adapt to weight initializer modification

example/resnet101_imagenet2012/train.py

@@ -64,11 +64,11 @@ if __name__ == '__main__':
         if isinstance(cell, nn.Conv2d):
             cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
                                                                 cell.weight.default_input.shape(),
-                                                                cell.weight.default_input.dtype())
+                                                                cell.weight.default_input.dtype()).to_tensor()
         if isinstance(cell, nn.Dense):
             cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
                                                                 cell.weight.default_input.shape(),
-                                                                cell.weight.default_input.dtype())
+                                                                cell.weight.default_input.dtype()).to_tensor()
     if not config.label_smooth:
         config.label_smooth_factor = 0.0
     loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)

example/resnet50_imagenet2012/train.py

@@ -61,11 +61,11 @@ if __name__ == '__main__':
         if isinstance(cell, nn.Conv2d):
             cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
                                                                 cell.weight.default_input.shape(),
-                                                                cell.weight.default_input.dtype())
+                                                                cell.weight.default_input.dtype()).to_tensor()
         if isinstance(cell, nn.Dense):
             cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
                                                                 cell.weight.default_input.shape(),
-                                                                cell.weight.default_input.dtype())
+                                                                cell.weight.default_input.dtype()).to_tensor()
     if not config.use_label_smooth:
         config.label_smooth_factor = 0.0