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体验新版 GitCode,发现更多精彩内容 >>
提交
91f0573b
编写于
7月 26, 2018
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix the overfix of 2to3 for print function
上级
559d3632
变更
32
隐藏空白更改
内联
并排
Showing
32 changed file
with
156 addition
and
155 deletion
+156
-155
CMakeLists.txt
CMakeLists.txt
+1
-0
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+1
-1
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
...d/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
+1
-1
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
.../image_classification/test_image_classification_resnet.py
+2
-2
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
...api/image_classification/test_image_classification_vgg.py
+2
-2
python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py
.../label_semantic_roles/test_label_semantic_roles_newapi.py
+10
-10
python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py
...level-api/machine_translation/test_machine_translation.py
+2
-2
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
...-level-api/recognize_digits/test_recognize_digits_conv.py
+5
-5
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
...h-level-api/recognize_digits/test_recognize_digits_mlp.py
+5
-5
python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py
...-api/recommender_system/test_recommender_system_newapi.py
+2
-2
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py
...pi/understand_sentiment/test_understand_sentiment_conv.py
+13
-13
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py
...rstand_sentiment/test_understand_sentiment_dynamic_rnn.py
+13
-13
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py
...stand_sentiment/test_understand_sentiment_stacked_lstm.py
+13
-13
python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py
...sts/book/high-level-api/word2vec/test_word2vec_new_api.py
+2
-2
python/paddle/fluid/tests/book/notest_understand_sentiment.py
...on/paddle/fluid/tests/book/notest_understand_sentiment.py
+10
-10
python/paddle/fluid/tests/book/test_fit_a_line.py
python/paddle/fluid/tests/book/test_fit_a_line.py
+3
-3
python/paddle/fluid/tests/book/test_image_classification.py
python/paddle/fluid/tests/book/test_image_classification.py
+4
-4
python/paddle/fluid/tests/book/test_label_semantic_roles.py
python/paddle/fluid/tests/book/test_label_semantic_roles.py
+11
-11
python/paddle/fluid/tests/book/test_machine_translation.py
python/paddle/fluid/tests/book/test_machine_translation.py
+3
-3
python/paddle/fluid/tests/book/test_recognize_digits.py
python/paddle/fluid/tests/book/test_recognize_digits.py
+3
-3
python/paddle/fluid/tests/book/test_recommender_system.py
python/paddle/fluid/tests/book/test_recommender_system.py
+7
-7
python/paddle/fluid/tests/book/test_rnn_encoder_decoder.py
python/paddle/fluid/tests/book/test_rnn_encoder_decoder.py
+11
-11
python/paddle/fluid/tests/book/test_word2vec.py
python/paddle/fluid/tests/book/test_word2vec.py
+7
-7
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
.../tests/book_memory_optimization/test_memopt_fit_a_line.py
+1
-1
python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
...ry_optimization/test_memopt_image_classification_train.py
+2
-2
python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
...ok_memory_optimization/test_memopt_machine_translation.py
+2
-2
python/paddle/fluid/tests/demo/fc_gan.py
python/paddle/fluid/tests/demo/fc_gan.py
+2
-2
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+4
-4
python/paddle/fluid/tests/test_if_else_op.py
python/paddle/fluid/tests/test_if_else_op.py
+2
-2
python/paddle/fluid/tests/unittests/benchmark.py
python/paddle/fluid/tests/unittests/benchmark.py
+4
-4
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
...ddle/fluid/tests/unittests/parallel_executor_test_base.py
+3
-3
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
...paddle/fluid/transpiler/memory_optimization_transpiler.py
+5
-5
未找到文件。
CMakeLists.txt
浏览文件 @
91f0573b
...
...
@@ -72,6 +72,7 @@ option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VER
if
(
NOT PY_VERSION
)
set
(
PY_VERSION 2.7
)
endif
()
set
(
PYBIND11_PYTHON_VERSION
${
PY_VERSION
}
)
# CMAKE_BUILD_TYPE
if
(
NOT CMAKE_BUILD_TYPE
)
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
91f0573b
...
...
@@ -106,7 +106,7 @@ class Optimizer(object):
param_lr
=
param
.
optimize_attr
[
'learning_rate'
]
if
type
(
param_lr
)
==
Variable
:
# param learning rate has been updated (LARS)
print
(
(
"returns updated param lr "
,
param_lr
)
)
print
(
"returns updated param lr "
,
param_lr
)
return
param_lr
else
:
if
param_lr
==
1.0
:
...
...
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
浏览文件 @
91f0573b
...
...
@@ -94,7 +94,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_x
=
numpy
.
random
.
uniform
(
0
,
10
,
[
batch_size
,
13
]).
astype
(
"float32"
)
results
=
inferencer
.
infer
({
'x'
:
tensor_x
})
print
(
(
"infer results: "
,
results
[
0
])
)
print
(
"infer results: "
,
results
[
0
]
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
浏览文件 @
91f0573b
...
...
@@ -105,7 +105,7 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'pixel'
,
'label'
])
print
(
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
)
))
print
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
))
if
accuracy
>
0.01
:
# Low threshold for speeding up CI
if
params_dirname
is
not
None
:
...
...
@@ -134,7 +134,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_img
=
numpy
.
random
.
rand
(
1
,
3
,
32
,
32
).
astype
(
"float32"
)
results
=
inferencer
.
infer
({
'pixel'
:
tensor_img
})
print
(
(
"infer results: "
,
results
)
)
print
(
"infer results: "
,
results
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
浏览文件 @
91f0573b
...
...
@@ -82,7 +82,7 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'pixel'
,
'label'
])
print
(
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
)
))
print
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
))
if
accuracy
>
0.01
:
# Low threshold for speeding up CI
if
params_dirname
is
not
None
:
...
...
@@ -111,7 +111,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_img
=
numpy
.
random
.
rand
(
1
,
3
,
32
,
32
).
astype
(
"float32"
)
results
=
inferencer
.
infer
({
'pixel'
:
tensor_img
})
print
(
(
"infer results: "
,
results
)
)
print
(
"infer results: "
,
results
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py
浏览文件 @
91f0573b
...
...
@@ -171,7 +171,7 @@ def train(use_cuda, train_program, params_dirname):
# get avg cost
avg_cost
=
np
.
array
(
avg_cost_set
).
mean
()
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
if
float
(
avg_cost
)
<
100.0
:
# Large value to increase CI speed
trainer
.
save_params
(
params_dirname
)
...
...
@@ -183,8 +183,8 @@ def train(use_cuda, train_program, params_dirname):
sys
.
exit
(
"got NaN loss, training failed."
)
elif
isinstance
(
event
,
fluid
.
EndStepEvent
):
print
(
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
)
print
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
if
event
.
step
==
1
:
# Run 2 iterations to speed CI
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
...
...
@@ -206,14 +206,14 @@ def infer(use_cuda, inference_program, params_dirname):
inference_program
,
param_path
=
params_dirname
,
place
=
place
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
3
,
4
,
2
]]
base_shape
=
[
1
]
...
...
@@ -248,7 +248,7 @@ def infer(use_cuda, inference_program, params_dirname):
},
return_numpy
=
False
)
print
(
(
"infer results: "
,
np
.
array
(
results
[
0
]).
shape
)
)
print
(
"infer results: "
,
np
.
array
(
results
[
0
]).
shape
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py
浏览文件 @
91f0573b
...
...
@@ -197,7 +197,7 @@ def train(use_cuda, is_sparse, is_local=True):
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
EndStepEvent
):
print
(
(
'pass_id='
+
str
(
event
.
epoch
)
+
' batch='
+
str
(
event
.
step
)
))
print
(
'pass_id='
+
str
(
event
.
epoch
)
+
' batch='
+
str
(
event
.
step
))
if
event
.
step
==
10
:
trainer
.
stop
()
...
