From 5f4af11a488996402adea15977b098421ace5418 Mon Sep 17 00:00:00 2001 From: Fan Zhang Date: Tue, 7 Sep 2021 13:16:50 +0800 Subject: [PATCH] [CPU-PSLIB] Add consistency insepection of use_var_list and data_generator data (#34988) --- python/paddle/fluid/dataset.py | 65 +++ .../test_dataset_consistency_inspection.py | 406 ++++++++++++++++++ 2 files changed, 471 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/test_dataset_consistency_inspection.py diff --git a/python/paddle/fluid/dataset.py b/python/paddle/fluid/dataset.py index a125cd40139..a308c87fe7b 100644 --- a/python/paddle/fluid/dataset.py +++ b/python/paddle/fluid/dataset.py @@ -321,6 +321,71 @@ class DatasetBase(object): def _dynamic_adjust_after_train(self): pass + def check_use_var_with_data_generator(self, var_list, data_generator_class, + test_file): + """ + Var consistency insepection of use_var_list and data_generator data. + + Examples: + .. code-block:: python + + # required: skiptest + import paddle.fluid as fluid + from dataset_generator import CTRDataset + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + generator_class = CTRDataset() + dataset.check_use_var_with_data_generator([data, label], generator_class, "data/part-00000") + + Args: + var_list(list): variable list + data_generator_class(class): data_generator class + test_file(str): local test file path + """ + + f = open(test_file, "r") + var_len = len(var_list) + + while True: + line = f.readline() + if line: + line_iter = data_generator_class.generate_sample(line) + for user_parsed_line in line_iter(): + data_gen_len = len(user_parsed_line) + if var_len != data_gen_len: + raise ValueError( + "var length mismatch error: var_list = %s vs data_generator = %s" + % (var_len, data_gen_len)) + + for i, ele in enumerate(user_parsed_line): + if len(ele[1]) == 0: + raise ValueError( + "var length error: var %s's length in data_generator is 0" + % ele[0]) + + if var_list[ + i].dtype == core.VarDesc.VarType.FP32 and not all( + isinstance(ele, float) for ele in ele[1]): + raise TypeError( + "var dtype mismatch error: var name = %s, var type in var_list = %s, while var in data_generator contains non-float value, which is %s \n" + "Please check if order of var_list and data_generator are aligned. \n" + "Please check if var's type in data_generator is correct." + % (ele[0], "float", ele[1])) + + if (var_list[i].dtype == core.VarDesc.VarType.INT64 or + var_list[i].dtype == core.VarDesc.VarType.INT32 + ) and not all( + isinstance(ele, int) for ele in ele[1]): + raise TypeError( + "var dtype mismatch error: var name = %s, var type in var_list = %s, while var in data_generator contains non-int value, which is %s \n" + "Please check if order of var_list and data_generator are aligned. \n" + "Please check if var's type in data_generator is correct." + % (ele[0], "int", ele[1])) + + else: + break + + f.close() + class InMemoryDataset(DatasetBase): """ diff --git a/python/paddle/fluid/tests/unittests/test_dataset_consistency_inspection.py b/python/paddle/fluid/tests/unittests/test_dataset_consistency_inspection.py new file mode 100644 index 00000000000..c83c6770135 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_dataset_consistency_inspection.py @@ -0,0 +1,406 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +TestCases for Dataset consistency insepection of use_var_list and data_generator. +""" + +from __future__ import print_function +import paddle +import paddle.fluid as fluid +import paddle.compat as cpt +import paddle.fluid.core as core +import numpy as np +import random +import math +import os +import shutil +import unittest +import paddle.fluid.incubate.data_generator as dg + +#paddle.enable_static() +# fluid.disable_dygraph() +fluid.disable_dygraph() +url_schema_len = 5 +query_schema = [ + 'Q_query_basic', 'Q_query_phrase', 'Q_quq', 'Q_timelevel', + 'Q_context_title_basic1', 'Q_context_title_basic2', + 'Q_context_title_basic3', 'Q_context_title_basic4', + 'Q_context_title_basic5', 'Q_context_title_phrase1', + 'Q_context_title_phrase2', 'Q_context_title_phrase3', + 'Q_context_title_phrase4', 'Q_context_title_phrase5', 'Q_context_site1', + 'Q_context_site2', 'Q_context_site3', 'Q_context_site4', 'Q_context_site5' +] + + +class CTRDataset(dg.MultiSlotDataGenerator): + def __init__(self, mode): + self.test = mode + + def generate_sample(self, line): + def reader(): + ins = line.strip().split(';') + label_pos_num = int(ins[1].split(' ')[0]) + label_neg_num = int(ins[1].split(' ')[1]) + + #query fea parse + bias = 2 + query_len = 0 + sparse_query_feature = [] + for index in range(len(query_schema)): + pos = index + bias + sparse_query_feature.append( + [int(x) for x in ins[pos].split(' ')]) + if index == 0: + query_len = len(ins[pos].split(' ')) + query_len = 1.0 / (1 + pow(2.7182818, 3 - 1.0 * query_len)) + + #positive url fea parse + bias = 2 + len(query_schema) + pos_url_feas = [] + pos_click_feas = [] + pos_context_feas = [] + for k in range(label_pos_num): + pos_url_fea = [] + pos = 0 + for index in range(url_schema_len - 1): + pos = bias + k * (url_schema_len) + index + pos_url_fea.append([int(x) for x in ins[pos].split(' ')]) + #click info + if (ins[pos + 1] == ''): + continue + item = ins[pos + 1].split(' ') + if len(item) != 17: + continue + stat_fea = [[max(float(item[i]), 0.0)] for i in range(len(item)) \ + if not (i == 5 or i == 9 or i == 13 or i == 14 or i ==15 or i ==16)] + pos_url_feas.append(pos_url_fea) + pos_click_feas.append(stat_fea) + + query_serach = float(item[5]) + if query_serach > 0.0: + query_serach = min(math.log(query_serach), 10.0) / 10.0 + pos_context_fea = [[query_serach], [query_len]] + pos_context_feas.append(pos_context_fea) + + #negative url fea parse + bias = 2 + len(query_schema) + label_pos_num * (url_schema_len) + neg_url_feas = [] + neg_click_feas = [] + neg_context_feas = [] + for k in range(label_neg_num): + neg_url_fea = [] + pos = 0 + for index in range(url_schema_len - 1): + pos = bias + k * (url_schema_len) + index + neg_url_fea.append([int(x) for x in ins[pos].split(' ')]) + if (ins[pos + 1] == ''): + continue + item = ins[pos + 1].split(' ') + #zdf_tmp + if len(item) != 17: + continue + #print ins[pos + 1] + stat_fea = [[max(float(item[i]), 0.0)] for i in range(len(item)) \ + if not (i == 5 or i == 9 or i == 13 or i == 14 or i == 15 or i == 16)] + neg_click_feas.append(stat_fea) + neg_url_feas.append(neg_url_fea) + + query_serach = float(item[5]) + if query_serach > 0.0: + query_serach = min(math.log(query_serach), 10.0) / 10.0 + neg_context_fea = [[query_serach], [query_len]] + neg_context_feas.append(neg_context_fea) + + #make train data + if self.test == 1: + for p in range(len(pos_url_feas)): + # feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + pos_url_fea = pos_url_feas[p] + pos_click_fea = pos_click_feas[p] + pos_context_fea = pos_context_feas[p] + yield zip(feature_name, [[1]] + sparse_query_feature + + pos_url_fea + pos_click_fea + pos_context_fea + + pos_url_fea + pos_click_fea + pos_context_fea) + for n in range(len(neg_url_feas)): + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + neg_url_fea = neg_url_feas[n] + neg_click_fea = neg_click_feas[n] + neg_context_fea = neg_context_feas[n] + yield zip(feature_name, [[0]] + sparse_query_feature + + neg_url_fea + neg_click_fea + neg_context_fea + + neg_url_fea + neg_click_fea + neg_context_fea) + elif self.test == 0: + for p in range(len(pos_url_feas)): + #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:] + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + #print("#######") + #print(feature_name) + #print("#######") + pos_url_fea = pos_url_feas[p] + pos_click_fea = pos_click_feas[p] + pos_context_fea = pos_context_feas[p] + for n in range(len(neg_url_feas)): + # prob = get_rand() + # if prob < sample_rate: + neg_url_fea = neg_url_feas[n] + neg_click_fea = neg_click_feas[n] + neg_context_fea = neg_context_feas[n] + #print("q:", query_feas) + #print("pos:", pos_url_fea) + #print("neg:", neg_url_fea) + # yield zip(feature_name[:3], sparse_query_feature[:3]) + yield list(zip(feature_name, [[1]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \ + neg_url_fea + neg_click_fea + neg_context_fea)) + elif self.test == 2: + for p in range(len(pos_url_feas)): + #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:] + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + #print("#######") + #print(feature_name) + #print("#######") + pos_url_fea = pos_url_feas[p] + pos_click_fea = pos_click_feas[p] + pos_context_fea = pos_context_feas[p] + for n in range(len(neg_url_feas)): + # prob = get_rand() + # if prob < sample_rate: + neg_url_fea = neg_url_feas[n] + neg_click_fea = neg_click_feas[n] + neg_context_fea = neg_context_feas[n] + #print("q:", query_feas) + #print("pos:", pos_url_fea) + #print("neg:", neg_url_fea) + # yield zip(feature_name[:3], sparse_query_feature[:3]) + yield list(zip(feature_name, [[1], [2]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \ + neg_url_fea + neg_click_fea + neg_context_fea)) + elif self.test == 3: + for p in range(len(pos_url_feas)): + #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:] + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + #print("#######") + #print(feature_name) + #print("#######") + pos_url_fea = pos_url_feas[p] + pos_click_fea = pos_click_feas[p] + pos_context_fea = pos_context_feas[p] + for n in range(len(neg_url_feas)): + # prob = get_rand() + # if prob < sample_rate: + neg_url_fea = neg_url_feas[n] + neg_click_fea = neg_click_feas[n] + neg_context_fea = neg_context_feas[n] + #print("q:", query_feas) + #print("pos:", pos_url_fea) + #print("neg:", neg_url_fea) + # yield zip(feature_name[:3], sparse_query_feature[:3]) + yield list(zip(feature_name, [[1], [2.0]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \ + neg_url_fea + neg_click_fea + neg_context_fea)) + elif self.test == 4: + for p in range(len(pos_url_feas)): + #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:] + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + #print("#######") + #print(feature_name) + #print("#######") + pos_url_fea = pos_url_feas[p] + pos_click_fea = pos_click_feas[p] + pos_context_fea = pos_context_feas[p] + for n in range(len(neg_url_feas)): + # prob = get_rand() + # if prob < sample_rate: + neg_url_fea = neg_url_feas[n] + neg_click_fea = neg_click_feas[n] + neg_context_fea = neg_context_feas[n] + #print("q:", query_feas) + #print("pos:", pos_url_fea) + #print("neg:", neg_url_fea) + # yield zip(feature_name[:3], sparse_query_feature[:3]) + yield list(zip(feature_name, [[], [2.0]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \ + neg_url_fea + neg_click_fea + neg_context_fea)) + elif self.test == 5: + for p in range(len(pos_url_feas)): + #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:] + feature_name = ["click"] + for i in range(1, 54): + feature_name.append(str(i)) + #print("#######") + #print(feature_name) + #print("#######") + pos_url_fea = pos_url_feas[p] + pos_click_fea = pos_click_feas[p] + pos_context_fea = pos_context_feas[p] + for n in range(len(neg_url_feas)): + # prob = get_rand() + # if prob < sample_rate: + neg_url_fea = neg_url_feas[n] + neg_click_fea = neg_click_feas[n] + neg_context_fea = neg_context_feas[n] + #print("q:", query_feas) + #print("pos:", pos_url_fea) + #print("neg:", neg_url_fea) + # yield zip(feature_name[:3], sparse_query_feature[:3]) + yield list(zip(feature_name, sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \ + neg_url_fea + neg_click_fea + neg_context_fea)) + + return reader + + +class TestDataset(unittest.TestCase): + """ TestCases for Dataset. """ + + def setUp(self): + pass + # use_data_loader = False + # epoch_num = 10 + # drop_last = False + + def test_var_consistency_insepection(self): + """ + Testcase for InMemoryDataset of consistency insepection of use_var_list and data_generator. + """ + with open("test_run_with_dump_a.txt", "w") as f: + # data = "\n" + # data += "\n" + data = "2 1;1 9;20002001 20001240 20001860 20003611 20000723;20002001 20001240 20001860 20003611 20000723;0;40000001;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20012788 20000157;20002001 20001240 20001860 20003611 20000623 20000251 20000157 20000723 20000070 20000001 20000057;20002640 20004695 20000157 20000723 20000070 20002001 20001240 20001860 20003611;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20003519 20000005;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20003519 20000005;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20131464;20002001 20001240 20001860 20003611 20018820 20000157 20000723 20000070 20000001 20000057;20002640 20034154 20000723 20000070 20002001 20001240 20001860 20003611;10000200;10000200;10063938;10000008;10000177;20002001 20001240 20001860 20003611 20010833 20000210 20000500 20000401 20000251 20012198 20001023 20000157;20002001 20001240 20001860 20003611 20012396 20000500 20002513 20012198 20001023 20000157;10000123;30000004;0.623 