Merge branch 'develop' into warpctc

.dockerignore

+.gitignore
\ No newline at end of file

.gitignore

@@ -8,3 +8,4 @@ build/
 .cproject
 .pydevproject
 Makefile
+.test_env/

CMakeLists.txt

 cmake_minimum_required(VERSION 2.8)
 
 project(paddle CXX C)
-set(PADDLE_MAJOR_VERSION 0)
-set(PADDLE_MINOR_VERSION 9)
-set(PADDLE_PATCH_VERSION 0)
-set(PADDLE_VERSION ${PADDLE_MAJOR_VERSION}.${PADDLE_MINOR_VERSION}.${PADDLE_PATCH_VERSION})
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake")
 set(PROJ_ROOT ${CMAKE_SOURCE_DIR})
@@ -56,7 +52,7 @@ option(ON_COVERALLS "Generating code coverage data on coveralls or not." OFF)
 option(COVERALLS_UPLOAD "Uploading the generated coveralls json." ON)
 
 if(NOT CMAKE_BUILD_TYPE)
-    set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING 
+    set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING
         "Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel"
         FORCE)
 endif()
@@ -75,31 +71,11 @@ include(check_packages)
 include(swig)
 include(coveralls)
 
-# add PaddlePaddle version
-if(DEFINED ENV{PADDLE_VERSION})
-    add_definitions(-DPADDLE_VERSION=\"$ENV{PADDLE_VERSION}\")
-else()
-    if(EXISTS ${PROJ_ROOT}/.svn/)
-        find_package(Subversion REQUIRED)
-        if(SUBVERSION_FOUND)
-            Subversion_WC_INFO(${PROJ_ROOT} Project)
-            add_definitions(-DPADDLE_VERSION=${Project_WC_REVISION})
-        endif()
-    elseif(EXISTS ${PROJ_ROOT}/.git/)
-        find_package(Git REQUIRED)
-        execute_process(
-            COMMAND ${GIT_EXECUTABLE} log -1 --format=%H
-            WORKING_DIRECTORY ${PROJ_ROOT}
-            OUTPUT_VARIABLE GIT_SHA1
-            RESULT_VARIABLE GIT_RESULT
-            ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-        if(NOT ${GIT_RESULT})
-            add_definitions(-DPADDLE_VERSION=\"${GIT_SHA1}\")
-        else()
-            message(WARNING "Cannot add paddle version from git tag")
-        endif()
-    endif()
-endif()
+# Set PaddlePaddle version to Git tag name or Git commit ID.
+find_package(Git REQUIRED)
+# version.cmake will get the current PADDLE_VERSION
+include(version)
+add_definitions(-DPADDLE_VERSION=\"${PADDLE_VERSION}\")
 
 
 if(NOT WITH_GPU)

cmake/version.cmake

+# Get the latest git tag.
+set(PADDLE_VERSION $ENV{PADDLE_VERSION})
+set(tmp_version "HEAD")
+while ("${PADDLE_VERSION}" STREQUAL "")
+  execute_process(
+    COMMAND ${GIT_EXECUTABLE} describe --tags --abbrev=0 ${tmp_version}
+    WORKING_DIRECTORY ${PROJ_ROOT}
+    OUTPUT_VARIABLE GIT_TAG_NAME
+    RESULT_VARIABLE GIT_RESULT
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+  if (NOT ${GIT_RESULT})
+    # Check the tag is a correct version
+    if (${GIT_TAG_NAME} MATCHES "v[0-9]+\\.[0-9]+\\.[0-9]+(\\.(a|b|rc)\\.[0-9]+)?")
+      string(REPLACE "v" "" PADDLE_VERSION ${GIT_TAG_NAME})
+    else()  # otherwise, get the previous git tag name.
+      set(tmp_version "${GIT_TAG_NAME}~1")
+    endif()
+  else()
+    set(PADDLE_VERSION "0.0.0")
+    message(WARNING "Cannot add paddle version from git tag")
+  endif()
+endwhile()
+
+message(STATUS "Paddle version is ${PADDLE_VERSION}")

demo/gan/.gitignore

+output/
+uniform_params/
+cifar_params/
+mnist_params/
+*.png
+.pydevproject
+.project
+*.log
+*.pyc
+data/mnist_data/
+data/cifar-10-batches-py/

demo/gan/README.md

+# Generative Adversarial Networks (GAN) 
+
+This demo implements GAN training described in the original GAN paper (https://arxiv.org/abs/1406.2661) and DCGAN (https://arxiv.org/abs/1511.06434).
+
+The general training procedures are implemented in gan_trainer.py. The neural network configurations are specified in gan_conf.py (for synthetic data) and gan_conf_image.py (for image data).
+
+In order to run the model, first download the corresponding data by running the shell script in ./data.
+Then you can run the command below. The flag -d specifies the training data (cifar, mnist or uniform) and flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).  
+
+$python gan_trainer.py -d cifar --use_gpu 1
+
+The generated images will be stored in ./cifar_samples/
+The corresponding models will be stored in ./cifar_params/
\ No newline at end of file

demo/gan/data/download_cifar.sh

+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+set -e
+wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
+tar zxf cifar-10-python.tar.gz
+rm cifar-10-python.tar.gz
+

demo/gan/data/get_mnist_data.sh

+#!/usr/bin/env sh
+# This script downloads the mnist data and unzips it.
+set -e
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+rm -rf "$DIR/mnist_data"
+mkdir "$DIR/mnist_data"
+cd "$DIR/mnist_data"
+
+echo "Downloading..."
+
+for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
+do
+    if [ ! -e $fname ]; then
+        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
+        gunzip ${fname}.gz
+    fi
+done
+
+

