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Author: qingqing01

demo/sentiment/predict.py

@@ -66,42 +66,27 @@ def load_label(self, label_file):
         for v in open(label_file, 'r'):
             self.label[int(v.split('\t')[1])] = v.split('\t')[0]
 
-    def get_data(self, data):
+    def get_index(self, data):
         """
-        Get input data of paddle format.
+        transform word into integer index according to the dictionary.
         """
-        for line in data:
-            words = line.strip().split()
-            word_slot = [
-                self.word_dict[w] for w in words if w in self.word_dict
-            ]
-            if not word_slot:
-                print "all words are not in dictionary: %s", line
-                continue
-            yield [word_slot]
-
-    def predict(self, batch_size):
-
-        def batch_predict(batch_data):
-            input = self.converter(self.get_data(batch_data))
-            output = self.network.forwardTest(input)
-            prob = output[0]["value"]
-            labs = np.argsort(-prob)
-            for idx, lab in enumerate(labs):
-                if self.label is None:
-                    print("predicting label is %d" % (lab[0]))
-                else:
-                    print("predicting label is %s" %
-                          (self.label[lab[0]]))
-
-        batch = []
-        for line in sys.stdin:
-            batch.append(line)
-            if len(batch) == batch_size:
-                batch_predict(batch)
-                batch=[]
-        if len(batch) > 0:
-            batch_predict(batch)
+        words = data.strip().split()
+        word_slot = [
+            self.word_dict[w] for w in words if w in self.word_dict
+        ]
+        return word_slot
+
+    def batch_predict(self, data_batch):
+        input = self.converter(data_batch)
+        output = self.network.forwardTest(input)
+        prob = output[0]["value"]
+        labs = np.argsort(-prob)
+        for idx, lab in enumerate(labs):
+            if self.label is None:
+                print("predicting label is %d" % (lab[0]))
+            else:
+                print("predicting label is %s" %
+                      (self.label[lab[0]]))
 
 def option_parser():
     usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
@@ -152,8 +137,15 @@ def main():
     label = options.label
     swig_paddle.initPaddle("--use_gpu=0")
     predict = SentimentPrediction(train_conf, dict_file, model_path, label)
-    predict.predict(batch_size)
 
+    batch = []
+    for line in sys.stdin:
+        batch.append([predict.get_index(line)])
+        if len(batch) == batch_size:
+            predict.batch_predict(batch)
+            batch=[]
+    if len(batch) > 0:
+        predict.batch_predict(batch)
 
 if __name__ == '__main__':
     main()

doc/tutorials/sentiment_analysis/sentiment_analysis.md

@@ -293,20 +293,21 @@ predict.sh:
 model=model_output/pass-00002/
 config=trainer_config.py
 label=data/pre-imdb/labels.list
-python predict.py \
-     -n $config\
-     -w $model \
-     -b $label \
-     -d data/pre-imdb/dict.txt \
-     -i data/aclImdb/test/pos/10007_10.txt
-```
-
-* `predict.py`: predicting interface.
-*  -n $config : set network configure.
-*  -w $model: set model path.
-*  -b $label: set dictionary about corresponding relation between integer label and string label.
-*  -d data/pre-imdb/dict.txt: set dictionary.
-*  -i data/aclImdb/test/pos/10014_7.txt: set one example file to predict.
+cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
+     --tconf=$config\
+     --model=$model \
+     --label=$label \
+     --dict=./data/pre-imdb/dict.txt \
+     --batch_size=1
+```
+
+* `cat ./data/aclImdb/test/pos/10007_10.txt` : the input sample.
+* `predict.py` : predicting interface.
+* `--tconf=$config` : set network configure.
+* ` --model=$model` : set model path.
+* `--label=$label` : set dictionary about corresponding relation between integer label and string label.
+* `--dict=data/pre-imdb/dict.txt` : set dictionary.
+* `--batch_size=1` : set batch size.
 
 Note you should make sure the default model path `model_output/pass-00002`
 exists or change the model path.

doc_cn/demo/sentiment_analysis/sentiment_analysis.md

@@ -291,20 +291,21 @@ predict.sh:
 model=model_output/pass-00002/
 config=trainer_config.py
 label=data/pre-imdb/labels.list
-python predict.py \
-     -n $config\
-     -w $model \
-     -b $label \
-     -d data/pre-imdb/dict.txt \
-     -i data/aclImdb/test/pos/10007_10.txt
-```
-
-* `predict.py`: 预测接口脚本。
-*  -n $config : 设置网络配置。
-*  -w $model: 设置模型路径。
-*  -b $label: 设置标签类别字典，这个字典是整数标签和字符串标签的一个对应。
-*  -d data/pre-imdb/dict.txt: 设置字典文件。
-*  -i data/aclImdb/test/pos/10014_7.txt: 设置一个要预测的示例文件。
+cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
+     --tconf=$config\
+     --model=$model \
+     --label=$label \
+     --dict=./data/pre-imdb/dict.txt \
+     --batch_size=1
+```
+
+* `cat ./data/aclImdb/test/pos/10007_10.txt` : 输入预测样本。
+* `predict.py` : 预测接口脚本。
+* `--tconf=$config` : 设置网络配置。
+* `--model=$model` : 设置模型路径。
+* `--label=$label` : 设置标签类别字典，这个字典是整数标签和字符串标签的一个对应。
+* `--dict=data/pre-imdb/dict.txt` : 设置字典文件。
+* `--batch_size=1` : 设置batch size。
 
 注意应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。