Enable ParallelExecutor in SSD-MobileNet and Refine code.

Author: qingqing01

fluid/object_detection/data/prepare_voc_data.py renamed to fluid/object_detection/data/pascalvoc/create_list.py

@@ -60,4 +60,5 @@ def prepare_filelist(devkit_dir, years, output_dir):
  ftest.write(item[0] + ' ' + item[1] + '\n')
 
 
-prepare_filelist(devkit_dir, years, '.')
+if __name__ == '__main__':
+ prepare_filelist(devkit_dir, years, '.')

fluid/object_detection/data/pascalvoc/download.sh

@@ -0,0 +1,16 @@
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+cd "$DIR"
+
+# Download the data.
+echo "Downloading..."
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
+# Extract the data.
+echo "Extractint..."
+tar -xf VOCtrainval_11-May-2012.tar
+tar -xf VOCtrainval_06-Nov-2007.tar
+tar -xf VOCtest_06-Nov-2007.tar
+
+echo "Creating data lists..."
+python create_list.py

fluid/object_detection/data/label_list renamed to fluid/object_detection/data/pascalvoc/label_list

@@ -13,7 +13,7 @@ def conv_bn(input,
  num_groups=1,
  act='relu',
  use_cudnn=True):
- parameter_attr = ParamAttr(initializer=MSRA())
+ parameter_attr = ParamAttr(learning_rate=0.1, initializer=MSRA())
  conv = fluid.layers.conv2d(
  input=input,
  num_filters=num_filters,
@@ -25,11 +25,9 @@ def conv_bn(input,
  use_cudnn=use_cudnn,
  param_attr=parameter_attr,
  bias_attr=False)
- #parameter_attr = ParamAttr(learning_rate=0.1, initializer=MSRA())
- #bias_attr = ParamAttr(learning_rate=0.2)
- return fluid.layers.batch_norm(input=conv, act=act, epsilon=0.00001)
- #param_attr=parameter_attr,
- #bias_attr=bias_attr)
+ parameter_attr = ParamAttr(learning_rate=0.1, initializer=MSRA())
+ bias_attr = ParamAttr(learning_rate=0.2)
+ return fluid.layers.batch_norm(input=conv, act=act)
 
 
 def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride,

fluid/object_detection/load_model.py

@@ -0,0 +1,8 @@
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+cd "$DIR"
+
+# Download the data.
+echo "Downloading..."
+wget http://paddlemodels.bj.bcebos.com/ssd_mobilenet_coco.tar.gz
+echo "Extractint..."
+tar -xf ssd_mobilenet_coco.tar.gz

fluid/object_detection/mobilenet_ssd.py

@@ -0,0 +1,8 @@
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+cd "$DIR"
+
+# Download the data.
+echo "Downloading..."
+wget http://paddlemodels.bj.bcebos.com/mobilenet_imagenet.tar.gz
+echo "Extractint..."
+tar -xf ssd_mobilenet_imagenet.tar.gz

fluid/object_detection/pretrained/download_coco.sh

@@ -23,10 +23,6 @@
 import time
 import copy
 
-# cocoapi 
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
 
 class Settings(object):
  def __init__(self, dataset, toy, data_dir, label_file, resize_h, resize_w,
@@ -101,6 +97,10 @@ def img_mean(self):
 def _reader_creator(settings, file_list, mode, shuffle):
  def reader():
  if settings.dataset == 'coco':
+ # cocoapi 
+ from pycocotools.coco import COCO
+ from pycocotools.cocoeval import COCOeval
+
  coco = COCO(file_list)
  image_ids = coco.getImgIds()
  images = coco.loadImgs(image_ids)
@@ -295,28 +295,28 @@ def draw_bounding_box_on_image(image,
 
 
 def train(settings, file_list, shuffle=True):
+ file_list = os.path.join(settings.data_dir, file_list)
  if settings.dataset == 'coco':
  train_settings = copy.copy(settings)
  if '2014' in file_list:
  sub_dir = "train2014"
  elif '2017' in file_list:
  sub_dir = "train2017"
  train_settings.data_dir = os.path.join(settings.data_dir, sub_dir)
- file_list = os.path.join(settings.data_dir, file_list)
  return _reader_creator(train_settings, file_list, 'train', shuffle)
  elif settings.dataset == 'pascalvoc':
  return _reader_creator(settings, file_list, 'train', shuffle)
 
 
 def test(settings, file_list):
+ file_list = os.path.join(settings.data_dir, file_list)
  if settings.dataset == 'coco':
  test_settings = copy.copy(settings)
  if '2014' in file_list:
  sub_dir = "val2014"
  elif '2017' in file_list:
  sub_dir = "val2017"
  test_settings.data_dir = os.path.join(settings.data_dir, sub_dir)
- file_list = os.path.join(settings.data_dir, file_list)
  return _reader_creator(test_settings, file_list, 'test', False)
  elif settings.dataset == 'pascalvoc':
  return _reader_creator(settings, file_list, 'test', False)