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体验新版 GitCode,发现更多精彩内容 >>
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87568cfd
编写于
3月 07, 2018
作者:
F
fengjiayi
提交者:
GitHub
3月 07, 2018
浏览文件
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差异文件
Merge pull request #8643 from JiayiFeng/remove_evaluator
Removes Accuracy
上级
f2cf2a7a
ea508c9e
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
162 addition
and
99 deletion
+162
-99
benchmark/cluster/vgg16/vgg16_fluid.py
benchmark/cluster/vgg16/vgg16_fluid.py
+20
-15
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+1
-0
python/paddle/fluid/average.py
python/paddle/fluid/average.py
+61
-0
python/paddle/fluid/evaluator.py
python/paddle/fluid/evaluator.py
+0
-38
python/paddle/fluid/layers/__init__.py
python/paddle/fluid/layers/__init__.py
+3
-0
python/paddle/fluid/layers/metric.py
python/paddle/fluid/layers/metric.py
+57
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+1
-36
python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
...ry_optimization/test_memopt_image_classification_train.py
+11
-6
python/paddle/fluid/tests/unittests/test_profiler.py
python/paddle/fluid/tests/unittests/test_profiler.py
+8
-4
未找到文件。
benchmark/cluster/vgg16/vgg16_fluid.py
浏览文件 @
87568cfd
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
...
@@ -138,13 +138,14 @@ def main():
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Evaluator
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
batch_size
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size
)
# inference program
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
test_target
=
accuracy
.
metrics
+
accuracy
.
states
inference_program
=
fluid
.
io
.
get_inference_program
(
test_target
)
inference_program
=
fluid
.
io
.
get_inference_program
(
batch_acc
)
# Optimization
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
args
.
learning_rate
)
...
...
@@ -157,27 +158,30 @@ def main():
# test
def
test
(
exe
):
accuracy
.
reset
(
exe
)
test_pass_acc
=
fluid
.
average
.
WeightedAverage
(
)
for
batch_id
,
data
in
enumerate
(
test_reader
()):
img_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
].
reshape
(
data_shape
),
data
)).
astype
(
"float32"
)
y_data
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
y_data
=
y_data
.
reshape
([
-
1
,
1
])
exe
.
run
(
inference_program
,
feed
=
{
"pixel"
:
img_data
,
"label"
:
y_data
})
outs
=
exe
.
run
(
inference_program
,
feed
=
{
"pixel"
:
img_data
,
"label"
:
y_data
},
fetch_list
=
[
batch_acc
,
batch_size
])
test_pass_acc
.
add
(
value
=
np
.
array
(
outs
[
0
]),
weight
=
np
.
array
(
outs
[
1
]))
return
accuracy
.
eval
(
exe
)
return
test_pass_acc
.
eval
(
)
def
train_loop
(
exe
,
trainer_prog
):
iters
=
0
ts
=
time
.
time
()
train_pass_acc
=
fluid
.
average
.
WeightedAverage
()
for
pass_id
in
range
(
args
.
num_passes
):
# train
start_time
=
time
.
time
()
num_samples
=
0
accuracy
.
reset
(
exe
)
train_pass_acc
.
reset
(
)
with
profiler
.
profiler
(
"CPU"
,
'total'
)
as
prof
:
for
batch_id
,
data
in
enumerate
(
train_reader
()):
ts
=
time
.
time
()
...
...
@@ -187,13 +191,14 @@ def main():
y_data
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
y_data
=
y_data
.
reshape
([
-
1
,
1
])
loss
,
acc
=
exe
.
run
(
loss
,
acc
,
b_size
=
exe
.
run
(
trainer_prog
,
feed
=
{
"pixel"
:
img_data
,
"label"
:
y_data
},
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
fetch_list
=
[
avg_cost
,
batch_acc
,
batch_size
]
)
iters
+=
1
num_samples
+=
len
(
data
)
train_pass_acc
.
add
(
value
=
acc
,
weight
=
b_size
)
print
(
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
%
(
pass_id
,
iters
,
loss
,
acc
,
...
...
@@ -201,7 +206,7 @@ def main():
)
# The accuracy is the accumulation of batches, but not the current batch.
pass_elapsed
=
time
.
time
()
-
start_time
pass_train_acc
=
accuracy
.
eval
(
exe
)
pass_train_acc
=
train_pass_acc
.
eval
(
)
pass_test_acc
=
test
(
exe
)
print
(
"Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f
\n
"
...
...
python/paddle/fluid/__init__.py
浏览文件 @
87568cfd
...
...
@@ -28,6 +28,7 @@ import nets
import
optimizer
import
backward
import
regularizer
import
average
from
param_attr
import
ParamAttr
,
WeightNormParamAttr
from
data_feeder
import
DataFeeder
from
core
import
LoDTensor
,
CPUPlace
,
CUDAPlace
...
...
python/paddle/fluid/average.py
0 → 100644
浏览文件 @
87568cfd
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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
numpy
as
np
"""
Class of all kinds of Average.
All Averages are accomplished via Python totally.