...
@@ -259,7 +259,7 @@ def decode_main(use_cuda, is_sparse):
feed
=
feed_dict
,
fetch_list
=
[
translation_ids
,
translation_scores
],
return_numpy
=
False
)
print
(
(
result_ids
.
recursive_sequence_lengths
()
))
print
(
result_ids
.
recursive_sequence_lengths
(
))
break
...
...
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
浏览文件 @
91f0573b
...
...
@@ -78,14 +78,14 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
acc
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'img'
,
'label'
])
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
(
"acc : %s"
%
acc
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
acc
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
params_dirname
)
else
:
print
(
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
)
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
elif
isinstance
(
event
,
fluid
.
EndStepEvent
):
...
...
@@ -118,7 +118,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
results
=
inferencer
.
infer
({
'img'
:
tensor_img
})
print
(
(
"infer results: "
,
results
[
0
])
)
print
(
"infer results: "
,
results
[
0
]
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
浏览文件 @
91f0573b
...
...
@@ -61,14 +61,14 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
acc
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'img'
,
'label'
])
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
(
"acc : %s"
%
acc
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
acc
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
params_dirname
)
else
:
print
(
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
)
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
...
...
@@ -96,7 +96,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
results
=
inferencer
.
infer
({
'img'
:
tensor_img
})
print
(
(
"infer results: "
,
results
[
0
])
)
print
(
"infer results: "
,
results
[
0
]
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py
浏览文件 @
91f0573b
...
...
@@ -180,7 +180,7 @@ def train(use_cuda, train_program, params_dirname):
# get avg cost
avg_cost
=
np
.
array
(
avg_cost_set
).
mean
()
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
if
float
(
avg_cost
)
<
4
:
# Smaller value to increase CI speed
trainer
.
save_params
(
params_dirname
)
...
...
@@ -240,7 +240,7 @@ def infer(use_cuda, inference_program, params_dirname):
},
return_numpy
=
False
)
print
(
(
"infer results: "
,
np
.
array
(
results
[
0
])
))
print
(
"infer results: "
,
np
.
array
(
results
[
0
]
))
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py
浏览文件 @
91f0573b
...
...
@@ -82,21 +82,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
acc
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'words'
,
'label'
])
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
(
"acc : %s"
%
acc
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
acc
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
else
:
print
(
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
)
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
elif
isinstance
(
event
,
fluid
.
EndStepEvent
):
print
(
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
)
print
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
if
event
.
step
==
1
:
# Run 2 iterations to speed CI
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
...
...
@@ -123,14 +123,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
place
=
place
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
3
,
4
,
2
]]
base_shape
=
[
1
]
...
...
@@ -138,7 +138,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_words
=
fluid
.
create_random_int_lodtensor
(
recursive_seq_lens
,
base_shape
,
place
,
low
=
0
,
high
=
len
(
word_dict
)
-
1
)
results
=
inferencer
.
infer
({
'words'
:
tensor_words
})
print
(
(
"infer results: "
,
results
)
)
print
(
"infer results: "
,
results
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py
浏览文件 @
91f0573b
...
...
@@ -97,21 +97,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
acc
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'words'
,
'label'
])
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
(
"acc : %s"
%
acc
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
acc
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
else
:
print
(
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
)
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
elif
isinstance
(
event
,
fluid
.
EndStepEvent
):
print
(
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
)
print
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
if
event
.
step
==
1
:
# Run 2 iterations to speed CI
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
...
...
@@ -138,14 +138,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
place
=
place
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
3
,
4
,
2
]]
base_shape
=
[
1
]
...
...
