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Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_expand_layer.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=30)
data_seq = data_layer(name='data_seq', size=30)
outputs(
expand_layer(
input=din, expand_as=data_seq, expand_level=ExpandLevel.FROM_SEQUENCE),
expand_layer(
input=din,
expand_as=data_seq,
expand_level=ExpandLevel.FROM_NO_SEQUENCE))
| 1,017 | 34.103448 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_detection_output_layer.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
input_loc = data_layer(name='input_loc', size=16, height=16, width=1)
input_conf = data_layer(name='input_conf', size=8, height=1, width=8)
priorbox = data_layer(name='priorbox', size=32, height=4, width=8)
detout = detection_output_layer(
input_loc=input_loc,
input_conf=input_conf,
priorbox=priorbox,
num_classes=21,
nms_threshold=0.45,
nms_top_k=400,
keep_top_k=200,
confidence_threshold=0.01,
background_id=0,
name='test_detection_output')
outputs(detout)
| 1,209 | 30.842105 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py
|
# 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.
from paddle.trainer_config_helpers import *
data = data_layer(name='data', size=100)
scale = scale_shift_layer(input=data, bias_attr=False)
scale_shift = scale_shift_layer(input=data)
outputs(scale, scale_shift)
| 829 | 33.583333 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/shared_gru.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
data_1 = data_layer(name='data_a', size=100)
data_2 = data_layer(name='data_b', size=100)
mixed_param = ParamAttr(name='mixed_param')
gru_param = ParamAttr(name='gru_param')
gru_bias = ParamAttr(name='gru_bias', initial_mean=0., initial_std=0.)
gru1 = simple_gru(
input=data_1,
size=200,
mixed_param_attr=mixed_param,
mixed_bias_param_attr=False,
gru_bias_attr=gru_bias,
gru_param_attr=gru_param)
gru2 = simple_gru(
input=data_2,
size=200,
mixed_param_attr=mixed_param,
mixed_bias_param_attr=False,
gru_bias_attr=gru_bias,
gru_param_attr=gru_param)
softmax_param = ParamAttr(name='softmax_param')
predict = fc_layer(
input=[last_seq(input=gru1), last_seq(input=gru2)],
size=10,
param_attr=[softmax_param, softmax_param],
bias_attr=False,
act=SoftmaxActivation())
outputs(
classification_cost(
input=predict, label=data_layer(
name='label', size=10)))
| 1,654 | 29.090909 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/layer_activations.py
|
# 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.
'''
Test all activations.
'''
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
din = data_layer(name='input', size=100)
acts = [
TanhActivation, SigmoidActivation, SoftmaxActivation, IdentityActivation,
LinearActivation, ExpActivation, ReluActivation, BReluActivation,
SoftReluActivation, STanhActivation, AbsActivation, SquareActivation
]
outputs([
fc_layer(
input=din, size=100, act=act(), name="layer_%d" % i)
for i, act in enumerate(acts)
])
| 1,132 | 31.371429 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_lstmemory_layer.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=128)
outputs(
lstmemory(
input=din,
reverse=True,
gate_act=TanhActivation(),
act=TanhActivation(),
size=32))
| 894 | 30.964286 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_gated_unit_layer.py
|
# 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.
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=256)
glu = gated_unit_layer(
size=512,
input=data,
act=TanhActivation(),
gate_attr=ExtraLayerAttribute(error_clipping_threshold=100.0),
gate_param_attr=ParamAttr(initial_std=1e-4),
gate_bias_attr=ParamAttr(initial_std=1),
inproj_attr=ExtraLayerAttribute(error_clipping_threshold=100.0),
inproj_param_attr=ParamAttr(initial_std=1e-4),
inproj_bias_attr=ParamAttr(initial_std=1),
layer_attr=ExtraLayerAttribute(error_clipping_threshold=100.0))
outputs(glu)
| 1,190 | 37.419355 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/simple_rnn_layers.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
din = data_layer(name='data', size=200)
hidden = fc_layer(input=din, size=200, act=SigmoidActivation())
rnn = recurrent_layer(input=hidden, act=SigmoidActivation())
rnn2 = recurrent_layer(input=hidden, act=SigmoidActivation(), reverse=True)
lstm1_param = fc_layer(
input=hidden, size=200 * 4, act=LinearActivation(), bias_attr=False)
lstm1 = lstmemory(input=lstm1_param, act=SigmoidActivation())
lstm2_param = fc_layer(
input=hidden, size=200 * 4, act=LinearActivation(), bias_attr=False)
lstm2 = lstmemory(input=lstm2_param, act=SigmoidActivation(), reverse=True)
gru1_param = fc_layer(
input=hidden, size=200 * 3, act=LinearActivation(), bias_attr=False)
gru1 = grumemory(input=gru1_param, act=SigmoidActivation())
gru2_param = fc_layer(
input=hidden, size=200 * 3, act=LinearActivation(), bias_attr=False)
gru2 = grumemory(input=gru2_param, act=SigmoidActivation(), reverse=True)
outputs(
last_seq(input=rnn),
first_seq(input=rnn2),
last_seq(input=lstm1),
first_seq(input=lstm2),
last_seq(input=gru1),
first_seq(gru2))
| 1,777 | 33.192308 | 75 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_bilinear_interp.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2304)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=1,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
bilinear = bilinear_interp_layer(input=conv, out_size_x=64, out_size_y=64)
pool = img_pool_layer(
input=bilinear,
num_channels=16,
pool_size=2,
stride=2,
pool_type=MaxPooling())
fc = fc_layer(input=pool, size=384, bias_attr=False)
outputs(fc)
| 1,176 | 27.02381 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_config_parser_for_non_file_config.py
|
#!/usr/bin/env python
# Copyright (c) 2016 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 sys
import re
import getopt
def main(print_whole_config, globals, locals):
'''
this test will all test_config.py
'''
cmdstr = """from paddle.trainer.config_parser import parse_config\n"""
importstr = ""
functionstr = ""
for line in sys.stdin:
if re.match("^import", line) or re.match("^from.*import", line):
importstr = importstr + line
else:
functionstr = functionstr + " " + line
cmdstr = cmdstr + importstr + """def configs():\n""" + functionstr
#cmdstr = cmdstr + """def configs():\n""" + importstr + functionstr
if print_whole_config:
cmdstr = cmdstr + """print parse_config(configs, "")"""
else:
cmdstr = cmdstr + """print parse_config(configs, "").model_config"""
exec (cmdstr, globals, locals)
if __name__ == '__main__':
whole = False
opts, args = getopt.getopt(sys.argv[1:], "", ["whole"])
for op, value in opts:
if op == "--whole":
whole = True
main(whole, globals(), locals())
| 1,675 | 31.230769 | 76 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/projections.py
|
# 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.
'''
Test mixed layer, projections and operators.
'''
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
din = data_layer(name='test', size=100)
din = embedding_layer(input=din, size=256)
with mixed_layer(size=100) as m1:
m1 += full_matrix_projection(input=din)
with mixed_layer(size=100) as m2:
m2 += table_projection(input=m1)
with mixed_layer(size=100) as m3:
m3 += identity_projection(input=m2)
with mixed_layer(size=100) as m4:
m4 += dotmul_projection(input=m3)
with mixed_layer() as m5:
m5 += context_projection(input=m4, context_len=3)
with mixed_layer() as m6:
m6 += dotmul_operator(a=m3, b=m4)
m6 += scaling_projection(m3)
img = data_layer(name='img', size=32 * 32)
flt = data_layer(name='filter', size=3 * 3 * 1 * 64)
with mixed_layer() as m7:
m7 += conv_operator(
img=img, filter=flt, num_filters=64, num_channels=1, filter_size=3)
m7 += conv_projection(img, filter_size=3, num_filters=64, num_channels=1)
with mixed_layer() as m8:
m8 += conv_operator(
img=img,
filter=flt,
num_filters=64,
num_channels=1,
filter_size=3,
stride=2,
padding=1,
trans=True)
m8 += conv_projection(
img,
filter_size=3,
num_filters=64,
num_channels=1,
stride=2,
padding=1,
trans=True)
end = mixed_layer(
input=[
full_matrix_projection(input=m5),
trans_full_matrix_projection(input=m6),
full_matrix_projection(input=m7), full_matrix_projection(input=m8)
],
size=100,
layer_attr=ExtraAttr(
drop_rate=0.5, error_clipping_threshold=40))
outputs(end)
| 2,317 | 27.617284 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_print_layer.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
din = data_layer(name='input', size=100)
print_layer(input=din)
outputs(din)
| 784 | 31.708333 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_ntm_layers.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
weight = data_layer(name='w', size=1)
a = data_layer(name='a', size=100)
b = data_layer(name='b', size=100)
c = data_layer(name='c', size=200)
d = data_layer(name='d', size=31)
outputs(
interpolation_layer(
input=[a, b], weight=weight),
power_layer(
input=a, weight=weight),
scaling_layer(
input=a, weight=weight),
cos_sim(
a=a, b=b),
cos_sim(
a=a, b=c, size=2),
sum_to_one_norm_layer(input=a),
conv_shift_layer(
a=a, b=d),
tensor_layer(
a=a, b=b, size=1000),
slope_intercept_layer(
input=a, slope=0.7, intercept=0.9),
linear_comb_layer(
weights=b, vectors=c))
| 1,379 | 29.666667 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
#data = data_layer(name='data', size=180, width=30, height=6)
#batchNorm = batch_norm_layer(data, num_channels=1)
#outputs(batchNorm)
data3D = data_layer(name='data3D', size=120 * 3, width=20, height=6, depth=3)
batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True)
outputs(batchNorm3D)
| 1,006 | 37.730769 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py
|
#!/usr/bin/env python
#coding=utf-8
from paddle.trainer_config_helpers import *
beam_size = 5
# the first beam expansion.
sentence_states = data_layer(name="sentence_states", size=32)
sentence_scores = data_layer(name="sentence_scores", size=1)
topk_sentence_ids = kmax_seq_score_layer(
input=sentence_scores, beam_size=beam_size)
# the second beam expansion.
topk_sen = sub_nested_seq_layer(
input=sentence_states, selected_indices=topk_sentence_ids)
start_pos_scores = fc_layer(input=topk_sen, size=1, act=LinearActivation())
topk_start_pos_ids = kmax_seq_score_layer(
input=sentence_scores, beam_size=beam_size)
# the final beam expansion.
topk_start_spans = seq_slice_layer(
input=topk_sen, starts=topk_start_pos_ids, ends=None)
end_pos_scores = fc_layer(
input=topk_start_spans, size=1, act=LinearActivation())
topk_end_pos_ids = kmax_seq_score_layer(
input=end_pos_scores, beam_size=beam_size)
# define the cost
sentence_idx = data_layer(name="sentences_ids", size=1)
start_idx = data_layer(name="start_ids", size=1)
end_idx = data_layer(name="end_ids", size=1)
cost = cross_entropy_over_beam(input=[
BeamInput(
candidate_scores=sentence_scores,
selected_candidates=topk_sentence_ids,
gold=sentence_idx), BeamInput(
candidate_scores=start_pos_scores,
selected_candidates=topk_start_pos_ids,
gold=start_idx), BeamInput(
candidate_scores=end_pos_scores,
selected_candidates=topk_end_pos_ids,
gold=end_idx)
])
outputs(cost)
| 1,569 | 33.130435 | 75 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_smooth_l1.py
|
# 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.
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
lbl = data_layer(name='label', size=300)
smooth_l1 = smooth_l1_cost(input=data, label=lbl)
outputs(smooth_l1)
| 811 | 35.909091 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/img_layers.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-3, batch_size=1000)
img = data_layer(name='image', size=256 * 256)
# the parse_conv in config_parse.py is not strictly accurate when filter_size
# is not square. So here set square filter_size.
img_conv = img_conv_layer(
input=img,
num_channels=1,
num_filters=64,
filter_size=(32, 32),
padding=(1, 1),
dilation=(1, 1),
stride=(1, 1),
act=LinearActivation())
img_bn = batch_norm_layer(input=img_conv, act=ReluActivation())
img_norm = img_cmrnorm_layer(input=img_bn, size=32)
img_pool = img_pool_layer(input=img_conv, pool_size=32, pool_type=MaxPooling())
outputs(img_pool, img_norm)
| 1,303 | 32.435897 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_sequence_pooling.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
din = data_layer(name='dat_in', size=100)
POOL_TYPE = [MaxPooling, AvgPooling, SumPooling]
AGG_LEVEL = [AggregateLevel.TO_SEQUENCE, AggregateLevel.TO_NO_SEQUENCE]
opts = []
for pt in POOL_TYPE:
for al in AGG_LEVEL:
opts.append(pooling_layer(input=din, agg_level=al, pooling_type=pt()))
for pt in POOL_TYPE:
opts.append(
pooling_layer(
input=din,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
pooling_type=pt(),
stride=5))
opts.append(
pooling_layer(
input=din, pooling_type=MaxPooling(output_max_index=True)))
outputs(opts)
| 1,315 | 28.909091 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_recursive_topology.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=100)
enc = din
for i in range(32):
enc = addto_layer([enc, enc])
pred = fc_layer(
input=fc_layer(
input=enc, size=32, act=ReluActivation()),
size=10,
act=SoftmaxActivation())
outputs(pred)
| 955 | 29.83871 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_crop.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2016, height=48, width=42)
refernce_data = data_layer(name='data', size=768, height=16, width=16)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=1,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
pool = img_pool_layer(input=conv, pool_size=2, stride=2, pool_type=MaxPooling())
crop = crop_layer(input=[pool, refernce_data], axis=2)
outputs(pad)
| 1,153 | 31.055556 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_maxout.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2304, height=48, width=48)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=1,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
maxout = maxout_layer(input=conv, num_channels=16, groups=2)
pool = img_pool_layer(
input=maxout, num_channels=8, pool_size=2, stride=2, pool_type=MaxPooling())
conv2 = img_conv_layer(
input=pool,
filter_size=3,
num_channels=8,
num_filters=128,
padding=1,
act=LinearActivation(),
bias_attr=True)
maxout2 = maxout_layer(input=conv2, num_channels=128, groups=4)
block = block_expand_layer(
input=maxout2,
num_channels=32,
stride_x=1,
stride_y=1,
block_x=1,
block_y=6)
fc = fc_layer(input=block, size=384, bias_attr=False)
outputs(fc)
| 1,525 | 25.77193 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_split_datasource.py
|
# 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.
from paddle.trainer_config_helpers import *
define_py_data_sources2(
train_list="train.list",
test_list="test.list",
module=["a", "b"],
obj=("c", "d"))
settings(learning_rate=1e-3, batch_size=1000)
outputs(data_layer(name="a", size=10))
| 868 | 33.76 | 74 |
py
|
Paddle
|
Paddle-master/cmake/make_resource.py
|
# 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 os
import re
import sys
res = sys.argv[1]
out = sys.argv[2]
var = re.sub(r'[ .-]', '_', os.path.basename(res))
open(out, "w").write("const unsigned char " + var + "[] = {" + ",".join([
"0x%02x" % ord(c) for c in open(res).read()
]) + ",0};\n" + "const unsigned " + var + "_size = sizeof(" + var + ");\n")
| 931 | 34.846154 | 75 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/fluid_benchmark.py
|
# 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 argparse
import cProfile
import time
import os
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
import paddle.fluid.transpiler.distribute_transpiler as distribute_transpiler
BENCHMARK_MODELS = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
]
def parse_args():
parser = argparse.ArgumentParser('Fluid model benchmarks.')
parser.add_argument(
'--model',
type=str,
choices=BENCHMARK_MODELS,
default='resnet',
help='The model to run benchmark with.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size.')
parser.add_argument(
'--learning_rate',
type=float,
default=0.001,
help='The minibatch size.')
# TODO(wuyi): add "--use_fake_data" option back.
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations', type=int, default=80, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=100, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
parser.add_argument(
'--data_set',
type=str,
default='flowers',
choices=['cifar10', 'flowers'],
help='Optional dataset for benchmark.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
parser.add_argument(
'--no_test',
action='store_false',
help='If set, test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='If set ommit the actual read data operators.')
parser.add_argument(
'--profile', action='store_true', help='If set, profile a few steps.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
args = parser.parse_args()
return args
def append_nccl2_prepare(trainer_id):
if trainer_id >= 0:
# append gen_nccl_id at the end of startup program
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
port = os.getenv("PADDLE_PSERVER_PORT")
worker_ips = os.getenv("PADDLE_TRAINER_IPS")
worker_endpoints = []
for ip in worker_ips.split(","):
worker_endpoints.append(':'.join([ip, port]))
num_trainers = len(worker_endpoints)
current_endpoint = os.getenv("PADDLE_CURRENT_IP") + ":" + port
worker_endpoints.remove(current_endpoint)
nccl_id_var = fluid.default_startup_program().global_block().create_var(
name="NCCLID",
persistable=True,
type=fluid.core.VarDesc.VarType.RAW)
fluid.default_startup_program().global_block().append_op(
type="gen_nccl_id",
inputs={},
outputs={"NCCLID": nccl_id_var},
attrs={
"endpoint": current_endpoint,
"endpoint_list": worker_endpoints,
"trainer_id": trainer_id
})
return nccl_id_var, num_trainers, trainer_id
else:
raise Exception("must set positive PADDLE_TRAINER_ID env variables for "
"nccl-based dist train.")
def dist_transpile(trainer_id):
if trainer_id < 0:
return None, None
# the port of all pservers, needed by both trainer and pserver
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
# comma separated ips of all pservers, needed by trainer and
# pserver
pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist)
# total number of workers/trainers in the job, needed by
# trainer and pserver
trainers = int(os.getenv("PADDLE_TRAINERS"))
# the IP of the local machine, needed by pserver only
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
# the role, should be either PSERVER or TRAINER
training_role = os.getenv("PADDLE_TRAINING_ROLE")
t = distribute_transpiler.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
pserver_program)
return pserver_program, pserver_startup_program
elif training_role == "TRAINER":
train_program = t.get_trainer_program()
return train_program, fluid.default_startup_program()
else:
raise ValueError(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
def test(exe, inference_program, test_reader, feeder, batch_acc):
accuracy_evaluator = fluid.metrics.Accuracy()
for batch_id, data in enumerate(test_reader()):
acc = exe.run(inference_program,
feed=feeder.feed(data),
fetch_list=[batch_acc])
accuracy_evaluator.update(value=np.array(acc), weight=len(data))
return accuracy_evaluator.eval()
# TODO(wuyi): replace train, train_parallel, test functions with new trainer
# API once it is ready.
def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
args, train_prog, startup_prog):
if os.getenv("PADDLE_TRAINING_ROLE") == "PSERVER":
place = core.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
exe.run(train_prog)
return
if args.use_fake_data:
raise Exception(
"fake data is not supported in single GPU test for now.")
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(startup_prog)
feed_var_list = [
var for var in train_prog.global_block().vars.itervalues()
if var.is_data
]
feeder = fluid.DataFeeder(feed_var_list, place)
iters, num_samples, start_time = 0, 0, time.time()
for pass_id in range(args.pass_num):
train_losses = []
for batch_id, data in enumerate(train_reader()):
if iters == args.skip_batch_num:
start_time = time.time()
num_samples = 0
if iters == args.iterations:
break
loss = exe.run(train_prog,
feed=feeder.feed(data),
fetch_list=[avg_loss])
iters += 1
num_samples += len(data)
train_losses.append(loss)
print("Pass: %d, Iter: %d, Loss: %f\n" %
(pass_id, iters, np.mean(train_losses)))
train_elapsed = time.time() - start_time
examples_per_sec = num_samples / train_elapsed
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sec\n' %
(num_samples, train_elapsed, examples_per_sec))
print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses)))
# evaluation
if not args.no_test and batch_acc != None:
pass_test_acc = test(exe, infer_prog, test_reader, feeder,
batch_acc)
print(", Test Accuracy: %f" % pass_test_acc)
print("\n")
# TODO(wuyi): add warmup passes to get better perf data.
exit(0)
# TODO(wuyi): replace train, train_parallel, test functions with new trainer
# API once it is ready.
def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
batch_acc, args, train_prog, startup_prog, nccl_id_var,
num_trainers, trainer_id):
feed_var_list = [
var for var in train_prog.global_block().vars.itervalues()
if var.is_data
]
# generate fake:
if args.use_fake_data:
for var in feed_var_list:
v = startup_prog.global_block().clone_variable(var)
var.persistable = True
v.persistable = True
real_shape = list(var.shape)
real_shape[0] = args.batch_size / args.gpus
startup_prog.global_block().append_op(
outputs={"Out": v},
type="fill_constant",
attrs={"shape": real_shape,
"value": 1.0,
"dtype": var.dtype})
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
if nccl_id_var and trainer_id == 0:
#FIXME(wuyi): wait other trainer to start listening
time.sleep(30)
startup_exe = fluid.Executor(place)
startup_exe.run(startup_prog)
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
exe = fluid.ParallelExecutor(
True,
avg_loss.name,
exec_strategy=strategy,
num_trainers=num_trainers,
trainer_id=trainer_id)
feeder = fluid.DataFeeder(feed_var_list, place)
for pass_id in range(args.pass_num):
num_samples = 0
iters = 0
start_time = time.time()
for batch_id, data in enumerate(train_reader()):
if args.profile and pass_id == 0 and batch_id == 5:
profiler.start_profiler("All")
elif args.profile and pass_id == 0 and batch_id == 10:
profiler.stop_profiler("total", "/tmp/profile_%d" % trainer_id)
if iters == args.skip_batch_num:
start_time = time.time()
num_samples = 0
if iters == args.iterations:
break
if args.use_fake_data:
loss, = exe.run([avg_loss.name])
else:
loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
if args.update_method == "pserver":
exe.bcast_params()
num_samples += len(data)
iters += 1
if batch_id % 1 == 0:
print("Pass %d, batch %d, loss %s" %
(pass_id, batch_id, np.array(loss)))
train_elapsed = time.time() - start_time
examples_per_sec = num_samples / train_elapsed
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
(num_samples, train_elapsed, examples_per_sec))
if not args.no_test and batch_acc != None:
test_acc = test(startup_exe, infer_prog, test_reader, feeder,
batch_acc)
print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))
exit(0)
def print_arguments(args):
vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
vars(args)['device'] == 'GPU')
print('----------- resnet Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def main():
args = parse_args()
print_arguments(args)
# the unique trainer id, starting from 0, needed by trainer
# only
nccl_id_var, num_trainers, trainer_id = (
None, 1, int(os.getenv("PADDLE_TRAINER_ID", "-1")))
if args.use_cprof:
pr = cProfile.Profile()
pr.enable()
model_def = __import__("models.%s" % args.model, fromlist=["models"])
train_args = list(model_def.get_model(args))
train_args.append(args)
# Run optimizer.minimize(avg_loss)
train_args[2].minimize(train_args[0])
if args.memory_optimize:
fluid.memory_optimize(fluid.default_main_program())
if args.update_method == "pserver":
train_prog, startup_prog = dist_transpile(trainer_id)
if not train_prog:
raise Exception(
"Must configure correct environments to run dist train.")
train_args.extend([train_prog, startup_prog])
if args.gpus > 1 and os.getenv("PADDLE_TRAINING_ROLE") == "TRAINER":
train_args.extend([nccl_id_var, num_trainers, trainer_id])
train_parallel(*train_args)
train(*train_args)
exit(0)
# for other update methods, use default programs
train_args.append(fluid.default_main_program())
train_args.append(fluid.default_startup_program())
if args.update_method == "nccl2":
nccl_id_var, num_trainers, trainer_id = append_nccl2_prepare(trainer_id)
if args.gpus == 1:
# NOTE: parallel executor use profiler interanlly
if args.use_nvprof and args.device == 'GPU':
with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
train(*train_args)
else:
train(*train_args)
else:
if args.device == "CPU":
raise Exception("Only support GPU perf with parallel exe")
train_args.extend([nccl_id_var, num_trainers, trainer_id])
train_parallel(*train_args)
if __name__ == "__main__":
main()
| 14,702 | 36.507653 | 82 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/kube_gen_job.py
|
# 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 yaml
import copy
import argparse
import random
import os
from kube_templates import pserver, trainer, envs
def parse_args():
parser = argparse.ArgumentParser(description='Generate dist job yamls.')
parser.add_argument(
'--jobname', default="paddlejob", help='unique job name')
parser.add_argument(
'--cpu', default=1, type=int, help='CPU cores per trainer node')
parser.add_argument(
'--pscpu', default=1, type=int, help='CPU cores per pserver node')
parser.add_argument(
'--gpu', default=0, type=int, help='num of GPUs per node')
parser.add_argument(
'--image',
default="bootstrapper:5000/fluid_benchmark:gpu",
help='num of GPUs per node')
parser.add_argument(
'--pservers', default=1, type=int, help='num of pservers')
parser.add_argument(
'--trainers', default=1, type=int, help='num of trainers')
parser.add_argument('--memory', default=1, type=int, help='trainer memory')
parser.add_argument(
'--psmemory', default=1, type=int, help='pserver memory')
parser.add_argument(
'--port', default=30236, type=int, help='num of trainers')
parser.add_argument(
'--entry', default="python train.py", help='command to run')
parser.add_argument(
'--fluid', default=1, type=int, help='whether is fluid job')
parser.add_argument(
'--rdma', action='store_ture', help='whether mount rdma libs')
parser.add_argument(
'--disttype',
default="pserver",
type=str,
choices=['pserver', 'nccl2', 'local'],
help='pserver or nccl2 or local')
args = parser.parse_args()
return args
def gen_job():
ps = pserver
tn = trainer
args = parse_args()
ps_container = ps["spec"]["template"]["spec"]["containers"][0]
tn_container = tn["spec"]["template"]["spec"]["containers"][0]
if args.fluid == 1:
ps_container["command"] = \
["paddle_k8s", "start_fluid"]
tn_container["command"] = \
["paddle_k8s", "start_fluid"]
ps["metadata"]["name"] = args.jobname + "-pserver"
ps["spec"]["template"]["metadata"]["labels"][
"paddle-job-pserver"] = args.jobname
tn["metadata"]["name"] = args.jobname + "-trainer"
tn["spec"]["template"]["metadata"]["labels"]["paddle-job"] = args.jobname
ps_container["image"] = args.image
tn_container["image"] = args.image
ps_container["resources"]["requests"]["cpu"] = str(args.pscpu)
ps_container["resources"]["requests"]["memory"] = str(args.psmemory) + "Gi"
ps_container["resources"]["limits"]["cpu"] = str(args.pscpu)
ps_container["resources"]["limits"]["memory"] = str(args.psmemory) + "Gi"
tn_container["resources"]["requests"]["cpu"] = str(args.cpu)
tn_container["resources"]["requests"]["memory"] = str(args.memory) + "Gi"
tn_container["resources"]["limits"]["cpu"] = str(args.cpu)
tn_container["resources"]["limits"]["memory"] = str(args.memory) + "Gi"
if args.gpu > 0:
tn_container["resources"]["requests"][
"alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
tn_container["resources"]["limits"][
"alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
ps["spec"]["replicas"] = int(args.pservers)
tn["spec"]["parallelism"] = int(args.trainers)
tn["spec"]["completions"] = int(args.trainers)
ps_container["ports"][0]["name"] = "jobport-" + str(args.port)
ps_container["ports"][0]["containerPort"] = args.port
spreadport = random.randint(40000, 60000)
tn_container["ports"][0]["name"] = "spr-" + str(spreadport)
tn_container["ports"][0]["containerPort"] = spreadport
envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
envs.append({"name": "TRAINERS", "value": str(args.trainers)})
envs.append({"name": "PSERVERS", "value": str(args.pservers)})
envs.append({"name": "ENTRY", "value": args.entry})
envs.append({"name": "PADDLE_INIT_PORT", "value": str(args.port)})
envs.append({"name": "PADDLE_PSERVER_PORT", "value": str(args.port)})
# NOTE: these directories below are cluster specific, please modify
# this settings before you run on your own cluster.
envs.append({
"name": "LD_LIBRARY_PATH",
"value":
"/usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind"
})
volumes = [{
"name": "nvidia-driver",
"hostPath": {
"path": "/usr/local/nvidia/lib64"
}
}]
volumeMounts = [{
"mountPath": "/usr/local/nvidia/lib64",
"name": "nvidia-driver"
}]
if args.rdma:
volumes.extend([{
"name": "ibetc",
"hostPath": {
"path": "/etc/libibverbs.d"
}
}, {
"name": "iblibs",
"hostPath": {
"path": "/usr/local/rdma"
}
}, {
"name": "valgrind",
"hostPath": {
"path": "/usr/lib64/mlnx_ofed/valgrind"
}
}])
volumeMounts.extend([{
"mountPath": "/etc/libibverbs.d",
"name": "ibetc"
}, {
"mountPath": "/usr/local/rdma",
"name": "iblibs"
}, {
"mountPath": "/usr/lib64/mlnx_ofed/valgrind",
"name": "valgrind"
}])
# append shm for NCCL2
volumes.append({"name": "dshm", "emptyDir": {"medium": "Memory"}})
volumeMounts.append({"mountPath": "/dev/shm", "name": "dshm"})
tn["spec"]["template"]["spec"]["volumes"] = volumes
tn_container["volumeMounts"] = volumeMounts
ps_container["env"] = envs
ps_container["env"].append({"name": "TRAINING_ROLE", "value": "PSERVER"})
tn_container["env"] = envs
if args.disttype == "pserver":
tn_container["env"].append({
"name": "TRAINING_ROLE",
"value": "TRAINER"
})
elif args.disttype == "nccl2" or args.disttype == "local":
# NCCL2 have no training role, set to plain WORKER
tn_container["env"].append({"name": "TRAINING_ROLE", "value": "WORKER"})
os.mkdir(args.jobname)
if args.disttype == "pserver":
with open("%s/pserver.yaml" % args.jobname, "w") as fn:
yaml.dump(ps, fn)
with open("%s/trainer.yaml" % args.jobname, "w") as fn:
yaml.dump(tn, fn)
if __name__ == "__main__":
gen_job()
| 7,071 | 35.833333 | 100 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/kube_templates/pserver.py
|
# 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.
pserver = {
"apiVersion": "extensions/v1beta1",
"kind": "ReplicaSet",
"metadata": {
"name": "jobname-pserver"
},
"spec": {
"replicas": 1,
"template": {
"metadata": {
"labels": {
"paddle-job-pserver": "jobname"
}
},
"spec": {
"hostNetwork": True,
"imagePullSecrets": [{
"name": "job-registry-secret"
}],
"containers": [{
"name": "pserver",
"image": "",
"imagePullPolicy": "Always",
"ports": [{
"name": "jobport-1",
"containerPort": 1
}],
"env": [],
"command": ["paddle_k8s", "start_pserver"],
"resources": {
"requests": {
"memory": "10Gi",
"cpu": "4"
},
"limits": {
"memory": "10Gi",
"cpu": "4"
}
}
}]
}
}
}
}
| 1,912 | 31.423729 | 74 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/kube_templates/__init__.py
|
# 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.
from pserver import pserver
from trainer import trainer
__all__ = ["pserver", "trainer", "envs"]
envs = [
# envs that don't need to change
{
"name": "GLOG_v",
"value": "0"
},
{
"name": "GLOG_logtostderr",
"value": "1"
},
{
"name": "TOPOLOGY",
"value": ""
},
{
"name": "TRAINER_PACKAGE",
"value": "/workspace"
},
{
"name": "PADDLE_INIT_NICS",
"value": "eth2"
},
{
"name": "NAMESPACE",
"valueFrom": {
"fieldRef": {
"fieldPath": "metadata.namespace"
}
}
},
{
"name": "POD_IP",
"valueFrom": {
"fieldRef": {
"fieldPath": "status.podIP"
}
}
},
{
"name": "PADDLE_CURRENT_IP",
"valueFrom": {
"fieldRef": {
"fieldPath": "status.podIP"
}
}
}
]
| 1,587 | 22.701493 | 74 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/kube_templates/trainer.py
|
# 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.
trainer = {
"apiVersion": "batch/v1",
"kind": "Job",
"metadata": {
"name": "jobname-pserver"
},
"spec": {
"parallelism": 4,
"completions": 4,
"template": {
"metadata": {
"labels": {
"paddle-job": "jobname"
}
},
"spec": {
"hostNetwork": True,
"imagePullSecrets": [{
"name": "job-registry-secret"
}],
"restartPolicy": "Never",
"containers": [{
"name": "trainer",
"image": "",
"imagePullPolicy": "Always",
# to let container set rlimit
"securityContext": {
"privileged": True
# TODO(wuyi): use below specific cap instead of privileged,
# using privileged will cause all GPU device are visible
# in the container.
# "capabilities": {
# "add": ["SYS_RESOURCE"]
# }
},
"ports": [{
"name": "jobport-1",
"containerPort": 1
}],
"env": [],
"command": ["paddle_k8s", "start_trainer", "v2"],
"resources": {
"requests": {
"memory": "10Gi",
"cpu": "4",
},
"limits": {
"memory": "10Gi",
"cpu": "4",
}
}
}]
}
}
}
}
| 2,458 | 33.633803 | 83 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/models/resnet.py
|
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import numpy as np
import time
import cProfile, pstats, StringIO
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
conv1 = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=False)
return fluid.layers.batch_norm(input=conv1, act=act)
def shortcut(input, ch_out, stride):
ch_in = input.shape[1] # if args.data_format == 'NCHW' else input.shape[-1]
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
else:
return input
def basicblock(input, ch_out, stride):
short = shortcut(input, ch_out, stride)
conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
def bottleneck(input, ch_out, stride):
short = shortcut(input, ch_out * 4, stride)
conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
def layer_warp(block_func, input, ch_out, count, stride):
res_out = block_func(input, ch_out, stride)
for i in range(1, count):
res_out = block_func(res_out, ch_out, 1)
return res_out
def resnet_imagenet(input, class_dim, depth=50, data_format='NCHW'):
cfg = {
18: ([2, 2, 2, 1], basicblock),
34: ([3, 4, 6, 3], basicblock),
50: ([3, 4, 6, 3], bottleneck),
101: ([3, 4, 23, 3], bottleneck),
152: ([3, 8, 36, 3], bottleneck)
}
stages, block_func = cfg[depth]
conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
pool1 = fluid.layers.pool2d(
input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
res2 = layer_warp(block_func, res1, 128, stages[1], 2)
res3 = layer_warp(block_func, res2, 256, stages[2], 2)
res4 = layer_warp(block_func, res3, 512, stages[3], 2)
pool2 = fluid.layers.pool2d(
input=res4,
pool_size=7,
pool_type='avg',
pool_stride=1,
global_pooling=True)
out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
return out
def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
assert (depth - 2) % 6 == 0
n = (depth - 2) // 6
conv1 = conv_bn_layer(
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, n, 1)
res2 = layer_warp(basicblock, res1, 32, n, 2)
res3 = layer_warp(basicblock, res2, 64, n, 2)
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
return out
def get_model(args):
model = resnet_cifar10
if args.data_set == "cifar10":
class_dim = 10
if args.data_format == 'NCHW':
dshape = [3, 32, 32]
else:
dshape = [32, 32, 3]
model = resnet_cifar10
else:
class_dim = 102
if args.data_format == 'NCHW':
dshape = [3, 224, 224]
else:
dshape = [224, 224, 3]
model = resnet_imagenet
input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
predict = model(input, class_dim)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program(
target_vars=[batch_acc, batch_size_tensor])
optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.cifar.test10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
batch_size=args.batch_size)
return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc
| 5,571 | 33.395062 | 87 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/models/vgg.py
|
# 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.
"""VGG16 benchmark in Fluid"""
from __future__ import print_function
import sys
import time
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import argparse
import functools
def vgg16_bn_drop(input):
def conv_block(input, num_filter, groups, dropouts):
return fluid.nets.img_conv_group(
input=input,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * groups,
conv_filter_size=3,
conv_act='relu',
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type='max')
conv1 = conv_block(input, 64, 2, [0.3, 0])
conv2 = conv_block(conv1, 128, 2, [0.4, 0])
conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=512, act=None)
bn = fluid.layers.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
return fc2
def get_model(args):
if args.data_set == "cifar10":
classdim = 10
if args.data_format == 'NCHW':
data_shape = [3, 32, 32]
else:
data_shape = [32, 32, 3]
else:
classdim = 102
if args.data_format == 'NCHW':
data_shape = [3, 224, 224]
else:
data_shape = [224, 224, 3]
# Input data
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
net = vgg16_bn_drop(images)
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
# Evaluator
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
# inference program
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program(
target_vars=[batch_acc, batch_size_tensor])
# Optimization
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
# data reader
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.cifar.test10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
batch_size=args.batch_size)
return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc
| 3,656 | 33.828571 | 87 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/models/stacked_dynamic_lstm.py
|
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import cPickle
import os
import random
import time
import numpy
import paddle
import paddle.dataset.imdb as imdb
import paddle.fluid as fluid
import paddle.batch as batch
import paddle.fluid.profiler as profiler
word_dict = imdb.word_dict()
def crop_sentence(reader, crop_size):
unk_value = word_dict['<unk>']
def __impl__():
for item in reader():
if len([x for x in item[0] if x != unk_value]) < crop_size:
yield item
return __impl__
def get_model(args):
lstm_size = 512
emb_dim = 512
crop_size = 1500
data = fluid.layers.data(
name="words", shape=[1], lod_level=1, dtype='int64')
sentence = fluid.layers.embedding(
input=data, size=[len(word_dict), emb_dim])
sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')
rnn = fluid.layers.DynamicRNN()
with rnn.block():
word = rnn.step_input(sentence)
prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
def gate_common(
ipt,
hidden,
size, ):
gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
gate = fluid.layers.sums(input=[gate0, gate1])
return gate
forget_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
input_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
output_gate = fluid.layers.sigmoid(
x=gate_common(word, prev_hidden, lstm_size))
cell_gate = fluid.layers.tanh(
x=gate_common(word, prev_hidden, lstm_size))
cell = fluid.layers.sums(input=[
fluid.layers.elementwise_mul(
x=forget_gate, y=prev_cell), fluid.layers.elementwise_mul(
x=input_gate, y=cell_gate)
])
hidden = fluid.layers.elementwise_mul(
x=output_gate, y=fluid.layers.tanh(x=cell))
rnn.update_memory(prev_cell, cell)
rnn.update_memory(prev_hidden, hidden)
rnn.output(hidden)
last = fluid.layers.sequence_pool(rnn(), 'last')
logit = fluid.layers.fc(input=last, size=2, act='softmax')
loss = fluid.layers.cross_entropy(
input=logit,
label=fluid.layers.data(
name='label', shape=[1], dtype='int64'))
loss = fluid.layers.mean(x=loss)
# add acc
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
shape=[1], dtype='int64'), total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program(
target_vars=[batch_acc, batch_size_tensor])
adam = fluid.optimizer.Adam()
train_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.train(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size)
test_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.test(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size)
return loss, inference_program, adam, train_reader, test_reader, batch_acc
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = numpy.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
| 4,607 | 31.914286 | 90 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/models/__init__.py
|
# 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__ = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
]
| 701 | 38 | 75 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/models/machine_translation.py
|
# 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.
"""seq2seq model for fluid."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import time
import distutils.util
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.framework as framework
from paddle.fluid.executor import Executor
def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
def linear(inputs):
return fluid.layers.fc(input=inputs, size=size, bias_attr=True)
forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t]))
cell_t = fluid.layers.sums(input=[
fluid.layers.elementwise_mul(
x=forget_gate, y=cell_t_prev), fluid.layers.elementwise_mul(
x=input_gate, y=cell_tilde)
])
hidden_t = fluid.layers.elementwise_mul(
x=output_gate, y=fluid.layers.tanh(x=cell_t))
return hidden_t, cell_t
def seq_to_seq_net(embedding_dim, encoder_size, decoder_size, source_dict_dim,
target_dict_dim, is_generating, beam_size, max_length):
"""Construct a seq2seq network."""
def bi_lstm_encoder(input_seq, gate_size):
# Linear transformation part for input gate, output gate, forget gate
# and cell activation vectors need be done outside of dynamic_lstm.
# So the output size is 4 times of gate_size.
input_forward_proj = fluid.layers.fc(input=input_seq,
size=gate_size * 4,
act=None,
bias_attr=False)
forward, _ = fluid.layers.dynamic_lstm(
input=input_forward_proj, size=gate_size * 4, use_peepholes=False)
input_reversed_proj = fluid.layers.fc(input=input_seq,
size=gate_size * 4,
act=None,
bias_attr=False)
reversed, _ = fluid.layers.dynamic_lstm(
input=input_reversed_proj,
size=gate_size * 4,
is_reverse=True,
use_peepholes=False)
return forward, reversed
src_word_idx = fluid.layers.data(
name='source_sequence', shape=[1], dtype='int64', lod_level=1)
src_embedding = fluid.layers.embedding(
input=src_word_idx,
size=[source_dict_dim, embedding_dim],
dtype='float32')
src_forward, src_reversed = bi_lstm_encoder(
input_seq=src_embedding, gate_size=encoder_size)
encoded_vector = fluid.layers.concat(
input=[src_forward, src_reversed], axis=1)
encoded_proj = fluid.layers.fc(input=encoded_vector,
size=decoder_size,
bias_attr=False)
backward_first = fluid.layers.sequence_pool(
input=src_reversed, pool_type='first')
decoder_boot = fluid.layers.fc(input=backward_first,
size=decoder_size,
bias_attr=False,
act='tanh')
def lstm_decoder_with_attention(target_embedding, encoder_vec, encoder_proj,
decoder_boot, decoder_size):
def simple_attention(encoder_vec, encoder_proj, decoder_state):
decoder_state_proj = fluid.layers.fc(input=decoder_state,
size=decoder_size,
bias_attr=False)
decoder_state_expand = fluid.layers.sequence_expand(
x=decoder_state_proj, y=encoder_proj)
concated = fluid.layers.concat(
input=[encoder_proj, decoder_state_expand], axis=1)
attention_weights = fluid.layers.fc(input=concated,
size=1,
act='tanh',
bias_attr=False)
attention_weights = fluid.layers.sequence_softmax(
input=attention_weights)
weigths_reshape = fluid.layers.reshape(
x=attention_weights, shape=[-1])
scaled = fluid.layers.elementwise_mul(
x=encoder_vec, y=weigths_reshape, axis=0)
context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')
return context
rnn = fluid.layers.DynamicRNN()
cell_init = fluid.layers.fill_constant_batch_size_like(
input=decoder_boot,
value=0.0,
shape=[-1, decoder_size],
dtype='float32')
cell_init.stop_gradient = False
with rnn.block():
current_word = rnn.step_input(target_embedding)
encoder_vec = rnn.static_input(encoder_vec)
encoder_proj = rnn.static_input(encoder_proj)
hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)
cell_mem = rnn.memory(init=cell_init)
context = simple_attention(encoder_vec, encoder_proj, hidden_mem)
decoder_inputs = fluid.layers.concat(
input=[context, current_word], axis=1)
h, c = lstm_step(decoder_inputs, hidden_mem, cell_mem, decoder_size)
rnn.update_memory(hidden_mem, h)
rnn.update_memory(cell_mem, c)
out = fluid.layers.fc(input=h,
size=target_dict_dim,
bias_attr=True,
act='softmax')
rnn.output(out)
return rnn()
if not is_generating:
trg_word_idx = fluid.layers.data(
name='target_sequence', shape=[1], dtype='int64', lod_level=1)
trg_embedding = fluid.layers.embedding(
input=trg_word_idx,
size=[target_dict_dim, embedding_dim],
dtype='float32')
prediction = lstm_decoder_with_attention(trg_embedding, encoded_vector,
encoded_proj, decoder_boot,
decoder_size)
label = fluid.layers.data(
name='label_sequence', shape=[1], dtype='int64', lod_level=1)
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
feeding_list = ["source_sequence", "target_sequence", "label_sequence"]
return avg_cost, feeding_list
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
lod_t = core.LoDTensor()
lod_t.set(flattened_data, place)
lod_t.set_lod([lod])
return lod_t, lod[-1]
def lodtensor_to_ndarray(lod_tensor):
dims = lod_tensor.get_dims()
ndarray = np.zeros(shape=dims).astype('float32')
for i in xrange(np.product(dims)):
ndarray.ravel()[i] = lod_tensor.get_float_element(i)
return ndarray
def get_model(args):
embedding_dim = 512
encoder_size = 512
decoder_size = 512
dict_size = 30000
beam_size = 3
max_length = 250
avg_cost, feeding_list = seq_to_seq_net(
embedding_dim,
encoder_size,
decoder_size,
dict_size,
dict_size,
False,
beam_size=beam_size,
max_length=max_length)
# clone from default main program
inference_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
train_batch_generator = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=args.batch_size)
test_batch_generator = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.test(dict_size), buf_size=1000),
batch_size=args.batch_size)
return avg_cost, inference_program, optimizer, train_batch_generator, \
test_batch_generator, None
| 9,045 | 37.824034 | 80 |
py
|
Paddle
|
Paddle-master/benchmark/fluid/models/mnist.py
|
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import time
import cProfile
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
SEED = 1
DTYPE = "float32"
# random seed must set before configuring the network.
# fluid.default_startup_program().random_seed = SEED
def cnn_model(data):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=data,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
# TODO(dzhwinter) : refine the initializer and random seed settting
SIZE = 10
input_shape = conv_pool_2.shape
param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
predict = fluid.layers.fc(
input=conv_pool_2,
size=SIZE,
act="softmax",
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)))
return predict
def get_model(args):
# Input data
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
predict = cnn_model(images)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
# Evaluator
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
# inference program
inference_program = fluid.default_main_program().clone()
# Optimization
opt = fluid.optimizer.AdamOptimizer(
learning_rate=0.001, beta1=0.9, beta2=0.999)
# Reader
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=args.batch_size)
return avg_cost, inference_program, opt, train_reader, test_reader, batch_acc
| 2,943 | 29.989474 | 81 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/plotlog.py
|
# Copyright (c) 2016 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 sys
import argparse
import matplotlib.pyplot as plt
def parse_args():
parser = argparse.ArgumentParser('Parse Log')
parser.add_argument(
'--file_path', '-f', type=str, help='the path of the log file')
parser.add_argument(
'--sample_rate',
'-s',
type=float,
default=1.0,
help='the rate to take samples from log')
parser.add_argument(
'--log_period', '-p', type=int, default=1, help='the period of log')
args = parser.parse_args()
return args
def parse_file(file_name):
loss = []
error = []
with open(file_name) as f:
for i, line in enumerate(f):
line = line.strip()
if not line.startswith('pass'):
continue
line_split = line.split(' ')
if len(line_split) != 5:
continue
loss_str = line_split[2][:-1]
cur_loss = float(loss_str.split('=')[-1])
loss.append(cur_loss)
err_str = line_split[3][:-1]
cur_err = float(err_str.split('=')[-1])
error.append(cur_err)
accuracy = [1.0 - err for err in error]
return loss, accuracy
def sample(metric, sample_rate):
interval = int(1.0 / sample_rate)
if interval > len(metric):
return metric[:1]
num = len(metric) / interval
idx = [interval * i for i in range(num)]
metric_sample = [metric[id] for id in idx]
return metric_sample
def plot_metric(metric,
batch_id,
graph_title,
line_style='b-',
line_label='y',
line_num=1):
plt.figure()
plt.title(graph_title)
if line_num == 1:
plt.plot(batch_id, metric, line_style, label=line_label)
else:
for i in range(line_num):
plt.plot(batch_id, metric[i], line_style[i], label=line_label[i])
plt.xlabel('batch')
plt.ylabel(graph_title)
plt.legend()
plt.savefig(graph_title + '.jpg')
plt.close()
def main():
args = parse_args()
assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
loss, accuracy = parse_file(args.file_path)
batch = [args.log_period * i for i in range(len(loss))]
batch_sample = sample(batch, args.sample_rate)
loss_sample = sample(loss, args.sample_rate)
accuracy_sample = sample(accuracy, args.sample_rate)
plot_metric(loss_sample, batch_sample, 'loss', line_label='loss')
plot_metric(
accuracy_sample,
batch_sample,
'accuracy',
line_style='g-',
line_label='accuracy')
if __name__ == '__main__':
main()
| 3,298 | 27.686957 | 107 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/smallnet_mnist_cifar.py
|
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 32
width = 32
num_class = 10
batch_size = get_config_arg('batch_size', int, 128)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(
input=net,
filter_size=5,
num_channels=3,
num_filters=32,
stride=1,
padding=2)
net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)
# conv2
net = img_conv_layer(
input=net, filter_size=5, num_filters=32, stride=1, padding=2)
net = img_pool_layer(
input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
# conv3
net = img_conv_layer(
input=net, filter_size=3, num_filters=64, stride=1, padding=1)
net = img_pool_layer(
input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
net = fc_layer(input=net, size=64, act=ReluActivation())
net = fc_layer(input=net, size=10, act=SoftmaxActivation())
lab = data_layer('label', num_class)
loss = classification_cost(input=net, label=lab)
outputs(loss)
| 1,367 | 26.36 | 80 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/resnet.py
|
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg("layer_num", int, 50)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
#######################Network Configuration #############
def conv_bn_layer(name,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
active_type=ReluActivation()):
"""
A wrapper for conv layer with batch normalization layers.
Note:
conv layer has no activation.
"""
tmp = img_conv_layer(
name=name + "_conv",
input=input,
filter_size=filter_size,
num_channels=channels,
num_filters=num_filters,
stride=stride,
padding=padding,
act=LinearActivation(),
bias_attr=False)
return batch_norm_layer(
name=name + "_bn",
input=tmp,
act=active_type,
use_global_stats=is_infer)
def bottleneck_block(name, input, num_filters1, num_filters2):
"""
A wrapper for bottlenect building block in ResNet.
Last conv_bn_layer has no activation.
Addto layer has activation of relu.
"""
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=1,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[input, last_name], act=ReluActivation())
def mid_projection(name, input, num_filters1, num_filters2, stride=2):
"""
A wrapper for middile projection in ResNet.
projection shortcuts are used for increasing dimensions,
and other shortcuts are identity
branch1: projection shortcuts are used for increasing
dimensions, has no activation.
branch2x: bottleneck building block, shortcuts are identity.
