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Simplify layer.v2

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avx_docs
Yu Yang 9 years ago
parent c679003d7b
commit 3758993393
4 changed files with 61 additions and 98 deletions
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4
python/paddle/trainer_config_helpers/default_decorators.py
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@ -52,6 +52,10 @@ def wrap_param_default(param_names=None,
kwargs[name] = default_factory(func)
return func(*args, **kwargs)
if hasattr(func, 'argspec'):
__wrapper__.argspec = func.argspec
else:
__wrapper__.argspec = inspect.getargspec(func)
return __wrapper__
return __impl__

6
python/paddle/trainer_config_helpers/layers.py
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@ -14,6 +14,7 @@
import functools
import collections
import inspect
from paddle.trainer.config_parser import *
from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
@ -316,6 +317,11 @@ def layer_support(*attrs):
val.check(method.__name__)
return method(*args, **kwargs)
if hasattr(method, 'argspec'):
wrapper.argspec = method.argspec
else:
wrapper.argspec = inspect.getargspec(method)
return wrapper
return decorator

143
python/paddle/v2/layer.py
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@ -67,6 +67,7 @@ paddle.v2.parameters.create, no longer exposed to users.
"""
import collections
import inspect
import paddle.trainer_config_helpers as conf_helps
from paddle.trainer_config_helpers.config_parser_utils import \
@ -74,26 +75,14 @@ from paddle.trainer_config_helpers.config_parser_utils import \
from paddle.trainer_config_helpers.default_decorators import wrap_name_default
from paddle.trainer_config_helpers.default_decorators import wrap_act_default
from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default
from paddle.trainer_config_helpers.default_decorators import \
wrap_bias_attr_default
from paddle.trainer_config_helpers.layers import layer_support
import data_type
import activation
import attr
__all__ = [
'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp',
'maxout', 'img_cmrnorm', 'batch_norm', 'sum_to_one_norm', 'recurrent',
'lstmemory', 'grumemory', 'pool', 'last_seq', 'first_seq', 'concat',
'seq_concat', 'block_expand', 'expand', 'repeat', 'seq_reshape', 'addto',
'linear_comb', 'interpolation', 'bilinear_interp', 'power', 'scaling',
'slope_intercept', 'tensor', 'cos_sim', 'trans', 'max_id', 'sampling_id',
'pad', 'classification_cost', 'cross_entropy_cost',
'cross_entropy_with_selfnorm_cost', 'regression_cost',
'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost',
'sum_cost', 'huber_cost', 'crf', 'crf_decoding', 'ctc', 'warp_ctc', 'nce',
'hsigmoid', 'eos'
]
__all__ = ['parse_network', 'data']
__projection_names__ = filter(lambda x: x.endswith('_projection'),
dir(conf_helps))
@ -288,83 +277,51 @@ data = DataLayerV2
AggregateLevel = conf_helps.layers.AggregateLevel
ExpandLevel = conf_helps.layers.ExpandLevel
layer_list = [
# [V2LayerImpl, V1_method_name, parent_names]
# fully connected layers
['fc', 'fc_layer', ['input']],
# conv layers
['conv_shift', 'conv_shift_layer', ['a', 'b']],
['img_conv', 'img_conv_layer', ['input']],
# image pooling layers
['img_pool', 'img_pool_layer', ['input']],
['spp', 'spp_layer', ['input']],
['maxout', 'maxout_layer', ['input']],
# norm layers
['img_cmrnorm', 'img_cmrnorm_layer', ['input']],
['batch_norm', 'batch_norm_layer', ['input']],
['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']],
# recurrent layers
['recurrent', 'recurrent_layer', ['input']],
['lstmemory', 'lstmemory', ['input']],
['grumemory', 'grumemory', ['input']],
# aggregate layers
['pool', 'pooling_layer', ['input']],
['last_seq', 'last_seq', ['input']],
['first_seq', 'first_seq', ['input']],
['concat', 'concat_layer', ['input']],
['seq_concat', 'seq_concat_layer', ['a', 'b']],
# reshaping layers
['block_expand', 'block_expand_layer', ['input']],
['expand', 'expand_layer', ['input', 'expand_as']],
['repeat', 'repeat_layer', ['input']],
