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PaddleDetection
“5ba9fe6ef8b3b52b7f871a5deb70853ee80c3b37”上不存在“paddle/phi/kernels/gpu/maxout_kernel.cu”
提交 1adc6a28 编写于 作者: G gangliao 提交者： GitHub
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Merge pull request #829 from reyoung/feature/travis_pre_commit_checks 

Feature/travis pre commit checks
上级 176cc7c1 579e5912
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Showing with 1609 addition and 1482 deletion
.travis.yml
浏览文件 @ 1adc6a28
...@@ -8,10 +8,13 @@ os: ...@@ -8,10 +8,13 @@ os:
env: env:
- JOB=DOCS - JOB=DOCS
- JOB=BUILD_AND_TEST - JOB=BUILD_AND_TEST
- JOB=PRE_COMMIT
matrix: matrix:
exclude: exclude:
- os: osx - os: osx
env: JOB=DOCS # Only generate documentation in linux env: JOB=DOCS # Only generate documentation in linux.
- os: osx
env: JOB=PRE_COMMIT # Only check pre-commit hook in linux
addons: addons:
apt: apt:
...@@ -39,6 +42,7 @@ addons: ...@@ -39,6 +42,7 @@ addons:
- lcov - lcov
- graphviz - graphviz
- swig - swig
- clang-format-3.8
before_install: before_install:
- | - |
if [ ${JOB} == "BUILD_AND_TEST" ]; then if [ ${JOB} == "BUILD_AND_TEST" ]; then
...@@ -53,7 +57,8 @@ before_install: ...@@ -53,7 +57,8 @@ before_install:
fi fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
- if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
- pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
- pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme pre-commit
script: script:
- paddle/scripts/travis/main.sh - paddle/scripts/travis/main.sh
notifications: notifications:
......
WORKSPACE
浏览文件 @ 1adc6a28
# External dependency to Google protobuf. # External dependency to Google protobuf.
http_archive( http_archive(
name = "protobuf", name="protobuf",
url = "http://github.com/google/protobuf/archive/v3.1.0.tar.gz", url="http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
sha256 = "0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7", sha256="0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
strip_prefix = "protobuf-3.1.0", strip_prefix="protobuf-3.1.0", )
)
# External dependency to gtest 1.7.0. This method comes from # External dependency to gtest 1.7.0. This method comes from
# https://www.bazel.io/versions/master/docs/tutorial/cpp.html. # https://www.bazel.io/versions/master/docs/tutorial/cpp.html.
new_http_archive( new_http_archive(
name = "gtest", name="gtest",
url = "https://github.com/google/googletest/archive/release-1.7.0.zip", url="https://github.com/google/googletest/archive/release-1.7.0.zip",
sha256 = "b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0", sha256="b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
build_file = "third_party/gtest.BUILD", build_file="third_party/gtest.BUILD",
strip_prefix = "googletest-release-1.7.0", strip_prefix="googletest-release-1.7.0", )
)
benchmark/tensorflow/rnn/run_multi.sh
浏览文件 @ 1adc6a28
...@@ -25,4 +25,3 @@ test 4 2 256 512 ...@@ -25,4 +25,3 @@ test 4 2 256 512
test 4 2 512 128 test 4 2 512 128
test 4 2 512 256 test 4 2 512 256
test 4 2 512 512 test 4 2 512 512
demo/gan/data/download_cifar.sh
浏览文件 @ 1adc6a28
...@@ -15,4 +15,3 @@ set -e ...@@ -15,4 +15,3 @@ set -e
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar zxf cifar-10-python.tar.gz tar zxf cifar-10-python.tar.gz
rm cifar-10-python.tar.gz rm cifar-10-python.tar.gz
demo/gan/data/get_mnist_data.sh
浏览文件 @ 1adc6a28
...@@ -15,5 +15,3 @@ do ...@@ -15,5 +15,3 @@ do
gunzip ${fname}.gz gunzip ${fname}.gz
fi fi
done done
demo/gan/gan_conf.py
浏览文件 @ 1adc6a28
...@@ -14,10 +14,9 @@ ...@@ -14,10 +14,9 @@
from paddle.trainer_config_helpers import * from paddle.trainer_config_helpers import *
mode = get_config_arg("mode", str, "generator") mode = get_config_arg("mode", str, "generator")
assert mode in set(["generator", assert mode in set([
"discriminator", "generator", "discriminator", "generator_training", "discriminator_training"
"generator_training", ])
"discriminator_training"])
is_generator_training = mode == "generator_training" is_generator_training = mode == "generator_training"
is_discriminator_training = mode == "discriminator_training" is_discriminator_training = mode == "discriminator_training"
...@@ -38,8 +37,8 @@ sample_dim = 2 ...@@ -38,8 +37,8 @@ sample_dim = 2
settings( settings(
batch_size=128, batch_size=128,
learning_rate=1e-4, learning_rate=1e-4,
learning_method=AdamOptimizer(beta1=0.5) learning_method=AdamOptimizer(beta1=0.5))
)
def discriminator(sample): def discriminator(sample):
""" """
...@@ -50,71 +49,88 @@ def discriminator(sample): ...@@ -50,71 +49,88 @@ def discriminator(sample):
of the sample is from real data. of the sample is from real data.
""" """
param_attr = ParamAttr(is_static=is_generator_training) param_attr = ParamAttr(is_static=is_generator_training)
bias_attr = ParamAttr(is_static=is_generator_training, bias_attr = ParamAttr(
initial_mean=1.0, is_static=is_generator_training, initial_mean=1.0, initial_std=0)
initial_std=0)
hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim, hidden = fc_layer(
input=sample,
name="dis_hidden",
size=hidden_dim,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=ReluActivation()) act=ReluActivation())
hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim, hidden2 = fc_layer(
input=hidden,
name="dis_hidden2",
size=hidden_dim,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=LinearActivation()) act=LinearActivation())
hidden_bn = batch_norm_layer(hidden2, hidden_bn = batch_norm_layer(
hidden2,
act=ReluActivation(), act=ReluActivation(),
name="dis_hidden_bn", name="dis_hidden_bn",
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=ParamAttr(is_static=is_generator_training, param_attr=ParamAttr(
initial_mean=1.0, is_static=is_generator_training, initial_mean=1.0,
initial_std=0.02), initial_std=0.02),
use_global_stats=False) use_global_stats=False)
return fc_layer(input=hidden_bn, name="dis_prob", size=2, return fc_layer(
input=hidden_bn,
name="dis_prob",
size=2,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=SoftmaxActivation()) act=SoftmaxActivation())
def generator(noise): def generator(noise):
""" """
generator generates a sample given noise generator generates a sample given noise
""" """
param_attr = ParamAttr(is_static=is_discriminator_training) param_attr = ParamAttr(is_static=is_discriminator_training)
bias_attr = ParamAttr(is_static=is_discriminator_training, bias_attr = ParamAttr(
initial_mean=1.0, is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
initial_std=0)
hidden = fc_layer(input=noise, hidden = fc_layer(
input=noise,
name="gen_layer_hidden", name="gen_layer_hidden",
size=hidden_dim, size=hidden_dim,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=ReluActivation()) act=ReluActivation())
hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim, hidden2 = fc_layer(
input=hidden,
name="gen_hidden2",
size=hidden_dim,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=LinearActivation()) act=LinearActivation())
hidden_bn = batch_norm_layer(hidden2, hidden_bn = batch_norm_layer(
hidden2,
act=ReluActivation(), act=ReluActivation(),
name="gen_layer_hidden_bn", name="gen_layer_hidden_bn",
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=ParamAttr(is_static=is_discriminator_training, param_attr=ParamAttr(
is_static=is_discriminator_training,
initial_mean=1.0, initial_mean=1.0,
initial_std=0.02), initial_std=0.02),
use_global_stats=False) use_global_stats=False)
return fc_layer(input=hidden_bn, return fc_layer(
input=hidden_bn,
name="gen_layer1", name="gen_layer1",
size=sample_dim, size=sample_dim,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=LinearActivation()) act=LinearActivation())
if is_generator_training: if is_generator_training:
noise = data_layer(name="noise", size=noise_dim) noise = data_layer(name="noise", size=noise_dim)
sample = generator(noise) sample = generator(noise)
...@@ -126,7 +142,8 @@ if is_generator_training or is_discriminator_training: ...@@ -126,7 +142,8 @@ if is_generator_training or is_discriminator_training:
label = data_layer(name="label", size=1) label = data_layer(name="label", size=1)
prob = discriminator(sample) prob = discriminator(sample)
cost = cross_entropy(input=prob, label=label) cost = cross_entropy(input=prob, label=label)
classification_error_evaluator(input=prob, label=label, name=mode+'_error') classification_error_evaluator(
input=prob, label=label, name=mode + '_error')
outputs(cost) outputs(cost)
if is_generator: if is_generator:
......
demo/gan/gan_conf_image.py
浏览文件 @ 1adc6a28
...@@ -15,10 +15,9 @@ from paddle.trainer_config_helpers import * ...@@ -15,10 +15,9 @@ from paddle.trainer_config_helpers import *
mode = get_config_arg("mode", str, "generator") mode = get_config_arg("mode", str, "generator")
dataSource = get_config_arg("data", str, "mnist") dataSource = get_config_arg("data", str, "mnist")
assert mode in set(["generator", assert mode in set([
"discriminator", "generator", "discriminator", "generator_training", "discriminator_training"
"generator_training", ])
"discriminator_training"])
is_generator_training = mode == "generator_training" is_generator_training = mode == "generator_training"
is_discriminator_training = mode == "discriminator_training" is_discriminator_training = mode == "discriminator_training"
...@@ -41,19 +40,28 @@ if dataSource == "mnist": ...@@ -41,19 +40,28 @@ if dataSource == "mnist":
else: else:
sample_dim = 32 sample_dim = 32
c_dim = 3 c_dim = 3
s2, s4 = int(sample_dim/2), int(sample_dim/4), s2, s4 = int(sample_dim / 2), int(sample_dim / 4),
s8, s16 = int(sample_dim/8), int(sample_dim/16) s8, s16 = int(sample_dim / 8), int(sample_dim / 16)
settings( settings(
batch_size=128, batch_size=128,
learning_rate=2e-4, learning_rate=2e-4,
learning_method=AdamOptimizer(beta1=0.5) learning_method=AdamOptimizer(beta1=0.5))
)
def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, def conv_bn(input,
param_attr, bias_attr, param_attr_bn, bn, trans=False, channels,
imgSize,
num_filters,
output_x,
stride,
name,
param_attr,
bias_attr,
param_attr_bn,
bn,
trans=False,
act=ReluActivation()): act=ReluActivation()):
""" """
conv_bn is a utility function that constructs a convolution/deconv layer conv_bn is a utility function that constructs a convolution/deconv layer
with an optional batch_norm layer with an optional batch_norm layer
...@@ -76,7 +84,7 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, ...@@ -76,7 +84,7 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
filter_size = tmp filter_size = tmp
padding = 0 padding = 0
print (imgSize, output_x, stride, filter_size, padding) print(imgSize, output_x, stride, filter_size, padding)
if trans: if trans:
nameApx = "_conv" nameApx = "_conv"
...@@ -84,16 +92,27 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, ...@@ -84,16 +92,27 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
nameApx = "_convt" nameApx = "_convt"
if bn: if bn:
conv = img_conv_layer(input, filter_size=filter_size, conv = img_conv_layer(
input,
filter_size=filter_size,
num_filters=num_filters, num_filters=num_filters,
name=name + nameApx, num_channels=channels, name=name + nameApx,
act=LinearActivation(), groups=1, stride=stride, num_channels=channels,
padding=padding, bias_attr=bias_attr, act=LinearActivation(),
param_attr=param_attr, shared_biases=True, layer_attr=None, groups=1,
filter_size_y=None, stride_y=None, padding_y=None, stride=stride,
padding=padding,
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=True,
layer_attr=None,
filter_size_y=None,
stride_y=None,
padding_y=None,
trans=trans) trans=trans)
conv_bn = batch_norm_layer(conv, conv_bn = batch_norm_layer(
conv,
act=act, act=act,
name=name + nameApx + "_bn", name=name + nameApx + "_bn",
bias_attr=bias_attr, bias_attr=bias_attr,
...@@ -102,49 +121,60 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, ...@@ -102,49 +121,60 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
return conv_bn return conv_bn
else: else:
conv = img_conv_layer(input, filter_size=filter_size, conv = img_conv_layer(
input,
filter_size=filter_size,
num_filters=num_filters, num_filters=num_filters,
name=name + nameApx, num_channels=channels, name=name + nameApx,
act=act, groups=1, stride=stride, num_channels=channels,
padding=padding, bias_attr=bias_attr, act=act,
param_attr=param_attr, shared_biases=True, layer_attr=None, groups=1,
filter_size_y=None, stride_y=None, padding_y=None, stride=stride,
padding=padding,
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=True,
layer_attr=None,
filter_size_y=None,
stride_y=None,
padding_y=None,
trans=trans) trans=trans)
return conv return conv
def generator(noise): def generator(noise):
""" """
generator generates a sample given noise generator generates a sample given noise
""" """
param_attr = ParamAttr(is_static=is_discriminator_training, param_attr = ParamAttr(
initial_mean=0.0, is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.02)
initial_std=0.02) bias_attr = ParamAttr(
bias_attr = ParamAttr(is_static=is_discriminator_training, is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.0)
initial_mean=0.0,
initial_std=0.0) param_attr_bn = ParamAttr(
is_static=is_discriminator_training, initial_mean=1.0, initial_std=0.02)
param_attr_bn=ParamAttr(is_static=is_discriminator_training,
initial_mean=1.0, h1 = fc_layer(
initial_std=0.02) input=noise,
h1 = fc_layer(input=noise,
name="gen_layer_h1", name="gen_layer_h1",
size=s8 * s8 * gf_dim * 4, size=s8 * s8 * gf_dim * 4,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=LinearActivation()) act=LinearActivation())
h1_bn = batch_norm_layer(h1, h1_bn = batch_norm_layer(
h1,
act=ReluActivation(), act=ReluActivation(),
name="gen_layer_h1_bn", name="gen_layer_h1_bn",
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr_bn, param_attr=param_attr_bn,
use_global_stats=False) use_global_stats=False)
h2_bn = conv_bn(h1_bn, h2_bn = conv_bn(
channels=gf_dim*4, h1_bn,
channels=gf_dim * 4,
output_x=s8, output_x=s8,
num_filters=gf_dim*2, num_filters=gf_dim * 2,
imgSize=s4, imgSize=s4,
stride=2, stride=2,
name="gen_layer_h2", name="gen_layer_h2",
...@@ -154,8 +184,9 @@ def generator(noise): ...@@ -154,8 +184,9 @@ def generator(noise):
bn=True, bn=True,
trans=True) trans=True)
h3_bn = conv_bn(h2_bn, h3_bn = conv_bn(
channels=gf_dim*2, h2_bn,
channels=gf_dim * 2,
output_x=s4, output_x=s4,
num_filters=gf_dim, num_filters=gf_dim,
imgSize=s2, imgSize=s2,
...@@ -167,8 +198,8 @@ def generator(noise): ...@@ -167,8 +198,8 @@ def generator(noise):
bn=True, bn=True,
trans=True) trans=True)
return conv_bn(
return conv_bn(h3_bn, h3_bn,
channels=gf_dim, channels=gf_dim,
output_x=s2, output_x=s2,
num_filters=c_dim, num_filters=c_dim,
...@@ -191,18 +222,16 @@ def discriminator(sample): ...@@ -191,18 +222,16 @@ def discriminator(sample):
of the sample is from generator and dimension 1 is the probabblity of the sample is from generator and dimension 1 is the probabblity
of the sample is from real data. of the sample is from real data.
