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0ef86cbd
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0ef86cbd
编写于
5月 30, 2017
作者:
E
emailweixu
提交者:
GitHub
5月 30, 2017
浏览文件
操作
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差异文件
Merge pull request #2288 from emailweixu/fix_v2_api
Fix V2 API
上级
94d83fcd
97c4d23f
变更
13
展开全部
隐藏空白更改
内联
并排
Showing
13 changed file
with
344 addition
and
861 deletion
+344
-861
paddle/parameter/Parameter.h
paddle/parameter/Parameter.h
+1
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+17
-14
python/paddle/trainer_config_helpers/config_parser_utils.py
python/paddle/trainer_config_helpers/config_parser_utils.py
+17
-4
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+6
-0
python/paddle/v2/config_base.py
python/paddle/v2/config_base.py
+48
-199
python/paddle/v2/evaluator.py
python/paddle/v2/evaluator.py
+3
-14
python/paddle/v2/inference.py
python/paddle/v2/inference.py
+12
-12
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+204
-542
python/paddle/v2/networks.py
python/paddle/v2/networks.py
+1
-14
python/paddle/v2/tests/test_layer.py
python/paddle/v2/tests/test_layer.py
+9
-9
python/paddle/v2/tests/test_rnn_layer.py
python/paddle/v2/tests/test_rnn_layer.py
+9
-0
python/paddle/v2/tests/test_topology.py
python/paddle/v2/tests/test_topology.py
+6
-6
python/paddle/v2/topology.py
python/paddle/v2/topology.py
+11
-47
未找到文件。
paddle/parameter/Parameter.h
浏览文件 @
0ef86cbd
...
...
@@ -324,6 +324,7 @@ protected:
std
::
vector
<
std
::
shared_ptr
<
IParameterUpdaterHook
>>
updaterHooks_
;
public:
void
setSharedCount
(
int
cnt
)
{
sharedCount_
=
cnt
;
}
int
getSharedCount
()
{
return
sharedCount_
;
}
bool
isSparse
()
{
return
config_
.
is_sparse
();
}
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
0ef86cbd
...
...
@@ -3371,7 +3371,7 @@ def make_importer(config_dir, config_args):
return
Import
settings
=
dict
(
DEFAULT_SETTING
=
dict
(
batch_size
=
None
,
mini_batch_size
=
None
,
algorithm
=
'async_sgd'
,
...
...
@@ -3404,6 +3404,8 @@ settings = dict(
adam_beta2
=
0.999
,
adam_epsilon
=
1e-8
,
)
settings
=
copy
.
deepcopy
(
DEFAULT_SETTING
)
settings_deprecated
=
dict
(
usage_ratio
=
1.
,
)
trainer_settings
=
dict
(
...
...
@@ -3544,10 +3546,8 @@ def update_g_config():
return
g_config
def
parse_config
(
trainer_config
,
config_arg_str
):
def
begin_parse
(
config_arg_str
=
''
):
'''
@param trainer_config: can be a string of config file name or a function name
with config logic
@param config_arg_str: a string of the form var1=val1,var2=val2. It will be
passed to config script as a dictionary CONFIG_ARGS
'''
...
...
@@ -3555,12 +3555,23 @@ def parse_config(trainer_config, config_arg_str):
for
hook
in
_parse_config_hooks
:
hook
()
config_args
=
{}
logger
.
findCaller
=
find_caller
logger
.
fatal
=
my_fatal
g_config
.
model_config
.
type
=
"nn"
global
g_current_submodel
,
g_root_submodel
g_root_submodel
=
g_config
.
model_config
.
sub_models
.
add
()
g_root_submodel
.
name
=
'root'
g_root_submodel
.
is_recurrent_layer_group
=
False
g_current_submodel
=
g_root_submodel
def
parse_config
(
trainer_config
,
config_arg_str
):
begin_parse
(
config_arg_str
)
config_args
=
{}
if
config_arg_str
:
config_args
=
dict
([
f
.
split
(
'='
)
for
f
in
config_arg_str
.
split
(
','
)])
...
...
@@ -3573,14 +3584,6 @@ def parse_config(trainer_config, config_arg_str):
extension_module
=
importlib
(
extension_module_name
)
g_extended_config_funcs
=
extension_module
.
get_config_funcs
(
g_config
)
g_config
.
model_config
.
type
=
'nn'
global
g_current_submodel
,
g_root_submodel
g_root_submodel
=
g_config
.
model_config
.
sub_models
.
add
()
g_root_submodel
.
name
=
'root'
g_root_submodel
.
is_recurrent_layer_group
=
False
g_current_submodel
=
g_root_submodel
if
hasattr
(
trainer_config
,
'__call__'
):
trainer_config
.
func_globals
.
update
(
make_config_environment
(
""
,
config_args
))
...
...
python/paddle/trainer_config_helpers/config_parser_utils.py
浏览文件 @
0ef86cbd
...
...
@@ -12,15 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
copy
import
paddle.trainer.config_parser
as
config_parser
from
paddle.proto.TrainerConfig_pb2
import
OptimizationConfig
'''
This file is a wrapper of formal config_parser. The main idea of this file is to
This file is a wrapper of formal config_parser. The main idea of this file is to
separete different config logic into different function, such as network configuration
and optimizer configuration.
'''
__all__
=
[
"parse_trainer_config"
,
"parse_network_config"
,
"parse_optimizer_config"
"parse_trainer_config"
,
"parse_network_config"
,
"parse_optimizer_config"
,
"reset_parser"
]
...
...
@@ -34,5 +37,15 @@ def parse_network_config(network_conf, config_arg_str=''):
def
parse_optimizer_config
(
optimizer_conf
,
config_arg_str
=
''
):
config
=
config_parser
.
parse_config
(
optimizer_conf
,
config_arg_str
)
return
config
.
opt_config
config_parser
.
settings
=
copy
.
deepcopy
(
config_parser
.
DEFAULT_SETTING
)
optimizer_conf
()
opt_config
=
OptimizationConfig
()
for
k
,
v
in
config_parser
.
settings
.
iteritems
():
if
v
is
None
:
continue
opt_config
.
__setattr__
(
k
,
v
)
return
opt_config
def
reset_parser
():
config_parser
.
begin_parse
()
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
0ef86cbd
...
...
@@ -287,6 +287,7 @@ class LayerOutput(object):
assert
size
is
not
None
assert
LayerType
.
is_layer_type
(
layer_type
)
self
.
name
=
name
self
.
full_name
=
MakeLayerNameInSubmodel
(
name
)
self
.
layer_type
=
layer_type
if
parents
is
not
None
and
type
(
parents
)
!=
list
:
parents
=
[
parents
]
...
...
@@ -3491,6 +3492,11 @@ def recurrent_group(step,
RecurrentLayerGroupEnd
(
name
=
name
)
for
layer_out
in
layer_outs
:
# Thee previous full_name is the name is the rnn group
# We need a full_name outside the rnn group
layer_out
.
full_name
=
MakeLayerNameInSubmodel
(
layer_out
.
name
)
if
len
(
layer_outs
)
==
1
:
return
layer_outs
[
0
]
else
:
...
...
python/paddle/v2/config_base.py
浏览文件 @
0ef86cbd
...
...
@@ -14,206 +14,55 @@
import
collections
import
re
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
import
paddle.trainer_config_helpers
as
conf_helps
from
topology
import
Topology
class
LayerType
(
type
):
def
__new__
(
cls
,
name
,
bases
,
attrs
):
method_name
=
attrs
.
get
(
'METHOD_NAME'
,
None
)
if
method_name
is
not
None
:
method
=
getattr
(
conf_helps
,
method_name
)
if
method
.
__doc__
is
not
None
:
mapper
=
attrs
.
get
(
"__map_docstr__"
,
None
)
if
mapper
is
not
None
:
attrs
[
'__doc__'
]
=
LayerType
.