"""
# stride = 2
branch1 = conv_bn_layer(
name=name + '_branch1',
input=input,
filter_size=1,
num_filters=num_filters2,
stride=stride,
padding=0,
active_type=LinearActivation())
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=stride,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
img = data_layer(name='image', size=height * width * 3)
def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
"""
A wrapper for 50,101,152 layers of ResNet.
res2_num: number of blocks stacked in conv2_x
res3_num: number of blocks stacked in conv3_x
res4_num: number of blocks stacked in conv4_x
res5_num: number of blocks stacked in conv5_x
"""
# For ImageNet
# conv1: 112x112
tmp = conv_bn_layer(
"conv1",
input=img,
filter_size=7,
channels=3,
num_filters=64,
stride=2,
padding=3)
tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
# conv2_x: 56x56
tmp = mid_projection(
name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
for i in xrange(2, res2_num + 1, 1):
tmp = bottleneck_block(
name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
# conv3_x: 28x28
tmp = mid_projection(
name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
for i in xrange(2, res3_num + 1, 1):
tmp = bottleneck_block(
name="res3_" + str(i),
input=tmp,
num_filters1=128,
num_filters2=512)
# conv4_x: 14x14
tmp = mid_projection(
name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
for i in xrange(2, res4_num + 1, 1):
tmp = bottleneck_block(
name="res4_" + str(i),
input=tmp,
num_filters1=256,
num_filters2=1024)
# conv5_x: 7x7
tmp = mid_projection(
name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
for i in xrange(2, res5_num + 1, 1):
tmp = bottleneck_block(
name="res5_" + str(i),
input=tmp,
num_filters1=512,
num_filters2=2048)
tmp = img_pool_layer(
name='avgpool',
input=tmp,
pool_size=7,
stride=1,
pool_type=AvgPooling())
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 50:
resnet = deep_res_net(3, 4, 6, 3)
elif layer_num == 101:
resnet = deep_res_net(3, 4, 23, 3)
elif layer_num == 152:
resnet = deep_res_net(3, 8, 36, 3)
else:
print("Wrong layer number.")
if is_infer:
outputs(resnet)
else:
lbl = data_layer(name="label", size=num_class)
loss = cross_entropy(name='loss', input=resnet, label=lbl)
outputs(loss)
| 6,378 | 26.614719 | 80 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/vgg.py
|
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg('layer_num', int, 19)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.001 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
img = data_layer(name='image', size=height * width * 3)
def vgg_network(vgg_num=3):
tmp = img_conv_group(
input=img,
num_channels=3,
conv_padding=1,
conv_num_filter=[64, 64],
conv_filter_size=3,
conv_act=ReluActivation(),
pool_size=2,
pool_stride=2,
pool_type=MaxPooling())
tmp = img_conv_group(
input=tmp,
conv_num_filter=[128, 128],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
channels = []
for i in range(vgg_num):
channels.append(256)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
channels = []
for i in range(vgg_num):
channels.append(512)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 16:
vgg = vgg_network(3)
elif layer_num == 19:
vgg = vgg_network(4)
else:
print("Wrong layer number.")
if is_infer:
outputs(vgg)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=vgg, label=lab)
outputs(loss)
| 2,910 | 23.258333 | 71 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/googlenet.py
|
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
conv_projection = conv_projection if use_gpu else img_conv_layer
def inception2(name, input, channels, \
filter1,
filter3R, filter3,
filter5R, filter5,
proj):
conv1 = name + '_1'
conv3r = name + '_3r'
conv3 = name + '_3'
conv5r = name + '_5r'
conv5 = name + '_5'
maxpool = name + '_max'
convproj = name + '_proj'
cov1 = img_conv_layer(
name=conv1,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = img_conv_layer(
name=conv3r,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = img_conv_layer(
name=conv3,
input=cov3r,
filter_size=3,
num_filters=filter3,
stride=1,
padding=1)
cov5r = img_conv_layer(
name=conv5r,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = img_conv_layer(
name=conv5,
input=cov5r,
filter_size=5,
num_filters=filter5,
stride=1,
padding=2)
pool1 = img_pool_layer(
name=maxpool,
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = img_conv_layer(
name=convproj,
input=pool1,
filter_size=1,
num_filters=proj,
stride=1,
padding=0)
cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj])
return cat
def inception(name, input, channels, \
filter1,
filter3R, filter3,
filter5R, filter5,
proj):
cov1 = conv_projection(
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = img_conv_layer(
name=name + '_3r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = conv_projection(
input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1)
cov5r = img_conv_layer(
name=name + '_5r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = conv_projection(
input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2)
pool1 = img_pool_layer(
name=name + '_max',
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = conv_projection(
input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0)
cat = concat_layer(
name=name,
input=[cov1, cov3, cov5, covprj],
bias_attr=True if use_gpu else False,
act=ReluActivation())
return cat
data = data_layer(name="input", size=3 * height * width)
# stage 1
conv1 = img_conv_layer(
name="conv1",
input=data,
filter_size=7,
num_channels=3,
num_filters=64,
stride=2,
padding=3)
pool1 = img_pool_layer(
name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2)
# stage 2
conv2_1 = img_conv_layer(
name="conv2_1",
input=pool1,
filter_size=1,
num_filters=64,
stride=1,
padding=0)
conv2_2 = img_conv_layer(
name="conv2_2",
input=conv2_1,
filter_size=3,
num_filters=192,
stride=1,
padding=1)
pool2 = img_pool_layer(
name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
# stage 3
ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
pool3 = img_pool_layer(
name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
# stage 4
ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
pool4 = img_pool_layer(
name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
# stage 5
ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
pool5 = img_pool_layer(
name="pool5",
input=ince5b,
num_channels=1024,
pool_size=7,
stride=7,
pool_type=AvgPooling())
# We remove loss1 and loss2 for all system when testing benchmark
# output 1
# pool_o1 = img_pool_layer(name="pool_o1", input=ince4a, num_channels=512, pool_size=5, stride=3, pool_type=AvgPooling())
# conv_o1 = img_conv_layer(name="conv_o1", input=pool_o1, filter_size=1, num_filters=128, stride=1, padding=0)
# fc_o1 = fc_layer(name="fc_o1", input=conv_o1, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
# out1 = fc_layer(name="output1", input=fc_o1, size=1000, act=SoftmaxActivation())
# loss1 = cross_entropy(name='loss1', input=out1, label=lab, coeff=0.3)
# output 2
#pool_o2 = img_pool_layer(name="pool_o2", input=ince4d, num_channels=528, pool_size=5, stride=3, pool_type=AvgPooling())
#conv_o2 = img_conv_layer(name="conv_o2", input=pool_o2, filter_size=1, num_filters=128, stride=1, padding=0)
#fc_o2 = fc_layer(name="fc_o2", input=conv_o2, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
#out2 = fc_layer(name="output2", input=fc_o2, size=1000, act=SoftmaxActivation())
#loss2 = cross_entropy(name='loss2', input=out2, label=lab, coeff=0.3)
# output 3
dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
out3 = fc_layer(
name="output3", input=dropout, size=1000, act=SoftmaxActivation())
if is_infer:
outputs(out3)
else:
lab = data_layer(name="label", size=num_class)
loss3 = cross_entropy(name='loss3', input=out3, label=lab)
outputs(loss3)
| 6,920 | 27.134146 | 121 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/provider.py
|
# 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 io, os
import random
import numpy as np
from paddle.trainer.PyDataProvider2 import *
def initHook(settings, height, width, color, num_class, **kwargs):
settings.height = height
settings.width = width
settings.color = color
settings.num_class = num_class
if settings.color:
settings.data_size = settings.height * settings.width * 3
else:
settings.data_size = settings.height * settings.width
settings.is_infer = kwargs.get('is_infer', False)
settings.num_samples = kwargs.get('num_samples', 2560)
if settings.is_infer:
settings.slots = [dense_vector(settings.data_size)]
else:
settings.slots = [dense_vector(settings.data_size), integer_value(1)]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_list):
for i in xrange(settings.num_samples):
img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
if settings.is_infer:
yield img.astype('float32')
else:
lab = random.randint(0, settings.num_class - 1)
yield img.astype('float32'), int(lab)
| 1,778 | 36.0625 | 77 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/image/alexnet.py
|
# 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.
from paddle.trainer_config_helpers import *
height = 227
width = 227
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
gp = get_config_arg('layer_num', int, 1)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(
input=net,
filter_size=11,
num_channels=3,
num_filters=96,
stride=4,
padding=1)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)
# conv2
net = img_conv_layer(
input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)
# conv3
net = img_conv_layer(
input=net, filter_size=3, num_filters=384, stride=1, padding=1)
# conv4
net = img_conv_layer(
input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
# conv5
net = img_conv_layer(
input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
net = img_pool_layer(input=net, pool_size=3, stride=2)
net = fc_layer(
input=net,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(
input=net,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
if is_infer:
outputs(net)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=net, label=lab)
outputs(loss)
| 2,747 | 28.234043 | 78 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/rnn/imdb.py
|
# 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.
from __future__ import print_function
import six.moves.cPickle as pickle
import gzip
import os
import numpy
def get_dataset_file(dataset, default_dataset, origin):
data_dir, data_file = os.path.split(dataset)
if (not os.path.isfile(dataset)) and data_file == default_dataset:
from six.moves import urllib
print('Downloading data from %s' % origin)
urllib.request.urlretrieve(origin, dataset)
return dataset
def create_data(path="imdb.pkl"):
if (not os.path.isfile('imdb.train.pkl')):
path = get_dataset_file(
path, "imdb.pkl",
"http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl")
if path.endswith(".gz"):
f = gzip.open(path, 'rb')
else:
f = open(path, 'rb')
train_set = pickle.load(f)
test_set = pickle.load(f)
f.close()
pickle.dump(train_set, open('imdb.train.pkl', 'wb'))
pickle.dump(test_set, open('imdb.test.pkl', 'wb'))
if (not os.path.isfile('train.list')):
file('train.list', 'w').write('imdb.train.pkl\n')
def main():
create_data('imdb.pkl')
if __name__ == "__main__":
main()
| 1,786 | 28.295082 | 74 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/rnn/rnn.py
|
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
import imdb
num_class = 2
vocab_size = 30000
fixedlen = 100
batch_size = get_config_arg('batch_size', int, 128)
lstm_num = get_config_arg('lstm_num', int, 1)
hidden_size = get_config_arg('hidden_size', int, 128)
# whether to pad sequence into fixed length
pad_seq = get_config_arg('pad_seq', bool, True)
imdb.create_data('imdb.pkl')
args = {'vocab_size': vocab_size, 'pad_seq': pad_seq, 'maxlen': fixedlen}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25)
net = data_layer('data', size=vocab_size)
net = embedding_layer(input=net, size=128)
for i in xrange(lstm_num):
net = simple_lstm(input=net, size=hidden_size)
net = last_seq(input=net)
net = fc_layer(input=net, size=2, act=SoftmaxActivation())
lab = data_layer('label', num_class)
loss = classification_cost(input=net, label=lab)
outputs(loss)
| 1,100 | 27.230769 | 73 |
py
|
Paddle
|
Paddle-master/benchmark/paddle/rnn/provider.py
|
# 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 io, os
import random
import numpy as np
import six.moves.cPickle as pickle
from paddle.trainer.PyDataProvider2 import *
def remove_unk(x, n_words):
return [[1 if w >= n_words else w for w in sen] for sen in x]
# ==============================================================
# tensorflow uses fixed length, but PaddlePaddle can process
# variable-length. Padding is used in benchmark in order to
# compare with other platform.
# ==============================================================
def pad_sequences(sequences,
maxlen=None,
dtype='int32',
padding='post',
truncating='post',
value=0.):
lengths = [len(s) for s in sequences]
nb_samples = len(sequences)
if maxlen is None:
maxlen = np.max(lengths)
x = (np.ones((nb_samples, maxlen)) * value).astype(dtype)
for idx, s in enumerate(sequences):
if len(s) == 0:
continue # empty list was found
if truncating == 'pre':
trunc = s[-maxlen:]
elif truncating == 'post':
trunc = s[:maxlen]
else:
raise ValueError("Truncating type '%s' not understood" % padding)
if padding == 'post':
x[idx, :len(trunc)] = trunc
elif padding == 'pre':
x[idx, -len(trunc):] = trunc
else:
raise ValueError("Padding type '%s' not understood" % padding)
return x
def initHook(settings, vocab_size, pad_seq, maxlen, **kwargs):
settings.vocab_size = vocab_size
settings.pad_seq = pad_seq
settings.maxlen = maxlen
settings.input_types = [
integer_value_sequence(vocab_size), integer_value(2)
]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file):
f = open(file, 'rb')
train_set = pickle.load(f)
f.close()
x, y = train_set
# remove unk, namely remove the words out of dictionary
x = remove_unk(x, settings.vocab_size)
if settings.pad_seq:
x = pad_sequences(x, maxlen=settings.maxlen, value=0.)
for i in range(len(y)):
yield map(int, x[i]), int(y[i])
| 2,816 | 31.37931 | 77 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/resnet.py
|
# 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.
"""
based on https://github.com/tensorflow/models/blob/master/official/resnet/resnet_model.py
Get help: python resnet.py --help
See performance on flowers: python resnet.py
Train on cifar10: python resnet.py --data=cifar10 --with_test
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import time
import numpy as np
import paddle.v2 as paddle
import tensorflow as tf
DTYPE = tf.float32
def parse_args():
parser = argparse.ArgumentParser('Convolution model benchmark.')
parser.add_argument(
'--model',
type=str,
choices=['resnet'],
default='resnet',
help='The model architecture.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='use real data or fake data')
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations',
type=int,
default=105,
help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=300, help='The number of passes.')
parser.add_argument(
'--order',
type=str,
default='NHWC',
choices=['NCHW', 'NHWC'],
help='The data order, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--data',
type=str,
default='flowers102',
choices=['flowers102', 'cifar10'],
help='The kinds of data.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--with_test',
action='store_true',
help='If set, test the testset during training.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
args = parser.parse_args()
return args
def print_arguments(args):
vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
vars(args)['device'] == 'GPU')
vars(args)['iterations'] = vars(args)['pass_num'] * 1000 if vars(args)[
'with_test'] else vars(args)['iterations']
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def fixed_padding(inputs, kernel_size, data_format):
"""Pads the input along the spatial dimensions independently of input size.
Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
[batch, height_in, width_in, channels] depending on data_format.
kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
Should be a positive integer.
data_format: The input format ('channels_last' or 'channels_first').
Returns:
A tensor with the same format as the input with the data either intact
(if kernel_size == 1) or padded (if kernel_size > 1).
"""
pad_total = kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
if data_format == 'channels_first':
padded_inputs = tf.pad(inputs, [[0, 0], [0, 0], [pad_beg, pad_end],
[pad_beg, pad_end]])
else:
padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
[pad_beg, pad_end], [0, 0]])
return padded_inputs
def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
"""Strided 2-D convolution with explicit padding."""
# The padding is consistent and is based only on `kernel_size`, not on the
# dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
# This is consistent with PaddlePaddle.
# In addition, the calculation for output size in TensorFlow can refer:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/common_shape_fns.cc
if strides > 1:
inputs = fixed_padding(inputs, kernel_size, data_format)
return tf.layers.conv2d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=('SAME' if strides == 1 else 'VALID'),
use_bias=False,
kernel_initializer=tf.variance_scaling_initializer(),
data_format=data_format)
def conv_bn(inputs,
filters,
kernel_size,
strides,
is_training,
data_format,
act=True):
# def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
# set fused=True for a significant performance boost. See
# https://www.tensorflow.org/performance/performance_guide#common_fused_ops
inputs = conv2d_fixed_padding(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
data_format=data_format)
inputs = tf.layers.batch_normalization(
inputs=inputs,
axis=1 if data_format == 'channels_first' else 3,
momentum=0.9,
epsilon=1e-05,
center=True,
scale=True,
training=is_training,
fused=True)
if act:
inputs = tf.nn.relu(inputs)
return inputs
def basicblock(inputs, filters, is_training, projection_shortcut, strides,
data_format):
shortcut = inputs
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
inputs = conv_bn(inputs, filters, 3, strides, is_training, data_format)
inputs = conv_bn(inputs, filters, 3, 1, is_training, data_format, act=False)
inputs = inputs + shortcut
inputs = tf.nn.relu(inputs)
return inputs
def bottleneck(inputs, filters, is_training, projection_shortcut, strides,
data_format):
shortcut = inputs
if projection_shortcut is not None:
shortcut = projection_shortcut(inputs)
inputs = conv_bn(inputs, filters, 1, strides, is_training, data_format)
inputs = conv_bn(inputs, filters, 3, 1, is_training, data_format, act=False)
inputs = conv_bn(
inputs, filters * 4, 1, 1, is_training, data_format, act=False)
inputs = inputs + shortcut
inputs = tf.nn.relu(inputs)
return inputs
def block_layer(inputs, filters, block_fn, blocks, strides, is_training, name,
data_format):
# Bottleneck blocks end with 4x the number of filters as they start with
filters_out = 4 * filters if block_fn is bottleneck else filters
def projection_shortcut(inputs):
return conv2d_fixed_padding(
inputs=inputs,
filters=filters_out,
kernel_size=1,
strides=strides,
data_format=data_format)
# Only the first block per block_layer uses projection_shortcut and strides
inputs = block_fn(inputs, filters, is_training, projection_shortcut,
strides, data_format)
for _ in range(1, blocks):
inputs = block_fn(inputs, filters, is_training, None, 1, data_format)
return tf.identity(inputs, name)
def resnet_imagenet(depth, class_dim, data_format):
"""Returns the ResNet model for a given size and number of output classes."""
def resnet_generator(block_fn,
layers,
num_classes,
data_format='channels_last'):
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
def model(inputs, is_training):
"""Constructs the ResNet model given the inputs."""
if data_format == 'channels_first':
# Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
# This provides a large performance boost on GPU. See
# https://www.tensorflow.org/performance/performance_guide#data_formats
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv_bn(inputs, 64, 7, 2, is_training, data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = tf.layers.max_pooling2d(
inputs=inputs,
pool_size=3,
strides=2,
padding='SAME',
data_format=data_format)
inputs = tf.identity(inputs, 'initial_max_pool')
inputs = block_layer(inputs, 64, block_fn, layers[0], 1,
is_training, 'block_layer1', data_format)
inputs = block_layer(inputs, 128, block_fn, layers[1], 2,
is_training, 'block_layer2', data_format)
inputs = block_layer(inputs, 256, block_fn, layers[2], 2,
is_training, 'block_layer3', data_format)
inputs = block_layer(inputs, 512, block_fn, layers[3], 2,
is_training, 'block_layer4', data_format)
inputs = tf.layers.average_pooling2d(
inputs=inputs,
pool_size=7,
strides=1,
padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs,
[-1, 512 if block_fn is basicblock else 2048])
inputs = tf.layers.dense(inputs=inputs, units=num_classes)
inputs = tf.identity(inputs, 'final_dense')
return inputs
return model
model_params = {
18: {
'block': basicblock,
'layers': [2, 2, 2, 2]
},
34: {
'block': basicblock,
'layers': [3, 4, 6, 3]
},
50: {
'block': bottleneck,
'layers': [3, 4, 6, 3]
},
101: {
'block': bottleneck,
'layers': [3, 4, 23, 3]
},
152: {
'block': bottleneck,
'layers': [3, 8, 36, 3]
},
200: {
'block': bottleneck,
'layers': [3, 24, 36, 3]
}
}
if depth not in model_params:
raise ValueError('Not a valid depth:', depth)
params = model_params[depth]
return resnet_generator(params['block'], params['layers'], class_dim,
data_format)
def resnet_cifar10(depth, num_classes, data_format):
if depth % 6 != 2:
raise ValueError('depth must be 6n + 2:', depth)
num_blocks = (depth - 2) // 6
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
def model(inputs, is_training):
inputs = conv_bn(inputs, 16, 3, 1, is_training, data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = block_layer(inputs, 16, basicblock, num_blocks, 1, is_training,
'block_layer1', data_format)
inputs = block_layer(inputs, 32, basicblock, num_blocks, 2, is_training,
'block_layer2', data_format)
inputs = block_layer(inputs, 64, basicblock, num_blocks, 2, is_training,
'block_layer3', data_format)
inputs = tf.layers.average_pooling2d(
inputs=inputs,
pool_size=8,
strides=1,
padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [-1, 64])
inputs = tf.layers.dense(inputs=inputs, units=num_classes)
inputs = tf.identity(inputs, 'final_dense')
return inputs
return model
def run_benchmark(args, data_format='channels_last', device='/cpu:0'):
"""Our model_fn for ResNet to be used with our Estimator."""
class_dim = 1000
dshape = (None, 224, 224, 3)
pdshape = (3, 224, 224)
if args.data == 'flowers102':
class_dim = 102
dshape = (None, 224, 224, 3)
pdshape = (3, 224, 224)
elif args.data == 'cifar10':
class_dim = 10
dshape = (None, 32, 32, 3)
pdshape = (3, 32, 32)
with tf.device(device):
images = tf.placeholder(DTYPE, shape=dshape)
labels = tf.placeholder(tf.int64, shape=(None, ))
is_training = tf.placeholder('bool')
onehot_labels = tf.one_hot(labels, depth=class_dim)
network = resnet_cifar10(
32, class_dim,
data_format) if args.data == 'cifar10' else resnet_imagenet(
50, class_dim, data_format)
logits = network(inputs=images, is_training=is_training)
cross_entropy = tf.losses.softmax_cross_entropy(
logits=logits, onehot_labels=onehot_labels)
avg_cost = tf.reduce_mean(cross_entropy)
correct = tf.equal(tf.argmax(logits, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
lr = 0.1 if args.data == 'cifar10' else 0.01
optimizer = tf.train.MomentumOptimizer(learning_rate=lr, momentum=0.9)
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10()
if args.data == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.cifar.test10()
if args.data == 'cifar10' else paddle.dataset.flowers.test(),
batch_size=100)
def test():
test_accs = []
for batch_id, data in enumerate(test_reader()):
test_images = np.array(
map(lambda x: np.transpose(x[0].reshape(pdshape),
axes=[1, 2, 0]), data)).astype("float32")
test_labels = np.array(map(lambda x: x[1], data)).astype('int64')
test_accs.append(
accuracy.eval(feed_dict={
images: test_images,
labels: test_labels,
is_training: False
}))
print("Pass = %d, Train performance = %f imgs/s, Test accuracy = %f\n" %
(pass_id, num_samples / train_elapsed, np.mean(test_accs)))
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
if args.use_fake_data:
data = train_reader().next()
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape(pdshape),
axes=[1, 2, 0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype('int64')
iters, num_samples, start_time = 0, 0, 0.0
for pass_id in range(args.pass_num):
if iters == args.iterations:
break
train_accs = []
train_losses = []
for batch_id, data in enumerate(train_reader()):
if iters == args.skip_batch_num:
start_time = time.time()
num_samples = 0
if iters == args.iterations:
break
if not args.use_fake_data:
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape(pdshape),
axes=[1, 2, 0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype(
'int64')
_, loss, acc = sess.run([train_op, avg_cost, accuracy],
feed_dict={
images: images_data,
labels: labels_data,
is_training: True
})
iters += 1
train_accs.append(acc)
train_losses.append(loss)
num_samples += len(data)
print("Pass=%d, Iter=%d, Loss=%f, Accuray=%f\n" %
(pass_id, iters, loss, acc))
train_elapsed = time.time() - start_time
print("Pass=%d, Loss=%f, Accuray=%f\n" %
(pass_id, np.mean(train_losses), np.mean(train_accs)))
# evaluation
if args.with_test:
test()
if not args.with_test:
duration = time.time() - start_time
examples_per_sec = num_samples / duration
sec_per_batch = duration / (iters - args.skip_batch_num)
print('Total examples: %d, total time: %.5f' %
(num_samples, duration))
print('%.5f examples/sec, %.5f sec/batch' %
(examples_per_sec, sec_per_batch))
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
if tf.test.is_built_with_cuda():
device = '/device:GPU:0'
if args.order == 'NHWC':
data_format = 'channels_last'
else:
data_format = 'channels_first'
else:
device = '/cpu:0'
if args.order == 'NHWC':
data_format = 'channels_last'
else:
raise ValueError('Only support NHWC order in CPU mode')
run_benchmark(args, data_format, device)
| 18,829 | 36.287129 | 104 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/vgg.py
|
# 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.
"""VGG16 benchmark in TensorFlow"""
import tensorflow as tf
import paddle.v2 as paddle
import numpy as np
import argparse
import time
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
'--batch_size', type=int, default=128, help="Batch size for training.")
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test')
parser.add_argument(
'--iterations', type=int, default=80, help='The number of minibatches.')
parser.add_argument(
'--learning_rate',
type=float,
default=1e-3,
help="Learning rate for training.")
parser.add_argument('--num_passes', type=int, default=50, help="No. of passes.")
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help="The device type.")
parser.add_argument(
'--data_format',
type=str,
default='NHWC',
choices=['NCHW', 'NHWC'],
help='The data order, NCHW=[batch, channels, height, width].'
'Only support NHWC right now.')
parser.add_argument(
'--data_set',
type=str,
default='cifar10',
choices=['cifar10', 'flowers'],
help='Optional dataset for benchmark.')
args = parser.parse_args()
class VGG16Model(object):
def __init__(self):
self.parameters = []
def batch_norm_relu(self, inputs, is_training):
"""Performs a batch normalization followed by a ReLU."""
# We set fused=True for a significant speed boost. See
# https://www.tensorflow.org/speed/speed_guide#common_fused_ops
inputs = tf.layers.batch_normalization(
inputs=inputs,
axis=1 if args.data_format == 'NCHW' else -1,
momentum=0.9,
epsilon=1e-05,
center=True,
scale=True,
training=is_training,
fused=True)
inputs = tf.nn.relu(inputs)
return inputs
def conv_bn_layer(self,
name,
images,
kernel_shape,
is_training,
drop_rate=0.0):
with tf.name_scope(name) as scope:
kernel = tf.Variable(
tf.truncated_normal(
kernel_shape, dtype=tf.float32, stddev=1e-1),
name='weights')
conv = tf.nn.conv2d(
images,
kernel, [1, 1, 1, 1],
data_format=args.data_format,
padding='SAME')
biases = tf.Variable(
tf.constant(
0.0, shape=[kernel_shape[-1]], dtype=tf.float32),
trainable=True,
name='biases')
out = tf.nn.bias_add(conv, biases)
out = self.batch_norm_relu(out, is_training)
out = tf.layers.dropout(out, rate=drop_rate, training=is_training)
return out
def fc_layer(self, name, inputs, shape):
with tf.name_scope(name) as scope:
fc_w = tf.Variable(
tf.truncated_normal(
shape, dtype=tf.float32, stddev=1e-1),
name='weights')
fc_b = tf.Variable(
tf.constant(
0.0, shape=[shape[-1]], dtype=tf.float32),
trainable=True,
name='biases')
out = tf.nn.bias_add(tf.matmul(inputs, fc_w), fc_b)
return out
def network(self, images, class_dim, is_training):
""" VGG16 model structure.
TODO(kuke): enable this network to support the 'NCHW' data format
"""
# conv1
conv1_1 = self.conv_bn_layer(
'conv1_1', images, [3, 3, 3, 64], is_training, drop_rate=0.3)
conv1_2 = self.conv_bn_layer(
'conv1_2', conv1_1, [3, 3, 64, 64], is_training, drop_rate=0.0)
# pool1
pool1 = tf.nn.max_pool(
conv1_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
# conv2
conv2_1 = self.conv_bn_layer(
'conv2_1', pool1, [3, 3, 64, 128], is_training, drop_rate=0.4)
conv2_2 = self.conv_bn_layer(
'conv2_2', conv2_1, [3, 3, 128, 128], is_training, drop_rate=0.0)
# pool2
pool2 = tf.nn.max_pool(
conv2_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# conv3
conv3_1 = self.conv_bn_layer(
'conv3_1', pool2, [3, 3, 128, 256], is_training, drop_rate=0.4)
conv3_2 = self.conv_bn_layer(
'conv3_2', conv3_1, [3, 3, 256, 256], is_training, drop_rate=0.4)
conv3_3 = self.conv_bn_layer(
'conv3_3', conv3_2, [3, 3, 256, 256], is_training, drop_rate=0.0)
# pool3
pool3 = tf.nn.max_pool(
conv3_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool3')
# conv4
conv4_1 = self.conv_bn_layer(
'conv4_1', pool3, [3, 3, 256, 512], is_training, drop_rate=0.4)
conv4_2 = self.conv_bn_layer(
'conv4_2', conv4_1, [3, 3, 512, 512], is_training, drop_rate=0.4)
conv4_3 = self.conv_bn_layer(
'conv4_3', conv4_2, [3, 3, 512, 512], is_training, drop_rate=0.0)
# pool4
pool4 = tf.nn.max_pool(
conv4_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
# conv5
conv5_1 = self.conv_bn_layer(
'conv5_1', pool4, [3, 3, 512, 512], is_training, drop_rate=0.4)
conv5_2 = self.conv_bn_layer(
'conv5_2', conv5_1, [3, 3, 512, 512], is_training, drop_rate=0.4)
conv5_3 = self.conv_bn_layer(
'conv5_3', conv5_2, [3, 3, 512, 512], is_training, drop_rate=0.0)
# pool5
pool5 = tf.nn.max_pool(
conv5_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
# flatten
shape = int(np.prod(pool5.get_shape()[1:]))
pool5_flat = tf.reshape(pool5, [-1, shape])
# fc1
drop = tf.layers.dropout(pool5_flat, rate=0.5, training=is_training)
fc1 = self.fc_layer('fc1', drop, [shape, 512])
# fc2
bn = self.batch_norm_relu(fc1, is_training)
drop = tf.layers.dropout(bn, rate=0.5, training=is_training)
fc2 = self.fc_layer('fc2', drop, [512, 512])
fc3 = self.fc_layer('fc3', fc2, [512, class_dim])
return fc3
def run_benchmark():
"""Run benchmark on cifar10 or flowers."""
if args.data_set == "cifar10":
class_dim = 10
raw_shape = (3, 32, 32)
dat_shape = (None, 32, 32, 3) if args.data_format == 'NHWC' else (
None, 3, 32, 32)
else:
class_dim = 102
raw_shape = (3, 224, 224)
dat_shape = (None, 224, 224, 3) if args.data_format == 'NHWC' else (
None, 3, 224, 224)
device = '/cpu:0' if args.device == 'CPU' else '/device:GPU:0'
with tf.device(device):
images = tf.placeholder(tf.float32, shape=dat_shape)
labels = tf.placeholder(tf.int64, shape=(None, ))
is_training = tf.placeholder('bool')
onehot_labels = tf.one_hot(labels, depth=class_dim)
vgg16 = VGG16Model()
logits = vgg16.network(images, class_dim, is_training)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
avg_loss = tf.reduce_mean(loss)
correct = tf.equal(tf.argmax(logits, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(avg_loss)
# data reader
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.test10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
buf_size=5120),
batch_size=args.batch_size)
# test
def test():
test_accs = []
for batch_id, data in enumerate(test_reader()):
test_images = np.array(
map(lambda x: np.transpose(x[0].reshape(raw_shape),
axes=[1, 2, 0]) if args.data_format == 'NHWC' else x[0], data)).astype("float32")
test_labels = np.array(map(lambda x: x[1], data)).astype('int64')
test_accs.append(
accuracy.eval(feed_dict={
images: test_images,
labels: test_labels,
is_training: False
}))
return np.mean(test_accs)
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
iters, num_samples, start_time = 0, 0, time.time()
for pass_id in range(args.num_passes):
# train
num_samples = 0
start_time = time.time()
for batch_id, data in enumerate(train_reader()):
if iters == args.skip_batch_num:
start_time = time.time()
num_samples = 0
if iters == args.iterations:
break
train_images = np.array(
map(lambda x: np.transpose(x[0].reshape(raw_shape),
axes=[1, 2, 0]) if args.data_format == 'NHWC' else x[0], data)).astype("float32")
train_labels = np.array(map(lambda x: x[1], data)).astype(
'int64')
_, loss, acc = sess.run([train_op, avg_loss, accuracy],
feed_dict={
images: train_images,
labels: train_labels,
is_training: True
})
iters += 1
num_samples += len(data)
print("Pass = %d, Iters = %d, Loss = %f, Accuracy = %f" %
(pass_id, iters, loss, acc))
train_elapsed = time.time() - start_time
# test
pass_test_acc = test()
print("Pass = %d, Train speed = %f imgs/s, Test accuracy = %f\n" %
(pass_id, num_samples / train_elapsed, pass_test_acc))
def print_arguments():
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == '__main__':
print_arguments()
run_benchmark()
| 12,034 | 36.030769 | 101 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/stacked_dynamic_lstm.py
|
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import time
import tensorflow as tf
import paddle.v2 as paddle
def parse_args():
parser = argparse.ArgumentParser("LSTM model benchmark.")
parser.add_argument(
'--batch_size',
type=int,
default=32,
help='The sequence number of a batch data. (default: %(default)d)')
parser.add_argument(
'--stacked_num',
type=int,
default=5,
help='Number of lstm layers to stack. (default: %(default)d)')
parser.add_argument(
'--embedding_dim',
type=int,
default=512,
help='Dimension of embedding table. (default: %(default)d)')
parser.add_argument(
'--hidden_dim',
type=int,
default=512,
help='Hidden size of lstm unit. (default: %(default)d)')
parser.add_argument(
'--pass_num',
type=int,
default=10,
help='Epoch number to train. (default: %(default)d)')
parser.add_argument(
'--learning_rate',
type=float,
default=0.0002,
help='Learning rate used to train. (default: %(default)f)')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
args = parser.parse_args()
return args
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def dynamic_lstm_model(dict_size,
embedding_dim,
hidden_dim,
stacked_num,
class_num=2,
is_train=True):
word_idx = tf.placeholder(tf.int64, shape=[None, None])
sequence_length = tf.placeholder(tf.int64, shape=[None, ])
embedding_weights = tf.get_variable('word_embeddings',
[dict_size, embedding_dim])
embedding = tf.nn.embedding_lookup(embedding_weights, word_idx)
lstm_cell = tf.nn.rnn_cell.LSTMCell(
num_units=hidden_dim, use_peepholes=False)
stacked_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * stacked_num)
# final_state [LSTMTuple(c, h), LSTMTuple(c, h) ...] total stacked_num LSTMTuples
_, final_state = tf.nn.dynamic_rnn(
cell=stacked_cell,
inputs=embedding,
dtype=tf.float32,
sequence_length=sequence_length)
w = tf.Variable(
tf.truncated_normal([hidden_dim, class_num]), dtype=tf.float32)
bias = tf.Variable(
tf.constant(
value=0.0, shape=[class_num], dtype=tf.float32))
prediction = tf.matmul(final_state[-1][1], w) + bias
if not is_train:
return (word_idx, sequence_length), tf.nn.softmax(prediction)
label = tf.placeholder(tf.int64, shape=[None, ])
loss = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(label, 2), logits=prediction)
avg_loss = tf.reduce_mean(loss)
correct_count = tf.equal(tf.argmax(prediction, 1), label)
acc = tf.reduce_mean(tf.cast(correct_count, tf.float32))
with tf.variable_scope("reset_metrics_accuracy_scope") as scope:
g_acc = tf.metrics.accuracy(label, tf.argmax(prediction, axis=1))
vars = tf.contrib.framework.get_variables(
scope, collection=tf.GraphKeys.LOCAL_VARIABLES)
reset_op = tf.variables_initializer(vars)
return (word_idx, sequence_length, label), avg_loss, acc, g_acc, reset_op
def padding_data(data, padding_size, value):
data = data + [value] * padding_size
return data[:padding_size]
def train(args):
word_dict = paddle.dataset.imdb.word_dict()
dict_size = len(word_dict)
feeding_list, avg_loss, acc, g_acc, reset_op = dynamic_lstm_model(
dict_size, args.embedding_dim, args.hidden_dim, args.stacked_num)
adam_optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
train_op = adam_optimizer.minimize(avg_loss)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.test(word_dict), buf_size=25000),
batch_size=args.batch_size)
def do_validation(sess):
sess.run(reset_op)
for batch_id, data in enumerate(test_reader()):
word_idx = map(lambda x: x[0], data)
sequence_length = np.array(
[len(seq) for seq in word_idx]).astype('int64')
maxlen = np.max(sequence_length)
word_idx = [padding_data(seq, maxlen, 0) for seq in word_idx]
word_idx = np.array(word_idx).astype('int64')
label = np.array(map(lambda x: x[1], data)).astype('int64')
_, loss, fetch_acc, fetch_g_acc = sess.run(
[train_op, avg_loss, acc, g_acc],
feed_dict={
feeding_list[0]: word_idx,
feeding_list[1]: sequence_length,
feeding_list[2]: label
})
return fetch_g_acc[1]
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_l)
sess.run(init_g)
for pass_id in xrange(args.pass_num):
# clear accuracy local variable
sess.run(reset_op)
pass_start_time = time.time()
words_seen = 0
for batch_id, data in enumerate(train_reader()):
word_idx = map(lambda x: x[0], data)
sequence_length = np.array(
[len(seq) for seq in word_idx]).astype('int64')
words_seen += np.sum(sequence_length)
maxlen = np.max(sequence_length)
word_idx = [padding_data(seq, maxlen, 0) for seq in word_idx]
word_idx = np.array(word_idx).astype('int64')
label = np.array(map(lambda x: x[1], data)).astype('int64')
_, loss, fetch_acc, fetch_g_acc = sess.run(
[train_op, avg_loss, acc, g_acc],
feed_dict={
feeding_list[0]: word_idx,
feeding_list[1]: sequence_length,
feeding_list[2]: label
})
print("pass_id=%d, batch_id=%d, loss: %f, acc: %f, avg_acc: %f"
% (pass_id, batch_id, loss, fetch_acc, fetch_g_acc[1]))
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
words_per_sec = words_seen / time_consumed
test_acc = do_validation(sess)
print("pass_id=%d, test_acc: %f, words/s: %f, sec/pass: %f" %
(pass_id, test_acc, words_per_sec, time_consumed))
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
if args.infer_only:
pass
else:
train(args)
| 7,975 | 35.090498 | 85 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/machine_translation.py
|
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import dtypes
from tensorflow.python.layers.core import Dense
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.framework import ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops.rnn_cell_impl import RNNCell, BasicLSTMCell
from tensorflow.python.ops.rnn_cell_impl import LSTMStateTuple
from tensorflow.contrib.rnn.python.ops import core_rnn_cell
from tensorflow.python.ops import array_ops
from tensorflow.python.util import nest
import tensorflow.contrib.seq2seq as seq2seq
from tensorflow.contrib.seq2seq.python.ops import beam_search_decoder
import numpy as np
import os
import argparse
import time
import paddle.v2 as paddle
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--embedding_dim",
type=int,
default=512,
help="The dimension of embedding table. (default: %(default)d)")
parser.add_argument(
"--encoder_size",
type=int,
default=512,
help="The size of encoder bi-rnn unit. (default: %(default)d)")
parser.add_argument(
"--decoder_size",
type=int,
default=512,
help="The size of decoder rnn unit. (default: %(default)d)")
parser.add_argument(
"--batch_size",
type=int,
default=128,
help="The sequence number of a mini-batch data. (default: %(default)d)")
parser.add_argument(
"--dict_size",
type=int,
default=30000,
help="The dictionary capacity. Dictionaries of source sequence and "
"target dictionary have same capacity. (default: %(default)d)")
parser.add_argument(
"--max_time_steps",
type=int,
default=81,
help="Max number of time steps for sequence. (default: %(default)d)")
parser.add_argument(
"--pass_num",
type=int,
default=10,
help="The pass number to train. (default: %(default)d)")
parser.add_argument(
"--learning_rate",
type=float,
default=0.0002,
help="Learning rate used to train the model. (default: %(default)f)")
parser.add_argument(
"--infer_only", action='store_true', help="If set, run forward only.")
parser.add_argument(
"--beam_size",
type=int,
default=3,
help="The width for beam searching. (default: %(default)d)")
parser.add_argument(
"--max_generation_length",
type=int,
default=250,
help="The maximum length of sequence when doing generation. "
"(default: %(default)d)")
parser.add_argument(
"--save_freq",
type=int,
default=500,
help="Save model checkpoint every this interation. (default: %(default)d)")
parser.add_argument(
"--model_dir",
type=str,
default='./checkpoint',
help="Path to save model checkpoints. (default: %(default)d)")
_Linear = core_rnn_cell._Linear # pylint: disable=invalid-name
START_TOKEN_IDX = 0
END_TOKEN_IDX = 1
class LSTMCellWithSimpleAttention(RNNCell):
"""Add attention mechanism to BasicLSTMCell.
This class is a wrapper based on tensorflow's `BasicLSTMCell`.
"""
def __init__(self,
num_units,
encoder_vector,
encoder_proj,
source_sequence_length,
forget_bias=1.0,
state_is_tuple=True,
activation=None,
reuse=None):
super(LSTMCellWithSimpleAttention, self).__init__(_reuse=reuse)
if not state_is_tuple:
logging.warn("%s: Using a concatenated state is slower and will "
"soon be deprecated. Use state_is_tuple=True.", self)
self._num_units = num_units
# set padding part to 0
self._encoder_vector = self._reset_padding(encoder_vector,
source_sequence_length)
self._encoder_proj = self._reset_padding(encoder_proj,
source_sequence_length)
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation or math_ops.tanh
self._linear = None
@property
def state_size(self):
return (LSTMStateTuple(self._num_units, self._num_units) \
if self._state_is_tuple else 2 * self._num_units)
@property
def output_size(self):
return self._num_units
def zero_state(self, batch_size, dtype):
state_size = self.state_size
if hasattr(self, "_last_zero_state"):
(last_state_size, last_batch_size, last_dtype,
last_output) = getattr(self, "_last_zero_state")
if (last_batch_size == batch_size and last_dtype == dtype and
last_state_size == state_size):
return last_output
with ops.name_scope(
type(self).__name__ + "ZeroState", values=[batch_size]):
output = _zero_state_tensors(state_size, batch_size, dtype)
self._last_zero_state = (state_size, batch_size, dtype, output)
return output
def call(self, inputs, state):
sigmoid = math_ops.sigmoid
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
# get context from encoder outputs
context = self._simple_attention(self._encoder_vector,
self._encoder_proj, h)
if self._linear is None:
self._linear = _Linear([inputs, context, h], 4 * self._num_units,
True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(
value=self._linear([inputs, context, h]),
num_or_size_splits=4,
axis=1)
new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) *
self._activation(j))
new_h = self._activation(new_c) * sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat([new_c, new_h], 1)
return new_h, new_state
def _simple_attention(self, encoder_vec, encoder_proj, decoder_state):
"""Implement the attention function.
The implementation has the same logic to the fluid decoder.
"""
decoder_state_proj = tf.contrib.layers.fully_connected(
inputs=decoder_state,
num_outputs=self._num_units,
activation_fn=None,
biases_initializer=None)
decoder_state_expand = tf.tile(
tf.expand_dims(
input=decoder_state_proj, axis=1),
[1, tf.shape(encoder_proj)[1], 1])
concated = tf.concat([decoder_state_expand, encoder_proj], axis=2)
# need reduce the first dimension
attention_weights = tf.contrib.layers.fully_connected(
inputs=tf.reshape(
concated, shape=[-1, self._num_units * 2]),
num_outputs=1,
activation_fn=tf.nn.tanh,
biases_initializer=None)
attention_weights_reshaped = tf.reshape(
attention_weights, shape=[tf.shape(encoder_vec)[0], -1, 1])
# normalize the attention weights using softmax
attention_weights_normed = tf.nn.softmax(
attention_weights_reshaped, dim=1)
scaled = tf.multiply(attention_weights_normed, encoder_vec)
context = tf.reduce_sum(scaled, axis=1)
return context
def _reset_padding(self,
memory,
memory_sequence_length,
check_inner_dims_defined=True):
"""Reset the padding part for encoder inputs.
This funtion comes from tensorflow's `_prepare_memory` function.
"""
memory = nest.map_structure(
lambda m: ops.convert_to_tensor(m, name="memory"), memory)
if memory_sequence_length is not None:
memory_sequence_length = ops.convert_to_tensor(
memory_sequence_length, name="memory_sequence_length")
if check_inner_dims_defined:
def _check_dims(m):
if not m.get_shape()[2:].is_fully_defined():
raise ValueError(
"Expected memory %s to have fully defined inner dims, "
"but saw shape: %s" % (m.name, m.get_shape()))
nest.map_structure(_check_dims, memory)
if memory_sequence_length is None:
seq_len_mask = None
else:
seq_len_mask = array_ops.sequence_mask(
memory_sequence_length,
maxlen=array_ops.shape(nest.flatten(memory)[0])[1],
dtype=nest.flatten(memory)[0].dtype)
seq_len_batch_size = (memory_sequence_length.shape[0].value or
array_ops.shape(memory_sequence_length)[0])
def _maybe_mask(m, seq_len_mask):
rank = m.get_shape().ndims
rank = rank if rank is not None else array_ops.rank(m)
extra_ones = array_ops.ones(rank - 2, dtype=dtypes.int32)
m_batch_size = m.shape[0].value or array_ops.shape(m)[0]
if memory_sequence_length is not None:
message = ("memory_sequence_length and memory tensor "
"batch sizes do not match.")
with ops.control_dependencies([
check_ops.assert_equal(
seq_len_batch_size, m_batch_size, message=message)
]):
seq_len_mask = array_ops.reshape(
seq_len_mask,
array_ops.concat(
(array_ops.shape(seq_len_mask), extra_ones), 0))
return m * seq_len_mask
else:
return m
return nest.map_structure(lambda m: _maybe_mask(m, seq_len_mask),
memory)
def seq_to_seq_net(embedding_dim, encoder_size, decoder_size, source_dict_dim,
target_dict_dim, is_generating, beam_size,
max_generation_length):
src_word_idx = tf.placeholder(tf.int32, shape=[None, None])
src_sequence_length = tf.placeholder(tf.int32, shape=[None, ])
src_embedding_weights = tf.get_variable("source_word_embeddings",
[source_dict_dim, embedding_dim])
src_embedding = tf.nn.embedding_lookup(src_embedding_weights, src_word_idx)
src_forward_cell = tf.nn.rnn_cell.BasicLSTMCell(encoder_size)
src_reversed_cell = tf.nn.rnn_cell.BasicLSTMCell(encoder_size)
# no peephole
encoder_outputs, _ = tf.nn.bidirectional_dynamic_rnn(
cell_fw=src_forward_cell,
cell_bw=src_reversed_cell,
inputs=src_embedding,
sequence_length=src_sequence_length,
dtype=tf.float32)
# concat the forward outputs and backward outputs
encoded_vec = tf.concat(encoder_outputs, axis=2)
# project the encoder outputs to size of decoder lstm
encoded_proj = tf.contrib.layers.fully_connected(
inputs=tf.reshape(
encoded_vec, shape=[-1, embedding_dim * 2]),
num_outputs=decoder_size,
activation_fn=None,
biases_initializer=None)
encoded_proj_reshape = tf.reshape(
encoded_proj, shape=[-1, tf.shape(encoded_vec)[1], decoder_size])
# get init state for decoder lstm's H
backword_first = tf.slice(encoder_outputs[1], [0, 0, 0], [-1, 1, -1])
decoder_boot = tf.contrib.layers.fully_connected(
inputs=tf.reshape(
backword_first, shape=[-1, embedding_dim]),
num_outputs=decoder_size,
activation_fn=tf.nn.tanh,
biases_initializer=None)
# prepare the initial state for decoder lstm
cell_init = tf.zeros(tf.shape(decoder_boot), tf.float32)
initial_state = LSTMStateTuple(cell_init, decoder_boot)
# create decoder lstm cell
decoder_cell = LSTMCellWithSimpleAttention(
decoder_size,
encoded_vec
if not is_generating else seq2seq.tile_batch(encoded_vec, beam_size),
encoded_proj_reshape if not is_generating else
seq2seq.tile_batch(encoded_proj_reshape, beam_size),
src_sequence_length if not is_generating else
seq2seq.tile_batch(src_sequence_length, beam_size),
forget_bias=0.0)
output_layer = Dense(target_dict_dim, name='output_projection')
if not is_generating:
trg_word_idx = tf.placeholder(tf.int32, shape=[None, None])
trg_sequence_length = tf.placeholder(tf.int32, shape=[None, ])
trg_embedding_weights = tf.get_variable(
"target_word_embeddings", [target_dict_dim, embedding_dim])
trg_embedding = tf.nn.embedding_lookup(trg_embedding_weights,
trg_word_idx)
training_helper = seq2seq.TrainingHelper(
inputs=trg_embedding,
sequence_length=trg_sequence_length,
time_major=False,
name='training_helper')
training_decoder = seq2seq.BasicDecoder(
cell=decoder_cell,
helper=training_helper,
initial_state=initial_state,
output_layer=output_layer)
# get the max length of target sequence
max_decoder_length = tf.reduce_max(trg_sequence_length)
decoder_outputs_train, _, _ = seq2seq.dynamic_decode(
decoder=training_decoder,
output_time_major=False,
impute_finished=True,
maximum_iterations=max_decoder_length)
decoder_logits_train = tf.identity(decoder_outputs_train.rnn_output)
decoder_pred_train = tf.argmax(
decoder_logits_train, axis=-1, name='decoder_pred_train')
masks = tf.sequence_mask(
lengths=trg_sequence_length,
maxlen=max_decoder_length,
dtype=tf.float32,
name='masks')
# place holder of label sequence
lbl_word_idx = tf.placeholder(tf.int32, shape=[None, None])
# compute the loss
loss = seq2seq.sequence_loss(
logits=decoder_logits_train,
targets=lbl_word_idx,
weights=masks,
average_across_timesteps=True,
average_across_batch=True)
# return feeding list and loss operator
return {
'src_word_idx': src_word_idx,
'src_sequence_length': src_sequence_length,
'trg_word_idx': trg_word_idx,
'trg_sequence_length': trg_sequence_length,
'lbl_word_idx': lbl_word_idx
}, loss
else:
start_tokens = tf.ones([tf.shape(src_word_idx)[0], ],
tf.int32) * START_TOKEN_IDX
# share the same embedding weights with target word
trg_embedding_weights = tf.get_variable(
"target_word_embeddings", [target_dict_dim, embedding_dim])
inference_decoder = beam_search_decoder.BeamSearchDecoder(
cell=decoder_cell,
embedding=lambda tokens: tf.nn.embedding_lookup(trg_embedding_weights, tokens),
start_tokens=start_tokens,
end_token=END_TOKEN_IDX,
initial_state=tf.nn.rnn_cell.LSTMStateTuple(
tf.contrib.seq2seq.tile_batch(initial_state[0], beam_size),
tf.contrib.seq2seq.tile_batch(initial_state[1], beam_size)),
beam_width=beam_size,
output_layer=output_layer)
decoder_outputs_decode, _, _ = seq2seq.dynamic_decode(
decoder=inference_decoder,
output_time_major=False,
#impute_finished=True,# error occurs
maximum_iterations=max_generation_length)
predicted_ids = decoder_outputs_decode.predicted_ids
return {
'src_word_idx': src_word_idx,
'src_sequence_length': src_sequence_length
}, predicted_ids
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in vars(args).iteritems():
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def padding_data(data, padding_size, value):
data = data + [value] * padding_size
return data[:padding_size]
def save(sess, path, var_list=None, global_step=None):
saver = tf.train.Saver(var_list)
save_path = saver.save(sess, save_path=path, global_step=global_step)
print('Model save at %s' % save_path)
def restore(sess, path, var_list=None):
# var_list = None returns the list of all saveable variables
saver = tf.train.Saver(var_list)
saver.restore(sess, save_path=path)
print('model restored from %s' % path)
def adapt_batch_data(data):
src_seq = map(lambda x: x[0], data)
trg_seq = map(lambda x: x[1], data)
lbl_seq = map(lambda x: x[2], data)
src_sequence_length = np.array(
[len(seq) for seq in src_seq]).astype('int32')
src_seq_maxlen = np.max(src_sequence_length)
trg_sequence_length = np.array(
[len(seq) for seq in trg_seq]).astype('int32')
trg_seq_maxlen = np.max(trg_sequence_length)
src_seq = np.array(
[padding_data(seq, src_seq_maxlen, END_TOKEN_IDX)
for seq in src_seq]).astype('int32')
trg_seq = np.array(
[padding_data(seq, trg_seq_maxlen, END_TOKEN_IDX)
for seq in trg_seq]).astype('int32')
lbl_seq = np.array(
[padding_data(seq, trg_seq_maxlen, END_TOKEN_IDX)
for seq in lbl_seq]).astype('int32')
return {
'src_word_idx': src_seq,
'src_sequence_length': src_sequence_length,
'trg_word_idx': trg_seq,
'trg_sequence_length': trg_sequence_length,
'lbl_word_idx': lbl_seq
}
def train():
feeding_dict, loss = seq_to_seq_net(
embedding_dim=args.embedding_dim,
encoder_size=args.encoder_size,
decoder_size=args.decoder_size,
source_dict_dim=args.dict_size,
target_dict_dim=args.dict_size,
is_generating=False,
beam_size=args.beam_size,
max_generation_length=args.max_generation_length)
global_step = tf.Variable(0, trainable=False, name='global_step')
trainable_params = tf.trainable_variables()
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
gradients = tf.gradients(loss, trainable_params)
# may clip the parameters
clip_gradients, _ = tf.clip_by_global_norm(gradients, 1.0)
updates = optimizer.apply_gradients(
zip(gradients, trainable_params), global_step=global_step)
src_dict, trg_dict = paddle.dataset.wmt14.get_dict(args.dict_size)
train_batch_generator = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(args.dict_size), buf_size=1000),
batch_size=args.batch_size)
test_batch_generator = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.test(args.dict_size), buf_size=1000),
batch_size=args.batch_size)
def do_validataion():
total_loss = 0.0
count = 0
for batch_id, data in enumerate(test_batch_generator()):
adapted_batch_data = adapt_batch_data(data)
outputs = sess.run([loss],
feed_dict={
item[1]: adapted_batch_data[item[0]]
for item in feeding_dict.items()
})
total_loss += outputs[0]
count += 1
return total_loss / count
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_l)
sess.run(init_g)
for pass_id in xrange(args.pass_num):
pass_start_time = time.time()
words_seen = 0
for batch_id, data in enumerate(train_batch_generator()):
adapted_batch_data = adapt_batch_data(data)
words_seen += np.sum(adapted_batch_data['src_sequence_length'])
words_seen += np.sum(adapted_batch_data['trg_sequence_length'])
outputs = sess.run([updates, loss],
feed_dict={
item[1]: adapted_batch_data[item[0]]
for item in feeding_dict.items()
})
print("pass_id=%d, batch_id=%d, train_loss: %f" %
(pass_id, batch_id, outputs[1]))
pass_end_time = time.time()
test_loss = do_validataion()
time_consumed = pass_end_time - pass_start_time
words_per_sec = words_seen / time_consumed
print("pass_id=%d, test_loss: %f, words/s: %f, sec/pass: %f" %
(pass_id, test_loss, words_per_sec, time_consumed))
def infer():
feeding_dict, predicted_ids = seq_to_seq_net(
embedding_dim=args.embedding_dim,
encoder_size=args.encoder_size,
decoder_size=args.decoder_size,
source_dict_dim=args.dict_size,
target_dict_dim=args.dict_size,
is_generating=True,
beam_size=args.beam_size,
max_generation_length=args.max_generation_length)
src_dict, trg_dict = paddle.dataset.wmt14.get_dict(args.dict_size)
test_batch_generator = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(args.dict_size), buf_size=1000),
batch_size=args.batch_size)
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
with tf.Session(config=config) as sess:
restore(sess, './checkpoint/tf_seq2seq-1500')
for batch_id, data in enumerate(test_batch_generator()):
src_seq = map(lambda x: x[0], data)
source_language_seq = [
src_dict[item] for seq in src_seq for item in seq
]
src_sequence_length = np.array(
[len(seq) for seq in src_seq]).astype('int32')
src_seq_maxlen = np.max(src_sequence_length)
src_seq = np.array([
padding_data(seq, src_seq_maxlen, END_TOKEN_IDX)
for seq in src_seq
]).astype('int32')
outputs = sess.run([predicted_ids],
feed_dict={
feeding_dict['src_word_idx']: src_seq,
feeding_dict['src_sequence_length']:
src_sequence_length
})
print("\nDecoder result comparison: ")
source_language_seq = ' '.join(source_language_seq).lstrip(
'<s>').rstrip('<e>').strip()
inference_seq = ''
print(" --> source: " + source_language_seq)
for item in outputs[0][0]:
if item[0] == END_TOKEN_IDX: break
inference_seq += ' ' + trg_dict.get(item[0], '<unk>')
print(" --> inference: " + inference_seq)
if __name__ == '__main__':
args = parser.parse_args()
print_arguments(args)
if args.infer_only:
infer()
else:
train()
| 24,265 | 37.701754 | 91 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/mnist.py
|
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import time
import numpy as np
import tensorflow as tf
import paddle.v2 as paddle
DTYPE = tf.float32
def parse_args():
parser = argparse.ArgumentParser("mnist model benchmark.")
parser.add_argument(
'--batch_size', type=int, default=128, help='The minibatch size.')
parser.add_argument(
'--iterations', type=int, default=35, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=5, help='The number of passes.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
args = parser.parse_args()
return args
def run_benchmark(args):
def weight_variable(dtype, shape):
initial = tf.truncated_normal(shape, stddev=0.1, dtype=dtype)
return tf.Variable(initial)
def bias_variable(dtype, shape):
initial = tf.constant(0.1, shape=shape, dtype=dtype)
return tf.Variable(initial)
device = '/cpu:0' if args.device == 'CPU' else '/device:GPU:0'
with tf.device(device):
images = tf.placeholder(DTYPE, shape=(None, 28, 28, 1))
labels = tf.placeholder(tf.int64, shape=(None, ))
# conv1, relu, pool1
conv1_weights = weight_variable(DTYPE, [5, 5, 1, 20])
conv1_bias = bias_variable(DTYPE, [20])
conv1 = tf.nn.conv2d(
images, conv1_weights, strides=[1, 1, 1, 1], padding="VALID")
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias))
pool1 = tf.nn.max_pool(
relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
# conv2, relu, pool2
conv2_weights = weight_variable(DTYPE, [5, 5, 20, 50])
conv2_bias = bias_variable(DTYPE, [50])
conv2 = tf.nn.conv2d(
pool1, conv2_weights, strides=[1, 1, 1, 1], padding="VALID")
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias))
pool2 = tf.nn.max_pool(
relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
# FC
pool_shape = pool2.get_shape().as_list()
hidden_dim = reduce(lambda a, b: a * b, pool_shape[1:], 1)
reshape = tf.reshape(pool2, shape=(tf.shape(pool2)[0], hidden_dim))
fc_weights = weight_variable(DTYPE, [hidden_dim, 10])
fc_bias = bias_variable(DTYPE, [10])
logits = tf.matmul(reshape, fc_weights) + fc_bias
# Get prediction
prediction = tf.nn.softmax(logits)
# Loss
one_hot_labels = tf.one_hot(labels, depth=10)
cost = -tf.reduce_sum(tf.log(prediction) * one_hot_labels, [1])
avg_cost = tf.reduce_mean(cost)
# Get accuracy
correct = tf.equal(tf.argmax(prediction, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# metrics, g_accuracy
with tf.variable_scope("reset_metrics_accuracy_scope") as scope:
g_accuracy = tf.metrics.accuracy(
labels, tf.argmax(
prediction, axis=1))
vars = tf.contrib.framework.get_variables(
scope, collection=tf.GraphKeys.LOCAL_VARIABLES)
g_accuracy_reset_op = tf.variables_initializer(vars)
# Optimizer
opt = tf.train.AdamOptimizer(
learning_rate=0.001, beta1=0.9, beta2=0.999)
train_op = opt.minimize(avg_cost)
# train_op = tf.train.AdamOptimizer(1e-4).minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=args.batch_size)
def eval_test():
sess.run(g_accuracy_reset_op)
for batch_id, data in enumerate(test_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype("int64")
loss, acc, g_acc = sess.run(
[avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
return g_acc[1]
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
for pass_id in range(args.pass_num):
sess.run(g_accuracy_reset_op)
pass_start = time.time()
for batch_id, data in enumerate(train_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype(
"int64")
start = time.time()
_, loss, acc, g_acc = sess.run(
[train_op, avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
end = time.time()
print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
(pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))
pass_end = time.time()
test_avg_acc = eval_test()
print(
"pass=%d, training_avg_accuracy=%f, test_avg_acc=%f, elapse=%f"
% (pass_id, g_acc[1], test_avg_acc,
(pass_end - pass_start) / 1000))
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
run_benchmark(args)
| 6,800 | 36.574586 | 113 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/image/smallnet_mnist_cifar.py
|
# 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.
from six.moves import xrange # pylint: disable=redefined-builtin
from datetime import datetime
import math
import time
import tensorflow.python.platform
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 128, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('forward_only', False,
"""Only run the forward pass.""")
tf.app.flags.DEFINE_boolean('forward_backward_only', False,
"""Only run the forward-forward pass.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
"""The data format for Convnet operations.