""" """
param_attr = ParamAttr(is_static=is_generator_training, param_attr = ParamAttr(
initial_mean=0.0, is_static=is_generator_training, initial_mean=0.0, initial_std=0.02)
initial_std=0.02) bias_attr = ParamAttr(
bias_attr = ParamAttr(is_static=is_generator_training, is_static=is_generator_training, initial_mean=0.0, initial_std=0.0)
initial_mean=0.0,
initial_std=0.0) param_attr_bn = ParamAttr(
is_static=is_generator_training, initial_mean=1.0, initial_std=0.02)
param_attr_bn=ParamAttr(is_static=is_generator_training,
initial_mean=1.0, h0 = conv_bn(
initial_std=0.02) sample,
h0 = conv_bn(sample,
channels=c_dim, channels=c_dim,
imgSize=sample_dim, imgSize=sample_dim,
num_filters=df_dim, num_filters=df_dim,
...@@ -214,10 +243,11 @@ def discriminator(sample): ...@@ -214,10 +243,11 @@ def discriminator(sample):
param_attr_bn=param_attr_bn, param_attr_bn=param_attr_bn,
bn=False) bn=False)
h1_bn = conv_bn(h0, h1_bn = conv_bn(
h0,
channels=df_dim, channels=df_dim,
imgSize=s2, imgSize=s2,
num_filters=df_dim*2, num_filters=df_dim * 2,
output_x=s4, output_x=s4,
stride=2, stride=2,
name="dis_h1", name="dis_h1",
...@@ -226,10 +256,11 @@ def discriminator(sample): ...@@ -226,10 +256,11 @@ def discriminator(sample):
param_attr_bn=param_attr_bn, param_attr_bn=param_attr_bn,
bn=True) bn=True)
h2_bn = conv_bn(h1_bn, h2_bn = conv_bn(
channels=df_dim*2, h1_bn,
channels=df_dim * 2,
imgSize=s4, imgSize=s4,
num_filters=df_dim*4, num_filters=df_dim * 4,
output_x=s8, output_x=s8,
stride=2, stride=2,
name="dis_h2", name="dis_h2",
...@@ -238,25 +269,28 @@ def discriminator(sample): ...@@ -238,25 +269,28 @@ def discriminator(sample):
param_attr_bn=param_attr_bn, param_attr_bn=param_attr_bn,
bn=True) bn=True)
return fc_layer(input=h2_bn, name="dis_prob", size=2, return fc_layer(
input=h2_bn,
name="dis_prob",
size=2,
bias_attr=bias_attr, bias_attr=bias_attr,
param_attr=param_attr, param_attr=param_attr,
act=SoftmaxActivation()) act=SoftmaxActivation())
if is_generator_training: if is_generator_training:
noise = data_layer(name="noise", size=noise_dim) noise = data_layer(name="noise", size=noise_dim)
sample = generator(noise) sample = generator(noise)
if is_discriminator_training: if is_discriminator_training:
sample = data_layer(name="sample", size=sample_dim * sample_dim*c_dim) sample = data_layer(name="sample", size=sample_dim * sample_dim * c_dim)
if is_generator_training or is_discriminator_training: if is_generator_training or is_discriminator_training:
label = data_layer(name="label", size=1) label = data_layer(name="label", size=1)
prob = discriminator(sample) prob = discriminator(sample)
cost = cross_entropy(input=prob, label=label) cost = cross_entropy(input=prob, label=label)
classification_error_evaluator(input=prob, label=label, name=mode+'_error') classification_error_evaluator(
input=prob, label=label, name=mode + '_error')
outputs(cost) outputs(cost)
if is_generator: if is_generator:
......
demo/gan/gan_trainer.py
浏览文件 @ 1adc6a28
...@@ -16,7 +16,7 @@ import argparse ...@@ -16,7 +16,7 @@ import argparse
import random import random
import numpy import numpy
import cPickle import cPickle
import sys,os import sys, os
from PIL import Image from PIL import Image
from paddle.trainer.config_parser import parse_config from paddle.trainer.config_parser import parse_config
...@@ -24,6 +24,7 @@ from paddle.trainer.config_parser import logger ...@@ -24,6 +24,7 @@ from paddle.trainer.config_parser import logger
import py_paddle.swig_paddle as api import py_paddle.swig_paddle as api
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def plot2DScatter(data, outputfile): def plot2DScatter(data, outputfile):
''' '''
Plot the data as a 2D scatter plot and save to outputfile Plot the data as a 2D scatter plot and save to outputfile
...@@ -41,9 +42,11 @@ def plot2DScatter(data, outputfile): ...@@ -41,9 +42,11 @@ def plot2DScatter(data, outputfile):
plt.scatter(x, y) plt.scatter(x, y)
plt.savefig(outputfile, bbox_inches='tight') plt.savefig(outputfile, bbox_inches='tight')
def CHECK_EQ(a, b): def CHECK_EQ(a, b):
assert a == b, "a=%s, b=%s" % (a, b) assert a == b, "a=%s, b=%s" % (a, b)
def copy_shared_parameters(src, dst): def copy_shared_parameters(src, dst):
''' '''
copy the parameters from src to dst copy the parameters from src to dst
...@@ -52,11 +55,9 @@ def copy_shared_parameters(src, dst): ...@@ -52,11 +55,9 @@ def copy_shared_parameters(src, dst):
:param dst: the destination of the parameters :param dst: the destination of the parameters
:type dst: GradientMachine :type dst: GradientMachine
''' '''
src_params = [src.getParameter(i) src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
for i in xrange(src.getParameterSize())]
src_params = dict([(p.getName(), p) for p in src_params]) src_params = dict([(p.getName(), p) for p in src_params])
for i in xrange(dst.getParameterSize()): for i in xrange(dst.getParameterSize()):
dst_param = dst.getParameter(i) dst_param = dst.getParameter(i)
src_param = src_params.get(dst_param.getName(), None) src_param = src_params.get(dst_param.getName(), None)
...@@ -68,14 +69,16 @@ def copy_shared_parameters(src, dst): ...@@ -68,14 +69,16 @@ def copy_shared_parameters(src, dst):
dst_value.copyFrom(src_value) dst_value.copyFrom(src_value)
dst_param.setValueUpdated() dst_param.setValueUpdated()
def print_parameters(src): def print_parameters(src):
src_params = [src.getParameter(i) src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
for i in xrange(src.getParameterSize())]
print "***************" print "***************"
for p in src_params: for p in src_params:
print "Name is %s" % p.getName() print "Name is %s" % p.getName()
print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray() print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray(
)
def load_mnist_data(imageFile): def load_mnist_data(imageFile):
f = open(imageFile, "rb") f = open(imageFile, "rb")
...@@ -87,32 +90,35 @@ def load_mnist_data(imageFile): ...@@ -87,32 +90,35 @@ def load_mnist_data(imageFile):
else: else:
n = 10000 n = 10000
data = numpy.fromfile(f, 'ubyte', count=n*28*28).reshape((n, 28*28)) data = numpy.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
data = data / 255.0 * 2.0 - 1.0 data = data / 255.0 * 2.0 - 1.0
f.close() f.close()
return data.astype('float32') return data.astype('float32')
def load_cifar_data(cifar_path): def load_cifar_data(cifar_path):
batch_size = 10000 batch_size = 10000
data = numpy.zeros((5*batch_size, 32*32*3), dtype = "float32") data = numpy.zeros((5 * batch_size, 32 * 32 * 3), dtype="float32")
for i in range(1, 6): for i in range(1, 6):
file = cifar_path + "/data_batch_" + str(i) file = cifar_path + "/data_batch_" + str(i)
fo = open(file, 'rb') fo = open(file, 'rb')
dict = cPickle.load(fo) dict = cPickle.load(fo)
fo.close() fo.close()
data[(i - 1)*batch_size:(i*batch_size), :] = dict["data"] data[(i - 1) * batch_size:(i * batch_size), :] = dict["data"]
data = data / 255.0 * 2.0 - 1.0 data = data / 255.0 * 2.0 - 1.0
return data return data
# synthesize 2-D uniform data # synthesize 2-D uniform data
def load_uniform_data(): def load_uniform_data():
data = numpy.random.rand(1000000, 2).astype('float32') data = numpy.random.rand(1000000, 2).astype('float32')
return data return data
def merge(images, size): def merge(images, size):
if images.shape[1] == 28*28: if images.shape[1] == 28 * 28:
h, w, c = 28, 28, 1 h, w, c = 28, 28, 1
else: else:
h, w, c = 32, 32, 3 h, w, c = 32, 32, 3
...@@ -124,6 +130,7 @@ def merge(images, size): ...@@ -124,6 +130,7 @@ def merge(images, size):
((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0) ((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
return img.astype('uint8') return img.astype('uint8')
def save_images(images, path): def save_images(images, path):
merged_img = merge(images, [8, 8]) merged_img = merge(images, [8, 8])
if merged_img.shape[2] == 1: if merged_img.shape[2] == 1:
...@@ -132,13 +139,16 @@ def save_images(images, path): ...@@ -132,13 +139,16 @@ def save_images(images, path):
im = Image.fromarray(merged_img, mode="RGB") im = Image.fromarray(merged_img, mode="RGB")
im.save(path) im.save(path)
def get_real_samples(batch_size, data_np): def get_real_samples(batch_size, data_np):
return data_np[numpy.random.choice(data_np.shape[0], batch_size, return data_np[numpy.random.choice(
replace=False),:] data_np.shape[0], batch_size, replace=False), :]
def get_noise(batch_size, noise_dim): def get_noise(batch_size, noise_dim):
return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32') return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
def get_fake_samples(generator_machine, batch_size, noise): def get_fake_samples(generator_machine, batch_size, noise):
gen_inputs = api.Arguments.createArguments(1) gen_inputs = api.Arguments.createArguments(1)
gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise)) gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
...@@ -147,12 +157,14 @@ def get_fake_samples(generator_machine, batch_size, noise): ...@@ -147,12 +157,14 @@ def get_fake_samples(generator_machine, batch_size, noise):
fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat() fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
return fake_samples return fake_samples
def get_training_loss(training_machine, inputs): def get_training_loss(training_machine, inputs):
outputs = api.Arguments.createArguments(0) outputs = api.Arguments.createArguments(0)
training_machine.forward(inputs, outputs, api.PASS_TEST) training_machine.forward(inputs, outputs, api.PASS_TEST)
loss = outputs.getSlotValue(0).copyToNumpyMat() loss = outputs.getSlotValue(0).copyToNumpyMat()
return numpy.mean(loss) return numpy.mean(loss)
def prepare_discriminator_data_batch_pos(batch_size, data_np): def prepare_discriminator_data_batch_pos(batch_size, data_np):
real_samples = get_real_samples(batch_size, data_np) real_samples = get_real_samples(batch_size, data_np)
labels = numpy.ones(batch_size, dtype='int32') labels = numpy.ones(batch_size, dtype='int32')
...@@ -161,6 +173,7 @@ def prepare_discriminator_data_batch_pos(batch_size, data_np): ...@@ -161,6 +173,7 @@ def prepare_discriminator_data_batch_pos(batch_size, data_np):
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels)) inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
return inputs return inputs
def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise): def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
fake_samples = get_fake_samples(generator_machine, batch_size, noise) fake_samples = get_fake_samples(generator_machine, batch_size, noise)
labels = numpy.zeros(batch_size, dtype='int32') labels = numpy.zeros(batch_size, dtype='int32')
...@@ -169,6 +182,7 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise): ...@@ -169,6 +182,7 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels)) inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
return inputs return inputs
def prepare_generator_data_batch(batch_size, noise): def prepare_generator_data_batch(batch_size, noise):
label = numpy.ones(batch_size, dtype='int32') label = numpy.ones(batch_size, dtype='int32')
inputs = api.Arguments.createArguments(2) inputs = api.Arguments.createArguments(2)
