Add CE to Sentiment (#2777)

* Update mnist_dygraph.py

fix bug

* add ce to se_resnext

* delete useless comments and fix unique_name bugs

* add sentiment ce

* delete some comment

* fix code style

dygraph/sentiment/.run_ce.sh

+#!/bin/bash
+
+# This file is only used for continuous evaluation.
+# dygraph single card
+export FLAGS_cudnn_deterministic=True
+export CUDA_VISIBLE_DEVICES=0
+python main.py --ce --epoch 1 --random_seed 33 --validation_steps 600 | python _ce.py
+

dygraph/sentiment/_ce.py

+####this file is only used for continuous evaluation test!
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import sys
+from kpi import CostKpi, DurationKpi, AccKpi
+sys.path.append(os.environ['ceroot'])
+
+#### NOTE kpi.py should shared in models in some way!!!!
+
+train_acc = AccKpi('train_acc', 0.1, 0, actived=True, desc="train acc")
+train_loss = CostKpi('train_loss', 0.1, 0, actived=True, desc="train loss")
+tracking_kpis = [train_acc, train_loss]
+
+def parse_log(log):
+    '''
+    This method should be implemented by model developers.
+
+    The suggestion:
+
+    each line in the log should be key, value, for example:
+
+    "
+    train_cost\t1.0
+    test_cost\t1.0
+    train_cost\t1.0
+    train_cost\t1.0
+    train_acc\t1.2
+    "
+    '''
+    for line in log.split('\n'):
+        fs = line.strip().split('\t')
+        print(fs)
+        if len(fs) == 3 and fs[0] == 'kpis':
+            print("-----%s" % fs)
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
+
+
+def log_to_ce(log):
+    kpi_tracker = {}
+    for kpi in tracking_kpis:
+        kpi_tracker[kpi.name] = kpi
+
+    for (kpi_name, kpi_value) in parse_log(log):
+        print(kpi_name, kpi_value)
+        kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
+
+
+if __name__ == '__main__':
+    log = sys.stdin.read()
+    print("*****")
+    print(log)
+    print("****")
+    log_to_ce(log)

dygraph/sentiment/main.py

@@ -52,12 +52,13 @@ data_g.add_arg("batch_size", int, 16,
 data_g.add_arg("random_seed", int, 0, "Random seed.")
 
 run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
-run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.")
+run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
 run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
 run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation.")
 run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.")
 run_type_g.add_arg("profile_steps", int, 15000,
                    "The steps interval to record the performance.")
+parser.add_argument("--ce", action="store_true", help="run ce")
 
 args = parser.parse_args()
 
@@ -81,8 +82,21 @@ def profile_context(profile=True):
         yield
 
 
+if args.ce:
+    print("ce mode")
+    seed = 90
+    np.random.seed(seed)
+    fluid.default_startup_program().random_seed = seed
+    fluid.default_main_program().random_seed = seed 
+
 def train():
     with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 90
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed 
         processor = reader.SentaProcessor(
             data_dir=args.data_dir,
             vocab_path=args.vocab_path,
@@ -92,19 +106,31 @@ def train():
         num_train_examples = processor.get_num_examples(phase="train")
 
         max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count
-
-        train_data_generator = processor.data_generator(
-            batch_size=args.batch_size,
-            phase='train',
-            epoch=args.epoch,
-            shuffle=True)
-
-        eval_data_generator = processor.data_generator(
-            batch_size=args.batch_size,
-            phase='dev',
-            epoch=args.epoch,
-            shuffle=False)
-
+        
+        if not args.ce:
+            train_data_generator = processor.data_generator(
+                batch_size=args.batch_size,
+                phase='train',
+                epoch=args.epoch,
+                shuffle=True)
+
+            eval_data_generator = processor.data_generator(
+                batch_size=args.batch_size,
+                phase='dev',
+                epoch=args.epoch,
+                shuffle=False)
+        else:
+            train_data_generator = processor.data_generator(
+                batch_size=args.batch_size,
+                phase='train',
+                epoch=args.epoch,
+                shuffle=False)
+
+            eval_data_generator = processor.data_generator(
+                batch_size=args.batch_size,
+                phase='dev',
+                epoch=args.epoch,
+                shuffle=False)
         cnn_net = nets.CNN("cnn_net", args.vocab_size, args.batch_size,
                            args.padding_size)
 
