Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Cannot completely remove function DataBase #1261

Closed
wangkuiyi opened this issue Feb 5, 2017 · 2 comments
Closed

Cannot completely remove function DataBase #1261

wangkuiyi opened this issue Feb 5, 2017 · 2 comments
Assignees
@reyoung @wangkuiyi

Comments

@wangkuiyi
Copy link
Collaborator

In #1253, after renaming function DataBase defined in python/paddle/trainer/config_parser.py into create_data_config_proto, I tried to remove it completely. However, once I do so, I'd fail a unit test test_layerHelpers with the following error message. It seems that DataBase or create_data_config_proto did something magic that cannot be ignored.

01:14 # CTEST_OUTPUT_ON_FAILURE=1  ctest -j12 -R test_layerHelpers
Test project /paddle/build
    Start 57: test_layerHelpers
1/1 Test #57: test_layerHelpers ................***Failed    8.04 sec
Generating  test_fc
[WARNING 2017-02-05 01:14:57,300 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:57,300 networks.py:1472] The input order is [data, mask]
[INFO 2017-02-05 01:14:57,300 networks.py:1478] The output order is [__fc_layer_0__, __selective_fc_layer_0__]
[WARNING 2017-02-05 01:14:57,436 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:57,437 networks.py:1472] The input order is [data, mask]
[INFO 2017-02-05 01:14:57,437 networks.py:1478] The output order is [__fc_layer_0__, __selective_fc_layer_0__]
Generating  layer_activations
[WARNING 2017-02-05 01:14:57,577 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:57,577 networks.py:1472] The input order is [input]
[INFO 2017-02-05 01:14:57,577 networks.py:1478] The output order is [layer_0, layer_1, layer_2, layer_3, layer_4, layer_5, layer_6, layer_7, layer_8, layer_9, layer_10, layer_11]
[WARNING 2017-02-05 01:14:57,717 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:57,717 networks.py:1472] The input order is [input]
[INFO 2017-02-05 01:14:57,717 networks.py:1478] The output order is [layer_0, layer_1, layer_2, layer_3, layer_4, layer_5, layer_6, layer_7, layer_8, layer_9, layer_10, layer_11]
Generating  projections
[INFO 2017-02-05 01:14:57,858 networks.py:1472] The input order is [test, img, filter]
[INFO 2017-02-05 01:14:57,858 networks.py:1478] The output order is [__mixed_7__]
[INFO 2017-02-05 01:14:57,998 networks.py:1472] The input order is [test, img, filter]
[INFO 2017-02-05 01:14:57,999 networks.py:1478] The output order is [__mixed_7__]
Generating  test_print_layer
[INFO 2017-02-05 01:14:58,135 networks.py:1472] The input order is [input]
[INFO 2017-02-05 01:14:58,135 networks.py:1478] The output order is [input]
[INFO 2017-02-05 01:14:58,269 networks.py:1472] The input order is [input]
[INFO 2017-02-05 01:14:58,269 networks.py:1478] The output order is [input]
Generating  test_sequence_pooling
[WARNING 2017-02-05 01:14:58,405 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:58,405 networks.py:1472] The input order is [dat_in]
[INFO 2017-02-05 01:14:58,405 networks.py:1478] The output order is [__seq_pooling_0__, __seq_pooling_1__, __seq_pooling_2__, __seq_pooling_3__, __seq_pooling_4__, __seq_pooling_5__, __seq_pooling_6__]
[WARNING 2017-02-05 01:14:58,542 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:58,542 networks.py:1472] The input order is [dat_in]
[INFO 2017-02-05 01:14:58,542 networks.py:1478] The output order is [__seq_pooling_0__, __seq_pooling_1__, __seq_pooling_2__, __seq_pooling_3__, __seq_pooling_4__, __seq_pooling_5__, __seq_pooling_6__]
Generating  test_lstmemory_layer
[WARNING 2017-02-05 01:14:58,678 layers.py:1134] NOTE: The lstmemory layer[__lstmemory_0__]'s size is set by previous input layer. The lstm size should be equal with input layer size/4. The size which is set explicitly will be ignored.
[INFO 2017-02-05 01:14:58,679 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:14:58,679 networks.py:1478] The output order is [__lstmemory_0__]