demo/gan/gan_conf.py

+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from paddle.trainer_config_helpers import *
+
+mode = get_config_arg("mode", str, "generator")
+assert mode in set(["generator",
+                    "discriminator",
+                    "generator_training",
+                    "discriminator_training"])
+
+is_generator_training = mode == "generator_training"
+is_discriminator_training = mode == "discriminator_training"
+is_generator = mode == "generator"
+is_discriminator = mode == "discriminator"
+
+# The network structure below follows the ref https://arxiv.org/abs/1406.2661
+# Here we used two hidden layers and batch_norm
+
+print('mode=%s' % mode)
+# the dim of the noise (z) as the input of the generator network
+noise_dim = 10
+# the dim of the hidden layer
+hidden_dim = 10
+# the dim of the generated sample
+sample_dim = 2
+
+settings(
+    batch_size=128,
+    learning_rate=1e-4,
+    learning_method=AdamOptimizer(beta1=0.5)
+)
+
+def discriminator(sample):
+    """
+    discriminator ouputs the probablity of a sample is from generator
+    or real data.
+    The output has two dimenstional: dimension 0 is the probablity
+    of the sample is from generator and dimension 1 is the probabblity
+    of the sample is from real data.
+    """
+    param_attr = ParamAttr(is_static=is_generator_training)
+    bias_attr = ParamAttr(is_static=is_generator_training,
+                          initial_mean=1.0,
+                          initial_std=0)
+
+    hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=ReluActivation())
+
+    hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=LinearActivation())
+    
+    hidden_bn = batch_norm_layer(hidden2, 
+                     act=ReluActivation(), 
+                     name="dis_hidden_bn", 
+                     bias_attr=bias_attr, 
+                     param_attr=ParamAttr(is_static=is_generator_training,
+                           initial_mean=1.0,
+                           initial_std=0.02),
+                     use_global_stats=False)
+    
+    return fc_layer(input=hidden_bn, name="dis_prob", size=2,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=SoftmaxActivation())
+
+def generator(noise):
+    """
+    generator generates a sample given noise
+    """
+    param_attr = ParamAttr(is_static=is_discriminator_training)
+    bias_attr = ParamAttr(is_static=is_discriminator_training,
+                           initial_mean=1.0,
+                           initial_std=0)
+    
+    hidden = fc_layer(input=noise,
+                    name="gen_layer_hidden",
+                    size=hidden_dim,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=ReluActivation())
+
+    hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=LinearActivation())
+    
+    hidden_bn = batch_norm_layer(hidden2, 
+                     act=ReluActivation(), 
+                     name="gen_layer_hidden_bn", 
+                     bias_attr=bias_attr, 
+                     param_attr=ParamAttr(is_static=is_discriminator_training,
+                           initial_mean=1.0,
+                           initial_std=0.02),
+                     use_global_stats=False)
+    
+    return fc_layer(input=hidden_bn,
+                    name="gen_layer1",
+                    size=sample_dim,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=LinearActivation())
+
+if is_generator_training:
+    noise = data_layer(name="noise", size=noise_dim)
+    sample = generator(noise)
+
+if is_discriminator_training:
+    sample = data_layer(name="sample", size=sample_dim)
+
+if is_generator_training or is_discriminator_training:
+    label = data_layer(name="label", size=1)
+    prob = discriminator(sample)
+    cost = cross_entropy(input=prob, label=label)
+    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
+    outputs(cost)
+
+if is_generator:
+    noise = data_layer(name="noise", size=noise_dim)
+    outputs(generator(noise))

demo/gan/gan_conf_image.py

+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from paddle.trainer_config_helpers import *
+
+mode = get_config_arg("mode", str, "generator")
+dataSource = get_config_arg("data", str, "mnist")
+assert mode in set(["generator",
+                    "discriminator",
+                    "generator_training",
+                    "discriminator_training"])
+
+is_generator_training = mode == "generator_training"
+is_discriminator_training = mode == "discriminator_training"
+is_generator = mode == "generator"
+is_discriminator = mode == "discriminator"
+
+# The network structure below follows the dcgan paper 
+# (https://arxiv.org/abs/1511.06434)
+
+print('mode=%s' % mode)
+# the dim of the noise (z) as the input of the generator network
+noise_dim = 100
+# the number of filters in the layer in generator/discriminator that is 
+# closet to the image
+gf_dim = 64
+df_dim = 64
+if dataSource == "mnist":
+    sample_dim = 28 # image dim
+    c_dim = 1 # image color
+else:
+    sample_dim = 32
+    c_dim = 3
+s2, s4 = int(sample_dim/2), int(sample_dim/4), 
+s8, s16 = int(sample_dim/8), int(sample_dim/16)
+
+settings(
+    batch_size=128,
+    learning_rate=2e-4,
+    learning_method=AdamOptimizer(beta1=0.5)
+)
+
+def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, 
+                 param_attr, bias_attr, param_attr_bn, bn, trans=False, 
+                 act=ReluActivation()):
+    
+    """
+    conv_bn is a utility function that constructs a convolution/deconv layer 
+    with an optional batch_norm layer
+
+    :param bn: whether to use batch_norm_layer
+    :type bn: bool
+    :param trans: whether to use conv (False) or deconv (True)
+    :type trans: bool
+    """
+    
+    # calculate the filter_size and padding size based on the given
+    # imgSize and ouput size
+    tmp =  imgSize - (output_x - 1) * stride
+    if tmp <= 1 or tmp > 5:
+        raise ValueError("conv input-output dimension does not fit")
+    elif tmp <= 3:
+        filter_size = tmp + 2
+        padding = 1
+    else:
+        filter_size = tmp
+        padding = 0
+
+    print (imgSize, output_x, stride, filter_size, padding)
+    
+    if trans:
+        nameApx = "_conv"
+    else:
+        nameApx = "_convt"
+    
+    if bn:
+        conv = img_conv_layer(input, filter_size=filter_size, 
+                   num_filters=num_filters,
+                   name=name + nameApx, num_channels=channels,
+                   act=LinearActivation(), groups=1, 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)
+        
+        conv_bn = batch_norm_layer(conv, 
+                         act=act, 
+                         name=name + nameApx + "_bn", 
+                         bias_attr=bias_attr, 
+                         param_attr=param_attr_bn,
+                         use_global_stats=False)
+        
+        return conv_bn
+    else:
+        conv = img_conv_layer(input, filter_size=filter_size, 
+                   num_filters=num_filters,
+                   name=name + nameApx, num_channels=channels,
+                   act=act, groups=1, 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)
+        return conv
+    
+def generator(noise):
+    """
+    generator generates a sample given noise
+    """
+    param_attr = ParamAttr(is_static=is_discriminator_training,
+                           initial_mean=0.0,
+                           initial_std=0.02)
+    bias_attr = ParamAttr(is_static=is_discriminator_training,
+                           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)
+    
+    h1 = fc_layer(input=noise,
+                    name="gen_layer_h1",
+                    size=s8 * s8 * gf_dim * 4,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=LinearActivation())
+    
+    h1_bn = batch_norm_layer(h1, 
+                     act=ReluActivation(), 
+                     name="gen_layer_h1_bn", 
+                     bias_attr=bias_attr, 
+                     param_attr=param_attr_bn,
+                     use_global_stats=False)
+    
+    h2_bn = conv_bn(h1_bn, 
+                    channels=gf_dim*4, 
+                    output_x=s8,
+                    num_filters=gf_dim*2, 
+                    imgSize=s4,
+                    stride=2,
+                    name="gen_layer_h2", 
+                    param_attr=param_attr, 
+                    bias_attr=bias_attr, 
+                    param_attr_bn=param_attr_bn,
+                    bn=True,
+                    trans=True)
+    
+    h3_bn = conv_bn(h2_bn, 
+                    channels=gf_dim*2, 
+                    output_x=s4,
+                    num_filters=gf_dim, 
+                    imgSize=s2,
+                    stride=2,
+                    name="gen_layer_h3", 
+                    param_attr=param_attr, 
+                    bias_attr=bias_attr, 
+                    param_attr_bn=param_attr_bn,
+                    bn=True,
+                    trans=True)
+     
+    
+    return conv_bn(h3_bn,
+                   channels=gf_dim, 
+                   output_x=s2,
+                   num_filters=c_dim, 
+                   imgSize=sample_dim,
+                   stride=2,
+                   name="gen_layer_h4", 
+                   param_attr=param_attr, 
+                   bias_attr=bias_attr, 
+                   param_attr_bn=param_attr_bn,
+                   bn=False,
+                   trans=True,
+                   act=TanhActivation())
+
+
+def discriminator(sample):
+    """
+    discriminator ouputs the probablity of a sample is from generator
+    or real data.
+    The output has two dimenstional: dimension 0 is the probablity
+    of the sample is from generator and dimension 1 is the probabblity
+    of the sample is from real data.
+    """
+    param_attr = ParamAttr(is_static=is_generator_training,
+                           initial_mean=0.0,
+                           initial_std=0.02)
+    bias_attr = ParamAttr(is_static=is_generator_training,
+                          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)
+    
+    h0 = conv_bn(sample, 
+                 channels=c_dim, 
+                 imgSize=sample_dim,
+                 num_filters=df_dim, 
+                 output_x=s2, 
+                 stride=2, 
+                 name="dis_h0", 
+                 param_attr=param_attr, 
+                 bias_attr=bias_attr, 
+                 param_attr_bn=param_attr_bn, 
+                 bn=False)
+    
+    h1_bn = conv_bn(h0, 
+                 channels=df_dim,
+                 imgSize=s2,
+                 num_filters=df_dim*2, 
+                 output_x=s4, 
+                 stride=2, 
+                 name="dis_h1", 
+                 param_attr=param_attr, 
+                 bias_attr=bias_attr, 
+                 param_attr_bn=param_attr_bn, 
+                 bn=True)
+
+    h2_bn = conv_bn(h1_bn, 
+                 channels=df_dim*2,
+                 imgSize=s4,
+                 num_filters=df_dim*4, 
+                 output_x=s8, 
+                 stride=2, 
+                 name="dis_h2", 
+                 param_attr=param_attr, 
+                 bias_attr=bias_attr, 
+                 param_attr_bn=param_attr_bn, 
+                 bn=True)
+        
+    return fc_layer(input=h2_bn, name="dis_prob", size=2,
+                    bias_attr=bias_attr,
+                    param_attr=param_attr,
+                    act=SoftmaxActivation())
+
+
+
+if is_generator_training:
+    noise = data_layer(name="noise", size=noise_dim)
+    sample = generator(noise)
+
+if is_discriminator_training:
+    sample = data_layer(name="sample", size=sample_dim * sample_dim*c_dim)
+
+if is_generator_training or is_discriminator_training:
+    label = data_layer(name="label", size=1)
+    prob = discriminator(sample)
+    cost = cross_entropy(input=prob, label=label)
+    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
+    outputs(cost)
+
+if is_generator:
+    noise = data_layer(name="noise", size=noise_dim)
+    outputs(generator(noise))

