update hub module name

demo/image-classification/create_module.py

@@ -56,7 +56,7 @@ def create_module(args):
         "feature_map", inputs=[image], outputs=[feature_map])
     hub.create_module(
         sign_arr=[sign1, sign2],
-        module_dir="hub_module_" + args.model,
+        module_dir=args.model + ".hub_module",
         module_info="resources/module_info.yml",
         processor=processor.Processor,
         assets=assets)

demo/image-classification/create_module.sh

@@ -16,7 +16,7 @@ do
 done
 
 script_path=$(cd `dirname $0`; pwd)
-module_path=hub_module_${model_name}
+module_path=${model_name}.hub_module
 
 if [ -d $script_path/$module_path ]
 then

demo/image-classification/infer.sh

-python ../../paddlehub/commands/hub.py run hub_module_ResNet50/ --signature classification --config resources/test/test.yml  --dataset resources/test/test.csv
+python ../../paddlehub/commands/hub.py run ResNet50.hub_module/ --signature classification --config resources/test/test.yml  --dataset resources/test/test.csv

demo/image-classification/resources/module_info.yml

-name: Resnet50
+name: resnet_v2_50_imagenet
 type: CV/classification
 author: paddlepaddle
 author_email: paddle-dev@baidu.com

demo/image-classification/retrain.py

@@ -5,11 +5,11 @@ import paddlehub as hub
 
 
 def train():
-    resnet_module = hub.Module(module_dir="hub_module_ResNet50")
+    resnet_module = hub.Module(module_dir="ResNet50.hub_module")
     input_dict, output_dict, program = resnet_module.context(
         sign_name="feature_map", trainable=True)
     dataset = hub.dataset.Flowers()
-    data_reader = hub.ImageClassificationReader(
+    data_reader = hub.reader.ImageClassificationReader(
         image_width=224, image_height=224, dataset=dataset)
     with fluid.program_guard(program):
         label = fluid.layers.data(name="label", dtype="int64", shape=[1])
@@ -24,7 +24,7 @@ def train():
 
         feed_list = [img.name, label.name]
 
-        task = hub.append_mlp_classifier(
+        task = hub.create_img_classification_task(
             feature=feature_map, label=label, num_classes=dataset.num_labels)
         hub.finetune_and_eval(
             task, feed_list=feed_list, data_reader=data_reader, config=config)

demo/lac/create_module.py

@@ -35,7 +35,7 @@ def create_module():
     # assets
     assets = [word_dict_path, label_dict_path, word_rep_dict_path]
 
-    # create a module and save as hub_module_lac
+    # create a module and save as lac.hub_module
     sign = hub.create_signature(
         name="lexical_analysis",
         inputs=[word],
@@ -43,7 +43,7 @@ def create_module():
         for_predict=True)
     hub.create_module(
         sign_arr=[sign],
-        module_dir="hub_module_lac",
+        module_dir="lac.hub_module",
         exe=exe,
         module_info="resources/module_info.yml",
         processor=processor.Processor,

demo/lac/create_module.sh

@@ -3,7 +3,7 @@ set -o nounset
 set -o errexit
 
 script_path=$(cd `dirname $0`; pwd)
-module_path=hub_module_lac
+module_path=lac.hub_module
 
 if [ -d $script_path/$module_path ]
 then

demo/lac/infer.sh

-python ../../paddlehub/commands/hub.py run hub_module_lac/ --signature lexical_analysis --config resources/test/test.yml  --input_file resources/test/test.txt
+python ../../paddlehub/commands/hub.py run lac.hub_module/ --signature lexical_analysis --config resources/test/test.yml  --input_file resources/test/test.txt

demo/lac/infer_by_code.py

@@ -4,7 +4,7 @@ import paddlehub as hub
 
 def infer_with_input_text():
     # get lac module
-    lac = hub.Module(module_dir="hub_module_lac")
+    lac = hub.Module(module_dir="lac.hub_module")
 
     test_text = ["今天是个好日子", "天气预报说今天要下雨", "下一班地铁马上就要到了"]
 
@@ -24,7 +24,7 @@ def infer_with_input_text():
 
 def infer_with_input_file():
     # get lac module
-    lac = hub.Module(module_dir="hub_module_lac")
+    lac = hub.Module(module_dir="lac.hub_module")
 
     # get the input keys for signature 'lexical_analysis'
     data_format = lac.processor.data_format(sign_name='lexical_analysis')

demo/senta/create_module.py

@@ -48,7 +48,7 @@ def create_module():
         for_predict=True)
     hub.create_module(
         sign_arr=[sign],
-        module_dir="hub_module_senta",
+        module_dir="senta.hub_module",
         exe=exe,
         module_info="resources/module_info.yml",
         processor=processor.Processor,

demo/senta/create_module.sh

@@ -3,7 +3,7 @@ set -o nounset
 set -o errexit
 
 script_path=$(cd `dirname $0`; pwd)
-module_path=hub_module_senta
+module_path=senta.hub_module
 
 if [ -d $script_path/$module_path ]
 then

demo/senta/infer.sh

-python ../../paddlehub/commands/hub.py run hub_module_senta/ --signature sentiment_classify --config resources/test/test.yml  --input_file resources/test/test.txt
+python ../../paddlehub/commands/hub.py run senta.hub_module/ --signature sentiment_classify --config resources/test/test.yml  --input_file resources/test/test.txt

demo/senta/infer_by_code.py

@@ -4,7 +4,7 @@ import paddlehub as hub
 
 def infer_with_input_text():
     # get senta module
-    senta = hub.Module(module_dir="hub_module_senta")
+    senta = hub.Module(module_dir="senta.hub_module")
 
     test_text = ["这家餐厅很好吃", "这部电影真的很差劲"]
 
@@ -24,7 +24,7 @@ def infer_with_input_text():
 
 def infer_with_input_file():
     # get senta module
-    senta = hub.Module(module_dir="hub_module_senta")
+    senta = hub.Module(module_dir="senta.hub_module")
 
     # get the input keys for signature 'sentiment_classify'
     data_format = senta.processor.data_format(sign_name='sentiment_classify')

demo/ssd/create_module.py

@@ -47,7 +47,7 @@ def create_module():
         for_predict=True)
     hub.create_module(
         sign_arr=[sign],
-        module_dir="hub_module_ssd",
+        module_dir="ssd_mobilenet_v1_pascal.hub_module",
         module_info="resources/module_info.yml",
         exe=exe,
         processor=processor.Processor,

demo/ssd/create_module.sh

@@ -3,7 +3,7 @@ set -o nounset
 set -o errexit
 
 script_path=$(cd `dirname $0`; pwd)
-module_path=hub_module_ssd
+module_path=ssd_mobilenet_v1_pascal.hub_module
 
 if [ -d $script_path/$module_path ]
 then

demo/ssd/infer.sh

-python ../../paddlehub/commands/hub.py run hub_module_ssd/ --signature object_detection --config resources/test/test.yml  --input_file resources/test/test.txt
+python ../../paddlehub/commands/hub.py run ssd_mobilenet_v1_pascal.hub_module/ --signature object_detection --config resources/test/test.yml  --input_file resources/test/test.txt

demo/ssd/infer_by_code.py

@@ -4,7 +4,7 @@ import paddlehub as hub
 
 def infer_with_input_text():
     # get ssd module
-    ssd = hub.Module(module_dir="hub_module_ssd")
+    ssd = hub.Module(module_dir="ssd_mobilenet_v1_pascal.hub_module")
 
     test_img_path = os.path.join("resources", "test", "test_img_bird.jpg")
 
@@ -23,7 +23,7 @@ def infer_with_input_text():
 
 def infer_with_input_file():
     # get ssd module
-    ssd = hub.Module(module_dir="hub_module_ssd")
+    ssd = hub.Module(module_dir="ssd_mobilenet_v1_pascal.hub_module")
 
     # get the input keys for signature 'object_detection'
     data_format = ssd.processor.data_format(sign_name='object_detection')

paddlehub/dataset/base_cv_dataset.py

@@ -34,7 +34,7 @@ class ImageClassificationDataset(object):
         if not os.path.exists(dataset_path):
             result, tips, dataset_path = default_downloader.download_file_and_uncompress(
                 url=url,
-                save_path=hub.dir.DATA_HOME,
+                save_path=hub.common.dir.DATA_HOME,
                 print_progress=True,
                 replace=True)
             if not result: