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Merge pull request #829 from reyoung/feature/travis_pre_commit_checks
Feature/travis pre commit checks
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‎.travis.yml‎

Lines changed: 7 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -8,10 +8,13 @@ os:
88
env:
99
- JOB=DOCS
1010
- JOB=BUILD_AND_TEST
11+
- JOB=PRE_COMMIT
1112
matrix:
1213
exclude:
1314
- os: osx
14-
env: JOB=DOCS # Only generate documentation in linux
15+
env: JOB=DOCS # Only generate documentation in linux.
16+
- os: osx
17+
env: JOB=PRE_COMMIT # Only check pre-commit hook in linux
1518

1619
addons:
1720
apt:
@@ -39,6 +42,7 @@ addons:
3942
- lcov
4043
- graphviz
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- swig
45+
- clang-format-3.8
4246
before_install:
4347
- |
4448
if [ ${JOB} == "BUILD_AND_TEST" ]; then
@@ -53,7 +57,8 @@ before_install:
5357
fi
5458
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
5559
- if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
56-
- pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme
60+
- if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
61+
- pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme pre-commit
5762
script:
5863
- paddle/scripts/travis/main.sh
5964
notifications:

‎WORKSPACE‎

Lines changed: 9 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -1,17 +1,15 @@
11
# External dependency to Google protobuf.
22
http_archive(
3-
name = "protobuf",
4-
url = "http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
5-
sha256 = "0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
6-
strip_prefix = "protobuf-3.1.0",
7-
)
3+
name="protobuf",
4+
url="http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
5+
sha256="0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
6+
strip_prefix="protobuf-3.1.0", )
87

98
# External dependency to gtest 1.7.0. This method comes from
109
# https://www.bazel.io/versions/master/docs/tutorial/cpp.html.
1110
new_http_archive(
12-
name = "gtest",
13-
url = "https://github.com/google/googletest/archive/release-1.7.0.zip",
14-
sha256 = "b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
15-
build_file = "third_party/gtest.BUILD",
16-
strip_prefix = "googletest-release-1.7.0",
17-
)
11+
name="gtest",
12+
url="https://github.com/google/googletest/archive/release-1.7.0.zip",
13+
sha256="b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
14+
build_file="third_party/gtest.BUILD",
15+
strip_prefix="googletest-release-1.7.0", )

‎benchmark/tensorflow/rnn/run_multi.sh‎

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,4 +25,3 @@ test 4 2 256 512
2525
test 4 2 512 128
2626
test 4 2 512 256
2727
test 4 2 512 512
28-

‎demo/gan/README.md‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -10,4 +10,4 @@ Then you can run the command below. The flag -d specifies the training data (cif
1010
$python gan_trainer.py -d cifar --use_gpu 1
1111

1212
The generated images will be stored in ./cifar_samples/
13-
The corresponding models will be stored in ./cifar_params/
13+
The corresponding models will be stored in ./cifar_params/

‎demo/gan/data/download_cifar.sh‎

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -15,4 +15,3 @@ set -e
1515
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
1616
tar zxf cifar-10-python.tar.gz
1717
rm cifar-10-python.tar.gz
18-

‎demo/gan/data/get_mnist_data.sh‎

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -15,5 +15,3 @@ do
1515
gunzip ${fname}.gz
1616
fi
1717
done
18-
19-

‎demo/gan/gan_conf.py‎

Lines changed: 82 additions & 65 deletions
Original file line numberDiff line numberDiff line change
@@ -14,10 +14,9 @@
1414
from paddle.trainer_config_helpers import *
1515

1616
mode = get_config_arg("mode", str, "generator")
17-
assert mode in set(["generator",
18-
"discriminator",
19-
"generator_training",
20-
"discriminator_training"])
17+
assert mode in set([
18+
"generator", "discriminator", "generator_training", "discriminator_training"
19+
])
2120

2221
is_generator_training = mode == "generator_training"
2322
is_discriminator_training = mode == "discriminator_training"
@@ -38,8 +37,8 @@
3837
settings(
3938
batch_size=128,
4039
learning_rate=1e-4,
41-
learning_method=AdamOptimizer(beta1=0.5)
42-
)
40+
learning_method=AdamOptimizer(beta1=0.5))
41+
4342

