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体验新版 GitCode,发现更多精彩内容 >>
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e11bf2a4
编写于
4月 03, 2019
作者:
L
lujun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
merge branch, test=develop
上级
a32c6ffa
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
140 addition
and
30 deletion
+140
-30
python/paddle/fluid/dygraph/layers.py
python/paddle/fluid/dygraph/layers.py
+2
-2
python/paddle/fluid/dygraph/nn.py
python/paddle/fluid/dygraph/nn.py
+124
-14
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+5
-5
python/paddle/fluid/tests/unittests/test_imperative_basic.py
python/paddle/fluid/tests/unittests/test_imperative_basic.py
+1
-1
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+1
-1
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
...addle/fluid/tests/unittests/test_imperative_se_resnext.py
+1
-1
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+6
-6
未找到文件。
python/paddle/fluid/dygraph/layers.py
浏览文件 @
e11bf2a4
...
...
@@ -141,12 +141,12 @@ class Layer(core.Layer):
for
p
in
self
.
parameters
():
p
.
clear_gradient
()
def
_
build_once
(
self
,
*
args
):
def
build_once
(
self
,
*
args
):
pass
def
__call__
(
self
,
*
inputs
):
if
not
self
.
_built
:
self
.
_
build_once
(
*
inputs
)
self
.
build_once
(
*
inputs
)
outputs
=
self
.
forward
(
*
inputs
)
self
.
_built
=
True
...
...
python/paddle/fluid/dygraph/nn.py
浏览文件 @
e11bf2a4
...
...
@@ -368,7 +368,7 @@ class Conv3D(layers.Layer):
self
.
_param_attr
=
param_attr
self
.
_bias_attr
=
bias_attr
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
num_channels
=
input
.
shape
[
1
]
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
input
)
...
...
@@ -435,6 +435,116 @@ class Conv3D(layers.Layer):
class
Conv3DTranspose
(
layers
.
Layer
):
"""
**Convlution3D transpose layer**
The convolution3D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCDHW format. Where N is batch size, C is the number of channels,
D is the depth of the feature, H is the height of the feature, and W
is the width of the feature. Parameters(dilations, strides, paddings) are
two elements. These two elements represent height and width, respectively.
The details of convolution transpose layer, please refer to the following
explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
If bias attribution and activation type are provided, bias is added to
the output of the convolution, and the corresponding activation function
is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \sigma (W
\\
ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`
\\
ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`
\\
sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
- Input:
Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
.. math::
D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1
\\\\
H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1
\\\\
W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain three integers, (image_D, image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of conv3d_transpose. If it is set to None or one attribute of ParamAttr, conv3d_transpose
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d_transpose.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, conv3d_transpose
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended.
Default: None.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution transpose result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
conv3d_transpose = nn.Conv3DTranspose(
'Conv3DTranspose',
num_filters=12,
filter_size=12,
use_cudnn=False)
transpose_res = conv3d_transpose(base.to_variable(input_array))
"""
def
__init__
(
self
,
name_scope
,
num_filters
,
...
...
@@ -465,7 +575,7 @@ class Conv3DTranspose(layers.Layer):
self
.
_bias_attr
=
bias_attr
self
.
_act
=
act
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
input
)
self
.
_input_channel
=
input
.
shape
[
1
]
...
...
@@ -769,7 +879,7 @@ class FC(layers.Layer):
assert
isinstance
(
value
,
Parameter
)
self
.
__w
[
i
]
=
value
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
i
=
0
for
inp
,
param
in
self
.
_helper
.
iter_inputs_and_params
(
input
,
self
.
_param_attr
):
...
...
@@ -998,7 +1108,7 @@ class BatchNorm(layers.Layer):
self
.
_fuse_with_relu
=
fuse_with_relu
self
.
_use_global_stats
=
use_global_stats
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
pass
def
forward
(
self
,
input
):
...
...
@@ -1202,7 +1312,7 @@ class LayerNorm(layers.Layer):
self
.
_bias_attr
=
bias_attr
self
.
_act
=
act
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
input
)
input_shape
=
input
.
shape
param_shape
=
[
...
...
@@ -1564,7 +1674,7 @@ class NCE(layers.Layer):
'remote_prefetch'
:
remote_prefetch
}
def
_
build_once
(
self
,
input
,
label
,
sample_weight
=
None
):
def
build_once
(
self
,
input
,
label
,
sample_weight
=
None
):
assert
isinstance
(
input
,
Variable
)
assert
isinstance
(
label
,
Variable
)
...
...
