simplify label_sementic_example

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -116,29 +116,6 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
     return feature_out
 
 
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = np.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = fluid.LoDTensor()
-    res.set(flattened_data, place)
-    res.set_lod([lod])
-    return res
-
-
-def create_random_lodtensor(lod, place, low, high):
-    data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
-    res = fluid.LoDTensor()
-    res.set(data, place)
-    res.set_lod([lod])
-    return res
-
-
 def train(use_cuda, save_dirname=None, is_local=True):
     # define network topology
     word = fluid.layers.data(
@@ -271,23 +248,33 @@ def infer(use_cuda, save_dirname=None):
         [inference_program, feed_target_names,
          fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
 
-        lod = [0, 4, 10]
-        word = create_random_lodtensor(
-            lod, place, low=0, high=word_dict_len - 1)
-        pred = create_random_lodtensor(
-            lod, place, low=0, high=pred_dict_len - 1)
-        ctx_n2 = create_random_lodtensor(
-            lod, place, low=0, high=word_dict_len - 1)
-        ctx_n1 = create_random_lodtensor(
-            lod, place, low=0, high=word_dict_len - 1)
-        ctx_0 = create_random_lodtensor(
-            lod, place, low=0, high=word_dict_len - 1)
-        ctx_p1 = create_random_lodtensor(
-            lod, place, low=0, high=word_dict_len - 1)
-        ctx_p2 = create_random_lodtensor(
-            lod, place, low=0, high=word_dict_len - 1)
-        mark = create_random_lodtensor(
-            lod, place, low=0, high=mark_dict_len - 1)
+        # Setup inputs by creating LoDTensors to represent sequences of words.
+        # Here each word is the basic element of these LoDTensors and the shape of 
+        # each word (base_shape) should be [1] since it is simply an index to 
+        # look up for the corresponding word vector.
+        # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
+        # which has only one lod level. Then the created LoDTensors will have only 
+        # one higher level structure (sequence of words, or sentence) than the basic 
+        # element (word). Hence the LoDTensor will hold data for three sentences of 
+        # length 3, 4 and 2, respectively. 
+        lod = [[3, 4, 2]]
+        base_shape = [1]
+        word = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=word_dict_len - 1)
+        pred = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=pred_dict_len - 1)
+        ctx_n2 = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=word_dict_len - 1)
+        ctx_n1 = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=word_dict_len - 1)
+        ctx_0 = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=word_dict_len - 1)
+        ctx_p1 = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=word_dict_len - 1)
+        ctx_p2 = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=word_dict_len - 1)
+        mark = fluid.create_random_lodtensor(
+            lod, base_shape, place, low=0, high=mark_dict_len - 1)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.