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体验新版 GitCode,发现更多精彩内容 >>
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1c58e27f
编写于
11月 09, 2016
作者:
W
wangyang59
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电子邮件补丁
差异文件
fixed a bug in parse_conv in config_parser.py
上级
af7a50c0
变更
2
显示空白变更内容
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并排
Showing
2 changed file
with
188 addition
and
5 deletion
+188
-5
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+12
-5
python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
..._helpers/tests/configs/protostr/img_trans_layers.protostr
+176
-0
未找到文件。
python/paddle/trainer/config_parser.py
浏览文件 @
1c58e27f
...
...
@@ -649,7 +649,8 @@ class ConvProjection(Projection):
parse_conv
(
conv_conf
,
input_layer_name
,
self
.
proj_conf
.
conv_conf
)
self
.
proj_conf
.
conv_conf
,
num_filters
)
# TODO: support rectangle input
self
.
proj_conf
.
output_size
=
(
self
.
proj_conf
.
conv_conf
.
output_x
**
2
)
*
num_filters
...
...
@@ -730,7 +731,8 @@ class ConvOperator(Operator):
parse_conv
(
conv_conf
,
MakeLayerNameInSubmodel
(
input_layer_names
[
0
]),
self
.
operator_conf
.
conv_conf
)
self
.
operator_conf
.
conv_conf
,
num_filters
)
self
.
operator_conf
.
output_size
=
(
self
.
operator_conf
.
conv_conf
.
output_x
**
2
)
*
num_filters
config_assert
(
len
(
input_layer_names
)
==
2
,
"Conv is binary operator"
)
...
...
@@ -1097,7 +1099,7 @@ def parse_norm(norm, input_layer_name, norm_conf):
caffe_mode: compute the output size using floor instead of ceil,
which is consistent of caffe and CuDNN's convention.
'''
def
parse_conv
(
conv
,
input_layer_name
,
conv_conf
,
trans
=
False
):
def
parse_conv
(
conv
,
input_layer_name
,
conv_conf
,
num_filters
,
trans
=
False
):
conv_conf
.
filter_size
=
conv
.
filter_size
conv_conf
.
filter_size_y
=
conv
.
filter_size_y
conv_conf
.
channels
=
conv
.
channels
...
...
@@ -1106,10 +1108,11 @@ def parse_conv(conv, input_layer_name, conv_conf, trans=False):
conv_conf
.
stride
=
conv
.
stride
conv_conf
.
stride_y
=
conv
.
stride_y
conv_conf
.
groups
=
conv
.
groups
conv_conf
.
filter_channels
=
conv
.
channels
/
conv
.
groups
conv_conf
.
caffe_mode
=
conv
.
caffe_mode
if
not
trans
:
conv_conf
.
filter_channels
=
conv
.
channels
/
conv
.
groups
img_pixels
=
g_layer_map
[
input_layer_name
].
size
/
conv
.
channels
print
(
'channels=%d size=%d'
%
(
conv
.
channels
,
g_layer_map
[
input_layer_name
].
size
))
...
...
@@ -1123,6 +1126,8 @@ def parse_conv(conv, input_layer_name, conv_conf, trans=False):
conv_conf
.
img_size
,
conv_conf
.
filter_size
,
conv_conf
.
padding
,
conv_conf
.
stride
,
conv_conf
.
caffe_mode
)
else
:
conv_conf
.
filter_channels
=
num_filters
/
conv
.
groups
outputSize
=
g_layer_map
[
input_layer_name
].
size
/
conv
.
channels
print
(
'channels=%d size=%d'
%
(
conv
.
channels
,
g_layer_map
[
input_layer_name
].
size
))
...
...
@@ -1616,7 +1621,8 @@ class ConvLayerBase(LayerBase):
parse_conv
(
self
.
inputs
[
input_index
].
conv
,
input_layer
.
name
,
self
.
config
.
inputs
[
input_index
].
conv_conf
)
self
.
config
.
inputs
[
input_index
].
conv_conf
,
num_filters
)
conv_conf
=
self
.
config
.
inputs
[
input_index
].
conv_conf
psize
=
self
.
calc_parameter_size
(
conv_conf
)
print
(
"output size for %s is %d "
%
(
name
,
conv_conf
.
output_x
))
...
...
@@ -1676,6 +1682,7 @@ class ConvTransLayerBase(LayerBase):
self
.
inputs
[
input_index
].
conv
,
input_layer
.
name
,
self
.
config
.
inputs
[
input_index
].
conv_conf
,
num_filters
,
trans
=
True
)
conv_conf
=
self
.
config
.
inputs
[
input_index
].
conv_conf
psize
=
self
.
calc_parameter_size
(
conv_conf
)
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
0 → 100644
浏览文件 @
1c58e27f
type: "nn"
layers {
name: "image"
type: "data"
size: 51529
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconvt"
size: 4194304
active_type: ""
inputs {
input_layer_name: "image"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 32
channels: 1
stride: 1
padding: 1
groups: 1
filter_channels: 64
output_x: 227
img_size: 256
caffe_mode: true
filter_size_y: 32
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 64
shared_biases: true
}
layers {
name: "__batch_norm_0__"
type: "batch_norm"
size: 4194304
active_type: "relu"
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w0"
image_conf {
channels: 64
img_size: 256
}
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w1"
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w2"
}
bias_parameter_name: "___batch_norm_0__.wbias"
moving_average_fraction: 0.9
}
layers {
name: "__crmnorm_0__"
type: "norm"
size: 4194304
active_type: ""
inputs {
input_layer_name: "__batch_norm_0__"
norm_conf {
norm_type: "cmrnorm-projection"
channels: 64
size: 32
scale: 0.0004
pow: 0.75
output_x: 256
img_size: 256
blocked: false
}
}
}
layers {
name: "__pool_0__"
type: "pool"
size: 3240000
active_type: ""
inputs {
input_layer_name: "__conv_0__"
pool_conf {
pool_type: "max-projection"
channels: 64
size_x: 32
stride: 1
output_x: 225
img_size: 256
padding: 0
size_y: 32
stride_y: 1
output_y: 225
img_size_y: 256
padding_y: 0
}
}
}
parameters {
name: "___conv_0__.w0"
size: 65536
initial_mean: 0.0
initial_std: 0.0441941738242
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 64
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w0"
size: 64
initial_mean: 1.0
initial_std: 0.0
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w1"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.w2"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
}
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
sub_models {
name: "root"
layer_names: "image"
layer_names: "__conv_0__"
layer_names: "__batch_norm_0__"
layer_names: "__crmnorm_0__"
layer_names: "__pool_0__"
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
is_recurrent_layer_group: false
}
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