demo/gan/gan_trainer.py

+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import random
+import numpy
+import cPickle
+import sys,os
+from PIL import Image
+
+from paddle.trainer.config_parser import parse_config
+from paddle.trainer.config_parser import logger
+import py_paddle.swig_paddle as api
+import matplotlib.pyplot as plt
+
+def plot2DScatter(data, outputfile):
+    '''
+    Plot the data as a 2D scatter plot and save to outputfile
+    data needs to be two dimensinoal
+    '''
+    x = data[:, 0]
+    y = data[:, 1]
+    logger.info("The mean vector is %s" % numpy.mean(data, 0))
+    logger.info("The std vector is %s" % numpy.std(data, 0))
+
+    heatmap, xedges, yedges = numpy.histogram2d(x, y, bins=50)
+    extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
+
+    plt.clf()
+    plt.scatter(x, y)
+    plt.savefig(outputfile, bbox_inches='tight')
+
+def CHECK_EQ(a, b):
+    assert a == b, "a=%s, b=%s" % (a, b)
+
+def copy_shared_parameters(src, dst):
+    '''
+    copy the parameters from src to dst
+    :param src: the source of the parameters
+    :type src: GradientMachine
+    :param dst: the destination of the parameters
+    :type dst: GradientMachine
+    '''
+    src_params = [src.getParameter(i)
+               for i in xrange(src.getParameterSize())]
+    src_params = dict([(p.getName(), p) for p in src_params])
+
+
+    for i in xrange(dst.getParameterSize()):
+        dst_param = dst.getParameter(i)
+        src_param = src_params.get(dst_param.getName(), None)
+        if src_param is None:
+            continue
+        src_value = src_param.getBuf(api.PARAMETER_VALUE)
+        dst_value = dst_param.getBuf(api.PARAMETER_VALUE)
+        CHECK_EQ(len(src_value), len(dst_value))
+        dst_value.copyFrom(src_value)
+        dst_param.setValueUpdated()
+        
+def print_parameters(src):
+    src_params = [src.getParameter(i)
+               for i in xrange(src.getParameterSize())]
+
+    print "***************"
+    for p in src_params:
+        print "Name is %s" % p.getName()
+        print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray()
+
+def load_mnist_data(imageFile):
+    f = open(imageFile, "rb")
+    f.read(16)
+
+    # Define number of samples for train/test
+    if "train" in imageFile:
+        n = 60000
+    else:
+        n = 10000
+    
+    data = numpy.fromfile(f, 'ubyte', count=n*28*28).reshape((n, 28*28))
+    data = data / 255.0 * 2.0 - 1.0
+
+    f.close()
+    return data.astype('float32')
+
+def load_cifar_data(cifar_path):
+    batch_size = 10000
+    data = numpy.zeros((5*batch_size, 32*32*3), dtype = "float32")
+    for i in range(1, 6):
+        file = cifar_path + "/data_batch_" + str(i)
+        fo = open(file, 'rb')
+        dict = cPickle.load(fo)
+        fo.close()
+        data[(i - 1)*batch_size:(i*batch_size), :] = dict["data"]
+    
+    data = data / 255.0 * 2.0 - 1.0
+    return data
+
+# synthesize 2-D uniform data
+def load_uniform_data():
+    data = numpy.random.rand(1000000, 2).astype('float32')
+    return data
+
+def merge(images, size):
+    if images.shape[1] == 28*28:
+        h, w, c = 28, 28, 1
+    else:
+        h, w, c = 32, 32, 3
+    img = numpy.zeros((h * size[0], w * size[1], c))
+    for idx in xrange(size[0] * size[1]):
+        i = idx % size[1]
+        j = idx // size[1]
+        img[j*h:j*h+h, i*w:i*w+w, :] = \
+          ((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
+    return img.astype('uint8')
+
+def save_images(images, path):
+    merged_img = merge(images, [8, 8])
+    if merged_img.shape[2] == 1:
+        im = Image.fromarray(numpy.squeeze(merged_img)).convert('RGB')
+    else:
+        im = Image.fromarray(merged_img, mode="RGB")
+    im.save(path)
+    
+def get_real_samples(batch_size, data_np):
+    return data_np[numpy.random.choice(data_np.shape[0], batch_size, 
+                                       replace=False),:]
+    
+def get_noise(batch_size, noise_dim):
+    return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
+
+def get_fake_samples(generator_machine, batch_size, noise):
+    gen_inputs = api.Arguments.createArguments(1)
+    gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
+    gen_outputs = api.Arguments.createArguments(0)
+    generator_machine.forward(gen_inputs, gen_outputs, api.PASS_TEST)
+    fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
+    return fake_samples
+
+def get_training_loss(training_machine, inputs):
+    outputs = api.Arguments.createArguments(0)
+    training_machine.forward(inputs, outputs, api.PASS_TEST)
+    loss = outputs.getSlotValue(0).copyToNumpyMat()
+    return numpy.mean(loss)
+
+def prepare_discriminator_data_batch_pos(batch_size, data_np):
+    real_samples = get_real_samples(batch_size, data_np)
+    labels = numpy.ones(batch_size, dtype='int32')
+    inputs = api.Arguments.createArguments(2)
+    inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(real_samples))
+    inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
+    return inputs
+
+def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
+    fake_samples = get_fake_samples(generator_machine, batch_size, noise)
+    labels = numpy.zeros(batch_size, dtype='int32')
+    inputs = api.Arguments.createArguments(2)
+    inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(fake_samples))
+    inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
+    return inputs
+
+def prepare_generator_data_batch(batch_size, noise):
+    label = numpy.ones(batch_size, dtype='int32')
+    inputs = api.Arguments.createArguments(2)
+    inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
+    inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(label))
+    return inputs
+
+
+def find(iterable, cond):
+    for item in iterable:
+        if cond(item):
+            return item
+    return None
+
+
+def get_layer_size(model_conf, layer_name):
+    layer_conf = find(model_conf.layers, lambda x: x.name == layer_name)
+    assert layer_conf is not None, "Cannot find '%s' layer" % layer_name
+    return layer_conf.size
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
+    parser.add_argument("--use_gpu", default="1", 
+                        help="1 means use gpu for training")
+    parser.add_argument("--gpu_id", default="0", 
+                        help="the gpu_id parameter")
+    args = parser.parse_args()
+    data_source = args.data_source
+    use_gpu = args.use_gpu
+    assert data_source in ["mnist", "cifar", "uniform"]
+    assert use_gpu in ["0", "1"]
+
+    if not os.path.exists("./%s_samples/" % data_source):
+        os.makedirs("./%s_samples/" % data_source)
+
+    if not os.path.exists("./%s_params/" % data_source):
+        os.makedirs("./%s_params/" % data_source)
