Polish nn code, test=develop (#22237)

* refine code, test=develop

* reuse contain_var, test=develop

python/paddle/fluid/framework.py

@@ -455,14 +455,14 @@ def name_scope(prefix=None):
     """
     # TODO(panyx0718): Only [0-9a-z].
     # in dygraph we don't need namescope since it will cause mem leak
-    if not in_dygraph_mode():
+    if in_dygraph_mode():
+        yield
+    else:
         assert prefix, "namescope prefix cannot be empty."
         global _name_scope
         _name_scope = _name_scope.child(prefix)
         yield
         _name_scope = _name_scope.parent()
-    else:
-        yield
 
 
 def _full_name_scope():
@@ -715,10 +715,9 @@ def _getitem_impl_(var, item):
     if (use_strided_slice == True):
         attrs['strides'] = []
     infer_flags = list(1 for i in range(len(slice_axis)))
+
     # starts
-    if not contain_var(slice_start):
-        attrs['starts'] = slice_start
-    else:
+    if contain_var(slice_start):
         inputs['StartsTensorList'] = get_new_list_tensor(slice_start)
         for i, dim in enumerate(slice_start):
             if isinstance(dim, Variable):
@@ -726,10 +725,11 @@ def _getitem_impl_(var, item):
                 infer_flags[i] = -1
             else:
                 attrs['starts'].append(dim)
-    # ends
-    if not contain_var(slice_end):
-        attrs['ends'] = slice_end
     else:
+        attrs['starts'] = slice_start
+
+    # ends
+    if contain_var(slice_end):
         inputs['EndsTensorList'] = get_new_list_tensor(slice_end)
         for i, dim in enumerate(slice_end):
             if isinstance(dim, Variable):
@@ -737,11 +737,12 @@ def _getitem_impl_(var, item):
                 infer_flags[i] = -1
             else:
                 attrs['ends'].append(dim)
+    else:
+        attrs['ends'] = slice_end
+
     # strides
     if use_strided_slice == True:
-        if not contain_var(slice_step):
-            attrs['strides'] = slice_step
-        else:
+        if contain_var(slice_step):
             inputs['StridesTensorList'] = get_new_list_tensor(slice_step)
             for i, dim in enumerate(slice_step):
                 if isinstance(dim, Variable):
@@ -749,6 +750,8 @@ def _getitem_impl_(var, item):
                     infer_flags[i] = -1
                 else:
                     attrs['strides'].append(dim)
+        else:
+            attrs['strides'] = slice_step
     # infer_flags
     attrs['infer_flags'] = infer_flags
 
@@ -2344,12 +2347,12 @@ class Block(object):
                 if isinstance(item[1], Parameter))
 
     def create_var(self, *args, **kwargs):
-        if not in_dygraph_mode():
+        if in_dygraph_mode():
+            var = _varbase_creator(*args, **kwargs)
+        else:
             var = Variable(block=self, *args, **kwargs)
             if 'initializer' in kwargs:
                 kwargs['initializer'](var, self)
-        else:
-            var = _varbase_creator(*args, **kwargs)
         return var
 
     def has_var(self, name):
@@ -2396,9 +2399,8 @@ class Block(object):
         # NOTE: v is destroyed by C++ after calling _rename_var.
         d = self.desc.find_var(cpt.to_bytes(new_name))
         if var_type == "Parameter":
-            if not in_dygraph_mode():
-                var = Parameter(
-                    self,
+            if in_dygraph_mode():
+                var = ParamBase(
                     d.shape(),
                     d.dtype(),
                     type=orig_var_type,
@@ -2410,7 +2412,8 @@ class Block(object):
                     gradient_clip_attr=gradient_clip_attr,
                     error_clip=error_clip)
             else:
-                var = ParamBase(
+                var = Parameter(
+                    self,
                     d.shape(),
                     d.dtype(),
                     type=orig_var_type,
@@ -2444,10 +2447,10 @@ class Block(object):
     def create_parameter(self, *args, **kwargs):
         global_block = self.program.global_block()
         param = None
-        if not in_dygraph_mode():
-            param = Parameter(global_block, *args, **kwargs)
-        else:
+        if in_dygraph_mode():
             param = ParamBase(*args, **kwargs)
+        else:
+            param = Parameter(global_block, *args, **kwargs)
         if 'initializer' in kwargs:
 