Can be either NHWC or NCHW.
""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
parameters = []
conv_counter = 1
pool_counter = 1
affine_counter = 1
def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005, act=True):
global conv_counter
global parameters
name = 'conv' + str(conv_counter)
conv_counter += 1
with tf.name_scope(name) as scope:
kernel = tf.Variable(
tf.truncated_normal(
[kH, kW, nIn, nOut], dtype=tf.float32, stddev=1e-1),
name='weights')
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
if FLAGS.data_format == 'NCHW':
strides = [1, 1, dH, dW]
else:
strides = [1, dH, dW, 1]
conv = tf.nn.conv2d(
inpOp,
kernel,
strides,
padding=padType,
data_format=FLAGS.data_format)
biases = tf.Variable(
tf.constant(
0.0, shape=[nOut], dtype=tf.float32),
trainable=True,
name='biases')
bias = tf.reshape(
tf.nn.bias_add(
conv, biases, data_format=FLAGS.data_format),
conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope) if act else bias
parameters += [kernel, biases]
return conv1
def _affine(inpOp, nIn, nOut, wd=None, act=True):
global affine_counter
global parameters
name = 'affine' + str(affine_counter)
affine_counter += 1
with tf.name_scope(name) as scope:
kernel = tf.Variable(
tf.truncated_normal(
[nIn, nOut], dtype=tf.float32, stddev=1e-1),
name='weights')
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
biases = tf.Variable(
tf.constant(
0.0, shape=[nOut], dtype=tf.float32),
trainable=True,
name='biases')
affine1 = tf.nn.relu_layer(
inpOp, kernel, biases,
name=name) if act else tf.matmul(inpOp, kernel) + biases
parameters += [kernel, biases]
return affine1
def _mpool(inpOp, kH, kW, dH, dW, padding):
global pool_counter
global parameters
name = 'pool' + str(pool_counter)
pool_counter += 1
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.max_pool(
inpOp,
ksize=ksize,
strides=strides,
padding=padding,
data_format=FLAGS.data_format,
name=name)
def _apool(inpOp, kH, kW, dH, dW, padding):
global pool_counter
global parameters
name = 'pool' + str(pool_counter)
pool_counter += 1
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.avg_pool(
inpOp,
ksize=ksize,
strides=strides,
padding=padding,
data_format=FLAGS.data_format,
name=name)
def _norm(name, l_input, lsize=4):
return tf.nn.lrn(l_input,
lsize,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name=name)
def loss(logits, labels):
batch_size = tf.size(labels)
labels = tf.expand_dims(labels, 1)
indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)
concated = tf.concat(1, [indices, labels])
onehot_labels = tf.sparse_to_dense(concated,
tf.pack([batch_size, 10]), 1.0, 0.0)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits, onehot_labels, name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
return loss
def get_incoming_shape(incoming):
""" Returns the incoming data shape """
if isinstance(incoming, tf.Tensor):
return incoming.get_shape().as_list()
elif type(incoming) in [np.array, list, tuple]:
return np.shape(incoming)
else:
raise Exception("Invalid incoming layer.")
def inference(images):
conv1 = _conv(images, 3, 32, 5, 5, 1, 1, 'SAME')
pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')
conv2 = _conv(pool1, 32, 32, 5, 5, 1, 1, 'SAME')
pool2 = _apool(conv2, 3, 3, 2, 2, 'SAME')
conv3 = _conv(pool2, 32, 64, 5, 5, 1, 1, 'SAME')
pool3 = _apool(conv3, 3, 3, 2, 2, 'SAME')
resh1 = tf.reshape(pool3, [-1, 64 * 4 * 4])
affn1 = _affine(resh1, 64 * 4 * 4, 64)
affn2 = _affine(affn1, 64, 10, act=False)
print('conv1:', get_incoming_shape(conv1))
print('pool1:', get_incoming_shape(pool1))
print('conv2:', get_incoming_shape(conv2))
print('pool2:', get_incoming_shape(pool2))
print('conv3:', get_incoming_shape(conv3))
print('pool3:', get_incoming_shape(pool3))
return affn2
def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
if not isinstance(target, list):
target = [target]
target_op = tf.group(*target)
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target_op)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' %
(datetime.now(), i - num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, FLAGS.num_batches, mn, sd))
def run_benchmark():
global parameters
with tf.Graph().as_default():
# Generate some dummy images.
image_size = 32
# Note that our padding definition is slightly different the cuda-convnet.
# In order to force the model to start with the same activations sizes,
# we add 3 to the image_size and employ VALID padding above.
if FLAGS.data_format == 'NCHW':
image_shape = [FLAGS.batch_size, 3, image_size, image_size]
else:
image_shape = [FLAGS.batch_size, image_size, image_size, 3]
images = tf.get_variable(
'image',
image_shape,
initializer=tf.truncated_normal_initializer(
stddev=0.1, dtype=tf.float32),
dtype=tf.float32,
trainable=False)
labels = tf.get_variable(
'label', [FLAGS.batch_size],
initializer=tf.constant_initializer(1),
dtype=tf.int32,
trainable=False)
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(images)
objective = loss(last_layer, labels)
# Compute gradients.
opt = tf.train.MomentumOptimizer(0.001, 0.9)
grads = opt.compute_gradients(objective)
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(
0.0, dtype=tf.float32),
trainable=False,
dtype=tf.float32)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(0.9, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables(
))
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
run_forward = True
run_forward_backward = True
if FLAGS.forward_only and FLAGS.forward_backward_only:
raise ValueError("Cannot specify --forward_only and "
"--forward_backward_only at the same time.")
if FLAGS.forward_only:
run_forward_backward = False
elif FLAGS.forward_backward_only:
run_forward = False
if run_forward:
# Run the forward benchmark.
time_tensorflow_run(sess, last_layer, "Forward")
if run_forward_backward:
with tf.control_dependencies(
[apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
time_tensorflow_run(sess, [train_op, objective], "Forward-backward")
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 10,609 | 32.260188 | 82 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/image/googlenet.py
|
# 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.
from six.moves import xrange
from datetime import datetime
import math
import time
import tensorflow.python.platform
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 128, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('forward_only', False,
"""Only run the forward pass.""")
tf.app.flags.DEFINE_boolean('forward_backward_only', False,
"""Only run the forward-forward pass.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
"""The data format for Convnet operations.
Can be either NHWC or NCHW.
""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
parameters = []
conv_counter = 1
pool_counter = 1
affine_counter = 1
def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.0005):
global conv_counter
global parameters
name = 'conv' + str(conv_counter)
conv_counter += 1
with tf.name_scope(name) as scope:
kernel = tf.Variable(
tf.truncated_normal(
[kH, kW, nIn, nOut], dtype=tf.float32, stddev=1e-1),
name='weights')
if wd is not None and wd > 0:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
if FLAGS.data_format == 'NCHW':
strides = [1, 1, dH, dW]
else:
strides = [1, dH, dW, 1]
conv = tf.nn.conv2d(
inpOp,
kernel,
strides,
padding=padType,
data_format=FLAGS.data_format)
biases = tf.Variable(
tf.constant(
0.0, shape=[nOut], dtype=tf.float32),
trainable=True,
name='biases')
bias = tf.reshape(
tf.nn.bias_add(
conv, biases, data_format=FLAGS.data_format),
conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
return conv1
def _affine(inpOp, nIn, nOut, act=True, wd=0.0005):
global affine_counter
global parameters
name = 'affine' + str(affine_counter)
affine_counter += 1
with tf.name_scope(name) as scope:
kernel = tf.Variable(
tf.truncated_normal(
[nIn, nOut], dtype=tf.float32, stddev=1e-1),
name='weights')
if wd is not None and wd > 0:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
biases = tf.Variable(
tf.constant(
0.0, shape=[nOut], dtype=tf.float32),
trainable=True,
name='biases')
affine1 = tf.nn.relu_layer(
inpOp, kernel, biases,
name=name) if act else tf.matmul(inpOp, kernel) + biases
parameters += [kernel, biases]
return affine1
def _mpool(inpOp, kH, kW, dH, dW, padding):
global pool_counter
global parameters
name = 'pool' + str(pool_counter)
pool_counter += 1
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.max_pool(
inpOp,
ksize=ksize,
strides=strides,
padding=padding,
data_format=FLAGS.data_format,
name=name)
def _apool(inpOp, kH, kW, dH, dW, padding):
global pool_counter
global parameters
name = 'pool' + str(pool_counter)
pool_counter += 1
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.avg_pool(
inpOp,
ksize=ksize,
strides=strides,
padding=padding,
data_format=FLAGS.data_format,
name=name)
def _inception(inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2):
conv1 = _conv(inp, inSize, o1s, 1, 1, 1, 1, 'VALID')
conv3_ = _conv(inp, inSize, o2s1, 1, 1, 1, 1, 'VALID')
conv3 = _conv(conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME')
conv5_ = _conv(inp, inSize, o3s1, 1, 1, 1, 1, 'VALID')
conv5 = _conv(conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME')
pool_ = _mpool(inp, o4s1, o4s1, 1, 1, 'SAME')
pool = _conv(pool_, inSize, o4s2, 1, 1, 1, 1, 'VALID')
if FLAGS.data_format == 'NCHW':
channel_dim = 1
else:
channel_dim = 3
incept = tf.concat(channel_dim, [conv1, conv3, conv5, pool])
return incept
def loss(logits, labels):
batch_size = tf.size(labels)
labels = tf.expand_dims(labels, 1)
indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)
concated = tf.concat(1, [indices, labels])
onehot_labels = tf.sparse_to_dense(concated,
tf.pack([batch_size, 1000]), 1.0, 0.0)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits, onehot_labels, name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
return loss
def inference(images):
# stage 1
conv1 = _conv(images, 3, 64, 7, 7, 2, 2, 'SAME')
pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')
# stage 2
conv2 = _conv(pool1, 64, 64, 1, 1, 1, 1, 'VALID')
conv3 = _conv(conv2, 64, 192, 3, 3, 1, 1, 'SAME')
pool3 = _mpool(conv3, 3, 3, 2, 2, 'SAME')
# stage 3
incept3a = _inception(pool3, 192, 64, 96, 128, 16, 32, 3, 32)
incept3b = _inception(incept3a, 256, 128, 128, 192, 32, 96, 3, 64)
pool4 = _mpool(incept3b, 3, 3, 2, 2, 'SAME')
# stage 4
incept4a = _inception(pool4, 480, 192, 96, 208, 16, 48, 3, 64)
incept4b = _inception(incept4a, 512, 160, 112, 224, 24, 64, 3, 64)
incept4c = _inception(incept4b, 512, 128, 128, 256, 24, 64, 3, 64)
incept4d = _inception(incept4c, 512, 112, 144, 288, 32, 64, 3, 64)
incept4e = _inception(incept4d, 528, 256, 160, 320, 32, 128, 3, 128)
pool5 = _mpool(incept4e, 3, 3, 2, 2, 'SAME')
# stage 5
incept5a = _inception(pool5, 832, 256, 160, 320, 32, 128, 3, 128)
incept5b = _inception(incept5a, 832, 384, 192, 384, 48, 128, 3, 128)
pool6 = _apool(incept5b, 7, 7, 1, 1, 'VALID')
# output 1
resh1 = tf.reshape(pool6, [-1, 1024])
drop = tf.nn.dropout(resh1, 0.4)
affn1 = _affine(resh1, 1024, 1000, act=False)
return affn1
def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
if not isinstance(target, list):
target = [target]
target_op = tf.group(*target)
for i in range(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target_op)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' %
(datetime.now(), i - num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, FLAGS.num_batches, mn, sd))
def run_benchmark():
global parameters
with tf.Graph().as_default():
# Generate some dummy images.
image_size = 224
if FLAGS.data_format == 'NCHW':
image_shape = [FLAGS.batch_size, 3, image_size, image_size]
else:
image_shape = [FLAGS.batch_size, image_size, image_size, 3]
images = tf.get_variable(
'image',
image_shape,
initializer=tf.truncated_normal_initializer(
stddev=0.1, dtype=tf.float32),
dtype=tf.float32,
trainable=False)
labels = tf.get_variable(
'label', [FLAGS.batch_size],
initializer=tf.constant_initializer(1),
dtype=tf.int32,
trainable=False)
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(images)
objective = loss(last_layer, labels)
# Compute gradients.
# opt = tf.train.GradientDescentOptimizer(0.001)
opt = tf.train.MomentumOptimizer(0.001, 0.9)
grads = opt.compute_gradients(objective)
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(
0.0, dtype=tf.float32),
trainable=False,
dtype=tf.float32)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(0.9, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables(
))
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
run_forward = True
run_forward_backward = True
if FLAGS.forward_only and FLAGS.forward_backward_only:
raise ValueError("Cannot specify --forward_only and "
"--forward_backward_only at the same time.")
if FLAGS.forward_only:
run_forward_backward = False
elif FLAGS.forward_backward_only:
run_forward = False
if run_forward:
# Run the forward benchmark.
time_tensorflow_run(sess, last_layer, "Forward")
if run_forward_backward:
with tf.control_dependencies(
[apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
time_tensorflow_run(sess, [train_op, objective], "Forward-backward")
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 11,036 | 32.855828 | 80 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/image/googlenet_multi_gpu.py
|
# 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.
from six.moves import xrange # pylint: disable=redefined-builtin
from datetime import datetime
import math
import re
import time
import tensorflow.python.platform
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 64, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
"""The data format for Convnet operations.
Can be either NHWC or NCHW.
""")
tf.app.flags.DEFINE_string('train_dir', '/train_model',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('num_gpus', 4, """How many GPUs to use.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EPOCHS_PER_DECAY = 50
INITIAL_LEARNING_RATE = 0.1
LEARNING_RATE_DECAY_FACTOR = 0.1
TOWER_NAME = 'tower'
def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [kH, kW, nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
if FLAGS.data_format == 'NCHW':
strides = [1, 1, dH, dW]
else:
strides = [1, dH, dW, 1]
conv = tf.nn.conv2d(
inpOp,
kernel,
strides,
padding=padType,
data_format=FLAGS.data_format)
biases = tf.get_variable(
name=name + '_b',
shape=[nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32)
bias = tf.reshape(
tf.nn.bias_add(
conv, biases, data_format=FLAGS.data_format),
conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope)
return conv1
def _affine(name, inpOp, nIn, nOut, wd=0.005, act=True):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
biases = tf.get_variable(
name + '_b', [nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32,
trainable=True)
affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
tf.matmul(inpOp, kernel) + biases
return affine1
def _mpool(name, inpOp, kH, kW, dH, dW, padding):
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.max_pool(
inpOp,
ksize=ksize,
strides=strides,
padding=padding,
data_format=FLAGS.data_format,
name=name)
def _apool(name, inpOp, kH, kW, dH, dW, padding):
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.avg_pool(
inpOp,
ksize=ksize,
strides=strides,
padding=padding,
data_format=FLAGS.data_format,
name=name)
def loss(logits, labels):
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def get_incoming_shape(incoming):
""" Returns the incoming data shape """
if isinstance(incoming, tf.Tensor):
return incoming.get_shape().as_list()
elif type(incoming) in [np.array, list, tuple]:
return np.shape(incoming)
else:
raise Exception("Invalid incoming layer.")
def _inception(name, inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2):
conv1 = _conv(name + '_1', inp, inSize, o1s, 1, 1, 1, 1, 'VALID')
conv3_ = _conv(name + '_3r', inp, inSize, o2s1, 1, 1, 1, 1, 'VALID')
conv3 = _conv(name + '_3', conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME')
conv5_ = _conv(name + '_5r', inp, inSize, o3s1, 1, 1, 1, 1, 'VALID')
conv5 = _conv(name + '5', conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME')
pool_ = _mpool(name + 'pool', inp, o4s1, o4s1, 1, 1, 'SAME')
pool = _conv(name + 'proj', pool_, inSize, o4s2, 1, 1, 1, 1, 'VALID')
if FLAGS.data_format == 'NCHW':
channel_dim = 1
else:
channel_dim = 3
incept = tf.concat(channel_dim, [conv1, conv3, conv5, pool])
return incept
def inference(images):
# stage 1
conv1 = _conv('conv1', images, 3, 64, 7, 7, 2, 2, 'SAME')
pool1 = _mpool('pool1', conv1, 3, 3, 2, 2, 'SAME')
# stage 2
conv2 = _conv('conv2', pool1, 64, 64, 1, 1, 1, 1, 'VALID')
conv3 = _conv('conv3', conv2, 64, 192, 3, 3, 1, 1, 'SAME')
pool3 = _mpool('pool3', conv3, 3, 3, 2, 2, 'SAME')
# stage 3
incept3a = _inception('ince3a', pool3, 192, 64, 96, 128, 16, 32, 3, 32)
incept3b = _inception('ince3b', incept3a, 256, 128, 128, 192, 32, 96, 3, 64)
pool4 = _mpool('pool4', incept3b, 3, 3, 2, 2, 'SAME')
# stage 4
incept4a = _inception('ince4a', pool4, 480, 192, 96, 208, 16, 48, 3, 64)
incept4b = _inception('ince4b', incept4a, 512, 160, 112, 224, 24, 64, 3, 64)
incept4c = _inception('ince4c', incept4b, 512, 128, 128, 256, 24, 64, 3, 64)
incept4d = _inception('ince4d', incept4c, 512, 112, 144, 288, 32, 64, 3, 64)
incept4e = _inception('ince4e', incept4d, 528, 256, 160, 320, 32, 128, 3,
128)
pool5 = _mpool('pool5', incept4e, 3, 3, 2, 2, 'SAME')
# stage 5
incept5a = _inception('ince5a', pool5, 832, 256, 160, 320, 32, 128, 3, 128)
incept5b = _inception('ince5b', incept5a, 832, 384, 192, 384, 48, 128, 3,
128)
pool6 = _apool('pool6', incept5b, 7, 7, 1, 1, 'VALID')
# output 1
resh1 = tf.reshape(pool6, [-1, 1024])
drop = tf.nn.dropout(resh1, 0.4)
affn1 = _affine('fc_out', resh1, 1024, 1000, act=False)
return affn1
def tower_loss(scope):
"""Calculate the total loss on a single tower running the model.
Args:
scope: unique prefix string identifying the tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
image_size = 224
if FLAGS.data_format == 'NCHW':
image_shape = [FLAGS.batch_size, 3, image_size, image_size]
else:
image_shape = [FLAGS.batch_size, image_size, image_size, 3]
images = tf.get_variable(
'image',
image_shape,
initializer=tf.truncated_normal_initializer(
stddev=0.1, dtype=tf.float32),
dtype=tf.float32,
trainable=False)
labels = tf.get_variable(
'label', [FLAGS.batch_size],
initializer=tf.constant_initializer(1),
dtype=tf.int32,
trainable=False)
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(images)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = loss(last_layer, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name)
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.scalar_summary(loss_name + ' (raw)', l)
tf.scalar_summary(loss_name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients. The inner list is over the gradient
calculation for each tower.
Returns:
List of pairs of (gradient, variable) where the gradient has been averaged
across all towers.
"""
average_grads = []
for grad_and_vars in zip(*tower_grads):
# Note that each grad_and_vars looks like the following:
# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
grads = []
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension.
grad = tf.concat(0, grads)
grad = tf.reduce_mean(grad, 0)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
def time_tensorflow_run(session, target):
num_steps_burn_in = 50
total_duration = 0.0
total_duration_squared = 0.0
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_, loss_value = session.run(target)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch batch_size = %d)')
print(format_str %
(datetime.now(), i - num_steps_burn_in, loss_value,
duration, sec_per_batch, num_examples_per_step))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), FLAGS.num_batches, mn, sd))
def run_benchmark():
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# Calculate the learning rate schedule.
num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(
INITIAL_LEARNING_RATE,
global_step,
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
# Create an optimizer that performs gradient descent.
opt = tf.train.MomentumOptimizer(lr, 0.9)
# Calculate the gradients for each model tower.
tower_grads = []
for i in xrange(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope:
# Calculate the loss for one tower of the model. This function
# constructs the entire model but shares the variables across
# all towers.
loss = tower_loss(scope)
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
# Retain the summaries from the final tower.
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
# Calculate the gradients for the batch of data on this tower.
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op)
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
time_tensorflow_run(sess, [train_op, loss])
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 15,686 | 35.823944 | 84 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/image/alexnet_multi_gpu.py
|
# 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.
from six.moves import xrange # pylint: disable=redefined-builtin
from datetime import datetime
import math
import re
import time
import tensorflow.python.platform
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 64, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
"""The data format for Convnet operations.
Can be either NHWC or NCHW.
""")
tf.app.flags.DEFINE_string('train_dir', '/train_model',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('num_gpus', 4, """How many GPUs to use.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EPOCHS_PER_DECAY = 50
INITIAL_LEARNING_RATE = 0.1
LEARNING_RATE_DECAY_FACTOR = 0.1
TOWER_NAME = 'tower'
def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [kH, kW, nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
if FLAGS.data_format == 'NCHW':
strides = [1, 1, dH, dW]
else:
strides = [1, dH, dW, 1]
conv = tf.nn.conv2d(
inpOp,
kernel,
strides,
padding=padType,
data_format=FLAGS.data_format)
biases = tf.get_variable(
name=name + '_b',
shape=[nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32)
bias = tf.reshape(
tf.nn.bias_add(
conv, biases, data_format=FLAGS.data_format),
conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope)
return conv1
def _affine(name, inpOp, nIn, nOut, wd=0.005, act=True):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
biases = tf.get_variable(
name + '_b', [nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32,
trainable=True)
affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
tf.matmul(inpOp, kernel) + biases
return affine1
def _mpool(name, inpOp, kH, kW, dH, dW):
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.max_pool(
inpOp,
ksize=ksize,
strides=strides,
padding='VALID',
data_format=FLAGS.data_format,
name=name)
def _norm(name, l_input, lsize=4):
return tf.nn.lrn(l_input,
lsize,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name=name)
def loss(logits, labels):
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def get_incoming_shape(incoming):
""" Returns the incoming data shape """
if isinstance(incoming, tf.Tensor):
return incoming.get_shape().as_list()
elif type(incoming) in [np.array, list, tuple]:
return np.shape(incoming)
else:
raise Exception("Invalid incoming layer.")
def inference(images):
conv1 = _conv('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID')
pool1 = _mpool('pool1', conv1, 3, 3, 2, 2)
norm1 = _norm('norm1', pool1, lsize=5)
conv2 = _conv('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME')
pool2 = _mpool('pool2', conv2, 3, 3, 2, 2)
norm2 = _norm('norm2', pool2, lsize=5)
conv3 = _conv('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME')
conv4 = _conv('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME')
conv5 = _conv('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME')
pool5 = _mpool('pool5', conv5, 3, 3, 2, 2)
resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6])
affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096)
affn2 = _affine('fc7', affn1, 4096, 4096)
affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc
return affn3
def tower_loss(scope):
"""Calculate the total loss on a single tower running the model.
Args:
scope: unique prefix string identifying the tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
image_size = 224
if FLAGS.data_format == 'NCHW':
image_shape = [FLAGS.batch_size, 3, image_size + 3, image_size + 3]
else:
image_shape = [FLAGS.batch_size, image_size + 3, image_size + 3, 3]
images = tf.get_variable(
'image',
image_shape,
initializer=tf.truncated_normal_initializer(
stddev=0.1, dtype=tf.float32),
dtype=tf.float32,
trainable=False)
labels = tf.get_variable(
'label', [FLAGS.batch_size],
initializer=tf.constant_initializer(1),
dtype=tf.int32,
trainable=False)
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(images)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = loss(last_layer, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name)
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.scalar_summary(loss_name + ' (raw)', l)
tf.scalar_summary(loss_name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients. The inner list is over the gradient
calculation for each tower.
Returns:
List of pairs of (gradient, variable) where the gradient has been averaged
across all towers.
"""
average_grads = []
for grad_and_vars in zip(*tower_grads):
# Note that each grad_and_vars looks like the following:
# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
grads = []
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension.
grad = tf.concat(0, grads)
grad = tf.reduce_mean(grad, 0)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
def time_tensorflow_run(session, target):
num_steps_burn_in = 50
total_duration = 0.0
total_duration_squared = 0.0
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_, loss_value = session.run(target)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch batch_size = %d)')
print(format_str %
(datetime.now(), i - num_steps_burn_in, loss_value,
duration, sec_per_batch, num_examples_per_step))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), FLAGS.num_batches, mn, sd))
def run_benchmark():
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# Calculate the learning rate schedule.
num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(
INITIAL_LEARNING_RATE,
global_step,
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
# Create an optimizer that performs gradient descent.
opt = tf.train.MomentumOptimizer(lr, 0.9)
# Calculate the gradients for each model tower.
tower_grads = []
for i in xrange(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope:
# Calculate the loss for one tower of the model. This function
# constructs the entire model but shares the variables across
# all towers.
loss = tower_loss(scope)
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
# Retain the summaries from the final tower.
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
# Calculate the gradients for the batch of data on this tower.
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op)
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
time_tensorflow_run(sess, [train_op, loss])
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 14,066 | 36.018421 | 84 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/image/alexnet.py
|
# 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.
from six.moves import xrange # pylint: disable=redefined-builtin
from datetime import datetime
import math
import time
import tensorflow.python.platform
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 128, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('forward_only', False,
"""Only run the forward pass.""")
tf.app.flags.DEFINE_boolean('forward_backward_only', False,
"""Only run the forward-forward pass.""")
tf.app.flags.DEFINE_string('data_format', 'NCHW',
"""The data format for Convnet operations.
Can be either NHWC or NCHW.
""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.0005):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [kH, kW, nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
if wd is not None and wd > 0:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
if FLAGS.data_format == 'NCHW':
strides = [1, 1, dH, dW]
else:
strides = [1, dH, dW, 1]
conv = tf.nn.conv2d(
inpOp,
kernel,
strides,
padding=padType,
data_format=FLAGS.data_format)
biases = tf.get_variable(
name=name + '_b',
shape=[nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32)
bias = tf.reshape(
tf.nn.bias_add(
conv, biases, data_format=FLAGS.data_format),
conv.get_shape())
conv1 = tf.nn.relu(bias, name=scope)
return conv1
def _affine(name, inpOp, nIn, nOut, wd=0.0005, act=True, drop=None):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
if wd is not None and wd > 0:
weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
biases = tf.get_variable(
name + '_b', [nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32,
trainable=True)
affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
tf.matmul(inpOp, kernel) + biases
output = tf.nn.dropout(affine1, drop) if drop else affine1
return output
def _mpool(name, inpOp, kH, kW, dH, dW):
if FLAGS.data_format == 'NCHW':
ksize = [1, 1, kH, kW]
strides = [1, 1, dH, dW]
else:
ksize = [1, kH, kW, 1]
strides = [1, dH, dW, 1]
return tf.nn.max_pool(
inpOp,
ksize=ksize,
strides=strides,
padding='VALID',
data_format=FLAGS.data_format,
name=name)
def _norm(name, l_input, lsize=4):
return tf.nn.lrn(l_input,
lsize,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name=name)
def loss(logits, labels):
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def get_incoming_shape(incoming):
""" Returns the incoming data shape """
if isinstance(incoming, tf.Tensor):
return incoming.get_shape().as_list()
elif type(incoming) in [np.array, list, tuple]:
return np.shape(incoming)
else:
raise Exception("Invalid incoming layer.")
def inference(images):
conv1 = _conv('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID')
pool1 = _mpool('pool1', conv1, 3, 3, 2, 2)
norm1 = _norm('norm1', pool1, lsize=5)
conv2 = _conv('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME')
pool2 = _mpool('pool2', conv2, 3, 3, 2, 2)
norm2 = _norm('norm2', pool2, lsize=5)
conv3 = _conv('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME')
conv4 = _conv('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME')
conv5 = _conv('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME')
pool5 = _mpool('pool5', conv5, 3, 3, 2, 2)
resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6])
affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096, 0.5)
affn2 = _affine('fc7', affn1, 4096, 4096, 0.5)
affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc
return affn3
def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
if not isinstance(target, list):
target = [target]
target_op = tf.group(*target)
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target_op)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' %
(datetime.now(), i - num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, FLAGS.num_batches, mn, sd))
def _add_loss_summaries(total_loss):
"""
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages of losses.
"""
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
losses = tf.get_collection('losses')
loss_averages_op = loss_averages.apply(losses + [total_loss])
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.scalar_summary(l.op.name + ' (raw)', l)
tf.scalar_summary(l.op.name, loss_averages.average(l))
return loss_averages_op
def run_benchmark():
with tf.Graph().as_default():
with tf.device('/gpu:0'):
# Generate some dummy images.
image_size = 224
# Note that our padding definition is slightly different the cuda-convnet.
# In order to force the model to start with the same activations sizes,
# we add 3 to the image_size and employ VALID padding above.
if FLAGS.data_format == 'NCHW':
image_shape = [
FLAGS.batch_size, 3, image_size + 3, image_size + 3
]
else:
image_shape = [
FLAGS.batch_size, image_size + 3, image_size + 3, 3
]
images = tf.get_variable(
'image',
image_shape,
initializer=tf.truncated_normal_initializer(
stddev=0.1, dtype=tf.float32),
dtype=tf.float32,
trainable=False)
labels = tf.get_variable(
'label', [FLAGS.batch_size],
initializer=tf.constant_initializer(1),
dtype=tf.int32,
trainable=False)
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(images)
objective = loss(last_layer, labels)
# Compute the gradient with respect to all the parameters.
# Compute gradients.
# opt = tf.train.GradientDescentOptimizer(0.001)
opt = tf.train.MomentumOptimizer(0.001, 0.9)
grads = opt.compute_gradients(objective)
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(
0.0, dtype=tf.float32),
trainable=False,
dtype=tf.float32)
apply_gradient_op = opt.apply_gradients(
grads, global_step=global_step)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(0.9,
global_step)
variables_averages_op = variable_averages.apply(
tf.trainable_variables())
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
run_forward = True
run_forward_backward = True
if FLAGS.forward_only and FLAGS.forward_backward_only:
raise ValueError("Cannot specify --forward_only and "
"--forward_backward_only at the same time.")
if FLAGS.forward_only:
run_forward_backward = False
elif FLAGS.forward_backward_only:
run_forward = False
if run_forward:
time_tensorflow_run(sess, last_layer, "Forward")
if run_forward_backward:
with tf.control_dependencies(
[apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
time_tensorflow_run(sess, [train_op, objective],
"Forward-backward")
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 11,535 | 35.85623 | 86 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/rnn/reader.py
|
# 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 os.path
import io
import numpy as np
import tensorflow as tf
# tflearn
import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb
FLAGS = tf.app.flags.FLAGS
class DataSet(object):
def __init__(self, data, labels):
assert data.shape[0] == labels.shape[0], (
'data.shape: %s labels.shape: %s' % (data.shape, labels.shape))
self._num_examples = data.shape[0]
self._data = data
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0
@property
def data(self):
return self._data
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size):
assert batch_size <= self._num_examples
start = self._index_in_epoch
self._index_in_epoch += batch_size
if self._index_in_epoch > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Shuffle the data
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._data = self._data[perm]
self._labels = self._labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size
end = self._index_in_epoch
return self._data[start:end], self._labels[start:end]
def create_datasets(file_path, vocab_size=30000, val_fraction=0.0):
# IMDB Dataset loading
train, test, _ = imdb.load_data(
path=file_path,
n_words=vocab_size,
valid_portion=val_fraction,
sort_by_len=False)
trainX, trainY = train
testX, testY = test
# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=FLAGS.max_len, value=0.)
testX = pad_sequences(testX, maxlen=FLAGS.max_len, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)
train_dataset = DataSet(trainX, trainY)
return train_dataset
def main():
create_datasets('imdb.pkl')
if __name__ == "__main__":
main()
| 2,948 | 26.560748 | 75 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/rnn/rnn_multi_gpu.py
|
#!/usr/bin/env python
from six.moves import xrange # pylint: disable=redefined-builtin
import re
import math
import time
import numpy as np
from datetime import datetime
import reader
import tensorflow as tf
from tensorflow.python.ops import rnn
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 64, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('num_layers', 1, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('max_len', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('hidden_size', 128, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('emb_size', 64, """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
tf.app.flags.DEFINE_integer('num_gpus', 4, """How many GPUs to use.""")
VOCAB_SIZE = 30000
NUM_CLASS = 2
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EPOCHS_PER_DECAY = 50
INITIAL_LEARNING_RATE = 0.1
LEARNING_RATE_DECAY_FACTOR = 0.1
TOWER_NAME = 'tower'
train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE)
def get_incoming_shape(incoming):
""" Returns the incoming data shape """
if isinstance(incoming, tf.Tensor):
return incoming.get_shape().as_list()
elif type(incoming) in [np.array, list, tuple]:
return np.shape(incoming)
else:
raise Exception("Invalid incoming layer.")
# Note input * W is done in LSTMCell,
# which is different from PaddlePaddle
def single_lstm(name,
incoming,
n_units,
use_peepholes=True,
return_seq=False,
return_state=False):
with tf.name_scope(name) as scope:
cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes)
output, _cell_state = rnn.rnn(cell, incoming, dtype=tf.float32)
out = output if return_seq else output[-1]
return (out, _cell_state) if return_state else out
def lstm(name,
incoming,
n_units,
use_peepholes=True,
return_seq=False,
return_state=False,
num_layers=1):
with tf.name_scope(name) as scope:
lstm_cell = tf.nn.rnn_cell.LSTMCell(
n_units, use_peepholes=use_peepholes)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * num_layers)
initial_state = cell.zero_state(FLAGS.batch_size, dtype=tf.float32)
if not isinstance(incoming, list):
# if the input is embeding, the Tensor shape : [None, time_step, emb_size]
incoming = [
tf.squeeze(input_, [1])
for input_ in tf.split(1, FLAGS.max_len, incoming)
]
outputs, state = tf.nn.rnn(cell,
incoming,
initial_state=initial_state,
dtype=tf.float32)
out = outputs if return_seq else outputs[-1]
return (out, _cell_state) if return_state else out
def embedding(name, incoming, vocab_size, emb_size):
with tf.name_scope(name) as scope:
#with tf.device("/cpu:0"):
embedding = tf.get_variable(
name + '_emb', [vocab_size, emb_size], dtype=tf.float32)
out = tf.nn.embedding_lookup(embedding, incoming)
return out
def fc(name, inpOp, nIn, nOut, act=True):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
biases = tf.get_variable(
name + '_b', [nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32,
trainable=True)
net = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
tf.matmul(inpOp, kernel) + biases
return net
def inference(seq):
net = embedding('emb', seq, VOCAB_SIZE, FLAGS.emb_size)
print "emb:", get_incoming_shape(net)
net = lstm('lstm', net, FLAGS.hidden_size, num_layers=FLAGS.num_layers)
print "lstm:", get_incoming_shape(net)
net = fc('fc1', net, FLAGS.hidden_size, 2)
return net
def loss(logits, labels):
# one label index for one sample
#labels = tf.cast(labels, tf.int64)
# cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
# logits, labels, name='cross_entropy_per_example')
labels = tf.cast(labels, tf.float32)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def tower_loss(scope):
"""Calculate the total loss on a single tower running the model.
Args:
scope: unique prefix string identifying the tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
data, label = train_dataset.next_batch(FLAGS.batch_size)
# Build a Graph that computes the logits predictions from the
# inference model.
last_layer = inference(data)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
#_ = loss(last_layer, label)
_ = loss(last_layer, label)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name)
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.scalar_summary(loss_name + ' (raw)', l)
#tf.scalar_summary(loss_name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients. The inner list is over the gradient
calculation for each tower.
Returns:
List of pairs of (gradient, variable) where the gradient has been averaged
across all towers.
"""
average_grads = []
for grad_and_vars in zip(*tower_grads):
# Note that each grad_and_vars looks like the following:
# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
grads = []
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension.
grad = tf.concat(0, grads)
grad = tf.reduce_mean(grad, 0)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
def time_tensorflow_run(session, target):
num_steps_burn_in = 80
total_duration = 0.0
total_duration_squared = 0.0
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target, feed_dict={x_input: data, y_input: label})
_, loss_value = session.run(target)
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
examples_per_sec = num_examples_per_step / duration
# sec_per_batch = duration / FLAGS.num_gpus
sec_per_batch = duration
format_str = (
'%s: step %d, loss= %.2f (%.1f examples/sec; %.3f '
'sec/batch batch_size= %d)')
print(format_str %
(datetime.now(), i - num_steps_burn_in, loss_value,
duration, sec_per_batch, num_examples_per_step))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), FLAGS.num_batches, mn, sd))
def run_benchmark():
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# Calculate the learning rate schedule.
num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
# Create an optimizer that performs gradient descent.
opt = tf.train.AdamOptimizer(0.001)
#train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE)
# Calculate the gradients for each model tower.
tower_grads = []
for i in xrange(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope:
# Calculate the loss for one tower of the model. This function
# constructs the entire model but shares the variables across
# all towers.
loss = tower_loss(scope)
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
# Retain the summaries from the final tower.
# summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
# Calculate the gradients for the batch of data on this tower.
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op)
# Build an initialization operation.
init = tf.initialize_all_variables()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
time_tensorflow_run(sess, [train_op, loss])
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 12,436 | 37.504644 | 86 |
py
|
Paddle
|
Paddle-master/benchmark/tensorflow/rnn/rnn.py
|
#!/usr/bin/env python
from six.moves import xrange # pylint: disable=redefined-builtin
import math
import time
import numpy as np
from datetime import datetime
import reader
import tensorflow as tf
from tensorflow.python.ops import rnn
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 128, """Batch size.""")
tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('num_layers', 1, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('max_len', 100, """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('forward_only', False,
"""Only run the forward pass.""")
tf.app.flags.DEFINE_boolean('forward_backward_only', False,
"""Only run the forward-forward pass.""")
tf.app.flags.DEFINE_integer('hidden_size', 128, """Number of batches to run.""")
tf.app.flags.DEFINE_integer('emb_size', 128, """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
VOCAB_SIZE = 30000
NUM_CLASS = 2
def get_feed_dict(x_data, y_data=None):
feed_dict = {}
if y_data is not None:
feed_dict[y_input] = y_data
for i in xrange(x_data.shape[0]):
feed_dict[x_input[i]] = x_data[i, :, :]
return feed_dict
def get_incoming_shape(incoming):
""" Returns the incoming data shape """
if isinstance(incoming, tf.Tensor):
return incoming.get_shape().as_list()
elif type(incoming) in [np.array, list, tuple]:
return np.shape(incoming)
else:
raise Exception("Invalid incoming layer.")
# Note input * W is done in LSTMCell,
# which is different from PaddlePaddle
def single_lstm(name,
incoming,
n_units,
use_peepholes=True,
return_seq=False,
return_state=False):
with tf.name_scope(name) as scope:
cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes)
output, _cell_state = rnn.rnn(cell, incoming, dtype=tf.float32)
out = output if return_seq else output[-1]
return (out, _cell_state) if return_state else out
def lstm(name,
incoming,
n_units,
use_peepholes=True,
return_seq=False,
return_state=False,
num_layers=1):
with tf.name_scope(name) as scope:
lstm_cell = tf.nn.rnn_cell.LSTMCell(
n_units, use_peepholes=use_peepholes)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * num_layers)
initial_state = cell.zero_state(FLAGS.batch_size, dtype=tf.float32)
if not isinstance(incoming, list):
# if the input is embeding, the Tensor shape : [None, time_step, emb_size]
incoming = [
tf.squeeze(input_, [1])
for input_ in tf.split(1, FLAGS.max_len, incoming)
]
outputs, state = tf.nn.rnn(cell,
incoming,
initial_state=initial_state,
dtype=tf.float32)
out = outputs if return_seq else outputs[-1]
return (out, _cell_state) if return_state else out
def embedding(name, incoming, vocab_size, emb_size):
with tf.name_scope(name) as scope:
#with tf.device("/cpu:0"):
embedding = tf.get_variable(
name + '_emb', [vocab_size, emb_size], dtype=tf.float32)
out = tf.nn.embedding_lookup(embedding, incoming)
return out
def fc(name, inpOp, nIn, nOut, act=True):
with tf.name_scope(name) as scope:
kernel = tf.get_variable(
name + '_w', [nIn, nOut],
initializer=tf.truncated_normal_initializer(
stddev=0.01, dtype=tf.float32),
dtype=tf.float32)
biases = tf.get_variable(
name + '_b', [nOut],
initializer=tf.constant_initializer(
value=0.0, dtype=tf.float32),
dtype=tf.float32,
trainable=True)
net = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
tf.matmul(inpOp, kernel) + biases
return net
def inference(seq):
net = embedding('emb', seq, VOCAB_SIZE, FLAGS.emb_size)
print "emb:", get_incoming_shape(net)
net = lstm('lstm', net, FLAGS.hidden_size, num_layers=FLAGS.num_layers)
print "lstm:", get_incoming_shape(net)
net = fc('fc1', net, FLAGS.hidden_size, 2)
return net
def loss(logits, labels):
# one label index for one sample
labels = tf.cast(labels, tf.float32)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def time_tensorflow_run(session, target, x_input, y_input, info_string):
num_steps_burn_in = 50
total_duration = 0.0
total_duration_squared = 0.0
if not isinstance(target, list):
target = [target]
target_op = tf.group(*target)
train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE)
for i in xrange(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
data, label = train_dataset.next_batch(FLAGS.batch_size)
_ = session.run(target_op, feed_dict={x_input: data, y_input: label})
duration = time.time() - start_time
if i > num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' %
(datetime.now(), i - num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
(datetime.now(), info_string, FLAGS.num_batches, mn, sd))
def run_benchmark():
with tf.Graph().as_default():
global_step = 0
with tf.device('/cpu:0'):
global_step = tf.Variable(0, trainable=False)
with tf.device('/gpu:0'):
#x_input = tf.placeholder(tf.int32, [None, FLAGS.max_len], name="x_input")
#y_input = tf.placeholder(tf.int32, [None, NUM_CLASS], name="y_input")
x_input = tf.placeholder(
tf.int32, [FLAGS.batch_size, FLAGS.max_len], name="x_input")
y_input = tf.placeholder(
tf.int32, [FLAGS.batch_size, NUM_CLASS], name="y_input")
# Generate some dummy sequnce.
last_layer = inference(x_input)
objective = loss(last_layer, y_input)
opt = tf.train.AdamOptimizer(0.001)
grads = opt.compute_gradients(objective)
apply_gradient_op = opt.apply_gradients(
grads, global_step=global_step)
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
run_forward = True
run_forward_backward = True
if FLAGS.forward_only and FLAGS.forward_backward_only:
raise ValueError("Cannot specify --forward_only and "
"--forward_backward_only at the same time.")