+        
+    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10', '--log_period=100', 
+                   '--gpu_id=' + args.gpu_id, '--save_dir=' + "./%s_params/" % data_source)
+    
+    if data_source == "uniform":
+        conf = "gan_conf.py"
+        num_iter = 10000
+    else:
+        conf = "gan_conf_image.py"
+        num_iter = 1000
+        
+    gen_conf = parse_config(conf, "mode=generator_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)
+    batch_size = dis_conf.opt_config.batch_size
+    noise_dim = get_layer_size(gen_conf.model_config, "noise")
+    
+    if data_source == "mnist":
+        data_np = load_mnist_data("./data/mnist_data/train-images-idx3-ubyte")
+    elif data_source == "cifar":
+        data_np = load_cifar_data("./data/cifar-10-batches-py/")
+    else:
+        data_np = load_uniform_data()
+    
+    # this creates a gradient machine for discriminator
+    dis_training_machine = api.GradientMachine.createFromConfigProto(
+        dis_conf.model_config)
+    # this create a gradient machine for generator    
+    gen_training_machine = api.GradientMachine.createFromConfigProto(
+        gen_conf.model_config)
+
+    # generator_machine is used to generate data only, which is used for
+    # training discriminator
+    logger.info(str(generator_conf.model_config))
+    generator_machine = api.GradientMachine.createFromConfigProto(
+        generator_conf.model_config)
+    
+    dis_trainer = api.Trainer.create(
+        dis_conf, dis_training_machine)
+
+    gen_trainer = api.Trainer.create(
+        gen_conf, gen_training_machine)
+    
+    dis_trainer.startTrain()
+    gen_trainer.startTrain()
+    
+    # Sync parameters between networks (GradientMachine) at the beginning
+    copy_shared_parameters(gen_training_machine, dis_training_machine)
+    copy_shared_parameters(gen_training_machine, generator_machine)
+    
+    # constrain that either discriminator or generator can not be trained
+    # consecutively more than MAX_strike times
+    curr_train = "dis"
+    curr_strike = 0
+    MAX_strike = 5
+     
+    for train_pass in xrange(100):
+        dis_trainer.startTrainPass()
+        gen_trainer.startTrainPass()
+        for i in xrange(num_iter):
+            # Do forward pass in discriminator to get the dis_loss
+            noise = get_noise(batch_size, noise_dim)
+            data_batch_dis_pos = prepare_discriminator_data_batch_pos(
+                batch_size, data_np)
+            dis_loss_pos = get_training_loss(dis_training_machine, data_batch_dis_pos)
+            
+            data_batch_dis_neg = prepare_discriminator_data_batch_neg(
+                generator_machine, batch_size, noise)
+            dis_loss_neg = get_training_loss(dis_training_machine, data_batch_dis_neg)            
+                         
+            dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
+            
+            # Do forward pass in generator to get the gen_loss
+            data_batch_gen = prepare_generator_data_batch(
+                    batch_size, noise)
+            gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
+             
+            if i % 100 == 0:
+                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)
+            
+            # Decide which network to train based on the training history
+            # And the relative size of the loss        
+            if (not (curr_train == "dis" and curr_strike == MAX_strike)) and \
+               ((curr_train == "gen" and curr_strike == MAX_strike) or dis_loss > gen_loss):
+                if curr_train == "dis":
+                    curr_strike += 1
+                else:
+                    curr_train = "dis"
+                    curr_strike = 1                
+                dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
+                dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)               
+                copy_shared_parameters(dis_training_machine, gen_training_machine)
+ 
+            else:
+                if curr_train == "gen":
+                    curr_strike += 1
+                else:
+                    curr_train = "gen"
+                    curr_strike = 1
+                gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
+                # TODO: add API for paddle to allow true parameter sharing between different GradientMachines 
+                # 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, generator_machine)
+ 
+        dis_trainer.finishTrainPass()
+        gen_trainer.finishTrainPass()
+        # At the end of each pass, save the generated samples/images
+        fake_samples = get_fake_samples(generator_machine, batch_size, noise)
+        if data_source == "uniform":
+            plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
+        else:
+            save_images(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
+    dis_trainer.finishTrain()
+    gen_trainer.finishTrain()
+
+if __name__ == '__main__':
+    main()

doc/getstarted/build_and_install/docker_install.rst

-Docker installation guide
-==========================
+Using and Building Docker Images
+================================
 
-PaddlePaddle provide the `Docker <https://www.docker.com/>`_ image. `Docker`_ is a lightweight container utilities. The performance of PaddlePaddle in `Docker`_ container is basically as same as run it in a normal linux. The `Docker`_ is a very convenient way to deliver the binary release for linux programs.
+We release PaddlePaddle in the form of `Docker <https://www.docker.com/>`_ images on `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_.   Running as Docker containers is currently the only officially-supported way to running PaddlePaddle.
 
-..  note::
+Run Docker images
+-----------------
 
-    The `Docker`_ image is the recommended way to run PaddlePaddle 
+For each version of PaddlePaddle, we release 4 variants of Docker images:
 
-PaddlePaddle Docker images
---------------------------
++-----------------+-------------+-------+
+|                 |   CPU AVX   |  GPU  |
++=================+=============+=======+
+|       cpu       |   yes       |  no   |
++-----------------+-------------+-------+
+|    cpu-noavx    |   no        |  no   |
++-----------------+-------------+-------+
+|       gpu       |   yes       |  yes  |
++-----------------+-------------+-------+
+|    gpu-noavx    |   no        |  yes  |
++-----------------+-------------+-------+
 
-There are 12 `images <https://hub.docker.com/r/paddledev/paddle/tags/>`_ for PaddlePaddle, and the name is :code:`paddle-dev/paddle`,  tags are\: 
+We run the following command on Linux to check if the CPU supports :code:`AVX`.
 