They do not change Paddle's Program, nor do anything to
modify NN model's configuration. They are completely
wrappers of Python functions.
"""
def
_is_number_
(
var
):
return
isinstance
(
var
,
int
)
or
isinstance
(
var
,
float
)
or
(
isinstance
(
var
,
np
.
ndarray
)
and
var
.
shape
==
(
1
,
))
def
_is_number_or_matrix_
(
var
):
return
_is_number_
(
var
)
or
isinstance
(
var
,
np
.
ndarray
)
class
WeightedAverage
(
object
):
def
__init__
(
self
):
self
.
reset
()
def
reset
(
self
):
self
.
numerator
=
None
self
.
denominator
=
None
def
add
(
self
,
value
,
weight
):
if
not
_is_number_or_matrix_
(
value
):
raise
ValueError
(
"The 'value' must be a number(int, float) or a numpy ndarray."
)
if
not
_is_number_
(
weight
):
raise
ValueError
(
"The 'weight' must be a number(int, float)."
)
if
self
.
numerator
is
None
or
self
.
denominator
is
None
:
self
.
numerator
=
value
*
weight
self
.
denominator
=
weight
else
:
self
.
numerator
+=
value
*
weight
self
.
denominator
+=
weight
def
eval
(
self
):
if
self
.
numerator
is
None
or
self
.
denominator
is
None
:
raise
ValueError
(
"There is no data to be averaged in WeightedAverage."
)
return
self
.
numerator
/
self
.
denominator
python/paddle/fluid/evaluator.py
浏览文件 @
87568cfd
...
...
@@ -108,44 +108,6 @@ class Evaluator(object):
return
state
class
Accuracy
(
Evaluator
):
"""
Average Accuracy for multiple mini-batches.
"""
def
__init__
(
self
,
input
,
label
,
k
=
1
,
**
kwargs
):
super
(
Accuracy
,
self
).
__init__
(
"accuracy"
,
**
kwargs
)
main_program
=
self
.
helper
.
main_program
if
main_program
.
current_block
().
idx
!=
0
:
raise
ValueError
(
"You can only invoke Evaluator in root block"
)
self
.
total
=
self
.
create_state
(
dtype
=
'int64'
,
shape
=
[
1
],
suffix
=
'total'
)
self
.
correct
=
self
.
create_state
(
dtype
=
'int64'
,
shape
=
[
1
],
suffix
=
'correct'
)
total
=
self
.
helper
.
create_tmp_variable
(
dtype
=
'int'
)
correct
=
self
.
helper
.
create_tmp_variable
(
dtype
=
'int'
)
acc
=
layers
.
accuracy
(
input
=
input
,
label
=
label
,
k
=
k
,
total
=
total
,
correct
=
correct
)
total
=
layers
.
cast
(
x
=
total
,
dtype
=
'int64'
)
correct
=
layers
.
cast
(
x
=
correct
,
dtype
=
'int64'
)
layers
.
sums
(
input
=
[
self
.
total
,
total
],
out
=
self
.
total
)
layers
.
sums
(
input
=
[
self
.
correct
,
correct
],
out
=
self
.
correct
)
self
.
metrics
.
append
(
acc
)
def
eval
(
self
,
executor
,
eval_program
=
None
):
if
eval_program
is
None
:
eval_program
=
Program
()
block
=
eval_program
.
current_block
()
with
program_guard
(
main_program
=
eval_program
):
total
=
_clone_var_
(
block
,
self
.
total
)
correct
=
_clone_var_
(
block
,
self
.
correct
)
total
=
layers
.
cast
(
total
,
dtype
=
'float32'
)
correct
=
layers
.
cast
(
correct
,
dtype
=
'float32'
)
out
=
layers
.
elementwise_div
(
x
=
correct
,
y
=
total
)
return
np
.
array
(
executor
.
run
(
eval_program
,
fetch_list
=
[
out
])[
0
])
class
ChunkEvaluator
(
Evaluator
):
"""
Accumulate counter numbers output by chunk_eval from mini-batches and
...
...
python/paddle/fluid/layers/__init__.py
浏览文件 @
87568cfd
...
...
@@ -28,6 +28,8 @@ import math_op_patch
from
math_op_patch
import
*
import
detection
from
detection
import
*
import
metric
from
metric
import
*
from
learning_rate_scheduler
import
*
__all__
=
[]
...
...
@@ -39,4 +41,5 @@ __all__ += control_flow.__all__
__all__
+=
ops
.
__all__
__all__
+=
device
.
__all__
__all__
+=
detection
.
__all__
__all__
+=
metric
.
__all__
__all__
+=
learning_rate_scheduler
.
__all__
python/paddle/fluid/layers/metric.py
0 → 100644
浏览文件 @
87568cfd
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
"""
All layers just related to metric.
"""
from
..layer_helper
import
LayerHelper
from
..initializer
import
Normal
,
Constant
from
..framework
import
Variable
from
..param_attr
import
ParamAttr
__all__
=
[
'accuracy'
]
def
accuracy
(
input
,
label
,
k
=
1
,
correct
=
None
,
total
=
None
):
"""
This function computes the accuracy using the input and label.