...@@ -193,10 +207,9 @@ def get_layer_size(model_conf, layer_name): ...@@ -193,10 +207,9 @@ def get_layer_size(model_conf, layer_name):
def main(): def main():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform") parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
parser.add_argument("--use_gpu", default="1", parser.add_argument(
help="1 means use gpu for training") "--use_gpu", default="1", help="1 means use gpu for training")
parser.add_argument("--gpu_id", default="0", parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
help="the gpu_id parameter")
args = parser.parse_args() args = parser.parse_args()
data_source = args.data_source data_source = args.data_source
use_gpu = args.use_gpu use_gpu = args.use_gpu
...@@ -209,8 +222,9 @@ def main(): ...@@ -209,8 +222,9 @@ def main():
if not os.path.exists("./%s_params/" % data_source): if not os.path.exists("./%s_params/" % data_source):
os.makedirs("./%s_params/" % data_source) os.makedirs("./%s_params/" % data_source)
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10', '--log_period=100', api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
'--gpu_id=' + args.gpu_id, '--save_dir=' + "./%s_params/" % data_source) '--log_period=100', '--gpu_id=' + args.gpu_id,
'--save_dir=' + "./%s_params/" % data_source)
if data_source == "uniform": if data_source == "uniform":
conf = "gan_conf.py" conf = "gan_conf.py"
...@@ -220,7 +234,8 @@ def main(): ...@@ -220,7 +234,8 @@ def main():
num_iter = 1000 num_iter = 1000
gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source) gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source)
dis_conf = parse_config(conf, "mode=discriminator_training,data=" + data_source) dis_conf = parse_config(conf,
"mode=discriminator_training,data=" + data_source)
generator_conf = parse_config(conf, "mode=generator,data=" + data_source) generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
batch_size = dis_conf.opt_config.batch_size batch_size = dis_conf.opt_config.batch_size
noise_dim = get_layer_size(gen_conf.model_config, "noise") noise_dim = get_layer_size(gen_conf.model_config, "noise")
...@@ -245,11 +260,9 @@ def main(): ...@@ -245,11 +260,9 @@ def main():
generator_machine = api.GradientMachine.createFromConfigProto( generator_machine = api.GradientMachine.createFromConfigProto(
generator_conf.model_config) generator_conf.model_config)
dis_trainer = api.Trainer.create( dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
dis_conf, dis_training_machine)
gen_trainer = api.Trainer.create( gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
gen_conf, gen_training_machine)
dis_trainer.startTrain() dis_trainer.startTrain()
gen_trainer.startTrain() gen_trainer.startTrain()
...@@ -272,21 +285,23 @@ def main(): ...@@ -272,21 +285,23 @@ def main():
noise = get_noise(batch_size, noise_dim) noise = get_noise(batch_size, noise_dim)
data_batch_dis_pos = prepare_discriminator_data_batch_pos( data_batch_dis_pos = prepare_discriminator_data_batch_pos(
batch_size, data_np) batch_size, data_np)
dis_loss_pos = get_training_loss(dis_training_machine, data_batch_dis_pos) dis_loss_pos = get_training_loss(dis_training_machine,
data_batch_dis_pos)
data_batch_dis_neg = prepare_discriminator_data_batch_neg( data_batch_dis_neg = prepare_discriminator_data_batch_neg(
generator_machine, batch_size, noise) generator_machine, batch_size, noise)
dis_loss_neg = get_training_loss(dis_training_machine, data_batch_dis_neg) dis_loss_neg = get_training_loss(dis_training_machine,
data_batch_dis_neg)
dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0 dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
# Do forward pass in generator to get the gen_loss # Do forward pass in generator to get the gen_loss
data_batch_gen = prepare_generator_data_batch( data_batch_gen = prepare_generator_data_batch(batch_size, noise)
batch_size, noise)
gen_loss = get_training_loss(gen_training_machine, data_batch_gen) gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
if i % 100 == 0: if i % 100 == 0:
print "d_pos_loss is %s d_neg_loss is %s" % (dis_loss_pos, dis_loss_neg) print "d_pos_loss is %s d_neg_loss is %s" % (dis_loss_pos,
dis_loss_neg)
print "d_loss is %s g_loss is %s" % (dis_loss, gen_loss) print "d_loss is %s g_loss is %s" % (dis_loss, gen_loss)
# Decide which network to train based on the training history # Decide which network to train based on the training history
...@@ -300,7 +315,8 @@ def main(): ...@@ -300,7 +315,8 @@ def main():
curr_strike = 1 curr_strike = 1
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg) dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos) dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
copy_shared_parameters(dis_training_machine, gen_training_machine) copy_shared_parameters(dis_training_machine,
gen_training_machine)
else: else:
if curr_train == "gen": if curr_train == "gen":
...@@ -311,7 +327,8 @@ def main(): ...@@ -311,7 +327,8 @@ def main():
gen_trainer.trainOneDataBatch(batch_size, data_batch_gen) gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
# TODO: add API for paddle to allow true parameter sharing between different GradientMachines # TODO: add API for paddle to allow true parameter sharing between different GradientMachines
# so that we do not need to copy shared parameters. # so that we do not need to copy shared parameters.
copy_shared_parameters(gen_training_machine, dis_training_machine) copy_shared_parameters(gen_training_machine,
dis_training_machine)
copy_shared_parameters(gen_training_machine, generator_machine) copy_shared_parameters(gen_training_machine, generator_machine)
dis_trainer.finishTrainPass() dis_trainer.finishTrainPass()
...@@ -319,11 +336,14 @@ def main(): ...@@ -319,11 +336,14 @@ def main():
# At the end of each pass, save the generated samples/images # At the end of each pass, save the generated samples/images
fake_samples = get_fake_samples(generator_machine, batch_size, noise) fake_samples = get_fake_samples(generator_machine, batch_size, noise)
if data_source == "uniform": if data_source == "uniform":
plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass)) plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
(data_source, train_pass))
else: else:
save_images(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass)) save_images(fake_samples, "./%s_samples/train_pass%s.png" %
(data_source, train_pass))
dis_trainer.finishTrain() dis_trainer.finishTrain()
gen_trainer.finishTrain() gen_trainer.finishTrain()
if __name__ == '__main__': if __name__ == '__main__':
main() main()
demo/quick_start/trainer_config.resnet-lstm.py
浏览文件 @ 1adc6a28
...@@ -13,7 +13,6 @@ ...@@ -13,7 +13,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
""" """
This configuration is a demonstration of how to implement the stacked LSTM This configuration is a demonstration of how to implement the stacked LSTM
with residual connections, i.e. an LSTM layer takes the sum of the hidden states with residual connections, i.e. an LSTM layer takes the sum of the hidden states
...@@ -46,7 +45,8 @@ is_predict = get_config_arg('is_predict', bool, False) ...@@ -46,7 +45,8 @@ is_predict = get_config_arg('is_predict', bool, False)
trn = 'data/train.list' if not is_predict else None trn = 'data/train.list' if not is_predict else None
tst = 'data/test.list' if not is_predict else 'data/pred.list' tst = 'data/test.list' if not is_predict else 'data/pred.list'
process = 'process' if not is_predict else 'process_predict' process = 'process' if not is_predict else 'process_predict'
define_py_data_sources2(train_list=trn, define_py_data_sources2(
train_list=trn,
test_list=tst, test_list=tst,
module="dataprovider_emb", module="dataprovider_emb",
obj=process, obj=process,
...@@ -58,10 +58,9 @@ settings( ...@@ -58,10 +58,9 @@ settings(
learning_rate=2e-3, learning_rate=2e-3,
learning_method=AdamOptimizer(), learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4), regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25 gradient_clipping_threshold=25)
)
bias_attr = ParamAttr(initial_std=0.,l2_rate=0.) bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
data = data_layer(name="word", size=len(word_dict)) data = data_layer(name="word", size=len(word_dict))
emb = embedding_layer(input=data, size=128) emb = embedding_layer(input=data, size=128)
...@@ -73,17 +72,15 @@ for i in range(3): ...@@ -73,17 +72,15 @@ for i in range(3):
# The input to the current layer is the sum of the hidden state # The input to the current layer is the sum of the hidden state
# and input of the previous layer. # and input of the previous layer.
current_input = addto_layer(input=[previous_input, previous_hidden_state]) current_input = addto_layer(input=[previous_input, previous_hidden_state])
hidden_state = simple_lstm(input=current_input, size=128, hidden_state = simple_lstm(
lstm_cell_attr=ExtraAttr(drop_rate=0.1)) input=current_input, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
previous_input, previous_hidden_state = current_input, hidden_state previous_input, previous_hidden_state = current_input, hidden_state
lstm = previous_hidden_state lstm = previous_hidden_state
lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling()) lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
output = fc_layer(input=lstm_last, size=2, output = fc_layer(
bias_attr=bias_attr, input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
act=SoftmaxActivation())
if is_predict: if is_predict:
maxid = maxid_layer(output) maxid = maxid_layer(output)
......
demo/semantic_role_labeling/data/extract_dict_feature.py
浏览文件 @ 1adc6a28
...@@ -69,7 +69,6 @@ def extract_dict_features(pair_file, feature_file): ...@@ -69,7 +69,6 @@ def extract_dict_features(pair_file, feature_file):
feature_out.write(feature_str + '\n') feature_out.write(feature_str + '\n')
if __name__ == '__main__': if __name__ == '__main__':
usage = '-p pair_file -f feature_file' usage = '-p pair_file -f feature_file'
......
demo/semantic_role_labeling/data/extract_pairs.py
浏览文件 @ 1adc6a28
...@@ -66,7 +66,7 @@ def transform_labels(sentences, labels): ...@@ -66,7 +66,7 @@ def transform_labels(sentences, labels):
else: else:
verb_list = [] verb_list = []
for x in labels[i][0]: for x in labels[i][0]:
if x !='-': if x != '-':
verb_list.append(x) verb_list.append(x)
for j in xrange(1, len(labels[i])): for j in xrange(1, len(labels[i])):
...@@ -93,7 +93,7 @@ def transform_labels(sentences, labels): ...@@ -93,7 +93,7 @@ def transform_labels(sentences, labels):
is_in_bracket = True is_in_bracket = True
else: else:
print 'error:', ll print 'error:', ll
sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq)) sen_lab_pair.append((sentences[i], verb_list[j - 1], label_seq))
return sen_lab_pair return sen_lab_pair
...@@ -103,7 +103,7 @@ def write_file(sen_lab_pair, output_file): ...@@ -103,7 +103,7 @@ def write_file(sen_lab_pair, output_file):
sentence = x[0] sentence = x[0]
label_seq = ' '.join(x[2]) label_seq = ' '.join(x[2])
assert len(sentence.split()) == len(x[2]) assert len(sentence.split()) == len(x[2])
fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n') fout.write(sentence + '\t' + x[1] + '\t' + label_seq + '\n')
if __name__ == '__main__': if __name__ == '__main__':
......
