Merge pull request #1465 from reyoung/feature/tester

Paddle.V2.Trainer.test method complete.

Author: jacquesqiao

demo/mnist/api_train_v2.py

@@ -20,26 +20,29 @@ def main():
 
     adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
 
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=adam_optimizer)
+
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
+            if event.batch_id % 1000 == 0:
+                result = trainer.test(reader=paddle.reader.batched(
+                    paddle.dataset.mnist.test(), batch_size=256))
+
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
         else:
             pass
 
-    trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
-
     trainer.train(
         reader=paddle.reader.batched(
             paddle.reader.shuffle(
                 paddle.dataset.mnist.train(), buf_size=8192),
             batch_size=32),
-        cost=cost,
-        parameters=parameters,
-        event_handler=event_handler,
-        reader_dict={images.name: 0,
-                     label.name: 1})
+        event_handler=event_handler)
 
 
 if __name__ == '__main__':

python/paddle/v2/dataset/mnist.py

@@ -9,9 +9,9 @@
 
 URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
 TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
-TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6'
+TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
 TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
-TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688'
+TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
 TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
 TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
 TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'

python/paddle/v2/event.py

@@ -11,7 +11,10 @@
 TODO(yuyang18): Complete it!
 """
 import py_paddle.swig_paddle as api
-__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass']
+
+__all__ = [
+    'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult'
+]
 
 
 class WithMetric(object):
@@ -30,6 +33,11 @@ def metrics(self):
         return retv
 
 
+class TestResult(WithMetric):
+    def __init__(self, evaluator):
+        super(TestResult, self).__init__(evaluator)
+
+
 class BeginPass(object):
     """
     Event On One Pass Training Start.

python/paddle/v2/topology.py

@@ -21,6 +21,14 @@
 __all__ = ['Topology']
 
 
+def __bfs_travel__(callback, *layers):
+    for each_layer in layers:
+        __break__ = callback(each_layer)
+        if __break__:
+            return
+        __bfs_travel__(callback, *each_layer.__parent_layers__.values())
+
+
 class Topology(object):
     """
     Topology is used to store the information about all layers
@@ -46,48 +54,41 @@ def get_layer(self, name):
         :param name:
         :return:
         """
-        result_layer = []
+        result_layer = [None]
 
-        def find_layer_by_name(layer, layer_name):
-            if len(result_layer) == 1:
-                return
-            elif layer.name == layer_name:
-                result_layer.append(layer)
-            else:
-                for parent_layer in layer.__parent_layers__.values():
-                    find_layer_by_name(parent_layer, layer_name)
+        def __impl__(l):
+            if l.name == name:
+                result_layer[0] = l
+                return True  # break
+            return False
 
-        for layer in self.layers:
-            find_layer_by_name(layer, name)
-
-        assert len(result_layer) == 1
+        __bfs_travel__(__impl__, *self.layers)
+        if result_layer[0] is None:
+            raise ValueError("No such layer %s" % name)
         return result_layer[0]
 
     def data_layers(self):
         """
         get all data layer
         :return:
         """
-        data_layers = set()
-
-        def find_data_layer(layer):
-            if isinstance(layer, v2_layer.DataLayerV2):
-                data_layers.add(layer)
-            for parent_layer in layer.__parent_layers__.values():
-                find_data_layer(parent_layer)
+        data_layers = dict()
 
-        for layer in self.layers:
-            find_data_layer(layer)
+        def __impl__(l):
+            if isinstance(l, v2_layer.DataLayerV2):
+                data_layers[l.name] = l
 
+        __bfs_travel__(__impl__, *self.layers)
         return data_layers
 
     def data_type(self):
         """
         get data_type from proto, such as:
         [('image', dense_vector(768)), ('label', integer_value(10))]
         """
-        return [(data_layer.name, data_layer.type)
-                for data_layer in self.data_layers()]
+        data_layers = self.data_layers()
+        return [(nm, data_layers[nm].type)
+                for nm in self.proto().input_layer_names]
 
 
 def __check_layer_type__(layer):

python/paddle/v2/trainer.py

@@ -42,25 +42,35 @@ def train(self, reader, topology, parameters, event_handler=None):
 
 
 class SGD(ITrainer):
-    def __init__(self, update_equation):
+    def __init__(self, cost, parameters, update_equation):
         """
         Simple SGD Trainer.
 