@@ -137,7 +163,6 @@ def train():
                     cnn_net.train()
                     avg_cost, prediction, acc = cnn_net(doc, label)
                     avg_cost.backward()
-
                     np_mask = (doc.numpy() != args.vocab_size).astype('int32')
                     word_num = np.sum(np_mask)
                     sgd_optimizer.minimize(avg_cost)
@@ -200,14 +225,16 @@ def train():
                              / np.sum(total_eval_num_seqs),
                              eval_steps / used_time))
                         time_begin = time.time()
+                        if args.ce:
+                            print("kpis\ttrain_loss\t%0.3f" % (np.sum(total_eval_cost) / np.sum(total_eval_num_seqs)))
+                            print("kpis\ttrain_acc\t%0.3f" % (np.sum(total_eval_acc) / np.sum(total_eval_num_seqs)))
 
                     if steps % args.save_steps == 0:
                         save_path = "save_dir_" + str(steps)
                         print('save model to: ' + save_path)
                         fluid.dygraph.save_persistables(cnn_net.state_dict(),
                                                         save_path)
-
-                    if enable_profile:
+                if enable_profile:
                         print('save profile result into /tmp/profile_file')
                         return
 

dygraph/sentiment/nets.py

@@ -76,7 +76,6 @@ class CNN(fluid.dygraph.Layer):
         emb = fluid.layers.reshape(
             emb, shape=[-1, 1, self.seq_len, self.hid_dim])
         conv_3 = self._simple_conv_pool_1(emb)
-
         fc_1 = self._fc1(conv_3)
         prediction = self._fc_prediction(fc_1)
 

dygraph/sentiment/reader.py

@@ -25,13 +25,13 @@ class SentaProcessor(object):
         self.num_examples = {"train": -1, "dev": -1, "infer": -1}
         np.random.seed(random_seed)
 
-    def get_train_examples(self, data_dir, epoch):
+    def get_train_examples(self, data_dir, epoch, shuffle):
         return data_reader((self.data_dir + "/train.tsv"), self.vocab,
-                           self.num_examples, "train", epoch)
+                           self.num_examples, "train", epoch, shuffle)
 
-    def get_dev_examples(self, data_dir, epoch):
+    def get_dev_examples(self, data_dir, epoch, shuffle):
         return data_reader((self.data_dir + "/dev.tsv"), self.vocab,
-                           self.num_examples, "dev", epoch)
+                           self.num_examples, "dev", epoch, shuffle)
 
     def get_test_examples(self, data_dir, epoch):
         return data_reader((self.data_dir + "/test.tsv"), self.vocab,
@@ -52,12 +52,12 @@ class SentaProcessor(object):
     def data_generator(self, batch_size, phase='train', epoch=1, shuffle=True):
         if phase == "train":
             return paddle.batch(
-                self.get_train_examples(self.data_dir, epoch),
+                self.get_train_examples(self.data_dir, epoch, shuffle),
                 batch_size,
                 drop_last=True)
         elif phase == "dev":
             return paddle.batch(
-                self.get_dev_examples(self.data_dir, epoch),
+                self.get_dev_examples(self.data_dir, epoch, shuffle),
                 batch_size,
                 drop_last=True)
         elif phase == "infer":

dygraph/sentiment/utils.py

@@ -38,7 +38,7 @@ class ArgumentGroup(object):
             **kwargs)
 
 
-def data_reader(file_path, word_dict, num_examples, phrase, epoch):
+def data_reader(file_path, word_dict, num_examples, phrase, epoch, shuffle=False):
     unk_id = len(word_dict)
     all_data = []
     with io.open(file_path, "r", encoding='utf8') as fin:
@@ -56,8 +56,9 @@ def data_reader(file_path, word_dict, num_examples, phrase, epoch):
             ]
             all_data.append((wids, label))
 
-    if phrase == "train":
-        random.shuffle(all_data)
+    if shuffle:
+        if phrase == "train":
+            random.shuffle(all_data)
 
     num_examples[phrase] = len(all_data)