demo/semantic_role_labeling/dataprovider.py
浏览文件 @ 1adc6a28
...@@ -30,8 +30,7 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs): ...@@ -30,8 +30,7 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)), integer_value_sequence(len(predicate_dict)), integer_value_sequence(2),
integer_value_sequence(2),
integer_value_sequence(len(label_dict)) integer_value_sequence(len(label_dict))
] ]
...@@ -40,8 +39,12 @@ def get_batch_size(yeild_data): ...@@ -40,8 +39,12 @@ def get_batch_size(yeild_data):
return len(yeild_data[0]) return len(yeild_data[0])
@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, @provider(
can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM) init_hook=hook,
should_shuffle=True,
calc_batch_size=get_batch_size,
can_over_batch_size=False,
cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_name): def process(settings, file_name):
with open(file_name, 'r') as fdata: with open(file_name, 'r') as fdata:
for line in fdata: for line in fdata:
......
demo/semantic_role_labeling/db_lstm.py
浏览文件 @ 1adc6a28
...@@ -20,7 +20,7 @@ from paddle.trainer_config_helpers import * ...@@ -20,7 +20,7 @@ from paddle.trainer_config_helpers import *
#file paths #file paths
word_dict_file = './data/wordDict.txt' word_dict_file = './data/wordDict.txt'
label_dict_file = './data/targetDict.txt' label_dict_file = './data/targetDict.txt'
predicate_file= './data/verbDict.txt' predicate_file = './data/verbDict.txt'
train_list_file = './data/train.list' train_list_file = './data/train.list'
test_list_file = './data/test.list' test_list_file = './data/test.list'
...@@ -47,7 +47,6 @@ if not is_predict: ...@@ -47,7 +47,6 @@ if not is_predict:
w = line.strip() w = line.strip()
predicate_dict[w] = i predicate_dict[w] = i
if is_test: if is_test:
train_list_file = None train_list_file = None
...@@ -57,9 +56,11 @@ if not is_predict: ...@@ -57,9 +56,11 @@ if not is_predict:
test_list=test_list_file, test_list=test_list_file,
module='dataprovider', module='dataprovider',
obj='process', obj='process',
args={'word_dict': word_dict, args={
'word_dict': word_dict,
'label_dict': label_dict, 'label_dict': label_dict,
'predicate_dict': predicate_dict }) 'predicate_dict': predicate_dict
})
word_dict_len = len(word_dict) word_dict_len = len(word_dict)
label_dict_len = len(label_dict) label_dict_len = len(label_dict)
...@@ -77,24 +78,16 @@ mark_dim = 5 ...@@ -77,24 +78,16 @@ mark_dim = 5
hidden_dim = 512 hidden_dim = 512
depth = 8 depth = 8
########################### Optimizer ####################################### ########################### Optimizer #######################################
settings( settings(
batch_size=150, batch_size=150,
learning_method=MomentumOptimizer(momentum=0), learning_method=MomentumOptimizer(momentum=0),
learning_rate=2e-2, learning_rate=2e-2,
regularization=L2Regularization(8e-4), regularization=L2Regularization(8e-4),
is_async=False, is_async=False,
model_average=ModelAverage(average_window=0.5, model_average=ModelAverage(
max_average_window=10000), average_window=0.5, max_average_window=10000), )
)
####################################### network ############################## ####################################### network ##############################
#8 features and 1 target #8 features and 1 target
...@@ -108,22 +101,28 @@ ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len) ...@@ -108,22 +101,28 @@ ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len) ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
mark = data_layer(name='mark_data', size=mark_dict_len) mark = data_layer(name='mark_data', size=mark_dict_len)
if not is_predict: if not is_predict:
target = data_layer(name='target', size=label_dict_len) target = data_layer(name='target', size=label_dict_len)
default_std = 1 / math.sqrt(hidden_dim) / 3.0
default_std=1/math.sqrt(hidden_dim)/3.0
emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.) emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
std_0 = ParameterAttribute(initial_std=0.) std_0 = ParameterAttribute(initial_std=0.)
std_default = ParameterAttribute(initial_std=default_std) std_default = ParameterAttribute(initial_std=default_std)
predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std)) predicate_embedding = embedding_layer(
mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0) size=word_dim,
input=predicate,
word_input=[word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2] param_attr=ParameterAttribute(
emb_layers = [embedding_layer(size=word_dim, input=x, param_attr=emb_para) for x in word_input] name='vemb', initial_std=default_std))
mark_embedding = embedding_layer(
name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
embedding_layer(
size=word_dim, input=x, param_attr=emb_para) for x in word_input
]
emb_layers.append(predicate_embedding) emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding) emb_layers.append(mark_embedding)
...@@ -131,14 +130,18 @@ hidden_0 = mixed_layer( ...@@ -131,14 +130,18 @@ hidden_0 = mixed_layer(
name='hidden0', name='hidden0',
size=hidden_dim, size=hidden_dim,
bias_attr=std_default, bias_attr=std_default,
input=[ full_matrix_projection(input=emb, param_attr=std_default ) for emb in emb_layers ]) input=[
full_matrix_projection(
input=emb, param_attr=std_default) for emb in emb_layers
])
mix_hidden_lr = 1e-3 mix_hidden_lr = 1e-3
lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0) lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr) hidden_para_attr = ParameterAttribute(
initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = lstmemory(name='lstm0', lstm_0 = lstmemory(
name='lstm0',
input=hidden_0, input=hidden_0,
act=ReluActivation(), act=ReluActivation(),
gate_act=SigmoidActivation(), gate_act=SigmoidActivation(),
...@@ -149,66 +152,67 @@ lstm_0 = lstmemory(name='lstm0', ...@@ -149,66 +152,67 @@ lstm_0 = lstmemory(name='lstm0',
#stack L-LSTM and R-LSTM with direct edges #stack L-LSTM and R-LSTM with direct edges
input_tmp = [hidden_0, lstm_0] input_tmp = [hidden_0, lstm_0]
for i in range(1, depth): for i in range(1, depth):
mix_hidden = mixed_layer(name='hidden'+str(i), mix_hidden = mixed_layer(
name='hidden' + str(i),
size=hidden_dim, size=hidden_dim,
bias_attr=std_default, bias_attr=std_default,
input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr), input=[
full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr) full_matrix_projection(
] input=input_tmp[0], param_attr=hidden_para_attr),
) full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
lstm = lstmemory(name='lstm'+str(i), ])
lstm = lstmemory(
name='lstm' + str(i),
input=mix_hidden, input=mix_hidden,
act=ReluActivation(), act=ReluActivation(),
gate_act=SigmoidActivation(), gate_act=SigmoidActivation(),
state_act=SigmoidActivation(), state_act=SigmoidActivation(),
reverse=((i % 2)==1), reverse=((i % 2) == 1),
bias_attr=std_0, bias_attr=std_0,
param_attr=lstm_para_attr) param_attr=lstm_para_attr)
input_tmp = [mix_hidden, lstm] input_tmp = [mix_hidden, lstm]
feature_out = mixed_layer(name='output', feature_out = mixed_layer(
name='output',
size=label_dict_len, size=label_dict_len,
bias_attr=std_default, bias_attr=std_default,
input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr), input=[
full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr) full_matrix_projection(
], input=input_tmp[0], param_attr=hidden_para_attr),
) full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
], )
if not is_predict: if not is_predict:
crf_l = crf_layer( name = 'crf', crf_l = crf_layer(
size = label_dict_len, name='crf',
input = feature_out, size=label_dict_len,
label = target, input=feature_out,
param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr) label=target,
param_attr=ParameterAttribute(
) name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
size = label_dict_len,
input = feature_out,
label = target,
param_attr=ParameterAttribute(name='crfw')
)
crf_dec_l = crf_decoding_layer(
name='crf_dec_l',
size=label_dict_len,
input=feature_out,
label=target,
param_attr=ParameterAttribute(name='crfw'))
eval = sum_evaluator(input=crf_dec_l) eval = sum_evaluator(input=crf_dec_l)
outputs(crf_l) outputs(crf_l)
else: else:
crf_dec_l = crf_decoding_layer(name = 'crf_dec_l', crf_dec_l = crf_decoding_layer(
size = label_dict_len, name='crf_dec_l',
input = feature_out, size=label_dict_len,
param_attr=ParameterAttribute(name='crfw') input=feature_out,
) param_attr=ParameterAttribute(name='crfw'))
outputs(crf_dec_l) outputs(crf_dec_l)
demo/semantic_role_labeling/predict.py
浏览文件 @ 1adc6a28
...@@ -26,7 +26,8 @@ UNK_IDX = 0 ...@@ -26,7 +26,8 @@ UNK_IDX = 0
class Prediction(): class Prediction():
def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file): def __init__(self, train_conf, dict_file, model_dir, label_file,
predicate_dict_file):
""" """
train_conf: trainer configure. train_conf: trainer configure.
dict_file: word dictionary file name. dict_file: word dictionary file name.
...@@ -35,7 +36,7 @@ class Prediction(): ...@@ -35,7 +36,7 @@ class Prediction():
self.dict = {} self.dict = {}
self.labels = {} self.labels = {}
self.predicate_dict={} self.predicate_dict = {}
self.labels_reverse = {} self.labels_reverse = {}
self.load_dict_label(dict_file, label_file, predicate_dict_file) self.load_dict_label(dict_file, label_file, predicate_dict_file)
...@@ -44,24 +45,17 @@ class Prediction(): ...@@ -44,24 +45,17 @@ class Prediction():
len_pred = len(self.predicate_dict) len_pred = len(self.predicate_dict)
conf = parse_config( conf = parse_config(
train_conf, train_conf, 'dict_len=' + str(len_dict) + ',label_len=' +
'dict_len=' + str(len_dict) + str(len_label) + ',pred_len=' + str(len_pred) + ',is_predict=True')
',label_len=' + str(len_label) +
',pred_len=' + str(len_pred) +
',is_predict=True')
self.network = swig_paddle.GradientMachine.createFromConfigProto( self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config) conf.model_config)
self.network.loadParameters(model_dir) self.network.loadParameters(model_dir)
slots = [ slots = [
integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict),
integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict),
integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict),
integer_value_sequence(len_dict), integer_value_sequence(len_pred), integer_value_sequence(2)
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_pred),
integer_value_sequence(2)
] ]
self.converter = DataProviderConverter(slots) self.converter = DataProviderConverter(slots)
...@@ -78,6 +72,7 @@ class Prediction(): ...@@ -78,6 +72,7 @@ class Prediction():
for line_count, line in enumerate(open(predicate_dict_file, 'r')): for line_count, line in enumerate(open(predicate_dict_file, 'r')):
self.predicate_dict[line.strip()] = line_count self.predicate_dict[line.strip()] = line_count
def get_data(self, data_file): def get_data(self, data_file):
""" """
Get input data of paddle format. Get input data of paddle format.
...@@ -90,7 +85,8 @@ class Prediction(): ...@@ -90,7 +85,8 @@ class Prediction():
sen_len = len(words) sen_len = len(words)
word_slot = [self.dict.get(w, UNK_IDX) for w in words] word_slot = [self.dict.get(w, UNK_IDX) for w in words]
predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)
] * sen_len
ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len
...@@ -123,7 +119,8 @@ class Prediction(): ...@@ -123,7 +119,8 @@ class Prediction():
def option_parser(): def option_parser():
usage = ("python predict.py -c config -w model_dir " usage = (
"python predict.py -c config -w model_dir "
"-d word dictionary -l label_file -i input_file -p pred_dict_file") "-d word dictionary -l label_file -i input_file -p pred_dict_file")
parser = OptionParser(usage="usage: %s [options]" % usage) parser = OptionParser(usage="usage: %s [options]" % usage)
parser.add_option( parser.add_option(
...@@ -187,8 +184,9 @@ def main(): ...@@ -187,8 +184,9 @@ def main():
output_file = options.output_file output_file = options.output_file
swig_paddle.initPaddle("--use_gpu=0") swig_paddle.initPaddle("--use_gpu=0")
predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file) predict = Prediction(train_conf, dict_file, model_path, label_file,
predict.predict(data_file,output_file) predict_dict_file)
predict.predict(data_file, output_file)
if __name__ == '__main__': if __name__ == '__main__':
......
demo/sentiment/predict.py
浏览文件 @ 1adc6a28
...@@ -71,9 +71,7 @@ class SentimentPrediction(): ...@@ -71,9 +71,7 @@ class SentimentPrediction():
transform word into integer index according to the dictionary. transform word into integer index according to the dictionary.