[WARNING 2017-02-05 01:14:58,815 layers.py:1134] NOTE: The lstmemory layer[__lstmemory_0__]'s size is set by previous input layer. The lstm size should be equal with input layer size/4. The size which is set explicitly will be ignored.
[INFO 2017-02-05 01:14:58,815 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:14:58,815 networks.py:1478] The output order is [__lstmemory_0__]
Generating  test_grumemory_layer
[WARNING 2017-02-05 01:14:58,951 layers.py:1254] NOTE: the gru memory layer's size is set by previous input layer, and should be input size / 3. Set size explicitly will be ignored.
[INFO 2017-02-05 01:14:58,951 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:14:58,952 networks.py:1478] The output order is [__gru_0__]
[WARNING 2017-02-05 01:14:59,088 layers.py:1254] NOTE: the gru memory layer's size is set by previous input layer, and should be input size / 3. Set size explicitly will be ignored.
[INFO 2017-02-05 01:14:59,088 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:14:59,088 networks.py:1478] The output order is [__gru_0__]
Generating  last_first_seq
[WARNING 2017-02-05 01:14:59,224 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:59,224 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:14:59,224 networks.py:1478] The output order is [__first_seq_0__, __first_seq_1__, __last_seq_0__, __last_seq_1__]
[WARNING 2017-02-05 01:14:59,359 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:59,359 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:14:59,359 networks.py:1478] The output order is [__first_seq_0__, __first_seq_1__, __last_seq_0__, __last_seq_1__]
Generating  test_expand_layer
[WARNING 2017-02-05 01:14:59,495 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:59,495 networks.py:1472] The input order is [data, data_seq]
[INFO 2017-02-05 01:14:59,495 networks.py:1478] The output order is [__expand_layer_0__, __expand_layer_1__]
[WARNING 2017-02-05 01:14:59,630 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:59,630 networks.py:1472] The input order is [data, data_seq]
[INFO 2017-02-05 01:14:59,630 networks.py:1478] The output order is [__expand_layer_0__, __expand_layer_1__]
Generating  test_ntm_layers
[WARNING 2017-02-05 01:14:59,767 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:59,767 networks.py:1472] The input order is [w, a, b, c, d]
[INFO 2017-02-05 01:14:59,767 networks.py:1478] The output order is [__interpolation_layer_0__, __power_layer_0__, __scaling_layer_0__, __cos_sim_0__, __cos_sim_1__, __sum_to_one_norm_layer_0__, __conv_shift_layer_0__, __tensor_layer_0__, __slope_intercept_layer_0__, __linear_comb_layer_0__]
[WARNING 2017-02-05 01:14:59,904 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:14:59,904 networks.py:1472] The input order is [w, a, b, c, d]
[INFO 2017-02-05 01:14:59,904 networks.py:1478] The output order is [__interpolation_layer_0__, __power_layer_0__, __scaling_layer_0__, __cos_sim_0__, __cos_sim_1__, __sum_to_one_norm_layer_0__, __conv_shift_layer_0__, __tensor_layer_0__, __slope_intercept_layer_0__, __linear_comb_layer_0__]
Generating  test_hsigmoid
[INFO 2017-02-05 01:15:00,040 networks.py:1472] The input order is [data, label]
[INFO 2017-02-05 01:15:00,040 networks.py:1478] The output order is [__hsigmoid_0__]
[INFO 2017-02-05 01:15:00,175 networks.py:1472] The input order is [data, label]
[INFO 2017-02-05 01:15:00,175 networks.py:1478] The output order is [__hsigmoid_0__]
Generating  img_layers
[INFO 2017-02-05 01:15:00,310 layers.py:1962] output for __conv_0__: c = 64, h = 227, w = 227, size = 3297856
[INFO 2017-02-05 01:15:00,311 layers.py:2071] output for __pool_0__: c = 64, h = 196, w = 196, size = 2458624
[WARNING 2017-02-05 01:15:00,312 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:00,312 networks.py:1472] The input order is [image]
[INFO 2017-02-05 01:15:00,312 networks.py:1478] The output order is [__pool_0__, __crmnorm_0__]
[INFO 2017-02-05 01:15:00,447 layers.py:1962] output for __conv_0__: c = 64, h = 227, w = 227, size = 3297856
[INFO 2017-02-05 01:15:00,448 layers.py:2071] output for __pool_0__: c = 64, h = 196, w = 196, size = 2458624
