polish

test=develop

python/paddle/fluid/framework.py

@@ -293,7 +293,6 @@ class Variable(core.VarBase):
         if is_new_var:
             self.desc.set_type(type)
         elif self.desc.type() != type:
-            # sys.stderr.write('%s vs %s\n' % (self.desc.type(), type))
             raise ValueError("Variable {0} has been created before. The "
                              "previous type is {1}; the new type is {2}. They"
                              " are not matched".format(self.name,
@@ -358,16 +357,16 @@ class Variable(core.VarBase):
         self.stop_gradient = stop_gradient
         self.is_data = is_data
 
-    def numpy(self):
+    def _numpy(self):
         scope = _imperative_tracer().get_scope(self.block.desc)
         tensor = core.get_variable_tensor(scope, self.desc.name())
         return np.array(tensor)
 
-    def backward(self):
+    def _backward(self):
         scope = _imperative_tracer().get_scope(self.block.desc)
         self._run_backward(scope)
 
-    def grad(self):
+    def _gradient(self):
         return np.array(self._grad())
 
     def __str__(self):

python/paddle/fluid/imperative/layers.py

@@ -35,13 +35,8 @@ class PyLayer(core.Layer):
 
         var_inputs = []
         for x in inputs:
-            if isinstance(x, np.ndarray):
-                py_var = base.to_variable(x)
-                var_inputs.append(py_var)
-            elif isinstance(x, framework.Variable):
-                var_inputs.append(x)
-            else:
-                raise ValueError("not var or ndarray %s" % type(x))
+            py_var = base.to_variable(x)
+            var_inputs.append(py_var)
         outputs = self.forward(var_inputs)
         return outputs
 

python/paddle/fluid/layer_helper.py

@@ -49,23 +49,8 @@ class LayerHelper(object):
     def startup_program(self):
         return default_startup_program()
 
-    def _np_to_variable(self, x):
-        tensor = core.LoDTensor()
-        tensor.set(x, core.CPUPlace())
-        return Variable(
-            self.main_program.current_block(),
-            type=core.VarDesc.VarType.LOD_TENSOR,
-            name=None,
-            shape=x.shape,
-            dtype=x.dtype)
-
     def to_variable(self, x):
-        if isinstance(x, Variable):
-            return x
-        elif isinstance(x, np.ndarray):
-            return base.to_variable(x, self.main_program.current_block())
-        else:
-            raise ValueError("inputs wrong type %s\n" % x)
+        return base.to_variable(x, self.main_program.current_block())
 
     def append_op(self, *args, **kwargs):
         return self.main_program.current_block().append_op(*args, **kwargs)

python/paddle/fluid/tests/unittests/test_imperative.py

@@ -43,9 +43,9 @@ class TestImperative(unittest.TestCase):
             l = MyLayer()
             x = l(np.array([1.0, 2.0, -1.0], dtype=np.float32))[0]
             self.assertIsNotNone(x)
-            sys.stderr.write("%s output: %s\n" % (x, x.numpy()))
-            x.backward()
-            sys.stderr.write("grad %s\n" % l._x_for_debug.grad())
+            sys.stderr.write("%s output: %s\n" % (x, x._numpy()))
+            x._backward()
+            sys.stderr.write("grad %s\n" % l._x_for_debug._gradient())
 
 
 if __name__ == '__main__':