""" """
words = data.strip().split() words = data.strip().split()
word_slot = [ word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
self.word_dict[w] for w in words if w in self.word_dict
]
return word_slot return word_slot
def batch_predict(self, data_batch): def batch_predict(self, data_batch):
...@@ -85,8 +83,8 @@ class SentimentPrediction(): ...@@ -85,8 +83,8 @@ class SentimentPrediction():
if self.label is None: if self.label is None:
print("predicting label is %d" % (lab[0])) print("predicting label is %d" % (lab[0]))
else: else:
print("predicting label is %s" % print("predicting label is %s" % (self.label[lab[0]]))
(self.label[lab[0]]))
def option_parser(): def option_parser():
usage = "python predict.py -n config -w model_dir -d dictionary -i input_file " usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
...@@ -143,9 +141,10 @@ def main(): ...@@ -143,9 +141,10 @@ def main():
batch.append([predict.get_index(line)]) batch.append([predict.get_index(line)])
if len(batch) == batch_size: if len(batch) == batch_size:
predict.batch_predict(batch) predict.batch_predict(batch)
batch=[] batch = []
if len(batch) > 0: if len(batch) > 0:
predict.batch_predict(batch) predict.batch_predict(batch)
if __name__ == '__main__': if __name__ == '__main__':
main() main()
doc_cn/cluster/k8s/start_paddle.py
浏览文件 @ 1adc6a28
...@@ -19,7 +19,6 @@ import socket ...@@ -19,7 +19,6 @@ import socket
import os import os
import argparse import argparse
# configuration for cluster # configuration for cluster
API = "/api/v1/namespaces/" API = "/api/v1/namespaces/"
JOBSELECTOR = "labelSelector=job-name=" JOBSELECTOR = "labelSelector=job-name="
...@@ -145,8 +144,8 @@ def startPaddle(idMap={}, train_args_dict=None): ...@@ -145,8 +144,8 @@ def startPaddle(idMap={}, train_args_dict=None):
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser(prog="start_paddle.py", parser = argparse.ArgumentParser(
description='simple tool for k8s') prog="start_paddle.py", description='simple tool for k8s')
args, train_args_list = parser.parse_known_args() args, train_args_list = parser.parse_known_args()
train_args = refine_unknown_args(train_args_list) train_args = refine_unknown_args(train_args_list)
train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2]))
......
paddle/api/GradientMachine.cpp
浏览文件 @ 1adc6a28
...@@ -15,8 +15,8 @@ limitations under the License. */ ...@@ -15,8 +15,8 @@ limitations under the License. */
#include "PaddleAPI.h" #include "PaddleAPI.h"
#include "PaddleAPIPrivate.h" #include "PaddleAPIPrivate.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
#include "Internal.h" #include "Internal.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
std::vector<int> GradientMachine::defaultParamTypes = { std::vector<int> GradientMachine::defaultParamTypes = {
PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM}; PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM};
......
paddle/api/Internal.h
浏览文件 @ 1adc6a28
...@@ -16,14 +16,13 @@ limitations under the License. */ ...@@ -16,14 +16,13 @@ limitations under the License. */
#include "PaddleAPI.h" #include "PaddleAPI.h"
#include <vector>
#include <algorithm> #include <algorithm>
#include <vector>
template <typename T1, typename T2> template <typename T1, typename T2>
void staticCastVector(std::vector<T2>* dest, const std::vector<T1>& src) { void staticCastVector(std::vector<T2>* dest, const std::vector<T1>& src) {
dest->resize(src.size()); dest->resize(src.size());
std::transform(src.begin(), std::transform(src.begin(), src.end(), dest->begin(), [](T1 t) {
src.end(), return static_cast<T2>(t);
dest->begin(), });
[](T1 t) { return static_cast<T2>(t); });
} }
paddle/api/Matrix.cpp
浏览文件 @ 1adc6a28
...@@ -12,12 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,12 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "PaddleAPI.h"
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/math/CpuSparseMatrix.h"
#include <iostream>
#include <cstring> #include <cstring>
#include <iostream>
#include "PaddleAPI.h"
#include "paddle/math/CpuSparseMatrix.h"
#include "paddle/math/SparseMatrix.h"
struct MatrixPrivate { struct MatrixPrivate {
std::shared_ptr<paddle::Matrix> mat; std::shared_ptr<paddle::Matrix> mat;
......
paddle/api/PaddleAPI.h
浏览文件 @ 1adc6a28
...@@ -16,8 +16,8 @@ limitations under the License. */ ...@@ -16,8 +16,8 @@ limitations under the License. */
#include <stddef.h> #include <stddef.h>
#include <stdint.h> #include <stdint.h>
#include <string>
#include <stdexcept> #include <stdexcept>
#include <string>
#include <vector> #include <vector>
#include "paddle/utils/GlobalConstants.h" #include "paddle/utils/GlobalConstants.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/TypeDefs.h"
......
paddle/api/Parameter.cpp
浏览文件 @ 1adc6a28
...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "PaddleAPI.h"
#include "paddle/parameter/Parameter.h" #include "paddle/parameter/Parameter.h"
#include "PaddleAPI.h"
struct ParameterPrivate { struct ParameterPrivate {
std::shared_ptr<paddle::Parameter> sharedPtr; std::shared_ptr<paddle::Parameter> sharedPtr;
......
paddle/api/ParameterOptimizer.cpp
浏览文件 @ 1adc6a28
...@@ -12,11 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,11 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
#include "paddle/parameter/ParameterOptimizer.h" #include "paddle/parameter/ParameterOptimizer.h"
#include "Internal.h"
#include <algorithm> #include <algorithm>
#include "Internal.h"
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
struct ParameterOptimizerPrivate { struct ParameterOptimizerPrivate {
std::unique_ptr<paddle::ParameterOptimizer> optimizer; std::unique_ptr<paddle::ParameterOptimizer> optimizer;
...@@ -36,10 +36,7 @@ struct ParameterTraverseCallbackPrivate { ...@@ -36,10 +36,7 @@ struct ParameterTraverseCallbackPrivate {
size_t sparseId) { size_t sparseId) {
std::vector<paddle::VectorPtr> real_vecs; std::vector<paddle::VectorPtr> real_vecs;
real_vecs.resize(vecs.size()); real_vecs.resize(vecs.size());
std::transform(vecs.begin(), std::transform(vecs.begin(), vecs.end(), real_vecs.begin(), [](Vector* v) {
vecs.end(),
real_vecs.begin(),
[](Vector* v) {
if (v) { if (v) {
return *(paddle::VectorPtr*)(v->getSharedPtr()); return *(paddle::VectorPtr*)(v->getSharedPtr());
} else { } else {
......
paddle/api/SequenceGenerator.cpp
浏览文件 @ 1adc6a28
...@@ -12,14 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,14 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <algorithm>
#include <iterator>
#include <sstream>
#include <vector>
#include "PaddleAPI.h" #include "PaddleAPI.h"
#include "paddle/gserver/gradientmachines/GradientMachine.h" #include "paddle/gserver/gradientmachines/GradientMachine.h"
#include "paddle/parameter/Argument.h" #include "paddle/parameter/Argument.h"
#include "paddle/utils/Flags.h" #include "paddle/utils/Flags.h"
#include <vector>
#include <sstream>
#include <algorithm>
#include <iterator>
// used to represent partial sequence // used to represent partial sequence
struct Path { struct Path {
......
paddle/api/Trainer.cpp
浏览文件 @ 1adc6a28
...@@ -16,12 +16,12 @@ limitations under the License. */ ...@@ -16,12 +16,12 @@ limitations under the License. */
#include "PaddleAPIPrivate.h" #include "PaddleAPIPrivate.h"
#include <stdlib.h> #include <stdlib.h>
#include <memory>
#include <atomic> #include <atomic>
#include <memory>
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
#include "paddle/trainer/ParamUtil.h" #include "paddle/trainer/ParamUtil.h"
#include "paddle/trainer/Trainer.h" #include "paddle/trainer/Trainer.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
#include "paddle/trainer/TrainerInternal.h" #include "paddle/trainer/TrainerInternal.h"
#include "paddle/utils/Flags.h" #include "paddle/utils/Flags.h"
......
paddle/api/Util.cpp
浏览文件 @ 1adc6a28
...@@ -14,16 +14,16 @@ limitations under the License. */ ...@@ -14,16 +14,16 @@ limitations under the License. */
#include "PaddleAPI.h" #include "PaddleAPI.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/Excepts.h"
#include "paddle/parameter/Parameter.h" #include "paddle/parameter/Parameter.h"
#include "paddle/utils/Excepts.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Util.h"
#include <fenv.h> #include <fenv.h>
#include <algorithm>
#include <iostream> #include <iostream>
#include <iterator> #include <iterator>
#include <algorithm>
void initPaddle(int argc, char** argv) { void initPaddle(int argc, char** argv) {
paddle::initMain(argc, argv); paddle::initMain(argc, argv);
......
paddle/api/test/testMatrix.py
浏览文件 @ 1adc6a28
...@@ -104,7 +104,8 @@ class TestMatrix(unittest.TestCase): ...@@ -104,7 +104,8 @@ class TestMatrix(unittest.TestCase):
def test_numpy(self): def test_numpy(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32") numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat) m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
self.assertEqual((int(m.getHeight()), int(m.getWidth())), numpy_mat.shape) self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu()) 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]): for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e) self.assertAlmostEqual(a, e)
......
paddle/api/test/testVector.py
浏览文件 @ 1adc6a28
...@@ -29,7 +29,7 @@ class TestIVector(unittest.TestCase): ...@@ -29,7 +29,7 @@ class TestIVector(unittest.TestCase):
m = swig_paddle.IVector.createZero(10) m = swig_paddle.IVector.createZero(10)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu()) self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(m.getData(), [0]*10) self.assertEqual(m.getData(), [0] * 10)
def test_create(self): def test_create(self):
m = swig_paddle.IVector.create(range(10), False) m = swig_paddle.IVector.create(range(10), False)
...@@ -88,7 +88,7 @@ class TestVector(unittest.TestCase): ...@@ -88,7 +88,7 @@ class TestVector(unittest.TestCase):
v = swig_paddle.Vector.createZero(10) v = swig_paddle.Vector.createZero(10)
self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu()) self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(v.getData(), [0]*10) self.assertEqual(v.getData(), [0] * 10)
def testCreate(self): def testCreate(self):
v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)], False) v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)], False)
...@@ -104,7 +104,6 @@ class TestVector(unittest.TestCase): ...@@ -104,7 +104,6 @@ class TestVector(unittest.TestCase):
for i in xrange(len(v)): for i in xrange(len(v)):
self.assertTrue(util.doubleEqual(vdata[i], i / 100.0)) self.assertTrue(util.doubleEqual(vdata[i], i / 100.0))
def testCpuNumpy(self): def testCpuNumpy(self):
numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32") numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, copy=False) vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, copy=False)
...@@ -137,7 +136,6 @@ class TestVector(unittest.TestCase): ...@@ -137,7 +136,6 @@ class TestVector(unittest.TestCase):
for n, v in zip(numpy_arr, vecData): for n, v in zip(numpy_arr, vecData):
self.assertTrue(util.doubleEqual(n, v)) self.assertTrue(util.doubleEqual(n, v))
def testCopyFromNumpy(self): def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1, False) vec = swig_paddle.Vector.createZero(1, False)
arr = np.array([1.3, 3.2, 2.4], dtype="float32") arr = np.array([1.3, 3.2, 2.4], dtype="float32")
......
paddle/cuda/include/hl_base.h
浏览文件 @ 1adc6a28
...@@ -223,9 +223,9 @@ typedef struct { ...@@ -223,9 +223,9 @@ typedef struct {
#ifdef __NVCC__ #ifdef __NVCC__
#include "paddle/utils/Logging.h"
#include "hl_cuda.h"
#include "cuda_runtime.h" #include "cuda_runtime.h"
#include "hl_cuda.h"
#include "paddle/utils/Logging.h"
extern __thread bool g_sync_flag; extern __thread bool g_sync_flag;
extern __thread cudaStream_t default_stream; extern __thread cudaStream_t default_stream;
......
paddle/cuda/include/hl_dso_loader.h
浏览文件 @ 1adc6a28
...@@ -16,8 +16,8 @@ limitations under the License. */ ...@@ -16,8 +16,8 @@ limitations under the License. */
#define HL_DSO_LOADER_H_ #define HL_DSO_LOADER_H_
#include <dlfcn.h> #include <dlfcn.h>
#include <string>
#include <memory> #include <memory>
#include <string>
#include "hl_base.h" #include "hl_base.h"
/** /**
......
