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Paddle
未验证 提交 d4bdbf8c 编写于 作者: L Leo Chen 提交者： GitHub
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Polish nn code, test=develop (#22237) 

* refine code, test=develop

* reuse contain_var, test=develop
上级 efcdeb51
隐藏空白更改
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Showing with 95 addition and 135 deletion
python/paddle/fluid/framework.py
浏览文件 @ d4bdbf8c
......@@ -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
浏览文件 @ d4bdbf8c
......@@ -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
浏览文件 @ d4bdbf8c
......@@ -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
浏览文件 @ d4bdbf8c
......@@ -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
浏览文件 @ d4bdbf8c
......@@ -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():
......
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