__map_docstr__
(
mapper
(
method
.
__doc__
),
method_name
=
method_name
,
name
=
name
)
else
:
attrs
[
'__doc__'
]
=
LayerType
.
__map_docstr__
(
method
.
__doc__
,
method_name
=
method_name
,
name
=
name
)
return
super
(
LayerType
,
cls
).
__new__
(
cls
,
name
,
bases
,
attrs
)
@
staticmethod
def
__map_docstr__
(
doc
,
name
,
method_name
):
assert
isinstance
(
doc
,
basestring
)
# replace LayerOutput to paddle.v2.config_base.Layer
doc
=
doc
.
replace
(
"LayerOutput"
,
"paddle.v2.config_base.Layer"
)
doc
=
doc
.
replace
(
'ParameterAttribute'
,
'paddle.v2.attr.ParameterAttribute'
)
doc
=
re
.
sub
(
r
'ExtraLayerAttribute[^\s]?'
,
'paddle.v2.attr.ExtraAttribute'
,
doc
)
# xxx_layer to xxx
doc
=
re
.
sub
(
r
"(?P<name>[a-z]+)_layer"
,
r
"\g<name>"
,
doc
)
# XxxxActivation to paddle.v2.Activation.Xxxx
doc
=
re
.
sub
(
r
"(?P<name>[A-Z][a-zA-Z]+)Activation"
,
r
"paddle.v2.Activation.\g<name>"
,
doc
)
# TODO(yuyang18): Add more rules if needed.
__layer_map__
=
{}
def
__map_docstr__
(
doc
,
name
):
if
doc
is
None
:
return
doc
assert
isinstance
(
doc
,
basestring
)
# replace LayerOutput to paddle.v2.config_base.Layer
doc
=
doc
.
replace
(
"LayerOutput"
,
"paddle.v2.config_base.Layer"
)
doc
=
doc
.
replace
(
'ParameterAttribute'
,
'paddle.v2.attr.ParameterAttribute'
)
doc
=
re
.
sub
(
r
'ExtraLayerAttribute[^\s]?'
,
'paddle.v2.attr.ExtraAttribute'
,
doc
)
# xxx_layer to xxx
doc
=
re
.
sub
(
r
"(?P<name>[a-z]+)_layer"
,
r
"\g<name>"
,
doc
)
# XxxxActivation to paddle.v2.Activation.Xxxx
doc
=
re
.
sub
(
r
"(?P<name>[A-Z][a-zA-Z]+)Activation"
,
r
"paddle.v2.Activation.\g<name>"
,
doc
)
# xxx_evaluator to paddle.v2.evaluator.xxx
doc
=
re
.
sub
(
r
"(?P<name>[a-z]+)_evaluator"
,
r
"evaluator.\g<name>"
,
doc
)
# TODO(yuyang18): Add more rules if needed.
return
doc
def
__convert_to_v2__
(
f
,
name
,
module
):
def
wrapped
(
*
args
,
**
xargs
):
out
=
f
(
*
args
,
**
xargs
)
outs
=
out
if
not
isinstance
(
out
,
collections
.
Sequence
):
outs
=
[
out
]
for
l
in
outs
:
if
isinstance
(
l
,
conf_helps
.
LayerOutput
):
__layer_map__
[
l
.
full_name
]
=
l
return
out
wrapped
.
__doc__
=
__map_docstr__
(
f
.
__doc__
,
name
)
wrapped
.
__name__
=
name
wrapped
.
__module__
=
module
return
wrapped
class
Layer
(
object
):
__metaclass__
=
LayerType
def
__init__
(
self
,
name
=
None
,
parent_layers
=
None
):
assert
isinstance
(
parent_layers
,
dict
)
self
.
name
=
name
self
.
__context__
=
{}
self
.
__parent_layers__
=
parent_layers
# some layer may have some extra parent layer
self
.
__extra_parent__
=
[]
# used for evaluator.
self
.