paddle/cuda/include/hl_gpu.h
浏览文件 @ 1adc6a28
...@@ -15,28 +15,28 @@ limitations under the License. */ ...@@ -15,28 +15,28 @@ limitations under the License. */
#ifndef HL_GPU_H_ #ifndef HL_GPU_H_
#define HL_GPU_H_ #define HL_GPU_H_
#include "hl_aggregate.h"
#include "hl_base.h" #include "hl_base.h"
#include "hl_cnn.h"
#include "hl_cuda.h" #include "hl_cuda.h"
#include "hl_cuda_cublas.h" #include "hl_cuda_cublas.h"
#include "hl_cuda_cudnn.h" #include "hl_cuda_cudnn.h"
#include "hl_matrix.h"
#include "hl_aggregate.h"
#include "hl_cnn.h"
#include "hl_sparse.h"
#include "hl_lstm.h" #include "hl_lstm.h"
#include "hl_matrix.h"
#include "hl_sequence.h" #include "hl_sequence.h"
#include "hl_sparse.h"
#include "hl_warpctc_wrap.h" #include "hl_warpctc_wrap.h"
#ifdef HPPL_STUB_FUNC #ifdef HPPL_STUB_FUNC
#include "stub/hl_cuda_stub.h"
#include "stub/hl_cuda_cublas_stub.h"
#include "stub/hl_cuda_cudnn_stub.h"
#include "stub/hl_matrix_stub.h"
#include "stub/hl_aggregate_stub.h" #include "stub/hl_aggregate_stub.h"
#include "stub/hl_cnn_stub.h" #include "stub/hl_cnn_stub.h"
#include "stub/hl_sparse_stub.h" #include "stub/hl_cuda_cublas_stub.h"
#include "stub/hl_cuda_cudnn_stub.h"
#include "stub/hl_cuda_stub.h"
#include "stub/hl_lstm_stub.h" #include "stub/hl_lstm_stub.h"
#include "stub/hl_matrix_stub.h"
#include "stub/hl_sequence_stub.h" #include "stub/hl_sequence_stub.h"
#include "stub/hl_sparse_stub.h"
#endif #endif
#endif /* HL_GPU_H_ */ #endif /* HL_GPU_H_ */
paddle/cuda/include/hl_time.h
浏览文件 @ 1adc6a28
...@@ -14,7 +14,7 @@ limitations under the License. */ ...@@ -14,7 +14,7 @@ limitations under the License. */
#ifndef HL_TIME_H_ #ifndef HL_TIME_H_
#define HL_TIME_H_ #define HL_TIME_H_
#include <cstdint>
/** /**
* @brief High resolution timer. * @brief High resolution timer.
* *
......
paddle/cuda/src/hl_cuda_cublas.cc
浏览文件 @ 1adc6a28
...@@ -12,12 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,12 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "hl_cuda_cublas.h"
#include <sys/time.h> #include <sys/time.h>
#include <mutex> #include <mutex>
#include "hl_cuda.h" #include "hl_cuda.h"
#include "hl_cuda_cublas.h"
#include "hl_thread.ph"
#include "hl_dso_loader.h" #include "hl_dso_loader.h"
#include "hl_thread.ph"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
namespace dynload { namespace dynload {
......
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @ 1adc6a28
...@@ -12,14 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,14 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "hl_cuda_cudnn.h"
#include <cudnn.h> #include <cudnn.h>
#include <mutex> #include <mutex>
#include "hl_cuda_cudnn.h"
#include "hl_cuda_cudnn.ph" #include "hl_cuda_cudnn.ph"
#include "hl_thread.ph"
#include "hl_dso_loader.h" #include "hl_dso_loader.h"
#include "paddle/utils/Logging.h" #include "hl_thread.ph"
#include "paddle/utils/CommandLineParser.h" #include "paddle/utils/CommandLineParser.h"
#include "paddle/utils/Logging.h"
P_DEFINE_int32(cudnn_conv_workspace_limit_in_mb, P_DEFINE_int32(cudnn_conv_workspace_limit_in_mb,
4096, 4096,
......
paddle/cuda/src/hl_cuda_device.cc
浏览文件 @ 1adc6a28
...@@ -12,13 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,13 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "hl_cuda.h"
#include <cuda_profiler_api.h> #include <cuda_profiler_api.h>
#include <string.h> #include <string.h>
#include <sys/syscall.h> #include <sys/syscall.h>
#include <sys/time.h> #include <sys/time.h>
#include <unistd.h> #include <unistd.h>
#include <mutex> #include <mutex>
#include "hl_cuda.h"
#include "hl_cuda.ph" #include "hl_cuda.ph"
#include "hl_dso_loader.h" #include "hl_dso_loader.h"
#include "hl_thread.ph" #include "hl_thread.ph"
......
paddle/cuda/src/hl_cudart_wrap.cc
浏览文件 @ 1adc6a28
...@@ -14,8 +14,8 @@ limitations under the License. */ ...@@ -14,8 +14,8 @@ limitations under the License. */
#ifdef PADDLE_USE_DSO #ifdef PADDLE_USE_DSO
#include <mutex>
#include <cuda_runtime.h> #include <cuda_runtime.h>
#include <mutex>
#include "hl_dso_loader.h" #include "hl_dso_loader.h"
/** /**
......
paddle/cuda/src/hl_time.cc
浏览文件 @ 1adc6a28
...@@ -12,10 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,10 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <chrono> #include "hl_time.h"
#include <stdlib.h> #include <stdlib.h>
#include <chrono>
#include <cstdint>
#include <iostream> #include <iostream>
#include "hl_time.h"
using std::chrono::high_resolution_clock; using std::chrono::high_resolution_clock;
......
paddle/cuda/src/hl_warpctc_wrap.cc
浏览文件 @ 1adc6a28
...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <mutex>
#include "hl_warpctc_wrap.h" #include "hl_warpctc_wrap.h"
#include <mutex>
#include "hl_dso_loader.h" #include "hl_dso_loader.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
......
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ 1adc6a28
...@@ -15,13 +15,13 @@ limitations under the License. */ ...@@ -15,13 +15,13 @@ limitations under the License. */
#include "ActivationFunction.h" #include "ActivationFunction.h"
#include <algorithm> #include <algorithm>
#include <memory>
#include <iostream> #include <iostream>
#include <type_traits> #include <memory>
#include <string> #include <string>
#include <thread> #include <thread>
#include "paddle/utils/ClassRegistrar.h" #include <type_traits>
#include "paddle/parameter/Argument.h" #include "paddle/parameter/Argument.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
......
paddle/gserver/dataproviders/DataProvider.cpp
浏览文件 @ 1adc6a28
...@@ -14,12 +14,12 @@ limitations under the License. */ ...@@ -14,12 +14,12 @@ limitations under the License. */
#include "DataProvider.h" #include "DataProvider.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Logging.h"
#include <algorithm>
#include <unistd.h> #include <unistd.h>
#include <algorithm>
#include "ProtoDataProvider.h" #include "ProtoDataProvider.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
namespace paddle { namespace paddle {
......
paddle/gserver/dataproviders/DataProvider.h
浏览文件 @ 1adc6a28
...@@ -14,28 +14,28 @@ limitations under the License. */ ...@@ -14,28 +14,28 @@ limitations under the License. */
#pragma once #pragma once
#include <vector>
#include <memory>
#include <mutex>
#include <iostream>
#include <fstream>
#include <stdint.h> #include <stdint.h>
#include <string.h>
#include <stdlib.h>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <fstream>
#include <iostream>
#include <memory>
#include <mutex>
#include <vector>
#include "DataConfig.pb.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/math/Vector.h"
#include "paddle/parameter/Argument.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Queue.h" #include "paddle/utils/Queue.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/ThreadLocal.h" #include "paddle/utils/ThreadLocal.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/TypeDefs.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "paddle/math/Vector.h"
#include "DataConfig.pb.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/parameter/Argument.h"
namespace paddle { namespace paddle {
/** /**
......
paddle/gserver/dataproviders/MultiDataProvider.cpp
浏览文件 @ 1adc6a28
...@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Util.h"
#include "MultiDataProvider.h" #include "MultiDataProvider.h"
#include "paddle/utils/Logging.h"
#include <algorithm> #include <algorithm>
#include "paddle/utils/Logging.h"
#include "paddle/utils/Util.h"
namespace paddle { namespace paddle {
......
paddle/gserver/dataproviders/ProtoDataProvider.cpp
浏览文件 @ 1adc6a28
...@@ -13,14 +13,14 @@ See the License for the specific language governing permissions and ...@@ -13,14 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "ProtoDataProvider.h" #include "ProtoDataProvider.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/StringUtil.h"
#include <algorithm> #include <algorithm>
#include <fstream> #include <fstream>
#include <istream> #include <istream>
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/Logging.h"
#include "DataProviderGroup.h" #include "DataProviderGroup.h"
#include "paddle/utils/Logging.h"
P_DEFINE_double(memory_threshold_on_load_data, P_DEFINE_double(memory_threshold_on_load_data,
1.0, 1.0,
...@@ -562,14 +562,14 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size, ...@@ -562,14 +562,14 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
auto mat = cpuArguments[slot].value; auto mat = cpuArguments[slot].value;
mat->resize(size, dim); mat->resize(size, dim);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) { if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat) std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
->copyFrom(dataPos.data(), dataPos.data(),
slots_[slot].indices.data(), slots_[slot].indices.data(),
slots_[slot].sparseNonValueData.data(), slots_[slot].sparseNonValueData.data(),
HPPL_STREAM_1); HPPL_STREAM_1);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) { } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat) std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
->copyFrom(dataPos.data(), dataPos.data(),
slots_[slot].indices.data(), slots_[slot].indices.data(),
slots_[slot].sparseNonValueData.data()); slots_[slot].sparseNonValueData.data());
} else { } else {
...@@ -598,14 +598,14 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size, ...@@ -598,14 +598,14 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
auto mat = cpuArguments[slot].value; auto mat = cpuArguments[slot].value;
mat->resize(size, dim); mat->resize(size, dim);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) { if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat) std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
->copyFrom(dataPos.data(), dataPos.data(),
slots_[slot].indices.data(), slots_[slot].indices.data(),
slots_[slot].sparseFloatValueData.data(), slots_[slot].sparseFloatValueData.data(),
HPPL_STREAM_1); HPPL_STREAM_1);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) { } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat) std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
->copyFrom(dataPos.data(), dataPos.data(),
slots_[slot].indices.data(), slots_[slot].indices.data(),
slots_[slot].sparseFloatValueData.data()); slots_[slot].sparseFloatValueData.data());
} else { } else {
......
paddle/gserver/dataproviders/ProtoDataProvider.h
浏览文件 @ 1adc6a28
...@@ -16,8 +16,8 @@ limitations under the License. */ ...@@ -16,8 +16,8 @@ limitations under the License. */
#include <vector> #include <vector>
#include "paddle/utils/Stat.h"
#include "DataFormat.pb.h" #include "DataFormat.pb.h"
#include "paddle/utils/Stat.h"
#include "DataProvider.h" #include "DataProvider.h"
#include "ProtoReader.h" #include "ProtoReader.h"
......
paddle/gserver/dataproviders/ProtoReader.h
浏览文件 @ 1adc6a28
...@@ -16,10 +16,10 @@ limitations under the License. */ ...@@ -16,10 +16,10 @@ limitations under the License. */
#include <memory> #include <memory>
#include <google/protobuf/message_lite.h>
#include <google/protobuf/io/coded_stream.h> #include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/io/gzip_stream.h> #include <google/protobuf/io/gzip_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/message_lite.h>
namespace paddle { namespace paddle {
......
paddle/gserver/dataproviders/PyDataProvider.cpp
浏览文件 @ 1adc6a28
...@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and ...@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "PyDataProvider.h" #include "PyDataProvider.h"
#include "paddle/utils/PythonUtil.h"
#include <fenv.h> #include <fenv.h>
#include "paddle/utils/Util.h"
#include "paddle/utils/Excepts.h" #include "paddle/utils/Excepts.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Util.h"
namespace paddle { namespace paddle {
...@@ -316,14 +316,14 @@ void PyDataProvider::handleSparseNonValueSlot( ...@@ -316,14 +316,14 @@ void PyDataProvider::handleSparseNonValueSlot(
auto mat = cpuArguments[slotIndex].value; auto mat = cpuArguments[slotIndex].value;
mat->resize(slot.sampleNum, dim, slot.sampleNum, NO_VALUE, SPARSE_CSR); mat->resize(slot.sampleNum, dim, slot.sampleNum, NO_VALUE, SPARSE_CSR);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) { if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat) std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
->copyFrom(slot.sampleSequenceIdVec.data(), slot.sampleSequenceIdVec.data(),
slot.indices.data(), slot.indices.data(),
slot.sparseNonValueData.data(), slot.sparseNonValueData.data(),
HPPL_STREAM_1); HPPL_STREAM_1);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) { } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat) std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
->copyFrom(slot.sampleSequenceIdVec.data(), slot.sampleSequenceIdVec.data(),
slot.indices.data(), slot.indices.data(),
slot.sparseNonValueData.data()); slot.sparseNonValueData.data());
} else { } else {
...@@ -347,14 +347,14 @@ void PyDataProvider::handleSparseValueSlot( ...@@ -347,14 +347,14 @@ void PyDataProvider::handleSparseValueSlot(
auto mat = cpuArguments[slotIndex].value; auto mat = cpuArguments[slotIndex].value;
mat->resize(slot.sampleNum, dim, slot.sampleNum, FLOAT_VALUE, SPARSE_CSR); mat->resize(slot.sampleNum, dim, slot.sampleNum, FLOAT_VALUE, SPARSE_CSR);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) { if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat) std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
->copyFrom(slot.sampleSequenceIdVec.data(), slot.sampleSequenceIdVec.data(),
slot.indices.data(), slot.indices.data(),
slot.sparseFloatValueData.data(), slot.sparseFloatValueData.data(),
HPPL_STREAM_DEFAULT); HPPL_STREAM_DEFAULT);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) { } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat) std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
->copyFrom(slot.sampleSequenceIdVec.data(), slot.sampleSequenceIdVec.data(),
slot.indices.data(), slot.indices.data(),
slot.sparseFloatValueData.data()); slot.sparseFloatValueData.data());
} else { } else {
......
