Make use of unittest to compelete unit testing. (#413)

* Make use of unittest to compelete unit testing.
delete 4 .py scripts and 2 shell scripts.

scripts/test_all_case.sh

-#!/bin/bash
-set -o errexit
-
-function usage() {
-    echo "usage: sh $0 {test_case_list_file}"
-}
-
-if [ $# -lt 1 ]
-then
-    usage
-    exit 1
-fi
-
-listfile=$1
-base_path=$(cd `dirname $0`/..; pwd)
-test_case_path=${base_path}/tests
-export PYTHONPATH=$base_path:$PYTHONPATH
-
-# install the require package
-cd ${base_path}
-pip install -r requirements.txt
-
-# run all case list in the {listfile}
-cd -
-for test_file in `cat $listfile | grep -v ^#`
-do
-    echo "run test case ${test_file}"
-	python ${test_case_path}/${test_file}.py
-done

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

-#coding:utf-8
-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

-#coding:utf-8
-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'])

tests/test_module.py

+# coding=utf-8
+import os
+import unittest
+import paddlehub as hub
+
+
+class TestHubModule(unittest.TestCase):
+    def test_lac(self):
+        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"]
+        inputs = {"text": test_text}
+        results = lac.lexical_analysis(data=inputs)
+        self.assertEqual(results[0]['word'], ['今天', '是', '个', '好日子'])
+        self.assertEqual(results[0]['tag'], ['TIME', 'v', 'q', 'n'])
+        self.assertEqual(results[1]['word'], ['天气预报', '说', '今天', '要', '下雨'])
+        self.assertEqual(results[1]['tag'], ['n', 'v', 'TIME', 'v', 'v'])
+        self.assertEqual(results[2]['word'],
+                         ['下', '一班', '地铁', '马上', '就要', '到', '了'])
+        self.assertEqual(results[2]['tag'],
+                         ['f', 'm', 'n', 'd', 'v', 'v', 'xc'])
+
+    def test_senta(self):
+        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)
+        self.assertEqual(results[0]['sentiment_label'], 1)
+        self.assertEqual(results[0]['sentiment_key'], 'positive')
+        self.assertEqual(results[1]['sentiment_label'], 0)
+        self.assertEqual(results[1]['sentiment_key'], 'negative')
+        for result in results:
+            print(result['text'])
+            print(result['positive_probs'])
+            print(result['negative_probs'])
+
+    def test_simnet(self):
+        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))
+
+    def test_ssd(self):
+        ssd = hub.Module(name="ssd_mobilenet_v1_pascal")
+        test_img_path = os.path.join(
+            os.path.dirname(__file__), "resources", "test_img_cat.jpg")
+        input_dict = {"image": [test_img_path]}
+        results = ssd.object_detection(data=input_dict)
+        for result in results:
+            print(result['path'])
+            print(result['data'])
+
+
+if __name__ == "__main__":
+    unittest.main()