__children_layers__
=
[]
def
extra_parent
(
self
):
return
self
.
__extra_parent__
def
append_extra_parent
(
self
,
parent
):
self
.
__extra_parent__
.
append
(
parent
)
def
append_child
(
self
,
layer
,
parent_names
):
self
.
__children_layers__
.
append
((
layer
,
parent_names
))
def
to_proto
(
self
,
context
):
"""
function to set proto attribute
"""
self
.
__context__
=
context
# STEP: short cut if this layer is parsed before.
if
self
.
context_name
()
in
context
:
if
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
# STEP: parse extra_parent that is not used by this layer but must
# be parsed before this layer.
for
p
in
self
.
__extra_parent__
:
p
.
to_proto
(
context
=
context
)
# STEP: parse parent that is used by this layer, get the result and
# insert into kwargs of the next layer's to_proto_impl method.
kwargs
=
dict
()
for
layer_name
in
self
.
__parent_layers__
:
if
not
isinstance
(
self
.
__parent_layers__
[
layer_name
],
collections
.
Sequence
):
v1_layer
=
self
.
__parent_layers__
[
layer_name
].
to_proto
(
context
=
context
)
else
:
v1_layer
=
map
(
lambda
x
:
x
.
to_proto
(
context
=
context
),
self
.
__parent_layers__
[
layer_name
])
kwargs
[
layer_name
]
=
v1_layer
# STEP: parse myself and add myself into context.
ret_val
=
self
.
to_proto_impl
(
**
kwargs
)
if
self
.
context_name
()
is
not
None
\
and
self
.
context_name
()
not
in
context
:
context
[
self
.
context_name
()]
=
ret_val
# STEP: parse children that should be pased after this layer.
for
layer
,
pnames
in
self
.
__children_layers__
:
drop
=
False
# child will only be parsed if all parents are in context.
for
pname
in
pnames
:
if
pname
not
in
context
:
drop
=
True
break
if
drop
:
continue
layer
.
to_proto
(
context
=
context
)
# STEP: return v1 layer result
if
self
.
context_name
()
is
None
:
return
ret_val
elif
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
def
to_proto_impl
(
self
,
**
kwargs
):
raise
NotImplementedError
()
def
context_name
(
self
):
"""
Context name means the context which stores `to_proto_impl` result.
If multiple layer share same context_name, the `to_proto_impl` of them
will be invoked only once.
"""
return
self
.
name
def
use_context_name
(
self
):
return
False
def
calculate_size
(
self
):
"""
lazy calculate size of the layer, should be called when to_proto_impl of
this layer is called.
:return:
"""
return
self
.
__context__
[
self
.
context_name
()].
size
def
attr
(
self
):
topo
=
Topology
(
self
)
return
topo
.
get_layer_proto
(
self
.
name
)
def
__convert_to_v2__
(
method_name
,
parent_names
,
is_default_name
=
True
,
attach_parent
=
False
):
if
is_default_name
:
wrapper
=
wrap_name_default
(
name_prefix
=
method_name
)
else
:
wrapper
=
None
class
V2LayerImpl
(
Layer
):
METHOD_NAME
=
method_name
def
__init__
(
self
,
**
kwargs
):
parent_layers
=
dict
()
other_kwargs
=
dict
()
for
pname
in
parent_names
:
if
pname
in
kwargs
:
parent_layers
[
pname
]
=
kwargs
[
pname
]
if
attach_parent
:
pnames
=
[
x
.
context_name
()
for
x
in
parent_layers
.
values
()]
for
pname
in
parent_layers
:
layers
=
kwargs
[
pname
]
if
not
isinstance
(
layers
,
collections
.
Sequence
):
layers
=
[
layers
]
for
layer
in
layers
:
layer
.
append_child
(
self
,
pnames
)
for
key
in
kwargs
.
keys
():
if
key
not
in
parent_names
:
other_kwargs
[
key
]
=
kwargs
[
key
]
name
=
kwargs
.
get
(
'name'
,
None
)
super
(
V2LayerImpl
,
self
).
