From 69e0af56cd1d36bce35c4d8f1f299fa5ba660c67 Mon Sep 17 00:00:00 2001 From: Kexin Zhao Date: Wed, 23 May 2018 15:43:11 -0700 Subject: [PATCH] do this to new_api example --- .../test_label_semantic_roles_newapi.py | 47 ++++++++++++------- .../tests/book/test_label_semantic_roles.py | 2 + 2 files changed, 31 insertions(+), 18 deletions(-) diff --git a/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py b/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py index 5f30ce195..e32b130ac 100755 --- a/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py +++ b/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py @@ -202,24 +202,35 @@ def infer(use_cuda, inference_program, save_path): inferencer = fluid.Inferencer( inference_program, param_path=save_path, place=place) - 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 - - # Create an input example - 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. + # Note that lod info should be a list of lists. + lod = [[3, 4, 2]] + base_shape = [1] + # The range of random integers is [low, high] + 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) results = inferencer.infer( { diff --git a/python/paddle/fluid/tests/book/test_label_semantic_roles.py b/python/paddle/fluid/tests/book/test_label_semantic_roles.py index 3eb0c1069..21839e588 100644 --- a/python/paddle/fluid/tests/book/test_label_semantic_roles.py +++ b/python/paddle/fluid/tests/book/test_label_semantic_roles.py @@ -257,8 +257,10 @@ def infer(use_cuda, save_dirname=None): # 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. + # Note that lod info should be a list of lists. lod = [[3, 4, 2]] base_shape = [1] + # The range of random integers is [low, high] word = fluid.create_random_lodtensor( lod, base_shape, place, low=0, high=word_dict_len - 1) pred = fluid.create_random_lodtensor( -- GitLab