0.233 0.290 0.208 0.354 49.000 0.000 0.000 0.000 -1.000 0.569 0.679 0.733 53 17 2 0;20002001 20001240 20001860 20003611 20000723;20002001 20001240 20001860 20003611 20000723;10000047;30000004;0.067 0.000 0.161 0.005 0.000 49.000 0.000 0.000 0.000 -1.000 0.000 0.378 0.043 0 6 0 0;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20003519 20000005;10000200;30000001;0.407 0.111 0.196 0.095 0.181 49.000 0.000 0.000 0.000 -1.000 0.306 0.538 0.355 48 8 0 0;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20003519 20000005;10000200;30000001;0.226 0.029 0.149 0.031 0.074 49.000 0.000 0.000 0.000 -1.000 0.220 0.531 0.286 26 6 0 0;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20012788 20000157;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20131464;10063938;30000001;0.250 0.019 0.138 0.012 0.027 49.000 0.000 0.000 0.000 -1.000 0.370 0.449 0.327 7 2 0 0;20002001 20001240 20001860 20003611 20000723;20002001 20001240 20001860 20003611 20000723;10000003;30000002;0.056 0.000 0.139 0.003 0.000 49.000 0.000 0.000 0.000 -1.000 0.000 0.346 0.059 15 3 0 0;20002001 20001240 20001860 20003611 20000623 20000251 20000157 20000723 20000070 20000001 20000057;20002001 20001240 20001860 20003611 20018820 20000157 20000723 20000070 20000001 20000057;10000008;30000001;0.166 0.004 0.127 0.001 0.004 49.000 0.000 0.000 0.000 -1.000 0.103 0.417 0.394 10 3 0 0;20002640 20004695 20000157 20000723 20000070 20002001 20001240 20001860 20003611;20002640 20034154 20000723 20000070 20002001 20001240 20001860 20003611;10000177;30000001;0.094 0.008 0.157 0.012 0.059 49.000 0.000 0.000 0.000 -1.000 0.051 0.382 0.142 21 0 0 0;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20000157;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20000157;10000134;30000001;0.220 0.016 0.181 0.037 0.098 49.000 0.000 0.000 0.000 -1.000 0.192 0.453 0.199 17 1 0 0;20002001 20001240 20001860 20003611 20002640 20004695 20000157 20000723 20000070 20002001 20001240 20001860 20003611;20002001 20001240 20001860 20003611 20002640 20034154 20000723 20000070 20002001 20001240 20001860 20003611;10000638;30000001;0.000 0.000 0.000 0.000 0.000 49.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 0 0 0;\n" + data += "2 1;1 11;20000025 20000404;20001923;20000002 20000157 20000028 20004205 20000500 20028809 20000571 20000007 20027523 20004940 20000651 20000043 20000051 20000520 20015398 20000066 20004720 20000070 20001648;40000001;20000025 20000404 20000571 20004940 20000001 20000017;20000025 20000404 20000029 20000500 20001408 20000404 20000001 20000017;0;0;0;20001923 20011130 20000027;20001923 20000029 20000500 20001408 20000404 20000027;0;0;0;10000005;10000005;0;0;0;20003316 20000392 20001979 20000474 20000025 20000194 20000025 20000404 20000019 20000109;20016528 20024913 20004748 20001923 20000019 20000109;10000015;30000002;0.572 0.043 0.401 0.352 0.562 32859.000 0.005 0.060 0.362 -1.000 0.448 0.673 0.222 16316 991 89 0;20000025 20000404 20000571 20004940 20000001 20000017;20001923 20011130 20000027;10000005;30000001;0.495 0.024 0.344 0.285 0.379 32859.000 0.002 0.050 0.362 -1.000 0.423 0.764 0.254 19929 896 72 0;20000202 20000026 20001314 20004289 20000025 20000404 20000451 20000089 20000007;20000202 20000026 20014094 20001314 20004289 20001923 20000451 20000089 20000007;10000035;30000003;0.133 0.006 0.162 0.042 0.174 32859.000 0.003 0.037 0.362 -1.000 0.363 0.542 0.122 14763 664 53 0;20000202 20000026 20001314 20004289 20000025 20000404;20000202 20000026 20014094 20001314 20004289 20001923;10000021;30000001;0.058 0.004 0.133 0.017 0.120 32859.000 0.000 0.006 0.362 -1.000 0.168 0.437 0.041 -1 -1 -1 -1;20000025 20000404 20000018 20012461 20001699 20000446 20000174 20000062 20000133 20003172 20000240 20007877 20067375 20000111 20000164 20001410 20000204 20016958;20001923 20000018 20012461 20001699 20007717 20000062 20000133 20003172 20000240 20007877 20067375 20000111 20000164 20001410 20000204 20016958;10000002;30000001;0.017 0.000 0.099 0.004 0.072 32859.000 0.000 0.009 0.362 -1.000 0.058 0.393 0.025 -1 -1 -1 -1;20000025 20000404;20001923;10000133;30000005;0.004 0.000 0.122 0.000 0.000 32859.000 0.000 0.000 0.362 -1.000 0.000 0.413 0.020 0 444 35 0;20000025 20000404;20001923;10005297;30000004;0.028 0.000 0.138 0.002 0.000 32859.000 0.000 0.000 0.362 -1.000 0.000 0.343 0.024 0 600 