__init__
(
name
,
parent_layers
)
self
.
__other_kwargs__
=
other_kwargs
if
wrapper
is
not
None
:
__init__
=
wrapper
(
__init__
)
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__other_kwargs__
:
args
[
each
]
=
self
.
__other_kwargs__
[
each
]
return
getattr
(
conf_helps
,
method_name
)(
**
args
)
return
V2LayerImpl
Layer
=
conf_helps
.
LayerOutput
python/paddle/v2/evaluator.py
浏览文件 @
0ef86cbd
...
...
@@ -13,8 +13,8 @@
# limitations under the License.
import
paddle.trainer_config_helpers.evaluators
as
evs
import
inspect
from
config_base
import
__convert_to_v2__
import
inspect
__all__
=
[]
...
...
@@ -25,21 +25,10 @@ def initialize():
for
__ev_name__
in
filter
(
lambda
x
:
x
.
endswith
(
'_evaluator'
),
evs
.
__all__
):
__ev__
=
getattr
(
evs
,
__ev_name__
)
if
hasattr
(
__ev__
,
'argspec'
):
argspec
=
__ev__
.
argspec
else
:
argspec
=
inspect
.
getargspec
(
__ev__
)
parent_names
=
filter
(
lambda
x
:
x
in
[
'input'
,
'label'
,
'weight'
],
argspec
.
args
)
v2_ev
=
__convert_to_v2__
(
__ev_name__
,
parent_names
=
parent_names
,
is_default_name
=
'name'
in
argspec
.
args
,
attach_parent
=
True
)
__new_name__
=
convert_to_new_name
(
__ev_name__
)
globals
()[
__new_name__
]
=
v2_ev
globals
()[
__new_name__
]
=
__convert_to_v2__
(
__ev__
,
__new_name__
,
__name__
)
globals
()[
__new_name__
].
__name__
=
__new_name__
__all__
.
append
(
__new_name__
)
...
...
python/paddle/v2/inference.py
浏览文件 @
0ef86cbd
...
...
@@ -12,9 +12,9 @@ class Inference(object):
"""
Inference combines neural network output and parameters together
to do inference.
.. code-block:: python
inferer = Inference(output_layer=prediction, parameters=parameters)
for data_batch in batches:
print inferer.infer(data_batch)
...
...
@@ -92,8 +92,8 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
.. code-block:: python
result = paddle.infer(output_layer=prediction,
parameters=parameters,
result = paddle.infer(output_layer=prediction,
parameters=parameters,
input=SomeData)
print result
...
...
@@ -101,14 +101,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
.. code-block:: python
result = paddle.infer(output_layer=[prediction1, prediction2],
parameters=parameters,
result = paddle.infer(output_layer=[prediction1, prediction2],
parameters=parameters,
input=SomeData,
field=[id, value]])
print result
:param output_layer: output of the neural network that would be inferred
:type output_layer: paddle.v2.config_base.Layer or a list of
:type output_layer: paddle.v2.config_base.Layer or a list of
paddle.v2.config_base.Layer
:param parameters: parameters of the neural network.
:type parameters: paddle.v2.parameters.Parameters
...
...
@@ -117,14 +117,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
:type input: collections.Iterable
:param feeding: Reader dictionary. Default could generate from input
value.
:param field: The prediction field. It should in [`value`, `id`, `prob`].
`value` and `prob` mean return the prediction probabilities,
:param field: The prediction field. It should in [`value`, `id`, `prob`].
`value` and `prob` mean return the prediction probabilities,
`id` means return the prediction labels. Default is `value`.
Note that `prob` only used when output_layer is beam_search
Note that `prob` only used when output_layer is beam_search
or max_id.
:type field: str
:return: The prediction result. If there are multiple outout_layers and fields,
the return order is outout_layer1.field1, outout_layer2.field1, ...,
:return: The prediction result. If there are multiple outout_layers and fields,
the return order is outout_layer1.field1, outout_layer2.field1, ...,
outout_layer1.field2, outout_layer2.field2 ...
