Refine code

python/paddle/v2/fluid/tests/book/test_word2vec.py

@@ -12,76 +12,104 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import unittest
 
-PASS_NUM = 100
-EMBED_SIZE = 32
-HIDDEN_SIZE = 256
-N = 5
-BATCH_SIZE = 32
-IS_SPARSE = True
-
-word_dict = paddle.dataset.imikolov.build_dict()
-dict_size = len(word_dict)
-
-first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
-second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
-third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
-forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
-next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
-
-embed_first = fluid.layers.embedding(
-    input=first_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_second = fluid.layers.embedding(
-    input=second_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_third = fluid.layers.embedding(
-    input=third_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_forth = fluid.layers.embedding(
-    input=forth_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-
-concat_embed = fluid.layers.concat(
-    input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
-hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
-predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
-avg_cost = fluid.layers.mean(x=cost)
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
-
-train_reader = paddle.batch(
-    paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(
-    feed_list=[first_word, second_word, third_word, forth_word, next_word],
-    place=place)
-
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
-    for data in train_reader():
-        avg_cost_np = exe.run(fluid.default_main_program(),
-                              feed=feeder.feed(data),
-                              fetch_list=[avg_cost])
-        if avg_cost_np[0] < 5.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-exit(1)
+
+def main_impl(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    PASS_NUM = 100
+    EMBED_SIZE = 32
+    HIDDEN_SIZE = 256
+    N = 5
+    BATCH_SIZE = 32
+    IS_SPARSE = True
+
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+
+    first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
+    second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
+    third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
+    forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
+    next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
+
+    embed_first = fluid.layers.embedding(
+        input=first_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+    embed_second = fluid.layers.embedding(
+        input=second_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+    embed_third = fluid.layers.embedding(
+        input=third_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+    embed_forth = fluid.layers.embedding(
+        input=forth_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+
+    concat_embed = fluid.layers.concat(
+        input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+    hidden1 = fluid.layers.fc(input=concat_embed,
+                              size=HIDDEN_SIZE,
+                              act='sigmoid')
+    predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
+    cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
+    avg_cost = fluid.layers.mean(x=cost)
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    sgd_optimizer.minimize(avg_cost)
+
+    train_reader = paddle.batch(
+        paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    feeder = fluid.DataFeeder(
+        feed_list=[first_word, second_word, third_word, forth_word, next_word],
+        place=place)
+
+    exe.run(fluid.default_startup_program())
+
+    for pass_id in range(PASS_NUM):
+        for data in train_reader():
+            avg_cost_np = exe.run(fluid.default_main_program(),
+                                  feed=feeder.feed(data),
+                                  fetch_list=[avg_cost])
+            if avg_cost_np[0] < 5.0:
+                return
+    raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
+
+
+def main(*args, **kwargs):
+    prog = fluid.Program()
+    startup_prog = fluid.Program()
+    scope = fluid.core.Scope()
+    with fluid.scope_guard(scope):
+        with fluid.program_guard(prog, startup_prog):
+            main_impl(*args, **kwargs)
+
+
+class W2VTest(unittest.TestCase):
+    def test_cpu_normal(self):
+        main(use_cuda=False)
+
+    def test_gpu_normal(self):
+        main(use_cuda=True)
+
+
+if __name__ == '__main__':
+    unittest.main()