if FLAGS.forward_only:
run_forward_backward = False
elif FLAGS.forward_backward_only:
run_forward = False
if run_forward:
time_tensorflow_run(sess, last_layer, x_input, y_input,
"Forward")
if run_forward_backward:
with tf.control_dependencies([apply_gradient_op]):
train_op = tf.no_op(name='train')
time_tensorflow_run(sess, [train_op, objective], x_input,
y_input, "Forward-backward")
def main(_):
run_benchmark()
if __name__ == '__main__':
tf.app.run()
| 8,285 | 35.991071 | 86 |
py
|
Paddle
|
Paddle-master/paddle/trainer/tests/simple_sparse_neural_network.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=17, learning_method=AdaGradOptimizer(), learning_rate=1e-4)
file_list = 'trainer/tests/fake_file_list.list'
define_py_data_sources2(
train_list=file_list,
test_list=file_list,
module="simple_sparse_neural_network_dp",
obj="process")
embedding = embedding_layer(
input=data_layer(
name="word_ids", size=8191),
size=128,
param_attr=ParamAttr(sparse_update=True))
prediction = fc_layer(input=embedding, size=10, act=SoftmaxActivation())
outputs(
classification_cost(
input=prediction, label=data_layer(
name='label', size=10)))
| 1,266 | 32.342105 | 79 |
py
|
Paddle
|
Paddle-master/paddle/trainer/tests/simple_sparse_neural_network_dp.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from paddle.trainer.PyDataProvider2 import provider, integer_sequence, integer_value
import random
def init_hook(settings, is_train, **kwargs):
settings.is_train = is_train
@provider(
input_types={'word_ids': integer_value(8191),
'label': integer_value(10)},
min_pool_size=0,
init_hook=init_hook)
def process(settings, filename):
if settings.is_train:
data_size = 2**10
else:
data_size = 2**5
for _ in xrange(data_size):
yield random.randint(0, 8190), random.randint(0, 9)
| 1,159 | 31.222222 | 84 |
py
|
Paddle
|
Paddle-master/paddle/trainer/tests/testPyDataWrapper.py
|
# Copyright (c) 2016 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 sys
sys.path.append("../")
from paddle.trainer.PyDataProviderWrapper import *
import random
import json
import string
SPARSE_ID_LIMIT = 1000
SPARSE_ID_COUNT = 100
SEQUENCE_LIMIT = 50
STRING_LIMIT = 10
sparse_id_randomer = lambda: random.randrange(0, SPARSE_ID_LIMIT - 1)
sparse_count_randomer = lambda: random.randrange(1, SPARSE_ID_COUNT)
val_randomer = lambda: random.uniform(-1.0, 1.0)
seq_count_randomer = lambda: random.randrange(1, SEQUENCE_LIMIT)
str_count_randomer = lambda: random.randrange(1, STRING_LIMIT)
class IDRandomer(): # A random generator, return unique id
def __init__(self):
self.id_set = set()
def __call__(self):
idx = sparse_id_randomer()
if idx not in self.id_set:
self.id_set.add(idx)
return idx
else:
return self.__call__()
# SparseValueSlot
def sparse_value_creator(_):
rand = IDRandomer()
return [(rand(), val_randomer()) for _ in xrange(sparse_count_randomer())]
sparse_value = map(sparse_value_creator, range(seq_count_randomer()))
# DenseSlot
def dense_creator(_):
return [val_randomer() for _ in xrange(SPARSE_ID_LIMIT)]
dense = map(dense_creator, range(seq_count_randomer()))
# SparseNonValueSlot
def sparse_creator(_):
rand = IDRandomer()
return [rand() for _ in xrange(sparse_count_randomer())]
sparse_nonvalue = map(sparse_creator, range(seq_count_randomer()))
# IndexSlot
ids = [sparse_id_randomer() for _ in range(seq_count_randomer())]
# StringSlot
def random_str(size=8, chars=string.ascii_letters + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
strs = [random_str(str_count_randomer()) for _ in range(seq_count_randomer())]
def processSeqAndGenerateDataInit(obj, *args, **kwargs):
obj.json_filename = kwargs.get("load_data_args", "test_data.json")
@provider(
slots=[
SparseValueSlot(SPARSE_ID_LIMIT), DenseSlot(SPARSE_ID_LIMIT),
SparseNonValueSlot(SPARSE_ID_LIMIT), IndexSlot(SPARSE_ID_LIMIT),
StringSlot(SPARSE_ID_LIMIT)
],
use_seq=True,
init_hook=processSeqAndGenerateDataInit)
def processSeqAndGenerateData(obj, name):
retv = [sparse_value, dense, sparse_nonvalue, ids, strs]
# Write to protoseq.
with open(obj.json_filename, "w") as f:
json.dump(retv, f)
yield retv
def processSubSeqAndGenerateDataInit(obj, *args, **kwargs):
obj.json_filename = kwargs.get("load_data_args", "test_data.json")
@provider(
slots=[
SparseValueSlot(SPARSE_ID_LIMIT), DenseSlot(SPARSE_ID_LIMIT),
SparseNonValueSlot(SPARSE_ID_LIMIT), IndexSlot(SPARSE_ID_LIMIT),
StringSlot(SPARSE_ID_LIMIT)
],
use_seq=True,
init_hook=processSubSeqAndGenerateDataInit)
def processSubSeqAndGenerateData(obj, name):
retv_json = [sparse_value, dense, sparse_nonvalue, ids, strs]
retv_wrapper = [[sparse_value], [dense], [sparse_nonvalue], [ids], [strs]]
# Write to protoseq.
with open(obj.json_filename, "w") as f:
json.dump(retv_json, f)
yield retv_wrapper
if __name__ == "__main__":
pvd = processSeqAndGenerateData("_")
print pvd.getNextBatch(100)
pvd = processSubSeqAndGenerateData("_")
print pvd.getNextBatch(1)
| 3,853 | 28.419847 | 78 |
py
|
Paddle
|
Paddle-master/paddle/trainer/tests/__init__.py
|
# Copyright (c) 2016 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.
| 609 | 42.571429 | 74 |
py
|
Paddle
|
Paddle-master/paddle/trainer/tests/config_parser_test.py
|
# Copyright (c) 2016 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.
from paddle.trainer.config_parser import parse_config_and_serialize
if __name__ == '__main__':
parse_config_and_serialize('trainer/tests/test_config.conf', '')
parse_config_and_serialize(
'trainer/tests/sample_trainer_config.conf',
'extension_module_name=paddle.trainer.config_parser_extension')
parse_config_and_serialize('gserver/tests/pyDataProvider/trainer.conf', '')
| 1,011 | 43 | 79 |
py
|
Paddle
|
Paddle-master/paddle/api/__init__.py
|
# Copyright (c) 2016 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.
| 609 | 42.571429 | 74 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testTrainConfig.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=100, learning_method=AdamOptimizer())
din = data_layer(name='input', size=784)
fc1 = fc_layer(name='hidden1', input=din, size=100)
fc2 = fc_layer(name='hidden2', input=fc1, size=100)
opt = fc_layer(input=fc2, size=10, act=SoftmaxActivation())
outputs(classification_cost(input=opt, label=data_layer('lbl', 10)))
| 993 | 37.230769 | 74 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testArguments.py
|
# Copyright (c) 2016 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.
from py_paddle import swig_paddle
import numpy as np
import unittest
class TestArguments(unittest.TestCase):
def test_load_arguments(self):
m = swig_paddle.Matrix.createDense([4, 2, 4, 3, 9, 5], 2, 3)
args = swig_paddle.Arguments.createArguments(1)
args.setSlotValue(0, m)
self.assertAlmostEqual(27.0, args.sum())
mat = args.getSlotValue(0)
assert isinstance(mat, swig_paddle.Matrix)
np_mat = mat.toNumpyMatInplace()
# The matrix unittest is in testMatrix.py
self.assertEqual(np_mat.shape, (2, 3))
args.setSlotIds(0, swig_paddle.IVector.create([1, 2, 3, 4, 5, 6]))
iv = args.getSlotIds(0)
assert isinstance(iv, swig_paddle.IVector)
np_arr = iv.toNumpyArrayInplace()
self.assertEqual(np_arr.shape, (6, ))
def test_arguments_shape(self):
h, w = 4, 6
v = np.random.rand(2, h * w)
m = swig_paddle.Matrix.createDense(v.flatten(), 2, h * w)
args = swig_paddle.Arguments.createArguments(1)
args.setSlotValue(0, m)
args.setSlotFrameHeight(0, h)
args.setSlotFrameWidth(0, w)
self.assertEqual(args.getSlotFrameHeight(), h)
self.assertEqual(args.getSlotFrameWidth(), w)
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=0")
unittest.main()
| 1,958 | 34.618182 | 74 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testGradientMachine.py
|
# Copyright (c) 2016 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.
from py_paddle import swig_paddle
import paddle.proto.ParameterConfig_pb2
import util
import unittest
import numpy
class TestGradientMachine(unittest.TestCase):
def test_create_gradient_machine(self):
conf_file_path = "./testTrainConfig.py"
trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
conf_file_path)
self.assertIsNotNone(trainer_config)
opt_config = trainer_config.getOptimizationConfig()
model_config = trainer_config.getModelConfig()
self.assertIsNotNone(model_config)
machine = swig_paddle.GradientMachine.createByModelConfig(
model_config, swig_paddle.CREATE_MODE_NORMAL,
swig_paddle.ParameterOptimizer.create(opt_config).getParameterTypes(
))
self.assertIsNotNone(machine)
ipt, _ = util.loadMNISTTrainData()
output = swig_paddle.Arguments.createArguments(0)
optimizers = {}
# Initial Machine Parameter all to 0.1
for param in machine.getParameters():
assert isinstance(param, swig_paddle.Parameter)
val = param.getBuf(swig_paddle.PARAMETER_VALUE)
assert isinstance(val, swig_paddle.Vector)
arr = numpy.full((len(val), ), 0.1, dtype="float32")
val.copyFromNumpyArray(arr)
self.assertTrue(param.save(param.getName()))
param_config = param.getConfig().toProto()
assert isinstance(param_config,
paddle.proto.ParameterConfig_pb2.ParameterConfig)
opt = swig_paddle.ParameterOptimizer.create(opt_config)
optimizers[param.getID()] = opt
num_rows = param_config.dims[1]
opt.init(num_rows, param.getConfig())
for k in optimizers:
opt = optimizers[k]
opt.startPass()
batch_size = ipt.getSlotValue(0).getHeight()
for k in optimizers:
opt = optimizers[k]
opt.startBatch(batch_size)
machine.forward(ipt, output, swig_paddle.PASS_TRAIN)
self.assertEqual(1, output.getSlotNum())
self.isCalled = False
def backward_callback(param_):
self.isCalled = isinstance(param_, swig_paddle.Parameter)
assert isinstance(param_, swig_paddle.Parameter)
vec = param_.getBuf(swig_paddle.PARAMETER_VALUE)
assert isinstance(vec, swig_paddle.Vector)
vec = vec.copyToNumpyArray()
for val_ in vec:
self.assertTrue(
util.doubleEqual(val_, 0.1)) # Assert All Value is 0.1
vecs = list(param_.getBufs())
opt_ = optimizers[param_.getID()]
opt_.update(vecs, param_.getConfig())
machine.backward(backward_callback)
for k in optimizers:
opt = optimizers[k]
opt.finishBatch()
for k in optimizers:
opt = optimizers[k]
opt.finishPass()
self.assertTrue(self.isCalled)
for param in machine.getParameters():
self.assertTrue(param.load(param.getName()))
def test_train_one_pass(self):
conf_file_path = './testTrainConfig.py'
trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
conf_file_path)
model_config = trainer_config.getModelConfig()
machine = swig_paddle.GradientMachine.createByModelConfig(model_config)
at_end = False
output = swig_paddle.Arguments.createArguments(0)
if not at_end:
input_, at_end = util.loadMNISTTrainData(1000)
machine.forwardBackward(input_, output, swig_paddle.PASS_TRAIN)
if __name__ == '__main__':
swig_paddle.initPaddle('--use_gpu=0')
unittest.main()
| 4,398 | 36.598291 | 80 |
py
|
Paddle
|
Paddle-master/paddle/api/test/util.py
|
# Copyright (c) 2016 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 random
import numpy as np
from py_paddle import swig_paddle
def doubleEqual(a, b):
return abs(a - b) < 1e-5
def __readFromFile():
for i in xrange(10002):
label = np.random.randint(0, 9)
sample = np.random.rand(784) + 0.1 * label
yield sample, label
def loadMNISTTrainData(batch_size=100):
if not hasattr(loadMNISTTrainData, "gen"):
generator = __readFromFile()
loadMNISTTrainData.gen = generator
else:
generator = loadMNISTTrainData.gen
args = swig_paddle.Arguments.createArguments(2)
# batch_size = 100
dense_slot = []
id_slot = []
atEnd = False
for _ in xrange(batch_size):
try:
result = generator.next()
dense_slot.extend(result[0])
id_slot.append(result[1])
except StopIteration:
atEnd = True
del loadMNISTTrainData.gen
break
dense_slot = swig_paddle.Matrix.createDense(dense_slot, batch_size, 784)
id_slot = swig_paddle.IVector.create(id_slot)
args.setSlotValue(0, dense_slot)
args.setSlotIds(1, id_slot)
return args, atEnd
| 1,752 | 28.216667 | 76 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testTrainer.py
|
# Copyright (c) 2016 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.
from paddle.trainer.config_parser import parse_config
from paddle.trainer.config_parser import logger
from py_paddle import swig_paddle
import util
def main():
trainer_config = parse_config("./testTrainConfig.py", "")
model = swig_paddle.GradientMachine.createFromConfigProto(
trainer_config.model_config)
trainer = swig_paddle.Trainer.create(trainer_config, model)
trainer.startTrain()
for train_pass in xrange(2):
trainer.startTrainPass()
num = 0
cost = 0
while True: # Train one batch
batch_size = 1000
data, atEnd = util.loadMNISTTrainData(batch_size)
if atEnd:
break
trainer.trainOneDataBatch(batch_size, data)
outs = trainer.getForwardOutput()
cost += sum(outs[0]['value'])
num += batch_size
trainer.finishTrainPass()
logger.info('train cost=%f' % (cost / num))
trainer.startTestPeriod()
num = 0
cost = 0
while True: # Test one batch
batch_size = 1000
data, atEnd = util.loadMNISTTrainData(batch_size)
if atEnd:
break
trainer.testOneDataBatch(batch_size, data)
outs = trainer.getForwardOutput()
cost += sum(outs[0]['value'])
num += batch_size
trainer.finishTestPeriod()
logger.info('test cost=%f' % (cost / num))
trainer.finishTrain()
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=0", "--trainer_count=1")
main()
| 2,188 | 33.203125 | 74 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testMatrix.py
|
# Copyright (c) 2016 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.
from py_paddle import swig_paddle
import numpy as np
import unittest
class TestMatrix(unittest.TestCase):
def test_createZero_get_set(self):
m = swig_paddle.Matrix.createZero(32, 24)
self.assertEqual(m.getWidth(), 24)
self.assertEqual(m.getHeight(), 32)
for x in xrange(24):
for y in xrange(32):
self.assertEqual(0.0, m.get(x, y))
with self.assertRaises(swig_paddle.RangeError):
m.get(51, 47)
m.set(3, 3, 3.0)
self.assertEqual(m.get(3, 3), 3.0)
def test_sparse(self):
m = swig_paddle.Matrix.createSparse(3, 3, 6, True, False, False)
self.assertIsNotNone(m)
self.assertTrue(m.isSparse())
self.assertEqual(m.getSparseValueType(), swig_paddle.SPARSE_NON_VALUE)
self.assertEqual(m.getSparseFormat(), swig_paddle.SPARSE_CSR)
m.sparseCopyFrom([0, 2, 3, 3], [0, 1, 2], [])
self.assertEqual(m.getSparseRowCols(0), [0, 1])
self.assertEqual(m.getSparseRowCols(1), [2])
self.assertEqual(m.getSparseRowCols(2), [])
def test_sparse_value(self):
m = swig_paddle.Matrix.createSparse(3, 3, 6, False, False, False)
self.assertIsNotNone(m)
m.sparseCopyFrom([0, 2, 3, 3], [0, 1, 2], [7.3, 4.2, 3.2])
def assertKVArraySame(actual, expect):
self.assertEqual(len(actual), len(expect))
for i in xrange(len(actual)):
a = actual[i]
e = expect[i]
self.assertIsInstance(a, tuple)
self.assertIsInstance(e, tuple)
self.assertEqual(len(a), 2)
self.assertEqual(len(e), 2)
self.assertEqual(a[0], e[0])
self.assertTrue(abs(a[1] - e[1]) < 1e-5)
first_row = m.getSparseRowColsVal(0)
assertKVArraySame(first_row, [(0, 7.3), (1, 4.2)])
def test_createDenseMat(self):
m = swig_paddle.Matrix.createDense([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], 2, 3)
self.assertIsNotNone(m)
self.assertTrue(abs(m.get(1, 1) - 0.5) < 1e-5)
def test_numpyCpu(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat, False)
self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
# the numpy matrix and paddle matrix shared the same memory.
numpy_mat[0, 1] = 342.23
for h in xrange(m.getHeight()):
for w in xrange(m.getWidth()):
self.assertEqual(m.get(h, w), numpy_mat[h, w])
mat2 = m.toNumpyMatInplace()
mat2[1, 1] = 32.2
self.assertTrue(np.array_equal(mat2, numpy_mat))
def test_numpyGpu(self):
if swig_paddle.isGpuVersion():
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype='float32')
gpu_m = swig_paddle.Matrix.createGpuDenseFromNumpy(numpy_mat)
assert isinstance(gpu_m, swig_paddle.Matrix)
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
def test_numpy(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
if __name__ == "__main__":
swig_paddle.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(TestMatrix)
unittest.TextTestRunner().run(suite)
if swig_paddle.isGpuVersion():
swig_paddle.setUseGpu(True)
unittest.main()
| 4,894 | 39.454545 | 80 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testVector.py
|
# Copyright (c) 2016 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.
from py_paddle import swig_paddle
import util
import numpy as np
import unittest
class TestIVector(unittest.TestCase):
def test_createZero(self):
m = swig_paddle.IVector.createZero(10, False)
self.assertIsNotNone(m)
for i in xrange(10):
self.assertEqual(m[i], 0)
m[i] = i
self.assertEqual(m[i], i)
m = swig_paddle.IVector.createZero(10)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(m.getData(), [0] * 10)
def test_create(self):
m = swig_paddle.IVector.create(range(10), False)
self.assertIsNotNone(m)
for i in xrange(10):
self.assertEqual(m[i], i)
m = swig_paddle.IVector.create(range(10))
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(m.getData(), range(10))
def test_cpu_numpy(self):
vec = np.array([1, 3, 4, 65, 78, 1, 4], dtype="int32")
iv = swig_paddle.IVector.createCpuVectorFromNumpy(vec, False)
self.assertEqual(vec.shape[0], int(iv.__len__()))
vec[4] = 832
for i in xrange(len(iv)):
self.assertEqual(vec[i], iv[i])
vec2 = iv.toNumpyArrayInplace()
vec2[1] = 384
for i in xrange(len(iv)):
self.assertEqual(vec[i], iv[i])
self.assertEqual(vec2[i], iv[i])
def test_gpu_numpy(self):
if swig_paddle.isGpuVersion():
vec = swig_paddle.IVector.create(range(0, 10), True)
assert isinstance(vec, swig_paddle.IVector)
self.assertTrue(vec.isGpu())
self.assertEqual(vec.getData(), range(0, 10))
num_arr = vec.copyToNumpyArray()
assert isinstance(num_arr, np.ndarray) # for code hint.
num_arr[4] = 7
self.assertEquals(vec.getData(), range(0, 10))
vec.copyFromNumpyArray(num_arr)
expect_vec = range(0, 10)
expect_vec[4] = 7
self.assertEqual(vec.getData(), expect_vec)
def test_numpy(self):
vec = np.array([1, 3, 4, 65, 78, 1, 4], dtype="int32")
iv = swig_paddle.IVector.createVectorFromNumpy(vec)
self.assertEqual(iv.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(iv.getData(), list(vec))
class TestVector(unittest.TestCase):
def testCreateZero(self):
v = swig_paddle.Vector.createZero(10, False)
self.assertIsNotNone(v)
for i in xrange(len(v)):
self.assertTrue(util.doubleEqual(v[i], 0))
v[i] = i
self.assertTrue(util.doubleEqual(v[i], i))
v = swig_paddle.Vector.createZero(10)
self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(v.getData(), [0] * 10)
def testCreate(self):
v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)], False)
self.assertIsNotNone(v)
for i in xrange(len(v)):
self.assertTrue(util.doubleEqual(v[i], i / 100.0))
self.assertEqual(100, len(v))
v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)])
self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(100, len(v))
vdata = v.getData()
for i in xrange(len(v)):
self.assertTrue(util.doubleEqual(vdata[i], i / 100.0))
def testCpuNumpy(self):
numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, False)
assert isinstance(vec, swig_paddle.Vector)
numpy_arr[0] = 0.1
for n, v in zip(numpy_arr, vec):
self.assertTrue(util.doubleEqual(n, v))
numpy_2 = vec.toNumpyArrayInplace()
vec[0] = 1.3
for x, y in zip(numpy_arr, numpy_2):
self.assertTrue(util.doubleEqual(x, y))
for x, y in zip(numpy_arr, vec):
self.assertTrue(util.doubleEqual(x, y))
numpy_3 = vec.copyToNumpyArray()
numpy_3[0] = 0.4
self.assertTrue(util.doubleEqual(vec[0], 1.3))
self.assertTrue(util.doubleEqual(numpy_3[0], 0.4))
for i in xrange(1, len(numpy_3)):
util.doubleEqual(numpy_3[i], vec[i])
def testNumpy(self):
numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
vec = swig_paddle.Vector.createVectorFromNumpy(numpy_arr)
self.assertEqual(vec.isGpu(), swig_paddle.isUsingGpu())
vecData = vec.getData()
for n, v in zip(numpy_arr, vecData):
self.assertTrue(util.doubleEqual(n, v))
def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1, False)
arr = np.array([1.3, 3.2, 2.4], dtype="float32")
vec.copyFromNumpyArray(arr)
for i in xrange(len(vec)):
self.assertTrue(util.doubleEqual(vec[i], arr[i]))
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(TestVector)
unittest.TextTestRunner().run(suite)
if swig_paddle.isGpuVersion():
swig_paddle.setUseGpu(True)
unittest.main()
| 5,734 | 36.24026 | 78 |
py
|
Paddle
|
Paddle-master/paddle/api/test/testTrain.py
|
# Copyright (c) 2016 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.
from py_paddle import swig_paddle
import paddle.trainer.config_parser
import numpy
import util
def init_params(params):
def init_param(p):
assert isinstance(p, swig_paddle.Parameter)
val = p.getBuf(swig_paddle.PARAMETER_VALUE)
assert isinstance(val, swig_paddle.Vector)
arr = val.toNumpyArrayInplace()
for i in xrange(len(arr)):
arr[i] = numpy.random.uniform(-1.0, 1.0)
for p in params:
init_param(p)
def init_optimizers(opt_conf, params):
opts = {}
for param in params:
param_conf = param.getConfig().toProto()
opts[param.getID()] = swig_paddle.ParameterOptimizer.create(opt_conf)
opts[param.getID()].init(param_conf.dims[1], param.getConfig())
retv_opts = [None for _ in xrange(len(opts))]
for k in opts:
assert k < len(retv_opts)
retv_opts[k] = opts[k]
return retv_opts
def main():
trainer_config = paddle.trainer.config_parser.parse_config(
"./testTrainConfig.py", "")
opt_config = trainer_config.opt_config
print "========Optimization Config ======="
print opt_config
print "==================================="
opt_config = swig_paddle.OptimizationConfig.createFromProto(opt_config)
_temp_optimizer_ = swig_paddle.ParameterOptimizer.create(opt_config)
enable_types = _temp_optimizer_.getParameterTypes()
m = swig_paddle.GradientMachine.createFromConfigProto(
trainer_config.model_config, swig_paddle.CREATE_MODE_NORMAL,
enable_types)
assert m is not None
assert isinstance(m, swig_paddle.GradientMachine)
init_params(m.getParameters())
optimizers = init_optimizers(opt_config, m.getParameters())
# Train One Pass.
for optimizer in optimizers:
optimizer.startPass()
batch_id = 0
while True: # Train one batch
batch_size = 1000
inArgs, atEnd = util.loadMNISTTrainData(batch_size)
if atEnd:
break
outArgs = swig_paddle.Arguments.createArguments(0)
for optimizer in optimizers:
optimizer.startBatch(batch_size)
def update_callback(param):
try:
bufs = list(param.getBufs())
opt = optimizers[param.getID()]
opt.update(bufs, param.getConfig())
callback = opt.needSpecialTraversal(param.getConfig())
if callback is not None:
callback(bufs, param.getConfig(), swig_paddle.NO_SPARSE_ID)
except Exception as e:
print e
ev = m.makeEvaluator()
ev.start()
m.forwardBackward(inArgs, outArgs, swig_paddle.PASS_TRAIN,
update_callback)
m.eval(ev)
ev.finish()
for name in ev.getNames():
print name, ev.getValue(name)
for optimizer in optimizers:
optimizer.finishBatch()
cost_vec = outArgs.getSlotValue(0)
assert isinstance(cost_vec, swig_paddle.Matrix)
cost_vec = cost_vec.copyToNumpyMat()
print 'Finish Batch', batch_id, 'with cost ', cost_vec.sum(
) / batch_size
batch_id += 1
for optimizer in optimizers:
optimizer.finishPass()
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=0", "--trainer_count=1")
main()
| 3,953 | 32.794872 | 79 |
py
|
Paddle
|
Paddle-master/paddle/fluid/train/demo/demo_network.py
|
# 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 paddle.fluid as fluid
import paddle.fluid.framework as framework
def train_network(with_optimize):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
if with_optimize:
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.00001)
sgd_optimizer.minimize(avg_cost)
else:
fluid.backward.append_backward(avg_cost)
def save_program_desc(network_func):
startup_program = framework.Program()
train_program = framework.Program()
with framework.program_guard(train_program, startup_program):
network_func(with_optimize=False)
with open("startup_program", "w") as f:
f.write(startup_program.desc.serialize_to_string())
with open("main_program", "w") as f:
f.write(train_program.desc.serialize_to_string())
save_program_desc(train_network)
| 1,676 | 33.9375 | 74 |
py
|
Paddle
|
Paddle-master/paddle/scripts/cpplint.py
|
#!/usr/bin/env python
#
# Copyright (c) 2009 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Does google-lint on c++ files.
The goal of this script is to identify places in the code that *may*
be in non-compliance with google style. It does not attempt to fix
up these problems -- the point is to educate. It does also not
attempt to find all problems, or to ensure that everything it does
find is legitimately a problem.
In particular, we can get very confused by /* and // inside strings!
We do a small hack, which is to ignore //'s with "'s after them on the
same line, but it is far from perfect (in either direction).
EDIT(yuyang18): Add #pragma once as include guard.
EDIT(yuyang18): Add NOLINTNEXTLINES_ to suppress multiline lint.
"""
import codecs
import copy
import getopt
import math # for log
import os
import re
import sre_compile
import string
import sys
import unicodedata
_USAGE = """
Syntax: cpplint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...]
[--counting=total|toplevel|detailed] [--root=subdir]
[--linelength=digits]
[--write-success=success_status_file]
<file> [file] ...
The style guidelines this tries to follow are those in
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml
Every problem is given a confidence score from 1-5, with 5 meaning we are
certain of the problem, and 1 meaning it could be a legitimate construct.
This will miss some errors, and is not a substitute for a code review.
To suppress false-positive errors of a certain category, add a
'NOLINT(category)' comment to the line. NOLINT or NOLINT(*)
suppresses errors of all categories on that line.
The files passed in will be linted; at least one file must be provided.
Default linted extensions are .cc, .cpp, .cu, .cuh and .h. Change the
extensions with the --extensions flag.
Flags:
output=vs7
By default, the output is formatted to ease emacs parsing. Visual Studio
compatible output (vs7) may also be used. Other formats are unsupported.
verbose=#
Specify a number 0-5 to restrict errors to certain verbosity levels.
filter=-x,+y,...
Specify a comma-separated list of category-filters to apply: only
error messages whose category names pass the filters will be printed.
(Category names are printed with the message and look like
"[whitespace/indent]".) Filters are evaluated left to right.
"-FOO" and "FOO" means "do not print categories that start with FOO".
"+FOO" means "do print categories that start with FOO".
Examples: --filter=-whitespace,+whitespace/braces
--filter=whitespace,runtime/printf,+runtime/printf_format
--filter=-,+build/include_what_you_use
To see a list of all the categories used in cpplint, pass no arg:
--filter=
counting=total|toplevel|detailed
The total number of errors found is always printed. If
'toplevel' is provided, then the count of errors in each of
the top-level categories like 'build' and 'whitespace' will
also be printed. If 'detailed' is provided, then a count
is provided for each category like 'build/class'.
root=subdir
The root directory used for deriving header guard CPP variable.
By default, the header guard CPP variable is calculated as the relative
path to the directory that contains .git, .hg, or .svn. When this flag
is specified, the relative path is calculated from the specified
directory. If the specified directory does not exist, this flag is
ignored.
Examples:
Assuming that src/.git exists, the header guard CPP variables for
src/chrome/browser/ui/browser.h are:
No flag => CHROME_BROWSER_UI_BROWSER_H_
--root=chrome => BROWSER_UI_BROWSER_H_
--root=chrome/browser => UI_BROWSER_H_
linelength=digits
This is the allowed line length for the project. The default value is
80 characters.
Examples:
--linelength=120
extensions=extension,extension,...
The allowed file extensions that cpplint will check
Examples:
--extensions=hpp,cpp
cpplint.py supports per-directory configurations specified in CPPLINT.cfg
files. CPPLINT.cfg file can contain a number of key=value pairs.
Currently the following options are supported:
set noparent
filter=+filter1,-filter2,...
exclude_files=regex
linelength=80
"set noparent" option prevents cpplint from traversing directory tree
upwards looking for more .cfg files in parent directories. This option
is usually placed in the top-level project directory.
The "filter" option is similar in function to --filter flag. It specifies
message filters in addition to the |_DEFAULT_FILTERS| and those specified
through --filter command-line flag.
"exclude_files" allows to specify a regular expression to be matched against
a file name. If the expression matches, the file is skipped and not run
through liner.
"linelength" allows to specify the allowed line length for the project.
CPPLINT.cfg has an effect on files in the same directory and all
sub-directories, unless overridden by a nested configuration file.
Example file:
filter=-build/include_order,+build/include_alpha
exclude_files=.*\.cc
The above example disables build/include_order warning and enables
build/include_alpha as well as excludes all .cc from being
processed by linter, in the current directory (where the .cfg
file is located) and all sub-directories.
"""
# We categorize each error message we print. Here are the categories.
# We want an explicit list so we can list them all in cpplint --filter=.
# If you add a new error message with a new category, add it to the list
# here! cpplint_unittest.py should tell you if you forget to do this.
_ERROR_CATEGORIES = [
'build/class',
'build/c++11',
'build/deprecated',
'build/endif_comment',
'build/explicit_make_pair',
'build/forward_decl',
'build/header_guard',
'build/include',
'build/include_alpha',
'build/include_order',
'build/include_what_you_use',
'build/namespaces',
'build/printf_format',
'build/storage_class',
'legal/copyright',
'readability/alt_tokens',
'readability/braces',
'readability/casting',
'readability/check',
'readability/constructors',
'readability/fn_size',
'readability/function',
'readability/inheritance',
'readability/multiline_comment',
'readability/multiline_string',
'readability/namespace',
'readability/nolint',
'readability/nul',
'readability/strings',
'readability/todo',
'readability/utf8',
'runtime/arrays',
'runtime/casting',
'runtime/explicit',
'runtime/int',
'runtime/init',
'runtime/invalid_increment',
'runtime/member_string_references',
'runtime/memset',
'runtime/indentation_namespace',
'runtime/operator',
'runtime/printf',
'runtime/printf_format',
'runtime/references',
'runtime/string',
'runtime/threadsafe_fn',
'runtime/vlog',
'whitespace/blank_line',
'whitespace/braces',
'whitespace/comma',
'whitespace/comments',
'whitespace/empty_conditional_body',
'whitespace/empty_loop_body',
'whitespace/end_of_line',
'whitespace/ending_newline',
'whitespace/forcolon',
'whitespace/indent',
'whitespace/line_length',
'whitespace/newline',
'whitespace/operators',
'whitespace/parens',
'whitespace/semicolon',
'whitespace/tab',
'whitespace/todo',
]
# These error categories are no longer enforced by cpplint, but for backwards-
# compatibility they may still appear in NOLINT comments.
_LEGACY_ERROR_CATEGORIES = ['readability/streams', ]
# The default state of the category filter. This is overridden by the --filter=
# flag. By default all errors are on, so only add here categories that should be
# off by default (i.e., categories that must be enabled by the --filter= flags).
# All entries here should start with a '-' or '+', as in the --filter= flag.
_DEFAULT_FILTERS = ['-build/include_alpha']
# We used to check for high-bit characters, but after much discussion we
# decided those were OK, as long as they were in UTF-8 and didn't represent
# hard-coded international strings, which belong in a separate i18n file.
# C++ headers
_CPP_HEADERS = frozenset([
# Legacy
'algobase.h',
'algo.h',
'alloc.h',
'builtinbuf.h',
'bvector.h',
'complex.h',
'defalloc.h',
'deque.h',
'editbuf.h',
'fstream.h',
'function.h',
'hash_map',
'hash_map.h',
'hash_set',
'hash_set.h',
'hashtable.h',
'heap.h',
'indstream.h',
'iomanip.h',
'iostream.h',
'istream.h',
'iterator.h',
'list.h',
'map.h',
'multimap.h',
'multiset.h',
'ostream.h',
'pair.h',
'parsestream.h',
'pfstream.h',
'procbuf.h',
'pthread_alloc',
'pthread_alloc.h',
'rope',
'rope.h',
'ropeimpl.h',
'set.h',
'slist',
'slist.h',
'stack.h',
'stdiostream.h',
'stl_alloc.h',
'stl_relops.h',
'streambuf.h',
'stream.h',
'strfile.h',
'strstream.h',
'tempbuf.h',
'tree.h',
'type_traits.h',
'vector.h',
# 17.6.1.2 C++ library headers
'algorithm',
'array',
'atomic',
'bitset',
'chrono',
'codecvt',
'complex',
'condition_variable',
'deque',
'exception',
'forward_list',
'fstream',
'functional',
'future',
'initializer_list',
'iomanip',
'ios',
'iosfwd',
'iostream',
'istream',
'iterator',
'limits',
'list',
'locale',
'map',
'memory',
'mutex',
'new',
'numeric',
'ostream',
'queue',
'random',
'ratio',
'regex',
'set',
'sstream',
'stack',
'stdexcept',
'streambuf',
'string',
'strstream',
'system_error',
'thread',
'tuple',
'typeindex',
'typeinfo',
'type_traits',
'unordered_map',
'unordered_set',
'utility',
'valarray',
'vector',
# 17.6.1.2 C++ headers for C library facilities
'cassert',
'ccomplex',
'cctype',
'cerrno',
'cfenv',
'cfloat',
'cinttypes',
'ciso646',
'climits',
'clocale',
'cmath',
'csetjmp',
'csignal',
'cstdalign',
'cstdarg',
'cstdbool',
'cstddef',
'cstdint',
'cstdio',
'cstdlib',
'cstring',
'ctgmath',
'ctime',
'cuchar',
'cwchar',
'cwctype',
])
# These headers are excluded from [build/include] and [build/include_order]
# checks:
# - Anything not following google file name conventions (containing an
# uppercase character, such as Python.h or nsStringAPI.h, for example).
# - Lua headers.
_THIRD_PARTY_HEADERS_PATTERN = re.compile(
r'^(?:[^/]*[A-Z][^/]*\.h|lua\.h|lauxlib\.h|lualib\.h)$')
# Assertion macros. These are defined in base/logging.h and
# testing/base/gunit.h. Note that the _M versions need to come first
# for substring matching to work.
_CHECK_MACROS = [
'DCHECK',
'CHECK',
'EXPECT_TRUE_M',
'EXPECT_TRUE',
'ASSERT_TRUE_M',
'ASSERT_TRUE',
'EXPECT_FALSE_M',
'EXPECT_FALSE',
'ASSERT_FALSE_M',
'ASSERT_FALSE',
]
# Replacement macros for CHECK/DCHECK/EXPECT_TRUE/EXPECT_FALSE
_CHECK_REPLACEMENT = dict([(m, {}) for m in _CHECK_MACROS])
for op, replacement in [('==', 'EQ'), ('!=', 'NE'), ('>=', 'GE'), ('>', 'GT'),
('<=', 'LE'), ('<', 'LT')]:
_CHECK_REPLACEMENT['DCHECK'][op] = 'DCHECK_%s' % replacement
_CHECK_REPLACEMENT['CHECK'][op] = 'CHECK_%s' % replacement
_CHECK_REPLACEMENT['EXPECT_TRUE'][op] = 'EXPECT_%s' % replacement
_CHECK_REPLACEMENT['ASSERT_TRUE'][op] = 'ASSERT_%s' % replacement
_CHECK_REPLACEMENT['EXPECT_TRUE_M'][op] = 'EXPECT_%s_M' % replacement
_CHECK_REPLACEMENT['ASSERT_TRUE_M'][op] = 'ASSERT_%s_M' % replacement
for op, inv_replacement in [('==', 'NE'), ('!=', 'EQ'), ('>=', 'LT'),
('>', 'LE'), ('<=', 'GT'), ('<', 'GE')]:
_CHECK_REPLACEMENT['EXPECT_FALSE'][op] = 'EXPECT_%s' % inv_replacement
_CHECK_REPLACEMENT['ASSERT_FALSE'][op] = 'ASSERT_%s' % inv_replacement
_CHECK_REPLACEMENT['EXPECT_FALSE_M'][op] = 'EXPECT_%s_M' % inv_replacement
_CHECK_REPLACEMENT['ASSERT_FALSE_M'][op] = 'ASSERT_%s_M' % inv_replacement
# Alternative tokens and their replacements. For full list, see section 2.5
# Alternative tokens [lex.digraph] in the C++ standard.
#
# Digraphs (such as '%:') are not included here since it's a mess to
# match those on a word boundary.
_ALT_TOKEN_REPLACEMENT = {
'and': '&&',
'bitor': '|',
'or': '||',
'xor': '^',
'compl': '~',
'bitand': '&',
'and_eq': '&=',
'or_eq': '|=',
'xor_eq': '^=',
'not': '!',
'not_eq': '!='
}
# Compile regular expression that matches all the above keywords. The "[ =()]"
# bit is meant to avoid matching these keywords outside of boolean expressions.
#
# False positives include C-style multi-line comments and multi-line strings
# but those have always been troublesome for cpplint.
_ALT_TOKEN_REPLACEMENT_PATTERN = re.compile(r'[ =()](' + ('|'.join(
_ALT_TOKEN_REPLACEMENT.keys())) + r')(?=[ (]|$)')
# These constants define types of headers for use with
# _IncludeState.CheckNextIncludeOrder().
_C_SYS_HEADER = 1
_CPP_SYS_HEADER = 2
_LIKELY_MY_HEADER = 3
_POSSIBLE_MY_HEADER = 4
_OTHER_HEADER = 5
# These constants define the current inline assembly state
_NO_ASM = 0 # Outside of inline assembly block
_INSIDE_ASM = 1 # Inside inline assembly block
_END_ASM = 2 # Last line of inline assembly block
_BLOCK_ASM = 3 # The whole block is an inline assembly block
# Match start of assembly blocks
_MATCH_ASM = re.compile(r'^\s*(?:asm|_asm|__asm|__asm__)'
r'(?:\s+(volatile|__volatile__))?'
r'\s*[{(]')
_regexp_compile_cache = {}
# {str, set(int)}: a map from error categories to sets of linenumbers
# on which those errors are expected and should be suppressed.
_error_suppressions = {}
# The root directory used for deriving header guard CPP variable.
# This is set by --root flag.
_root = None
# The allowed line length of files.
# This is set by --linelength flag.
_line_length = 80
# The allowed extensions for file names
# This is set by --extensions flag.
_valid_extensions = set(['cc', 'h', 'cpp', 'cu', 'cuh'])
_write_success = None
def ParseNolintSuppressions(filename, raw_line, linenum, error):
"""Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler.
"""
matched = Search(r'\bNOLINT(NEXTLINE(S_\d+)?)?\b(\([^)]+\))?', raw_line)
if matched:
if matched.group(1):
lines = matched.group(2)
if lines:
lines = int(lines[2:])
suppressed_line = [linenum + i for i in xrange(lines)]
else:
suppressed_line = linenum + 1
else:
suppressed_line = linenum
category = matched.group(3)
if category in (None, '(*)'): # => "suppress all"
if isinstance(suppressed_line, int):
_error_suppressions.setdefault(None, set()).add(suppressed_line)
else:
for _line in suppressed_line:
_error_suppressions.setdefault(None, set()).add(_line)
else:
if category.startswith('(') and category.endswith(')'):
category = category[1:-1]
if category in _ERROR_CATEGORIES:
if isinstance(suppressed_line, int):
_error_suppressions.setdefault(
category, set()).add(suppressed_line)
else:
for _line in suppressed_line:
_error_suppressions.setdefault(category,
set()).add(_line)
elif category not in _LEGACY_ERROR_CATEGORIES:
error(filename, linenum, 'readability/nolint', 5,
'Unknown NOLINT error category: %s' % category)
def ResetNolintSuppressions():
"""Resets the set of NOLINT suppressions to empty."""
_error_suppressions.clear()
def IsErrorSuppressedByNolint(category, linenum):
"""Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment.
"""
return (linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set()))
def Match(pattern, s):
"""Matches the string with the pattern, caching the compiled regexp."""
# The regexp compilation caching is inlined in both Match and Search for
# performance reasons; factoring it out into a separate function turns out
# to be noticeably expensive.
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].match(s)
def ReplaceAll(pattern, rep, s):
"""Replaces instances of pattern in a string with a replacement.
The compiled regex is kept in a cache shared by Match and Search.
Args:
pattern: regex pattern
rep: replacement text
s: search string
Returns:
string with replacements made (or original string if no replacements)
"""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].sub(rep, s)
def Search(pattern, s):
"""Searches the string for the pattern, caching the compiled regexp."""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].search(s)
class _IncludeState(object):
"""Tracks line numbers for includes, and the order in which includes appear.
include_list contains list of lists of (header, line number) pairs.
It's a lists of lists rather than just one flat list to make it
easier to update across preprocessor boundaries.
Call CheckNextIncludeOrder() once for each header in the file, passing
in the type constants defined above. Calls in an illegal order will
raise an _IncludeError with an appropriate error message.
"""
# self._section will move monotonically through this set. If it ever
# needs to move backwards, CheckNextIncludeOrder will raise an error.
_INITIAL_SECTION = 0
_MY_H_SECTION = 1
_C_SECTION = 2
_CPP_SECTION = 3
_OTHER_H_SECTION = 4
_TYPE_NAMES = {
_C_SYS_HEADER: 'C system header',
_CPP_SYS_HEADER: 'C++ system header',
_LIKELY_MY_HEADER: 'header this file implements',
_POSSIBLE_MY_HEADER: 'header this file may implement',
_OTHER_HEADER: 'other header',
}
_SECTION_NAMES = {
_INITIAL_SECTION: "... nothing. (This can't be an error.)",
_MY_H_SECTION: 'a header this file implements',
_C_SECTION: 'C system header',
_CPP_SECTION: 'C++ system header',
_OTHER_H_SECTION: 'other header',
}
def __init__(self):
self.include_list = [[]]
self.ResetSection('')
def FindHeader(self, header):
"""Check if a header has already been included.
Args:
header: header to check.
Returns:
Line number of previous occurrence, or -1 if the header has not
been seen before.
"""
for section_list in self.include_list:
for f in section_list:
if f[0] == header:
return f[1]
return -1
def ResetSection(self, directive):
"""Reset section checking for preprocessor directive.
Args:
directive: preprocessor directive (e.g. "if", "else").
"""
# The name of the current section.
self._section = self._INITIAL_SECTION
# The path of last found header.
self._last_header = ''
# Update list of includes. Note that we never pop from the
# include list.
if directive in ('if', 'ifdef', 'ifndef'):
self.include_list.append([])
elif directive in ('else', 'elif'):
self.include_list[-1] = []
def SetLastHeader(self, header_path):
self._last_header = header_path
def CanonicalizeAlphabeticalOrder(self, header_path):
"""Returns a path canonicalized for alphabetical comparison.
- replaces "-" with "_" so they both cmp the same.
- removes '-inl' since we don't require them to be after the main header.
- lowercase everything, just in case.
Args:
header_path: Path to be canonicalized.
Returns:
Canonicalized path.
"""
return header_path.replace('-inl.h', '.h').replace('-', '_').lower()
def IsInAlphabeticalOrder(self, clean_lines, linenum, header_path):
"""Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order.
"""
# If previous section is different from current section, _last_header will
# be reset to empty string, so it's always less than current header.
#
# If previous line was a blank line, assume that the headers are
# intentionally sorted the way they are.
if (self._last_header > header_path and
Match(r'^\s*#\s*include\b', clean_lines.elided[linenum - 1])):
return False
return True
def CheckNextIncludeOrder(self, header_type):
"""Returns a non-empty error message if the next header is out of order.
This function also updates the internal state to be ready to check
the next include.
Args:
header_type: One of the _XXX_HEADER constants defined above.
Returns:
The empty string if the header is in the right order, or an
error message describing what's wrong.
"""
error_message = ('Found %s after %s' % (
self._TYPE_NAMES[header_type], self._SECTION_NAMES[self._section]))
last_section = self._section
if header_type == _C_SYS_HEADER:
if self._section <= self._C_SECTION:
self._section = self._C_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _CPP_SYS_HEADER:
if self._section <= self._CPP_SECTION:
self._section = self._CPP_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _LIKELY_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
self._section = self._OTHER_H_SECTION
elif header_type == _POSSIBLE_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
# This will always be the fallback because we're not sure
# enough that the header is associated with this file.
self._section = self._OTHER_H_SECTION
else:
assert header_type == _OTHER_HEADER
self._section = self._OTHER_H_SECTION
if last_section != self._section:
self._last_header = ''
return ''
class _CppLintState(object):
"""Maintains module-wide state.."""
def __init__(self):
self.verbose_level = 1 # global setting.
self.error_count = 0 # global count of reported errors
# filters to apply when emitting error messages
self.filters = _DEFAULT_FILTERS[:]
# backup of filter list. Used to restore the state after each file.
self._filters_backup = self.filters[:]
self.counting = 'total' # In what way are we counting errors?
self.errors_by_category = {} # string to int dict storing error counts
# output format:
# "emacs" - format that emacs can parse (default)
# "vs7" - format that Microsoft Visual Studio 7 can parse
self.output_format = 'emacs'
def SetOutputFormat(self, output_format):
"""Sets the output format for errors."""
self.output_format = output_format
def SetVerboseLevel(self, level):
"""Sets the module's verbosity, and returns the previous setting."""
last_verbose_level = self.verbose_level
self.verbose_level = level
return last_verbose_level
def SetCountingStyle(self, counting_style):
"""Sets the module's counting options."""
self.counting = counting_style
def SetFilters(self, filters):
"""Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter"
"""
# Default filters always have less priority than the flag ones.
self.filters = _DEFAULT_FILTERS[:]
self.AddFilters(filters)
def AddFilters(self, filters):
""" Adds more filters to the existing list of error-message filters. """
for filt in filters.split(','):
clean_filt = filt.strip()
if clean_filt:
self.filters.append(clean_filt)
for filt in self.filters:
if not (filt.startswith('+') or filt.startswith('-')):
raise ValueError(
'Every filter in --filters must start with + or -'
' (%s does not)' % filt)
def BackupFilters(self):
""" Saves the current filter list to backup storage."""
self._filters_backup = self.filters[:]
def RestoreFilters(self):
""" Restores filters previously backed up."""
self.filters = self._filters_backup[:]
def ResetErrorCounts(self):
"""Sets the module's error statistic back to zero."""
self.error_count = 0
self.errors_by_category = {}
def IncrementErrorCount(self, category):
"""Bumps the module's error statistic."""
self.error_count += 1
if self.counting in ('toplevel', 'detailed'):
if self.counting != 'detailed':
category = category.split('/')[0]
if category not in self.errors_by_category:
self.errors_by_category[category] = 0
self.errors_by_category[category] += 1
def PrintErrorCounts(self):
"""Print a summary of errors by category, and the total."""
for category, count in self.errors_by_category.iteritems():
sys.stdout.write('Category \'%s\' errors found: %d\n' %
(category, count))
sys.stdout.write('Total errors found: %d\n' % self.error_count)
_cpplint_state = _CppLintState()
def _OutputFormat():
"""Gets the module's output format."""
return _cpplint_state.output_format
def _SetOutputFormat(output_format):
"""Sets the module's output format."""
_cpplint_state.SetOutputFormat(output_format)
def _VerboseLevel():
"""Returns the module's verbosity setting."""
return _cpplint_state.verbose_level
def _SetVerboseLevel(level):
"""Sets the module's verbosity, and returns the previous setting."""
return _cpplint_state.SetVerboseLevel(level)
def _SetCountingStyle(level):
"""Sets the module's counting options."""
_cpplint_state.SetCountingStyle(level)
def _Filters():
"""Returns the module's list of output filters, as a list."""
return _cpplint_state.filters
def _SetFilters(filters):
"""Sets the module's error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
_cpplint_state.SetFilters(filters)
def _AddFilters(filters):
"""Adds more filter overrides.
Unlike _SetFilters, this function does not reset the current list of filters
available.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
_cpplint_state.AddFilters(filters)
def _BackupFilters():
""" Saves the current filter list to backup storage."""
_cpplint_state.BackupFilters()
def _RestoreFilters():
""" Restores filters previously backed up."""
_cpplint_state.RestoreFilters()
class _FunctionState(object):
"""Tracks current function name and the number of lines in its body."""
_NORMAL_TRIGGER = 250 # for --v=0, 500 for --v=1, etc.
_TEST_TRIGGER = 400 # about 50% more than _NORMAL_TRIGGER.
def __init__(self):
self.in_a_function = False
self.lines_in_function = 0
self.current_function = ''
def Begin(self, function_name):
"""Start analyzing function body.
Args:
function_name: The name of the function being tracked.
"""
self.in_a_function = True
self.lines_in_function = 0
self.current_function = function_name
def Count(self):
"""Count line in current function body."""
if self.in_a_function:
self.lines_in_function += 1
def Check(self, error, filename, linenum):
"""Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check.
"""
if Match(r'T(EST|est)', self.current_function):
base_trigger = self._TEST_TRIGGER
else:
base_trigger = self._NORMAL_TRIGGER
trigger = base_trigger * 2**_VerboseLevel()
if self.lines_in_function > trigger:
error_level = int(
math.log(self.lines_in_function / base_trigger, 2))
# 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ...
if error_level > 5:
error_level = 5
error(filename, linenum, 'readability/fn_size', error_level,
'Small and focused functions are preferred:'
' %s has %d non-comment lines'
' (error triggered by exceeding %d lines).' % (
self.current_function, self.lines_in_function, trigger))
def End(self):
"""Stop analyzing function body."""
self.in_a_function = False
class _IncludeError(Exception):
"""Indicates a problem with the include order in a file."""
pass
class FileInfo(object):
"""Provides utility functions for filenames.
FileInfo provides easy access to the components of a file's path
relative to the project root.
"""
def __init__(self, filename):
self._filename = filename
def FullName(self):
"""Make Windows paths like Unix."""
return os.path.abspath(self._filename).replace('\\', '/')
def RepositoryName(self):
"""FullName after removing the local path to the repository.
If we have a real absolute path name here we can try to do something smart:
detecting the root of the checkout and truncating /path/to/checkout from
the name so that we get header guards that don't include things like
"C:\Documents and Settings\..." or "/home/username/..." in them and thus
people on different computers who have checked the source out to different
locations won't see bogus errors.
"""
fullname = self.FullName()
if os.path.exists(fullname):
project_dir = os.path.dirname(fullname)
if os.path.exists(os.path.join(project_dir, ".svn")):
# If there's a .svn file in the current directory, we recursively look
# up the directory tree for the top of the SVN checkout
root_dir = project_dir
one_up_dir = os.path.dirname(root_dir)
while os.path.exists(os.path.join(one_up_dir, ".svn")):
root_dir = os.path.dirname(root_dir)
one_up_dir = os.path.dirname(one_up_dir)
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by
# searching up from the current path.
root_dir = os.path.dirname(fullname)
while (root_dir != os.path.dirname(root_dir) and
not os.path.exists(os.path.join(root_dir, ".git")) and
not os.path.exists(os.path.join(root_dir, ".hg")) and
not os.path.exists(os.path.join(root_dir, ".svn"))):
root_dir = os.path.dirname(root_dir)
if (os.path.exists(os.path.join(root_dir, ".git")) or
os.path.exists(os.path.join(root_dir, ".hg")) or
os.path.exists(os.path.join(root_dir, ".svn"))):
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Don't know what to do; header guard warnings may be wrong...
return fullname
def Split(self):
"""Splits the file into the directory, basename, and extension.
For 'chrome/browser/browser.cc', Split() would
return ('chrome/browser', 'browser', '.cc')
Returns:
A tuple of (directory, basename, extension).
"""
googlename = self.RepositoryName()
project, rest = os.path.split(googlename)
return (project, ) + os.path.splitext(rest)
def BaseName(self):
"""File base name - text after the final slash, before the final period."""
return self.Split()[1]
def Extension(self):
"""File extension - text following the final period."""
return self.Split()[2]
def NoExtension(self):
"""File has no source file extension."""
return '/'.join(self.Split()[0:2])
def IsSource(self):
"""File has a source file extension."""
return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx')
def _ShouldPrintError(category, confidence, linenum):
"""If confidence >= verbose, category passes filter and is not suppressed."""
# There are three ways we might decide not to print an error message:
# a "NOLINT(category)" comment appears in the source,
# the verbosity level isn't high enough, or the filters filter it out.
if IsErrorSuppressedByNolint(category, linenum):
return False
if confidence < _cpplint_state.verbose_level:
return False
is_filtered = False
for one_filter in _Filters():
if one_filter.startswith('-'):
if category.startswith(one_filter[1:]):
is_filtered = True
elif one_filter.startswith('+'):
if category.startswith(one_filter[1:]):
is_filtered = False
else:
assert False # should have been checked for in SetFilter.
if is_filtered:
return False
return True
def Error(filename, linenum, category, confidence, message):
"""Logs the fact we've found a lint error.
We log where the error was found, and also our confidence in the error,
that is, how certain we are this is a legitimate style regression, and
not a misidentification or a use that's sometimes justified.
False positives can be suppressed by the use of
"cpplint(category)" comments on the offending line. These are
parsed into _error_suppressions.
Args:
filename: The name of the file containing the error.
linenum: The number of the line containing the error.
category: A string used to describe the "category" this bug
falls under: "whitespace", say, or "runtime". Categories
may have a hierarchy separated by slashes: "whitespace/indent".
confidence: A number from 1-5 representing a confidence score for
the error, with 5 meaning that we are certain of the problem,
and 1 meaning that it could be a legitimate construct.
message: The error message.
"""
if _ShouldPrintError(category, confidence, linenum):
_cpplint_state.IncrementErrorCount(category)
if _cpplint_state.output_format == 'vs7':
sys.stderr.write('%s(%s): %s [%s] [%d]\n' %
(filename, linenum, message, category, confidence))
elif _cpplint_state.output_format == 'eclipse':
sys.stderr.write('%s:%s: warning: %s [%s] [%d]\n' %
(filename, linenum, message, category, confidence))
else:
sys.stderr.write('%s:%s: %s [%s] [%d]\n' %
(filename, linenum, message, category, confidence))
# Matches standard C++ escape sequences per 2.13.2.3 of the C++ standard.
_RE_PATTERN_CLEANSE_LINE_ESCAPES = re.compile(
r'\\([abfnrtv?"\\\']|\d+|x[0-9a-fA-F]+)')
# Match a single C style comment on the same line.
_RE_PATTERN_C_COMMENTS = r'/\*(?:[^*]|\*(?!/))*\*/'
# Matches multi-line C style comments.
# This RE is a little bit more complicated than one might expect, because we
# have to take care of space removals tools so we can handle comments inside
# statements better.
# The current rule is: We only clear spaces from both sides when we're at the
# end of the line. Otherwise, we try to remove spaces from the right side,
# if this doesn't work we try on left side but only if there's a non-character
# on the right.
_RE_PATTERN_CLEANSE_LINE_C_COMMENTS = re.compile(
r'(\s*' + _RE_PATTERN_C_COMMENTS + r'\s*$|' + _RE_PATTERN_C_COMMENTS +
r'\s+|' + r'\s+' + _RE_PATTERN_C_COMMENTS + r'(?=\W)|' +
_RE_PATTERN_C_COMMENTS + r')')
def IsCppString(line):
"""Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
string constant.
"""
line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1
def CleanseRawStrings(raw_lines):
"""Removes C++11 raw strings from lines.
Before:
static const char kData[] = R"(
multi-line string
)";
After:
static const char kData[] = ""
(replaced by blank line)
"";
Args:
raw_lines: list of raw lines.
Returns:
list of lines with C++11 raw strings replaced by empty strings.
"""
delimiter = None
lines_without_raw_strings = []
for line in raw_lines:
if delimiter:
# Inside a raw string, look for the end
end = line.find(delimiter)
if end >= 0:
# Found the end of the string, match leading space for this
# line and resume copying the original lines, and also insert
# a "" on the last line.
leading_space = Match(r'^(\s*)\S', line)
line = leading_space.group(1) + '""' + line[end + len(
delimiter):]
delimiter = None
else:
# Haven't found the end yet, append a blank line.
line = '""'
# Look for beginning of a raw string, and replace them with
# empty strings. This is done in a loop to handle multiple raw
# strings on the same line.
while delimiter is None:
# Look for beginning of a raw string.
# See 2.14.15 [lex.string] for syntax.
matched = Match(r'^(.*)\b(?:R|u8R|uR|UR|LR)"([^\s\\()]*)\((.*)$',
line)
if matched:
delimiter = ')' + matched.group(2) + '"'
end = matched.group(3).find(delimiter)
if end >= 0:
# Raw string ended on same line
line = (matched.group(1) + '""' +
matched.group(3)[end + len(delimiter):])
delimiter = None
else:
# Start of a multi-line raw string
line = matched.group(1) + '""'
else:
break
lines_without_raw_strings.append(line)
# TODO(unknown): if delimiter is not None here, we might want to
# emit a warning for unterminated string.
return lines_without_raw_strings
def FindNextMultiLineCommentStart(lines, lineix):
"""Find the beginning marker for a multiline comment."""
while lineix < len(lines):
if lines[lineix].strip().startswith('/*'):
# Only return this marker if the comment goes beyond this line
if lines[lineix].strip().find('*/', 2) < 0:
return lineix
lineix += 1
return len(lines)
def FindNextMultiLineCommentEnd(lines, lineix):
"""We are inside a comment, find the end marker."""
while lineix < len(lines):
if lines[lineix].strip().endswith('*/'):
return lineix
lineix += 1
return len(lines)
def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '/**/'
def RemoveMultiLineComments(filename, lines, error):
"""Removes multiline (c-style) comments from lines."""
lineix = 0
while lineix < len(lines):
lineix_begin = FindNextMultiLineCommentStart(lines, lineix)
if lineix_begin >= len(lines):
return
lineix_end = FindNextMultiLineCommentEnd(lines, lineix_begin)
if lineix_end >= len(lines):
error(filename, lineix_begin + 1, 'readability/multiline_comment',
5, 'Could not find end of multi-line comment')
return
RemoveMultiLineCommentsFromRange(lines, lineix_begin, lineix_end + 1)
lineix = lineix_end + 1
def CleanseComments(line):
"""Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
The line with single-line comments removed.
"""
commentpos = line.find('//')
if commentpos != -1 and not IsCppString(line[:commentpos]):
line = line[:commentpos].rstrip()
# get rid of /* ... */
return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)
class CleansedLines(object):
"""Holds 4 copies of all lines with different preprocessing applied to them.
1) elided member contains lines without strings and comments.
2) lines member contains lines without comments.
3) raw_lines member contains all the lines without processing.
4) lines_without_raw_strings member is same as raw_lines, but with C++11 raw
strings removed.
All these members are of <type 'list'>, and of the same length.
"""
def __init__(self, lines):
self.elided = []
self.lines = []
self.raw_lines = lines
self.num_lines = len(lines)
self.lines_without_raw_strings = CleanseRawStrings(lines)
for linenum in range(len(self.lines_without_raw_strings)):
self.lines.append(
CleanseComments(self.lines_without_raw_strings[linenum]))
elided = self._CollapseStrings(self.lines_without_raw_strings[
linenum])
self.elided.append(CleanseComments(elided))
def NumLines(self):
"""Returns the number of lines represented."""
return self.num_lines
@staticmethod
def _CollapseStrings(elided):
"""Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings.