:rtype: numpy.ndarray
"""
...
...
python/paddle/v2/layer.py
浏览文件 @
0ef86cbd
此差异已折叠。
点击以展开。
python/paddle/v2/networks.py
浏览文件 @
0ef86cbd
...
...
@@ -24,20 +24,7 @@ def __initialize__():
if
each_subnetwork
in
[
'inputs'
,
'outputs'
]:
continue
func
=
getattr
(
conf_nw
,
each_subnetwork
)
if
hasattr
(
func
,
'argspec'
):
argspec
=
func
.
argspec
else
:
argspec
=
inspect
.
getargspec
(
func
)
if
each_subnetwork
==
'simple_attention'
:
parents
=
[
'encoded_sequence'
,
'encoded_proj'
,
'decoder_state'
]
else
:
parents
=
filter
(
lambda
x
:
x
.
startswith
(
'input'
),
argspec
.
args
)
assert
len
(
parents
)
!=
0
,
each_subnetwork
v2_subnet
=
__convert_to_v2__
(
each_subnetwork
,
parent_names
=
parents
,
is_default_name
=
'name'
in
argspec
.
args
)
globals
()[
each_subnetwork
]
=
v2_subnet
globals
()[
each_subnetwork
]
=
func
globals
()[
each_subnetwork
].
__name__
=
each_subnetwork
global
__all__
__all__
.
append
(
each_subnetwork
)
...
...
python/paddle/v2/tests/test_layer.py
浏览文件 @
0ef86cbd
...
...
@@ -173,9 +173,9 @@ class OtherLayerTest(unittest.TestCase):
class
ProjOpTest
(
unittest
.
TestCase
):
def
test_projection
(
self
):
input
=
layer
.
data
(
name
=
'data'
,
type
=
data_type
.
dense_vector
(
784
))
input
=
layer
.
data
(
name
=
'data
2
'
,
type
=
data_type
.
dense_vector
(
784
))
word
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
integer_value_sequence
(
10000
))
name
=
'word
2
'
,
type
=
data_type
.
integer_value_sequence
(
10000
))
fc0
=
layer
.
fc
(
input
=
input
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
input
,
size
=
200
,
act
=
activation
.
Sigmoid
())
mixed0
=
layer
.
mixed
(
...
...
@@ -204,8 +204,8 @@ class ProjOpTest(unittest.TestCase):
dotmul1
+=
dotmul
context
=
layer
.
context_projection
(
input
=
fc0
,
context_len
=
5
)
context0
=
layer
.
mixed
(
size
=
1
00
,
input
=
context
)
with
layer
.
mixed
(
size
=
1
00
)
as
context1
:
context0
=
layer
.
mixed
(
size
=
5
00
,
input
=
context
)
with
layer
.
mixed
(
size
=
5
00
)
as
context1
:
context1
+=
context
conv
=
layer
.
conv_projection
(
...
...
@@ -231,8 +231,8 @@ class ProjOpTest(unittest.TestCase):
print
layer
.
parse_network
(
conv1
)
def
test_operator
(
self
):
ipt0
=
layer
.
data
(
name
=
'data'
,
type
=
data_type
.
dense_vector
(
784
))
ipt1
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
dense_vector
(
128
))
ipt0
=
layer
.
data
(
name
=
'data
1
'
,
type
=
data_type
.
dense_vector
(
784
))
ipt1
=
layer
.
data
(
name
=
'word
1
'
,
type
=
data_type
.
dense_vector
(
128
))
fc0
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
...
...
@@ -261,7 +261,7 @@ class ProjOpTest(unittest.TestCase):
class
NetworkTests
(
unittest
.