The output is the top_k inputs and their indices.
"""
helper
=
LayerHelper
(
"accuracy"
,
**
locals
())
topk_out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
topk_indices
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"top_k"
,
inputs
=
{
"X"
:
[
input
]},
outputs
=
{
"Out"
:
[
topk_out
],
"Indices"
:
[
topk_indices
]},
attrs
=
{
"k"
:
k
})
acc_out
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
if
correct
is
None
:
correct
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
if
total
is
None
:
total
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"accuracy"
,
inputs
=
{
"Out"
:
[
topk_out
],
"Indices"
:
[
topk_indices
],
"Label"
:
[
label
]
},
outputs
=
{
"Accuracy"
:
[
acc_out
],
"Correct"
:
[
correct
],
"Total"
:
[
total
],
})
return
acc_out
python/paddle/fluid/layers/nn.py
浏览文件 @
87568cfd
...
...
@@ -35,7 +35,6 @@ __all__ = [
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'accuracy'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
...
...
@@ -1022,40 +1021,6 @@ def square_error_cost(input, label):
return
square_out
def
accuracy
(
input
,
label
,
k
=
1
,
correct
=
None
,
total
=
None
):
"""
This function computes the accuracy using the input and label.
The output is the top_k inputs and their indices.
"""
helper
=
LayerHelper
(
"accuracy"
,
**
locals
())
topk_out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
topk_indices
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"top_k"
,
inputs
=
{
"X"
:
[
input
]},
outputs
=
{
"Out"
:
[
topk_out
],
"Indices"
:
[
topk_indices
]},
attrs
=
{
"k"
:
k
})
acc_out
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
if
correct
is
None
:
correct
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
if
total
is
None
:
total
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"accuracy"
,
inputs
=
{
"Out"
:
[
topk_out
],
"Indices"
:
[
topk_indices
],
"Label"
:
[
label
]
},
outputs
=
{
"Accuracy"
:
[
acc_out
],
"Correct"
:
[
correct
],
"Total"
:
[
total
],
})
return
acc_out
def
chunk_eval
(
input
,
label
,
chunk_scheme
,
...
...
@@ -3182,7 +3147,7 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[100], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.smooth_l1(
logits=fc, label
=label)
out = fluid.layers.smooth_l1(
x=fc, y
=label)
"""
helper
=
LayerHelper
(
'smooth_l1_loss'
,
**
locals
())
diff
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
...
...
python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
浏览文件 @
87568cfd
...
...
@@ -122,7 +122,8 @@ avg_cost = fluid.layers.mean(cost)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opts
=
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
batch_size
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size
)
fluid
.
memory_optimize
(
fluid
.
default_main_program
())
...
...
@@ -144,13 +145,17 @@ feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe
.
run
(
fluid
.
default_startup_program
())
i
=
0
accuracy
=
fluid
.
average
.
WeightedAverage
()
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
accuracy
.
reset
()
for
data
in
train_reader
():
loss
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
loss
,
acc
,
weight
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
,
batch_acc
,
batch_size
])
accuracy
.
add
(
value
=
acc
,
weight
=
weight
)
pass_acc
=
accuracy
.
eval
()
print
(
"loss:"
+
str
(
loss
)
+
" acc:"
+
str
(
acc
)
+
" pass_acc:"
+
str
(
pass_acc
))
# this model is slow, so if we can train two mini batch, we think it works properly.
...
...
python/paddle/fluid/tests/unittests/test_profiler.py
浏览文件 @
87568cfd
...
...
@@ -37,7 +37,9 @@ class TestProfiler(unittest.TestCase):
label
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'int64'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
batch_size
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size
)
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
0.001
,
momentum
=
0.9
)
opts
=
optimizer
.
minimize
(
avg_cost
,
startup_program
=
startup_program
)
...
...
@@ -46,7 +48,7 @@ class TestProfiler(unittest.TestCase):
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_program
)
accuracy
.
reset
(
exe
)
pass_acc_calculator
=
fluid
.
average
.
WeightedAverage
(
)
with
profiler
.
profiler
(
state
,
'total'
,
profile_path
)
as
prof
:
for
iter
in
range
(
10
):
if
iter
==
2
:
...
...
@@ -57,9 +59,11 @@ class TestProfiler(unittest.TestCase):
outs
=
exe
.
run
(
main_program
,
feed
=
{
'x'
:
x
,
'y'
:
y
},
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
fetch_list
=
[
avg_cost
,
batch_acc
,
batch_size
]
)
acc
=
np
.
array
(
outs
[
1
])
pass_acc
=
accuracy
.
eval
(
exe
)
b_size
=
np
.
array
(
outs
[
2
])
pass_acc_calculator
.
add
(
value
=
acc
,
weight
=
b_size
)
pass_acc
=
pass_acc_calculator
.
eval
()
def
test_cpu_profiler
(
self
):
self
.
net_profiler
(
'CPU'
)
...
...
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