replace dataset with fake data (#27519)

python/paddle/fluid/contrib/tests/test_weight_decay_extend.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 
+import six
 import unittest
 from functools import partial
 import numpy as np
@@ -24,6 +25,24 @@ import contextlib
 paddle.enable_static()
 
 
+def fake_imdb_reader(word_dict_size,
+                     sample_num,
+                     lower_seq_len=100,
+                     upper_seq_len=200,
+                     class_dim=2):
+    def __reader__():
+        for _ in six.moves.range(sample_num):
+            length = np.random.random_integers(
+                low=lower_seq_len, high=upper_seq_len, size=[1])[0]
+            ids = np.random.random_integers(
+                low=0, high=word_dict_size - 1, size=[length]).astype('int64')
+            label = np.random.random_integers(
+                low=0, high=class_dim - 1, size=[1]).astype('int64')[0]
+            yield ids, label
+
+    return __reader__
+
+
 def get_places():
     places = [fluid.CPUPlace()]
     if fluid.core.is_compiled_with_cuda():
@@ -68,10 +87,11 @@ def bow_net(data,
 
 class TestWeightDecay(unittest.TestCase):
     def setUp(self):
-        self.word_dict = paddle.dataset.imdb.word_dict()
-        reader = paddle.batch(
-            paddle.dataset.imdb.train(self.word_dict), batch_size=2)()
-        self.train_data = [next(reader) for _ in range(5)]
+        self.word_dict_len = 5147
+        batch_size = 2
+        reader = fake_imdb_reader(self.word_dict_len, batch_size * 100)
+        reader = paddle.batch(reader, batch_size=batch_size)()
+        self.train_data = [next(reader) for _ in range(3)]
         self.learning_rate = .5
 
     def run_program(self, place, feed_list):
@@ -103,7 +123,7 @@ class TestWeightDecay(unittest.TestCase):
             data = fluid.layers.data(
                 name="words", shape=[1], dtype="int64", lod_level=1)
             label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-            avg_cost = model(data, label, len(self.word_dict))
+            avg_cost = model(data, label, self.word_dict_len)
             AdamW = fluid.contrib.extend_with_decoupled_weight_decay(
                 fluid.optimizer.Adam)
 
@@ -127,7 +147,7 @@ class TestWeightDecay(unittest.TestCase):
                 name="words", shape=[1], dtype="int64", lod_level=1)
             label = fluid.layers.data(name="label", shape=[1], dtype="int64")
 
-            avg_cost = model(data, label, len(self.word_dict))
+            avg_cost = model(data, label, self.word_dict_len)
 
             param_list = [(var, var * self.learning_rate)
                           for var in main_prog.block(0).all_parameters()]