             def _is_inited_by(block, var):
@@ -2687,9 +2690,8 @@ class Block(object):
                                  "same topology")
             assert isinstance(v, Variable)
             new_p = None
-            if not in_dygraph_mode():
-                new_p = Parameter(
-                    block=self,
+            if in_dygraph_mode():
+                new_p = ParamBase(
                     shape=v.shape,
                     dtype=v.dtype,
                     type=v.type,
@@ -2702,7 +2704,8 @@ class Block(object):
                     error_clip=p.error_clip,
                     name=v.name)
             else:
-                new_p = ParamBase(
+                new_p = Parameter(
+                    block=self,
                     shape=v.shape,
                     dtype=v.dtype,
                     type=v.type,

python/paddle/fluid/layers/nn.py

@@ -4346,24 +4346,23 @@ def split(input, num_or_sections, dim=-1, name=None):
         if isinstance(num_or_sections, int):
             num = num_or_sections
             attrs['num'] = num_or_sections
-            res = core.ops.split(inputs, attrs, {}, {'Out': num})
-            return res['Out']
-        elif isinstance(num_or_sections, list):
+        elif isinstance(num_or_sections, (list, tuple)):
             num = len(num_or_sections)
-            attrs['sections'] = list(
-                map(lambda ele: -1 if isinstance(ele, Variable) else ele,
-                    num_or_sections))
-            contain_var = not all(not isinstance(ele, Variable)
-                                  for ele in num_or_sections)
-            if contain_var:
+            if utils._contain_var(num_or_sections):
                 raise TypeError(
-                    "The type of 'num_or_sections' in split must be int or list[int] in Dygraph mode, but "
-                    "received %s." % ('list[Variable]'))
+                    "The type of 'num_or_sections' in split must be int or list[int] or tuple[int] in Dygraph mode, but "
+                    "received %s, which contains Variable." %
+                    (type(num_or_sections)))
+            else:
+                attrs['sections'] = list(num_or_sections)
         else:
             raise TypeError(
                 "The type of 'num_or_sections' in split must be int or list in Dygraph mode, but "
                 "received %s." % (type(num_or_sections)))
 
+        res = core.ops.split(inputs, attrs, {}, {'Out': num})
+        return res['Out']
+
     if not isinstance(num_or_sections, (int, list, tuple)):
         raise TypeError(
             "The type of 'num_or_sections' in split must be int, list or "
@@ -4422,9 +4421,7 @@ def split(input, num_or_sections, dim=-1, name=None):
         attrs['sections'] = list(
             map(lambda ele: -1 if isinstance(ele, Variable) else ele,
                 num_or_sections))
-        contain_var = not all(not isinstance(ele, Variable)
-                              for ele in num_or_sections)
-        if contain_var:
+        if utils._contain_var(num_or_sections):
             inputs['SectionsTensorList'] = _get_SectionsTensorList(
                 num_or_sections)
 
@@ -5572,16 +5569,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
             # the shape of reshaped_3 is [6,8].
     """
     if in_dygraph_mode():
-        #TODO(zhiqiu): open inplace if we can.
+        #TODO(zhiqiu): enable inplace in dygraph mode.
         if inplace:
             warnings.warn(
                 "Inplace on reshape is not allowed and will be discarded in dygraph mode currently."
             )
         attrs = {}
         if isinstance(shape, (list, tuple)):
-            contain_var = not all(not isinstance(ele, Variable)
-                                  for ele in shape)
-            if contain_var:
+            if utils._contain_var(shape):
                 raise TypeError(
                     "The type of 'shape' in reshape must be list[int] or tuple(int) in Dygraph mode, but "
                     "received %s, which contains Variable." % type(shape))
@@ -5604,12 +5599,6 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
 
     helper = LayerHelper("reshape2", **locals())
 