@@ -153,7 +153,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_words
=
fluid
.
create_random_int_lodtensor
(
recursive_seq_lens
,
base_shape
,
place
,
low
=
0
,
high
=
len
(
word_dict
)
-
1
)
results
=
inferencer
.
infer
({
'words'
:
tensor_words
})
print
(
(
"infer results: "
,
results
)
)
print
(
"infer results: "
,
results
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py
浏览文件 @
91f0573b
...
...
@@ -91,21 +91,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost
,
acc
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'words'
,
'label'
])
print
(
(
"avg_cost: %s"
%
avg_cost
)
)
print
(
(
"acc : %s"
%
acc
)
)
print
(
"avg_cost: %s"
%
avg_cost
)
print
(
"acc : %s"
%
acc
)
if
acc
>
0.2
:
# Smaller value to increase CI speed
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
else
:
print
(
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
)
print
(
'BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'
.
format
(
event
.
epoch
+
1
,
avg_cost
,
acc
))
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
elif
isinstance
(
event
,
fluid
.
EndStepEvent
):
print
(
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
)
print
(
"Step {0}, Epoch {1} Metrics {2}"
.
format
(
event
.
step
,
event
.
epoch
,
list
(
map
(
np
.
array
,
event
.
metrics
))))
if
event
.
step
==
1
:
# Run 2 iterations to speed CI
trainer
.
save_params
(
params_dirname
)
trainer
.
stop
()
...
...
@@ -133,14 +133,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
place
=
place
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
3
,
4
,
2
]]
base_shape
=
[
1
]
...
...
@@ -148,7 +148,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_words
=
fluid
.
create_random_int_lodtensor
(
recursive_seq_lens
,
base_shape
,
place
,
low
=
0
,
high
=
len
(
word_dict
)
-
1
)
results
=
inferencer
.
infer
({
'words'
:
tensor_words
})
print
(
(
"infer results: "
,
results
)
)
print
(
"infer results: "
,
results
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py
浏览文件 @
91f0573b
...
...
@@ -98,7 +98,7 @@ def train(use_cuda, train_program, params_dirname):
reader
=
test_reader
,
feed_order
=
[
'firstw'
,
'secondw'
,
'thirdw'
,
'forthw'
,
'nextw'
])
avg_cost
=
outs
[
0
]
print
(
(
"loss= "
,
avg_cost
)
)
print
(
"loss= "
,
avg_cost
)
if
avg_cost
<
10.0
:
trainer
.
save_params
(
params_dirname
)
...
...
@@ -149,7 +149,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
'forthw'
:
fourth_word
},
return_numpy
=
False
)
print
(
(
np
.
array
(
result
[
0
])
))
print
(
np
.
array
(
result
[
0
]
))
def
main
(
use_cuda
,
is_sparse
):
...
...
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @
91f0573b
...
...
@@ -180,7 +180,7 @@ def train(word_dict,
cost_val
,
acc_val
=
exe
.
run
(
main_program
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
cost
,
acc_out
])
print
(
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
)
))
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
))
if
cost_val
<
0.4
and
acc_val
>
0.8
:
if
save_dirname
is
not
None
:
fluid
.
io
.
save_inference_model
(
save_dirname
,
[
"words"
],
...
...
@@ -235,14 +235,14 @@ def infer(word_dict, use_cuda, save_dirname=None):
word_dict_len
=
len
(
word_dict
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
3
,
4
,
2
]]
base_shape
=
[
1
]
...
...
@@ -261,10 +261,10 @@ def infer(word_dict, use_cuda, save_dirname=None):
feed
=
{
feed_target_names
[
0
]:
tensor_words
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
(
results
[
0
].
recursive_sequence_lengths
()
))
print
(
results
[
0
].
recursive_sequence_lengths
(
))
np_data
=
np
.
array
(
results
[
0
])
print
(
(
"Inference Shape: "
,
np_data
.
shape
)
)
print
(
(
"Inference results: "
,
np_data
)
)
print
(
"Inference Shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
def
main
(
word_dict
,
net_method
,
use_cuda
,
parallel
=
False
,
save_dirname
=
None
):
...