+.. code-block:: bash
 
-+-----------------+------------------+------------------------+-----------------------+
-|                 |   normal         |           devel        |          demo         |
-+=================+==================+========================+=======================+
-|       CPU       | cpu-latest       | cpu-devel-latest       | cpu-demo-latest       |
-+-----------------+------------------+------------------------+-----------------------+
-|       GPU       | gpu-latest       | gpu-devel-latest       | gpu-demo-latest       |
-+-----------------+------------------+------------------------+-----------------------+
-| CPU WITHOUT AVX | cpu-noavx-latest | cpu-devel-noavx-latest | cpu-demo-noavx-latest |
-+-----------------+------------------+------------------------+-----------------------+
-| GPU WITHOUT AVX | gpu-noavx-latest | gpu-devel-noavx-latest | gpu-demo-noavx-latest |
-+-----------------+------------------+------------------------+-----------------------+
+   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
 
-And the three columns are:
+On Mac OS X, we need to run
 
-* normal\: The docker image only contains binary of PaddlePaddle.
-* devel\: The docker image contains PaddlePaddle binary, source code and essential build environment.
-* demo\: The docker image contains the dependencies to run PaddlePaddle demo.
+.. code-block:: bash
 
-And the four rows are:
+   sysctl -a | grep machdep.cpu.leaf7_features
 
-* CPU\: CPU Version. Support CPU which has :code:`AVX` instructions.
-* GPU\: GPU Version. Support GPU, and cpu has :code:`AVX` instructions.
-* CPU WITHOUT AVX\: CPU Version, which support most CPU even doesn't have :code:`AVX` instructions.
-* GPU WITHOUT AVX\: GPU Version, which support most CPU even doesn't have :code:`AVX` instructions.
 
-User can choose any version depends on machine. The following script can help you to detect your CPU support :code:`AVX` or not.
+Once we determine the proper variant, we can cope with the Docker image tag name by appending the version number.  For example, the following command runs the AVX-enabled image of the most recent version:
 
-..  code-block:: bash
-    
-    if cat /proc/cpuinfo | grep -q avx ; then echo "Support AVX"; else echo "Not support AVX"; fi
+.. code-block:: bash
 
-If the output is :code:`Support AVX`, then you can choose the AVX version of PaddlePaddle, otherwise, you need select :code:`noavx` version of PaddlePaddle. For example, the CPU develop version of PaddlePaddle is :code:`paddle-dev/paddle:cpu-devel-latest`.
+    docker run -it --rm paddledev/paddle:cpu-latest /bin/bash
 
-The PaddlePaddle images don't contain any entry command. You need to write your entry command to use this image. See :code:`Remote Access` part or just use following command to run a :code:`bash`
+To run a GPU-enabled image, you need to install CUDA and let Docker knows about it:
 
-..  code-block:: bash
-
-    docker run -it paddledev/paddle:cpu-latest /bin/bash
-
-
-Download and Run Docker images
-------------------------------
-
-You have to install Docker in your machine which has linux kernel version 3.10+ first. You can refer to the official guide https://docs.docker.com/engine/installation/ for further information.
-
-You can use :code:`docker pull ` to download images first, or just launch a container with :code:`docker run` \:
-
-..  code-block:: bash
-
-    docker run -it paddledev/paddle:cpu-latest
-
-
-If you want to launch container with GPU support, you need to set some environment variables at the same time:
-
-..  code-block:: bash
+.. code-block:: bash
 
     export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
     export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
     docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest
 
+The default entry point of all our Docker images starts the OpenSSH server.  To run PaddlePaddle and to expose OpenSSH port to 2202 on the host computer:
 
-Some notes for docker
----------------------
-
-Performance
-+++++++++++
-
-Since Docker is based on the lightweight virtual containers, the CPU computing performance maintains well. And GPU driver and equipments are all mapped to the container, so the GPU computing performance would not be seriously affected.
-
-If you use high performance nic, such as RDMA(RoCE 40GbE or IB 56GbE), Ethernet(10GbE), it is recommended to use config "-net = host".
-
-
-
-
-Remote access
-+++++++++++++
-
+.. code-block:: bash
 
-If you want to enable ssh access background, you need to build an image by yourself. Please refer to official guide https://docs.docker.com/engine/reference/builder/ for further information.
+    docker run -d -p 2202:22 paddledev/paddle:cpu-latest
 
-Following is a simple Dockerfile with ssh:
+Then we can login to the container using username :code:`root` and password :code:`root`:
 
-..  literalinclude:: ../../doc_cn/build_and_install/install/paddle_ssh.Dockerfile
+.. code-block:: bash
 
-Then you can build an image with Dockerfile and launch a container:
+    ssh -p 2202 root@localhost
 
-..  code-block:: bash
 
-    # cd into Dockerfile directory
-    docker build . -t paddle_ssh
-    # run container, and map host machine port 8022 to container port 22
-    docker run -d -p 8022:22 --name paddle_ssh_machine paddle_ssh
+Build Docker images
+-------------------
 
-Now, you can ssh on port 8022 to access the container, username is root, password is also root:
+Developers might want to build Docker images from their local commit or from a tagged version.  Suppose that your local repo is at :code:`~/work/Paddle`, the following steps builds a cpu variant from your current work:
 
-..  code-block:: bash
+.. code-block:: bash
 
-    ssh -p 8022 root@YOUR_HOST_MACHINE
+  cd ~/Paddle
+  ./paddle/scripts/docker/generates.sh # Use m4 to generate Dockerfiles for each variant.
+  docker build -t paddle:latest -f ./paddle/scripts/docker/Dockerfile.cpu
 
-You can stop and delete the container as following:
+As a release engineer, you might want to build Docker images for a certain version and publish them to dockerhub.com.  You can do this by switching to the right Git tag, or create a new tag, before running `docker build`.  For example, the following commands build Docker images for v0.9.0:
 
-..  code-block:: bash
+.. code-block:: bash
 
-    # stop
-    docker stop paddle_ssh_machine
-    # delete
-    docker rm paddle_ssh_machine
+   cd ~/Paddle
+   git checkout tags/v0.9.0
+   ./paddle/scripts/docker/generates.sh # Use m4 to generate Dockerfiles for each variant.
+   docker build -t paddle:cpu-v0.9.0 -f ./paddle/scripts/docker/Dockerfile.cpu

paddle/api/Arguments.cpp

@@ -27,11 +27,6 @@ Arguments* Arguments::createArguments(size_t slotNum) {
 
 void Arguments::resize(size_t slotNum) { m->outputs.resize(slotNum); }
 
-Matrix* Arguments::getSlotValue(size_t idx) const throw(RangeError) {
-  auto& a = m->getArg(idx);
-  return Matrix::createByPaddleMatrixPtr(&a.value);
-}
-
 Arguments::Arguments() : m(new ArgumentsPrivate()) {}
 
 Arguments::~Arguments() { delete m; }
@@ -43,6 +38,16 @@ Arguments* Arguments::createByPaddleArgumentVector(void* ptr) {
   return args;
 }
 
+Matrix* Arguments::getSlotValue(size_t idx) const throw(RangeError) {
+  auto& a = m->getArg(idx);
+  return Matrix::createByPaddleMatrixPtr(&a.value);
+}
+
+Matrix* Arguments::getSlotGrad(size_t idx) const throw(RangeError) {
+  auto& a = m->getArg(idx);
+  return Matrix::createByPaddleMatrixPtr(&a.grad);
+}
+
 IVector* Arguments::getSlotIds(size_t idx) const throw(RangeError) {
   auto& a = m->getArg(idx);
   return IVector::createByPaddleVectorPtr(&a.ids);
@@ -58,6 +63,11 @@ void Arguments::setSlotValue(size_t idx, Matrix* mat) throw(RangeError) {
   a.value = m->cast<paddle::Matrix>(mat->getSharedPtr());
 }
 
