update the SRL demo.

demo/semantic_role_labeling/api_train_v2.py

@@ -1,26 +1,33 @@
-import sys
 import math
 import numpy as np
-import paddle.v2 as paddle
+import gzip
+import logging
 import paddle.v2.dataset.conll05 as conll05
+import paddle.v2.evaluator as evaluator
+import paddle.v2 as paddle
 
+logger = logging.getLogger('paddle')
 
-def db_lstm():
-    word_dict, verb_dict, label_dict = conll05.get_dict()
-    word_dict_len = len(word_dict)
-    label_dict_len = len(label_dict)
-    pred_len = len(verb_dict)
+word_dict, verb_dict, label_dict = conll05.get_dict()
+word_dict_len = len(word_dict)
+label_dict_len = len(label_dict)
+pred_len = len(verb_dict)
 
-    mark_dict_len = 2
-    word_dim = 32
-    mark_dim = 5
-    hidden_dim = 512
-    depth = 8
+mark_dict_len = 2
+word_dim = 32
+mark_dim = 5
+hidden_dim = 512
+depth = 8
+default_std = 1 / math.sqrt(hidden_dim) / 3.0
+mix_hidden_lr = 1e-3
 
-    #8 features
-    def d_type(size):
-        return paddle.data_type.integer_value_sequence(size)
 
+def d_type(size):
+    return paddle.data_type.integer_value_sequence(size)
+
+
+def db_lstm():
+    #8 features
     word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
     predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))
 
@@ -31,11 +38,7 @@ def d_type(size):
     ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
     mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))
 
-    target = paddle.layer.data(name='target', type=d_type(label_dict_len))
-
-    default_std = 1 / math.sqrt(hidden_dim) / 3.0
-
-    emb_para = paddle.attr.Param(name='emb', initial_std=0., learning_rate=0.)
+    emb_para = paddle.attr.Param(name='emb', initial_std=0., is_static=True)
     std_0 = paddle.attr.Param(initial_std=0.)
     std_default = paddle.attr.Param(initial_std=default_std)
 
@@ -63,7 +66,6 @@ def d_type(size):
                 input=emb, param_attr=std_default) for emb in emb_layers
         ])
 
-    mix_hidden_lr = 1e-3
     lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
     hidden_para_attr = paddle.attr.Param(
         initial_std=default_std, learning_rate=mix_hidden_lr)
@@ -111,6 +113,21 @@ def d_type(size):
                 input=input_tmp[1], param_attr=lstm_para_attr)
         ], )
 
+    return feature_out
+
+
+def load_parameter(file_name, h, w):
+    with open(file_name, 'rb') as f:
+        f.read(16)  # skip header.
+        return np.fromfile(f, dtype=np.float32).reshape(h, w)
+
+
+def train():
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    # define network topology
+    feature_out = db_lstm()
+    target = paddle.layer.data(name='target', type=d_type(label_dict_len))
     crf_cost = paddle.layer.crf(size=label_dict_len,
                                 input=feature_out,
                                 label=target,
@@ -120,29 +137,15 @@ def d_type(size):
                                     learning_rate=mix_hidden_lr))
 
     crf_dec = paddle.layer.crf_decoding(
-        name='crf_dec_l',
         size=label_dict_len,
         input=feature_out,
         label=target,
         param_attr=paddle.attr.Param(name='crfw'))
-
-    return crf_cost, crf_dec
-
-
-def load_parameter(file_name, h, w):
-    with open(file_name, 'rb') as f:
-        f.read(16)  # skip header.
-        return np.fromfile(f, dtype=np.float32).reshape(h, w)
-
-
-def main():
-    paddle.init(use_gpu=False, trainer_count=1)
-
-    # define network topology
-    crf_cost, crf_dec = db_lstm()
+    evaluator.sum(input=crf_dec)
 
     # create parameters
-    parameters = paddle.parameters.create([crf_cost, crf_dec])
+    parameters = paddle.parameters.create(crf_cost)
+    parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
 