...
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @
91f0573b
...
...
@@ -124,9 +124,9 @@ def infer(use_cuda, save_dirname=None):
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
numpy
.
array
(
test_feat
)},
fetch_list
=
fetch_targets
)
print
(
(
"infer shape: "
,
results
[
0
].
shape
)
)
print
(
(
"infer results: "
,
results
[
0
])
)
print
(
(
"ground truth: "
,
test_label
)
)
print
(
"infer shape: "
,
results
[
0
].
shape
)
print
(
"infer results: "
,
results
[
0
]
)
print
(
"ground truth: "
,
test_label
)
def
main
(
use_cuda
,
is_local
=
True
):
...
...
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @
91f0573b
...
...
@@ -119,7 +119,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
# Test program
# Test program
test_program
=
fluid
.
default_main_program
().
clone
(
for_test
=
True
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
...
...
@@ -163,10 +163,10 @@ def train(net_type, use_cuda, save_dirname, is_local):
acc_value
=
numpy
.
array
(
acc_list
).
mean
()
avg_loss_value
=
numpy
.
array
(
avg_loss_list
).
mean
()
print
(
(
print
(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'
.
format
(
pass_id
,
batch_id
+
1
,
float
(
avg_loss_value
),
float
(
acc_value
)))
)
float
(
avg_loss_value
),
float
(
acc_value
)))
if
acc_value
>
0.01
:
# Low threshold for speeding up CI
fluid
.
io
.
save_inference_model
(
save_dirname
,
[
"pixel"
],
...
...
@@ -239,7 +239,7 @@ def infer(use_cuda, save_dirname=None):
np
.
testing
.
assert_almost_equal
(
results
[
0
][
i
],
transpiler_results
[
0
][
i
],
decimal
=
5
)
print
(
(
"infer results: "
,
results
[
0
])
)
print
(
"infer results: "
,
results
[
0
]
)
fluid
.
io
.
save_inference_model
(
save_dirname
,
feed_target_names
,
fetch_targets
,
exe
,
...
...
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
91f0573b
...
...
@@ -189,10 +189,10 @@ def train(use_cuda, save_dirname=None, is_local=True):
cost
=
cost
[
0
]
if
batch_id
%
10
==
0
:
print
(
(
"avg_cost:"
+
str
(
cost
)
))
print
(
"avg_cost:"
+
str
(
cost
))
if
batch_id
!=
0
:
print
(
(
"second per batch: "
+
str
(
(
time
.
time
()
-
start_time
)
/
batch_id
)
))
print
(
"second per batch: "
+
str
((
time
.
time
(
)
-
start_time
)
/
batch_id
))
# Set the threshold low to speed up the CI test
if
float
(
cost
)
<
60.0
:
if
save_dirname
is
not
None
:
...
...
@@ -248,14 +248,14 @@ def infer(use_cuda, save_dirname=None):
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
3
,
4
,
2
]]
base_shape
=
[
1
]
...
...
@@ -333,9 +333,9 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
(
results
[
0
].
recursive_sequence_lengths
()
))
print
(
results
[
0
].
recursive_sequence_lengths
(
))
np_data
=
np
.
array
(
results
[
0
])
print
(
(
"Inference Shape: "
,
np_data
.
shape
)
)
print
(
"Inference Shape: "
,
np_data
.
shape
)
def
main
(
use_cuda
,
is_local
=
True
):
...
...
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @
91f0573b
...
...
@@ -205,8 +205,8 @@ def train_main(use_cuda, is_sparse, is_local=True):
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
])
avg_cost_val
=
np
.
array
(
outs
[
0
])
print
(
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
)
))
print
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
))
if
batch_id
>
3
:
break
batch_id
+=
1
...
...
@@ -282,7 +282,7 @@ def decode_main(use_cuda, is_sparse):
feed
=
feed_dict
,
fetch_list
=
[
translation_ids
,
translation_scores
],
return_numpy
=
False
)
print
(
(
result_ids
.
recursive_sequence_lengths
()
))
print
(
result_ids
.
recursive_sequence_lengths
(
))
break
...
...
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @
91f0573b
...
...
@@ -142,10 +142,10 @@ def train(nn_type,
params_filename
=
params_filename
)
return
else
:
print
(
(
print
(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'
.
format
(
pass_id
,
batch_id
+
1
,
float
(
avg_loss_val
),
float
(
acc_val
)))
)
float
(
avg_loss_val
),
float
(
acc_val
)))
if
math
.
isnan
(
float
(
avg_loss_val
)):
sys
.
exit
(
"got NaN loss, training failed."
)
raise
AssertionError
(
"Loss of recognize digits is too large"
)
...
...
@@ -206,7 +206,7 @@ def infer(use_cuda,
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
print
(
(
"infer results: "
,
results
[
0
])
)
print
(
"infer results: "
,
results
[
0
]
)
def
main
(
use_cuda
,
parallel
,
nn_type
,
combine
):
...
...
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @
91f0573b
...
...
@@ -260,15 +260,15 @@ def infer(use_cuda, save_dirname=None):
# Use the first data from paddle.dataset.movielens.test() as input
assert
feed_target_names
[
0
]
==
"user_id"
# Use create_lod_tensor(data, recursive_sequence_lengths, place) API
# to generate LoD Tensor where `data` is a list of sequences of index
# numbers, `recursive_sequence_lengths` is the length-based level of detail
# Use create_lod_tensor(data, recursive_sequence_lengths, place) API
# to generate LoD Tensor where `data` is a list of sequences of index
# numbers, `recursive_sequence_lengths` is the length-based level of detail
# (lod) info associated with `data`.
# For example, data = [[10, 2, 3], [2, 3]] means that it contains
# two sequences of indexes, of length 3 and 2, respectively.
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
user_id
=
fluid
.
create_lod_tensor
([[
1
]],
[[
1
]],
place
)
assert
feed_target_names
[
1
]
==
"gender_id"
...
...
@@ -304,7 +304,7 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
(
"inferred score: "
,
np
.
array
(
results
[
0
])
))
print
(
"inferred score: "
,
np
.
array
(
results
[
0
]
))
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_rnn_encoder_decoder.py
浏览文件 @
91f0573b
...
...
@@ -182,8 +182,8 @@ def train(use_cuda, save_dirname=None):
fetch_list
=
[
avg_cost
])
avg_cost_val
=
np
.
array
(
outs
[
0
])
print
(
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
)
))
print
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
))
if
math
.
isnan
(
float
(
avg_cost_val
[
0
])):
sys
.
exit
(
"got NaN loss, training failed."
)
if
batch_id
>
3
:
...
...
@@ -213,14 +213,14 @@ def infer(use_cuda, save_dirname=None):
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[4, 6]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for two sentences of
# length 4 and 6, respectively.
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for two sentences of
# length 4 and 6, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
4
,
6
]]
base_shape
=
[
1
]
...
...
@@ -241,10 +241,10 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
(
results
[
0
].
recursive_sequence_lengths
()
))
print
(
results
[
0
].
recursive_sequence_lengths
(
))
np_data
=
np
.
array
(
results
[
0
])
print
(
(
"Inference shape: "
,
np_data
.
shape
)
)
print
(
(
"Inference results: "
,
np_data
)
)
print
(
"Inference shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @
91f0573b
...
...
@@ -169,11 +169,11 @@ def infer(use_cuda, save_dirname=None):
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
# which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
# meaning there is only one level of detail and there is only one sequence of
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
# which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
# meaning there is only one level of detail and there is only one sequence of
# one word on this level.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens
=
[[
1
]]
...
...