"""
if _RE_PATTERN_INCLUDE.match(elided):
return elided
# Remove escaped characters first to make quote/single quote collapsing
# basic. Things that look like escaped characters shouldn't occur
# outside of strings and chars.
elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
# Replace quoted strings and digit separators. Both single quotes
# and double quotes are processed in the same loop, otherwise
# nested quotes wouldn't work.
collapsed = ''
while True:
# Find the first quote character
match = Match(r'^([^\'"]*)([\'"])(.*)$', elided)
if not match:
collapsed += elided
break
head, quote, tail = match.groups()
if quote == '"':
# Collapse double quoted strings
second_quote = tail.find('"')
if second_quote >= 0:
collapsed += head + '""'
elided = tail[second_quote + 1:]
else:
# Unmatched double quote, don't bother processing the rest
# of the line since this is probably a multiline string.
collapsed += elided
break
else:
# Found single quote, check nearby text to eliminate digit separators.
#
# There is no special handling for floating point here, because
# the integer/fractional/exponent parts would all be parsed
# correctly as long as there are digits on both sides of the
# separator. So we are fine as long as we don't see something
# like "0.'3" (gcc 4.9.0 will not allow this literal).
if Search(r'\b(?:0[bBxX]?|[1-9])[0-9a-fA-F]*$', head):
match_literal = Match(r'^((?:\'?[0-9a-zA-Z_])*)(.*)$',
"'" + tail)
collapsed += head + match_literal.group(1).replace("'", '')
elided = match_literal.group(2)
else:
second_quote = tail.find('\'')
if second_quote >= 0:
collapsed += head + "''"
elided = tail[second_quote + 1:]
else:
# Unmatched single quote
collapsed += elided
break
return collapsed
def FindEndOfExpressionInLine(line, startpos, stack):
"""Find the position just after the end of current parenthesized expression.
Args:
line: a CleansedLines line.
startpos: start searching at this position.
stack: nesting stack at startpos.
Returns:
On finding matching end: (index just after matching end, None)
On finding an unclosed expression: (-1, None)
Otherwise: (-1, new stack at end of this line)
"""
for i in xrange(startpos, len(line)):
char = line[i]
if char in '([{':
# Found start of parenthesized expression, push to expression stack
stack.append(char)
elif char == '<':
# Found potential start of template argument list
if i > 0 and line[i - 1] == '<':
# Left shift operator
if stack and stack[-1] == '<':
stack.pop()
if not stack:
return (-1, None)
elif i > 0 and Search(r'\boperator\s*$', line[0:i]):
# operator<, don't add to stack
continue
else:
# Tentative start of template argument list
stack.append('<')
elif char in ')]}':
# Found end of parenthesized expression.
#
# If we are currently expecting a matching '>', the pending '<'
# must have been an operator. Remove them from expression stack.
while stack and stack[-1] == '<':
stack.pop()
if not stack:
return (-1, None)
if ((stack[-1] == '(' and char == ')') or
(stack[-1] == '[' and char == ']') or
(stack[-1] == '{' and char == '}')):
stack.pop()
if not stack:
return (i + 1, None)
else:
# Mismatched parentheses
return (-1, None)
elif char == '>':
# Found potential end of template argument list.
# Ignore "->" and operator functions
if (i > 0 and (line[i - 1] == '-' or Search(r'\boperator\s*$',
line[0:i - 1]))):
continue
# Pop the stack if there is a matching '<'. Otherwise, ignore
# this '>' since it must be an operator.
if stack:
if stack[-1] == '<':
stack.pop()
if not stack:
return (i + 1, None)
elif char == ';':
# Found something that look like end of statements. If we are currently
# expecting a '>', the matching '<' must have been an operator, since
# template argument list should not contain statements.
while stack and stack[-1] == '<':
stack.pop()
if not stack:
return (-1, None)
# Did not find end of expression or unbalanced parentheses on this line
return (-1, stack)
def CloseExpression(clean_lines, linenum, pos):
"""If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
TODO(unknown): cpplint spends a fair bit of time matching parentheses.
Ideally we would want to index all opening and closing parentheses once
and have CloseExpression be just a simple lookup, but due to preprocessor
tricks, this is not so easy.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
if (line[pos] not in '({[<') or Match(r'<[<=]', line[pos:]):
return (line, clean_lines.NumLines(), -1)
# Check first line
(end_pos, stack) = FindEndOfExpressionInLine(line, pos, [])
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while stack and linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, stack) = FindEndOfExpressionInLine(line, 0, stack)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find end of expression before end of file, give up
return (line, clean_lines.NumLines(), -1)
def FindStartOfExpressionInLine(line, endpos, stack):
"""Find position at the matching start of current expression.
This is almost the reverse of FindEndOfExpressionInLine, but note
that the input position and returned position differs by 1.
Args:
line: a CleansedLines line.
endpos: start searching at this position.
stack: nesting stack at endpos.
Returns:
On finding matching start: (index at matching start, None)
On finding an unclosed expression: (-1, None)
Otherwise: (-1, new stack at beginning of this line)
"""
i = endpos
while i >= 0:
char = line[i]
if char in ')]}':
# Found end of expression, push to expression stack
stack.append(char)
elif char == '>':
# Found potential end of template argument list.
#
# Ignore it if it's a "->" or ">=" or "operator>"
if (i > 0 and
(line[i - 1] == '-' or Match(r'\s>=\s', line[i - 1:]) or
Search(r'\boperator\s*$', line[0:i]))):
i -= 1
else:
stack.append('>')
elif char == '<':
# Found potential start of template argument list
if i > 0 and line[i - 1] == '<':
# Left shift operator
i -= 1
else:
# If there is a matching '>', we can pop the expression stack.
# Otherwise, ignore this '<' since it must be an operator.
if stack and stack[-1] == '>':
stack.pop()
if not stack:
return (i, None)
elif char in '([{':
# Found start of expression.
#
# If there are any unmatched '>' on the stack, they must be
# operators. Remove those.
while stack and stack[-1] == '>':
stack.pop()
if not stack:
return (-1, None)
if ((char == '(' and stack[-1] == ')') or
(char == '[' and stack[-1] == ']') or
(char == '{' and stack[-1] == '}')):
stack.pop()
if not stack:
return (i, None)
else:
# Mismatched parentheses
return (-1, None)
elif char == ';':
# Found something that look like end of statements. If we are currently
# expecting a '<', the matching '>' must have been an operator, since
# template argument list should not contain statements.
while stack and stack[-1] == '>':
stack.pop()
if not stack:
return (-1, None)
i -= 1
return (-1, stack)
def ReverseCloseExpression(clean_lines, linenum, pos):
"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
if line[pos] not in ')}]>':
return (line, 0, -1)
# Check last line
(start_pos, stack) = FindStartOfExpressionInLine(line, pos, [])
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while stack and linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, stack) = FindStartOfExpressionInLine(line,
len(line) - 1, stack)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find start of expression before beginning of file, give up
return (line, 0, -1)
def CheckForCopyright(filename, lines, error):
"""Logs an error if no Copyright message appears at the top of the file."""
# We'll say it should occur by line 10. Don't forget there's a
# dummy line at the front.
for line in xrange(1, min(len(lines), 11)):
if re.search(r'Copyright', lines[line], re.I): break
else: # means no copyright line was found
error(filename, 0, 'legal/copyright', 5, 'No copyright message found. '
'You should have a line: "Copyright [year] <Copyright Owner>"')
def GetIndentLevel(line):
"""Return the number of leading spaces in line.
Args:
line: A string to check.
Returns:
An integer count of leading spaces, possibly zero.
"""
indent = Match(r'^( *)\S', line)
if indent:
return len(indent.group(1))
else:
return 0
def GetHeaderGuardCPPVariable(filename):
"""Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
"""
filename = os.path.basename(filename)
return re.sub(r'[^a-zA-Z0-9]', '_', filename).upper() + '_'
def CheckForHeaderGuard(filename, clean_lines, error):
"""Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
clean_lines: A CleansedLines instance containing the file.
error: The function to call with any errors found.
"""
# Don't check for header guards if there are error suppression
# comments somewhere in this file.
#
# Because this is silencing a warning for a nonexistent line, we
# only support the very specific NOLINT(build/header_guard) syntax,
# and not the general NOLINT or NOLINT(*) syntax.
raw_lines = clean_lines.lines_without_raw_strings
for i in raw_lines:
if Search(r'//\s*NOLINT\(build/header_guard\)', i):
return
cppvar = GetHeaderGuardCPPVariable(filename)
ifndef = ''
ifndef_linenum = 0
define = ''
endif = ''
endif_linenum = 0
pragma_linenum = -1
for linenum, line in enumerate(raw_lines):
linesplit = line.split()
if len(linesplit) >= 2:
if linesplit[0] == '#pragma' and linesplit[1] == 'once':
pragma_linenum = linenum
# find the first occurrence of #ifndef and #define, save arg
if not ifndef and linesplit[0] == '#ifndef':
# set ifndef to the header guard presented on the #ifndef line.
ifndef = linesplit[1]
ifndef_linenum = linenum
if not define and linesplit[0] == '#define':
define = linesplit[1]
# find the last occurrence of #endif, save entire line
if line.startswith('#endif'):
endif = line
endif_linenum = linenum
if pragma_linenum != -1:
return # short path for pragma once
if not ifndef or not define or ifndef != define:
error(filename, 0, 'build/header_guard', 5,
'No #ifndef header guard found, suggested CPP variable is: %s' %
cppvar)
return
# The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
# for backward compatibility.
if ifndef != cppvar:
error_level = 0
if ifndef != cppvar + '_':
error_level = 5
ParseNolintSuppressions(filename, raw_lines[ifndef_linenum],
ifndef_linenum, error)
error(filename, ifndef_linenum, 'build/header_guard', error_level,
'#ifndef header guard has wrong style, please use: %s' % cppvar)
# Check for "//" comments on endif line.
ParseNolintSuppressions(filename, raw_lines[endif_linenum], endif_linenum,
error)
match = Match(r'#endif\s*//\s*' + cppvar + r'(_)?\b', endif)
if match:
if match.group(1) == '_':
# Issue low severity warning for deprecated double trailing underscore
error(filename, endif_linenum, 'build/header_guard', 0,
'#endif line should be "#endif // %s"' % cppvar)
return
# Didn't find the corresponding "//" comment. If this file does not
# contain any "//" comments at all, it could be that the compiler
# only wants "/**/" comments, look for those instead.
no_single_line_comments = True
for i in xrange(1, len(raw_lines) - 1):
line = raw_lines[i]
if Match(r'^(?:(?:\'(?:\.|[^\'])*\')|(?:"(?:\.|[^"])*")|[^\'"])*//',
line):
no_single_line_comments = False
break
if no_single_line_comments:
match = Match(r'#endif\s*/\*\s*' + cppvar + r'(_)?\s*\*/', endif)
if match:
if match.group(1) == '_':
# Low severity warning for double trailing underscore
error(filename, endif_linenum, 'build/header_guard', 0,
'#endif line should be "#endif /* %s */"' % cppvar)
return
# Didn't find anything
error(filename, endif_linenum, 'build/header_guard', 5,
'#endif line should be "#endif // %s"' % cppvar)
def CheckHeaderFileIncluded(filename, include_state, error):
"""Logs an error if a .cc file does not include its header."""
# Do not check test files
if filename.endswith('_test.cc') or filename.endswith('_unittest.cc'):
return
fileinfo = FileInfo(filename)
headerfile = filename[0:len(filename) - 2] + 'h'
if not os.path.exists(headerfile):
return
headername = FileInfo(headerfile).RepositoryName()
first_include = 0
for section_list in include_state.include_list:
for f in section_list:
if headername in f[0] or f[0] in headername:
return
if not first_include:
first_include = f[1]
error(filename, first_include, 'build/include', 5,
'%s should include its header file %s' % (fileinfo.RepositoryName(),
headername))
def CheckForBadCharacters(filename, lines, error):
"""Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
for linenum, line in enumerate(lines):
if u'\ufffd' in line:
error(
filename, linenum, 'readability/utf8', 5,
'Line contains invalid UTF-8 (or Unicode replacement character).'
)
if '\0' in line:
error(filename, linenum, 'readability/nul', 5,
'Line contains NUL byte.')
def CheckForNewlineAtEOF(filename, lines, error):
"""Logs an error if there is no newline char at the end of the file.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
# The array lines() was created by adding two newlines to the
# original file (go figure), then splitting on \n.
# To verify that the file ends in \n, we just have to make sure the
# last-but-two element of lines() exists and is empty.
if len(lines) < 3 or lines[-2]:
error(filename,
len(lines) - 2, 'whitespace/ending_newline', 5,
'Could not find a newline character at the end of the file.')
def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
"""Logs an error if we see /* ... */ or "..." that extend past one line.
/* ... */ comments are legit inside macros, for one line.
Otherwise, we prefer // comments, so it's ok to warn about the
other. Likewise, it's ok for strings to extend across multiple
lines, as long as a line continuation character (backslash)
terminates each line. Although not currently prohibited by the C++
style guide, it's ugly and unnecessary. We don't do well with either
in this lint program, so we warn about both.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Remove all \\ (escaped backslashes) from the line. They are OK, and the
# second (escaped) slash may trigger later \" detection erroneously.
line = line.replace('\\\\', '')
if line.count('/*') > line.count('*/'):
error(filename, linenum, 'readability/multiline_comment', 5,
'Complex multi-line /*...*/-style comment found. '
'Lint may give bogus warnings. '
'Consider replacing these with //-style comments, '
'with #if 0...#endif, '
'or with more clearly structured multi-line comments.')
if (line.count('"') - line.count('\\"')) % 2:
error(filename, linenum, 'readability/multiline_string', 5,
'Multi-line string ("...") found. This lint script doesn\'t '
'do well with such strings, and may give bogus warnings. '
'Use C++11 raw strings or concatenation instead.')
# (non-threadsafe name, thread-safe alternative, validation pattern)
#
# The validation pattern is used to eliminate false positives such as:
# _rand(); // false positive due to substring match.
# ->rand(); // some member function rand().
# ACMRandom rand(seed); // some variable named rand.
# ISAACRandom rand(); // another variable named rand.
#
# Basically we require the return value of these functions to be used
# in some expression context on the same line by matching on some
# operator before the function name. This eliminates constructors and
# member function calls.
_UNSAFE_FUNC_PREFIX = r'(?:[-+*/=%^&|(<]\s*|>\s+)'
_THREADING_LIST = (
('asctime(', 'asctime_r(', _UNSAFE_FUNC_PREFIX + r'asctime\([^)]+\)'),
('ctime(', 'ctime_r(', _UNSAFE_FUNC_PREFIX + r'ctime\([^)]+\)'),
('getgrgid(', 'getgrgid_r(', _UNSAFE_FUNC_PREFIX + r'getgrgid\([^)]+\)'),
('getgrnam(', 'getgrnam_r(', _UNSAFE_FUNC_PREFIX + r'getgrnam\([^)]+\)'),
('getlogin(', 'getlogin_r(', _UNSAFE_FUNC_PREFIX + r'getlogin\(\)'),
('getpwnam(', 'getpwnam_r(', _UNSAFE_FUNC_PREFIX + r'getpwnam\([^)]+\)'),
('getpwuid(', 'getpwuid_r(', _UNSAFE_FUNC_PREFIX + r'getpwuid\([^)]+\)'),
('gmtime(', 'gmtime_r(', _UNSAFE_FUNC_PREFIX + r'gmtime\([^)]+\)'),
('localtime(', 'localtime_r(', _UNSAFE_FUNC_PREFIX + r'localtime\([^)]+\)'),
('rand(', 'rand_r(', _UNSAFE_FUNC_PREFIX + r'rand\(\)'),
('strtok(', 'strtok_r(', _UNSAFE_FUNC_PREFIX + r'strtok\([^)]+\)'),
('ttyname(', 'ttyname_r(', _UNSAFE_FUNC_PREFIX + r'ttyname\([^)]+\)'), )
def CheckPosixThreading(filename, clean_lines, linenum, error):
"""Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
multi-threading. Also, engineers are relying on their old experience;
they have learned posix before threading extensions were added. These
tests guide the engineers to use thread-safe functions (when using
posix directly).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for single_thread_func, multithread_safe_func, pattern in _THREADING_LIST:
# Additional pattern matching check to confirm that this is the
# function we are looking for
if Search(pattern, line):
error(filename, linenum, 'runtime/threadsafe_fn', 2,
'Consider using ' + multithread_safe_func + '...) instead of '
+ single_thread_func + '...) for improved thread safety.')
def CheckVlogArguments(filename, clean_lines, linenum, error):
"""Checks that VLOG() is only used for defining a logging level.
For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
VLOG(FATAL) are not.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line):
error(filename, linenum, 'runtime/vlog', 5,
'VLOG() should be used with numeric verbosity level. '
'Use LOG() if you want symbolic severity levels.')
# Matches invalid increment: *count++, which moves pointer instead of
# incrementing a value.
_RE_PATTERN_INVALID_INCREMENT = re.compile(r'^\s*\*\w+(\+\+|--);')
def CheckInvalidIncrement(filename, clean_lines, linenum, error):
"""Checks for invalid increment *count++.
For example following function:
void increment_counter(int* count) {
*count++;
}
is invalid, because it effectively does count++, moving pointer, and should
be replaced with ++*count, (*count)++ or *count += 1.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if _RE_PATTERN_INVALID_INCREMENT.match(line):
error(
filename, linenum, 'runtime/invalid_increment', 5,
'Changing pointer instead of value (or unused value of operator*).')
def IsMacroDefinition(clean_lines, linenum):
if Search(r'^#define', clean_lines[linenum]):
return True
if linenum > 0 and Search(r'\\$', clean_lines[linenum - 1]):
return True
return False
def IsForwardClassDeclaration(clean_lines, linenum):
return Match(r'^\s*(\btemplate\b)*.*class\s+\w+;\s*$', clean_lines[linenum])
class _BlockInfo(object):
"""Stores information about a generic block of code."""
def __init__(self, seen_open_brace):
self.seen_open_brace = seen_open_brace
self.open_parentheses = 0
self.inline_asm = _NO_ASM
self.check_namespace_indentation = False
def CheckBegin(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
blocks, there isn't much to check, so we always pass.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass
def CheckEnd(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text after the closing brace.
This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass
def IsBlockInfo(self):
"""Returns true if this block is a _BlockInfo.
This is convenient for verifying that an object is an instance of
a _BlockInfo, but not an instance of any of the derived classes.
Returns:
True for this class, False for derived classes.
"""
return self.__class__ == _BlockInfo
class _ExternCInfo(_BlockInfo):
"""Stores information about an 'extern "C"' block."""
def __init__(self):
_BlockInfo.__init__(self, True)
class _ClassInfo(_BlockInfo):
"""Stores information about a class."""
def __init__(self, name, class_or_struct, clean_lines, linenum):
_BlockInfo.__init__(self, False)
self.name = name
self.starting_linenum = linenum
self.is_derived = False
self.check_namespace_indentation = True
if class_or_struct == 'struct':
self.access = 'public'
self.is_struct = True
else:
self.access = 'private'
self.is_struct = False
# Remember initial indentation level for this class. Using raw_lines here
# instead of elided to account for leading comments.
self.class_indent = GetIndentLevel(clean_lines.raw_lines[linenum])
# Try to find the end of the class. This will be confused by things like:
# class A {
# } *x = { ...
#
# But it's still good enough for CheckSectionSpacing.
self.last_line = 0
depth = 0
for i in range(linenum, clean_lines.NumLines()):
line = clean_lines.elided[i]
depth += line.count('{') - line.count('}')
if not depth:
self.last_line = i
break
def CheckBegin(self, filename, clean_lines, linenum, error):
# Look for a bare ':'
if Search('(^|[^:]):($|[^:])', clean_lines.elided[linenum]):
self.is_derived = True
def CheckEnd(self, filename, clean_lines, linenum, error):
# If there is a DISALLOW macro, it should appear near the end of
# the class.
seen_last_thing_in_class = False
for i in xrange(linenum - 1, self.starting_linenum, -1):
match = Search(
r'\b(DISALLOW_COPY_AND_ASSIGN|DISALLOW_IMPLICIT_CONSTRUCTORS)\('
+ self.name + r'\)', clean_lines.elided[i])
if match:
if seen_last_thing_in_class:
error(filename, i, 'readability/constructors', 3,
match.group(1) +
' should be the last thing in the class')
break
if not Match(r'^\s*$', clean_lines.elided[i]):
seen_last_thing_in_class = True
# Check that closing brace is aligned with beginning of the class.
# Only do this if the closing brace is indented by only whitespaces.
# This means we will not check single-line class definitions.
indent = Match(r'^( *)\}', clean_lines.elided[linenum])
if indent and len(indent.group(1)) != self.class_indent:
if self.is_struct:
parent = 'struct ' + self.name
else:
parent = 'class ' + self.name
error(filename, linenum, 'whitespace/indent', 3,
'Closing brace should be aligned with beginning of %s' %
parent)
class _NamespaceInfo(_BlockInfo):
"""Stores information about a namespace."""
def __init__(self, name, linenum):
_BlockInfo.__init__(self, False)
self.name = name or ''
self.starting_linenum = linenum
self.check_namespace_indentation = True
def CheckEnd(self, filename, clean_lines, linenum, error):
"""Check end of namespace comments."""
line = clean_lines.raw_lines[linenum]
# Check how many lines is enclosed in this namespace. Don't issue
# warning for missing namespace comments if there aren't enough
# lines. However, do apply checks if there is already an end of
# namespace comment and it's incorrect.
#
# TODO(unknown): We always want to check end of namespace comments
# if a namespace is large, but sometimes we also want to apply the
# check if a short namespace contained nontrivial things (something
# other than forward declarations). There is currently no logic on
# deciding what these nontrivial things are, so this check is
# triggered by namespace size only, which works most of the time.
if (linenum - self.starting_linenum < 10 and
not Match(r'};*\s*(//|/\*).*\bnamespace\b', line)):
return
# Look for matching comment at end of namespace.
#
# Note that we accept C style "/* */" comments for terminating
# namespaces, so that code that terminate namespaces inside
# preprocessor macros can be cpplint clean.
#
# We also accept stuff like "// end of namespace <name>." with the
# period at the end.
#
# Besides these, we don't accept anything else, otherwise we might
# get false negatives when existing comment is a substring of the
# expected namespace.
if self.name:
# Named namespace
if not Match((r'};*\s*(//|/\*).*\bnamespace\s+' +
re.escape(self.name) + r'[\*/\.\\\s]*$'), line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace %s"' %
self.name)
else:
# Anonymous namespace
if not Match(r'};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line):
# If "// namespace anonymous" or "// anonymous namespace (more text)",
# mention "// anonymous namespace" as an acceptable form
if Match(r'}.*\b(namespace anonymous|anonymous namespace)\b',
line):
error(
filename, linenum, 'readability/namespace', 5,
'Anonymous namespace should be terminated with "// namespace"'
' or "// anonymous namespace"')
else:
error(
filename, linenum, 'readability/namespace', 5,
'Anonymous namespace should be terminated with "// namespace"'
)
class _PreprocessorInfo(object):
"""Stores checkpoints of nesting stacks when #if/#else is seen."""
def __init__(self, stack_before_if):
# The entire nesting stack before #if
self.stack_before_if = stack_before_if
# The entire nesting stack up to #else
self.stack_before_else = []
# Whether we have already seen #else or #elif
self.seen_else = False
class NestingState(object):
"""Holds states related to parsing braces."""
def __init__(self):
# Stack for tracking all braces. An object is pushed whenever we
# see a "{", and popped when we see a "}". Only 3 types of
# objects are possible:
# - _ClassInfo: a class or struct.
# - _NamespaceInfo: a namespace.
# - _BlockInfo: some other type of block.
self.stack = []
# Top of the previous stack before each Update().
#
# Because the nesting_stack is updated at the end of each line, we
# had to do some convoluted checks to find out what is the current
# scope at the beginning of the line. This check is simplified by
# saving the previous top of nesting stack.
#
# We could save the full stack, but we only need the top. Copying
# the full nesting stack would slow down cpplint by ~10%.
self.previous_stack_top = []
# Stack of _PreprocessorInfo objects.
self.pp_stack = []
def SeenOpenBrace(self):
"""Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
block is still expecting an opening brace.
"""
return (not self.stack) or self.stack[-1].seen_open_brace
def InNamespaceBody(self):
"""Check if we are currently one level inside a namespace body.
Returns:
True if top of the stack is a namespace block, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _NamespaceInfo)
def InExternC(self):
"""Check if we are currently one level inside an 'extern "C"' block.
Returns:
True if top of the stack is an extern block, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _ExternCInfo)
def InClassDeclaration(self):
"""Check if we are currently one level inside a class or struct declaration.
Returns:
True if top of the stack is a class/struct, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _ClassInfo)
def InAsmBlock(self):
"""Check if we are currently one level inside an inline ASM block.
Returns:
True if the top of the stack is a block containing inline ASM.
"""
return self.stack and self.stack[-1].inline_asm != _NO_ASM
def InTemplateArgumentList(self, clean_lines, linenum, pos):
"""Check if current position is inside template argument list.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: position just after the suspected template argument.
Returns:
True if (linenum, pos) is inside template arguments.
"""
while linenum < clean_lines.NumLines():
# Find the earliest character that might indicate a template argument
line = clean_lines.elided[linenum]
match = Match(r'^[^{};=\[\]\.<>]*(.)', line[pos:])
if not match:
linenum += 1
pos = 0
continue
token = match.group(1)
pos += len(match.group(0))
# These things do not look like template argument list:
# class Suspect {
# class Suspect x; }
if token in ('{', '}', ';'): return False
# These things look like template argument list:
# template <class Suspect>
# template <class Suspect = default_value>
# template <class Suspect[]>
# template <class Suspect...>
if token in ('>', '=', '[', ']', '.'): return True
# Check if token is an unmatched '<'.
# If not, move on to the next character.
if token != '<':
pos += 1
if pos >= len(line):
linenum += 1
pos = 0
continue
# We can't be sure if we just find a single '<', and need to
# find the matching '>'.
(_, end_line, end_pos) = CloseExpression(clean_lines, linenum,
pos - 1)
if end_pos < 0:
# Not sure if template argument list or syntax error in file
return False
linenum = end_line
pos = end_pos
return False
def UpdatePreprocessor(self, line):
"""Update preprocessor stack.
We need to handle preprocessors due to classes like this:
#ifdef SWIG
struct ResultDetailsPageElementExtensionPoint {
#else
struct ResultDetailsPageElementExtensionPoint : public Extension {
#endif
We make the following assumptions (good enough for most files):
- Preprocessor condition evaluates to true from #if up to first
#else/#elif/#endif.
- Preprocessor condition evaluates to false from #else/#elif up
to #endif. We still perform lint checks on these lines, but
these do not affect nesting stack.
Args:
line: current line to check.
"""
if Match(r'^\s*#\s*(if|ifdef|ifndef)\b', line):
# Beginning of #if block, save the nesting stack here. The saved
# stack will allow us to restore the parsing state in the #else case.
self.pp_stack.append(_PreprocessorInfo(copy.deepcopy(self.stack)))
elif Match(r'^\s*#\s*(else|elif)\b', line):
# Beginning of #else block
if self.pp_stack:
if not self.pp_stack[-1].seen_else:
# This is the first #else or #elif block. Remember the
# whole nesting stack up to this point. This is what we
# keep after the #endif.
self.pp_stack[-1].seen_else = True
self.pp_stack[-1].stack_before_else = copy.deepcopy(
self.stack)
# Restore the stack to how it was before the #if
self.stack = copy.deepcopy(self.pp_stack[-1].stack_before_if)
else:
# TODO(unknown): unexpected #else, issue warning?
pass
elif Match(r'^\s*#\s*endif\b', line):
# End of #if or #else blocks.
if self.pp_stack:
# If we saw an #else, we will need to restore the nesting
# stack to its former state before the #else, otherwise we
# will just continue from where we left off.
if self.pp_stack[-1].seen_else:
# Here we can just use a shallow copy since we are the last
# reference to it.
self.stack = self.pp_stack[-1].stack_before_else
# Drop the corresponding #if
self.pp_stack.pop()
else:
# TODO(unknown): unexpected #endif, issue warning?
pass
# TODO(unknown): Update() is too long, but we will refactor later.
def Update(self, filename, clean_lines, linenum, error):
"""Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Remember top of the previous nesting stack.
#
# The stack is always pushed/popped and not modified in place, so
# we can just do a shallow copy instead of copy.deepcopy. Using
# deepcopy would slow down cpplint by ~28%.
if self.stack:
self.previous_stack_top = self.stack[-1]
else:
self.previous_stack_top = None
# Update pp_stack
self.UpdatePreprocessor(line)
# Count parentheses. This is to avoid adding struct arguments to
# the nesting stack.
if self.stack:
inner_block = self.stack[-1]
depth_change = line.count('(') - line.count(')')
inner_block.open_parentheses += depth_change
# Also check if we are starting or ending an inline assembly block.
if inner_block.inline_asm in (_NO_ASM, _END_ASM):
if (depth_change != 0 and inner_block.open_parentheses == 1 and
_MATCH_ASM.match(line)):
# Enter assembly block
inner_block.inline_asm = _INSIDE_ASM
else:
# Not entering assembly block. If previous line was _END_ASM,
# we will now shift to _NO_ASM state.
inner_block.inline_asm = _NO_ASM
elif (inner_block.inline_asm == _INSIDE_ASM and
inner_block.open_parentheses == 0):
# Exit assembly block
inner_block.inline_asm = _END_ASM
# Consume namespace declaration at the beginning of the line. Do
# this in a loop so that we catch same line declarations like this:
# namespace proto2 { namespace bridge { class MessageSet; } }
while True:
# Match start of namespace. The "\b\s*" below catches namespace
# declarations even if it weren't followed by a whitespace, this
# is so that we don't confuse our namespace checker. The
# missing spaces will be flagged by CheckSpacing.
namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$',
line)
if not namespace_decl_match:
break
new_namespace = _NamespaceInfo(
namespace_decl_match.group(1), linenum)
self.stack.append(new_namespace)
line = namespace_decl_match.group(2)
if line.find('{') != -1:
new_namespace.seen_open_brace = True
line = line[line.find('{') + 1:]
# Look for a class declaration in whatever is left of the line
# after parsing namespaces. The regexp accounts for decorated classes
# such as in:
# class LOCKABLE API Object {
# };
class_decl_match = Match(
r'^(\s*(?:template\s*<[\w\s<>,:]*>\s*)?'
r'(class|struct)\s+(?:[A-Z_]+\s+)*(\w+(?:::\w+)*))'
r'(.*)$', line)
if (class_decl_match and
(not self.stack or self.stack[-1].open_parentheses == 0)):
# We do not want to accept classes that are actually template arguments:
# template <class Ignore1,
# class Ignore2 = Default<Args>,
# template <Args> class Ignore3>
# void Function() {};
#
# To avoid template argument cases, we scan forward and look for
# an unmatched '>'. If we see one, assume we are inside a
# template argument list.
end_declaration = len(class_decl_match.group(1))
if not self.InTemplateArgumentList(clean_lines, linenum,
end_declaration):
self.stack.append(
_ClassInfo(
class_decl_match.group(3),
class_decl_match.group(2), clean_lines, linenum))
line = class_decl_match.group(4)
# If we have not yet seen the opening brace for the innermost block,
# run checks here.
if not self.SeenOpenBrace():
self.stack[-1].CheckBegin(filename, clean_lines, linenum, error)
# Update access control if we are inside a class/struct
if self.stack and isinstance(self.stack[-1], _ClassInfo):
classinfo = self.stack[-1]
access_match = Match(
r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?'
r':(?:[^:]|$)', line)
if access_match:
classinfo.access = access_match.group(2)
# Check that access keywords are indented +1 space. Skip this
# check if the keywords are not preceded by whitespaces.
indent = access_match.group(1)
if (len(indent) != classinfo.class_indent + 1 and
Match(r'^\s*$', indent)):
if classinfo.is_struct:
parent = 'struct ' + classinfo.name
else:
parent = 'class ' + classinfo.name
slots = ''
if access_match.group(3):
slots = access_match.group(3)
error(filename, linenum, 'whitespace/indent', 3,
'%s%s: should be indented +1 space inside %s' % (
access_match.group(2), slots, parent))
# Consume braces or semicolons from what's left of the line
while True:
# Match first brace, semicolon, or closed parenthesis.
matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line)
if not matched:
break
token = matched.group(1)
if token == '{':
# If namespace or class hasn't seen a opening brace yet, mark
# namespace/class head as complete. Push a new block onto the
# stack otherwise.
if not self.SeenOpenBrace():
self.stack[-1].seen_open_brace = True
elif Match(r'^extern\s*"[^"]*"\s*\{', line):
self.stack.append(_ExternCInfo())
else:
self.stack.append(_BlockInfo(True))
if _MATCH_ASM.match(line):
self.stack[-1].inline_asm = _BLOCK_ASM
elif token == ';' or token == ')':
# If we haven't seen an opening brace yet, but we already saw
# a semicolon, this is probably a forward declaration. Pop
# the stack for these.
#
# Similarly, if we haven't seen an opening brace yet, but we
# already saw a closing parenthesis, then these are probably
# function arguments with extra "class" or "struct" keywords.
# Also pop these stack for these.
if not self.SeenOpenBrace():
self.stack.pop()
else: # token == '}'
# Perform end of block checks and pop the stack.
if self.stack:
self.stack[-1].CheckEnd(filename, clean_lines, linenum,
error)
self.stack.pop()
line = matched.group(2)
def InnermostClass(self):
"""Get class info on the top of the stack.
Returns:
A _ClassInfo object if we are inside a class, or None otherwise.
"""
for i in range(len(self.stack), 0, -1):
classinfo = self.stack[i - 1]
if isinstance(classinfo, _ClassInfo):
return classinfo
return None
def CheckCompletedBlocks(self, filename, error):
"""Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
for obj in self.stack:
if isinstance(obj, _ClassInfo):
error(filename, obj.starting_linenum, 'build/class', 5,
'Failed to find complete declaration of class %s' %
obj.name)
elif isinstance(obj, _NamespaceInfo):
error(filename, obj.starting_linenum, 'build/namespaces', 5,
'Failed to find complete declaration of namespace %s' %
obj.name)
def CheckForNonStandardConstructs(filename, clean_lines, linenum, nesting_state,
error):
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
"""
# Remove comments from the line, but leave in strings for now.
line = clean_lines.lines[linenum]
if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line):
error(filename, linenum, 'runtime/printf_format', 3,
'%q in format strings is deprecated. Use %ll instead.')
if Search(r'printf\s*\(.*".*%\d+\$', line):
error(filename, linenum, 'runtime/printf_format', 2,
'%N$ formats are unconventional. Try rewriting to avoid them.')
# Remove escaped backslashes before looking for undefined escapes.
line = line.replace('\\\\', '')
if Search(r'("|\').*\\(%|\[|\(|{)', line):
error(filename, linenum, 'build/printf_format', 3,
'%, [, (, and { are undefined character escapes. Unescape them.')
# For the rest, work with both comments and strings removed.
line = clean_lines.elided[linenum]
if Search(r'\b(const|volatile|void|char|short|int|long'
r'|float|double|signed|unsigned'
r'|schar|u?int8|u?int16|u?int32|u?int64)'
r'\s+(register|static|extern|typedef)\b', line):
error(filename, linenum, 'build/storage_class', 5,
'Storage class (static, extern, typedef, etc) should be first.')
if Match(r'\s*#\s*endif\s*[^/\s]+', line):
error(filename, linenum, 'build/endif_comment', 5,
'Uncommented text after #endif is non-standard. Use a comment.')
if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line):
error(
filename, linenum, 'build/forward_decl', 5,
'Inner-style forward declarations are invalid. Remove this line.')
if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?',
line):
error(
filename, linenum, 'build/deprecated', 3,
'>? and <? (max and min) operators are non-standard and deprecated.')
if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line):
# TODO(unknown): Could it be expanded safely to arbitrary references,
# without triggering too many false positives? The first
# attempt triggered 5 warnings for mostly benign code in the regtest, hence
# the restriction.
# Here's the original regexp, for the reference:
# type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?'
# r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;'
error(filename, linenum, 'runtime/member_string_references', 2,
'const string& members are dangerous. It is much better to use '
'alternatives, such as pointers or simple constants.')
# Everything else in this function operates on class declarations.
# Return early if the top of the nesting stack is not a class, or if
# the class head is not completed yet.
classinfo = nesting_state.InnermostClass()
if not classinfo or not classinfo.seen_open_brace:
return
# The class may have been declared with namespace or classname qualifiers.
# The constructor and destructor will not have those qualifiers.
base_classname = classinfo.name.split('::')[-1]
# Look for single-argument constructors that aren't marked explicit.
# Technically a valid construct, but against style. Also look for
# non-single-argument constructors which are also technically valid, but
# strongly suggest something is wrong.
explicit_constructor_match = Match(
r'\s+(?:inline\s+)?(explicit\s+)?(?:inline\s+)?%s\s*'
r'\(((?:[^()]|\([^()]*\))*)\)' % re.escape(base_classname), line)
if explicit_constructor_match:
is_marked_explicit = explicit_constructor_match.group(1)
if not explicit_constructor_match.group(2):
constructor_args = []
else:
constructor_args = explicit_constructor_match.group(2).split(',')
# collapse arguments so that commas in template parameter lists and function
# argument parameter lists don't split arguments in two
i = 0
while i < len(constructor_args):
constructor_arg = constructor_args[i]
while (constructor_arg.count('<') > constructor_arg.count('>') or
constructor_arg.count('(') > constructor_arg.count(')')):
constructor_arg += ',' + constructor_args[i + 1]
del constructor_args[i + 1]
constructor_args[i] = constructor_arg
i += 1
defaulted_args = [arg for arg in constructor_args if '=' in arg]
noarg_constructor = (
not constructor_args or # empty arg list
# 'void' arg specifier
(len(constructor_args) == 1 and
constructor_args[0].strip() == 'void'))
onearg_constructor = (
(
len(constructor_args) == 1 and # exactly one arg
not noarg_constructor) or
# all but at most one arg defaulted
(len(constructor_args) >= 1 and not noarg_constructor and
len(defaulted_args) >= len(constructor_args) - 1))
initializer_list_constructor = bool(
onearg_constructor and
Search(r'\bstd\s*::\s*initializer_list\b', constructor_args[0]))
copy_constructor = bool(
onearg_constructor and
Match(r'(const\s+)?%s(\s*<[^>]*>)?(\s+const)?\s*(?:<\w+>\s*)?&' %
re.escape(base_classname), constructor_args[0].strip()))
if (not is_marked_explicit and onearg_constructor and
not initializer_list_constructor and not copy_constructor):
if defaulted_args:
error(filename, linenum, 'runtime/explicit', 5,
'Constructors callable with one argument '
'should be marked explicit.')
else:
error(
filename, linenum, 'runtime/explicit', 5,
'Single-parameter constructors should be marked explicit.')
elif is_marked_explicit and not onearg_constructor:
if noarg_constructor:
error(
filename, linenum, 'runtime/explicit', 5,
'Zero-parameter constructors should not be marked explicit.')
else:
error(filename, linenum, 'runtime/explicit', 0,
'Constructors that require multiple arguments '
'should not be marked explicit.')
def CheckSpacingForFunctionCall(filename, clean_lines, linenum, error):
"""Checks for the correctness of various spacing around function calls.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Since function calls often occur inside if/for/while/switch
# expressions - which have their own, more liberal conventions - we
# first see if we should be looking inside such an expression for a
# function call, to which we can apply more strict standards.
fncall = line # if there's no control flow construct, look at whole line
for pattern in (r'\bif\s*\((.*)\)\s*{', r'\bfor\s*\((.*)\)\s*{',
r'\bwhile\s*\((.*)\)\s*[{;]', r'\bswitch\s*\((.*)\)\s*{'):
match = Search(pattern, line)
if match:
fncall = match.group(1) # look inside the parens for function calls
break
# Except in if/for/while/switch, there should never be space
# immediately inside parens (eg "f( 3, 4 )"). We make an exception
# for nested parens ( (a+b) + c ). Likewise, there should never be
# a space before a ( when it's a function argument. I assume it's a
# function argument when the char before the whitespace is legal in
# a function name (alnum + _) and we're not starting a macro. Also ignore
# pointers and references to arrays and functions coz they're too tricky:
# we use a very simple way to recognize these:
# " (something)(maybe-something)" or
# " (something)(maybe-something," or
# " (something)[something]"
# Note that we assume the contents of [] to be short enough that
# they'll never need to wrap.
if ( # Ignore control structures.
not Search(
r'\b(if|for|while|switch|return|new|delete|catch|sizeof)\b',
fncall) and
# Ignore pointers/references to functions.
not Search(r' \([^)]+\)\([^)]*(\)|,$)', fncall) and
# Ignore pointers/references to arrays.
not Search(r' \([^)]+\)\[[^\]]+\]', fncall)):
if Search(r'\w\s*\(\s(?!\s*\\$)', fncall): # a ( used for a fn call
error(filename, linenum, 'whitespace/parens', 4,
'Extra space after ( in function call')
elif Search(r'\(\s+(?!(\s*\\)|\()', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Extra space after (')
if (Search(r'\w\s+\(', fncall) and
not Search(r'#\s*define|typedef|using\s+\w+\s*=', fncall) and
not Search(r'\w\s+\((\w+::)*\*\w+\)\(', fncall) and
not Search(r'\bcase\s+\(', fncall)):
# TODO(unknown): Space after an operator function seem to be a common
# error, silence those for now by restricting them to highest verbosity.
if Search(r'\boperator_*\b', line):
error(filename, linenum, 'whitespace/parens', 0,
'Extra space before ( in function call')
else:
error(filename, linenum, 'whitespace/parens', 4,
'Extra space before ( in function call')
# If the ) is followed only by a newline or a { + newline, assume it's
# part of a control statement (if/while/etc), and don't complain
if Search(r'[^)]\s+\)\s*[^{\s]', fncall):
# If the closing parenthesis is preceded by only whitespaces,
# try to give a more descriptive error message.
if Search(r'^\s+\)', fncall):
error(filename, linenum, 'whitespace/parens', 2,
'Closing ) should be moved to the previous line')
else:
error(filename, linenum, 'whitespace/parens', 2,
'Extra space before )')
def IsBlankLine(line):
"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
return not line or line.isspace()
def CheckForNamespaceIndentation(filename, nesting_state, clean_lines, line,
error):
is_namespace_indent_item = (
len(nesting_state.stack) > 1 and
nesting_state.stack[-1].check_namespace_indentation and
isinstance(nesting_state.previous_stack_top, _NamespaceInfo) and
nesting_state.previous_stack_top == nesting_state.stack[-2])
if ShouldCheckNamespaceIndentation(nesting_state, is_namespace_indent_item,
clean_lines.elided, line):
CheckItemIndentationInNamespace(filename, clean_lines.elided, line,
error)
def CheckForFunctionLengths(filename, clean_lines, linenum, function_state,
error):
"""Reports for long function bodies.
For an overview why this is done, see:
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
Trivial bodies are unchecked, so constructors with huge initializer lists
may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
of vertical space and comments just to get through a lint check.
NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found.
"""
lines = clean_lines.lines
line = lines[linenum]
joined_line = ''
starting_func = False
regexp = r'(\w(\w|::|\*|\&|\s)*)\(' # decls * & space::name( ...
match_result = Match(regexp, line)
if match_result:
# If the name is all caps and underscores, figure it's a macro and
# ignore it, unless it's TEST or TEST_F.
function_name = match_result.group(1).split()[-1]
if function_name == 'TEST' or function_name == 'TEST_F' or (
not Match(r'[A-Z_]+$', function_name)):
starting_func = True
if starting_func:
body_found = False
for start_linenum in xrange(linenum, clean_lines.NumLines()):
start_line = lines[start_linenum]
joined_line += ' ' + start_line.lstrip()
if Search(r'(;|})',
start_line): # Declarations and trivial functions
body_found = True
break # ... ignore
elif Search(r'{', start_line):
body_found = True
function = Search(r'((\w|:)*)\(', line).group(1)
if Match(r'TEST', function): # Handle TEST... macros
parameter_regexp = Search(r'(\(.*\))', joined_line)
if parameter_regexp: # Ignore bad syntax
function += parameter_regexp.group(1)
else:
function += '()'
function_state.Begin(function)
break
if not body_found:
# No body for the function (or evidence of a non-function) was found.
error(filename, linenum, 'readability/fn_size', 5,
'Lint failed to find start of function body.')
elif Match(r'^\}\s*$', line): # function end
function_state.Check(error, filename, linenum)
function_state.End()
elif not Match(r'^\s*$', line):
function_state.Count() # Count non-blank/non-comment lines.
_RE_PATTERN_TODO = re.compile(r'^//(\s*)TODO(\(.+?\))?:?(\s|$)?')
def CheckComment(line, filename, linenum, next_line_start, error):
"""Checks for common mistakes in comments.
Args:
line: The line in question.
filename: The name of the current file.
linenum: The number of the line to check.
next_line_start: The first non-whitespace column of the next line.
error: The function to call with any errors found.
"""
commentpos = line.find('//')
if commentpos != -1:
# Check if the // may be in quotes. If so, ignore it
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if (line.count('"', 0, commentpos) - line.count('\\"', 0, commentpos)
) % 2 == 0: # not in quotes
# Allow one space for new scopes, two spaces otherwise:
if (not (Match(r'^.*{ *//', line) and next_line_start == commentpos)
and ((commentpos >= 1 and
line[commentpos - 1] not in string.whitespace) or
(commentpos >= 2 and
line[commentpos - 2] not in string.whitespace))):
error(filename, linenum, 'whitespace/comments', 2,
'At least two spaces is best between code and comments')
# Checks for common mistakes in TODO comments.
comment = line[commentpos:]
match = _RE_PATTERN_TODO.match(comment)
if match:
# One whitespace is correct; zero whitespace is handled elsewhere.
leading_whitespace = match.group(1)
if len(leading_whitespace) > 1:
error(filename, linenum, 'whitespace/todo', 2,
'Too many spaces before TODO')
username = match.group(2)
if not username:
error(filename, linenum, 'readability/todo', 2,
'Missing username in TODO; it should look like '
'"// TODO(my_username): Stuff."')
middle_whitespace = match.group(3)
# Comparisons made explicit for correctness -- pylint: disable=g-explicit-bool-comparison
if middle_whitespace != ' ' and middle_whitespace != '':
error(filename, linenum, 'whitespace/todo', 2,
'TODO(my_username) should be followed by a space')
# If the comment contains an alphanumeric character, there
# should be a space somewhere between it and the // unless
# it's a /// or //! Doxygen comment.
if (Match(r'//[^ ]*\w', comment) and
not Match(r'(///|//\!)(\s+|$)', comment)):
error(filename, linenum, 'whitespace/comments', 4,
'Should have a space between // and comment')
def CheckAccess(filename, clean_lines, linenum, nesting_state, error):
"""Checks for improper use of DISALLOW* macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
matched = Match((r'\s*(DISALLOW_COPY_AND_ASSIGN|'
r'DISALLOW_IMPLICIT_CONSTRUCTORS)'), line)
if not matched:
return
if nesting_state.stack and isinstance(nesting_state.stack[-1], _ClassInfo):
if nesting_state.stack[-1].access != 'private':
error(filename, linenum, 'readability/constructors', 3,
'%s must be in the private: section' % matched.group(1))
else:
# Found DISALLOW* macro outside a class declaration, or perhaps it
# was used inside a function when it should have been part of the
# class declaration. We could issue a warning here, but it
# probably resulted in a compiler error already.
pass
def CheckSpacing(filename, clean_lines, linenum, nesting_state, error):
"""Checks for the correctness of various spacing issues in the code.
Things we check for: spaces around operators, spaces after
if/for/while/switch, no spaces around parens in function calls, two
spaces between code and comment, don't start a block with a blank
line, don't end a function with a blank line, don't add a blank line
after public/protected/private, don't have too many blank lines in a row.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw = clean_lines.lines_without_raw_strings
line = raw[linenum]
# Before nixing comments, check if the line is blank for no good
# reason. This includes the first line after a block is opened, and
# blank lines at the end of a function (ie, right before a line like '}'
#
# Skip all the blank line checks if we are immediately inside a
# namespace body. In other words, don't issue blank line warnings
# for this block:
# namespace {
#
# }
#
# A warning about missing end of namespace comments will be issued instead.
#
# Also skip blank line checks for 'extern "C"' blocks, which are formatted
# like namespaces.
if (IsBlankLine(line) and not nesting_state.InNamespaceBody() and
not nesting_state.InExternC()):
elided = clean_lines.elided
prev_line = elided[linenum - 1]
prevbrace = prev_line.rfind('{')
# TODO(unknown): Don't complain if line before blank line, and line after,
# both start with alnums and are indented the same amount.
# This ignores whitespace at the start of a namespace block
# because those are not usually indented.
if prevbrace != -1 and prev_line[prevbrace:].find('}') == -1:
# OK, we have a blank line at the start of a code block. Before we
# complain, we check if it is an exception to the rule: The previous
# non-empty line has the parameters of a function header that are indented
# 4 spaces (because they did not fit in a 80 column line when placed on
# the same line as the function name). We also check for the case where
# the previous line is indented 6 spaces, which may happen when the
# initializers of a constructor do not fit into a 80 column line.
exception = False
if Match(r' {6}\w', prev_line): # Initializer list?
# We are looking for the opening column of initializer list, which
# should be indented 4 spaces to cause 6 space indentation afterwards.
search_position = linenum - 2
while (search_position >= 0 and
Match(r' {6}\w', elided[search_position])):
search_position -= 1
exception = (search_position >= 0 and
elided[search_position][:5] == ' :')
else:
# Search for the function arguments or an initializer list. We use a
# simple heuristic here: If the line is indented 4 spaces; and we have a
# closing paren, without the opening paren, followed by an opening brace
# or colon (for initializer lists) we assume that it is the last line of
# a function header. If we have a colon indented 4 spaces, it is an
# initializer list.
exception = (Match(r' {4}\w[^\(]*\)\s*(const\s*)?(\{\s*$|:)',
prev_line) or Match(r' {4}:', prev_line))
if not exception:
error(filename, linenum, 'whitespace/blank_line', 2,
'Redundant blank line at the start of a code block '
'should be deleted.')