paddle/gserver/dataproviders/PyDataProvider2.cpp
浏览文件 @ 1adc6a28
...@@ -15,18 +15,18 @@ limitations under the License. */ ...@@ -15,18 +15,18 @@ limitations under the License. */
#ifndef PADDLE_NO_PYTHON #ifndef PADDLE_NO_PYTHON
#include <Python.h> #include <Python.h>
#include <numpy/numpyconfig.h>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <unordered_set>
#include <list> #include <list>
#include <numpy/numpyconfig.h> #include <unordered_set>
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include <numpy/ndarrayobject.h> #include <numpy/ndarrayobject.h>
#include "DataProvider.h" #include "DataProvider.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Locks.h" #include "paddle/utils/Locks.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
...@@ -400,8 +400,7 @@ private: ...@@ -400,8 +400,7 @@ private:
if (this->loadThread_) { // wait poolActualSize < poolSize; if (this->loadThread_) { // wait poolActualSize < poolSize;
std::unique_lock<std::mutex> l(mtx_); std::unique_lock<std::mutex> l(mtx_);
pushCV_.wait(l, pushCV_.wait(l, [this, additionalBatchSize] {
[this, additionalBatchSize] {
return this->poolActualSize_ < poolSize_; return this->poolActualSize_ < poolSize_;
}); });
} }
...@@ -529,10 +528,8 @@ public: ...@@ -529,10 +528,8 @@ public:
// but, loading from cache, cache object should ensure // but, loading from cache, cache object should ensure
// data pool ready. // data pool ready.
std::unique_lock<std::mutex> l(mtx_); std::unique_lock<std::mutex> l(mtx_);
pullCV_.wait(l, pullCV_.wait(l, [this, &size] {
[this, &size] { return this->poolActualSize_ >= std::max(size, this->minPoolSize_) ||
return this->poolActualSize_ >=
std::max(size, this->minPoolSize_) ||
callingContexts_.empty(); callingContexts_.empty();
}); });
......
paddle/gserver/evaluators/Evaluator.cpp
浏览文件 @ 1adc6a28
...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "paddle/gserver/evaluators/Evaluator.h" #include "paddle/gserver/evaluators/Evaluator.h"
#include "paddle/utils/Stat.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h" #include "paddle/gserver/gradientmachines/NeuralNetwork.h"
...@@ -842,9 +842,9 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) { ...@@ -842,9 +842,9 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) {
auto start = predictArray.begin(); auto start = predictArray.begin();
while (start != predictArray.end()) { while (start != predictArray.end()) {
auto end = std::find_if( auto end = std::find_if(
start + 1, start + 1, predictArray.end(), [=](const PredictionResult& x) {
predictArray.end(), return x.queryid != start->queryid;
[=](const PredictionResult& x) { return x.queryid != start->queryid; }); });
CHECK(end != start); CHECK(end != start);
stat(start - predictArray.begin(), stat(start - predictArray.begin(),
end - predictArray.begin(), end - predictArray.begin(),
......
paddle/gserver/evaluators/Evaluator.h
浏览文件 @ 1adc6a28
...@@ -14,11 +14,11 @@ limitations under the License. */ ...@@ -14,11 +14,11 @@ limitations under the License. */
#pragma once #pragma once
#include "paddle/pserver/ParameterClient2.h" #include <fstream>
#include "paddle/utils/ClassRegistrar.h"
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "paddle/parameter/Argument.h" #include "paddle/parameter/Argument.h"
#include <fstream> #include "paddle/pserver/ParameterClient2.h"
#include "paddle/utils/ClassRegistrar.h"
namespace paddle { namespace paddle {
......
paddle/gserver/gradientmachines/GradientMachine.cpp
浏览文件 @ 1adc6a28
...@@ -14,16 +14,16 @@ limitations under the License. */ ...@@ -14,16 +14,16 @@ limitations under the License. */
#include "GradientMachine.h" #include "GradientMachine.h"
#include "paddle/utils/Logging.h"
#include <fstream> #include <fstream>
#include "paddle/utils/Logging.h"
#include "hl_gpu.h" #include "GradientMachineMode.h"
#include "NeuralNetwork.h"
#include "ParallelNeuralNetwork.h"
#include "MultiGradientMachine.h" #include "MultiGradientMachine.h"
#include "NeuralNetwork.h"
#include "MultiNetwork.h" #include "MultiNetwork.h"
#include "GradientMachineMode.h" #include "NeuralNetwork.h"
#include "NeuralNetwork.h"
#include "ParallelNeuralNetwork.h"
#include "hl_gpu.h"
namespace paddle { namespace paddle {
......
paddle/gserver/gradientmachines/GradientMachine.h
浏览文件 @ 1adc6a28
...@@ -17,15 +17,15 @@ limitations under the License. */ ...@@ -17,15 +17,15 @@ limitations under the License. */
#include <iostream> #include <iostream>
#include <vector> #include <vector>
#include "paddle/math/Matrix.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/parameter/ParameterUpdaterBase.h"
#include "paddle/utils/Thread.h"
#include "TrainerConfig.pb.h"
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "TrainerConfig.pb.h"
#include "paddle/gserver/dataproviders/DataProvider.h" #include "paddle/gserver/dataproviders/DataProvider.h"
#include "paddle/gserver/evaluators/Evaluator.h" #include "paddle/gserver/evaluators/Evaluator.h"
#include "paddle/gserver/layers/Layer.h" #include "paddle/gserver/layers/Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/parameter/ParameterUpdaterBase.h"
#include "paddle/utils/Thread.h"
namespace paddle { namespace paddle {
/** /**
......
paddle/gserver/gradientmachines/MultiGradientMachine.h
浏览文件 @ 1adc6a28
...@@ -18,9 +18,9 @@ limitations under the License. */ ...@@ -18,9 +18,9 @@ limitations under the License. */
#include "GradientMachine.h" #include "GradientMachine.h"
#include "paddle/utils/Queue.h"
#include "paddle/utils/Locks.h"
#include "hl_gpu.h" #include "hl_gpu.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/Queue.h"
namespace paddle { namespace paddle {
......
paddle/gserver/gradientmachines/MultiNetwork.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <algorithm>
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include <algorithm>
#include "MultiNetwork.h" #include "MultiNetwork.h"
......
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @ 1adc6a28
...@@ -14,15 +14,15 @@ limitations under the License. */ ...@@ -14,15 +14,15 @@ limitations under the License. */
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/CustomStackTrace.h" #include "paddle/utils/CustomStackTrace.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "MultiNetwork.h"
#include "hl_gpu.h"
#include "NeuralNetwork.h" #include "NeuralNetwork.h"
#include "RecurrentGradientMachine.h" #include "RecurrentGradientMachine.h"
#include "MultiNetwork.h" #include "hl_gpu.h"
#include "paddle/gserver/layers/AgentLayer.h" #include "paddle/gserver/layers/AgentLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
void parameterInitNN(int paramId, void parameterInitNN(int paramId,
......
paddle/gserver/gradientmachines/NeuralNetwork.h
浏览文件 @ 1adc6a28
...@@ -14,18 +14,18 @@ limitations under the License. */ ...@@ -14,18 +14,18 @@ limitations under the License. */
#pragma once #pragma once
#include <memory>
#include <map>
#include <functional> #include <functional>
#include <map>
#include <memory>
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/parameter/Parameter.h"
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "paddle/gserver/dataproviders/DataProvider.h"
#include "paddle/gserver/gradientmachines/GradientMachine.h" #include "paddle/gserver/gradientmachines/GradientMachine.h"
#include "paddle/gserver/layers/CostLayer.h" #include "paddle/gserver/layers/CostLayer.h"
#include "paddle/gserver/layers/DataLayer.h" #include "paddle/gserver/layers/DataLayer.h"
#include "paddle/gserver/dataproviders/DataProvider.h"
#include "paddle/gserver/layers/Layer.h" #include "paddle/gserver/layers/Layer.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/utils/ClassRegistrar.h"
namespace paddle { namespace paddle {
/* /*
...@@ -57,11 +57,10 @@ void parameterInitNN(int paramId, ...@@ -57,11 +57,10 @@ void parameterInitNN(int paramId,
class NeuralNetwork : public GradientMachine { class NeuralNetwork : public GradientMachine {
public: public:
virtual void init( virtual void init(const ModelConfig& config,
const ModelConfig& config,
ParamInitCallback callback = nullptr, ParamInitCallback callback = nullptr,
const std::vector<ParameterType>& const std::vector<ParameterType>& parameterTypes =
parameterTypes = std::vector<ParameterType>{PARAMETER_VALUE, std::vector<ParameterType>{PARAMETER_VALUE,
PARAMETER_GRADIENT, PARAMETER_GRADIENT,
PARAMETER_MOMENTUM}, PARAMETER_MOMENTUM},
bool useGpu = FLAGS_use_gpu); bool useGpu = FLAGS_use_gpu);
......
paddle/gserver/gradientmachines/ParallelNeuralNetwork.h
浏览文件 @ 1adc6a28
...@@ -37,11 +37,10 @@ public: ...@@ -37,11 +37,10 @@ public:
NeuralNetwork *rootNetwork = nullptr) NeuralNetwork *rootNetwork = nullptr)
: NeuralNetwork(subModelName, rootNetwork) {} : NeuralNetwork(subModelName, rootNetwork) {}
virtual void init( virtual void init(const ModelConfig &config,
const ModelConfig &config,
ParamInitCallback callback = nullptr, ParamInitCallback callback = nullptr,
const std::vector<ParameterType> const std::vector<ParameterType> &parameterTypes =
&parameterTypes = std::vector<ParameterType>{PARAMETER_VALUE, std::vector<ParameterType>{PARAMETER_VALUE,
PARAMETER_GRADIENT, PARAMETER_GRADIENT,
PARAMETER_MOMENTUM}, PARAMETER_MOMENTUM},
bool useGpu = FLAGS_use_gpu); bool useGpu = FLAGS_use_gpu);
......
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @ 1adc6a28
...@@ -12,17 +12,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,17 +12,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h" #include "RecurrentGradientMachine.h"
#include "paddle/utils/Util.h" #include <dlfcn.h>
#include "paddle/utils/Flags.h"
#include <algorithm> #include <algorithm>
#include <cmath>
#include <functional> #include <functional>
#include <dlfcn.h>
#include <limits> #include <limits>
#include <cmath>
#include "RecurrentGradientMachine.h"
#include "NeuralNetwork.h" #include "NeuralNetwork.h"
#include "paddle/gserver/layers/AgentLayer.h" #include "paddle/gserver/layers/AgentLayer.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/Stat.h"
#include "paddle/utils/Util.h"
P_DEFINE_string(diy_beam_search_prob_so, "", "the diy beam search cost so"); P_DEFINE_string(diy_beam_search_prob_so, "", "the diy beam search cost so");
...@@ -78,7 +78,8 @@ static inline SymbolType loadDiySymbol(const char* symbolName) { ...@@ -78,7 +78,8 @@ static inline SymbolType loadDiySymbol(const char* symbolName) {
return reinterpret_cast<SymbolType>(sym); return reinterpret_cast<SymbolType>(sym);
} }
static InitFunction __init__diy_prob_method([] { static InitFunction __init__diy_prob_method(
[] {
std::string soName = FLAGS_diy_beam_search_prob_so; std::string soName = FLAGS_diy_beam_search_prob_so;
if (!soName.empty()) { if (!soName.empty()) {
gDiyProbHandle = dlopen(soName.c_str(), RTLD_LAZY); gDiyProbHandle = dlopen(soName.c_str(), RTLD_LAZY);
...@@ -86,12 +87,13 @@ static InitFunction __init__diy_prob_method([] { ...@@ -86,12 +87,13 @@ static InitFunction __init__diy_prob_method([] {
atexit(exit_diy_prob); atexit(exit_diy_prob);
gDiyProbMethod = gDiyProbMethod =
loadDiySymbol<decltype(gDiyProbMethod)>(DIY_CALC_PROB_SYMBOL_NAME); loadDiySymbol<decltype(gDiyProbMethod)>(DIY_CALC_PROB_SYMBOL_NAME);
gDiyProbStart = gDiyProbStart = loadDiySymbol<decltype(gDiyProbStart)>(
loadDiySymbol<decltype(gDiyProbStart)>(DIY_START_CALC_PROB_SYMBOL_NAME); DIY_START_CALC_PROB_SYMBOL_NAME);
gDiyProbStop = gDiyProbStop = loadDiySymbol<decltype(gDiyProbStop)>(
loadDiySymbol<decltype(gDiyProbStop)>(DIY_FINISH_CALC_PROB_SYMBOL_NAME); DIY_FINISH_CALC_PROB_SYMBOL_NAME);
} }
}, std::numeric_limits<int>::max()); },
std::numeric_limits<int>::max());
class BeamSearchControlCallbacks { class BeamSearchControlCallbacks {
public: public:
...@@ -1281,10 +1283,9 @@ void RecurrentGradientMachine::beamSearch(size_t batchSize) { ...@@ -1281,10 +1283,9 @@ void RecurrentGradientMachine::beamSearch(size_t batchSize) {
std::vector<std::vector<int>*> prefixes; std::vector<std::vector<int>*> prefixes;
prefixes.resize(paths.size()); prefixes.resize(paths.size());
std::transform( std::transform(
paths.begin(), paths.begin(), paths.end(), prefixes.begin(), [](const Path& p) {
paths.end(), return const_cast<std::vector<int>*>(&p.ids);
prefixes.begin(), });
[](const Path& p) { return const_cast<std::vector<int>*>(&p.ids); });
beamSearchCtrlCallbacks_->beamSearchCandidateAdjust( beamSearchCtrlCallbacks_->beamSearchCandidateAdjust(
prefixes, frames_[machineCur].get(), i); prefixes, frames_[machineCur].get(), i);
} }
......