@@ -204,9 +204,9 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
(
results
[
0
].
recursive_sequence_lengths
()
))
print
(
results
[
0
].
recursive_sequence_lengths
(
))
np_data
=
np
.
array
(
results
[
0
])
print
(
(
"Inference Shape: "
,
np_data
.
shape
)
)
print
(
"Inference Shape: "
,
np_data
.
shape
)
def
main
(
use_cuda
,
is_sparse
,
is_parallel
):
...
...
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
浏览文件 @
91f0573b
...
...
@@ -78,7 +78,7 @@ for pass_id in range(PASS_NUM):
if
avg_loss_value
[
0
]
<
10.0
:
exit
(
0
)
# if avg cost less than 10.0, we think our code is good.
print
(
(
avg_loss_value
[
0
])
)
print
(
avg_loss_value
[
0
]
)
if
math
.
isnan
(
float
(
avg_loss_value
)):
sys
.
exit
(
"got NaN loss, training failed."
)
exit
(
1
)
python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
浏览文件 @
91f0573b
...
...
@@ -155,8 +155,8 @@ for pass_id in range(PASS_NUM):
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
)
))
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.
if
i
>
0
:
exit
(
0
)
...
...
python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
浏览文件 @
91f0573b
...
...
@@ -124,8 +124,8 @@ def main():
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
])
avg_cost_val
=
np
.
array
(
outs
[
0
])
print
(
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
)
))
print
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
))
if
batch_id
>
2
:
exit
(
0
)
if
math
.
isnan
(
float
(
avg_cost_val
)):
...
...
python/paddle/fluid/tests/demo/fc_gan.py
浏览文件 @
91f0573b
...
...
@@ -158,8 +158,8 @@ def main():
dg_loss_np
=
exe
.
run
(
dg_program
,
feed
=
{
'noise'
:
n
},
fetch_list
=
{
dg_loss
})[
0
]
print
(
(
"Pass ID={0}, Batch ID={1}, D-Loss={2}, DG-Loss={3}"
.
format
(
pass_id
,
batch_id
,
d_loss_np
,
dg_loss_np
))
)
print
(
"Pass ID={0}, Batch ID={1}, D-Loss={2}, DG-Loss={3}"
.
format
(
pass_id
,
batch_id
,
d_loss_np
,
dg_loss_np
))
# generate image each batch
fig
=
plot
(
generated_img
)
plt
.
savefig
(
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
91f0573b
...
...
@@ -46,7 +46,7 @@ class TestDetection(unittest.TestCase):
scores
=
scores
,
loc
=
loc
,
prior_box
=
pb
,
prior_box_var
=
pbv
)
self
.
assertIsNotNone
(
out
)
self
.
assertEqual
(
out
.
shape
[
-
1
],
6
)
print
(
(
str
(
program
)
))
print
(
str
(
program
))
def
test_detection_api
(
self
):
program
=
Program
()
...
...
@@ -81,7 +81,7 @@ class TestDetection(unittest.TestCase):
self
.
assertIsNotNone
(
trg
)
self
.
assertIsNotNone
(
trg_weight
)
print
(
(
str
(
program
)
))
print
(
str
(
program
))
def
test_ssd_loss
(
self
):
program
=
Program
()
...
...
@@ -105,7 +105,7 @@ class TestDetection(unittest.TestCase):
loss
=
layers
.
ssd_loss
(
loc
,
scores
,
gt_box
,
gt_label
,
pb
,
pbv
)
self
.
assertIsNotNone
(
loss
)
self
.
assertEqual
(
loss
.
shape
[
-
1
],
1
)
print
(
(
str
(
program
)
))
print
(
str
(
program
))
class
TestPriorBox
(
unittest
.
TestCase
):
...
...
@@ -196,7 +196,7 @@ class TestDetectionMAP(unittest.TestCase):
map_out
=
layers
.
detection_map
(
detect_res
,
label
,
21
)
self
.
assertIsNotNone
(
map_out
)
self
.
assertEqual
(
map_out
.
shape
,
(
1
,
))
print
(
(
str
(
program
)
))
print
(
str
(
program
))
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/test_if_else_op.py
浏览文件 @
91f0573b
...
...
@@ -84,7 +84,7 @@ class TestMNISTIfElseOp(unittest.TestCase):
feed
=
{
'x'
:
x_data
,
'y'
:
y_data
},
fetch_list
=
[
avg_loss
])
print
(
(
outs
[
0
])
)
print
(
outs
[
0
]
)
if
outs
[
0
]
<
1.0
:
return
self
.
assertFalse
(
True
)
...
...
@@ -139,7 +139,7 @@ class TestMNISTIfElseOp(unittest.TestCase):
feed
=
{
'x'
:
x_data
,
'y'
:
y_data
},
fetch_list
=
[
avg_loss
])
print
(
(
outs
[
0
])
)
print
(
outs
[
0
]
)
if
outs
[
0
]
<
1.0
:
return
self
.
assertFalse
(
True
)
...
...
python/paddle/fluid/tests/unittests/benchmark.py
浏览文件 @
91f0573b
...
...
@@ -88,8 +88,8 @@ class BenchmarkSuite(OpTest):
for
place
in
places
:
elapses
.
append
(
self
.
timeit_output_with_place
(
place
,
iters
))
for
place
,
elapse
in
zip
(
places
,
elapses
):
print
(
(
"One pass of ({2}_op) at {0} cost {1}"
.
format
(
str
(
place
),
elapse
,
self
.
op_type
))
)
print
(
"One pass of ({2}_op) at {0} cost {1}"
.
format
(
str
(
place
),
elapse
,
self
.
op_type
))
def
timeit_grad_with_place
(
self
,
place
,
iters
=
100
):
inputs_to_check
=
self
.
_get_input_names
()
...
...
@@ -108,5 +108,5 @@ class BenchmarkSuite(OpTest):
for
place
in
places
:
elapses
.
append
(
self
.
timeit_grad_with_place
(
place
,
iters
))
for
place
,
elapse
in
zip
(
places
,
elapses
):
print
(
(
"One pass of ({2}_grad_op) at {0} cost {1}"
.
format
(
str
(
place
),
elapse
,
self
.
op_type
))
)
print
(
"One pass of ({2}_grad_op) at {0} cost {1}"
.
format
(
str
(
place
),
elapse
,
self
.
op_type
))
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @
91f0573b
...
...
@@ -99,8 +99,8 @@ class TestParallelExecutorBase(unittest.TestCase):
end
=
time
.
time
()
if
batch_size
is
not
None
:
print
(
(
"%.4f Instance per second"
%
(
(
batch_size
*
iter
+
2
)
/
(
end
-
begin
)))
)
print
(
"%.4f Instance per second"
%
(
(
batch_size
*
iter
+
2
)
/
(
end
-
begin
)))
avg_last_loss_val
=
np
.
array
(
last_loss
).
mean
()
avg_first_loss_val
=
np
.
array
(
first_loss
).
mean
()
...
...
@@ -108,6 +108,6 @@ class TestParallelExecutorBase(unittest.TestCase):
float
(
avg_first_loss_val
)):
sys
.
exit
(
"got NaN loss, training failed."
)
print
(
(
first_loss
,
last_loss
)
)
print
(
first_loss
,
last_loss
)
# self.assertGreater(first_loss[0], last_loss[0])
return
first_loss
,
last_loss
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @
91f0573b
...
...
@@ -246,11 +246,11 @@ class ControlFlowGraph(object):
continue
if
PRINT_LOG
:
print
((
(
"Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s "
)
%
(
index
,
x
,
cache_var
,
str
(
cache_shape
)
)))
print
((
"Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s "
)
%
(
index
,
x
,
cache_var
,
str
(
cache_shape
)))
self
.
pool
.
pop
(
index
)
if
x
==
cache_var
:
break
...
...
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