# Ignore blank lines at the end of a block in a long if-else
# chain, like this:
# if (condition1) {
# // Something followed by a blank line
#
# } else if (condition2) {
# // Something else
# }
if linenum + 1 < clean_lines.NumLines():
next_line = raw[linenum + 1]
if (next_line and Match(r'\s*}', next_line) and
next_line.find('} else ') == -1):
error(filename, linenum, 'whitespace/blank_line', 3,
'Redundant blank line at the end of a code block '
'should be deleted.')
matched = Match(r'\s*(public|protected|private):', prev_line)
if matched:
error(filename, linenum, 'whitespace/blank_line', 3,
'Do not leave a blank line after "%s:"' % matched.group(1))
# Next, check comments
next_line_start = 0
if linenum + 1 < clean_lines.NumLines():
next_line = raw[linenum + 1]
next_line_start = len(next_line) - len(next_line.lstrip())
CheckComment(line, filename, linenum, next_line_start, error)
# get rid of comments and strings
line = clean_lines.elided[linenum]
# You shouldn't have spaces before your brackets, except maybe after
# 'delete []' or 'return []() {};'
if Search(r'\w\s+\[', line) and not Search(r'(?:delete|return)\s+\[', line):
error(filename, linenum, 'whitespace/braces', 5, 'Extra space before [')
# In range-based for, we wanted spaces before and after the colon, but
# not around "::" tokens that might appear.
if (Search(r'for *\(.*[^:]:[^: ]', line) or
Search(r'for *\(.*[^: ]:[^:]', line)):
error(filename, linenum, 'whitespace/forcolon', 2,
'Missing space around colon in range-based for loop')
def CheckOperatorSpacing(filename, clean_lines, linenum, error):
"""Checks for horizontal spacing around operators.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Don't try to do spacing checks for operator methods. Do this by
# replacing the troublesome characters with something else,
# preserving column position for all other characters.
#
# The replacement is done repeatedly to avoid false positives from
# operators that call operators.
while True:
match = Match(r'^(.*\boperator\b)(\S+)(\s*\(.*)$', line)
if match:
line = match.group(1) + ('_' * len(match.group(2))) + match.group(3)
else:
break
# We allow no-spaces around = within an if: "if ( (a=Foo()) == 0 )".
# Otherwise not. Note we only check for non-spaces on *both* sides;
# sometimes people put non-spaces on one side when aligning ='s among
# many lines (not that this is behavior that I approve of...)
if ((Search(r'[\w.]=', line) or
Search(r'=[\w.]', line)) and not Search(r'\b(if|while|for) ', line)
# Operators taken from [lex.operators] in C++11 standard.
and
not Search(r'(>=|<=|==|!=|&=|\^=|\|=|\+=|\*=|\/=|\%=)', line) and
not Search(r'operator=', line)):
error(filename, linenum, 'whitespace/operators', 4,
'Missing spaces around =')
# It's ok not to have spaces around binary operators like + - * /, but if
# there's too little whitespace, we get concerned. It's hard to tell,
# though, so we punt on this one for now. TODO.
# You should always have whitespace around binary operators.
#
# Check <= and >= first to avoid false positives with < and >, then
# check non-include lines for spacing around < and >.
#
# If the operator is followed by a comma, assume it's be used in a
# macro context and don't do any checks. This avoids false
# positives.
#
# Note that && is not included here. Those are checked separately
# in CheckRValueReference
match = Search(r'[^<>=!\s](==|!=|<=|>=|\|\|)[^<>=!\s,;\)]', line)
if match:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around %s' % match.group(1))
elif not Match(r'#.*include', line):
# Look for < that is not surrounded by spaces. This is only
# triggered if both sides are missing spaces, even though
# technically should should flag if at least one side is missing a
# space. This is done to avoid some false positives with shifts.
match = Match(r'^(.*[^\s<])<[^\s=<,]', line)
if match:
(_, _, end_pos) = CloseExpression(clean_lines, linenum,
len(match.group(1)))
if end_pos <= -1:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around <')
# Look for > that is not surrounded by spaces. Similar to the
# above, we only trigger if both sides are missing spaces to avoid
# false positives with shifts.
match = Match(r'^(.*[^-\s>])>[^\s=>,]', line)
if match:
(_, _, start_pos) = ReverseCloseExpression(clean_lines, linenum,
len(match.group(1)))
if start_pos <= -1:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around >')
# We allow no-spaces around << when used like this: 10<<20, but
# not otherwise (particularly, not when used as streams)
#
# We also allow operators following an opening parenthesis, since
# those tend to be macros that deal with operators.
match = Search(r'(operator|[^\s(<])(?:L|UL|ULL|l|ul|ull)?<<([^\s,=<])',
line)
if (match and
not (match.group(1).isdigit() and match.group(2).isdigit()) and
not (match.group(1) == 'operator' and match.group(2) == ';')):
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around <<')
# We allow no-spaces around >> for almost anything. This is because
# C++11 allows ">>" to close nested templates, which accounts for
# most cases when ">>" is not followed by a space.
#
# We still warn on ">>" followed by alpha character, because that is
# likely due to ">>" being used for right shifts, e.g.:
# value >> alpha
#
# When ">>" is used to close templates, the alphanumeric letter that
# follows would be part of an identifier, and there should still be
# a space separating the template type and the identifier.
# type<type<type>> alpha
match = Search(r'>>[a-zA-Z_]', line)
if match:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around >>')
# There shouldn't be space around unary operators
match = Search(r'(!\s|~\s|[\s]--[\s;]|[\s]\+\+[\s;])', line)
if match:
error(filename, linenum, 'whitespace/operators', 4,
'Extra space for operator %s' % match.group(1))
def CheckParenthesisSpacing(filename, clean_lines, linenum, error):
"""Checks for horizontal spacing around parentheses.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# No spaces after an if, while, switch, or for
match = Search(r' (if\(|for\(|while\(|switch\()', line)
if match:
error(filename, linenum, 'whitespace/parens', 5,
'Missing space before ( in %s' % match.group(1))
# For if/for/while/switch, the left and right parens should be
# consistent about how many spaces are inside the parens, and
# there should either be zero or one spaces inside the parens.
# We don't want: "if ( foo)" or "if ( foo )".
# Exception: "for ( ; foo; bar)" and "for (foo; bar; )" are allowed.
match = Search(r'\b(if|for|while|switch)\s*'
r'\(([ ]*)(.).*[^ ]+([ ]*)\)\s*{\s*$', line)
if match:
if len(match.group(2)) != len(match.group(4)):
if not (match.group(3) == ';' and
len(match.group(2)) == 1 + len(match.group(4)) or
not match.group(2) and Search(r'\bfor\s*\(.*; \)', line)):
error(filename, linenum, 'whitespace/parens', 5,
'Mismatching spaces inside () in %s' % match.group(1))
if len(match.group(2)) not in [0, 1]:
error(filename, linenum, 'whitespace/parens', 5,
'Should have zero or one spaces inside ( and ) in %s' %
match.group(1))
def CheckCommaSpacing(filename, clean_lines, linenum, error):
"""Checks for horizontal spacing near commas and semicolons.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
raw = clean_lines.lines_without_raw_strings
line = clean_lines.elided[linenum]
# You should always have a space after a comma (either as fn arg or operator)
#
# This does not apply when the non-space character following the
# comma is another comma, since the only time when that happens is
# for empty macro arguments.
#
# We run this check in two passes: first pass on elided lines to
# verify that lines contain missing whitespaces, second pass on raw
# lines to confirm that those missing whitespaces are not due to
# elided comments.
if (Search(r',[^,\s]', ReplaceAll(r'\boperator\s*,\s*\(', 'F(', line)) and
Search(r',[^,\s]', raw[linenum])):
error(filename, linenum, 'whitespace/comma', 3, 'Missing space after ,')
# You should always have a space after a semicolon
# except for few corner cases
# TODO(unknown): clarify if 'if (1) { return 1;}' is requires one more
# space after ;
if Search(r';[^\s};\\)/]', line):
error(filename, linenum, 'whitespace/semicolon', 3,
'Missing space after ;')
def CheckBracesSpacing(filename, clean_lines, linenum, error):
"""Checks for horizontal spacing near commas.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Except after an opening paren, or after another opening brace (in case of
# an initializer list, for instance), you should have spaces before your
# braces. And since you should never have braces at the beginning of a line,
# this is an easy test.
match = Match(r'^(.*[^ ({>]){', line)
if match:
# Try a bit harder to check for brace initialization. This
# happens in one of the following forms:
# Constructor() : initializer_list_{} { ... }
# Constructor{}.MemberFunction()
# Type variable{};
# FunctionCall(type{}, ...);
# LastArgument(..., type{});
# LOG(INFO) << type{} << " ...";
# map_of_type[{...}] = ...;
# ternary = expr ? new type{} : nullptr;
# OuterTemplate<InnerTemplateConstructor<Type>{}>
#
# We check for the character following the closing brace, and
# silence the warning if it's one of those listed above, i.e.
# "{.;,)<>]:".
#
# To account for nested initializer list, we allow any number of
# closing braces up to "{;,)<". We can't simply silence the
# warning on first sight of closing brace, because that would
# cause false negatives for things that are not initializer lists.
# Silence this: But not this:
# Outer{ if (...) {
# Inner{...} if (...){ // Missing space before {
# }; }
#
# There is a false negative with this approach if people inserted
# spurious semicolons, e.g. "if (cond){};", but we will catch the
# spurious semicolon with a separate check.
(endline, endlinenum, endpos) = CloseExpression(clean_lines, linenum,
len(match.group(1)))
trailing_text = ''
if endpos > -1:
trailing_text = endline[endpos:]
for offset in xrange(endlinenum + 1,
min(endlinenum + 3, clean_lines.NumLines() - 1)):
trailing_text += clean_lines.elided[offset]
if not Match(r'^[\s}]*[{.;,)<>\]:]', trailing_text):
error(filename, linenum, 'whitespace/braces', 5,
'Missing space before {')
# Make sure '} else {' has spaces.
if Search(r'}else', line):
error(filename, linenum, 'whitespace/braces', 5,
'Missing space before else')
# You shouldn't have a space before a semicolon at the end of the line.
# There's a special case for "for" since the style guide allows space before
# the semicolon there.
if Search(r':\s*;\s*$', line):
error(filename, linenum, 'whitespace/semicolon', 5,
'Semicolon defining empty statement. Use {} instead.')
elif Search(r'^\s*;\s*$', line):
error(
filename, linenum, 'whitespace/semicolon', 5,
'Line contains only semicolon. If this should be an empty statement, '
'use {} instead.')
elif (Search(r'\s+;\s*$', line) and not Search(r'\bfor\b', line)):
error(filename, linenum, 'whitespace/semicolon', 5,
'Extra space before last semicolon. If this should be an empty '
'statement, use {} instead.')
def IsDecltype(clean_lines, linenum, column):
"""Check if the token ending on (linenum, column) is decltype().
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: the number of the line to check.
column: end column of the token to check.
Returns:
True if this token is decltype() expression, False otherwise.
"""
(text, _, start_col) = ReverseCloseExpression(clean_lines, linenum, column)
if start_col < 0:
return False
if Search(r'\bdecltype\s*$', text[0:start_col]):
return True
return False
def IsTemplateParameterList(clean_lines, linenum, column):
"""Check if the token ending on (linenum, column) is the end of template<>.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: the number of the line to check.
column: end column of the token to check.
Returns:
True if this token is end of a template parameter list, False otherwise.
"""
(_, startline, startpos) = ReverseCloseExpression(clean_lines, linenum,
column)
if (startpos > -1 and Search(r'\btemplate\s*$',
clean_lines.elided[startline][0:startpos])):
return True
return False
def IsRValueType(typenames, clean_lines, nesting_state, linenum, column):
"""Check if the token ending on (linenum, column) is a type.
Assumes that text to the right of the column is "&&" or a function
name.
Args:
typenames: set of type names from template-argument-list.
clean_lines: A CleansedLines instance containing the file.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
linenum: the number of the line to check.
column: end column of the token to check.
Returns:
True if this token is a type, False if we are not sure.
"""
prefix = clean_lines.elided[linenum][0:column]
# Get one word to the left. If we failed to do so, this is most
# likely not a type, since it's unlikely that the type name and "&&"
# would be split across multiple lines.
match = Match(r'^(.*)(\b\w+|[>*)&])\s*$', prefix)
if not match:
return False
# Check text following the token. If it's "&&>" or "&&," or "&&...", it's
# most likely a rvalue reference used inside a template.
suffix = clean_lines.elided[linenum][column:]
if Match(r'&&\s*(?:[>,]|\.\.\.)', suffix):
return True
# Check for known types and end of templates:
# int&& variable
# vector<int>&& variable
#
# Because this function is called recursively, we also need to
# recognize pointer and reference types:
# int* Function()
# int& Function()
if (match.group(2) in typenames or match.group(2) in [
'char', 'char16_t', 'char32_t', 'wchar_t', 'bool', 'short', 'int',
'long', 'signed', 'unsigned', 'float', 'double', 'void', 'auto',
'>', '*', '&'
]):
return True
# If we see a close parenthesis, look for decltype on the other side.
# decltype would unambiguously identify a type, anything else is
# probably a parenthesized expression and not a type.
if match.group(2) == ')':
return IsDecltype(clean_lines, linenum,
len(match.group(1)) + len(match.group(2)) - 1)
# Check for casts and cv-qualifiers.
# match.group(1) remainder
# -------------- ---------
# const_cast< type&&
# const type&&
# type const&&
if Search(r'\b(?:const_cast\s*<|static_cast\s*<|dynamic_cast\s*<|'
r'reinterpret_cast\s*<|\w+\s)\s*$', match.group(1)):
return True
# Look for a preceding symbol that might help differentiate the context.
# These are the cases that would be ambiguous:
# match.group(1) remainder
# -------------- ---------
# Call ( expression &&
# Declaration ( type&&
# sizeof ( type&&
# if ( expression &&
# while ( expression &&
# for ( type&&
# for( ; expression &&
# statement ; type&&
# block { type&&
# constructor { expression &&
start = linenum
line = match.group(1)
match_symbol = None
while start >= 0:
# We want to skip over identifiers and commas to get to a symbol.
# Commas are skipped so that we can find the opening parenthesis
# for function parameter lists.
match_symbol = Match(r'^(.*)([^\w\s,])[\w\s,]*$', line)
if match_symbol:
break
start -= 1
line = clean_lines.elided[start]
if not match_symbol:
# Probably the first statement in the file is an rvalue reference
return True
if match_symbol.group(2) == '}':
# Found closing brace, probably an indicate of this:
# block{} type&&
return True
if match_symbol.group(2) == ';':
# Found semicolon, probably one of these:
# for(; expression &&
# statement; type&&
# Look for the previous 'for(' in the previous lines.
before_text = match_symbol.group(1)
for i in xrange(start - 1, max(start - 6, 0), -1):
before_text = clean_lines.elided[i] + before_text
if Search(r'for\s*\([^{};]*$', before_text):
# This is the condition inside a for-loop
return False
# Did not find a for-init-statement before this semicolon, so this
# is probably a new statement and not a condition.
return True
if match_symbol.group(2) == '{':
# Found opening brace, probably one of these:
# block{ type&& = ... ; }
# constructor{ expression && expression }
# Look for a closing brace or a semicolon. If we see a semicolon
# first, this is probably a rvalue reference.
line = clean_lines.elided[start][0:len(match_symbol.group(1)) + 1]
end = start
depth = 1
while True:
for ch in line:
if ch == ';':
return True
elif ch == '{':
depth += 1
elif ch == '}':
depth -= 1
if depth == 0:
return False
end += 1
if end >= clean_lines.NumLines():
break
line = clean_lines.elided[end]
# Incomplete program?
return False
if match_symbol.group(2) == '(':
# Opening parenthesis. Need to check what's to the left of the
# parenthesis. Look back one extra line for additional context.
before_text = match_symbol.group(1)
if linenum > 1:
before_text = clean_lines.elided[linenum - 1] + before_text
before_text = match_symbol.group(1)
# Patterns that are likely to be types:
# [](type&&
# for (type&&
# sizeof(type&&
# operator=(type&&
#
if Search(r'(?:\]|\bfor|\bsizeof|\boperator\s*\S+\s*)\s*$',
before_text):
return True
# Patterns that are likely to be expressions:
# if (expression &&
# while (expression &&
# : initializer(expression &&
# , initializer(expression &&
# ( FunctionCall(expression &&
# + FunctionCall(expression &&
# + (expression &&
#
# The last '+' represents operators such as '+' and '-'.
if Search(r'(?:\bif|\bwhile|[-+=%^(<!?:,&*]\s*)$', before_text):
return False
# Something else. Check that tokens to the left look like
# return_type function_name
match_func = Match(r'^(.*\S.*)\s+\w(?:\w|::)*(?:<[^<>]*>)?\s*$',
match_symbol.group(1))
if match_func:
# Check for constructors, which don't have return types.
if Search(r'\b(?:explicit|inline)$', match_func.group(1)):
return True
implicit_constructor = Match(r'\s*(\w+)\((?:const\s+)?(\w+)',
prefix)
if (implicit_constructor and implicit_constructor.group(1) ==
implicit_constructor.group(2)):
return True
return IsRValueType(typenames, clean_lines, nesting_state, linenum,
len(match_func.group(1)))
# Nothing before the function name. If this is inside a block scope,
# this is probably a function call.
return not (nesting_state.previous_stack_top and
nesting_state.previous_stack_top.IsBlockInfo())
if match_symbol.group(2) == '>':
# Possibly a closing bracket, check that what's on the other side
# looks like the start of a template.
return IsTemplateParameterList(clean_lines, start,
len(match_symbol.group(1)))
# Some other symbol, usually something like "a=b&&c". This is most
# likely not a type.
return False
def IsDeletedOrDefault(clean_lines, linenum):
"""Check if current constructor or operator is deleted or default.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if this is a deleted or default constructor.
"""
open_paren = clean_lines.elided[linenum].find('(')
if open_paren < 0:
return False
(close_line, _, close_paren) = CloseExpression(clean_lines, linenum,
open_paren)
if close_paren < 0:
return False
return Match(r'\s*=\s*(?:delete|default)\b', close_line[close_paren:])
def IsRValueAllowed(clean_lines, linenum, typenames):
"""Check if RValue reference is allowed on a particular line.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
typenames: set of type names from template-argument-list.
Returns:
True if line is within the region where RValue references are allowed.
"""
# Allow region marked by PUSH/POP macros
for i in xrange(linenum, 0, -1):
line = clean_lines.elided[i]
if Match(r'GOOGLE_ALLOW_RVALUE_REFERENCES_(?:PUSH|POP)', line):
if not line.endswith('PUSH'):
return False
for j in xrange(linenum, clean_lines.NumLines(), 1):
line = clean_lines.elided[j]
if Match(r'GOOGLE_ALLOW_RVALUE_REFERENCES_(?:PUSH|POP)', line):
return line.endswith('POP')
# Allow operator=
line = clean_lines.elided[linenum]
if Search(r'\boperator\s*=\s*\(', line):
return IsDeletedOrDefault(clean_lines, linenum)
# Allow constructors
match = Match(r'\s*(?:[\w<>]+::)*([\w<>]+)\s*::\s*([\w<>]+)\s*\(', line)
if match and match.group(1) == match.group(2):
return IsDeletedOrDefault(clean_lines, linenum)
if Search(r'\b(?:explicit|inline)\s+[\w<>]+\s*\(', line):
return IsDeletedOrDefault(clean_lines, linenum)
if Match(r'\s*[\w<>]+\s*\(', line):
previous_line = 'ReturnType'
if linenum > 0:
previous_line = clean_lines.elided[linenum - 1]
if Match(r'^\s*$', previous_line) or Search(r'[{}:;]\s*$',
previous_line):
return IsDeletedOrDefault(clean_lines, linenum)
# Reject types not mentioned in template-argument-list
while line:
match = Match(r'^.*?(\w+)\s*&&(.*)$', line)
if not match:
break
if match.group(1) not in typenames:
return False
line = match.group(2)
# All RValue types that were in template-argument-list should have
# been removed by now. Those were allowed, assuming that they will
# be forwarded.
#
# If there are no remaining RValue types left (i.e. types that were
# not found in template-argument-list), flag those as not allowed.
return line.find('&&') < 0
def GetTemplateArgs(clean_lines, linenum):
"""Find list of template arguments associated with this function declaration.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Line number containing the start of the function declaration,
usually one line after the end of the template-argument-list.
Returns:
Set of type names, or empty set if this does not appear to have
any template parameters.
"""
# Find start of function
func_line = linenum
while func_line > 0:
line = clean_lines.elided[func_line]
if Match(r'^\s*$', line):
return set()
if line.find('(') >= 0:
break
func_line -= 1
if func_line == 0:
return set()
# Collapse template-argument-list into a single string
argument_list = ''
match = Match(r'^(\s*template\s*)<', clean_lines.elided[func_line])
if match:
# template-argument-list on the same line as function name
start_col = len(match.group(1))
_, end_line, end_col = CloseExpression(clean_lines, func_line,
start_col)
if end_col > -1 and end_line == func_line:
start_col += 1 # Skip the opening bracket
argument_list = clean_lines.elided[func_line][start_col:end_col]
elif func_line > 1:
# template-argument-list one line before function name
match = Match(r'^(.*)>\s*$', clean_lines.elided[func_line - 1])
if match:
end_col = len(match.group(1))
_, start_line, start_col = ReverseCloseExpression(
clean_lines, func_line - 1, end_col)
if start_col > -1:
start_col += 1 # Skip the opening bracket
while start_line < func_line - 1:
argument_list += clean_lines.elided[start_line][start_col:]
start_col = 0
start_line += 1
argument_list += clean_lines.elided[func_line - 1][start_col:
end_col]
if not argument_list:
return set()
# Extract type names
typenames = set()
while True:
match = Match(r'^[,\s]*(?:typename|class)(?:\.\.\.)?\s+(\w+)(.*)$',
argument_list)
if not match:
break
typenames.add(match.group(1))
argument_list = match.group(2)
return typenames
def CheckRValueReference(filename, clean_lines, linenum, nesting_state, error):
"""Check for rvalue references.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Find lines missing spaces around &&.
# TODO(unknown): currently we don't check for rvalue references
# with spaces surrounding the && to avoid false positives with
# boolean expressions.
line = clean_lines.elided[linenum]
match = Match(r'^(.*\S)&&', line)
if not match:
match = Match(r'(.*)&&\S', line)
if (not match) or '(&&)' in line or Search(r'\boperator\s*$',
match.group(1)):
return
# Either poorly formed && or an rvalue reference, check the context
# to get a more accurate error message. Mostly we want to determine
# if what's to the left of "&&" is a type or not.
typenames = GetTemplateArgs(clean_lines, linenum)
and_pos = len(match.group(1))
if IsRValueType(typenames, clean_lines, nesting_state, linenum, and_pos):
if not IsRValueAllowed(clean_lines, linenum, typenames):
error(filename, linenum, 'build/c++11', 3,
'RValue references are an unapproved C++ feature.')
else:
error(filename, linenum, 'whitespace/operators', 3,
'Missing spaces around &&')
def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
"""Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Skip checks if the class is small, where small means 25 lines or less.
# 25 lines seems like a good cutoff since that's the usual height of
# terminals, and any class that can't fit in one screen can't really
# be considered "small".
#
# Also skip checks if we are on the first line. This accounts for
# classes that look like
# class Foo { public: ... };
#
# If we didn't find the end of the class, last_line would be zero,
# and the check will be skipped by the first condition.
if (class_info.last_line - class_info.starting_linenum <= 24 or
linenum <= class_info.starting_linenum):
return
matched = Match(r'\s*(public|protected|private):',
clean_lines.lines[linenum])
if matched:
# Issue warning if the line before public/protected/private was
# not a blank line, but don't do this if the previous line contains
# "class" or "struct". This can happen two ways:
# - We are at the beginning of the class.
# - We are forward-declaring an inner class that is semantically
# private, but needed to be public for implementation reasons.
# Also ignores cases where the previous line ends with a backslash as can be
# common when defining classes in C macros.
prev_line = clean_lines.lines[linenum - 1]
if (not IsBlankLine(prev_line) and
not Search(r'\b(class|struct)\b', prev_line) and
not Search(r'\\$', prev_line)):
# Try a bit harder to find the beginning of the class. This is to
# account for multi-line base-specifier lists, e.g.:
# class Derived
# : public Base {
end_class_head = class_info.starting_linenum
for i in range(class_info.starting_linenum, linenum):
if Search(r'\{\s*$', clean_lines.lines[i]):
end_class_head = i
break
if end_class_head < linenum - 1:
error(filename, linenum, 'whitespace/blank_line', 3,
'"%s:" should be preceded by a blank line' %
matched.group(1))
def GetPreviousNonBlankLine(clean_lines, linenum):
"""Return the most recent non-blank line and its line number.
Args:
clean_lines: A CleansedLines instance containing the file contents.
linenum: The number of the line to check.
Returns:
A tuple with two elements. The first element is the contents of the last
non-blank line before the current line, or the empty string if this is the
first non-blank line. The second is the line number of that line, or -1
if this is the first non-blank line.
"""
prevlinenum = linenum - 1
while prevlinenum >= 0:
prevline = clean_lines.elided[prevlinenum]
if not IsBlankLine(prevline): # if not a blank line...
return (prevline, prevlinenum)
prevlinenum -= 1
return ('', -1)
def CheckBraces(filename, clean_lines, linenum, error):
"""Looks for misplaced braces (e.g. at the end of line).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
if Match(r'\s*{\s*$', line):
# We allow an open brace to start a line in the case where someone is using
# braces in a block to explicitly create a new scope, which is commonly used
# to control the lifetime of stack-allocated variables. Braces are also
# used for brace initializers inside function calls. We don't detect this
# perfectly: we just don't complain if the last non-whitespace character on
# the previous non-blank line is ',', ';', ':', '(', '{', or '}', or if the
# previous line starts a preprocessor block.
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if (not Search(r'[,;:}{(]\s*$', prevline) and
not Match(r'\s*#', prevline)):
error(filename, linenum, 'whitespace/braces', 4,
'{ should almost always be at the end of the previous line')
# An else clause should be on the same line as the preceding closing brace.
if Match(r'\s*else\b\s*(?:if\b|\{|$)', line):
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if Match(r'\s*}\s*$', prevline):
error(filename, linenum, 'whitespace/newline', 4,
'An else should appear on the same line as the preceding }')
# If braces come on one side of an else, they should be on both.
# However, we have to worry about "else if" that spans multiple lines!
if Search(r'else if\s*\(', line): # could be multi-line if
brace_on_left = bool(Search(r'}\s*else if\s*\(', line))
# find the ( after the if
pos = line.find('else if')
pos = line.find('(', pos)
if pos > 0:
(endline, _, endpos) = CloseExpression(clean_lines, linenum, pos)
brace_on_right = endline[endpos:].find('{') != -1
if brace_on_left != brace_on_right: # must be brace after if
error(
filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both'
)
elif Search(r'}\s*else[^{]*$', line) or Match(r'[^}]*else\s*{', line):
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
# Likewise, an else should never have the else clause on the same line
if Search(r'\belse [^\s{]', line) and not Search(r'\belse if\b', line):
error(filename, linenum, 'whitespace/newline', 4,
'Else clause should never be on same line as else (use 2 lines)')
# In the same way, a do/while should never be on one line
if Match(r'\s*do [^\s{]', line):
error(filename, linenum, 'whitespace/newline', 4,
'do/while clauses should not be on a single line')
# Check single-line if/else bodies. The style guide says 'curly braces are not
# required for single-line statements'. We additionally allow multi-line,
# single statements, but we reject anything with more than one semicolon in
# it. This means that the first semicolon after the if should be at the end of
# its line, and the line after that should have an indent level equal to or
# lower than the if. We also check for ambiguous if/else nesting without
# braces.
if_else_match = Search(r'\b(if\s*\(|else\b)', line)
if if_else_match and not Match(r'\s*#', line):
if_indent = GetIndentLevel(line)
endline, endlinenum, endpos = line, linenum, if_else_match.end()
if_match = Search(r'\bif\s*\(', line)
if if_match:
# This could be a multiline if condition, so find the end first.
pos = if_match.end() - 1
(endline, endlinenum, endpos) = CloseExpression(clean_lines,
linenum, pos)
# Check for an opening brace, either directly after the if or on the next
# line. If found, this isn't a single-statement conditional.
if (not Match(r'\s*{', endline[endpos:]) and
not (Match(r'\s*$', endline[endpos:]) and endlinenum <
(len(clean_lines.elided) - 1) and
Match(r'\s*{', clean_lines.elided[endlinenum + 1]))):
while (endlinenum < len(clean_lines.elided) and
';' not in clean_lines.elided[endlinenum][endpos:]):
endlinenum += 1
endpos = 0
if endlinenum < len(clean_lines.elided):
endline = clean_lines.elided[endlinenum]
# We allow a mix of whitespace and closing braces (e.g. for one-liner
# methods) and a single \ after the semicolon (for macros)
endpos = endline.find(';')
if not Match(r';[\s}]*(\\?)$', endline[endpos:]):
# Semicolon isn't the last character, there's something trailing.
# Output a warning if the semicolon is not contained inside
# a lambda expression.
if not Match(
r'^[^{};]*\[[^\[\]]*\][^{}]*\{[^{}]*\}\s*\)*[;,]\s*$',
endline):
error(
filename, linenum, 'readability/braces', 4,
'If/else bodies with multiple statements require braces'
)
elif endlinenum < len(clean_lines.elided) - 1:
# Make sure the next line is dedented
next_line = clean_lines.elided[endlinenum + 1]
next_indent = GetIndentLevel(next_line)
# With ambiguous nested if statements, this will error out on the
# if that *doesn't* match the else, regardless of whether it's the
# inner one or outer one.
if (if_match and Match(r'\s*else\b', next_line) and
next_indent != if_indent):
error(
filename, linenum, 'readability/braces', 4,
'Else clause should be indented at the same level as if. '
'Ambiguous nested if/else chains require braces.')
elif next_indent > if_indent:
error(
filename, linenum, 'readability/braces', 4,
'If/else bodies with multiple statements require braces'
)
def CheckTrailingSemicolon(filename, clean_lines, linenum, error):
"""Looks for redundant trailing semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Block bodies should not be followed by a semicolon. Due to C++11
# brace initialization, there are more places where semicolons are
# required than not, so we use a whitelist approach to check these
# rather than a blacklist. These are the places where "};" should
# be replaced by just "}":
# 1. Some flavor of block following closing parenthesis:
# for (;;) {};
# while (...) {};
# switch (...) {};
# Function(...) {};
# if (...) {};
# if (...) else if (...) {};
#
# 2. else block:
# if (...) else {};
#
# 3. const member function:
# Function(...) const {};
#
# 4. Block following some statement:
# x = 42;
# {};
#
# 5. Block at the beginning of a function:
# Function(...) {
# {};
# }
#
# Note that naively checking for the preceding "{" will also match
# braces inside multi-dimensional arrays, but this is fine since
# that expression will not contain semicolons.
#
# 6. Block following another block:
# while (true) {}
# {};
#
# 7. End of namespaces:
# namespace {};
#
# These semicolons seems far more common than other kinds of
# redundant semicolons, possibly due to people converting classes
# to namespaces. For now we do not warn for this case.
#
# Try matching case 1 first.
match = Match(r'^(.*\)\s*)\{', line)
if match:
# Matched closing parenthesis (case 1). Check the token before the
# matching opening parenthesis, and don't warn if it looks like a
# macro. This avoids these false positives:
# - macro that defines a base class
# - multi-line macro that defines a base class
# - macro that defines the whole class-head
#
# But we still issue warnings for macros that we know are safe to
# warn, specifically:
# - TEST, TEST_F, TEST_P, MATCHER, MATCHER_P
# - TYPED_TEST
# - INTERFACE_DEF
# - EXCLUSIVE_LOCKS_REQUIRED, SHARED_LOCKS_REQUIRED, LOCKS_EXCLUDED:
#
# We implement a whitelist of safe macros instead of a blacklist of
# unsafe macros, even though the latter appears less frequently in
# google code and would have been easier to implement. This is because
# the downside for getting the whitelist wrong means some extra
# semicolons, while the downside for getting the blacklist wrong
# would result in compile errors.
#
# In addition to macros, we also don't want to warn on
# - Compound literals
# - Lambdas
# - alignas specifier with anonymous structs:
closing_brace_pos = match.group(1).rfind(')')
opening_parenthesis = ReverseCloseExpression(clean_lines, linenum,
closing_brace_pos)
if opening_parenthesis[2] > -1:
line_prefix = opening_parenthesis[0][0:opening_parenthesis[2]]
macro = Search(r'\b([A-Z_]+)\s*$', line_prefix)
func = Match(r'^(.*\])\s*$', line_prefix)
if ((macro and macro.group(1) not in
('TEST', 'TEST_F', 'MATCHER', 'MATCHER_P', 'TYPED_TEST',
'EXCLUSIVE_LOCKS_REQUIRED', 'SHARED_LOCKS_REQUIRED',
'LOCKS_EXCLUDED', 'INTERFACE_DEF')) or
(func and not Search(r'\boperator\s*\[\s*\]', func.group(1))) or
Search(r'\b(?:struct|union)\s+alignas\s*$', line_prefix) or
Search(r'\s+=\s*$', line_prefix)):
match = None
if (match and opening_parenthesis[1] > 1 and Search(
r'\]\s*$', clean_lines.elided[opening_parenthesis[1] - 1])):
# Multi-line lambda-expression
match = None
else:
# Try matching cases 2-3.
match = Match(r'^(.*(?:else|\)\s*const)\s*)\{', line)
if not match:
# Try matching cases 4-6. These are always matched on separate lines.
#
# Note that we can't simply concatenate the previous line to the
# current line and do a single match, otherwise we may output
# duplicate warnings for the blank line case:
# if (cond) {
# // blank line
# }
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if prevline and Search(r'[;{}]\s*$', prevline):
match = Match(r'^(\s*)\{', line)
# Check matching closing brace
if match:
(endline, endlinenum, endpos) = CloseExpression(clean_lines, linenum,
len(match.group(1)))
if endpos > -1 and Match(r'^\s*;', endline[endpos:]):
# Current {} pair is eligible for semicolon check, and we have found
# the redundant semicolon, output warning here.
#
# Note: because we are scanning forward for opening braces, and
# outputting warnings for the matching closing brace, if there are
# nested blocks with trailing semicolons, we will get the error
# messages in reversed order.
error(filename, endlinenum, 'readability/braces', 4,
"You don't need a ; after a }")
def CheckEmptyBlockBody(filename, clean_lines, linenum, error):
"""Look for empty loop/conditional body with only a single semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Search for loop keywords at the beginning of the line. Because only
# whitespaces are allowed before the keywords, this will also ignore most
# do-while-loops, since those lines should start with closing brace.
#
# We also check "if" blocks here, since an empty conditional block
# is likely an error.
line = clean_lines.elided[linenum]
matched = Match(r'\s*(for|while|if)\s*\(', line)
if matched:
# Find the end of the conditional expression
(end_line, end_linenum, end_pos) = CloseExpression(clean_lines, linenum,
line.find('('))
# Output warning if what follows the condition expression is a semicolon.
# No warning for all other cases, including whitespace or newline, since we
# have a separate check for semicolons preceded by whitespace.
if end_pos >= 0 and Match(r';', end_line[end_pos:]):
if matched.group(1) == 'if':
error(filename, end_linenum,
'whitespace/empty_conditional_body', 5,
'Empty conditional bodies should use {}')
else:
error(filename, end_linenum, 'whitespace/empty_loop_body', 5,
'Empty loop bodies should use {} or continue')
def FindCheckMacro(line):
"""Find a replaceable CHECK-like macro.
Args:
line: line to search on.
Returns:
(macro name, start position), or (None, -1) if no replaceable
macro is found.
"""
for macro in _CHECK_MACROS:
i = line.find(macro)
if i >= 0:
# Find opening parenthesis. Do a regular expression match here
# to make sure that we are matching the expected CHECK macro, as
# opposed to some other macro that happens to contain the CHECK
# substring.
matched = Match(r'^(.*\b' + macro + r'\s*)\(', line)
if not matched:
continue
return (macro, len(matched.group(1)))
return (None, -1)
def CheckCheck(filename, clean_lines, linenum, error):
"""Checks the use of CHECK and EXPECT macros.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Decide the set of replacement macros that should be suggested
lines = clean_lines.elided
(check_macro, start_pos) = FindCheckMacro(lines[linenum])
if not check_macro:
return
# Find end of the boolean expression by matching parentheses
(last_line, end_line, end_pos) = CloseExpression(clean_lines, linenum,
start_pos)
if end_pos < 0:
return
# If the check macro is followed by something other than a
# semicolon, assume users will log their own custom error messages
# and don't suggest any replacements.
if not Match(r'\s*;', last_line[end_pos:]):
return
if linenum == end_line:
expression = lines[linenum][start_pos + 1:end_pos - 1]
else:
expression = lines[linenum][start_pos + 1:]
for i in xrange(linenum + 1, end_line):
expression += lines[i]
expression += last_line[0:end_pos - 1]
# Parse expression so that we can take parentheses into account.
# This avoids false positives for inputs like "CHECK((a < 4) == b)",
# which is not replaceable by CHECK_LE.
lhs = ''
rhs = ''
operator = None
while expression:
matched = Match(r'^\s*(<<|<<=|>>|>>=|->\*|->|&&|\|\||'
r'==|!=|>=|>|<=|<|\()(.*)$', expression)
if matched:
token = matched.group(1)
if token == '(':
# Parenthesized operand
expression = matched.group(2)
(end, _) = FindEndOfExpressionInLine(expression, 0, ['('])
if end < 0:
return # Unmatched parenthesis
lhs += '(' + expression[0:end]
expression = expression[end:]
elif token in ('&&', '||'):
# Logical and/or operators. This means the expression
# contains more than one term, for example:
# CHECK(42 < a && a < b);
#
# These are not replaceable with CHECK_LE, so bail out early.
return
elif token in ('<<', '<<=', '>>', '>>=', '->*', '->'):
# Non-relational operator
lhs += token
expression = matched.group(2)
else:
# Relational operator
operator = token
rhs = matched.group(2)
break
else:
# Unparenthesized operand. Instead of appending to lhs one character
# at a time, we do another regular expression match to consume several
# characters at once if possible. Trivial benchmark shows that this
# is more efficient when the operands are longer than a single
# character, which is generally the case.
matched = Match(r'^([^-=!<>()&|]+)(.*)$', expression)
if not matched:
matched = Match(r'^(\s*\S)(.*)$', expression)
if not matched:
break
lhs += matched.group(1)
expression = matched.group(2)
# Only apply checks if we got all parts of the boolean expression
if not (lhs and operator and rhs):
return
# Check that rhs do not contain logical operators. We already know
# that lhs is fine since the loop above parses out && and ||.
if rhs.find('&&') > -1 or rhs.find('||') > -1:
return
# At least one of the operands must be a constant literal. This is
# to avoid suggesting replacements for unprintable things like
# CHECK(variable != iterator)
#
# The following pattern matches decimal, hex integers, strings, and
# characters (in that order).
lhs = lhs.strip()
rhs = rhs.strip()
match_constant = r'^([-+]?(\d+|0[xX][0-9a-fA-F]+)[lLuU]{0,3}|".*"|\'.*\')$'
if Match(match_constant, lhs) or Match(match_constant, rhs):
# Note: since we know both lhs and rhs, we can provide a more
# descriptive error message like:
# Consider using CHECK_EQ(x, 42) instead of CHECK(x == 42)
# Instead of:
# Consider using CHECK_EQ instead of CHECK(a == b)
#
# We are still keeping the less descriptive message because if lhs
# or rhs gets long, the error message might become unreadable.
error(filename, linenum, 'readability/check', 2,
'Consider using %s instead of %s(a %s b)' %
(_CHECK_REPLACEMENT[check_macro][operator], check_macro,
operator))
def CheckAltTokens(filename, clean_lines, linenum, error):
"""Check alternative keywords being used in boolean expressions.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Avoid preprocessor lines
if Match(r'^\s*#', line):
return
# Last ditch effort to avoid multi-line comments. This will not help
# if the comment started before the current line or ended after the
# current line, but it catches most of the false positives. At least,
# it provides a way to workaround this warning for people who use
# multi-line comments in preprocessor macros.
#
# TODO(unknown): remove this once cpplint has better support for
# multi-line comments.
if line.find('/*') >= 0 or line.find('*/') >= 0:
return
for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
error(filename, linenum, 'readability/alt_tokens', 2,
'Use operator %s instead of %s' % (
_ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))
def GetLineWidth(line):
"""Determines the width of the line in column positions.
Args:
line: A string, which may be a Unicode string.
Returns:
The width of the line in column positions, accounting for Unicode
combining characters and wide characters.
"""
if isinstance(line, unicode):
width = 0
for uc in unicodedata.normalize('NFC', line):
if unicodedata.east_asian_width(uc) in ('W', 'F'):
width += 2
elif not unicodedata.combining(uc):
width += 1
return width
else:
return len(line)
def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
error):
"""Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw_lines = clean_lines.lines_without_raw_strings
line = raw_lines[linenum]
if line.find('\t') != -1:
error(filename, linenum, 'whitespace/tab', 1,
'Tab found; better to use spaces')
# One or three blank spaces at the beginning of the line is weird; it's
# hard to reconcile that with 2-space indents.
# NOTE: here are the conditions rob pike used for his tests. Mine aren't
# as sophisticated, but it may be worth becoming so: RLENGTH==initial_spaces
# if(RLENGTH > 20) complain = 0;
# if(match($0, " +(error|private|public|protected):")) complain = 0;
# if(match(prev, "&& *$")) complain = 0;
# if(match(prev, "\\|\\| *$")) complain = 0;
# if(match(prev, "[\",=><] *$")) complain = 0;
# if(match($0, " <<")) complain = 0;
# if(match(prev, " +for \\(")) complain = 0;
# if(prevodd && match(prevprev, " +for \\(")) complain = 0;
scope_or_label_pattern = r'\s*\w+\s*:\s*\\?$'
classinfo = nesting_state.InnermostClass()
initial_spaces = 0
cleansed_line = clean_lines.elided[linenum]
while initial_spaces < len(line) and line[initial_spaces] == ' ':
initial_spaces += 1
if line and line[-1].isspace():
error(filename, linenum, 'whitespace/end_of_line', 4,
'Line ends in whitespace. Consider deleting these extra spaces.')
# There are certain situations we allow one space, notably for
# section labels, and also lines containing multi-line raw strings.
elif ((initial_spaces == 1 or initial_spaces == 3) and
not Match(scope_or_label_pattern, cleansed_line) and
not (clean_lines.raw_lines[linenum] != line and
Match(r'^\s*""', line))):
error(filename, linenum, 'whitespace/indent', 3,
'Weird number of spaces at line-start. '
'Are you using a 2-space indent?')
# Check if the line is a header guard.
is_header_guard = False
if file_extension == 'h':
cppvar = GetHeaderGuardCPPVariable(filename)
if (line.startswith('#ifndef %s' % cppvar) or
line.startswith('#define %s' % cppvar) or
line.startswith('#endif // %s' % cppvar)):
is_header_guard = True
# #include lines and header guards can be long, since there's no clean way to
# split them.
#
# URLs can be long too. It's possible to split these, but it makes them
# harder to cut&paste.
#
# The "$Id:...$" comment may also get very long without it being the
# developers fault.
if (not line.startswith('#include') and not is_header_guard and
not Match(r'^\s*//.*http(s?)://\S*$', line) and
not Match(r'^// \$Id:.*#[0-9]+ \$$', line)):
line_width = GetLineWidth(line)
extended_length = int((_line_length * 1.25))
if line_width > extended_length:
error(filename, linenum, 'whitespace/line_length', 4,
'Lines should very rarely be longer than %i characters' %
extended_length)
elif line_width > _line_length:
error(filename, linenum, 'whitespace/line_length', 2,
'Lines should be <= %i characters long' % _line_length)
if (cleansed_line.count(';') > 1 and
# for loops are allowed two ;'s (and may run over two lines).
cleansed_line.find('for') == -1 and
(GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or
GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and
# It's ok to have many commands in a switch case that fits in 1 line
not ((cleansed_line.find('case ') != -1 or
cleansed_line.find('default:') != -1) and
cleansed_line.find('break;') != -1)):
error(filename, linenum, 'whitespace/newline', 0,
'More than one command on the same line')
# Some more style checks
CheckBraces(filename, clean_lines, linenum, error)
CheckTrailingSemicolon(filename, clean_lines, linenum, error)
CheckEmptyBlockBody(filename, clean_lines, linenum, error)
CheckAccess(filename, clean_lines, linenum, nesting_state, error)
CheckSpacing(filename, clean_lines, linenum, nesting_state, error)
CheckOperatorSpacing(filename, clean_lines, linenum, error)
CheckParenthesisSpacing(filename, clean_lines, linenum, error)
CheckCommaSpacing(filename, clean_lines, linenum, error)
CheckBracesSpacing(filename, clean_lines, linenum, error)
CheckSpacingForFunctionCall(filename, clean_lines, linenum, error)
CheckRValueReference(filename, clean_lines, linenum, nesting_state, error)
CheckCheck(filename, clean_lines, linenum, error)
CheckAltTokens(filename, clean_lines, linenum, error)
classinfo = nesting_state.InnermostClass()
if classinfo:
CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error)
_RE_PATTERN_INCLUDE = re.compile(r'^\s*#\s*include\s*([<"])([^>"]*)[>"].*$')
# Matches the first component of a filename delimited by -s and _s. That is:
# _RE_FIRST_COMPONENT.match('foo').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo.cc').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo-bar_baz.cc').group(0) == 'foo'
# _RE_FIRST_COMPONENT.match('foo_bar-baz.cc').group(0) == 'foo'
_RE_FIRST_COMPONENT = re.compile(r'^[^-_.]+')
def _DropCommonSuffixes(filename):
"""Drops common suffixes like _test.cc or -inl.h from filename.
For example:
>>> _DropCommonSuffixes('foo/foo-inl.h')
'foo/foo'
>>> _DropCommonSuffixes('foo/bar/foo.cc')
'foo/bar/foo'
>>> _DropCommonSuffixes('foo/foo_internal.h')
'foo/foo'
>>> _DropCommonSuffixes('foo/foo_unusualinternal.h')
'foo/foo_unusualinternal'
Args:
filename: The input filename.
Returns:
The filename with the common suffix removed.
"""
for suffix in ('test.cc', 'regtest.cc', 'unittest.cc', 'inl.h', 'impl.h',
'internal.h'):
if (filename.endswith(suffix) and len(filename) > len(suffix) and
filename[-len(suffix) - 1] in ('-', '_')):
return filename[:-len(suffix) - 1]
return os.path.splitext(filename)[0]
def _IsTestFilename(filename):
"""Determines if the given filename has a suffix that identifies it as a test.
Args:
filename: The input filename.
Returns:
True if 'filename' looks like a test, False otherwise.
"""
if (filename.endswith('_test.cc') or filename.endswith('_unittest.cc') or
filename.endswith('_regtest.cc')):
return True
else:
return False
def _ClassifyInclude(fileinfo, include, is_system):
"""Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER
"""
# This is a list of all standard c++ header files, except
# those already checked for above.
is_cpp_h = include in _CPP_HEADERS
if is_system:
if is_cpp_h:
return _CPP_SYS_HEADER
else:
return _C_SYS_HEADER
# If the target file and the include we're checking share a
# basename when we drop common extensions, and the include
# lives in . , then it's likely to be owned by the target file.
target_dir, target_base = (
os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
if target_base == include_base and (
include_dir == target_dir or
include_dir == os.path.normpath(target_dir + '/../public')):
return _LIKELY_MY_HEADER
# If the target and include share some initial basename
# component, it's possible the target is implementing the
# include, so it's allowed to be first, but we'll never
# complain if it's not there.
target_first_component = _RE_FIRST_COMPONENT.match(target_base)
include_first_component = _RE_FIRST_COMPONENT.match(include_base)
if (target_first_component and include_first_component and
target_first_component.group(0) ==
include_first_component.group(0)):
return _POSSIBLE_MY_HEADER
return _OTHER_HEADER
def CheckIncludeLine(filename, clean_lines, linenum, include_state, error):
"""Check rules that are applicable to #include lines.
Strings on #include lines are NOT removed from elided line, to make
certain tasks easier. However, to prevent false positives, checks
applicable to #include lines in CheckLanguage must be put here.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
include_state: An _IncludeState instance in which the headers are inserted.
error: The function to call with any errors found.
"""
fileinfo = FileInfo(filename)
line = clean_lines.lines[linenum]
# "include" should use the new style "foo/bar.h" instead of just "bar.h"
# Only do this check if the included header follows google naming
# conventions. If not, assume that it's a 3rd party API that
# requires special include conventions.
#
# We also make an exception for Lua headers, which follow google
# naming convention but not the include convention.
match = Match(r'#include\s*"([^/]+\.h)"', line)
if match and not _THIRD_PARTY_HEADERS_PATTERN.match(match.group(1)):
error(filename, linenum, 'build/include', 4,
'Include the directory when naming .h files')
# we shouldn't include a file more than once. actually, there are a
# handful of instances where doing so is okay, but in general it's
# not.
match = _RE_PATTERN_INCLUDE.search(line)
if match:
include = match.group(2)
is_system = (match.group(1) == '<')
duplicate_line = include_state.FindHeader(include)
if duplicate_line >= 0:
error(filename, linenum, 'build/include', 4,
'"%s" already included at %s:%s' %
(include, filename, duplicate_line))
elif (include.endswith('.cc') and
os.path.dirname(fileinfo.RepositoryName()) !=
os.path.dirname(include)):
error(filename, linenum, 'build/include', 4,
'Do not include .cc files from other packages')
elif not _THIRD_PARTY_HEADERS_PATTERN.match(include):
include_state.include_list[-1].append((include, linenum))
# We want to ensure that headers appear in the right order:
# 1) for foo.cc, foo.h (preferred location)
# 2) c system files
# 3) cpp system files
# 4) for foo.cc, foo.h (deprecated location)
# 5) other google headers
#
# We classify each include statement as one of those 5 types
# using a number of techniques. The include_state object keeps
# track of the highest type seen, and complains if we see a
# lower type after that.
error_message = include_state.CheckNextIncludeOrder(
_ClassifyInclude(fileinfo, include, is_system))
if error_message:
error(filename, linenum, 'build/include_order', 4,
'%s. Should be: %s.h, c system, c++ system, other.' %
(error_message, fileinfo.BaseName()))
canonical_include = include_state.CanonicalizeAlphabeticalOrder(
include)
if not include_state.IsInAlphabeticalOrder(clean_lines, linenum,
canonical_include):
error(filename, linenum, 'build/include_alpha', 4,
'Include "%s" not in alphabetical order' % include)
include_state.SetLastHeader(canonical_include)
def _GetTextInside(text, start_pattern):
r"""Retrieves all the text between matching open and close parentheses.
Given a string of lines and a regular expression string, retrieve all the text
following the expression and between opening punctuation symbols like
(, [, or {, and the matching close-punctuation symbol. This properly nested
occurrences of the punctuations, so for the text like
printf(a(), b(c()));
a call to _GetTextInside(text, r'printf\(') will return 'a(), b(c())'.
start_pattern must match string having an open punctuation symbol at the end.
Args:
text: The lines to extract text. Its comments and strings must be elided.
It can be single line and can span multiple lines.
start_pattern: The regexp string indicating where to start extracting
the text.
Returns:
The extracted text.
None if either the opening string or ending punctuation could not be found.
"""
# TODO(unknown): Audit cpplint.py to see what places could be profitably
# rewritten to use _GetTextInside (and use inferior regexp matching today).
# Give opening punctuations to get the matching close-punctuations.
matching_punctuation = {'(': ')', '{': '}', '[': ']'}
closing_punctuation = set(matching_punctuation.itervalues())
# Find the position to start extracting text.
match = re.search(start_pattern, text, re.M)
if not match: # start_pattern not found in text.
return None
start_position = match.end(0)
assert start_position > 0, (
'start_pattern must ends with an opening punctuation.')
assert text[start_position - 1] in matching_punctuation, (
'start_pattern must ends with an opening punctuation.')
# Stack of closing punctuations we expect to have in text after position.
punctuation_stack = [matching_punctuation[text[start_position - 1]]]
position = start_position
while punctuation_stack and position < len(text):
if text[position] == punctuation_stack[-1]:
punctuation_stack.pop()
elif text[position] in closing_punctuation:
# A closing punctuation without matching opening punctuations.
return None
elif text[position] in matching_punctuation:
punctuation_stack.append(matching_punctuation[text[position]])
position += 1
if punctuation_stack:
# Opening punctuations left without matching close-punctuations.
return None
# punctuations match.
return text[start_position:position - 1]
# Patterns for matching call-by-reference parameters.
#
# Supports nested templates up to 2 levels deep using this messy pattern:
# < (?: < (?: < [^<>]*
# >
# | [^<>] )*
# >
# | [^<>] )*
# >
_RE_PATTERN_IDENT = r'[_a-zA-Z]\w*' # =~ [[:alpha:]][[:alnum:]]*
_RE_PATTERN_TYPE = (
r'(?:const\s+)?(?:typename\s+|class\s+|struct\s+|union\s+|enum\s+)?'
r'(?:\w|'
r'\s*<(?:<(?:<[^<>]*>|[^<>])*>|[^<>])*>|'
r'::)+')
# A call-by-reference parameter ends with '& identifier'.
_RE_PATTERN_REF_PARAM = re.compile(
r'(' + _RE_PATTERN_TYPE + r'(?:\s*(?:\bconst\b|[*]))*\s*'
r'&\s*' + _RE_PATTERN_IDENT + r')\s*(?:=[^,()]+)?[,)]')
# A call-by-const-reference parameter either ends with 'const& identifier'
# or looks like 'const type& identifier' when 'type' is atomic.
_RE_PATTERN_CONST_REF_PARAM = (
r'(?:.*\s*\bconst\s*&\s*' + _RE_PATTERN_IDENT + r'|const\s+' +
_RE_PATTERN_TYPE + r'\s*&\s*' + _RE_PATTERN_IDENT + r')')
def CheckLanguage(filename, clean_lines, linenum, file_extension, include_state,
nesting_state, error):
"""Checks rules from the 'C++ language rules' section of cppguide.html.
Some of these rules are hard to test (function overloading, using
uint32 inappropriately), but we do the best we can.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
include_state: An _IncludeState instance in which the headers are inserted.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# If the line is empty or consists of entirely a comment, no need to
# check it.
line = clean_lines.elided[linenum]
if not line:
return
match = _RE_PATTERN_INCLUDE.search(line)
if match:
CheckIncludeLine(filename, clean_lines, linenum, include_state, error)
return
# Reset include state across preprocessor directives. This is meant
# to silence warnings for conditional includes.
match = Match(r'^\s*#\s*(if|ifdef|ifndef|elif|else|endif)\b', line)
if match:
include_state.ResetSection(match.group(1))
# Make Windows paths like Unix.
fullname = os.path.abspath(filename).replace('\\', '/')
# Perform other checks now that we are sure that this is not an include line
CheckCasts(filename, clean_lines, linenum, error)
CheckGlobalStatic(filename, clean_lines, linenum, error)
CheckPrintf(filename, clean_lines, linenum, error)
if file_extension == 'h':
# TODO(unknown): check that 1-arg constructors are explicit.
# How to tell it's a constructor?
# (handled in CheckForNonStandardConstructs for now)
# TODO(unknown): check that classes declare or disable copy/assign
# (level 1 error)
pass
# Check if people are using the verboten C basic types. The only exception
# we regularly allow is "unsigned short port" for port.
if Search(r'\bshort port\b', line):
if not Search(r'\bunsigned short port\b', line):
error(filename, linenum, 'runtime/int', 4,
'Use "unsigned short" for ports, not "short"')
else:
match = Search(r'\b(short|long(?! +double)|long long)\b', line)
if match:
error(filename, linenum, 'runtime/int', 4,
'Use int16/int64/etc, rather than the C type %s' %
match.group(1))
# Check if some verboten operator overloading is going on
# TODO(unknown): catch out-of-line unary operator&:
# class X {};
# int operator&(const X& x) { return 42; } // unary operator&
# The trick is it's hard to tell apart from binary operator&:
# class Y { int operator&(const Y& x) { return 23; } }; // binary operator&
if Search(r'\boperator\s*&\s*\(\s*\)', line):
error(filename, linenum, 'runtime/operator', 4,
'Unary operator& is dangerous. Do not use it.')
# Check for suspicious usage of "if" like
# } if (a == b) {
if Search(r'\}\s*if\s*\(', line):
error(filename, linenum, 'readability/braces', 4,
'Did you mean "else if"? If not, start a new line for "if".')
# Check for potential format string bugs like printf(foo).
# We constrain the pattern not to pick things like DocidForPrintf(foo).
# Not perfect but it can catch printf(foo.c_str()) and printf(foo->c_str())
# TODO(unknown): Catch the following case. Need to change the calling
# convention of the whole function to process multiple line to handle it.
# printf(
# boy_this_is_a_really_long_variable_that_cannot_fit_on_the_prev_line);
printf_args = _GetTextInside(line, r'(?i)\b(string)?printf\s*\(')
if printf_args:
match = Match(r'([\w.\->()]+)$', printf_args)
if match and match.group(1) != '__VA_ARGS__':
function_name = re.search(r'\b((?:string)?printf)\s*\(', line,
re.I).group(1)
error(filename, linenum, 'runtime/printf', 4,
'Potential format string bug. Do %s("%%s", %s) instead.' %
(function_name, match.group(1)))
# Check for potential memset bugs like memset(buf, sizeof(buf), 0).
match = Search(r'memset\s*\(([^,]*),\s*([^,]*),\s*0\s*\)', line)
if match and not Match(r"^''|-?[0-9]+|0x[0-9A-Fa-f]$", match.group(2)):
error(filename, linenum, 'runtime/memset', 4,
'Did you mean "memset(%s, 0, %s)"?' %
(match.group(1), match.group(2)))
if Search(r'\busing namespace\b', line):
error(filename, linenum, 'build/namespaces', 5,
'Do not use namespace using-directives. '
'Use using-declarations instead.')
