train', False), \
+                    chainer.function.no_backprop_mode():
                 x = chainer.Variable(self.xp.asarray(img[np.newaxis]))
                 score = self.__call__(x)[0].data
             score = chainer.cuda.to_cpu(score)

chainercv/links/model/ssd/ssd.py

@@ -199,7 +199,8 @@ def predict(self, imgs):
             x.append(self.xp.array(img))
             sizes.append((H, W))
 
-        with chainer.function.no_backprop_mode():
+        with chainer.using_config('train', False), \
+                chainer.function.no_backprop_mode():
             x = chainer.Variable(self.xp.stack(x))
             mb_locs, mb_confs = self(x)
         mb_locs, mb_confs = mb_locs.data, mb_confs.data

examples/classification/eval_imagenet.py

@@ -33,8 +33,6 @@ def __call__(self, imgs, pred_values, gt_values):
 
 
 def main():
-    chainer.config.train = False
-
     parser = argparse.ArgumentParser(
         description='Learning convnet from ILSVRC2012 dataset')
     parser.add_argument('val', help='Path to root of the validation dataset')

examples/detection/eval_voc07.py

@@ -33,8 +33,6 @@ def __call__(self, imgs, pred_values, gt_values):
 
 
 def main():
-    chainer.config.train = False
-
     parser = argparse.ArgumentParser()
     parser.add_argument(
         '--model', choices=('faster_rcnn', 'ssd300', 'ssd512'),

examples/detection/visualize_models.py

@@ -1,8 +1,6 @@
 from matplotlib.font_manager import FontProperties
 import matplotlib.pyplot as plot
 
-import chainer
-
 from chainercv.links import FasterRCNNVGG16
 from chainercv.links import SSD300
 from chainercv.links import SSD512
@@ -13,8 +11,6 @@
 
 
 def main():
-    chainer.config.train = False
-
     dataset = VOCDetectionDataset(year='2007', split='test')
     models = [
         ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),

examples/faster_rcnn/demo.py

@@ -10,8 +10,6 @@
 
 
 def main():
-    chainer.config.train = False
-
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', type=int, default=-1)
     parser.add_argument('--pretrained_model', default='voc07')

examples/segnet/eval_camvid.py

@@ -55,8 +55,6 @@ def main():
 
     model = calc_bn_statistics(model, args.batchsize)
 
-    chainer.config.train = False
-
     test = CamVidDataset(split='test')
     it = chainer.iterators.SerialIterator(test, batch_size=args.batchsize,
                                           repeat=False, shuffle=False)

examples/ssd/demo.py

@@ -11,8 +11,6 @@
 
 
 def main():
-    chainer.config.train = False
-
     parser = argparse.ArgumentParser()
     parser.add_argument(
         '--model', choices=('ssd300', 'ssd512'), default='ssd300')