['rotate', 'rotate_layer', ['input']],
['seq_reshape', 'seq_reshape_layer', ['input']],
# math layers
['addto', 'addto_layer', ['input']],
['linear_comb', 'linear_comb_layer', ['weights', 'vectors']],
['interpolation', 'interpolation_layer', ['input', 'weight']],
['bilinear_interp', 'bilinear_interp_layer', ['input']],
['power', 'power_layer', ['input', 'weight']],
['scaling', 'scaling_layer', ['input', 'weight']],
['slope_intercept', 'slope_intercept_layer', ['input']],
['tensor', 'tensor_layer', ['a', 'b']],
['cos_sim', 'cos_sim', ['a', 'b']],
['trans', 'trans_layer', ['input']],
# sampling layers
['max_id', 'maxid_layer', ['input']],
['sampling_id', 'sampling_id_layer', ['input']],
# slicing and joining layers
['pad', 'pad_layer', ['input']],
# cost layers
[
'classification_cost', 'classification_cost',
['input', 'label', 'weight']
],
['regression_cost', 'regression_cost', ['input', 'label', 'weight']],
['cross_entropy_cost', 'cross_entropy', ['input', 'label']],
[
'cross_entropy_with_selfnorm_cost', 'cross_entropy_with_selfnorm',
['input', 'label']
],
[
'multi_binary_label_cross_entropy_cost',
'multi_binary_label_cross_entropy', ['input', 'label']
],
['rank_cost', 'rank_cost', ['left', 'right', 'label', 'weight']],
['lambda_cost', 'lambda_cost', ['input', 'score']],
['sum_cost', 'sum_cost', ['input']],
['huber_cost', 'huber_cost', ['input', 'label']],
['crf', 'crf_layer', ['input', 'label']],
['crf_decoding', 'crf_decoding_layer', ['input']],
['ctc', 'ctc_layer', ['input', 'label']],
['warp_ctc', 'warp_ctc_layer', ['input', 'label']],
['nce', 'nce_layer', ['input', 'label']],
['hsigmoid', 'hsigmoid', ['input', 'label']],
# check layers
['eos', 'eos_layer', ['input']]
]
for l in layer_list:
globals()[l[0]] = __convert_to_v2__(l[1], l[2])
def __layer_name_mapping__(inname):
if inname in ['data_layer', 'memory', 'mixed_layer']:
# Do Not handle these layers
return
elif inname == 'maxid_layer':
return 'max_id'
elif inname.endswith('memory') or inname.endswith(
'_seq') or inname.endswith('_sim') or inname == 'hsigmoid':
return inname
elif inname in [
'cross_entropy', 'multi_binary_label_cross_entropy',
'cross_entropy_with_selfnorm'
]:
return inname + "_cost"
elif inname.endswith('_cost'):
return inname
elif inname.endswith("_layer"):
return inname[:-len("_layer")]
def __layer_name_mapping_parent_names__(inname):
all_args = getattr(conf_helps, inname).argspec.args
return filter(
lambda x: x in ['input1', 'input2','label', 'input', 'a', 'b', 'expand_as',
'weights', 'vectors', 'weight', 'score', 'left', 'right'],
all_args)
def __convert_layer__(_new_name_, _old_name_, _parent_names_):
global __all__
__all__.append(_new_name_)
globals()[new_name] = __convert_to_v2__(_old_name_, _parent_names_)
for each_layer_name in dir(conf_helps):
new_name = __layer_name_mapping__(each_layer_name)
if new_name is not None:
parent_names = __layer_name_mapping_parent_names__(each_layer_name)
assert len(parent_names) != 0, each_layer_name
__convert_layer__(new_name, each_layer_name, parent_names)
del parent_names
del new_name
del each_layer_name
# convert projection
for prj in __projection_names__:

6
python/paddle/v2/tests/test_layer.py
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@ -11,17 +11,13 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import difflib
import unittest
import paddle.trainer_config_helpers as conf_helps
import paddle.v2.activation as activation
import paddle.v2.attr as attr
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
import paddle.v2.pooling as pooling
from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as parse_network
pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
label = layer.data(name='label', type=data_type.integer_value(10))
@ -70,7 +66,7 @@ class ImageLayerTest(unittest.TestCase):
class AggregateLayerTest(unittest.TestCase):
def test_aggregate_layer(self):
pool = layer.pool(
pool = layer.pooling(
input=pixel,
pooling_type=pooling.Avg(),
agg_level=layer.AggregateLevel.EACH_SEQUENCE)

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