+void Arguments::setSlotGrad(size_t idx, Matrix* mat) throw(RangeError) {
+  auto& a = m->getArg(idx);
+  a.grad = m->cast<paddle::Matrix>(mat->getSharedPtr());
+}
+
 void Arguments::setSlotIn(size_t idx, Matrix* mat) throw(RangeError) {
   auto& a = m->getArg(idx);
   a.in = m->cast<paddle::Matrix>(mat->getSharedPtr());

paddle/api/Paddle.swig

@@ -193,5 +193,4 @@ namespace std {
 %ignore OptimizationConfigPrivate;
 %ignore ParameterTraverseCallbackPrivate;
 %include "utils/GlobalConstants.h"
-%include "api/PaddleAPI.h"
-
+%include "api/PaddleAPI.h"
\ No newline at end of file

paddle/api/PaddleAPI.h

@@ -156,12 +156,15 @@ public:
    *  @param dim1  dimension of data.
    *  @param dim2  dimension of data.
    *  @param copy  true if copy into a new matrix, false will create
-   *               matrix inplace.
+   *               matrix inplace. copy = false should be used with extreme
+   *               care because Matrix will share the memory with the given
+   *               numpy array. If the numpy array object is no longer valid,
+   *               the memory space will not be usable.
    */
   static Matrix* createCpuDenseFromNumpy(float* data,
                                          int dim1,
                                          int dim2,
-                                         bool copy = false);
+                                         bool copy = true);
 
   /// Create Gpu Dense Matrix from numpy matrix, dtype=float32
   static Matrix* createGpuDenseFromNumpy(float* data, int dim1, int dim2);
@@ -271,11 +274,18 @@ public:
    */
   static Vector* createCpuVectorFromNumpy(float* data,
                                           int dim,
-                                          bool copy = false);
+                                          bool copy = true);
 
   /// Create Gpu Vector from numpy array, which dtype=float32
   static Vector* createGpuVectorFromNumpy(float* data, int dim);
 
+  /**
+   * copy from another vector
+   * throw(RangeError) if size of src vector is different from size of this
+   * vector
+   */
+  void copyFrom(Vector* src) throw(RangeError);
+
   /// Cast to numpy array inplace.
   void toNumpyArrayInplace(float** view_data, int* dim1) throw(UnsupportError);
 
@@ -339,7 +349,7 @@ public:
    */
   static IVector* createCpuVectorFromNumpy(int* data,
                                            int dim,
-                                           bool copy = false);
+                                           bool copy = true);
   /**
    * Create Gpu IVector from numpy array, which dtype=int32
    */
@@ -418,6 +428,7 @@ public:
    * the param idx is the slot id
    */
   Matrix* getSlotValue(size_t idx) const throw(RangeError);
+  Matrix* getSlotGrad(size_t idx) const throw(RangeError);
   IVector* getSlotIds(size_t idx) const throw(RangeError);
   Matrix* getSlotIn(size_t idx) const throw(RangeError);
   IVector* getSlotSequenceStartPositions(size_t idx) const throw(RangeError);
@@ -434,6 +445,7 @@ public:
    * The other param is the input Matrix or vector.
    */
   void setSlotValue(size_t idx, Matrix* mat) throw(RangeError);
+  void setSlotGrad(size_t idx, Matrix* mat) throw(RangeError);
   void setSlotIn(size_t idx, Matrix* mat) throw(RangeError);
   void setSlotIds(size_t idx, IVector* vec) throw(RangeError);
   void setSlotSequenceStartPositions(size_t idx,
@@ -535,6 +547,7 @@ public:
   size_t getID() const;
 
   ParameterConfig* getConfig();
+  void setValueUpdated();
 
 private:
   static Parameter* createFromRawPtr(void* ptr);

paddle/api/Parameter.cpp

@@ -68,3 +68,5 @@ ParameterConfig* Parameter::getConfig() {
 }
 
 size_t Parameter::getID() const { return m->getPtr()->getID(); }
+
+void Parameter::setValueUpdated() { m->getPtr()->setValueUpdated(); }

paddle/api/Vector.cpp

@@ -281,6 +281,13 @@ FloatArray Vector::getData() const {
   }
 }
 
+void Vector::copyFrom(Vector* src) throw(RangeError) {
+  if (src->m->vec->getSize() !=  m->vec->getSize()) {
+    throw RangeError();
+  }
+  m->vec->copyFrom(*src->m->vec);
+}
+
 bool Vector::isGpu() const {
   return std::dynamic_pointer_cast<paddle::GpuVector>(m->vec) != nullptr;
 }

paddle/api/test/testMatrix.py

@@ -68,7 +68,7 @@ class TestMatrix(unittest.TestCase):
 
     def test_numpyCpu(self):
         numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
-        m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat)
+        m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat, copy=False)
         self.assertEqual((int(m.getHeight()), int(m.getWidth())),
                          numpy_mat.shape)
 

paddle/api/test/testVector.py

@@ -43,7 +43,7 @@ class TestIVector(unittest.TestCase):
 
     def test_cpu_numpy(self):
         vec = np.array([1, 3, 4, 65, 78, 1, 4], dtype="int32")
-        iv = swig_paddle.IVector.createCpuVectorFromNumpy(vec)
+        iv = swig_paddle.IVector.createCpuVectorFromNumpy(vec, copy=False)
         self.assertEqual(vec.shape[0], int(iv.__len__()))
         vec[4] = 832
         for i in xrange(len(iv)):
@@ -107,7 +107,7 @@ class TestVector(unittest.TestCase):
 
     def testCpuNumpy(self):
         numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
-        vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr)
+        vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, copy=False)
         assert isinstance(vec, swig_paddle.Vector)
         numpy_arr[0] = 0.1
         for n, v in zip(numpy_arr, vec):
@@ -152,4 +152,4 @@ if __name__ == '__main__':
     unittest.TextTestRunner().run(suite)
     if swig_paddle.isGpuVersion():
         swig_paddle.setUseGpu(True)
-        unittest.main()
\ No newline at end of file
+        unittest.main()

paddle/api/test/util.py

@@ -24,7 +24,9 @@ def doubleEqual(a, b):
 
 def __readFromFile():
     for i in xrange(10002):
-        yield np.random.rand(784), random.randint(0, 9)
+        label = np.random.randint(0, 9)
+        sample = np.random.rand(784) + 0.1 * label
+        yield sample, label
 
 
 def loadMNISTTrainData(batch_size=100):

paddle/gserver/dataproviders/DataProvider.h

@@ -271,7 +271,9 @@ public:
   void finishAsyncLoad() {
     stopping_ = true;
     taskReadySem_.post();
-    asyncLoader_->join();
+    if (asyncLoader_) {
+      asyncLoader_->join();
+    }
   }
 
   void setPending(bool pending) { pending_ = pending; }

paddle/gserver/layers/BatchNormBaseLayer.cpp

@@ -68,10 +68,10 @@ void BatchNormBaseLayer::calFeatureMapSize() {
   } else {
     imageH_ = inputLayers_[0]->getOutput().getFrameHeight();
     imageW_ = inputLayers_[0]->getOutput().getFrameWidth();
+    getOutput().setFrameHeight(imageH_);
+    getOutput().setFrameWidth(imageW_);
   }
   imgPixels_ = imageH_ * imageW_;
-  getOutput().setFrameHeight(imageH_);
-  getOutput().setFrameWidth(imageW_);
 }
 