-    def contain_var(one_list):
-        for ele in one_list:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     def get_new_shape_tensor(list_shape):
         new_shape_tensor = []
         for dim in list_shape:
@@ -5659,7 +5648,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
         assert len(shape) > 0, ("The size of 'shape' in reshape can't be zero, "
                                 "but received %s." % len(shape))
         attrs["shape"] = get_attr_shape(shape)
-        if contain_var(shape):
+        if utils._contain_var(shape):
             inputs['ShapeTensor'] = get_new_shape_tensor(shape)
         elif isinstance(actual_shape, Variable):
             actual_shape.stop_gradient = True
@@ -5804,8 +5793,7 @@ def unsqueeze(input, axes, name=None):
         axes.stop_gradient = True
         inputs["AxesTensor"] = axes
     elif isinstance(axes, (list, tuple)):
-        contain_var = not all(not isinstance(ele, Variable) for ele in axes)
-        if contain_var:
+        if utils._contain_var(axes):
             inputs["AxesTensorList"] = _to_Variable_list(axes)
         else:
             attrs["axes"] = axes
@@ -8256,12 +8244,6 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
     ipts = {'X': x}
     attrs = {}
 
-    def _contain_var(input_list):
-        for ele in input_list:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     def _attr_shape_check(shape_val):
         if not isinstance(shape_val, int):
             raise TypeError(
@@ -8290,7 +8272,7 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
         offsets.stop_gradient = True
         ipts['Offsets'] = offsets
         attrs['offsets'] = [-1] * len(x.shape)
-    elif _contain_var(offsets):
+    elif utils._contain_var(offsets):
         new_offsets_tensor = []
         offsets_attr = []
         for dim in offsets:
@@ -8314,7 +8296,7 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
     if isinstance(shape, Variable):
         shape.stop_gradient = True
         ipts['Shape'] = shape
-    elif _contain_var(shape):
+    elif utils._contain_var(shape):
         new_shape_tensor = []
         shape_attr = []
         for dim_size in shape:
@@ -9344,20 +9326,12 @@ def expand(x, expand_times, name=None):
             expanded_2 = fluid.layers.expand(data_2, expand_times=expand_times)
             # the shape of expanded_2 is [48, 56].
     """
-
-    def contain_var(expand_times):
-        for ele in expand_times:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     inputs = {"X": [x]}
     attrs = {}
 
     if in_dygraph_mode():
         if isinstance(expand_times, (list, tuple)):
-            contain_var = contain_var(expand_times)
-            if contain_var:
+            if utils._contain_var(expand_times):
                 raise TypeError(
                     "The type of 'expand_times' in expand must be list[int] or tuple(int) in Dygraph mode, but "
                     "received %s, which contains Variable." % type(shape))
@@ -9404,18 +9378,14 @@ def expand(x, expand_times, name=None):
                 new_expand_times_tensor.append(temp_out)
         return new_expand_times_tensor
 
-    if in_dygraph_mode():
-        inputs = {'X': x}
-        attrs = {'expand_times': expand_times}
-    else:
-        if isinstance(expand_times, Variable):
-            expand_times.stop_gradient = True
-            inputs['ExpandTimes'] = expand_times
-        elif isinstance(expand_times, (list, tuple)):
-            attrs['expand_times'] = get_attr_expand_times(expand_times)
-            if contain_var(expand_times):
-                inputs['expand_times_tensor'] = get_new_expand_times_tensor(
-                    expand_times)
+    if isinstance(expand_times, Variable):
+        expand_times.stop_gradient = True
+        inputs['ExpandTimes'] = expand_times
+    elif isinstance(expand_times, (list, tuple)):
+        attrs['expand_times'] = get_attr_expand_times(expand_times)
+        if utils._contain_var(expand_times):
+            inputs['expand_times_tensor'] = get_new_expand_times_tensor(
+                expand_times)
 
     dtype = helper.input_dtype(input_param_name='x')
     out = helper.create_variable_for_type_inference(dtype)
@@ -9912,19 +9882,12 @@ def slice(input, axes, starts, ends):
             sliced_2 = fluid.layers.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends)
             # sliced_2 is input[0:3, 0:2, 2:4].
     """
-
-    def contain_var(one_list):
-        for ele in one_list:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     if in_dygraph_mode():
         infer_flags = list(1 for i in range(len(axes)))
         inputs = {'Input': [input]}
 
         if isinstance(starts, (list, tuple)):
-            if contain_var(starts):
+            if utils._contain_var(starts):
                 raise TypeError(
                     "The type of 'starts' in slice must be list[int] or tuple(int) in Dygraph mode, but "
                     "received %s, which contains Variable." % type(shape))
@@ -9934,7 +9897,7 @@ def slice(input, axes, starts, ends):
                 "received %s." % type(shape))
 