TestCase
):
def
test_vgg
(
self
):
img
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
img
=
layer
.
data
(
name
=
'pixel
1
'
,
type
=
data_type
.
dense_vector
(
784
))
vgg_out
=
networks
.
small_vgg
(
input_image
=
img
,
num_channels
=
1
,
num_classes
=
2
)
print
layer
.
parse_network
(
vgg_out
)
...
...
@@ -269,12 +269,12 @@ class NetworkTests(unittest.TestCase):
class
EvaluatorTest
(
unittest
.
TestCase
):
def
test_evaluator
(
self
):
img
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
img
=
layer
.
data
(
name
=
'pixel
2
'
,
type
=
data_type
.
dense_vector
(
784
))
output
=
layer
.
fc
(
input
=
img
,
size
=
10
,
act
=
activation
.
Softmax
(),
name
=
'fc_here'
)
lbl
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
lbl
=
layer
.
data
(
name
=
'label
2
'
,
type
=
data_type
.
integer_value
(
10
))
cost
=
layer
.
cross_entropy_cost
(
input
=
output
,
label
=
lbl
)
evaluator
.
classification_error
(
input
=
output
,
label
=
lbl
)
...
...
python/paddle/v2/tests/test_rnn_layer.py
浏览文件 @
0ef86cbd
...
...
@@ -20,6 +20,8 @@ import paddle.v2.data_type as data_type
import
paddle.v2.layer
as
layer
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
parse_network
from
paddle.trainer_config_helpers.config_parser_utils
import
\
reset_parser
class
RNNTest
(
unittest
.
TestCase
):
...
...
@@ -29,6 +31,8 @@ class RNNTest(unittest.TestCase):
hidden_dim
=
8
def
parse_old_rnn
():
reset_parser
()
def
step
(
y
):
mem
=
conf_helps
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
conf_helps
.
fc_layer
(
...
...
@@ -48,6 +52,8 @@ class RNNTest(unittest.TestCase):
return
str
(
parse_network
(
test
))
def
parse_new_rnn
():
reset_parser
()
def
new_step
(
y
):
mem
=
layer
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
layer
.
fc
(
input
=
[
y
,
mem
],
...
...
@@ -75,6 +81,8 @@ class RNNTest(unittest.TestCase):
label_dim
=
3
def
parse_old_rnn
():
reset_parser
()
def
test
():
data
=
conf_helps
.
data_layer
(
name
=
"word"
,
size
=
dict_dim
)
label
=
conf_helps
.
data_layer
(
name
=
"label"
,
size
=
label_dim
)
...
...
@@ -114,6 +122,7 @@ class RNNTest(unittest.TestCase):
return
str
(
parse_network
(
test
))
def
parse_new_rnn
():
reset_parser
()
data
=
layer
.
data
(
name
=
"word"
,
type
=
data_type
.
dense_vector
(
dict_dim
))
label
=
layer
.
data
(
...
...
python/paddle/v2/tests/test_topology.py
浏览文件 @
0ef86cbd
...
...
@@ -46,8 +46,8 @@ class TestTopology(unittest.TestCase):
self
.
assertEqual
(
label_data_type
[
1
].
dim
,
10
)
def
test_get_layer
(
self
):
pixel
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
label
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
pixel
=
layer
.
data
(
name
=
'pixel
2
'
,
type
=
data_type
.
dense_vector
(
784
))
label
=
layer
.
data
(
name
=
'label
2
'
,
type
=
data_type
.
integer_value
(
10
))
hidden
=
layer
.
fc
(
input
=
pixel
,
size
=
100
,
act
=
conf_helps
.
SigmoidActivation
())
...
...
@@ -56,14 +56,14 @@ class TestTopology(unittest.TestCase):
act
=
conf_helps
.
SoftmaxActivation
())
cost
=
layer
.
classification_cost
(
input
=
inference
,
label
=
label
)
topo
=
topology
.
