Support Python3 in DeepLab v3+ (#1321)

fluid/deeplabv3+/.gitignore

+deeplabv3plus_xception65_initialize.params
+deeplabv3plus.params
+deeplabv3plus.tar.gz

fluid/deeplabv3+/README.md

-DeepLab运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。如果您的PaddlePaddle安装版本低于此要求，请按照[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明更新PaddlePaddle安装版本。
+DeepLab运行本目录下的程序示例需要使用PaddlePaddle Fluid v1.0.0版本或以上。如果您的PaddlePaddle安装版本低于此要求，请按照安装文档中的说明更新PaddlePaddle安装版本，如果使用GPU，该程序需要使用cuDNN v7版本。
 
 
 ## 代码结构
@@ -41,10 +41,12 @@ data/cityscape/
 如果需要从头开始训练模型，用户需要下载我们的初始化模型
 ```
 wget http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus_xception65_initialize.tar.gz
+tar -xf deeplabv3plus_xception65_initialize.tar.gz && rm deeplabv3plus_xception65_initialize.tar.gz
 ```
 如果需要最终训练模型进行fine tune或者直接用于预测，请下载我们的最终模型
 ```
 wget http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus.tar.gz
+tar -xf deeplabv3plus.tar.gz && rm deeplabv3plus.tar.gz
 ```
 
 
@@ -70,11 +72,11 @@ python train.py --help
 ```
 python ./train.py \
     --batch_size=8 \
-    --parallel=true
+    --parallel=true \
     --train_crop_size=769 \
     --total_step=90000 \
-    --init_weights_path=$INIT_WEIGHTS_PATH \
-    --save_weights_path=$SAVE_WEIGHTS_PATH \
+    --init_weights_path=deeplabv3plus_xception65_initialize.params \
+    --save_weights_path=output \
     --dataset_path=$DATASET_PATH
 ```
 
@@ -82,11 +84,10 @@ python ./train.py \
 执行以下命令在`Cityscape`测试数据集上进行测试：
 ```
 python ./eval.py \
-    --init_weights_path=$INIT_WEIGHTS_PATH \
+    --init_weights=deeplabv3plus.params \
     --dataset_path=$DATASET_PATH
 ```
-需要通过选项`--model_path`指定模型文件。
-测试脚本的输出的评估指标为[mean IoU]()。
+需要通过选项`--model_path`指定模型文件。测试脚本的输出的评估指标为mean IoU。
 
 
 ## 实验结果

fluid/deeplabv3+/eval.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98'
 
@@ -91,7 +94,7 @@ exe = fluid.Executor(place)
 exe.run(sp)
 
 if args.init_weights_path:
-    print "load from:", args.init_weights_path
+    print("load from:", args.init_weights_path)
     load_model()
 
 dataset = CityscapeDataset(args.dataset_path, 'val')
@@ -118,7 +121,7 @@ for i, imgs, labels, names in batches:
     mp = (wrong + right) != 0
     miou2 = np.mean((right[mp] * 1.0 / (right[mp] + wrong[mp])))
     if args.verbose:
-        print 'step: %s, mIoU: %s' % (i + 1, miou2)
+        print('step: %s, mIoU: %s' % (i + 1, miou2))
     else:
-        print '\rstep: %s, mIoU: %s' % (i + 1, miou2),
+        print('\rstep: %s, mIoU: %s' % (i + 1, miou2))
         sys.stdout.flush()

fluid/deeplabv3+/models.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 
@@ -50,7 +53,7 @@ def append_op_result(result, name):
 
 def conv(*args, **kargs):
     kargs['param_attr'] = name_scope + 'weights'
-    if kargs.has_key('bias_attr') and kargs['bias_attr']:
+    if 'bias_attr' in kargs and kargs['bias_attr']:
         kargs['bias_attr'] = name_scope + 'biases'
     else:
         kargs['bias_attr'] = False
@@ -62,7 +65,7 @@ def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None):
 
     N, C, H, W = input.shape
     if C % G != 0:
-        print "group can not divide channle:", C, G
+        print("group can not divide channle:", C, G)
         for d in range(10):
             for t in [d, -d]:
                 if G + t <= 0: continue
@@ -70,7 +73,7 @@ def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None):
                     G = G + t
                     break
             if C % G == 0:
-                print "use group size:", G
+                print("use group size:", G)
                 break
     assert C % G == 0
     param_shape = (G, )
@@ -139,7 +142,7 @@ def seq_conv(input, channel, stride, filter, dilation=1, act=None):
             filter,
             stride,
             groups=input.shape[1],
-            padding=(filter / 2) * dilation,
+            padding=(filter // 2) * dilation,
             dilation=dilation)
         input = bn(input)
         if act: input = act(input)

