Merge pull request #1561 from reyoung/feature/better_infer_interface

Add input data interface for inference

demo/mnist/api_train_v2.py

@@ -122,13 +122,14 @@ def main():
     test_creator = paddle.dataset.mnist.test()
     test_data = []
     for item in test_creator():
-        test_data.append(item[0])
+        test_data.append((item[0], ))
         if len(test_data) == 100:
             break
 
     # output is a softmax layer. It returns probabilities.
     # Shape should be (100, 10)
-    probs = paddle.infer(output=predict, parameters=parameters, input=test_data)
+    probs = paddle.infer(
+        output_layer=predict, parameters=parameters, input=test_data)
     print probs.shape
 
 

python/paddle/v2/inference.py

@@ -9,8 +9,8 @@ __all__ = ['infer']
 
 
 class Inference(object):
-    def __init__(self, output, parameters):
-        topo = topology.Topology(output)
+    def __init__(self, output_layer, parameters):
+        topo = topology.Topology(output_layer)
         gm = api.GradientMachine.createFromConfigProto(
             topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
         for param in gm.getParameters():
@@ -21,33 +21,16 @@ class Inference(object):
         self.__gradient_machine__ = gm
         self.__data_types__ = topo.data_type()
 
-    def iter_infer(self, input=None, batch_size=None, reader=None,
-                   feeding=None):
+    def iter_infer(self, input, feeding=None):
         feeder = DataFeeder(self.__data_types__, feeding)
-        if reader is None:
-            assert input is not None and isinstance(input, collections.Iterable)
-            if not isinstance(input, collections.Iterable):
-                raise TypeError("When reader is None, input should be whole "
-                                "inference data and should be iterable")
-
-            if batch_size is None:
-                if not hasattr(input, '__len__'):
-                    raise ValueError("Should set batch size when input data "
-                                     "don't contain length.")
-                batch_size = len(input)
-
-            def __reader_impl__():
-                for each_sample in input:
-                    if len(feeder) == 1:
-                        yield [each_sample]
-                    else:
-                        yield each_sample
-
-            reader = minibatch.batch(__reader_impl__, batch_size=batch_size)
-        else:
-            if input is not None:
-                raise ValueError("User should set either input or reader, "
-                                 "should not set them both.")
+        batch_size = len(input)
+
+        def __reader_impl__():
+            for each_sample in input:
+                yield each_sample
+
+        reader = minibatch.batch(__reader_impl__, batch_size=batch_size)
+
         self.__gradient_machine__.start()
         for data_batch in reader():
             yield self.__gradient_machine__.forwardTest(feeder(data_batch))
@@ -71,13 +54,7 @@ class Inference(object):
             return retv
 
 
-def infer(output,
-          parameters,
-          input=None,
-          batch_size=None,
-          reader=None,
-          feeding=None,
-          field='value'):
+def infer(output_layer, parameters, input, feeding=None, field='value'):
     """
     Infer a neural network by given neural network output and parameters.  The
     user should pass either a batch of input data or reader method.
@@ -90,19 +67,13 @@ def infer(output,
                               batch_size=32)
         print result
 
-    :param output: output of the neural network that would be inferred
-    :type output: paddle.v2.config_base.Layer
+    :param output_layer: output of the neural network that would be inferred
+    :type output_layer: paddle.v2.config_base.Layer
     :param parameters: parameters of the neural network.
     :type parameters: paddle.v2.parameters.Parameters
     :param input: input data batch. Should be a python iterable object, and each
                   element is the data batch.
     :type input: collections.Iterable
-    :param batch_size: the batch size when perform inference. Default is the
-                       length of input.
-    :type batch_size: int
-    :param reader: input data reader creator in batch. If this field is set, the
-                   `input` and `batch_size` will be ignored.
-    :type reader: callable
     :param feeding: Reader dictionary. Default could generate from input
                         value.
     :param field: The prediction field. It should in [`value`, `ids`]. `value`
@@ -113,10 +84,5 @@ def infer(output,
     :rtype: numpy.ndarray
     """
 
-    inferer = Inference(output=output, parameters=parameters)
-    return inferer.infer(
-        field=field,
-        input=input,
-        batch_size=batch_size,
-        reader=reader,
-        feeding=feeding)
+    inferer = Inference(output_layer=output_layer, parameters=parameters)
+    return inferer.infer(field=field, input=input, feeding=feeding)