# Detect variable-length arrays.
match = Match(r'\s*(.+::)?(\w+) [a-z]\w*\[(.+)];', line)
if (match and match.group(2) != 'return' and match.group(2) != 'delete' and
match.group(3).find(']') == -1):
# Split the size using space and arithmetic operators as delimiters.
# If any of the resulting tokens are not compile time constants then
# report the error.
tokens = re.split(r'\s|\+|\-|\*|\/|<<|>>]', match.group(3))
is_const = True
skip_next = False
for tok in tokens:
if skip_next:
skip_next = False
continue
if Search(r'sizeof\(.+\)', tok): continue
if Search(r'arraysize\(\w+\)', tok): continue
tok = tok.lstrip('(')
tok = tok.rstrip(')')
if not tok: continue
if Match(r'\d+', tok): continue
if Match(r'0[xX][0-9a-fA-F]+', tok): continue
if Match(r'k[A-Z0-9]\w*', tok): continue
if Match(r'(.+::)?k[A-Z0-9]\w*', tok): continue
if Match(r'(.+::)?[A-Z][A-Z0-9_]*', tok): continue
# A catch all for tricky sizeof cases, including 'sizeof expression',
# 'sizeof(*type)', 'sizeof(const type)', 'sizeof(struct StructName)'
# requires skipping the next token because we split on ' ' and '*'.
if tok.startswith('sizeof'):
skip_next = True
continue
is_const = False
break
if not is_const:
error(
filename, linenum, 'runtime/arrays', 1,
'Do not use variable-length arrays. Use an appropriately named '
"('k' followed by CamelCase) compile-time constant for the size."
)
# Check for use of unnamed namespaces in header files. Registration
# macros are typically OK, so we allow use of "namespace {" on lines
# that end with backslashes.
if (file_extension == 'h' and Search(r'\bnamespace\s*{', line) and
line[-1] != '\\'):
error(
filename, linenum, 'build/namespaces', 4,
'Do not use unnamed namespaces in header files. See '
'http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Namespaces'
' for more information.')
def CheckGlobalStatic(filename, clean_lines, linenum, error):
"""Check for unsafe global or static objects.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Match two lines at a time to support multiline declarations
if linenum + 1 < clean_lines.NumLines() and not Search(r'[;({]', line):
line += clean_lines.elided[linenum + 1].strip()
# Check for people declaring static/global STL strings at the top level.
# This is dangerous because the C++ language does not guarantee that
# globals with constructors are initialized before the first access.
match = Match(r'((?:|static +)(?:|const +))string +([a-zA-Z0-9_:]+)\b(.*)',
line)
# Remove false positives:
# - String pointers (as opposed to values).
# string *pointer
# const string *pointer
# string const *pointer
# string *const pointer
#
# - Functions and template specializations.
# string Function<Type>(...
# string Class<Type>::Method(...
#
# - Operators. These are matched separately because operator names
# cross non-word boundaries, and trying to match both operators
# and functions at the same time would decrease accuracy of
# matching identifiers.
# string Class::operator*()
if (match and
not Search(r'\bstring\b(\s+const)?\s*\*\s*(const\s+)?\w', line) and
not Search(r'\boperator\W', line) and not Match(
r'\s*(<.*>)?(::[a-zA-Z0-9_]+)*\s*\(([^"]|$)', match.group(3))):
error(
filename, linenum, 'runtime/string', 4,
'For a static/global string constant, use a C style string instead: '
'"%schar %s[]".' % (match.group(1), match.group(2)))
if Search(r'\b([A-Za-z0-9_]*_)\(\1\)', line):
error(filename, linenum, 'runtime/init', 4,
'You seem to be initializing a member variable with itself.')
def CheckPrintf(filename, clean_lines, linenum, error):
"""Check for printf related issues.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# When snprintf is used, the second argument shouldn't be a literal.
match = Search(r'snprintf\s*\(([^,]*),\s*([0-9]*)\s*,', line)
if match and match.group(2) != '0':
# If 2nd arg is zero, snprintf is used to calculate size.
error(filename, linenum, 'runtime/printf', 3,
'If you can, use sizeof(%s) instead of %s as the 2nd arg '
'to snprintf.' % (match.group(1), match.group(2)))
# Check if some verboten C functions are being used.
if Search(r'\bsprintf\s*\(', line):
error(filename, linenum, 'runtime/printf', 5,
'Never use sprintf. Use snprintf instead.')
match = Search(r'\b(strcpy|strcat)\s*\(', line)
if match:
error(filename, linenum, 'runtime/printf', 4,
'Almost always, snprintf is better than %s' % match.group(1))
def IsDerivedFunction(clean_lines, linenum):
"""Check if current line contains an inherited function.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if current line contains a function with "override"
virt-specifier.
"""
# Scan back a few lines for start of current function
for i in xrange(linenum, max(-1, linenum - 10), -1):
match = Match(r'^([^()]*\w+)\(', clean_lines.elided[i])
if match:
# Look for "override" after the matching closing parenthesis
line, _, closing_paren = CloseExpression(clean_lines, i,
len(match.group(1)))
return (closing_paren >= 0 and
Search(r'\boverride\b', line[closing_paren:]))
return False
def IsOutOfLineMethodDefinition(clean_lines, linenum):
"""Check if current line contains an out-of-line method definition.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if current line contains an out-of-line method definition.
"""
# Scan back a few lines for start of current function
for i in xrange(linenum, max(-1, linenum - 10), -1):
if Match(r'^([^()]*\w+)\(', clean_lines.elided[i]):
return Match(r'^[^()]*\w+::\w+\(',
clean_lines.elided[i]) is not None
return False
def IsInitializerList(clean_lines, linenum):
"""Check if current line is inside constructor initializer list.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if current line appears to be inside constructor initializer
list, False otherwise.
"""
for i in xrange(linenum, 1, -1):
line = clean_lines.elided[i]
if i == linenum:
remove_function_body = Match(r'^(.*)\{\s*$', line)
if remove_function_body:
line = remove_function_body.group(1)
if Search(r'\s:\s*\w+[({]', line):
# A lone colon tend to indicate the start of a constructor
# initializer list. It could also be a ternary operator, which
# also tend to appear in constructor initializer lists as
# opposed to parameter lists.
return True
if Search(r'\}\s*,\s*$', line):
# A closing brace followed by a comma is probably the end of a
# brace-initialized member in constructor initializer list.
return True
if Search(r'[{};]\s*$', line):
# Found one of the following:
# - A closing brace or semicolon, probably the end of the previous
# function.
# - An opening brace, probably the start of current class or namespace.
#
# Current line is probably not inside an initializer list since
# we saw one of those things without seeing the starting colon.
return False
# Got to the beginning of the file without seeing the start of
# constructor initializer list.
return False
def CheckForNonConstReference(filename, clean_lines, linenum, nesting_state,
error):
"""Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Do nothing if there is no '&' on current line.
line = clean_lines.elided[linenum]
if '&' not in line:
return
# If a function is inherited, current function doesn't have much of
# a choice, so any non-const references should not be blamed on
# derived function.
if IsDerivedFunction(clean_lines, linenum):
return
# Don't warn on out-of-line method definitions, as we would warn on the
# in-line declaration, if it isn't marked with 'override'.
if IsOutOfLineMethodDefinition(clean_lines, linenum):
return
# Long type names may be broken across multiple lines, usually in one
# of these forms:
# LongType
# ::LongTypeContinued &identifier
# LongType::
# LongTypeContinued &identifier
# LongType<
# ...>::LongTypeContinued &identifier
#
# If we detected a type split across two lines, join the previous
# line to current line so that we can match const references
# accordingly.
#
# Note that this only scans back one line, since scanning back
# arbitrary number of lines would be expensive. If you have a type
# that spans more than 2 lines, please use a typedef.
if linenum > 1:
previous = None
if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line):
# previous_line\n + ::current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$',
clean_lines.elided[linenum - 1])
elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line):
# previous_line::\n + current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$',
clean_lines.elided[linenum - 1])
if previous:
line = previous.group(1) + line.lstrip()
else:
# Check for templated parameter that is split across multiple lines
endpos = line.rfind('>')
if endpos > -1:
(_, startline, startpos) = ReverseCloseExpression(
clean_lines, linenum, endpos)
if startpos > -1 and startline < linenum:
# Found the matching < on an earlier line, collect all
# pieces up to current line.
line = ''
for i in xrange(startline, linenum + 1):
line += clean_lines.elided[i].strip()
# Check for non-const references in function parameters. A single '&' may
# found in the following places:
# inside expression: binary & for bitwise AND
# inside expression: unary & for taking the address of something
# inside declarators: reference parameter
# We will exclude the first two cases by checking that we are not inside a
# function body, including one that was just introduced by a trailing '{'.
# TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare].
if (nesting_state.previous_stack_top and
not (isinstance(nesting_state.previous_stack_top, _ClassInfo) or
isinstance(nesting_state.previous_stack_top, _NamespaceInfo))):
# Not at toplevel, not within a class, and not within a namespace
return
# Avoid initializer lists. We only need to scan back from the
# current line for something that starts with ':'.
#
# We don't need to check the current line, since the '&' would
# appear inside the second set of parentheses on the current line as
# opposed to the first set.
if linenum > 0:
for i in xrange(linenum - 1, max(0, linenum - 10), -1):
previous_line = clean_lines.elided[i]
if not Search(r'[),]\s*$', previous_line):
break
if Match(r'^\s*:\s+\S', previous_line):
return
# Avoid preprocessors
if Search(r'\\\s*$', line):
return
# Avoid constructor initializer lists
if IsInitializerList(clean_lines, linenum):
return
# We allow non-const references in a few standard places, like functions
# called "swap()" or iostream operators like "<<" or ">>". Do not check
# those function parameters.
#
# We also accept & in static_assert, which looks like a function but
# it's actually a declaration expression.
whitelisted_functions = (r'(?:[sS]wap(?:<\w:+>)?|'
r'operator\s*[<>][<>]|'
r'static_assert|COMPILE_ASSERT'
r')\s*\(')
if Search(whitelisted_functions, line):
return
elif not Search(r'\S+\([^)]*$', line):
# Don't see a whitelisted function on this line. Actually we
# didn't see any function name on this line, so this is likely a
# multi-line parameter list. Try a bit harder to catch this case.
for i in xrange(2):
if (linenum > i and Search(whitelisted_functions,
clean_lines.elided[linenum - i - 1])):
return
decls = ReplaceAll(r'{[^}]*}', ' ', line) # exclude function body
for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls):
if not Match(_RE_PATTERN_CONST_REF_PARAM, parameter):
error(filename, linenum, 'runtime/references', 2,
'Is this a non-const reference? '
'If so, make const or use a pointer: ' + ReplaceAll(
' *<', '<', parameter))
def CheckCasts(filename, clean_lines, linenum, error):
"""Various cast related checks.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Check to see if they're using an conversion function cast.
# I just try to capture the most common basic types, though there are more.
# Parameterless conversion functions, such as bool(), are allowed as they are
# probably a member operator declaration or default constructor.
match = Search(r'(\bnew\s+|\S<\s*(?:const\s+)?)?\b'
r'(int|float|double|bool|char|int32|uint32|int64|uint64)'
r'(\([^)].*)', line)
expecting_function = ExpectingFunctionArgs(clean_lines, linenum)
if match and not expecting_function:
matched_type = match.group(2)
# matched_new_or_template is used to silence two false positives:
# - New operators
# - Template arguments with function types
#
# For template arguments, we match on types immediately following
# an opening bracket without any spaces. This is a fast way to
# silence the common case where the function type is the first
# template argument. False negative with less-than comparison is
# avoided because those operators are usually followed by a space.
#
# function<double(double)> // bracket + no space = false positive
# value < double(42) // bracket + space = true positive
matched_new_or_template = match.group(1)
# Avoid arrays by looking for brackets that come after the closing
# parenthesis.
if Match(r'\([^()]+\)\s*\[', match.group(3)):
return
# Other things to ignore:
# - Function pointers
# - Casts to pointer types
# - Placement new
# - Alias declarations
matched_funcptr = match.group(3)
if (matched_new_or_template is None and not (matched_funcptr and (Match(
r'\((?:[^() ]+::\s*\*\s*)?[^() ]+\)\s*\(',
matched_funcptr) or matched_funcptr.startswith('(*)'))) and
not Match(r'\s*using\s+\S+\s*=\s*' + matched_type, line) and
not Search(r'new\(\S+\)\s*' + matched_type, line)):
error(filename, linenum, 'readability/casting', 4,
'Using deprecated casting style. '
'Use static_cast<%s>(...) instead' % matched_type)
if not expecting_function:
CheckCStyleCast(filename, clean_lines, linenum, 'static_cast',
r'\((int|float|double|bool|char|u?int(16|32|64))\)',
error)
# This doesn't catch all cases. Consider (const char * const)"hello".
#
# (char *) "foo" should always be a const_cast (reinterpret_cast won't
# compile).
if CheckCStyleCast(filename, clean_lines, linenum, 'const_cast',
r'\((char\s?\*+\s?)\)\s*"', error):
pass
else:
# Check pointer casts for other than string constants
CheckCStyleCast(filename, clean_lines, linenum, 'reinterpret_cast',
r'\((\w+\s?\*+\s?)\)', error)
# In addition, we look for people taking the address of a cast. This
# is dangerous -- casts can assign to temporaries, so the pointer doesn't
# point where you think.
#
# Some non-identifier character is required before the '&' for the
# expression to be recognized as a cast. These are casts:
# expression = &static_cast<int*>(temporary());
# function(&(int*)(temporary()));
#
# This is not a cast:
# reference_type&(int* function_param);
match = Search(r'(?:[^\w]&\(([^)*][^)]*)\)[\w(])|'
r'(?:[^\w]&(static|dynamic|down|reinterpret)_cast\b)', line)
if match:
# Try a better error message when the & is bound to something
# dereferenced by the casted pointer, as opposed to the casted
# pointer itself.
parenthesis_error = False
match = Match(r'^(.*&(?:static|dynamic|down|reinterpret)_cast\b)<',
line)
if match:
_, y1, x1 = CloseExpression(clean_lines, linenum,
len(match.group(1)))
if x1 >= 0 and clean_lines.elided[y1][x1] == '(':
_, y2, x2 = CloseExpression(clean_lines, y1, x1)
if x2 >= 0:
extended_line = clean_lines.elided[y2][x2:]
if y2 < clean_lines.NumLines() - 1:
extended_line += clean_lines.elided[y2 + 1]
if Match(r'\s*(?:->|\[)', extended_line):
parenthesis_error = True
if parenthesis_error:
error(filename, linenum, 'readability/casting', 4,
('Are you taking an address of something dereferenced '
'from a cast? Wrapping the dereferenced expression in '
'parentheses will make the binding more obvious'))
else:
error(filename, linenum, 'runtime/casting', 4,
('Are you taking an address of a cast? '
'This is dangerous: could be a temp var. '
'Take the address before doing the cast, rather than after'))
def CheckCStyleCast(filename, clean_lines, linenum, cast_type, pattern, error):
"""Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise.
"""
line = clean_lines.elided[linenum]
match = Search(pattern, line)
if not match:
return False
# Exclude lines with keywords that tend to look like casts
context = line[0:match.start(1) - 1]
if Match(r'.*\b(?:sizeof|alignof|alignas|[_A-Z][_A-Z0-9]*)\s*$', context):
return False
# Try expanding current context to see if we one level of
# parentheses inside a macro.
if linenum > 0:
for i in xrange(linenum - 1, max(0, linenum - 5), -1):
context = clean_lines.elided[i] + context
if Match(r'.*\b[_A-Z][_A-Z0-9]*\s*\((?:\([^()]*\)|[^()])*$', context):
return False
# operator++(int) and operator--(int)
if context.endswith(' operator++') or context.endswith(' operator--'):
return False
# A single unnamed argument for a function tends to look like old
# style cast. If we see those, don't issue warnings for deprecated
# casts, instead issue warnings for unnamed arguments where
# appropriate.
#
# These are things that we want warnings for, since the style guide
# explicitly require all parameters to be named:
# Function(int);
# Function(int) {
# ConstMember(int) const;
# ConstMember(int) const {
# ExceptionMember(int) throw (...);
# ExceptionMember(int) throw (...) {
# PureVirtual(int) = 0;
# [](int) -> bool {
#
# These are functions of some sort, where the compiler would be fine
# if they had named parameters, but people often omit those
# identifiers to reduce clutter:
# (FunctionPointer)(int);
# (FunctionPointer)(int) = value;
# Function((function_pointer_arg)(int))
# Function((function_pointer_arg)(int), int param)
# <TemplateArgument(int)>;
# <(FunctionPointerTemplateArgument)(int)>;
remainder = line[match.end(0):]
if Match(r'^\s*(?:;|const\b|throw\b|final\b|override\b|[=>{),]|->)',
remainder):
# Looks like an unnamed parameter.
# Don't warn on any kind of template arguments.
if Match(r'^\s*>', remainder):
return False
# Don't warn on assignments to function pointers, but keep warnings for
# unnamed parameters to pure virtual functions. Note that this pattern
# will also pass on assignments of "0" to function pointers, but the
# preferred values for those would be "nullptr" or "NULL".
matched_zero = Match(r'^\s=\s*(\S+)\s*;', remainder)
if matched_zero and matched_zero.group(1) != '0':
return False
# Don't warn on function pointer declarations. For this we need
# to check what came before the "(type)" string.
if Match(r'.*\)\s*$', line[0:match.start(0)]):
return False
# Don't warn if the parameter is named with block comments, e.g.:
# Function(int /*unused_param*/);
raw_line = clean_lines.raw_lines[linenum]
if '/*' in raw_line:
return False
# Passed all filters, issue warning here.
error(filename, linenum, 'readability/function', 3,
'All parameters should be named in a function')
return True
# At this point, all that should be left is actual casts.
error(filename, linenum, 'readability/casting', 4,
'Using C-style cast. Use %s<%s>(...) instead' %
(cast_type, match.group(1)))
return True
def ExpectingFunctionArgs(clean_lines, linenum):
"""Checks whether where function type arguments are expected.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if the line at 'linenum' is inside something that expects arguments
of function types.
"""
line = clean_lines.elided[linenum]
return (Match(r'^\s*MOCK_(CONST_)?METHOD\d+(_T)?\(', line) or
(linenum >= 2 and
(Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\((?:\S+,)?\s*$',
clean_lines.elided[linenum - 1]) or
Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\(\s*$',
clean_lines.elided[linenum - 2]) or
Search(r'\bstd::m?function\s*\<\s*$',
clean_lines.elided[linenum - 1]))))
_HEADERS_CONTAINING_TEMPLATES = (
('<deque>', ('deque', )),
('<functional>', (
'unary_function',
'binary_function',
'plus',
'minus',
'multiplies',
'divides',
'modulus',
'negate',
'equal_to',
'not_equal_to',
'greater',
'less',
'greater_equal',
'less_equal',
'logical_and',
'logical_or',
'logical_not',
'unary_negate',
'not1',
'binary_negate',
'not2',
'bind1st',
'bind2nd',
'pointer_to_unary_function',
'pointer_to_binary_function',
'ptr_fun',
'mem_fun_t',
'mem_fun',
'mem_fun1_t',
'mem_fun1_ref_t',
'mem_fun_ref_t',
'const_mem_fun_t',
'const_mem_fun1_t',
'const_mem_fun_ref_t',
'const_mem_fun1_ref_t',
'mem_fun_ref', )),
('<limits>', ('numeric_limits', )),
('<list>', ('list', )),
('<map>', (
'map',
'multimap', )),
('<memory>', ('allocator', )),
('<queue>', (
'queue',
'priority_queue', )),
('<set>', (
'set',
'multiset', )),
('<stack>', ('stack', )),
('<string>', (
'char_traits',
'basic_string', )),
('<tuple>', ('tuple', )),
('<utility>', ('pair', )),
('<vector>', ('vector', )),
# gcc extensions.
# Note: std::hash is their hash, ::hash is our hash
('<hash_map>', (
'hash_map',
'hash_multimap', )),
('<hash_set>', (
'hash_set',
'hash_multiset', )),
('<slist>', ('slist', )), )
_RE_PATTERN_STRING = re.compile(r'\bstring\b')
_re_pattern_algorithm_header = []
for _template in ('copy', 'max', 'min', 'min_element', 'sort', 'swap',
'transform'):
# Match max<type>(..., ...), max(..., ...), but not foo->max, foo.max or
# type::max().
_re_pattern_algorithm_header.append(
(re.compile(r'[^>.]\b' + _template + r'(<.*?>)?\([^\)]'), _template,
'<algorithm>'))
_re_pattern_templates = []
for _header, _templates in _HEADERS_CONTAINING_TEMPLATES:
for _template in _templates:
_re_pattern_templates.append(
(re.compile(r'(\<|\b)' + _template + r'\s*\<'), _template + '<>',
_header))
def FilesBelongToSameModule(filename_cc, filename_h):
"""Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the .cc file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file.
"""
if not filename_cc.endswith('.cc'):
return (False, '')
filename_cc = filename_cc[:-len('.cc')]
if filename_cc.endswith('_unittest'):
filename_cc = filename_cc[:-len('_unittest')]
elif filename_cc.endswith('_test'):
filename_cc = filename_cc[:-len('_test')]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
if not filename_h.endswith('.h'):
return (False, '')
filename_h = filename_h[:-len('.h')]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path
def UpdateIncludeState(filename, include_dict, io=codecs):
"""Fill up the include_dict with new includes found from the file.
Args:
filename: the name of the header to read.
include_dict: a dictionary in which the headers are inserted.
io: The io factory to use to read the file. Provided for testability.
Returns:
True if a header was successfully added. False otherwise.
"""
headerfile = None
try:
headerfile = io.open(filename, 'r', 'utf8', 'replace')
except IOError:
return False
linenum = 0
for line in headerfile:
linenum += 1
clean_line = CleanseComments(line)
match = _RE_PATTERN_INCLUDE.search(clean_line)
if match:
include = match.group(2)
include_dict.setdefault(include, linenum)
return True
def CheckForIncludeWhatYouUse(filename,
clean_lines,
include_state,
error,
io=codecs):
"""Reports for missing stl includes.
This function will output warnings to make sure you are including the headers
necessary for the stl containers and functions that you use. We only give one
reason to include a header. For example, if you use both equal_to<> and
less<> in a .h file, only one (the latter in the file) of these will be
reported as a reason to include the <functional>.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
include_state: An _IncludeState instance.
error: The function to call with any errors found.
io: The IO factory to use to read the header file. Provided for unittest
injection.
"""
required = {} # A map of header name to linenumber and the template entity.
# Example of required: { '<functional>': (1219, 'less<>') }
for linenum in xrange(clean_lines.NumLines()):
line = clean_lines.elided[linenum]
if not line or line[0] == '#':
continue
# String is special -- it is a non-templatized type in STL.
matched = _RE_PATTERN_STRING.search(line)
if matched:
# Don't warn about strings in non-STL namespaces:
# (We check only the first match per line; good enough.)
prefix = line[:matched.start()]
if prefix.endswith('std::') or not prefix.endswith('::'):
required['<string>'] = (linenum, 'string')
for pattern, template, header in _re_pattern_algorithm_header:
if pattern.search(line):
required[header] = (linenum, template)
# The following function is just a speed up, no semantics are changed.
if not '<' in line: # Reduces the cpu time usage by skipping lines.
continue
for pattern, template, header in _re_pattern_templates:
if pattern.search(line):
required[header] = (linenum, template)
# The policy is that if you #include something in foo.h you don't need to
# include it again in foo.cc. Here, we will look at possible includes.
# Let's flatten the include_state include_list and copy it into a dictionary.
include_dict = dict(
[item for sublist in include_state.include_list for item in sublist])
# Did we find the header for this file (if any) and successfully load it?
header_found = False
# Use the absolute path so that matching works properly.
abs_filename = FileInfo(filename).FullName()
# For Emacs's flymake.
# If cpplint is invoked from Emacs's flymake, a temporary file is generated
# by flymake and that file name might end with '_flymake.cc'. In that case,
# restore original file name here so that the corresponding header file can be
# found.
# e.g. If the file name is 'foo_flymake.cc', we should search for 'foo.h'
# instead of 'foo_flymake.h'
abs_filename = re.sub(r'_flymake\.cc$', '.cc', abs_filename)
# include_dict is modified during iteration, so we iterate over a copy of
# the keys.
header_keys = include_dict.keys()
for header in header_keys:
(same_module, common_path) = FilesBelongToSameModule(abs_filename,
header)
fullpath = common_path + header
if same_module and UpdateIncludeState(fullpath, include_dict, io):
header_found = True
# If we can't find the header file for a .cc, assume it's because we don't
# know where to look. In that case we'll give up as we're not sure they
# didn't include it in the .h file.
# TODO(unknown): Do a better job of finding .h files so we are confident that
# not having the .h file means there isn't one.
if filename.endswith('.cc') and not header_found:
return
# All the lines have been processed, report the errors found.
for required_header_unstripped in required:
template = required[required_header_unstripped][1]
if required_header_unstripped.strip('<>"') not in include_dict:
error(filename, required[required_header_unstripped][0],
'build/include_what_you_use', 4, 'Add #include ' +
required_header_unstripped + ' for ' + template)
_RE_PATTERN_EXPLICIT_MAKEPAIR = re.compile(r'\bmake_pair\s*<')
def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error):
"""Check that make_pair's template arguments are deduced.
G++ 4.6 in C++11 mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line)
if match:
error(
filename,
linenum,
'build/explicit_make_pair',
4, # 4 = high confidence
'For C++11-compatibility, omit template arguments from make_pair'
' OR use pair directly OR if appropriate, construct a pair directly')
def CheckDefaultLambdaCaptures(filename, clean_lines, linenum, error):
"""Check that default lambda captures are not used.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# A lambda introducer specifies a default capture if it starts with "[="
# or if it starts with "[&" _not_ followed by an identifier.
match = Match(r'^(.*)\[\s*(?:=|&[^\w])', line)
if match:
# Found a potential error, check what comes after the lambda-introducer.
# If it's not open parenthesis (for lambda-declarator) or open brace
# (for compound-statement), it's not a lambda.
line, _, pos = CloseExpression(clean_lines, linenum,
len(match.group(1)))
if pos >= 0 and Match(r'^\s*[{(]', line[pos:]):
error(
filename,
linenum,
'build/c++11',
4, # 4 = high confidence
'Default lambda captures are an unapproved C++ feature.')
def CheckRedundantVirtual(filename, clean_lines, linenum, error):
"""Check if line contains a redundant "virtual" function-specifier.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Look for "virtual" on current line.
line = clean_lines.elided[linenum]
virtual = Match(r'^(.*)(\bvirtual\b)(.*)$', line)
if not virtual: return
# Ignore "virtual" keywords that are near access-specifiers. These
# are only used in class base-specifier and do not apply to member
# functions.
if (Search(r'\b(public|protected|private)\s+$', virtual.group(1)) or
Match(r'^\s+(public|protected|private)\b', virtual.group(3))):
return
# Ignore the "virtual" keyword from virtual base classes. Usually
# there is a column on the same line in these cases (virtual base
# classes are rare in google3 because multiple inheritance is rare).
if Match(r'^.*[^:]:[^:].*$', line): return
# Look for the next opening parenthesis. This is the start of the
# parameter list (possibly on the next line shortly after virtual).
# TODO(unknown): doesn't work if there are virtual functions with
# decltype() or other things that use parentheses, but csearch suggests
# that this is rare.
end_col = -1
end_line = -1
start_col = len(virtual.group(2))
for start_line in xrange(linenum, min(linenum + 3, clean_lines.NumLines())):
line = clean_lines.elided[start_line][start_col:]
parameter_list = Match(r'^([^(]*)\(', line)
if parameter_list:
# Match parentheses to find the end of the parameter list
(_, end_line, end_col) = CloseExpression(
clean_lines, start_line,
start_col + len(parameter_list.group(1)))
break
start_col = 0
if end_col < 0:
return # Couldn't find end of parameter list, give up
# Look for "override" or "final" after the parameter list
# (possibly on the next few lines).
for i in xrange(end_line, min(end_line + 3, clean_lines.NumLines())):
line = clean_lines.elided[i][end_col:]
match = Search(r'\b(override|final)\b', line)
if match:
error(filename, linenum, 'readability/inheritance', 4,
('"virtual" is redundant since function is '
'already declared as "%s"' % match.group(1)))
# Set end_col to check whole lines after we are done with the
# first line.
end_col = 0
if Search(r'[^\w]\s*$', line):
break
def CheckRedundantOverrideOrFinal(filename, clean_lines, linenum, error):
"""Check if line contains a redundant "override" or "final" virt-specifier.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Look for closing parenthesis nearby. We need one to confirm where
# the declarator ends and where the virt-specifier starts to avoid
# false positives.
line = clean_lines.elided[linenum]
declarator_end = line.rfind(')')
if declarator_end >= 0:
fragment = line[declarator_end:]
else:
if linenum > 1 and clean_lines.elided[linenum - 1].rfind(')') >= 0:
fragment = line
else:
return
# Check that at most one of "override" or "final" is present, not both
if Search(r'\boverride\b', fragment) and Search(r'\bfinal\b', fragment):
error(filename, linenum, 'readability/inheritance', 4,
('"override" is redundant since function is '
'already declared as "final"'))
# Returns true if we are at a new block, and it is directly
# inside of a namespace.
def IsBlockInNameSpace(nesting_state, is_forward_declaration):
"""Checks that the new block is directly in a namespace.
Args:
nesting_state: The _NestingState object that contains info about our state.
is_forward_declaration: If the class is a forward declared class.
Returns:
Whether or not the new block is directly in a namespace.
"""
if is_forward_declaration:
if len(nesting_state.stack) >= 1 and (
isinstance(nesting_state.stack[-1], _NamespaceInfo)):
return True
else:
return False
return (len(nesting_state.stack) > 1 and
nesting_state.stack[-1].check_namespace_indentation and
isinstance(nesting_state.stack[-2], _NamespaceInfo))
def ShouldCheckNamespaceIndentation(nesting_state, is_namespace_indent_item,
raw_lines_no_comments, linenum):
"""This method determines if we should apply our namespace indentation check.
Args:
nesting_state: The current nesting state.
is_namespace_indent_item: If we just put a new class on the stack, True.
If the top of the stack is not a class, or we did not recently
add the class, False.
raw_lines_no_comments: The lines without the comments.
linenum: The current line number we are processing.
Returns:
True if we should apply our namespace indentation check. Currently, it
only works for classes and namespaces inside of a namespace.
"""
is_forward_declaration = IsForwardClassDeclaration(raw_lines_no_comments,
linenum)
if not (is_namespace_indent_item or is_forward_declaration):
return False
# If we are in a macro, we do not want to check the namespace indentation.
if IsMacroDefinition(raw_lines_no_comments, linenum):
return False
return IsBlockInNameSpace(nesting_state, is_forward_declaration)
# Call this method if the line is directly inside of a namespace.
# If the line above is blank (excluding comments) or the start of
# an inner namespace, it cannot be indented.
def CheckItemIndentationInNamespace(filename, raw_lines_no_comments, linenum,
error):
line = raw_lines_no_comments[linenum]
if Match(r'^\s+', line):
error(filename, linenum, 'runtime/indentation_namespace', 4,
'Do not indent within a namespace')
def ProcessLine(filename,
file_extension,
clean_lines,
line,
include_state,
function_state,
nesting_state,
error,
extra_check_functions=[]):
"""Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
raw_lines = clean_lines.raw_lines
ParseNolintSuppressions(filename, raw_lines[line], line, error)
nesting_state.Update(filename, clean_lines, line, error)
CheckForNamespaceIndentation(filename, nesting_state, clean_lines, line,
error)
if nesting_state.InAsmBlock(): return
CheckForFunctionLengths(filename, clean_lines, line, function_state, error)
CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
CheckStyle(filename, clean_lines, line, file_extension, nesting_state,
error)
CheckLanguage(filename, clean_lines, line, file_extension, include_state,
nesting_state, error)
CheckForNonConstReference(filename, clean_lines, line, nesting_state, error)
CheckForNonStandardConstructs(filename, clean_lines, line, nesting_state,
error)
CheckVlogArguments(filename, clean_lines, line, error)
CheckPosixThreading(filename, clean_lines, line, error)
CheckInvalidIncrement(filename, clean_lines, line, error)
CheckMakePairUsesDeduction(filename, clean_lines, line, error)
CheckDefaultLambdaCaptures(filename, clean_lines, line, error)
CheckRedundantVirtual(filename, clean_lines, line, error)
CheckRedundantOverrideOrFinal(filename, clean_lines, line, error)
for check_fn in extra_check_functions:
check_fn(filename, clean_lines, line, error)
def FlagCxx11Features(filename, clean_lines, linenum, error):
"""Flag those c++11 features that we only allow in certain places.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Flag unapproved C++11 headers.
include = Match(r'\s*#\s*include\s+[<"]([^<"]+)[">]', line)
if include and include.group(1) in (
'cfenv',
'condition_variable',
'fenv.h',
'future',
'mutex',
'thread',
'chrono',
'ratio',
'regex',
'system_error', ):
error(filename, linenum, 'build/c++11', 5,
('<%s> is an unapproved C++11 header.') % include.group(1))
# The only place where we need to worry about C++11 keywords and library
# features in preprocessor directives is in macro definitions.
if Match(r'\s*#', line) and not Match(r'\s*#\s*define\b', line): return
# These are classes and free functions. The classes are always
# mentioned as std::*, but we only catch the free functions if
# they're not found by ADL. They're alphabetical by header.
for top_name in (
# type_traits
'alignment_of',
'aligned_union', ):
if Search(r'\bstd::%s\b' % top_name, line):
error(filename, linenum, 'build/c++11', 5, (
'std::%s is an unapproved C++11 class or function. Send c-style '
'an example of where it would make your code more readable, and '
'they may let you use it.') % top_name)
def ProcessFileData(filename,
file_extension,
lines,
error,
extra_check_functions=[]):
"""Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is terminated with a newline.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
lines = (['// marker so line numbers and indices both start at 1'] + lines +
['// marker so line numbers end in a known way'])
include_state = _IncludeState()
function_state = _FunctionState()
nesting_state = NestingState()
ResetNolintSuppressions()
CheckForCopyright(filename, lines, error)
RemoveMultiLineComments(filename, lines, error)
clean_lines = CleansedLines(lines)
if file_extension == 'h':
CheckForHeaderGuard(filename, clean_lines, error)
for line in xrange(clean_lines.NumLines()):
ProcessLine(filename, file_extension, clean_lines, line, include_state,
function_state, nesting_state, error, extra_check_functions)
FlagCxx11Features(filename, clean_lines, line, error)
nesting_state.CheckCompletedBlocks(filename, error)
CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error)
# Check that the .cc file has included its header if it exists.
if file_extension == 'cc':
CheckHeaderFileIncluded(filename, include_state, error)
# We check here rather than inside ProcessLine so that we see raw
# lines rather than "cleaned" lines.
CheckForBadCharacters(filename, lines, error)
CheckForNewlineAtEOF(filename, lines, error)
def ProcessConfigOverrides(filename):
""" Loads the configuration files and processes the config overrides.
Args:
filename: The name of the file being processed by the linter.
Returns:
False if the current |filename| should not be processed further.
"""
abs_filename = os.path.abspath(filename)
cfg_filters = []
keep_looking = True
while keep_looking:
abs_path, base_name = os.path.split(abs_filename)
if not base_name:
break # Reached the root directory.
cfg_file = os.path.join(abs_path, "CPPLINT.cfg")
abs_filename = abs_path
if not os.path.isfile(cfg_file):
continue
try:
with open(cfg_file) as file_handle:
for line in file_handle:
line, _, _ = line.partition('#') # Remove comments.
if not line.strip():
continue
name, _, val = line.partition('=')
name = name.strip()
val = val.strip()
if name == 'set noparent':
keep_looking = False
elif name == 'filter':
cfg_filters.append(val)
elif name == 'exclude_files':
# When matching exclude_files pattern, use the base_name of
# the current file name or the directory name we are processing.
# For example, if we are checking for lint errors in /foo/bar/baz.cc
# and we found the .cfg file at /foo/CPPLINT.cfg, then the config
# file's "exclude_files" filter is meant to be checked against "bar"
# and not "baz" nor "bar/baz.cc".
if base_name:
pattern = re.compile(val)
if pattern.match(base_name):
sys.stderr.write(
'Ignoring "%s": file excluded by "%s". '
'File path component "%s" matches '
'pattern "%s"\n' %
(filename, cfg_file, base_name, val))
return False
elif name == 'linelength':
global _line_length
try:
_line_length = int(val)
except ValueError:
sys.stderr.write('Line length must be numeric.')
else:
sys.stderr.write(
'Invalid configuration option (%s) in file %s\n' %
(name, cfg_file))
except IOError:
sys.stderr.write(
"Skipping config file '%s': Can't open for reading\n" %
cfg_file)
keep_looking = False
# Apply all the accumulated filters in reverse order (top-level directory
# config options having the least priority).
for filter in reversed(cfg_filters):
_AddFilters(filter)
return True
def ProcessFile(filename, vlevel, extra_check_functions=[]):
"""Does google-lint on a single file.
Args:
filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
>= verbose_level will be reported. 0 is a good default.
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
_SetVerboseLevel(vlevel)
_BackupFilters()
if not ProcessConfigOverrides(filename):
_RestoreFilters()
return
lf_lines = []
crlf_lines = []
try:
# Support the UNIX convention of using "-" for stdin. Note that
# we are not opening the file with universal newline support
# (which codecs doesn't support anyway), so the resulting lines do
# contain trailing '\r' characters if we are reading a file that
# has CRLF endings.
# If after the split a trailing '\r' is present, it is removed
# below.
if filename == '-':
lines = codecs.StreamReaderWriter(sys.stdin,
codecs.getreader('utf8'),
codecs.getwriter('utf8'),
'replace').read().split('\n')
else:
lines = codecs.open(filename, 'r', 'utf8',
'replace').read().split('\n')
# Remove trailing '\r'.
# The -1 accounts for the extra trailing blank line we get from split()
for linenum in range(len(lines) - 1):
if lines[linenum].endswith('\r'):
lines[linenum] = lines[linenum].rstrip('\r')
crlf_lines.append(linenum + 1)
else:
lf_lines.append(linenum + 1)
except IOError:
sys.stderr.write("Skipping input '%s': Can't open for reading\n" %
filename)
_RestoreFilters()
return
# Note, if no dot is found, this will give the entire filename as the ext.
file_extension = filename[filename.rfind('.') + 1:]
# When reading from stdin, the extension is unknown, so no cpplint tests
# should rely on the extension.
if filename != '-' and file_extension not in _valid_extensions:
sys.stderr.write('Ignoring %s; not a valid file name '
'(%s)\n' % (filename, ', '.join(_valid_extensions)))
else:
ProcessFileData(filename, file_extension, lines, Error,
extra_check_functions)
# If end-of-line sequences are a mix of LF and CR-LF, issue
# warnings on the lines with CR.
#
# Don't issue any warnings if all lines are uniformly LF or CR-LF,
# since critique can handle these just fine, and the style guide
# doesn't dictate a particular end of line sequence.
#
# We can't depend on os.linesep to determine what the desired
# end-of-line sequence should be, since that will return the
# server-side end-of-line sequence.
if lf_lines and crlf_lines:
# Warn on every line with CR. An alternative approach might be to
# check whether the file is mostly CRLF or just LF, and warn on the
# minority, we bias toward LF here since most tools prefer LF.
for linenum in crlf_lines:
Error(filename, linenum, 'whitespace/newline', 1,
'Unexpected \\r (^M) found; better to use only \\n')
sys.stdout.write('Done processing %s\n' % filename)
_RestoreFilters()
def PrintUsage(message):
"""Prints a brief usage string and exits, optionally with an error message.
Args:
message: The optional error message.
"""
sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
else:
sys.exit(1)
def PrintCategories():
"""Prints a list of all the error-categories used by error messages.
These are the categories used to filter messages via --filter.
"""
sys.stderr.write(''.join(' %s\n' % cat for cat in _ERROR_CATEGORIES))
sys.exit(0)
def ParseArguments(args):
"""Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint.
"""
try:
(opts, filenames) = getopt.getopt(args, '', [
'help', 'output=', 'verbose=', 'counting=', 'filter=', 'root=',
'linelength=', 'extensions=', 'write-success='
])
except getopt.GetoptError:
PrintUsage('Invalid arguments.')
verbosity = _VerboseLevel()
output_format = _OutputFormat()
filters = ''
counting_style = ''
for (opt, val) in opts:
if opt == '--help':
PrintUsage(None)
elif opt == '--output':
if val not in ('emacs', 'vs7', 'eclipse'):
PrintUsage(
'The only allowed output formats are emacs, vs7 and eclipse.'
)
output_format = val
elif opt == '--verbose':
verbosity = int(val)
elif opt == '--filter':
filters = val
if not filters:
PrintCategories()
elif opt == '--counting':
if val not in ('total', 'toplevel', 'detailed'):
PrintUsage(
'Valid counting options are total, toplevel, and detailed')
counting_style = val
elif opt == '--root':
global _root
_root = val
elif opt == '--linelength':
global _line_length
try:
_line_length = int(val)
except ValueError:
PrintUsage('Line length must be digits.')
elif opt == '--extensions':
global _valid_extensions
try:
_valid_extensions = set(val.split(','))
except ValueError:
PrintUsage('Extensions must be comma seperated list.')
elif opt == '--write-success':
global _write_success
_write_success = val
if not filenames:
PrintUsage('No files were specified.')
_SetOutputFormat(output_format)
_SetVerboseLevel(verbosity)
_SetFilters(filters)
_SetCountingStyle(counting_style)
return filenames
def main():
filenames = ParseArguments(sys.argv[1:])
# Change stderr to write with replacement characters so we don't die
# if we try to print something containing non-ASCII characters.
sys.stderr = codecs.StreamReaderWriter(sys.stderr,
codecs.getreader('utf8'),
codecs.getwriter('utf8'), 'replace')
_cpplint_state.ResetErrorCounts()
for filename in filenames:
ProcessFile(filename, _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
if _cpplint_state.error_count == 0 and _write_success is not None:
with open(_write_success, 'a'):
os.utime(_write_success, None)
sys.exit(_cpplint_state.error_count > 0)
if __name__ == '__main__':
main()
| 260,730 | 39.574385 | 105 |
py
|
Paddle
|
Paddle-master/paddle/scripts/cluster_train_v2/fabric/conf.py
|
# Copyright (c) 2016 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.
HOSTS = [
"[email protected]",
"[email protected]",
"[email protected]",
]
'''
workspace configuration
'''
#root dir for workspace, can be set as any director with real user account
ROOT_DIR = "/root"
'''
network configuration
'''
#pserver nics
PADDLE_NIC = "eth0"
#pserver port
PADDLE_PORT = 7164
#pserver ports num
PADDLE_PORTS_NUM = 1
#pserver sparse ports num
PADDLE_PORTS_NUM_FOR_SPARSE = 1
#trainer whether use gpu
PADDLE_USE_GPU = "False"
#environments setting for all processes in cluster job
LD_LIBRARY_PATH = "/usr/local/cuda/lib64:/usr/lib64"
| 1,166 | 28.175 | 74 |
py
|
Paddle
|
Paddle-master/paddle/scripts/cluster_train/paddle.py
|
#!/usr/bin/python
# Copyright (c) 2016 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.
""" module for launching cluster job """
import os
import argparse
import socket
import copy
import time
import signal
from fabric.api import run, put, settings, env, prefix
from fabric.tasks import execute
#configuration for cluster
import conf
def refine_unknown_args(cmd_args):
'''
refine unknown parameters to handle some special parameters
'''
new_args = []
for arg in cmd_args:
if arg.startswith("--") and arg.find("=") != -1:
equal_pos = arg.find("=") #find first = pos
arglist = list(arg)
arglist[equal_pos] = " "
arg = "".join(arglist)
arg = arg.lstrip("-")
new_args += arg.split(" ")
elif arg.startswith("--") and arg.find("=") == -1:
arg = arg.lstrip("-")
new_args.append(arg)
else:
new_args.append(arg)
return new_args
def kill_process():
'''
kill comments threads
'''
run("ps aux \
| grep paddle_process_by_paddle \
| grep -v grep \
| awk '{print $2}' \
| xargs kill > /dev/null 2>&1")
def job_prepare(jobdir, data=None):
'''
prepare job related workspace data
Assuming you already installed PaddlePaddle in all nodes which means
PaddlePaddle related bins and dependencies libraries.
Assuming the train/test data have already been installed.
This function just prepare all related model and other resources
needed at runtime.
'''
def job_create_workspace(jobdir, data=None):
'''
prepare job workspace, common file, etc.
'''
log = os.path.join(jobdir, "log")
if data is not None:
#create job dir
run('rm ' + jobdir + ' -fr && ' + 'mkdir -p ' + jobdir)
#push data and paddle bin
| 2,450 | 28.53012 | 74 |
py
|
Paddle
|
Paddle-master/paddle/scripts/cluster_train/conf.py
|
# Copyright (c) 2016 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.
HOSTS = [
"[email protected]",
"[email protected]",
]
'''
workspace configuration
'''
#root dir for workspace, can be set as any director with real user account
ROOT_DIR = "/home/paddle"
'''
network configuration
'''
#pserver nics
PADDLE_NIC = "eth0"
#pserver port
PADDLE_PORT = 7164
#pserver ports num
PADDLE_PORTS_NUM = 2
#pserver sparse ports num
PADDLE_PORTS_NUM_FOR_SPARSE = 2
#environments setting for all processes in cluster job
LD_LIBRARY_PATH = "/usr/local/cuda/lib64:/usr/lib64"
| 1,113 | 28.315789 | 74 |
py
|
Paddle
|
Paddle-master/paddle/utils/enable_virtualenv.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 os
def __activate_virtual_env__():
__path__ = os.getenv('VIRTUAL_ENV')
if __path__ is None:
return
__script__ = os.path.join(__path__, 'bin', 'activate_this.py')
execfile(__script__, {'__file__': __script__})
__activate_virtual_env__()
| 883 | 31.740741 | 74 |
py
|
Paddle
|
Paddle-master/paddle/contrib/float16/float16_transpiler.py
|
# 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
import paddle.fluid.core as core
from paddle.fluid.framework import Program
from paddle.fluid.executor import global_scope
class Float16Transpiler:
def transpile(self, program, place, scope=None):
'''
Transpile the program desc and cast the weights to float16 data type to
enable float16 inference.
Since the operator in a program desc will automatically choose the
right compute kernel to run based on the data type of the input tensor.
We actually don't need to change the program desc to run in float16 mode.
However, in this way, users who are used to feeding and fetching tensors
of float32 data type when running typical inference may find it confusing
and difficult to run inference in float16 mode as they need to convert
input data to float16 dtype and then convert the results back to float32
dtype to match the rest of code.
So this function appends cast ops to the program desc where necessary so
that users are able to run inference in float16 mode while providing input
tensor (feed_holder) of float data type and obtaining output tensor
(fetch_holder) of float data type.
Moreover, it is desired that when we have the scope and program desc to run
inference in float32 mode, we can use a single API to do the necessary
modification and then user can run float16 inference on the fly. To make
this happen, this function also create new parameters in the scope to have the
converted float16 weights and change the operators in program desc to use
these new parameters.
:param program: program to transpile
:type program: Program
:param place: inference place
:type place: Place
:param scope: inference scope
:type scope: Scope
'''
if not isinstance(program, Program):
raise TypeError("program should be as Program type")
if not isinstance(place, core.CPUPlace) and not isinstance(
place, core.CUDAPlace):
raise TypeError("place should be as CPUPlace/CUDAPlace type")
if scope is None:
scope = global_scope()
if not isinstance(scope, core.Scope):
raise TypeError("scope should be as Scope type or None")
self.scope = scope
self.place = place
self.block = program.block(0)
self.input_map = {} # store the input names should be adjusted
self._modify_feed_fetch()
self._convert_param_to_float16()
self._adjust_input(skip=True)
self._remove_unused_var()
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
# ====================== private transpiler functions =====================
def _adjust_input(self, skip=False):
'''
Change the input variable name in operators.
When we are in the process of modifying a program desc, we usually
replace some variables with some other variables, where we create
a dictionary input_map to record the one-to-one correspondence
between each old variable and the new one.
After that, this function will search all the operators that use the
old variables and change the info in op to use the new variables. There
maybe some exceptions to this rule when we are using the float16 transpiler
and insert cast ops to cast float32 variable to float16 one. After we
insert the cast op to cast var_1 to var_1_fp16, we don't want to change
the input of cast op to var_1_fp16 after using this function.
'''
skip_ops = {"cast"}
for i in range(len(self.block.ops)):
current_op = self.block.ops[i]
if skip and current_op.type in skip_ops:
continue
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
remove unused varibles in program
'''
args = []
for i in range(len(self.block.ops)):
current_op = self.block.ops[i]
args += current_op.input_arg_names
args += current_op.output_arg_names
args = list(set(args)) # unique the input and output arguments
for var in self.block.vars.keys():
if var not in args:
self.block.remove_var(var)
def _modify_feed_fetch(self):
'''
Modify feed fetch op/vars for float16 inference.
For each feed op:
feed_op->feed_target_var
Change it to:
feed_op->feed_target_var->cast_op(from other dtype to float16)->tmp_var
For each fetch op:
fetch_target_var->fetch_op
Change it to:
tmp_var->cast_op(from float16 to other dtype)->fetch_target_var->fetch_op
:return: None
'''
def find_op(var):
# It is possible that var.op is not up to date after some
# modifications to program desc. Here we force to make it up to date.
var.op = None
for op in self.block.ops:
if var.name in op.output_arg_names:
var.op = op
break
if var.op is None:
raise ValueError("The target variable must have an "
"associated operator that generates it.")
i = 0
while i < len(self.block.ops):
cur_op = self.block.ops[i]
if cur_op.type == "feed":
var_name = cur_op.output("Out")[0]
tmp_var_name = var_name + ".fp16"
var = self.block.vars[var_name]
tmp_var = self.block.create_var(
name=tmp_var_name.encode('ascii'),
type=var.type,
dtype=core.VarDesc.VarType.FP16,
shape=var.shape,
persistable=var.persistable)
self.block.insert_op(
i + 1,
type="cast",
inputs={"X": var},
outputs={"Out": tmp_var},
attrs={
'in_dtype': int(var.dtype),
'out_dtype': int(tmp_var.dtype)
})
self.input_map[var_name] = tmp_var_name
i = i + 1
elif cur_op.type == "fetch":
var_name = cur_op.input("X")[0]
tmp_var_name = var_name + ".fp16"
var = self.block.vars[var_name]
tmp_var = self.block.create_var(
name=tmp_var_name.encode('ascii'),
type=var.type,
dtype=core.VarDesc.VarType.FP16,
shape=var.shape,
persistable=var.persistable)
find_op(var)
var.op.rename_output(var_name, tmp_var_name)
self.block.insert_op(
i,
type="cast",
inputs={"X": tmp_var},
outputs={"Out": var},
attrs={
'in_dtype': int(tmp_var.dtype),
'out_dtype': int(var.dtype)
})
i = i + 1
i = i + 1
def _convert_param_to_float16(self):
def _get_no_fp16_conversion_var_names():
'''
Get the set of input variable names that shouldn't be converted to float16.
When we want to run inference in float16 mode, most parameters need to be
firstly converted to float16. However, there are some parameters that
shouldn't be converted to float16 because the corresponding operator
requires float32 parameters even in float16 mode (when the input data is
of float16 data type). Currently, the only operator that has this exclusion
is the batch norm op.