@@ -1650,7 +1760,7 @@ class PRelu(layers.Layer):
raise
ValueError
(
'mode should be one of all, channel, element.'
)
self
.
_alpha_shape
=
[
1
]
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
if
self
.
_mode
==
'channel'
:
self
.
_alpha_shape
=
[
1
,
input
.
shape
[
1
],
1
,
1
]
elif
self
.
_mode
==
'element'
:
...
...
@@ -1728,7 +1838,7 @@ class BilinearTensorProduct(layers.Layer):
self
.
_name
=
name
self
.
_inputs
=
dict
()
def
_
build_once
(
self
,
x
,
y
):
def
build_once
(
self
,
x
,
y
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
x
)
param_shape
=
[
self
.
_size
,
x
.
shape
[
1
],
y
.
shape
[
1
]]
...
...
@@ -1904,7 +2014,7 @@ class Conv2DTranspose(layers.Layer):
self
.
_output_size
=
output_size
self
.
_op_type
=
'conv2d_transpose'
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
input_channel
=
input
.
shape
[
1
]
if
(
input_channel
==
self
.
_groups
and
self
.
_num_filters
==
input_channel
and
not
self
.
_use_cudnn
):
...
...
@@ -2028,7 +2138,7 @@ class SequenceConv(layers.Layer):
self
.
_bias_attr
=
bias_attr
self
.
_param_attr
=
param_attr
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
input
)
filter_shape
=
[
self
.
_filter_size
*
input
.
shape
[
1
],
self
.
_num_filters
]
self
.
_filter_param
=
self
.
create_parameter
(
...
...
@@ -2065,7 +2175,7 @@ class RowConv(layers.Layer):
self
.
_param_attr
=
param_attr
self
.
_future_context_size
=
future_context_size
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
input
)
filter_shape
=
[
self
.
_future_context_size
+
1
,
input
.
shape
[
1
]]
self
.
_filter_param
=
self
.
create_parameter
(
...
...
@@ -2128,7 +2238,7 @@ class GroupNorm(layers.Layer):
if
data_layout
!=
'NCHW'
:
raise
ValueError
(
"unsupported data layout:"
+
data_layout
)
def
_
build_once
(
self
,
input
):
def
build_once
(
self
,
input
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
input
)
param_shape
=
[
input
.
shape
[
1
]]
if
self
.
_bias_attr
:
...
...
@@ -2181,7 +2291,7 @@ class SpectralNorm(layers.Layer):
self
.
_eps
=
eps
self
.
_dim
=
dim
def
_
build_once
(
self
,
weight
):
def
build_once
(
self
,
weight
):
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
weight
)
input_shape
=
weight
.
shape
h
=
input_shape
[
self
.
_dim
]
...
...
@@ -2236,7 +2346,7 @@ class TreeConv(layers.Layer):
self
.
_bias_attr
=
bias_attr
self
.
_param_attr
=
param_attr
def
_
build_once
(
self
,
nodes_vector
,
edge_set
):
def
build_once
(
self
,
nodes_vector
,
edge_set
):
assert
isinstance
(
nodes_vector
,
Variable
)
assert
isinstance
(
edge_set
,
Variable
)
self
.
_dtype
=
self
.
_helper
.
input_dtype
(
nodes_vector
)
...
...
python/paddle/fluid/framework.py
浏览文件 @
e11bf2a4
...
...
@@ -715,7 +715,7 @@ class Variable(object):
raise
IndexError
(
"Valid index accept int or slice or ellipsis"
)
return
True
,
[
starts
,
ends
]
def
cloneVar
(
self
,
copy
=
False
):
def
_
cloneVar
(
self
,
copy
=
False
):
if
not
copy
:
return
self
.
block
.
create_var
(
name
=
unique_name
.
generate
(
"."
.
join
(
self
.
name
)),
...
...
@@ -726,7 +726,7 @@ class Variable(object):
return
self
def
_sliceVar
(
self
,
axes
,
starts
,
ends
):
new_var
=
self
.
cloneVar
()
new_var
=
self
.
_
cloneVar
()
self
.
block
.
append_op
(
type
=
"slice"
,
inputs
=
{
'Input'
:
[
self
]},
...
...
@@ -737,7 +737,7 @@ class Variable(object):
return
new_var
def
_concatVar
(
self
,
inputs
,
axis
):
new_var
=
self
.
cloneVar
()
new_var
=
self
.
_
cloneVar
()
self
.
block
.
append_op
(
type
=
"concat"
,
inputs
=
{
'X'
:
inputs
},
...