         if isinstance(ends, (list, tuple)):
-            if contain_var(ends):
+            if utils._contain_var(ends):
                 raise TypeError(
                     "The type of 'ends' in slice must be list[int] or tuple(int) in Dygraph mode, but "
                     "received %s, which contains Variable." % type(shape))
@@ -9985,9 +9948,7 @@ def slice(input, axes, starts, ends):
         infer_flags = list(-1 for i in range(len(axes)))
     elif isinstance(starts, (list, tuple)):
         attrs['starts'] = []
-        if not contain_var(starts):
-            attrs['starts'] = starts
-        else:
+        if utils._contain_var(starts):
             inputs['StartsTensorList'] = get_new_list_tensor(starts)
             for i, dim in enumerate(starts):
                 if isinstance(dim, Variable):
@@ -9995,6 +9956,8 @@ def slice(input, axes, starts, ends):
                     infer_flags[i] = -1
                 else:
                     attrs['starts'].append(dim)
+        else:
+            attrs['starts'] = starts
 
     # ends
     if isinstance(ends, Variable):
@@ -10003,9 +9966,7 @@ def slice(input, axes, starts, ends):
         infer_flags = list(-1 for i in range(len(axes)))
     elif isinstance(ends, (list, tuple)):
         attrs['ends'] = []
-        if not contain_var(ends):
-            attrs['ends'] = ends
-        else:
+        if utils._contain_var(ends):
             inputs['EndsTensorList'] = get_new_list_tensor(ends)
             for i, dim in enumerate(ends):
                 if isinstance(dim, Variable):
@@ -10013,6 +9974,9 @@ def slice(input, axes, starts, ends):
                     infer_flags[i] = -1
                 else:
                     attrs['ends'].append(dim)
+        else:
+            attrs['ends'] = ends
+
     # infer_flags
     attrs['infer_flags'] = infer_flags
     out = helper.create_variable_for_type_inference(
@@ -10130,12 +10094,6 @@ def strided_slice(input, axes, starts, ends, strides):
 
     helper = LayerHelper('strided_slice', **locals())
 
-    def contain_var(one_list):
-        for ele in one_list:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     def get_new_list_tensor(old_list):
         new_list_tensor = []
         for dim in old_list:
@@ -10169,9 +10127,7 @@ def strided_slice(input, axes, starts, ends, strides):
             inputs['StartsTensor'] = starts
         elif isinstance(starts, (list, tuple)):
             attrs['starts'] = []
-            if not contain_var(starts):
-                attrs['starts'] = starts
-            else:
+            if utils._contain_var(starts):
                 inputs['StartsTensorList'] = get_new_list_tensor(starts)
                 for i, dim in enumerate(starts):
                     if isinstance(dim, Variable):
@@ -10179,6 +10135,8 @@ def strided_slice(input, axes, starts, ends, strides):
                         infer_flags[i] = -1
                     else:
                         attrs['starts'].append(dim)
+            else:
+                attrs['starts'] = starts
 
         # ends
         if isinstance(ends, Variable):
@@ -10186,9 +10144,7 @@ def strided_slice(input, axes, starts, ends, strides):
             inputs['EndsTensor'] = ends
         elif isinstance(ends, (list, tuple)):
             attrs['ends'] = []
-            if not contain_var(ends):
-                attrs['ends'] = ends
-            else:
+            if utils._contain_var(ends):
                 inputs['EndsTensorList'] = get_new_list_tensor(ends)
                 for i, dim in enumerate(ends):
                     if isinstance(dim, Variable):
@@ -10196,15 +10152,16 @@ def strided_slice(input, axes, starts, ends, strides):
                         infer_flags[i] = -1
                     else:
                         attrs['ends'].append(dim)
+            else:
+                attrs['ends'] = ends
+
         # strides
         if isinstance(strides, Variable):
             strides.stop_gradient = True
             inputs['StridesTensor'] = strides
         elif isinstance(strides, (list, tuple)):
             attrs['strides'] = []
-            if not contain_var(strides):
-                attrs['strides'] = strides
-            else:
+            if utils._contain_var(strides):
                 inputs['StridesTensorList'] = get_new_list_tensor(strides)
                 for i, dim in enumerate(strides):
                     if isinstance(dim, Variable):
@@ -10212,6 +10169,8 @@ def strided_slice(input, axes, starts, ends, strides):
                         infer_flags[i] = -1
                     else:
                         attrs['strides'].append(dim)
+            else:
+                attrs['strides'] = strides
         attrs['infer_flags'] = infer_flags
     out = helper.create_variable_for_type_inference(
         dtype=helper.input_dtype('input'))
@@ -13894,12 +13853,6 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
         dtype = convert_np_dtype_to_dtype_(dtype)
     check_dtype(dtype, 'dtype', ['float32', 'float64'], 'uniform_random')
 