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
浏览文件 @ 1adc6a28
...@@ -14,9 +14,9 @@ limitations under the License. */ ...@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once #pragma once
#include <functional>
#include "GradientMachine.h" #include "GradientMachine.h"
#include "NeuralNetwork.h" #include "NeuralNetwork.h"
#include <functional>
#include "paddle/utils/Locks.h" #include "paddle/utils/Locks.h"
......
paddle/gserver/layers/BatchNormBaseLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "BatchNormBaseLayer.h" #include "BatchNormBaseLayer.h"
#include "BatchNormalizationLayer.h" #include "BatchNormalizationLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
#ifndef PADDLE_ONLY_CPU #ifndef PADDLE_ONLY_CPU
#include "CudnnBatchNormLayer.h" #include "CudnnBatchNormLayer.h"
#endif #endif
......
paddle/gserver/layers/BatchNormBaseLayer.h
浏览文件 @ 1adc6a28
...@@ -14,8 +14,8 @@ limitations under the License. */ ...@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once #pragma once
#include "paddle/utils/Stat.h"
#include "Layer.h" #include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/BatchNormalizationLayer.h
浏览文件 @ 1adc6a28
...@@ -14,8 +14,8 @@ limitations under the License. */ ...@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once #pragma once
#include "Layer.h"
#include "BatchNormBaseLayer.h" #include "BatchNormBaseLayer.h"
#include "Layer.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ConcatenateLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "Layer.h" #include "Layer.h"
#include "Projection.h" #include "Projection.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ContextProjection.cpp
浏览文件 @ 1adc6a28
...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "ContextProjection.h" #include "ContextProjection.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ConvBaseLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "ConvBaseLayer.h" #include "ConvBaseLayer.h"
#include "paddle/math/MathUtils.h" #include "paddle/math/MathUtils.h"
#include "paddle/utils/Logging.h"
namespace paddle { namespace paddle {
bool ConvBaseLayer::init(const LayerMap& layerMap, bool ConvBaseLayer::init(const LayerMap& layerMap,
......
paddle/gserver/layers/ConvOperator.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/math/Matrix.h"
#include "paddle/math/MathUtils.h"
#include "Operator.h" #include "Operator.h"
#include "paddle/math/MathUtils.h"
#include "paddle/math/Matrix.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ConvProjection.cpp
浏览文件 @ 1adc6a28
...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "ConvProjection.h" #include "ConvProjection.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ConvShiftLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "Layer.h" #include "Layer.h"
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ConvexCombinationLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "Layer.h" #include "Layer.h"
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/CosSimVecMatLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "Layer.h" #include "Layer.h"
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/CostLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,11 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,11 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <memory> #include "CostLayer.h"
#include <algorithm> #include <algorithm>
#include "paddle/utils/Logging.h"
#include <cmath> #include <cmath>
#include "CostLayer.h" #include <memory>
#include "paddle/utils/Logging.h"
#include "paddle/math/SparseMatrix.h" #include "paddle/math/SparseMatrix.h"
......
paddle/gserver/layers/CudnnBatchNormLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "CudnnBatchNormLayer.h" #include "CudnnBatchNormLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/CudnnBatchNormLayer.h
浏览文件 @ 1adc6a28
...@@ -14,9 +14,9 @@ limitations under the License. */ ...@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once #pragma once
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "BatchNormBaseLayer.h" #include "BatchNormBaseLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/CudnnConvLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "CudnnConvLayer.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "CudnnConvLayer.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/CudnnConvLayer.h
浏览文件 @ 1adc6a28
...@@ -14,10 +14,10 @@ limitations under the License. */ ...@@ -14,10 +14,10 @@ limitations under the License. */
#pragma once #pragma once
#include <vector>
#include "ConvBaseLayer.h" #include "ConvBaseLayer.h"
#include "paddle/math/Matrix.h"
#include "Projection.h" #include "Projection.h"
#include <vector> #include "paddle/math/Matrix.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/CudnnPoolLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "CudnnPoolLayer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "paddle/math/Matrix.h"
#include "CudnnPoolLayer.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/EosIdCheckLayer.cpp
浏览文件 @ 1adc6a28
...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "Layer.h" #include "Layer.h"
#include "paddle/utils/Logging.h"
namespace paddle { namespace paddle {
/** /**
......
paddle/gserver/layers/ExpandConvBaseLayer.h
浏览文件 @ 1adc6a28
...@@ -14,9 +14,9 @@ limitations under the License. */ ...@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once #pragma once
#include <vector>
#include "ConvBaseLayer.h" #include "ConvBaseLayer.h"
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include <vector>
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ExpandConvLayer.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "ExpandConvLayer.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "ExpandConvLayer.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ExpandConvLayer.h
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#pragma once #pragma once
#include "paddle/math/Matrix.h"
#include <vector> #include <vector>
#include "ExpandConvBaseLayer.h" #include "ExpandConvBaseLayer.h"
#include "paddle/math/Matrix.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/ExpandConvTransLayer.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "ExpandConvTransLayer.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "ExpandConvTransLayer.h"
/* The implementation of the convTransLayer is basically a swap of forward and /* The implementation of the convTransLayer is basically a swap of forward and
* backward of the original convLayer. * backward of the original convLayer.
......
paddle/gserver/layers/ExpandConvTransLayer.h
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#pragma once #pragma once
#include "paddle/math/Matrix.h"
#include <vector> #include <vector>
#include "ExpandConvBaseLayer.h" #include "ExpandConvBaseLayer.h"
#include "paddle/math/Matrix.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/FullyConnectedLayer.cpp
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limitations under the License. */ limitations under the License. */
#include "FullyConnectedLayer.h" #include "FullyConnectedLayer.h"
#include <algorithm>
#include <vector>
#include "paddle/math/SparseMatrix.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "paddle/math/SparseMatrix.h"
#include <vector>
#include <algorithm>
namespace paddle { namespace paddle {
......
paddle/gserver/layers/GatedRecurrentLayer.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "Layer.h"
#include "GatedRecurrentLayer.h" #include "GatedRecurrentLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
...@@ -386,8 +386,9 @@ void GatedRecurrentLayer::backwardBatch(int batchSize, MatrixPtr inputGrad) { ...@@ -386,8 +386,9 @@ void GatedRecurrentLayer::backwardBatch(int batchSize, MatrixPtr inputGrad) {
{ {
batchSize = outputGradTmp->getHeight(); batchSize = outputGradTmp->getHeight();
gruValue.prevOutValue = gruValue.prevOutValue =
(n == 0 ? nullptr : (batchValue_->getBatchValue(n - 1, batchSize)) (n == 0
->getData()); ? nullptr
: (batchValue_->getBatchValue(n - 1, batchSize))->getData());
gruGrad.prevOutGrad = gruGrad.prevOutGrad =
(n == 0 ? nullptr (n == 0 ? nullptr
: (batchGrad_->getBatchValue(n - 1, batchSize))->getData()); : (batchGrad_->getBatchValue(n - 1, batchSize))->getData());
......
paddle/gserver/layers/GatedRecurrentLayer.h
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#pragma once #pragma once
#include "paddle/math/Matrix.h"
#include "SequenceToBatch.h"
#include "GruCompute.h" #include "GruCompute.h"
#include "Layer.h" #include "Layer.h"
#include "SequenceToBatch.h"
#include "paddle/math/Matrix.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/GruCompute.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Util.h"
#include "GruCompute.h" #include "GruCompute.h"
#include "hl_recurrent_apply.cuh" #include "hl_recurrent_apply.cuh"
#include "paddle/utils/Util.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/GruCompute.h
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#pragma once #pragma once
#include "paddle/utils/TypeDefs.h"
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "hl_gpu.h" #include "hl_gpu.h"
#include "paddle/utils/TypeDefs.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/GruStepLayer.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "Layer.h"
#include "GruCompute.h" #include "GruCompute.h"
#include "Layer.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
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paddle/gserver/layers/IdentityProjection.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Stat.h"
#include "Projection.h" #include "Projection.h"
#include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/InterpolationLayer.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Logging.h"
#include "Layer.h" #include "Layer.h"
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/Layer.cpp
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#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "paddle/utils/Logging.h"
#include "paddle/math/SparseMatrix.h" #include "paddle/math/SparseMatrix.h"
#include "paddle/utils/Logging.h"
#include "AddtoLayer.h" #include "AddtoLayer.h"
#include "CRFLayer.h"
#include "CosSimLayer.h" #include "CosSimLayer.h"
#include "CostLayer.h" #include "CostLayer.h"
#include "ExpandConvLayer.h"
#include "CRFLayer.h"
#include "DataLayer.h" #include "DataLayer.h"
#include "ExpandConvLayer.h"
#include "FullyConnectedLayer.h" #include "FullyConnectedLayer.h"
#include "HierarchicalSigmoidLayer.h" #include "HierarchicalSigmoidLayer.h"
#include "MaxLayer.h" #include "MaxLayer.h"
......
paddle/gserver/layers/Layer.h
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#pragma once #pragma once
#include <memory>
#include <functional>
#include <paddle/parameter/Argument.h> #include <paddle/parameter/Argument.h>
#include "paddle/utils/ClassRegistrar.h" #include <functional>
#include <memory>
#include "ModelConfig.pb.h"
#include "paddle/math/CpuSparseMatrix.h" #include "paddle/math/CpuSparseMatrix.h"
#include "paddle/parameter/Parameter.h" #include "paddle/parameter/Parameter.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "ModelConfig.pb.h"
#include "paddle/gserver/activations/ActivationFunction.h"
#include <paddle/parameter/ParallelParameter.h> #include <paddle/parameter/ParallelParameter.h>
#include <paddle/parameter/Weight.h> #include <paddle/parameter/Weight.h>
#include "paddle/gserver/activations/ActivationFunction.h"
/// Macro for registering a layer type. /// Macro for registering a layer type.
/// Example: REGISTER_LAYER(crf_error, CRFDecodingErrorLayer); /// Example: REGISTER_LAYER(crf_error, CRFDecodingErrorLayer);
......
paddle/gserver/layers/LinearChainCRF.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <algorithm>
#include "LinearChainCRF.h" #include "LinearChainCRF.h"
#include <algorithm>
namespace paddle { namespace paddle {
......
paddle/gserver/layers/LinearChainCTC.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <math.h>
#include "LinearChainCTC.h" #include "LinearChainCTC.h"
#include <math.h>
#include <limits> #include <limits>
namespace paddle { namespace paddle {
......
paddle/gserver/layers/LstmCompute.cpp
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See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/utils/Util.h"
#include "hl_recurrent_apply.cuh"
#include "LstmCompute.h" #include "LstmCompute.h"
#include "hl_recurrent_apply.cuh"
#include "paddle/utils/Util.h"
namespace paddle { namespace paddle {
......
paddle/gserver/layers/LstmCompute.h
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#pragma once #pragma once
#include "paddle/utils/TypeDefs.h"
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "hl_gpu.h" #include "hl_gpu.h"
#include "paddle/utils/TypeDefs.h"
namespace paddle { namespace paddle {
......
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