     # create optimizer
     optimizer = paddle.optimizer.Momentum(
@@ -152,18 +155,12 @@ def main():
         model_average=paddle.optimizer.ModelAverage(
             average_window=0.5, max_average_window=10000), )
 
-    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)
-
     trainer = paddle.trainer.SGD(cost=crf_cost,
                                  parameters=parameters,
-                                 update_equation=optimizer)
-    parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
+                                 update_equation=optimizer,
+                                 extra_layers=crf_dec)
 
-    trn_reader = paddle.batch(
+    reader = paddle.batch(
         paddle.reader.shuffle(
             conll05.test(), buf_size=8192), batch_size=10)
 
@@ -179,12 +176,102 @@ def event_handler(event):
         'target': 8
     }
 
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                logger.info("Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics))
+            if event.batch_id and event.batch_id % 1000 == 0:
+                result = trainer.test(reader=reader, feeding=feeding)
+                logger.info("\nTest with Pass %d, Batch %d, %s" %
+                            (event.pass_id, event.batch_id, result.metrics))
+
+        if isinstance(event, paddle.event.EndPass):
+            # save parameters
+            with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+                parameters.to_tar(f)
+
+            result = trainer.test(reader=reader, feeding=feeding)
+            logger.info("\nTest with Pass %d, %s" %
+                        (event.pass_id, result.metrics))
+
     trainer.train(
-        reader=trn_reader,
+        reader=reader,
         event_handler=event_handler,
-        num_passes=10000,
+        num_passes=10,
         feeding=feeding)
 
 
+def infer_a_batch(inferer, test_data, word_dict, pred_dict, label_dict):
+    probs = inferer.infer(input=test_data, field='id')
+    assert len(probs) == sum(len(x[0]) for x in test_data)
+
+    for idx, test_sample in enumerate(test_data):
+        start_id = 0
+        pred_str = "%s\t" % (pred_dict[test_sample[6][0]])
+
+        for w, tag in zip(test_sample[0],
+                          probs[start_id:start_id + len(test_sample[0])]):
+            pred_str += "%s[%s] " % (word_dict[w], label_dict[tag])
+        print(pred_str.strip())
+        start_id += len(test_sample[0])
+
+
+def infer():
+    label_dict_reverse = dict((value, key)
+                              for key, value in label_dict.iteritems())
+    word_dict_reverse = dict((value, key)
+                             for key, value in word_dict.iteritems())
+    pred_dict_reverse = dict((value, key)
+                             for key, value in verb_dict.iteritems())
+
+    test_creator = paddle.dataset.conll05.test()
+
+    paddle.init(use_gpu=False, trainer_count=1)
+
+    # define network topology
+    feature_out = db_lstm()
+    predict = paddle.layer.crf_decoding(
+        size=label_dict_len,
+        input=feature_out,
+        param_attr=paddle.attr.Param(name='crfw'))
+
+    test_pass = 0
+    with gzip.open('params_pass_%d.tar.gz' % (test_pass)) as f:
+        parameters = paddle.parameters.Parameters.from_tar(f)
+        inferer = paddle.inference.Inference(
+            output_layer=predict, parameters=parameters)
+
+        # prepare test data
+        test_data = []
+        test_batch_size = 50
+
+        for idx, item in enumerate(test_creator()):
+            test_data.append(item[0:8])
+
+            if idx and (not idx % test_batch_size):
+                infer_a_batch(
+                    inferer,
+                    test_data,
+                    word_dict_reverse,
+                    pred_dict_reverse,
+                    label_dict_reverse, )
+                test_data = []
+        infer_a_batch(
+            inferer,
+            test_data,
+            word_dict_reverse,
+            pred_dict_reverse,
+            label_dict_reverse, )
+        test_data = []
+
+
+def main(is_inferring=False):
+    if is_inferring:
+        infer()
+    else:
+        train()
+
+
 if __name__ == '__main__':
-    main()
+    main(is_inferring=False)