 }  // namespace paddle

paddle/gserver/tests/CMakeLists.txt

@@ -39,9 +39,17 @@ add_unittest_without_exec(test_ConvUnify
     test_ConvUnify.cpp
     LayerGradUtil.cpp
     TestUtil.cpp)
-
+    
 add_test(NAME test_ConvUnify
     COMMAND test_ConvUnify)
+################# test_BatchNorm #######################
+add_unittest_without_exec(test_BatchNorm
+    test_BatchNorm.cpp
+    LayerGradUtil.cpp
+    TestUtil.cpp)
+
+add_test(NAME test_BatchNorm
+    COMMAND test_BatchNorm)
 ################## test_Evaluator #######################
 add_unittest(test_Evaluator
     test_Evaluator.cpp

paddle/gserver/tests/test_BatchNorm.cpp

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+#include <vector>
+#include <string>
+#include "paddle/gserver/layers/DataLayer.h"
+#include "ModelConfig.pb.h"
+#include "paddle/trainer/Trainer.h"
+#include "paddle/utils/GlobalConstants.h"
+#include "paddle/gserver/layers/ExpandConvTransLayer.h"
+
+#include "TestUtil.h"
+#include "LayerGradUtil.h"
+
+using namespace paddle;  // NOLINT
+using namespace std;     // NOLINT
+
+P_DECLARE_bool(use_gpu);
+P_DECLARE_int32(gpu_id);
+P_DECLARE_double(checkgrad_eps);
+P_DECLARE_bool(thread_local_rand_use_global_seed);
+P_DECLARE_bool(prev_batch_state);
+
+// Test that the batchNormLayer can be followed by a ConvLayer
+TEST(Layer, batchNorm) {
+    FLAGS_use_gpu = false;
+    TestConfig configBN;
+    const int CHANNELS = 6272;
+    const int IMG_SIZE = 1;
+    configBN.layerConfig.set_type("batch_norm");
+    configBN.layerConfig.set_name("bn");
+    configBN.layerConfig.set_size(CHANNELS * IMG_SIZE * IMG_SIZE);
+    configBN.layerConfig.set_active_type("relu");
+    configBN.biasSize = CHANNELS;
+    configBN.inputDefs.push_back({INPUT_DATA, "layer_0",
+                                /* dim= */ IMG_SIZE * IMG_SIZE * CHANNELS,
+                                /* paraSize= */ CHANNELS});
+
+    configBN.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean",
+                                    1, CHANNELS});
+    configBN.inputDefs.back().isStatic = true;
+    configBN.inputDefs.push_back({INPUT_DATA, "layer_2_running_var",
+                                    1, CHANNELS});
+    configBN.inputDefs.back().isStatic = true;
+
+    LayerInputConfig* input = configBN.layerConfig.add_inputs();
+    configBN.layerConfig.add_inputs();
+    configBN.layerConfig.add_inputs();
+
+    ImageConfig* img_conf = input->mutable_image_conf();
+    img_conf->set_channels(CHANNELS);
+    img_conf->set_img_size(IMG_SIZE);
+
+    // Setting up conv-layer config
+    TestConfig config;
+    config.biasSize = 64;
+    config.layerConfig.set_type("exconv");
+    config.layerConfig.set_num_filters(64);
+    config.layerConfig.set_partial_sum(1);
+    config.layerConfig.set_shared_biases(true);
+
+    config.inputDefs.push_back({INPUT_DATA, "bn", 6272, 204800});
+    input = config.layerConfig.add_inputs();
+    ConvConfig* conv = input->mutable_conv_conf();
+    conv->set_filter_size(5);
+    conv->set_filter_size_y(5);
+    conv->set_channels(128);
+    conv->set_padding(1);
+    conv->set_padding_y(1);
+    conv->set_stride(2);
+    conv->set_stride_y(2);
+    conv->set_groups(1);
+    conv->set_filter_channels(conv->channels() / conv->groups());
+    conv->set_img_size(7);
+    conv->set_output_x(3);
+    config.layerConfig.set_size(conv->output_x() * conv->output_x() *
+                                config.layerConfig.num_filters());
+    config.layerConfig.set_name("conv");
+
+    // data layer initialize
+    std::vector<DataLayerPtr> dataLayers;
+    LayerMap layerMap;
+    vector<Argument> datas;
+    initDataLayer(configBN, &dataLayers, &datas, &layerMap, "batch_norm",
+                  100, false, false);
+    // test layer initialize
+    std::vector<ParameterPtr> parameters;
+    LayerPtr bnLayer;
+    initTestLayer(configBN, &layerMap, &parameters, &bnLayer);
+
+    std::vector<ParameterPtr> parameters2;
+    LayerPtr convLayer;
+    initTestLayer(config, &layerMap, &parameters2, &convLayer);
+
+    bnLayer->forward(PASS_GC);
+    convLayer->forward(PASS_GC);
+
+    CHECK_EQ(convLayer->getOutputValue()->getHeight(), 100);
+    CHECK_EQ(convLayer->getOutputValue()->getWidth(), 576);
+}
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  initMain(argc, argv);
+  FLAGS_thread_local_rand_use_global_seed = true;
+  srand(1);
+  return RUN_ALL_TESTS();
+}

paddle/py_paddle/util.py

@@ -559,10 +559,10 @@ def __monkey_patch_trainer__():
 
 
 def monkeypatches():
-    patches = [
-        __monkeypatch_init_paddle__, __monkeypatch_gradient_machine__,
-        __monkey_patch_protobuf_objects__, __monkey_patch_parameter__,
-        __monkey_patch_trainer__
-    ]
+    patches = [__monkeypatch_init_paddle__,
+               __monkeypatch_gradient_machine__,
+               __monkey_patch_protobuf_objects__,
+               __monkey_patch_parameter__,
+               __monkey_patch_trainer__]
     for patch in patches:
         patch()

paddle/scripts/docker/Dockerfile.cpu

-FROM ubuntu:14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=OFF
-ENV IS_DEVEL=OFF
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=ON
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.cpu-demo

-FROM ubuntu:14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=OFF
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=ON
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=ON
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.cpu-devel

-FROM ubuntu:14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=OFF
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=ON
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.cpu-noavx

-FROM ubuntu:14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=OFF
-ENV IS_DEVEL=OFF
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=OFF
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.cpu-noavx-demo

-FROM ubuntu:14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=OFF
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=ON
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=OFF
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.cpu-noavx-devel

-FROM ubuntu:14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=OFF
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=OFF
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.gpu

-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=ON
-ENV IS_DEVEL=OFF
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=ON
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.gpu-demo

-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=ON
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=ON
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=ON
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.gpu-devel

-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=ON
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=ON
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.gpu-noavx

-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=ON
-ENV IS_DEVEL=OFF
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=OFF
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.gpu-noavx-demo

-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=ON
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=ON
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=OFF
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.gpu-noavx-devel