Topology
(
cost
)
pixel_layer
=
topo
.
get_layer
(
"pixel"
)
label_layer
=
topo
.
get_layer
(
"label"
)
pixel_layer
=
topo
.
get_layer
(
"pixel
2
"
)
label_layer
=
topo
.
get_layer
(
"label
2
"
)
self
.
assertEqual
(
pixel_layer
,
pixel
)
self
.
assertEqual
(
label_layer
,
label
)
def
test_parse
(
self
):
pixel
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
label
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
pixel
=
layer
.
data
(
name
=
'pixel
3
'
,
type
=
data_type
.
dense_vector
(
784
))
label
=
layer
.
data
(
name
=
'label
3
'
,
type
=
data_type
.
integer_value
(
10
))
hidden
=
layer
.
fc
(
input
=
pixel
,
size
=
100
,
act
=
conf_helps
.
SigmoidActivation
())
...
...
python/paddle/v2/topology.py
浏览文件 @
0ef86cbd
...
...
@@ -15,36 +15,13 @@
import
collections
from
paddle.proto.ModelConfig_pb2
import
ModelConfig
import
paddle.trainer_config_helpers
as
conf_helps
import
layer
as
v2_layer
import
config_base
__all__
=
[
'Topology'
]
def
__flatten__
(
lis
):
"""
Given a list, possibly nested to any level, return it flattened.
"""
new_lis
=
[]
for
item
in
lis
:
if
isinstance
(
item
,
collections
.
Sequence
):
new_lis
.
extend
(
__flatten__
(
item
))
else
:
new_lis
.
append
(
item
)
return
new_lis
def
__bfs_travel__
(
callback
,
*
layers
):
layers
=
__flatten__
(
layers
)
for
each_layer
in
layers
:
__break__
=
callback
(
each_layer
)
if
__break__
:
return
__layers__
=
each_layer
.
__parent_layers__
.
values
()
+
\
each_layer
.
extra_parent
()
__bfs_travel__
(
callback
,
*
__layers__
)
class
Topology
(
object
):
"""
Topology is used to store the information about all layers
...
...
@@ -94,31 +71,18 @@ class Topology(object):
:param name:
:return:
"""
result_layer
=
[
None
]
def
__impl__
(
l
):
if
l
.
name
==
name
:
result_layer
[
0
]
=
l
return
True
# break
return
False
__bfs_travel__
(
__impl__
,
*
self
.
layers
)
if
result_layer
[
0
]
is
None
:
raise
ValueError
(
"No such layer %s"
%
name
)
return
result_layer
[
0
]
return
v2_layer
.
get_layer
(
name
)
def
data_layers
(
self
):
"""
get all data layer
:return:
"""
data_layers
=
dict
()
def
__impl__
(
l
):
if
isinstance
(
l
,
v2_layer
.
DataLayerV2
):
data_layers
[
l
.
name
]
=
l
__bfs_travel__
(
__impl__
,
*
self
.
layers
)
data_layers
=
{}
for
layer
in
self
.
proto
().
layers
:
l
=
v2_layer
.
get_layer
(
layer
.
name
)
if
l
and
l
.
layer_type
==
conf_helps
.
LayerType
.
DATA
:
data_layers
[
layer
.
name
]
=
l
return
data_layers
def
data_type
(
self
):
...
...
@@ -127,7 +91,7 @@ class Topology(object):
[('image', dense_vector(768)), ('label', integer_value(10))]
"""
data_layers
=
self
.
data_layers
()
return
[(
nm
,
data_layers
[
nm
].
type
)
return
[(
nm
,
data_layers
[
nm
].
data_
type
)
for
nm
in
self
.
proto
().
input_layer_names
]
def
get_layer_proto
(
self
,
name
):
...
...
@@ -138,5 +102,5 @@ class Topology(object):
def
__check_layer_type__
(
layer
):
if
not
isinstance
(
layer
,
v2_layer
.
LayerV2
):
raise
ValueError
(
'layer should have type paddle.
layer
.Layer'
)
if
not
isinstance
(
layer
,
config_base
.
Layer
):
raise
ValueError
(
'layer should have type paddle.
v2.config_base
.Layer'
)
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