:return: set of input variable names
:type var_names: set
'''
op_names = {'batch_norm'}
var_names = []
for op in self.block.ops:
if op.type in op_names:
var_names += op.input_arg_names
return set(var_names)
def _should_be_converted(var):
return var.persistable and \
var.name not in self.no_conversion_vars and \
var.type != core.VarDesc.VarType.FEED_MINIBATCH and \
var.type != core.VarDesc.VarType.FETCH_LIST
self.no_conversion_vars = _get_no_fp16_conversion_var_names()
conversion_var_list = filter(_should_be_converted,
self.block.vars.values())
for var in conversion_var_list:
fp16_var_name = var.name + ".fp16"
fp16_var = self.block.create_parameter(
name=fp16_var_name.encode('ascii'),
type=var.type,
dtype=core.VarDesc.VarType.FP16,
shape=var.shape)
# cast the data in the tensor of the original var to float16
# data type and store it in the tensor of the new float16 var
self.scope.var(fp16_var_name)
fp16_tensor = self.scope.find_var(fp16_var_name).get_tensor()
tensor = np.array(self.scope.find_var(var.name).get_tensor())
# After the old tensor data is converted to np.float16, view(np.uint16)
# is used so that the internal memory of the numpy array will be
# reinterpreted to be of np.uint16 data type, which is binded to fluid
# float16 data type via the help of pybind in tensor_py.h.
fp16_tensor.set(
tensor.astype(np.float16).view(np.uint16), self.place)
# old var will be replaced by the fp16 var in program desc
self.input_map[var.name] = fp16_var_name
self.block.remove_var(var.name)
| 11,063 | 42.050584 | 88 |
py
|
Paddle
|
Paddle-master/paddle/contrib/float16/float16_inference_demo.py
|
# 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.
from __future__ import print_function
from float16_transpiler import Float16Transpiler
import argparse
import paddle
import paddle.fluid as fluid
import contextlib
import math
import sys
import numpy as np
import os
parser = argparse.ArgumentParser(
'Float16 inference accuracy test and benchmark.')
parser.add_argument(
'--train_batch_size', type=int, default=16, help="Batch size for training.")
parser.add_argument(
'--inf_batch_size', type=int, default=32, help="Batch size for inference.")
parser.add_argument(
'--repeat', type=int, default=1, help="How many times to run the test.")
parser.add_argument(
'--data_set',
type=str,
default='cifar10',
choices=['cifar10', 'imagenet'],
help="Optional dataset for benchmark.")
parser.add_argument(
'--model',
type=str,
default='vgg',
choices=['vgg', 'resnet'],
help="Optional model for benchmark.")
parser.add_argument(
'--threshold',
type=float,
default=0.005,
help='Save inference model when test accuracy reach this threshold.')
parser.add_argument('--learning_rate', type=float, default=0.001)
args = parser.parse_args()
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
conv1 = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=False)
return fluid.layers.batch_norm(input=conv1, act=act)
def shortcut(input, ch_out, stride):
ch_in = input.shape[1]
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
else:
return input
def basicblock(input, ch_out, stride):
short = shortcut(input, ch_out, stride)
conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
def bottleneck(input, ch_out, stride):
short = shortcut(input, ch_out * 4, stride)
conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
def layer_warp(block_func, input, ch_out, count, stride):
res_out = block_func(input, ch_out, stride)
for i in range(1, count):
res_out = block_func(res_out, ch_out, 1)
return res_out
def resnet_imagenet(input, depth=50):
cfg = {
18: ([2, 2, 2, 1], basicblock),
34: ([3, 4, 6, 3], basicblock),
50: ([3, 4, 6, 3], bottleneck),
101: ([3, 4, 23, 3], bottleneck),
152: ([3, 8, 36, 3], bottleneck)
}
stages, block_func = cfg[depth]
conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
pool1 = fluid.layers.pool2d(
input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
res2 = layer_warp(block_func, res1, 128, stages[1], 2)
res3 = layer_warp(block_func, res2, 256, stages[2], 2)
res4 = layer_warp(block_func, res3, 512, stages[3], 2)
pool2 = fluid.layers.pool2d(
input=res4,
pool_size=7,
pool_type='avg',
pool_stride=1,
global_pooling=True)
return pool2
def resnet_cifar10(input, depth=32):
assert (depth - 2) % 6 == 0
n = (depth - 2) // 6
conv1 = conv_bn_layer(
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, n, 1)
res2 = layer_warp(basicblock, res1, 32, n, 2)
res3 = layer_warp(basicblock, res2, 64, n, 2)
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
return pool
def vgg16(input):
def conv_block(input, num_filter, groups, dropouts):
return fluid.nets.img_conv_group(
input=input,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * groups,
conv_filter_size=3,
conv_act='relu',
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type='max')
conv1 = conv_block(input, 64, 2, [0.3, 0])
conv2 = conv_block(conv1, 128, 2, [0.4, 0])
conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
bn = fluid.layers.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
return fc2
def train(place, save_dirname):
if args.data_set == "cifar10":
class_dim = 10
data_shape = [3, 32, 32]
elif args.data_set == "imagenet":
class_dim = 102
data_shape = [3, 224, 224]
else:
raise ValueError("%s dataset is not supported" % data_set)
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if args.model == "vgg":
print("train vgg")
net = vgg16(images)
elif args.model == "resnet":
print("train resnet")
if args.data_set == "cifar10":
net = resnet_cifar10(images)
elif args.data_set == "imagenet":
net = resnet_imagenet(images)
else:
raise ValueError("%s dataset is not supported" % args.data_set)
else:
raise ValueError("%s network is not supported" % args.model)
predict = fluid.layers.fc(input=net, size=class_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=predict, label=label)
#Test program
test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
optimizer.minimize(avg_cost)
BATCH_SIZE = args.train_batch_size
PASS_NUM = 100
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.flowers.train()
if args.data_set == 'imagenet' else paddle.dataset.cifar.train10(),
buf_size=128 * 10),
batch_size=args.train_batch_size)
test_reader = paddle.batch(
paddle.dataset.flowers.test()
if args.data_set == 'imagenet' else paddle.dataset.cifar.test10(),
batch_size=args.inf_batch_size)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program())
main_program = fluid.default_main_program()
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
train_image = np.array(
map(lambda x: x[0].reshape(data_shape), data)).astype("float32")
train_label = np.array(map(lambda x: x[1], data)).astype("int64")
train_label = train_label.reshape([-1, 1])
exe.run(main_program,
feed={'pixel': train_image,
'label': train_label})
if (batch_id % 100) == 0:
acc_list = []
avg_loss_list = []
for tid, test_data in enumerate(test_reader()):
test_image = np.array(
map(lambda x: x[0].reshape(data_shape),
test_data)).astype("float32")
test_label = np.array(map(lambda x: x[1],
test_data)).astype("int64")
test_label = test_label.reshape([-1, 1])
loss_t, acc_t = exe.run(
program=test_program,
feed={"pixel": test_image,
"label": test_label},
fetch_list=[avg_cost, acc])
if math.isnan(float(loss_t)):
sys.exit("got NaN loss, training failed.")
acc_list.append(float(acc_t))
avg_loss_list.append(float(loss_t))
acc_value = np.array(acc_list).mean()
avg_loss_value = np.array(avg_loss_list).mean()
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Accuracy {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_value), float(acc_value)))
if acc_value > args.threshold:
print(
'Save inference model with test accuracy of {0} at {1}'.
format(float(acc_value), save_dirname))
fluid.io.save_inference_model(save_dirname, ["pixel"],
[predict], exe)
return
def test_accuracy(executor, inference_program, feed_target_names,
fetch_targets):
if args.data_set == "cifar10":
data_shape = [3, 32, 32]
elif args.data_set == "imagenet":
data_shape = [3, 224, 224]
else:
raise ValueError("%s dataset is not supported" % data_set)
test_reader = paddle.batch(
paddle.dataset.cifar.test10()
if args.data_set == "cifar10" else paddle.dataset.flowers.test(),
batch_size=args.inf_batch_size)
test_num = 0
correct_num = 0
for test_data in test_reader():
test_image = np.array(
map(lambda x: x[0].reshape(data_shape), test_data)).astype(
"float32")
test_label = np.array(map(lambda x: x[1], test_data)).astype("int64")
test_label = test_label.reshape([-1, 1])
results = executor.run(program=inference_program,
feed={feed_target_names[0]: test_image},
fetch_list=fetch_targets)
prediction = np.argmax(results[0], axis=1).reshape([-1, 1])
correct_num += np.sum(prediction == test_label)
test_num += test_label.size
print("{0} out of {1} predictions are correct.".format(correct_num,
test_num))
print("Test accuray is {0}.".format(float(correct_num) / float(test_num)))
def infer(place, save_dirname):
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
print("Load inference model from {0}".format(save_dirname))
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
print("The test set accuracy of inference in float mode is:")
test_accuracy(exe, inference_program, feed_target_names, fetch_targets)
float16_inference_program = inference_program.clone()
t = Float16Transpiler()
t.transpile(float16_inference_program, place)
print("The test set accuracy of inference in float16 mode is:")
test_accuracy(exe, float16_inference_program, feed_target_names,
fetch_targets)
fp16_save_dirname = "float16_" + save_dirname
fluid.io.save_inference_model(fp16_save_dirname, feed_target_names,
fetch_targets, exe,
float16_inference_program)
@contextlib.contextmanager
def scope_prog_guard():
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
if __name__ == "__main__":
if not fluid.core.is_compiled_with_cuda():
raise Exception("This test requires CUDA GPUs!")
place = fluid.CUDAPlace(0)
if not fluid.core.is_float16_supported(place):
raise Exception(
"This test requires compute capability of CUDA GPU >= 5.3!")
for i in range(args.repeat):
with scope_prog_guard():
save_dirname = "image_classification_" + args.data_set + "_" + args.model + ".inference.model"
train(place, save_dirname)
infer(place, save_dirname)
| 13,296 | 35.630854 | 106 |
py
|
Paddle
|
Paddle-master/paddle/capi/examples/model_inference/sparse_binary/trainer_config.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reservedd.
#
# 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.
| 613 | 42.857143 | 74 |
py
|
Paddle
|
Paddle-master/paddle/capi/examples/model_inference/sequence/trainer_config.py
|
# 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.
from paddle.trainer_config_helpers import *
WORD_DIM = 3000
sentence = data_layer(name='sentence', size=WORD_DIM)
sentence_embedding = embedding_layer(
input=sentence,
size=64,
param_attr=ParameterAttribute(
initial_max=1.0, initial_min=0.5))
lstm = simple_lstm(input=sentence_embedding, size=64)
lstm_last = last_seq(input=lstm)
outputs(fc_layer(input=lstm_last, size=2, act=SoftmaxActivation()))
| 1,033 | 35.928571 | 74 |
py
|
Paddle
|
Paddle-master/paddle/capi/examples/model_inference/multi_thread/trainer_config.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reservedd.
#
# 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.
| 613 | 42.857143 | 74 |
py
|
Paddle
|
Paddle-master/paddle/capi/examples/model_inference/dense/mnist_v2.py
|
# 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 os
import sys
import gzip
import logging
import argparse
from PIL import Image
import numpy as np
import paddle.v2 as paddle
from paddle.utils.dump_v2_config import dump_v2_config
logger = logging.getLogger("paddle")
logger.setLevel(logging.INFO)
def multilayer_perceptron(img, layer_size, lbl_dim):
for idx, size in enumerate(layer_size):
hidden = paddle.layer.fc(input=(img if not idx else hidden),
size=size,
act=paddle.activation.Relu())
return paddle.layer.fc(input=hidden,
size=lbl_dim,
act=paddle.activation.Softmax())
def network(input_dim=784, lbl_dim=10, is_infer=False):
images = paddle.layer.data(
name='pixel', type=paddle.data_type.dense_vector(input_dim))
predict = multilayer_perceptron(
images, layer_size=[128, 64], lbl_dim=lbl_dim)
if is_infer:
return predict
else:
label = paddle.layer.data(
name='label', type=paddle.data_type.integer_value(lbl_dim))
return paddle.layer.classification_cost(input=predict, label=label)
def main(task="train", use_gpu=False, trainer_count=1, save_dir="models"):
if task == "train":
if not os.path.exists(save_dir):
os.mkdir(save_dir)
paddle.init(use_gpu=use_gpu, trainer_count=trainer_count)
cost = network()
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1 / 128.0,
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
logger.info("Pass %d, Batch %d, Cost %f, %s" %
(event.pass_id, event.batch_id, event.cost,
event.metrics))
if isinstance(event, paddle.event.EndPass):
with gzip.open(
os.path.join(save_dir, "params_pass_%d.tar" %
event.pass_id), "w") as f:
trainer.save_parameter_to_tar(f)
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=128),
event_handler=event_handler,
num_passes=5)
elif task == "dump_config":
predict = network(is_infer=True)
dump_v2_config(predict, "trainer_config.bin", True)
else:
raise RuntimeError(("Error value for parameter task. "
"Available options are: train and dump_config."))
def parse_cmd():
parser = argparse.ArgumentParser(
description="PaddlePaddle MNIST demo for CAPI.")
parser.add_argument(
"--task",
type=str,
required=False,
help=("A string indicating the taks type. "
"Available options are: \"train\", \"dump_config\"."),
default="train")
parser.add_argument(
"--use_gpu",
type=bool,
help=("A bool flag indicating whether to use GPU device or not."),
default=False)
parser.add_argument(
"--trainer_count",
type=int,
help=("This parameter is only used in training task. It indicates "
"how many computing threads are created in training."),
default=1)
parser.add_argument(
"--save_dir",
type=str,
help=("This parameter is only used in training task. It indicates "
"path of the directory to save the trained models."),
default="models")
return parser.parse_args()
if __name__ == "__main__":
args = parse_cmd()
main(args.task, args.use_gpu, args.trainer_count, args.save_dir)
| 4,740 | 34.916667 | 80 |
py
|
Paddle
|
Paddle-master/paddle/capi/examples/model_inference/dense/merge_v2_model.py
|
# 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.
from paddle.utils.merge_model import merge_v2_model
from mnist_v2 import network
net = network(is_infer=True)
param_file = "models/params_pass_4.tar"
output_file = "output.paddle.model"
merge_v2_model(net, param_file, output_file)
| 846 | 35.826087 | 74 |
py
|
Paddle
|
Paddle-master/paddle/capi/examples/model_inference/dense/trainer_config.py
|
# 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.
| 612 | 42.785714 | 74 |
py
|
Paddle
|
Paddle-master/paddle/capi/tests/test_predict_network.py
|
# 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.
from paddle.trainer_config_helpers import *
settings(batch_size=100)
x = data_layer(name='x', size=100)
y = fc_layer(
input=x,
size=100,
bias_attr=ParamAttr(name='b'),
param_attr=ParamAttr(name='w'))
outputs(y)
| 844 | 29.178571 | 74 |
py
|
Paddle
|
Paddle-master/paddle/py_paddle/dataprovider_converter.py
|
# Copyright (c) 2016 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 paddle.trainer.PyDataProvider2 as dp2
import collections
import swig_paddle
import numpy
import itertools
from functools import reduce
__all__ = ['DataProviderConverter']
class IScanner(object):
"""
The scanner will scan Python object two passes, then convert it to Paddle's
argument.
In the first pass, `pre_scan` will be invoked by every data instance, and
then invoke `finish_pre_scan` to arguments. And the second pass do the same
thing except the functions changed to `scan`, `finish_scan`.
During the first pass, a scanner may count the shape of input matrix and
allocate memory for this argument. Then fill the data into this argument
in second pass.
"""
def __init__(self, input_type, pos):
self.input_type = input_type
if not isinstance(self.input_type, dp2.InputType):
raise ValueError("input type should be dataprovider2.InputType")
self.pos = pos
# data_in_gpu is used to indicate whether to create argument on GPU
# or not in GPU mode. Now if using one thread (trainer_count=1),
# trainer uses NeuralNetwork which needs to create argument on GPU
# before calling forward function. So, set data_in_gpu to True.
# Otherwise, trainer uses MultiGradientMachine which will transfer
# data from CPU to GPU in the forward function, set data_in_gpu to
# False in this case.
self.data_in_gpu = swig_paddle.isUsingGpu(
) and swig_paddle.getTrainerCount() == 1
def pre_scan(self, dat):
"""
First pass scan method. During this method, the scanner could count the
data number, and get the total memory size this batch would use.
:param dat: The python object.
"""
pass
def finish_pre_scan(self, argument):
"""
Finish first scan pass. Allocate the memory.
:param argument: Output arguments object.
:type argument: swig_paddle.Arguments
:param dat: Output arguments object.
:type dat: The Python object, numpy.array or List.
:return:
"""
pass
def scan(self, dat):
"""
Second pass scan method. Copy the data to arguments.
:param dat: The python object.
"""
pass
def finish_scan(self, argument):
"""
Finish second pass. Finalize the resources, etc.
:param argument: Output arguments object.
:type argument: swig_paddle.Arguments
"""
pass
class DenseScanner(IScanner):
"""
:type __mat__: numpy.ndarray
"""
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__mat__ = None
self.__shape__ = None
self.__height__ = 0
self.__dim__ = 0
def pre_scan(self, dat):
self.__height__ += 1
if self.__shape__ is None:
self.__shape__ = numpy.array(dat).shape
if len(self.__shape__) > 3:
raise ValueError(
"The dimension of input cannot be greater than 3.")
if len(self.__shape__) == 0:
raise ValueError(
"The input should be a vector, please check your input data."
)
self.__dim__ = reduce(lambda x, y: x * y, self.__shape__)
if len(self.__shape__) == 1 and self.__dim__ != self.input_type.dim:
raise ValueError(
"The data size must be equal to it in data layer.")
else:
if self.__shape__ != numpy.array(dat).shape:
raise ValueError(
"The data shape must be same in one mini-batch.")
def finish_pre_scan(self, argument):
self.__mat__ = numpy.ndarray(
shape=(self.__height__, self.__dim__), dtype=numpy.float32)
self.__height__ = 0
def scan(self, dat):
# It's better to use NumPy array for speed.
dat = numpy.array(dat)
dat = dat.flatten()
self.__mat__[self.__height__] = dat
self.__height__ += 1
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
if self.__mat__.dtype != numpy.float32:
self.__mat__ = self.__mat__.astype(numpy.float32)
m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True,
self.data_in_gpu)
argument.setSlotValue(self.pos, m)
if len(self.__shape__) > 1:
# The last-two dimenstions are the frame height and width.
# For example, the layout is CHW for 3-D feature of image.
# The H and W are the frame height and width.
h, w = self.__shape__[-2:]
argument.setSlotFrameHeight(self.pos, h)
argument.setSlotFrameWidth(self.pos, w)
self.__shape__ = None
class SparseBinaryScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__rows__ = [0]
self.__cols__ = []
self.__height__ = 0
self.__value__ = []
def scan(self, dat):
self.extend_cols(dat)
self.__rows__.append(len(self.__cols__))
self.__height__ += 1
def extend_cols(self, dat):
self.__cols__.extend(dat)
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
m = swig_paddle.Matrix.createSparse(
self.__height__,
self.input_type.dim,
len(self.__cols__),
len(self.__value__) == 0,
False, # trans
False) # TODO supoort GPU
assert isinstance(m, swig_paddle.Matrix)
m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__)
argument.setSlotValue(self.pos, m)
class SparseFloatScanner(SparseBinaryScanner):
def __init__(self, input_type, pos):
SparseBinaryScanner.__init__(self, input_type, pos)
def extend_cols(self, dat):
self.__cols__.extend((x[0] for x in dat))
self.__value__.extend((x[1] for x in dat))
class IndexScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__ids__ = None
self.__idx__ = 0
def pre_scan(self, dat):
self.__idx__ += 1
def finish_pre_scan(self, argument):
self.__ids__ = [0] * self.__idx__
self.__idx__ = 0
def scan(self, dat):
self.__ids__[self.__idx__] = dat
self.__idx__ += 1
def finish_scan(self, argument):
ids = swig_paddle.IVector.create(self.__ids__, self.data_in_gpu)
assert isinstance(argument, swig_paddle.Arguments)
argument.setSlotIds(self.pos, ids)
class SequenceScanner(IScanner):
def __init__(self, input_type, pos, inner_scanner, setter):
IScanner.__init__(self, input_type, pos)
self.__seq__ = [0]
self.__inner_scanner__ = inner_scanner
self.__setter__ = setter
def pre_scan(self, dat):
for each in dat:
self.__inner_scanner__.pre_scan(each)
def finish_pre_scan(self, argument):
self.__inner_scanner__.finish_pre_scan(argument)
def scan(self, dat):
self.__seq__.append(self.__seq__[-1] + self.get_size(dat))
for each in dat:
self.__inner_scanner__.scan(each)
def finish_scan(self, argument):
seq = swig_paddle.IVector.create(self.__seq__, False)
self.__setter__(argument, self.pos, seq)
self.__inner_scanner__.finish_scan(argument)
def get_size(self, dat):
if isinstance(self.__inner_scanner__, SequenceScanner):
return sum(self.__inner_scanner__.get_size(item) for item in dat)
else:
return len(dat)
class DataProviderConverter(object):
def __init__(self, input_types):
self.input_types = input_types
assert isinstance(self.input_types, collections.Sequence)
for each in self.input_types:
assert isinstance(each, dp2.InputType)
def convert(self, dat, argument=None):
if argument is None:
argument = swig_paddle.Arguments.createArguments(0)
assert isinstance(argument, swig_paddle.Arguments)
argument.resize(len(self.input_types))
scanners = [
DataProviderConverter.create_scanner(i, each_type)
for i, each_type in enumerate(self.input_types)
]
for each_sample in dat:
for each_step, scanner in itertools.izip(each_sample, scanners):
scanner.pre_scan(each_step)
for scanner in scanners:
scanner.finish_pre_scan(argument)
for each_sample in dat:
for each_step, scanner in itertools.izip(each_sample, scanners):
scanner.scan(each_step)
for scanner in scanners:
scanner.finish_scan(argument)
return argument
def __call__(self, dat, argument=None):
return self.convert(dat, argument)
@staticmethod
def create_scanner(i, each):
assert isinstance(each, dp2.InputType)
retv = None
if each.type == dp2.DataType.Dense:
retv = DenseScanner(each, i)
elif each.type == dp2.DataType.Index:
retv = IndexScanner(each, i)
elif each.type == dp2.DataType.SparseNonValue:
retv = SparseBinaryScanner(each, i)
elif each.type == dp2.DataType.SparseValue:
retv = SparseFloatScanner(each, i)
assert retv is not None
if each.seq_type == dp2.SequenceType.SUB_SEQUENCE:
retv = SequenceScanner(
each, i, retv,
lambda a, p, seq: a.setSlotSubSequenceStartPositions(p, seq))
if each.seq_type in [
dp2.SequenceType.SUB_SEQUENCE, dp2.SequenceType.SEQUENCE
]:
retv = SequenceScanner(
each, i, retv,
lambda a, p, seq: a.setSlotSequenceStartPositions(p, seq))
return retv
| 10,678 | 33.448387 | 81 |
py
|
Paddle
|
Paddle-master/paddle/py_paddle/util.py
|
# Copyright (c) 2016 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.
"""
Some Useful method for py_paddle.
"""
import swig_paddle
import os
import paddle.trainer.PyDataProviderWrapper
import paddle.proto.ParameterConfig_pb2
import paddle.proto.ModelConfig_pb2
import paddle.proto.TrainerConfig_pb2
import weakref
import numpy
import struct
import sys
import copy
def initializePaddle(*args):
"""
To initialize paddle process.
:param args: Command line options, such as --use_gpu=0, etc.
:return: Nothing.
"""
old_argv = copy.deepcopy(sys.argv)
old_pypath = os.getenv("PYTHONPATH")
pypath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if old_pypath is not None:
pypath = os.pathsep.join([pypath, old_pypath])
os.putenv("PYTHONPATH", pypath)
args = [""] + list(args) # argv[0] is command name, it is not important.
swig_paddle.__initPaddle__(args)
sys.argv = old_argv
def __monkeypatch_init_paddle__():
swig_paddle.__initPaddle__ = swig_paddle.initPaddle
swig_paddle.initPaddle = initializePaddle
class __ParameterCallbackWrapper__(swig_paddle.UpdateCallback):
"""
Wrap the python callable object to paddle.UpdateCallback.
INTERNAL USE ONLY.
"""
def __init__(self, callback):
swig_paddle.UpdateCallback.__init__(self)
self.callback = callback
def apply(self, param):
self.callback(param)
@staticmethod
def wrap(callback):
"""
Cast the python callable object/paddle.UpdateCallback to
swig_paddle.UpdateCallback.__disown__
:param callback: callable or swig_paddle.UpdateCallback object.
"""
if isinstance(callback, swig_paddle.UpdateCallback):
return callback.__disown__()
elif isinstance(callback, weakref.ProxyType):
raise RuntimeError("Should not pass __disown__ object")
else:
return __ParameterCallbackWrapper__(callback).__disown__()
def __arguments_to_numpy__(i, arg):
assert isinstance(arg, swig_paddle.Arguments)
value = arg.getSlotValue(i)
ids = arg.getSlotIds(i)
prob = arg.getSlotIn(i)
if value is not None:
assert isinstance(value, swig_paddle.Matrix)
value = value.copyToNumpyMat()
if ids is not None:
assert isinstance(ids, swig_paddle.IVector)
ids = ids.copyToNumpyArray()
if prob is not None:
assert isinstance(prob, swig_paddle.Matrix)
prob = prob.copyToNumpyMat()
return {"value": value, "id": ids, "prob": prob}
def __monkeypatch_gradient_machine__():
"""
Add some class methods to GradientMachine.
This method should be only used internally.
"""
swig_paddle.GradientMachine.loadFromConfigFile = \
staticmethod(loadGradientMachine)
def __matrix_to_numpy__(m):
if isinstance(m, swig_paddle.Matrix):
return m.copyToNumpyMat()
elif isinstance(m, swig_paddle.IVector):
return m.copyToNumpyArra()
else:
raise RuntimeError("Input arg should be matrix or vecotr.")
def createFromConfigProto(protoObj,
createMode=swig_paddle.CREATE_MODE_NORMAL,
paramTypes=[
swig_paddle.PARAMETER_VALUE,
swig_paddle.PARAMETER_GRADIENT,
swig_paddle.PARAMETER_MOMENTUM
]):
"""
Create Gradient Machine From Proto object.
:param protoObj: Model config
:type protoObj: proto.ModelConfig_pb2.ModelConfig
:param createMode: Create Mode, default is normal.
:type createMode: int
:param paramTypes: the gradient machine parameter type.
:type paramTypes: list of int
:return: paddle.GradientMachine
"""
assert isinstance(protoObj, paddle.proto.ModelConfig)
return swig_paddle.GradientMachine.createByConfigProtoStr(
protoObj.SerializeToString(), createMode, paramTypes)
swig_paddle.GradientMachine.createFromConfigProto = \
staticmethod(createFromConfigProto)
def forwardTest(self, inArgs):
"""
forwardTest. forward gradient machine in test mode, and return a numpy
matrix dict.
:param inArgs: The input arguments
:type inArgs: paddle.Arguments
:return: A dictionary with keys ['id', 'value'], each value is a
numpy.ndarray.
"""
outArgs = swig_paddle.Arguments.createArguments(0)
self.forward(inArgs, outArgs, swig_paddle.PASS_TEST)
return [
__arguments_to_numpy__(i, outArgs)
for i in xrange(outArgs.getSlotNum())
]
swig_paddle.GradientMachine.forwardTest = forwardTest
# Monkey patching backward
swig_paddle.GradientMachine.__backward__ = swig_paddle.GradientMachine.backward
def backward(self, callback):
"""
GradientMachine Backward
:param callback: a callback which parameter is (paddle.Parameter) or
a paddle.UpdateCallback object.
"""
self.__backward__(__ParameterCallbackWrapper__.wrap(callback))
swig_paddle.GradientMachine.backward = backward
# Monkey patching forwardBackward.
swig_paddle.GradientMachine.__forwardBackward__ = \
swig_paddle.GradientMachine.forwardBackward
def forwardBackward(self,
inArgs,
outArgs,
passType,
callback=swig_paddle.UpdateCallback()):
"""
GradientMachine forward backward.
:param inArgs: Input Arguments for GradientMachine.
:type inArgs: paddle.Arguments
:param outArgs: Output Arguments for GradientMachine.
:type outArgs: paddle.Arguments
:param passType: gradient machine's pass type.
:type passType: paddle.PassType
:param callback: a callable object with arguments (paddle.Parameter) or
a paddle.UpdateCallback it will be called when
backward
"""
self.__forwardBackward__(inArgs, outArgs, passType,
__ParameterCallbackWrapper__.wrap(callback))
swig_paddle.GradientMachine.forwardBackward = forwardBackward
def getParameters(self):
return (self.getParameter(i) for i in xrange(self.getParameterSize()))
swig_paddle.GradientMachine.getParameters = getParameters
def getNonStaticParameters(self):
return (self.getNonStaticParameter(i)
for i in xrange(self.getNonStaticParameterSize()))
swig_paddle.GradientMachine.getNonStaticParameters = getNonStaticParameters
def getLayerOutputs(self, layerNames):
"""
getLayerOutputs. get outputs of layers and return a numpy matrix dict.
:param layerNames: layer names.
:type layerNames: string or list.
"""
if isinstance(layerNames, basestring):
layerNames = [layerNames]
elif not isinstance(layerNames, list):
raise RuntimeError("Input args shuld be string or a sting list.")
output = dict()
for name in layerNames:
output[name] = __arguments_to_numpy__(0, self.getLayerOutput(name))
return output
swig_paddle.GradientMachine.getLayerOutputs = getLayerOutputs
def loadGradientMachine(config_filename, model_dir=None):
"""
Load a gradient machine from config file name/path.
:param config_filename: The trainer config file name/path
:param model_dir: The model parameter directory. None if same as the
directory of config_filename
:return: GradientMachine with some enhance methods.
:rtype: paddle.GradientMachine
"""
trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
config_filename)
assert isinstance(trainer_config, swig_paddle.TrainerConfig)
model_conf = trainer_config.getModelConfig()
network = swig_paddle.GradientMachine.createByModelConfig(model_conf)
assert isinstance(network, swig_paddle.GradientMachine)
if model_dir is None:
model_dir = os.path.dirname(config_filename)
network.loadParameters(model_dir)
return network
def loadParameterFile(fn):
"""
Load Paddle Parameter file to numpy.ndarray
:param fn: file name or file like object.
:type fn: str or file like object.
:return: numpy array
:rtype: numpy.ndarray
:raise: paddle.UnsupportError when parameter format is wrong.
"""
if isinstance(fn, str):
with open(fn, 'rb') as f:
return loadParameterFile(f)
elif hasattr(fn, 'read'): # File like object
version, = struct.unpack('i', fn.read(4))
if version != 0:
raise swig_paddle.UnsupportError()
value_length, = struct.unpack("I", fn.read(4))
if value_length != 4 and value_length != 8:
raise swig_paddle.UnsupportError()
dtype = 'float32' if value_length == 4 else 'float64'
param_size, = struct.unpack("L", fn.read(8))
value = numpy.fromfile(fn, dtype)
if len(value) != param_size:
raise swig_paddle.UnsupportError()
return value
else:
raise swig_paddle.UnsupportError()
class DataProviderWrapperConverter(object):
"""
A class convert DataFormat from PyDataProvider Wrapper to
py_paddle.paddle.Arguemnts.
"""
class DenseValueConverter(object):
"""
Internal class
"""
def __init__(self, header_def):
self.__dim__ = header_def.dim
self.buf = []
def append(self, other):
assert len(other) == self.__dim__
self.buf += other
def __call__(self, slot_idx, arg):
mat = swig_paddle.Matrix.createDense(self.buf,
len(self.buf) / self.__dim__,
self.__dim__)
arg.setSlotValue(slot_idx, mat)
class IdValueConverter(object):
"""
Internal class
"""
def __init__(self, *args):
self.buf = []
def append(self, other):
assert isinstance(other, int)
self.buf.append(other)
def __call__(self, slot_idx, arg):
arg.setSlotIds(slot_idx, swig_paddle.IVector.create(self.buf))
class SparseNonValueConverter(object):
"""
Internal class
"""
def __init__(self, slot_def):
self.indices = [0]
self.cols = []
self.dim = slot_def.dim
def append(self, other):
self.indices.append(self.indices[-1] + len(other))
self.cols += other
def __call__(self, slot_idx, arg):
mat = swig_paddle.Matrix.createSparse(
len(self.indices) - 1, self.dim, len(self.cols), True)
assert isinstance(mat, swig_paddle.Matrix)
mat.sparseCopyFrom(self.indices, self.cols)
self.putIntoArg(slot_idx, arg, mat)
def putIntoArg(self, slot_idx, arg, mat):
arg.setSlotValue(slot_idx, mat)
class SparseValueConverter(SparseNonValueConverter):
"""
Internal class
"""
def __init__(self, slot_def):
super(DataProviderWrapperConverter.SparseValueConverter,
self).__init__(slot_def)
self.values = []
def append(self, other):
super(DataProviderWrapperConverter.SparseValueConverter,
self).append(map(lambda x: x[0], other))
self.values += map(lambda x: x[1], other)
def __call__(self, slot_idx, arg):
mat = swig_paddle.Matrix.createSparse(
len(self.indices) - 1, self.dim, len(self.cols), False)
assert isinstance(mat, swig_paddle.Matrix)
mat.sparseCopyFrom(self.indices, self.cols, self.values)
self.putIntoArg(slot_idx, arg, mat)
__SLOT_VALUE_CONVERTER_MAP__ = {
paddle.trainer.PyDataProviderWrapper.DenseSlot: DenseValueConverter,
paddle.trainer.PyDataProviderWrapper.IndexSlot: IdValueConverter,
paddle.trainer.PyDataProviderWrapper.SparseNonValueSlot:
SparseNonValueConverter,
paddle.trainer.PyDataProviderWrapper.SparseValueSlot:
SparseValueConverter
}
def __init__(self, use_seq, header):
"""
Ctor
:param use_seq: True if use sequence.
:param header: List of slots type,
trainer.PyDataProviderWrapper.SlotType
"""
self.__use_seq__ = use_seq
self.__header__ = header
def convert(self, wrapper_data, argument=None):
"""
Convert PyDataProviderWrapper format to paddle.Argument
:param wrapper_data: PyDataProviderWrapper yield's data list.
:param argument: The output paddle.Arguments.
If it is not None, it will assign data in this
arguments, else it will create new arguments.
:return: arguments that contains data.
:rtype: paddle.Arguments
"""
if argument is None:
argument = swig_paddle.Arguments.createArguments(0)
assert isinstance(argument, swig_paddle.Arguments)
argument.resize(len(self.__header__))
values = map(
lambda x: DataProviderWrapperConverter.__SLOT_VALUE_CONVERTER_MAP__[x.__class__](x),
self.__header__)
if self.__use_seq__:
seq_dim = [[] for _ in xrange(self.__header__.__len__())]
seq_start_pos = [[0] for _ in xrange(self.__header__.__len__())]
for each_sample in wrapper_data:
for slot_idx, sequence in enumerate(each_sample):
for raw_data in sequence:
values[slot_idx].append(raw_data)
seq_start_pos[slot_idx].append(seq_start_pos[slot_idx][-1] +
len(sequence))
seq_dim[slot_idx].append(len(sequence))
for slot_idx in xrange(len(self.__header__)):
argument.setSlotSequenceDim(
slot_idx, swig_paddle.IVector.create(seq_dim[slot_idx]))
argument.setSlotSequenceStartPositions(
slot_idx,
swig_paddle.IVector.create(seq_start_pos[slot_idx]))
else:
for each_sample in wrapper_data:
for raw_data, value in zip(each_sample, values):
value.append(raw_data)
for i, v in enumerate(values):
v(i, argument)
return argument
def __call__(self, wrapper_data, argument=None):
"""
Invoke self.convert. See documents in self.convert.
"""
return self.convert(wrapper_data, argument)
def __monkey_patch_protobuf_objects__():
def ParameterConfig_toProto(self):
"""
Convert paddle.ParameterConfig to
proto.ParameterConfig_pb2.ParameterConfig
:return: proto.ParameterConfig_pb2.ParameterConfig object.
"""
param_conf = paddle.proto.ParameterConfig_pb2.ParameterConfig()
param_conf.ParseFromString(self.toProtoString())
return param_conf
swig_paddle.ParameterConfig.toProto = ParameterConfig_toProto
def OptimizationConfig_toProto(self):
"""
Convert paddle.OptimizationConfig to
proto.TrainerConfig_pb2.OptimizationConfig
:return: proto.TrainerConfig_pb2.OptimizationConfig
"""
opt_conf = proto.TrainerConfig_pb2.OptimizationConfig()
opt_conf.ParseFromString(self.toProtoString())
return opt_conf
swig_paddle.OptimizationConfig.toProto = OptimizationConfig_toProto
def OptimizationConfig_createFromProto(protoObj):
"""
Create a new paddle.OptimizationConfig from
proto.TrainerConfig_pb2.OptimizationConfig
:param protoObj: proto.TrainerConfig_pb2.OptimizationConfig
:return: paddle.OptimizationConfig
"""
assert isinstance(protoObj, paddle.proto.OptimizationConfig)
return swig_paddle.OptimizationConfig.createFromProtoString(
protoObj.SerializeToString())
swig_paddle.OptimizationConfig.createFromProto = staticmethod(
OptimizationConfig_createFromProto)
def TrainerConfig_createFromProto(protoObj):
"""
Create a new paddle.TrainerConfig from
proto.OptimizationConfig
:param protoObj: proto.TrainerConfig
:return: paddle.TrainerConfig
"""
assert isinstance(protoObj, paddle.proto.TrainerConfig)
return swig_paddle.TrainerConfig.createFromProtoString(
protoObj.SerializeToString())
swig_paddle.TrainerConfig.createFromProto = staticmethod(
TrainerConfig_createFromProto)
def __monkey_patch_parameter__():
def getBufs(self):
"""
get all parameter vectors.
NOTE: the return value is a generator. Maybe you need to cast to
list or tuple or something else.
:return: generator of all parameter vectors.
:rtype: generator
"""
return (self.getBuf(i) for i in xrange(swig_paddle.NUM_PARAMETER_TYPES))
swig_paddle.Parameter.getBufs = getBufs
def __monkey_patch_trainer__():
swig_paddle.Trainer.__create__ = staticmethod(swig_paddle.Trainer.create)
def Trainer_create(config, model=None):
"""
Create a trainer for model with TrainerCOnfig trainer_config
trainer_config.model_config will be ignored when model is supplied.
Trainer.trainOneBatch() and Trainer.forwardOneBatch() can be used only
when trainer_config.data_config is set.
A typical usage for Trainer is:
.. code-block:: python
trainer = Trainer.create(trainer_config, model)
for p in xrange(num_passes)
while True:
data = get_next_batch(batch_size)
if not data:
break
trainer.trainOneDataBatch(batch_size, data)
trainer.finishTrainPass()
trainer.finishTrain()
The trainer will take care of logging, model saving, distributed
training, etc.
:param config: trainer configuration
:type config: paddle.proto.TrainerConfig
:param model: the model to be trained
:type model: swig_paddle.GradientMachine
:return: a trainer
:rtype swig_paddle.Trainer
"""
assert isinstance(config, paddle.proto.TrainerConfig)
if model is not None:
assert isinstance(model, swig_paddle.GradientMachine)
return swig_paddle.Trainer.__create__(
swig_paddle.TrainerConfig.createFromProto(config), model)
swig_paddle.Trainer.create = staticmethod(Trainer_create)
swig_paddle.Trainer.__getForwardOutput__ = \
swig_paddle.Trainer.getForwardOutput
def getForwardOutput(self):
"""
Get the netword outputs from the previous trainOneBatch(),
trainOneDataBatch(), testOneDataPatch(), or forwardOneBatch() call.
:return: list of dictionary with keys ['id', 'value'], each value is a
numpy.ndarray.
"""
outArgs = self.__getForwardOutput__()
return [
__arguments_to_numpy__(i, outArgs)
for i in xrange(outArgs.getSlotNum())
]
swig_paddle.Trainer.getForwardOutput = getForwardOutput
def monkeypatches():
patches = [
__monkeypatch_init_paddle__, __monkeypatch_gradient_machine__,
__monkey_patch_protobuf_objects__, __monkey_patch_parameter__,
__monkey_patch_trainer__
]
for patch in patches:
patch()
| 20,527 | 34.454231 | 96 |
py
|
Paddle
|
Paddle-master/paddle/py_paddle/__init__.py
|
# Copyright (c) 2016 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.
from util import DataProviderWrapperConverter
from dataprovider_converter import DataProviderConverter
__all__ = [
'paddle',
'DataProviderConverter',
'DataProviderWrapperConverter', # for deprecated usage.
'loadParameterFile'
]
util.monkeypatches()
| 877 | 34.12 | 74 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequence_recurrent_group.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3
# This config is designed to be equivalent with sequence_recurrent.py
data = data_layer(name="word", size=dict_dim)
emb = embedding_layer(
input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
def step(y):
mem = memory(name="rnn_state", size=hidden_dim)
with mixed_layer(
name="rnn_state",
size=hidden_dim,
bias_attr=False,
act=SoftmaxActivation()) as out:
out += identity_projection(input=y)
out += full_matrix_projection(
input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
return out
recurrent = recurrent_group(name="rnn", step=step, input=emb)
recurrent_last = last_seq(input=recurrent)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=recurrent_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
| 2,250 | 31.623188 | 75 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequence_rnn_matched_inputs.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
define_py_data_sources2(
train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_mixed')
settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 2
hidden_dim = 2
label_dim = 2
data1 = data_layer(name="word1", size=dict_dim)
data2 = data_layer(name="word2", size=dict_dim)
label = data_layer(name="label", size=label_dim)
encoding = embedding_layer(input=data2, size=word_dim)
subseq = embedding_layer(input=data1, size=word_dim)
seq = embedding_layer(input=data2, size=word_dim)
nonseq = embedding_layer(input=label, size=word_dim)
# This hierarchical RNN is designed to be equivalent to the simple RNN in
# sequence_rnn_mixed_inputs.conf
def outer_step(subseq, seq, nonseq, encoding):
outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
def inner_step(subseq, seq, nonseq):
inner_mem = memory(
name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
out = fc_layer(
input=[subseq, seq, nonseq, inner_mem],
size=hidden_dim,
act=TanhActivation(),
bias_attr=True,
name='inner_rnn_state')
return out
decoder = recurrent_group(
step=inner_step, name='inner', input=[subseq, seq, nonseq])
last = last_seq(name="outer_rnn_state", input=decoder)
context = simple_attention(
encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
return context
out = recurrent_group(
name="outer",
step=outer_step,
input=[
subseq, expand_layer(
seq, expand_as=subseq,
expand_level=ExpandLevel.FROM_SEQUENCE), expand_layer(
nonseq,
expand_as=subseq,
expand_level=ExpandLevel.FROM_NO_SEQUENCE),
StaticInput(encoding)
])
rep = last_seq(input=out)
prob = fc_layer(
size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
outputs(classification_cost(input=prob, label=label))
| 2,817 | 32.152941 | 77 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequence_rnn_mixed_inputs.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
define_py_data_sources2(
train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_mixed')
settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 2
hidden_dim = 2
label_dim = 2
data1 = data_layer(name="word1", size=dict_dim)
data2 = data_layer(name="word2", size=dict_dim)
label = data_layer(name="label", size=label_dim)
encoding = embedding_layer(input=data2, size=word_dim)
# This hierarchical RNN is designed to be equivalent to the simple RNN in
# sequence_rnn_matched_inputs.conf
def outer_step(subseq, seq, nonseq, encoding):
outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
def inner_step(data1, data2, label):
inner_mem = memory(
name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
subseq = embedding_layer(input=data1, size=word_dim)
seq = embedding_layer(input=data2, size=word_dim)
nonseq = embedding_layer(input=label, size=word_dim)
print_layer(input=[data1, seq, label, inner_mem])
out = fc_layer(
input=[subseq, seq, nonseq, inner_mem],
size=hidden_dim,
act=TanhActivation(),
bias_attr=True,
name='inner_rnn_state')
return out
decoder = recurrent_group(
step=inner_step, name='inner',
input=[subseq, StaticInput(seq), nonseq])
last = last_seq(name="outer_rnn_state", input=decoder)
context = simple_attention(
encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
return context
out = recurrent_group(
name="outer",
step=outer_step,
input=[data1, data2, StaticInput(label), StaticInput(encoding)])
rep = last_seq(input=out)
prob = fc_layer(
size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
outputs(classification_cost(input=prob, label=label))
| 2,692 | 33.088608 | 77 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/rnn_data_provider.py
|
# Copyright (c) 2016 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.
from paddle.trainer.PyDataProvider2 import *
# Note that each config should has an independent provider
# in current design of PyDataProvider2.
#######################################################
data = [
[[[1, 3, 2], [4, 5, 2]], 0],
[[[0, 2], [2, 5], [0, 1, 2]], 1],
]
# Used for sequence_nest_rnn.conf
@provider(
input_types=[integer_value_sub_sequence(10), integer_value(3)],
should_shuffle=False)
def process_subseq(settings, file_name):
for d in data:
yield d
# Used for sequence_rnn.conf
@provider(
input_types=[integer_value_sequence(10), integer_value(3)],
should_shuffle=False)
def process_seq(settings, file_name):
for d in data:
seq = []
for subseq in d[0]:
seq += subseq
yield seq, d[1]
# Used for sequence_nest_rnn_multi_input.conf
@provider(
input_types=[integer_value_sub_sequence(10), integer_value(3)],
should_shuffle=False)
def process_subseq2(settings, file_name):
for d in data:
yield d
# Used for sequence_rnn_multi_input.conf
@provider(
input_types=[integer_value_sequence(10), integer_value(3)],
should_shuffle=False)
def process_seq2(settings, file_name):
for d in data:
seq = []
for subseq in d[0]:
seq += subseq
yield seq, d[1]
###########################################################
data2 = [
[[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]], 0],
[[[0, 2], [2, 5], [0, 1, 2]], [[1, 5], [4], [2, 3, 6, 1]], 1],
]
# Used for sequence_nest_rnn_multi_unequalength_inputs.conf
@provider(
input_types=[
integer_value_sub_sequence(10), integer_value_sub_sequence(10),
integer_value(2)
],
should_shuffle=False)
def process_unequalength_subseq(settings, file_name):
for d in data2:
yield d
# Used for sequence_rnn_multi_unequalength_inputs.conf
@provider(
input_types=[
integer_value_sequence(10), integer_value_sequence(10), integer_value(2)
],
should_shuffle=False)
def process_unequalength_seq(settings, file_name):
for d in data2:
words1 = reduce(lambda x, y: x + y, d[0])
words2 = reduce(lambda x, y: x + y, d[1])
yield words1, words2, d[2]
###########################################################
data3 = [
[[[1, 2], [4, 5, 2]], [1, 2], 0],
[[[0, 2], [2, 5], [0, 1, 2]], [2, 3, 0], 1],
]
# Used for sequence_nest_mixed_inputs.conf
@provider(
input_types=[
integer_value_sub_sequence(10), integer_value_sequence(10),
integer_value(2)
],
should_shuffle=False)
def process_mixed(settings, file_name):
for d in data3:
yield d
| 3,259 | 27.103448 | 80 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
define_py_data_sources2(
train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_unequalength_subseq')
settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 8
hidden_dim = 8
label_dim = 2
speaker1 = data_layer(name="word1", size=dict_dim)
speaker2 = data_layer(name="word2", size=dict_dim)
emb1 = embedding_layer(input=speaker1, size=word_dim)
emb2 = embedding_layer(input=speaker2, size=word_dim)
# This hierarchical RNN is designed to be equivalent to the simple RNN in
# sequence_rnn_multi_unequalength_inputs.conf
def outer_step(x1, x2):
index = [0]
def inner_step(ipt):
index[0] += 1
i = index[0]
outer_mem = memory(name="outer_rnn_state_%d" % i, size=hidden_dim)
def inner_step_impl(y):
inner_mem = memory(
name="inner_rnn_state_" + y.name,
size=hidden_dim,
boot_layer=outer_mem)
out = fc_layer(
input=[y, inner_mem],
size=hidden_dim,
act=TanhActivation(),
bias_attr=True,
name='inner_rnn_state_' + y.name)
return out
encoder = recurrent_group(
step=inner_step_impl, name='inner_%d' % i, input=ipt)
last = last_seq(name="outer_rnn_state_%d" % i, input=encoder)
return encoder, last
encoder1, sentence_last_state1 = inner_step(ipt=x1)
encoder2, sentence_last_state2 = inner_step(ipt=x2)
encoder1_expand = expand_layer(
input=sentence_last_state1, expand_as=encoder2)
return [encoder1_expand, encoder2]
encoder1_rep, encoder2_rep = recurrent_group(
name="outer",
step=outer_step,
input=[SubsequenceInput(emb1), SubsequenceInput(emb2)],
targetInlink=emb2)
encoder1_last = last_seq(input=encoder1_rep)
encoder1_expandlast = expand_layer(input=encoder1_last, expand_as=encoder2_rep)
context = mixed_layer(
input=[
identity_projection(encoder1_expandlast),
identity_projection(encoder2_rep)
],
size=hidden_dim)
rep = last_seq(input=context)
prob = fc_layer(
size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
outputs(
classification_cost(
input=prob, label=data_layer(
name="label", size=label_dim)))
| 3,142 | 31.402062 | 79 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
define_py_data_sources2(
train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_unequalength_seq')
settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 8
hidden_dim = 8
label_dim = 2
speaker1 = data_layer(name="word1", size=dict_dim)
speaker2 = data_layer(name="word2", size=dict_dim)
emb1 = embedding_layer(input=speaker1, size=word_dim)
emb2 = embedding_layer(input=speaker2, size=word_dim)
# This hierachical RNN is designed to be equivalent to the RNN in
# sequence_nest_rnn_multi_unequalength_inputs.conf
def step(x1, x2):
def calrnn(y):
mem = memory(name='rnn_state_' + y.name, size=hidden_dim)
out = fc_layer(
input=[y, mem],
size=hidden_dim,
act=TanhActivation(),
bias_attr=True,
name='rnn_state_' + y.name)
return out
encoder1 = calrnn(x1)
encoder2 = calrnn(x2)
return [encoder1, encoder2]
encoder1_rep, encoder2_rep = recurrent_group(
name="stepout", step=step, input=[emb1, emb2])
encoder1_last = last_seq(input=encoder1_rep)
encoder1_expandlast = expand_layer(input=encoder1_last, expand_as=encoder2_rep)
context = mixed_layer(
input=[
identity_projection(encoder1_expandlast),
identity_projection(encoder2_rep)
],
size=hidden_dim)
rep = last_seq(input=context)
prob = fc_layer(
size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
outputs(
classification_cost(
input=prob, label=data_layer(
name="label", size=label_dim)))
| 2,398 | 30.155844 | 79 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequence_recurrent.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3
# This config is designed to be equivalent with sequence_recurrent_group.py
data = data_layer(name="word", size=dict_dim)
emb = embedding_layer(
input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
recurrent_last = last_seq(input=recurrent)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=recurrent_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
| 1,871 | 32.428571 | 80 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/__init__.py
|
# Copyright (c) 2016 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.
| 609 | 42.571429 | 74 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/img_conv_cudnn.py
|
# Copyright (c) 2016 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.
from paddle.trainer_config_helpers import *
settings(batch_size=10)
data = data_layer(name="input", size=8 * 16 * 16)
conv = img_conv_layer(
input=data,
filter_size=1,
filter_size_y=1,
num_channels=8,
num_filters=16,
stride=1,
bias_attr=True,
act=LinearActivation(),
groups=2,
layer_type="cudnn_conv")
outputs(conv)
| 961 | 29.0625 | 73 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/sequenceGen.py
|
# Copyright (c) 2016 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 os
import sys
from paddle.trainer.PyDataProvider2 import *
def hook(settings, dict_file, **kwargs):
settings.word_dict = dict_file
settings.input_types = [
integer_value_sequence(len(settings.word_dict)), integer_value(3)
]
settings.logger.info('dict len : %d' % (len(settings.word_dict)))
@provider(init_hook=hook, should_shuffle=False)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line in fdata:
label, comment = line.strip().split('\t')
label = int(''.join(label.split()))
words = comment.split()
words = [
settings.word_dict[w] for w in words if w in settings.word_dict
]
yield words, label
## for hierarchical sequence network
def hook2(settings, dict_file, **kwargs):
settings.word_dict = dict_file
settings.input_types = [
integer_value_sub_sequence(len(settings.word_dict)),
integer_value_sequence(3)
]
settings.logger.info('dict len : %d' % (len(settings.word_dict)))
@provider(init_hook=hook2, should_shuffle=False)
def process2(settings, file_name):
with open(file_name) as fdata:
labels = []
sentences = []
for line in fdata:
if (len(line)) > 1:
label, comment = line.strip().split('\t')
label = int(''.join(label.split()))
words = comment.split()
words = [
settings.word_dict[w] for w in words
if w in settings.word_dict
]
labels.append(label)
sentences.append(words)
else:
yield sentences, labels
labels = []
sentences = []
| 2,393 | 32.71831 | 79 |
py
|
Paddle
|
Paddle-master/paddle/gserver/tests/test_PyDataProvider2.py
|
# Copyright (c) 2016 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 random
from paddle.trainer.PyDataProvider2 import *
@provider(slots=[dense_vector(200, seq_type=SequenceType.NO_SEQUENCE)])
def test_dense_no_seq(setting, filename):
for i in xrange(200):
yield [(float(j - 100) * float(i + 1)) / 200.0 for j in xrange(200)]
@provider(input_types=[integer_value(200, seq_type=SequenceType.NO_SEQUENCE)])
def test_index_no_seq(setting, filename):
for i in xrange(200):
yield i
def test_init_hooker(setting, value, **kwargs):
setting.value = value
@provider(
input_types=[dense_vector(
20, seq_type=SequenceType.NO_SEQUENCE)],
init_hook=test_init_hooker)
def test_init_hook(setting, filename):
for i in xrange(200):
yield setting.value
@provider(input_types=[
sparse_binary_vector(
30000, seq_type=SequenceType.NO_SEQUENCE)
])
def test_sparse_non_value_no_seq(setting, filename):
for i in xrange(200):
yield [(i + 1) * (j + 1) for j in xrange(10)]
@provider(input_types=[
sparse_float_vector(
30000, seq_type=SequenceType.NO_SEQUENCE)
])
def test_sparse_value_no_seq(setting, filename):
for i in xrange(200):
yield [((i + 1) * (j + 1), float(j) / float(i + 1)) for j in xrange(10)]
@provider(input_types=[integer_value(200, seq_type=SequenceType.SEQUENCE)])
def test_index_seq(setting, filename):
for i in xrange(200):
yield range(i + 1)
@provider(input_types=[index_slot(200, seq_type=SequenceType.SUB_SEQUENCE)])
def test_index_sub_seq(setting, filename):
def gen_sub_seq(l):
l += 1
for j in xrange(l):
yield range(j + 1)
for i in xrange(200):
yield list(gen_sub_seq(i))
@provider(input_types=[index_slot(100)], min_pool_size=1000)
def test_min_pool_size(setting, filename):
for _ in xrange(1 << 14):
yield random.randint(0, 100 - 1)
@provider(
input_types=[index_slot(
100, seq_type=SequenceType.SEQUENCE)],
can_over_batch_size=False,
calc_batch_size=lambda x: len(x[0]))
def test_can_over_batch_size(setting, filename):
for _ in xrange(1 << 10):
seq_len = random.randint(0, 99)
yield [random.randint(0, 100 - 1) for _ in xrange(seq_len)]
@provider(input_types={'input1': index_slot(10), 'input2': index_slot(10)})
def test_input_order(setting, filename):
for _ in xrange(1000):
yield {'input1': 0, 'input2': 1}
@provider(
input_types=[index_slot(10)],
check=True,
check_fail_continue=True,
should_shuffle="123") # also test should shuffle
def test_check(settings, filename):
yield_good_value = False
while not yield_good_value:
for _ in xrange(10000):
i = random.randint(0, 100)
if i < 10:
yield_good_value = True
yield i
@provider(
input_types=[index_slot(10)],
min_pool_size=1000,
cache=CacheType.CACHE_PASS_IN_MEM, )
def test_min_pool_size_with_cache(settings, filename):
import random
for _ in xrange(2**20):
yield random.randint(0, 9)
| 3,649 | 27.968254 | 80 |
py
|
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