         :param update_equation: The optimizer object.
         :type update_equation: v2_optimizer.Optimizer
         """
+
+        if not isinstance(parameters, v2_parameters.Parameters):
+            raise TypeError('parameters should be parameters')
+
         if not isinstance(update_equation, v2_optimizer.Optimizer):
-            raise ValueError("update equation parameter must be "
-                             "paddle.v2.optimizer.Optimizer")
+            raise TypeError("update equation parameter must be "
+                            "paddle.v2.optimizer.Optimizer")
+        topology = Topology(cost)
         self.__optimizer__ = update_equation
+        self.__topology__ = topology
+        self.__parameters__ = parameters
+        self.__topology_in_proto__ = topology.proto()
+        self.__data_types__ = topology.data_type()
+        gm = api.GradientMachine.createFromConfigProto(
+            self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
+            self.__optimizer__.enable_types())
+        assert isinstance(gm, api.GradientMachine)
+        parameters.append_gradient_machine(gm)
+        self.__gradient_machine__ = gm
+        self.__gradient_machine__.randParameters()
 
-    def train(self,
-              reader,
-              cost,
-              parameters,
-              num_passes=1,
-              event_handler=None,
-              reader_dict=None):
+    def train(self, reader, num_passes=1, event_handler=None, reader_dict=None):
         """
         Training method. Will train num_passes of input data.
 
@@ -76,44 +86,41 @@ def train(self,
         if event_handler is None:
             event_handler = default_event_handler
 
-        topology = Topology(cost)
+        if reader_dict is None:
+            reader_dict = self.default_reader_dict()
 
         __check_train_args__(**locals())
 
-        gm = api.GradientMachine.createFromConfigProto(
-            topology.proto(), api.CREATE_MODE_NORMAL,
-            self.__optimizer__.enable_types())
-        assert isinstance(gm, api.GradientMachine)
-        parameters.append_gradient_machine(gm)
-        gm.randParameters()
         updater = self.__optimizer__.create_local_updater()
-        updater.init(gm)
+        updater.init(self.__gradient_machine__)
 
-        gm.start()
-        batch_evaluator = gm.makeEvaluator()
+        self.__gradient_machine__.start()
+        batch_evaluator = self.__gradient_machine__.makeEvaluator()
         assert isinstance(batch_evaluator, api.Evaluator)
-        pass_evaluator = gm.makeEvaluator()
+        pass_evaluator = self.__gradient_machine__.makeEvaluator()
         assert isinstance(pass_evaluator, api.Evaluator)
         out_args = api.Arguments.createArguments(0)
 
-        feeder = DataFeeder(topology.data_type(), reader_dict)
+        feeder = DataFeeder(self.__data_types__, reader_dict)
 
         for pass_id in xrange(num_passes):
             event_handler(v2_event.BeginPass(pass_id))
             pass_evaluator.start()
             updater.startPass()
             for batch_id, data_batch in enumerate(reader()):
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.forwardBackward(
+                    feeder(data_batch), out_args, pass_type)
                 batch_evaluator.start()
                 event_handler(
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
-                gm.eval(pass_evaluator)
-                gm.eval(batch_evaluator)
-                for each_param in gm.getParameters():
+                self.__gradient_machine__.forwardBackward(
+                    feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.eval(pass_evaluator)
+                self.__gradient_machine__.eval(batch_evaluator)
+                for each_param in self.__gradient_machine__.getParameters():
                     updater.update(each_param)
                 # Get cost. We use numpy to calculate total cost for this batch.
                 cost_vec = out_args.getSlotValue(0)
@@ -131,22 +138,37 @@ def train(self,
             updater.finishPass()
             pass_evaluator.finish()
             event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
-        gm.finish()
+        self.__gradient_machine__.finish()
+
+    def default_reader_dict(self):
+        reader_dict = dict()
+        for i, tp in enumerate(self.__data_types__):
+            reader_dict[tp[0]] = i
+        return reader_dict
+
+    def test(self, reader, reader_dict=None):
+        if reader_dict is None:
+            reader_dict = self.default_reader_dict()
+
+        feeder = DataFeeder(self.__data_types__, reader_dict)
+        evaluator = self.__gradient_machine__.makeEvaluator()
+        out_args = api.Arguments.createArguments(0)
+        evaluator.start()
+        for data_batch in reader():
+            self.__gradient_machine__.forward(
+                feeder(data_batch), out_args, api.PASS_TEST)
+            self.__gradient_machine__.eval(evaluator)
+
+        evaluator.finish()
+        return v2_event.TestResult(evaluator=evaluator)
 
 
-def __check_train_args__(reader, topology, parameters, event_handler, **kwargs):
+def __check_train_args__(reader, event_handler, **kwargs):
     """
     Check train function's argument types
     """
     if not callable(reader) or not isinstance(reader(), collections.Iterator):
         raise TypeError('train_data_reader should be a function, '
                         'which can return a iterator')
-
-    if not isinstance(topology, Topology):
-        raise TypeError('topology should be a model config')
-
-    if not isinstance(parameters, v2_parameters.Parameters):
-        raise TypeError('parameters should be a parameter pool')
-
     if not callable(event_handler):
         raise TypeError('event handler should be a function')