...
@@ -748,7 +748,7 @@ class Variable(object):
def
_sliceAndConcatVar
(
self
,
item
,
axis
):
if
isinstance
(
item
,
slice
):
if
self
.
shape
[
axis
]
<
0
:
return
self
.
cloneVar
(
True
)
return
self
.
_
cloneVar
(
True
)
start
,
stop
,
step
=
self
.
_slice_indices
(
item
,
self
.
shape
[
axis
])
if
step
==
1
:
return
self
.
_sliceVar
([
axis
],
[
start
],
[
stop
])
...
...
@@ -767,7 +767,7 @@ class Variable(object):
return
self
.
_concatVar
(
vars
,
axis
)
elif
isinstance
(
item
,
int
):
if
self
.
shape
[
axis
]
<
0
:
return
self
.
cloneVar
(
True
)
return
self
.
_
cloneVar
(
True
)
index
=
int
(
item
)
if
(
index
>
0
and
index
>=
self
.
shape
[
axis
])
\
or
(
index
<
0
and
(
index
+
self
.
shape
[
axis
])
<
0
):
...
...
python/paddle/fluid/tests/unittests/test_imperative_basic.py
浏览文件 @
e11bf2a4
...
...
@@ -358,7 +358,7 @@ class TestImperative(unittest.TestCase):
x
=
fluid
.
layers
.
elementwise_add
(
inp1
,
inp2
)
else
:
x
=
fluid
.
layers
.
elementwise_sub
(
inp1
,
inp2
)
dygraph_result
=
x
.
_
numpy
()
dygraph_result
=
x
.
numpy
()
# static graph
with
new_program_scope
():
...
...
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
浏览文件 @
e11bf2a4
...
...
@@ -128,7 +128,7 @@ class TestImperativeMnist(unittest.TestCase):
img
=
to_variable
(
dy_x_data
)
label
=
to_variable
(
y_data
)
label
.
_
stop_gradient
=
True
label
.
stop_gradient
=
True
cost
=
mnist
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
cost
,
label
)
...
...
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
浏览文件 @
e11bf2a4
...
...
@@ -344,7 +344,7 @@ class TestImperativeResneXt(unittest.TestCase):
img
=
to_variable
(
dy_x_data
)
label
=
to_variable
(
y_data
)
label
.
_
stop_gradient
=
True
label
.
stop_gradient
=
True
out
=
se_resnext
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
e11bf2a4
...
...
@@ -109,7 +109,7 @@ class TestLayer(LayerTest):
dy_ret
=
fc2
(
ret
)
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
static_ret2
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_ret
.
_
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_ret
.
numpy
()))
def
test_layer_norm
(
self
):
inp
=
np
.
ones
([
3
,
32
,
32
],
dtype
=
'float32'
)
...
...
@@ -620,7 +620,7 @@ class TestLayer(LayerTest):
conv3d
=
nn
.
Conv3D
(
'conv3d'
,
num_filters
=
3
,
filter_size
=
2
)
dy_ret
=
conv3d
(
base
.
to_variable
(
images
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
_
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
def
test_row_conv
(
self
):
...
...
@@ -714,7 +714,7 @@ class TestLayer(LayerTest):
groupNorm
=
nn
.
GroupNorm
(
'GroupNorm'
,
groups
=
2
)
dy_ret
=
groupNorm
(
base
.
to_variable
(
input
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
_
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
def
test_spectral_norm
(
self
):
...
...
@@ -764,7 +764,7 @@ class TestLayer(LayerTest):
spectralNorm
=
nn
.
SpectralNorm
(
'SpectralNorm'
,
dim
=
1
,
power_iters
=
2
)
dy_ret
=
spectralNorm
(
base
.
to_variable
(
input
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
_
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
def
test_tree_conv
(
self
):
...
...
@@ -837,7 +837,7 @@ class TestLayer(LayerTest):
dy_ret
=
treeConv
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
_
numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
numpy
()))
def
test_conv3d_transpose
(
self
):
input_array
=
np
.
arange
(
0
,
48
).
reshape
(
...
...
@@ -867,7 +867,7 @@ class TestLayer(LayerTest):
use_cudnn
=
False
)
dy_rlt
=
conv3d_transpose
(
base
.
to_variable
(
input_array
))
self
.
assertTrue
(
np
.
allclose
(
static_rlt2
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_rlt
.
_
numpy
(),
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_rlt
.
numpy
(),
static_rlt
))
class
TestBook
(
unittest
.
TestCase
):
...
...
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