Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into mobilenet_gpu

paddle/gserver/layers/Layer.cpp

@@ -359,12 +359,11 @@ void Layer::backwardActivation() {
   /* Do error clipping */
   if (config_.error_clipping_threshold() > 0.0f) {
     if (FLAGS_log_error_clipping) {
-      CpuVector outGradVec(0, nullptr);
-      outGradVec.subVecFrom(
-          output_.grad->getData(), 0, output_.grad->getElementCnt());
-      real maxAbsGrad = outGradVec.getAbsMax();
+      VectorPtr outGradVec = Vector::create(
+          output_.grad->getData(), output_.grad->getElementCnt(), useGpu_);
+      real maxAbsGrad = outGradVec->getAbsMax();
       if (maxAbsGrad > config_.error_clipping_threshold()) {
-        real avgAbsGrad = outGradVec.getAbsSum() / outGradVec.getSize();
+        real avgAbsGrad = outGradVec->getAbsSum() / outGradVec->getSize();
         LOG(INFO) << " layer=" << config_.name() << " need clipping,"
                   << " max error=" << maxAbsGrad << " avg error=" << avgAbsGrad;
       }

paddle/scripts/travis/check_style.sh

 #!/bin/bash
 function abort(){
     echo "Your change doesn't follow PaddlePaddle's code style." 1>&2
-    echo "Please use pre-commit to reformat your code and git push again." 1>&2
+    echo "Please use pre-commit to check what is wrong." 1>&2
     exit 1
 }
 
@@ -19,7 +19,8 @@ ln -sf $TRAVIS_BUILD_DIR $GOPATH/src/github.com/PaddlePaddle/Paddle
 cd  $GOPATH/src/github.com/PaddlePaddle/Paddle/go; glide install; cd -
 
 if ! pre-commit run -a ; then
-  git diff  --exit-code
+    git diff
+    exit 1
 fi
 
 trap : 0

python/paddle/trainer/config_parser.py

@@ -1575,7 +1575,13 @@ class MultiClassCrossEntropySelfNormCostLayer(LayerBase):
 
 @config_layer('fc')
 class FCLayer(LayerBase):
-    def __init__(self, name, size, inputs, bias=True, **xargs):
+    def __init__(self,
+                 name,
+                 size,
+                 inputs,
+                 bias=True,
+                 error_clipping_threshold=None,
+                 **xargs):
         super(FCLayer, self).__init__(name, 'fc', size, inputs=inputs, **xargs)
         for input_index in xrange(len(self.inputs)):
             input_layer = self.get_input_layer(input_index)
@@ -1592,6 +1598,8 @@ class FCLayer(LayerBase):
             self.create_input_parameter(input_index, psize, dims, sparse,
                                         format)
         self.create_bias_parameter(bias, self.config.size)
+        if error_clipping_threshold is not None:
+            self.config.error_clipping_threshold = error_clipping_threshold
 
 
 @config_layer('selective_fc')

python/paddle/v2/dataset/__init__.py

@@ -26,8 +26,9 @@ import sentiment
 import wmt14
 import mq2007
 import flowers
+import voc2012
 
 __all__ = [
     'mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05', 'sentiment'
-    'uci_housing', 'wmt14', 'mq2007', 'flowers'
+    'uci_housing', 'wmt14', 'mq2007', 'flowers', 'voc2012'
 ]

python/paddle/v2/dataset/tests/voc2012_test.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.v2.dataset.voc2012
+import unittest
+
+
+class TestVOC(unittest.TestCase):
+    def check_reader(self, reader):
+        sum = 0
+        label = 0
+        for l in reader():
+            self.assertEqual(l[0].size, 3 * l[1].size)
+            sum += 1
+        return sum
+
+    def test_train(self):
+        count = self.check_reader(paddle.v2.dataset.voc_seg.train())
+        self.assertEqual(count, 2913)
+
+    def test_test(self):
+        count = self.check_reader(paddle.v2.dataset.voc_seg.test())
+        self.assertEqual(count, 1464)
+
+    def test_val(self):
+        count = self.check_reader(paddle.v2.dataset.voc_seg.val())
+        self.assertEqual(count, 1449)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/dataset/voc2012.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Image dataset for segmentation.
+The 2012 dataset contains images from 2008-2011 for which additional
+segmentations have been prepared. As in previous years the assignment
+to training/test sets has been maintained. The total number of images
+with segmentation has been increased from 7,062 to 9,993.
+"""
+
+import tarfile
+import io
+import numpy as np
+from paddle.v2.dataset.common import download
+from paddle.v2.image import *
+from PIL import Image
+
+__all__ = ['train', 'test', 'val']
+
+VOC_URL = 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/\
+VOCtrainval_11-May-2012.tar'
+
+VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
+SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
+DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
+LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'
+
+CACHE_DIR = 'voc2012'
+
+
+def reader_creator(filename, sub_name):
+
+    tarobject = tarfile.open(filename)
+    name2mem = {}
+    for ele in tarobject.getmembers():
+        name2mem[ele.name] = ele
+
+    def reader():
+        set_file = SET_FILE.format(sub_name)
+        sets = tarobject.extractfile(name2mem[set_file])
+        for line in sets:
+            line = line.strip()
+            data_file = DATA_FILE.format(line)
+            label_file = LABEL_FILE.format(line)
+            data = tarobject.extractfile(name2mem[data_file]).read()
+            label = tarobject.extractfile(name2mem[label_file]).read()
+            data = Image.open(io.BytesIO(data))
+            label = Image.open(io.BytesIO(label))
+            data = np.array(data)
+            label = np.array(label)
+            yield data, label
+
+    return reader
+
+
+def train():
+    """
+    Create a train dataset reader containing 2913 images in HWC order.
+    """
+    return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'trainval')
+
+
+def test():
+    """
+    Create a test dataset reader containing 1464 images in HWC order.
+    """
+    return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'train')
+
+
+def val():
+    """
+    Create a val dataset reader containing 1449 images in HWC order.
+    """
+    return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'val')

python/setup.py.in

@@ -20,6 +20,7 @@ setup_requires=["requests",
                 "matplotlib",
                 "rarfile",
                 "scipy>=0.19.0",
+                "Pillow",
                 "nltk"]
 
 if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']: