Test module in travis ci

.travis.yml

@@ -11,6 +11,7 @@ env:
 script:
   - if [[ $TRAVIS_PYTHON_VERSION != 2.7 ]]; then /bin/bash ./scripts/check_code_style.sh; fi
   - pip install paddlepaddle; /bin/bash ./scripts/test_cml.sh
+  - /bin/bash ./scripts/test_all_module.sh
 
 notifications:
   email:

scripts/test_all_module.sh

+#!/bin/bash
+set -o errexit
+
+base_path=$(cd `dirname $0`/..; pwd)
+test_module_path=${base_path}/tests/modules
+
+# install the require package
+cd ${base_path}
+
+# run all case list in the {listfile}
+cd -
+for test_file in `ls $test_module_path | grep test`
+do
+    echo "run module ${test_file}"
+    python $test_module_path/$test_file
+done

tests/modules/test_lac.py

+#coding:utf-8
+import paddlehub as hub
+import six
+import json
+
+# Load LAC Module
+lac = hub.Module(name="lac")
+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"]
+
+# Set input dict
+inputs = {"text": test_text}
+
+# execute predict and print the result
+results = lac.lexical_analysis(data=inputs)
+for result in results:
+    if six.PY2:
+        print(json.dumps(result['word'], encoding="utf8", ensure_ascii=False))
+        print(json.dumps(result['tag'], encoding="utf8", ensure_ascii=False))
+    else:
+        print(result['word'])
+        print(result['tag'])

tests/modules/test_senta.py

+import paddlehub as hub
+
+senta = hub.Module(name="senta_bilstm")
+test_text = ["这家餐厅很好吃", "这部电影真的很差劲"]
+input_dict = {"text": test_text}
+results = senta.sentiment_classify(data=input_dict)
+
+for result in results:
+    print(result['text'])
+    print(result['sentiment_label'])
+    print(result['sentiment_key'])
+    print(result['positive_probs'])
+    print(result['negative_probs'])

tests/modules/test_simnet.py

+import paddlehub as hub
+
+simnet_bow = hub.Module(name="simnet_bow")
+test_text_1 = ["这道题太难了", "这道题太难了", "这道题太难了"]
+test_text_2 = ["这道题是上一年的考题", "这道题不简单", "这道题很有意思"]
+
+inputs = {"text_1": test_text_1, "text_2": test_text_2}
+results = simnet_bow.similarity(data=inputs)
+
+max_score = -1
+result_text = ""
+for result in results:
+    if result['similarity'] > max_score:
+        max_score = result['similarity']
+        result_text = result['text_2']
+
+print("The most matching with the %s is %s" % (test_text_1[0], result_text))

tests/modules/test_ssd.py

+import paddlehub as hub
+import os
+
+ssd = hub.Module(name="ssd_mobilenet_v1_pascal")
+
+base_dir = os.path.dirname(__file__)
+test_img_path = os.path.join(base_dir, "resources", "test_img_cat.jpg")
+
+# set input dict
+input_dict = {"image": [test_img_path]}
+
+# execute predict and print the result
+results = ssd.object_detection(data=input_dict)
+for result in results:
+    print(result['path'])
+    print(result['data'])