Support predicting the samples from sys.stdin

demo/sentiment/predict.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
+import os, sys
 import numpy as np
 from optparse import OptionParser
 from py_paddle import swig_paddle, DataProviderConverter
@@ -66,12 +66,11 @@ class SentimentPrediction():
         for v in open(label_file, 'r'):
             self.label[int(v.split('\t')[1])] = v.split('\t')[0]
 
-    def get_data(self, data_file):
+    def get_data(self, data):
         """
         Get input data of paddle format.
         """
-        with open(data_file, 'r') as fdata:
-            for line in fdata:
+        for line in data:
             words = line.strip().split()
             word_slot = [
                 self.word_dict[w] for w in words if w in self.word_dict
@@ -81,20 +80,28 @@ class SentimentPrediction():
                 continue
             yield [word_slot]
 
-    def predict(self, data_file):
-        """
-        data_file: file name of input data.
-        """
-        input = self.converter(self.get_data(data_file))
+    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"]
-        lab = np.argsort(-prob)
+            labs = np.argsort(-prob)
+            for idx, lab in enumerate(labs):
                 if self.label is None:
-            print("%s: predicting label is %d" % (data_file, lab[0][0]))
+                    print("predicting label is %d" % (lab[0]))
                 else:
-            print("%s: predicting label is %s" %
-                  (data_file, self.label[lab[0][0]]))
+                    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)
 
 def option_parser():
     usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
@@ -119,11 +126,13 @@ def option_parser():
         default=None,
         help="dictionary file")
     parser.add_option(
-        "-i",
-        "--data",
+        "-c",
+        "--batch_size",
+        type="int",
         action="store",
-        dest="data",
-        help="data file to predict")
+        dest="batch_size",
+        default=1,
+        help="the batch size for prediction")
     parser.add_option(
         "-w",
         "--model",
@@ -137,13 +146,13 @@ def option_parser():
 def main():
     options, args = option_parser()
     train_conf = options.train_conf
-    data = options.data
+    batch_size = options.batch_size
     dict_file = options.dict_file
     model_path = options.model_path
     label = options.label
     swig_paddle.initPaddle("--use_gpu=0")
     predict = SentimentPrediction(train_conf, dict_file, model_path, label)
-    predict.predict(data)
+    predict.predict(batch_size)
 
 
 if __name__ == '__main__':

demo/sentiment/predict.sh

@@ -19,9 +19,9 @@ set -e
 model=model_output/pass-00002/
 config=trainer_config.py
 label=data/pre-imdb/labels.list
-python predict.py \
-     -n $config\
-     -w $model \
-     -b $label \
-     -d ./data/pre-imdb/dict.txt \
-     -i ./data/aclImdb/test/pos/10007_10.txt 
+cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
+     --tconf=$config\
+     --model=$model \
+     --label=$label \
+     --dict=./data/pre-imdb/dict.txt \
+     --batch_size=1