4443
def discriminator(sample):
4544
"""
@@ -50,70 +49,87 @@ def discriminator(sample):
5049
of the sample is from real data.
5150
"""
5251
param_attr = ParamAttr(is_static=is_generator_training)
53-
bias_attr = ParamAttr(is_static=is_generator_training,
54-
initial_mean=1.0,
55-
initial_std=0)
56-
57-
hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
58-
bias_attr=bias_attr,
59-
param_attr=param_attr,
60-
act=ReluActivation())
61-
62-
hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
63-
bias_attr=bias_attr,
64-
param_attr=param_attr,
65-
act=LinearActivation())
66-
67-
hidden_bn = batch_norm_layer(hidden2,
68-
act=ReluActivation(),
69-
name="dis_hidden_bn",
70-
bias_attr=bias_attr,
71-
param_attr=ParamAttr(is_static=is_generator_training,
72-
initial_mean=1.0,
73-
initial_std=0.02),
74-
use_global_stats=False)
75-
76-
return fc_layer(input=hidden_bn, name="dis_prob", size=2,
77-
bias_attr=bias_attr,
78-
param_attr=param_attr,
79-
act=SoftmaxActivation())
52+
bias_attr = ParamAttr(
53+
is_static=is_generator_training, initial_mean=1.0, initial_std=0)
54+
55+
hidden = fc_layer(
56+
input=sample,
57+
name="dis_hidden",
58+
size=hidden_dim,
59+
bias_attr=bias_attr,
60+
param_attr=param_attr,
61+
act=ReluActivation())
62+
63+
hidden2 = fc_layer(
64+
input=hidden,
65+
name="dis_hidden2",
66+
size=hidden_dim,
67+
bias_attr=bias_attr,
68+
param_attr=param_attr,
69+
act=LinearActivation())
70+
71+
hidden_bn = batch_norm_layer(
72+
hidden2,
73+
act=ReluActivation(),
74+
name="dis_hidden_bn",
75+
bias_attr=bias_attr,
76+
param_attr=ParamAttr(
77+
is_static=is_generator_training, initial_mean=1.0,
78+
initial_std=0.02),
79+
use_global_stats=False)
80+
81+
return fc_layer(
82+
input=hidden_bn,
83+
name="dis_prob",
84+
size=2,
85+
bias_attr=bias_attr,
86+
param_attr=param_attr,
87+
act=SoftmaxActivation())
88+
8089

8190
def generator(noise):
8291
"""
8392
generator generates a sample given noise
8493
"""
8594
param_attr = ParamAttr(is_static=is_discriminator_training)
86-
bias_attr = ParamAttr(is_static=is_discriminator_training,
87-
initial_mean=1.0,
88-
initial_std=0)
89-
90-
hidden = fc_layer(input=noise,
91-
name="gen_layer_hidden",
92-
size=hidden_dim,
93-
bias_attr=bias_attr,
94-
param_attr=param_attr,
95-
act=ReluActivation())
96-
97-
hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
98-
bias_attr=bias_attr,
99-
param_attr=param_attr,
100-
act=LinearActivation())
101-
102-
hidden_bn = batch_norm_layer(hidden2,
103-
act=ReluActivation(),
104-
name="gen_layer_hidden_bn",
105-
bias_attr=bias_attr,
106-
param_attr=ParamAttr(is_static=is_discriminator_training,
107-
initial_mean=1.0,
108-
initial_std=0.02),
109-
use_global_stats=False)
110-
111-
return fc_layer(input=hidden_bn,
112-
name="gen_layer1",
113-
size=sample_dim,
114-
bias_attr=bias_attr,
115-
param_attr=param_attr,
116-
act=LinearActivation())
95+
bias_attr = ParamAttr(
96+
is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
97+
98+
hidden = fc_layer(
99+
input=noise,
100+
name="gen_layer_hidden",
101+
size=hidden_dim,
102+
bias_attr=bias_attr,
103+
param_attr=param_attr,
104+
act=ReluActivation())
105+
106+
hidden2 = fc_layer(
107+
input=hidden,
108+
name="gen_hidden2",
109+
size=hidden_dim,
110+
bias_attr=bias_attr,
111+
param_attr=param_attr,
112+
act=LinearActivation())
113+
114+
hidden_bn = batch_norm_layer(
115+
hidden2,
116+
act=ReluActivation(),
117+
name="gen_layer_hidden_bn",
118+
bias_attr=bias_attr,
119+
param_attr=ParamAttr(
120+
is_static=is_discriminator_training,
121+
initial_mean=1.0,
122+
initial_std=0.02),
123+
use_global_stats=False)
124+
125+
return fc_layer(
126+
input=hidden_bn,
127+
name="gen_layer1",
128+
size=sample_dim,
129+
bias_attr=bias_attr,
130+
param_attr=param_attr,
131+
act=LinearActivation())
132+
117133

118134
if is_generator_training:
119135
noise = data_layer(name="noise", size=noise_dim)
@@ -126,7 +142,8 @@ def generator(noise):
126142
label = data_layer(name="label", size=1)
127143
prob = discriminator(sample)
128144
cost = cross_entropy(input=prob, label=label)
129-
classification_error_evaluator(input=prob, label=label, name=mode+'_error')
145+
classification_error_evaluator(
146+
input=prob, label=label, name=mode + '_error')
130147
outputs(cost)
131148

132149
if is_generator:

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