48 0;20000025 20000404;20001923;10000060;30000005;0.107 0.000 0.110 0.027 0.077 32859.000 0.000 0.005 0.362 -1.000 0.095 0.398 0.062 1338 491 39 0;20002960 20005534 20000043 20000025 20000404 20000025 20000007;20002960 20005534 20000043 20001923 20000025 20000007;10000020;30000003;0.041 0.000 0.122 0.012 0.101 32859.000 0.001 0.025 0.362 -1.000 0.302 0.541 0.065 9896 402 35 0;20000025 20000404 20000259 20000228 20000235 20000142;20001923 20000259 20000264 20000142;10000024;30000003;0.072 0.002 0.156 0.026 0.141 32859.000 0.002 0.032 0.362 -1.000 0.386 0.569 0.103 9896 364 35 0;20000025 20000404 20000029 20000500 20001408 20000404 20000001 20000017;20001923 20000029 20000500 20001408 20000404 20000027;10000005;30000001;0.328 0.006 0.179 0.125 0.181 32859.000 0.003 0.058 0.362 -1.000 0.300 0.445 0.141 9896 402 32 0;20000025 20000404;20001923;10012839;30000002;0.012 0.000 0.108 0.002 0.048 32859.000 0.000 0.000 0.362 -1.000 0.021 0.225 0.016 2207 120 12 0;\n" + # data += "" + f.write(data) + + slot_data = [] + label = fluid.layers.data( + name="click", + shape=[-1, 1], + dtype="int64", + lod_level=0, + append_batch_size=False) + slot_data.append(label) + + # sprase_query_feat_names + len_sparse_query = 19 + for feat_name in range(1, len_sparse_query + 1): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='int64', lod_level=1)) + + # sparse_url_feat_names + for feat_name in range(len_sparse_query + 1, len_sparse_query + 5): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='int64', lod_level=1)) + + # dense_feat_names + for feat_name in range(len_sparse_query + 5, len_sparse_query + 16): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='float32')) + + # context_feat_namess + for feat_name in range(len_sparse_query + 16, len_sparse_query + 18): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='float32')) + + # neg sparse_url_feat_names + for feat_name in range(len_sparse_query + 18, len_sparse_query + 22): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='int64', lod_level=1)) + + # neg dense_feat_names + for feat_name in range(len_sparse_query + 22, len_sparse_query + 33): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='float32')) + + # neg context_feat_namess + for feat_name in range(len_sparse_query + 33, len_sparse_query + 35): + slot_data.append( + fluid.layers.data( + name=str(feat_name), shape=[1], dtype='float32')) + + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + + print("========================================") + generator_class = CTRDataset(mode=0) + try: + dataset.check_use_var_with_data_generator( + slot_data, generator_class, "test_run_with_dump_a.txt") + print("case 1: check passed!") + except Exception as e: + print("warning: catch expected error") + print(e) + print("========================================") + print("\n") + + print("========================================") + generator_class = CTRDataset(mode=2) + try: + dataset.check_use_var_with_data_generator( + slot_data, generator_class, "test_run_with_dump_a.txt") + except Exception as e: + print("warning: case 2 catch expected error") + print(e) + print("========================================") + print("\n") + + print("========================================") + generator_class = CTRDataset(mode=3) + try: + dataset.check_use_var_with_data_generator( + slot_data, generator_class, "test_run_with_dump_a.txt") + except Exception as e: + print("warning: case 3 catch expected error") + print(e) + print("========================================") + print("\n") + + print("========================================") + generator_class = CTRDataset(mode=4) + try: + dataset.check_use_var_with_data_generator( + slot_data, generator_class, "test_run_with_dump_a.txt") + except Exception as e: + print("warning: case 4 catch expected error") + print(e) + print("========================================") + print("\n") + + print("========================================") + generator_class = CTRDataset(mode=5) + try: + dataset.check_use_var_with_data_generator( + slot_data, generator_class, "test_run_with_dump_a.txt") + except Exception as e: + print("warning: case 5 catch expected error") + print(e) + print("========================================") + + os.remove("./test_run_with_dump_a.txt") + + +if __name__ == '__main__': + unittest.main() -- GitLab