-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0a0
-ENV WITH_GPU=ON
-ENV IS_DEVEL=ON
-ENV WITH_DEMO=OFF
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
-ENV WITH_AVX=OFF
-RUN cd /root/ && bash build.sh

paddle/scripts/docker/Dockerfile.m4

 FROM PADDLE_BASE_IMAGE
 MAINTAINER PaddlePaddle Dev Team <paddle-dev@baidu.com>
-COPY build.sh /root/
-ENV GIT_CHECKOUT=v0.9.0
+
+# It is good to run apt-get install with Dockerfile RUN directive,
+# because if the following invocation to /root/build.sh fails, `docker
+# build` wouldn't have to re-install packages after we fix
+# /root/build.sh.  For more about Docker build cache, please refer to
+# https://docs.docker.com/engine/userguide/eng-image/dockerfile_best-practices/#/build-cache.
+RUN apt-get update && \
+    apt-get install -y cmake libprotobuf-dev protobuf-compiler git \
+    libgoogle-glog-dev libgflags-dev libatlas-dev libatlas3-base g++ m4 python-pip \
+    python-protobuf python-numpy python-dev swig openssh-server \
+    wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \
+    sed grep graphviz libjpeg-dev zlib1g-dev doxygen && \
+    apt-get clean -y
+RUN pip install BeautifulSoup docopt PyYAML pillow \
+    'sphinx>=1.4.0' sphinx_rtd_theme breathe recommonmark
+
 ENV WITH_GPU=PADDLE_WITH_GPU
-ENV IS_DEVEL=PADDLE_IS_DEVEL
-ENV WITH_DEMO=PADDLE_WITH_DEMO
-ENV PIP_INSTALL_ARGS ""
-ENV PIP_GENERAL_ARGS ""
-ENV USE_UBUNTU_MIRROR OFF
 ENV WITH_AVX=PADDLE_WITH_AVX
-RUN cd /root/ && bash build.sh
+
+RUN mkdir /paddle
+COPY . /paddle/
+COPY paddle/scripts/docker/build.sh /root/
+RUN /root/build.sh
+
+RUN echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile
+RUN pip install /usr/local/opt/paddle/share/wheels/*.whl
+RUN paddle version  # print version after build
+
+# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service
+RUN mkdir /var/run/sshd
+RUN echo 'root:root' | chpasswd
+RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
+RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
+EXPOSE 22
+CMD ["/usr/sbin/sshd", "-D"]

paddle/scripts/docker/build.sh

@@ -7,43 +7,21 @@ function abort(){
 
 trap 'abort' 0
 set -e
-if [ ${USE_UBUNTU_MIRROR} == "ON" ]; then
-    sed -i 's#http://archive\.ubuntu\.com/ubuntu/#mirror://mirrors\.ubuntu\.com/mirrors\.txt#g'\
-      /etc/apt/sources.list
-fi
-apt-get update
-apt-get install -y cmake libprotobuf-dev protobuf-compiler git \
-    libgoogle-glog-dev libgflags-dev libatlas-dev libatlas3-base g++ m4 python-pip\
-    python-protobuf python-numpy python-dev swig
 
 if [ ${WITH_GPU} == 'ON' ]; then
   ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so
 fi
 
-cd ~
-git clone https://github.com/PaddlePaddle/Paddle.git paddle
-cd paddle
-git checkout ${GIT_CHECKOUT}
-mkdir build
-cd build
-cmake .. -DWITH_DOC=OFF -DWITH_GPU=${WITH_GPU} -DWITH_SWIG_PY=ON\
-   -DCUDNN_ROOT=/usr/ -DWITH_STYLE_CHECK=OFF -DWITH_AVX=${WITH_AVX}
+mkdir -p /paddle/build # -p means no error if exists
+cd /paddle/build
+cmake .. \
+      -DWITH_DOC=ON \
+      -DWITH_GPU=${WITH_GPU} \
+      -DWITH_AVX=${WITH_AVX} \
+      -DWITH_SWIG_PY=ON \
+      -DCUDNN_ROOT=/usr/ \
+      -DWITH_STYLE_CHECK=OFF
 make -j `nproc`
-# because durning make install, there are several warning, so set +e, do not cause abort
 make install
-echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile
-pip ${PIP_GENERAL_ARGS} install ${PIP_INSTALL_ARGS} /usr/local/opt/paddle/share/wheels/*.whl
-paddle version  # print version after build
 
-if [ ${WITH_DEMO} == "ON" ]; then
-  apt-get install -y wget unzip perl python-matplotlib tar xz-utils bzip2 gzip coreutils\
-	          sed grep graphviz libjpeg-dev zlib1g-dev
-  pip ${PIP_GENERAL_ARGS} install ${PIP_INSTALL_ARGS}  BeautifulSoup docopt \
-    PyYAML pillow
-fi
-if [ ${IS_DEVEL} == "OFF" ]; then  # clean build packages.
-  cd ~
-  rm -rf paddle
-fi
-apt-get clean -y
 trap : 0

paddle/scripts/docker/generate.sh

 #!/bin/bash
+
 set -e
 cd `dirname $0`
-m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=ON\
+
+m4 -DPADDLE_WITH_GPU=OFF \
+   -DPADDLE_WITH_AVX=ON  \
+   -DPADDLE_BASE_IMAGE=ubuntu:14.04 \
    Dockerfile.m4 > Dockerfile.cpu
 
-m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=OFF\
+m4 -DPADDLE_WITH_GPU=OFF \
+   -DPADDLE_WITH_AVX=OFF \
+   -DPADDLE_BASE_IMAGE=ubuntu:14.04 \
    Dockerfile.m4 > Dockerfile.cpu-noavx
 
-m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=OFF\
-   Dockerfile.m4 > Dockerfile.cpu-noavx-devel
-
-m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=ON\
-   Dockerfile.m4 > Dockerfile.cpu-devel
-
-
-m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \
-   -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=ON\
-   Dockerfile.m4 > Dockerfile.cpu-demo
-
-m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \
-   -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=OFF\
-   Dockerfile.m4 > Dockerfile.cpu-noavx-demo
-
-
-m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
+m4 -DPADDLE_WITH_GPU=ON \
    -DPADDLE_WITH_AVX=ON \
-   Dockerfile.m4 > Dockerfile.gpu
-
-m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \
    -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
-   -DPADDLE_WITH_AVX=OFF \
-   Dockerfile.m4 > Dockerfile.gpu-noavx
-
-
-m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
-   -DPADDLE_WITH_AVX=ON \
-   Dockerfile.m4 > Dockerfile.gpu-devel
+   Dockerfile.m4 > Dockerfile.gpu
 
-m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \
-   -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
+m4 -DPADDLE_WITH_GPU=ON  \
    -DPADDLE_WITH_AVX=OFF \
-   Dockerfile.m4 > Dockerfile.gpu-noavx-devel
-
-m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \
    -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
-   -DPADDLE_WITH_AVX=ON \
-   Dockerfile.m4 > Dockerfile.gpu-demo
-
-
-m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \
-   -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
-   -DPADDLE_WITH_AVX=OFF \
-   Dockerfile.m4 > Dockerfile.gpu-noavx-demo
+   Dockerfile.m4 > Dockerfile.gpu-noavx

paddle/trainer/Trainer.cpp

@@ -205,7 +205,7 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
       (!IGradientMachineMode::dataMustInCpu(mode_, FLAGS_trainer_count));
 
   dataProvider_ = dataProvider;
-  if (!dataProvider_ && config_->hasDataConfig()) {
+  if (!dataProvider_ && config_->hasDataConfig() && !testing_) {
     dataProvider_.reset(DataProvider::create(*config_, *config_, gpuData));
   }
   if (!testDataProvider_) {