-    def contain_var(one_list):
-        for ele in one_list:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     def get_new_shape_tensor(list_shape):
         new_shape_tensor = []
         for dim in list_shape:
@@ -13939,7 +13892,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
             assert len(shape) > 0, (
                 "The size of argument(shape) can't be zero.")
             attrs["shape"] = get_attr_shape(shape)
-            if contain_var(shape):
+            if utils._contain_var(shape):
                 inputs['ShapeTensorList'] = get_new_shape_tensor(shape)
 
     out = helper.create_variable_for_type_inference(dtype)

python/paddle/fluid/layers/tensor.py

@@ -22,6 +22,7 @@ from ..initializer import Constant, force_init_on_cpu
 from ..core import VarDesc
 from .. import core
 from .layer_function_generator import templatedoc
+from . import utils
 from ..data_feeder import check_type_and_dtype, check_type, check_dtype, convert_dtype
 import numpy
 import warnings
@@ -552,13 +553,6 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
           shape = fluid.layers.fill_constant([1,2], "int32", 2) # shape=[2,2]
           data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
     """
-
-    def _contain_var(one_list):
-        for ele in one_list:
-            if isinstance(ele, Variable):
-                return True
-        return False
-
     attrs = {
         'value': float(value),
         'force_cpu': force_cpu or force_init_on_cpu()
@@ -571,8 +565,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
 
     if in_dygraph_mode():
         if isinstance(shape, (list, tuple)):
-            contain_var = _contain_var(shape)
-            if contain_var:
+            if utils._contain_var(shape):
                 raise TypeError(
                     "The type of 'shape' in fill_constant must be list[int] or tuple(int) in Dygraph mode, but "
                     "received %s, which contains Variable." % type(shape))
@@ -644,7 +637,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
             "The size of 'shape' in fill_constant can't be zero, "
             "but received %s." % len(shape))
         attrs["shape"] = _get_attr_shape(shape)
-        if _contain_var(shape):
+        if utils._contain_var(shape):
             inputs['ShapeTensorList'] = _get_shape_tensor(shape)
 
     if out is None:

python/paddle/fluid/layers/utils.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 import collections
 import six
 import numpy as np
+from ..framework import Variable
 
 
 def convert_to_list(value, n, name, dtype=np.int):
@@ -244,3 +245,13 @@ def _is_symmetric_padding(padding, data_dim):
             if padding[i * 2] != padding[i * 2 + 1]:
                 is_sys = False
     return is_sys
+
+
+def _contain_var(list_or_tuple):
+    """
+    Check whether list or tuple contains variable.
+    """
+    for item in list_or_tuple:
+        if isinstance(item, Variable):
+            return True
+    return False

python/paddle/fluid/optimizer.py

@@ -134,11 +134,11 @@ class Optimizer(object):
         # global step if use lr decay
         if isinstance(self._learning_rate, LearningRateDecay):
             var_tmp = None
-            if not framework.in_dygraph_mode():
-                var_temp = Variable(None, name='global_step', dtype='int32')
-            else:
+            if framework.in_dygraph_mode():
                 var_temp = framework._varbase_creator(
                     None, name='global_step', dtype='int32')
+            else:
+                var_temp = Variable(None, name='global_step', dtype='int32')
 
             tensor.fill_constant(
                 [1], "int32", self._learning_rate.step_num, out=var_temp)
@@ -546,10 +546,10 @@ class Optimizer(object):
             See examples in ``apply_gradients``.
         """
         act_no_grad_set = None
-        if not framework.in_dygraph_mode():
-            act_no_grad_set = self._get_no_grad_set(loss, no_grad_set)
-        else:
+        if framework.in_dygraph_mode():
             pass
+        else:
+            act_no_grad_set = self._get_no_grad_set(loss, no_grad_set)
 
         self._dtype = loss.dtype
         if framework.in_dygraph_mode():