fluid/deeplabv3+/reader.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import cv2
 import numpy as np
+import os
+import six
 
 default_config = {
     "shuffle": True,
@@ -30,7 +35,7 @@ def slice_with_pad(a, s, value=0):
                 pr = 0
             pads.append([pl, pr])
             slices.append([l, r])
-    slices = map(lambda x: slice(x[0], x[1], 1), slices)
+    slices = list(map(lambda x: slice(x[0], x[1], 1), slices))
     a = a[slices]
     a = np.pad(a, pad_width=pads, mode='constant', constant_values=value)
     return a
@@ -38,11 +43,17 @@ def slice_with_pad(a, s, value=0):
 
 class CityscapeDataset:
     def __init__(self, dataset_dir, subset='train', config=default_config):
-        import commands
-        label_dirname = dataset_dir + 'gtFine/' + subset
-        label_files = commands.getoutput(
-            "find %s -type f | grep labelTrainIds | sort" %
-            label_dirname).splitlines()
+        label_dirname = os.path.join(dataset_dir, 'gtFine/' + subset)
+        if six.PY2:
+            import commands
+            label_files = commands.getoutput(
+                "find %s -type f | grep labelTrainIds | sort" %
+                label_dirname).splitlines()
+        else:
+            import subprocess
+            label_files = subprocess.getstatusoutput(
+                "find %s -type f | grep labelTrainIds | sort" %
+                label_dirname)[-1].splitlines()
         self.label_files = label_files
         self.label_dirname = label_dirname
         self.index = 0
@@ -50,7 +61,7 @@ class CityscapeDataset:
         self.dataset_dir = dataset_dir
         self.config = config
         self.reset()
-        print "total number", len(label_files)
+        print("total number", len(label_files))
 
     def reset(self, shuffle=False):
         self.index = 0
@@ -66,13 +77,14 @@ class CityscapeDataset:
         shape = self.config["crop_size"]
         while True:
             ln = self.label_files[self.index]
-            img_name = self.dataset_dir + 'leftImg8bit/' + self.subset + ln[len(
-                self.label_dirname):]
+            img_name = os.path.join(
+                self.dataset_dir,
+                'leftImg8bit/' + self.subset + ln[len(self.label_dirname):])
             img_name = img_name.replace('gtFine_labelTrainIds', 'leftImg8bit')
             label = cv2.imread(ln)
             img = cv2.imread(img_name)
             if img is None:
-                print "load img failed:", img_name
+                print("load img failed:", img_name)
                 self.next_img()
             else:
                 break
@@ -128,5 +140,7 @@ class CityscapeDataset:
             from prefetch_generator import BackgroundGenerator
             batches = BackgroundGenerator(batches, 100)
         except:
-            print "You can install 'prefetch_generator' for acceleration of data reading."
+            print(
+                "You can install 'prefetch_generator' for acceleration of data reading."
+            )
         return batches

fluid/deeplabv3+/train.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98'
 
@@ -126,13 +129,12 @@ exe = fluid.Executor(place)
 exe.run(sp)
 
 if args.init_weights_path:
-    print "load from:", args.init_weights_path
+    print("load from:", args.init_weights_path)
     load_model()
 
 dataset = CityscapeDataset(args.dataset_path, 'train')
 
 if args.parallel:
-    print "Using ParallelExecutor."
     exe_p = fluid.ParallelExecutor(
         use_cuda=True, loss_name=loss_mean.name, main_program=tp)
 
@@ -149,9 +151,9 @@ for i, imgs, labels, names in batches:
                              'label': labels},
                        fetch_list=[pred, loss_mean])
     if i % 100 == 0:
-        print "Model is saved to", args.save_weights_path
+        print("Model is saved to", args.save_weights_path)
         save_model()
-    print "step %s, loss: %s" % (i, np.mean(retv[1]))
+    print("step %s, loss: %s" % (i, np.mean(retv[1])))
 
-print "Training done. Model is saved to", args.save_weights_path
+print("Training done. Model is saved to", args.save_weights_path)
 save_model()

fluid/face_detection/.gitignore

@@ -10,3 +10,4 @@ output*
 pred
 eval_tools
 box*
+PyramidBox_WiderFace*

fluid/image_classification/README_cn.md

@@ -14,7 +14,7 @@
 
 ## 安装
 
-在当前目录下运行样例代码需要PadddlePaddle Fluid的v0.13.0或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明来更新PaddlePaddle。
+在当前目录下运行样例代码需要PadddlePaddle Fluid的v0.13.0或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据安装文档中的说明来更新PaddlePaddle。
 
 ## 数据准备
 

fluid/object_detection/.gitignore

@@ -20,3 +20,4 @@ data/pascalvoc/trainval.txt
 
 log*
 *.log
+ssd_mobilenet_v1_pascalvoc*