[WARNING 2017-02-05 01:15:00,449 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:00,449 networks.py:1472] The input order is [image]
[INFO 2017-02-05 01:15:00,449 networks.py:1478] The output order is [__pool_0__, __crmnorm_0__]
Generating  img_trans_layers
[INFO 2017-02-05 01:15:00,586 layers.py:1962] output size for __conv_0__ is 227 
[INFO 2017-02-05 01:15:00,587 layers.py:2071] output for __pool_0__: c = 64, h = 225, w = 225, size = 3240000
[WARNING 2017-02-05 01:15:00,587 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:00,587 networks.py:1472] The input order is [image]
[INFO 2017-02-05 01:15:00,587 networks.py:1478] The output order is [__pool_0__, __crmnorm_0__]
[INFO 2017-02-05 01:15:00,724 layers.py:1962] output size for __conv_0__ is 227 
[INFO 2017-02-05 01:15:00,725 layers.py:2071] output for __pool_0__: c = 64, h = 225, w = 225, size = 3240000
[WARNING 2017-02-05 01:15:00,725 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:00,725 networks.py:1472] The input order is [image]
[INFO 2017-02-05 01:15:00,725 networks.py:1478] The output order is [__pool_0__, __crmnorm_0__]
Generating  util_layers
[WARNING 2017-02-05 01:15:00,862 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:00,862 networks.py:1472] The input order is [a, b]
[INFO 2017-02-05 01:15:00,862 networks.py:1478] The output order is [__addto_0__, __concat_0__, __concat_1__]
[WARNING 2017-02-05 01:15:00,997 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:00,997 networks.py:1472] The input order is [a, b]
[INFO 2017-02-05 01:15:00,997 networks.py:1478] The output order is [__addto_0__, __concat_0__, __concat_1__]
Generating  simple_rnn_layers
[WARNING 2017-02-05 01:15:01,136 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:01,137 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:01,137 networks.py:1478] The output order is [__last_seq_0__, __first_seq_0__, __last_seq_1__, __first_seq_1__, __last_seq_2__, __first_seq_2__]
[WARNING 2017-02-05 01:15:01,276 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:01,276 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:01,276 networks.py:1478] The output order is [__last_seq_0__, __first_seq_0__, __last_seq_1__, __first_seq_1__, __last_seq_2__, __first_seq_2__]
Generating  unused_layers
[INFO 2017-02-05 01:15:01,413 networks.py:1472] The input order is [probs]
[INFO 2017-02-05 01:15:01,413 networks.py:1478] The output order is [__sampling_id_layer_0__]
[INFO 2017-02-05 01:15:01,548 networks.py:1472] The input order is [probs]
[INFO 2017-02-05 01:15:01,548 networks.py:1478] The output order is [__sampling_id_layer_0__]
Generating  test_cost_layers
[WARNING 2017-02-05 01:15:01,685 layers.py:4886] None is not recommend for multi_binary_label_cross_entropy's activation, maybe the sigmoid is better
[WARNING 2017-02-05 01:15:01,686 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:01,686 networks.py:1472] The input order is [input, labels, crf_label, left, right, label, list_feature, list_scores, probs, xe-label, huber_probs, huber_label]
[INFO 2017-02-05 01:15:01,686 networks.py:1478] The output order is [__ctc_layer_0__, __warp_ctc_layer_0__, __crf_layer_0__, __rank_cost_0__, __lambda_cost_0__, __cross_entropy_0__, __cross_entropy_with_selfnorm_0__, __huber_cost_0__, __multi_binary_label_cross_entropy_0__, __sum_cost_0__]
[WARNING 2017-02-05 01:15:01,824 layers.py:4886] None is not recommend for multi_binary_label_cross_entropy's activation, maybe the sigmoid is better
[WARNING 2017-02-05 01:15:01,825 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:01,825 networks.py:1472] The input order is [input, labels, crf_label, left, right, label, list_feature, list_scores, probs, xe-label, huber_probs, huber_label]
[INFO 2017-02-05 01:15:01,825 networks.py:1478] The output order is [__ctc_layer_0__, __warp_ctc_layer_0__, __crf_layer_0__, __rank_cost_0__, __lambda_cost_0__, __cross_entropy_0__, __cross_entropy_with_selfnorm_0__, __huber_cost_0__, __multi_binary_label_cross_entropy_0__, __sum_cost_0__]
Generating  test_rnn_group
[WARNING 2017-02-05 01:15:01,967 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:01,967 networks.py:1472] The input order is [seq_input, sub_seq_input]
[INFO 2017-02-05 01:15:01,967 networks.py:1478] The output order is [__last_seq_0__, __first_seq_0__, __last_seq_1__, __last_seq_2__, __last_seq_3__]
[WARNING 2017-02-05 01:15:02,108 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:02,109 networks.py:1472] The input order is [seq_input, sub_seq_input]
[INFO 2017-02-05 01:15:02,109 networks.py:1478] The output order is [__last_seq_0__, __first_seq_0__, __last_seq_1__, __last_seq_2__, __last_seq_3__]
Generating  shared_fc
[INFO 2017-02-05 01:15:02,248 networks.py:1472] The input order is [feature_a, feature_b, label]
[INFO 2017-02-05 01:15:02,248 networks.py:1478] The output order is [__cost_0__]
[INFO 2017-02-05 01:15:02,384 networks.py:1472] The input order is [feature_a, feature_b, label]
[INFO 2017-02-05 01:15:02,384 networks.py:1478] The output order is [__cost_0__]
Generating  shared_lstm
[INFO 2017-02-05 01:15:02,524 networks.py:1472] The input order is [data_a, data_b, label]
[INFO 2017-02-05 01:15:02,524 networks.py:1478] The output order is [__cost_0__]
[INFO 2017-02-05 01:15:02,664 networks.py:1472] The input order is [data_a, data_b, label]
[INFO 2017-02-05 01:15:02,665 networks.py:1478] The output order is [__cost_0__]
Generating  shared_gru
[INFO 2017-02-05 01:15:02,804 networks.py:1472] The input order is [data_a, data_b, label]
[INFO 2017-02-05 01:15:02,804 networks.py:1478] The output order is [__cost_0__]
[INFO 2017-02-05 01:15:02,942 networks.py:1472] The input order is [data_a, data_b, label]
[INFO 2017-02-05 01:15:02,942 networks.py:1478] The output order is [__cost_0__]
Generating  test_cost_layers_with_weight
[WARNING 2017-02-05 01:15:03,079 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:03,080 networks.py:1472] The input order is [input, label, weight]
[INFO 2017-02-05 01:15:03,080 networks.py:1478] The output order is [__cost_0__, __regression_cost_0__]
[WARNING 2017-02-05 01:15:03,215 networks.py:1444] `outputs` routine try to calculate network's inputs and outputs order. It might not work well.Please see follow log carefully.
[INFO 2017-02-05 01:15:03,215 networks.py:1472] The input order is [input, label, weight]
[INFO 2017-02-05 01:15:03,215 networks.py:1478] The output order is [__cost_0__, __regression_cost_0__]
Generating  test_spp_layer
[INFO 2017-02-05 01:15:03,350 layers.py:2130] output for __spp_0__: c = 16, h = 1, w = 5, size = 80
[INFO 2017-02-05 01:15:03,350 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:03,350 networks.py:1478] The output order is [__spp_0__]
[INFO 2017-02-05 01:15:03,486 layers.py:2130] output for __spp_0__: c = 16, h = 1, w = 5, size = 80
[INFO 2017-02-05 01:15:03,486 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:03,486 networks.py:1478] The output order is [__spp_0__]
Generating  test_bilinear_interp
[INFO 2017-02-05 01:15:03,622 layers.py:1962] output for __conv_0__: c = 16, h = 48, w = 48, size = 36864
[INFO 2017-02-05 01:15:03,623 layers.py:1546] output for __bilinear_interp_layer_0__: c = 16, h = 64, w = 64, size = 65536
[INFO 2017-02-05 01:15:03,623 layers.py:2071] output for __pool_0__: c = 16, h = 32, w = 32, size = 16384
[INFO 2017-02-05 01:15:03,623 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:03,624 networks.py:1478] The output order is [__fc_layer_0__]
[INFO 2017-02-05 01:15:03,759 layers.py:1962] output for __conv_0__: c = 16, h = 48, w = 48, size = 36864
[INFO 2017-02-05 01:15:03,760 layers.py:1546] output for __bilinear_interp_layer_0__: c = 16, h = 64, w = 64, size = 65536
[INFO 2017-02-05 01:15:03,760 layers.py:2071] output for __pool_0__: c = 16, h = 32, w = 32, size = 16384
[INFO 2017-02-05 01:15:03,761 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:03,761 networks.py:1478] The output order is [__fc_layer_0__]
Generating  test_maxout
[INFO 2017-02-05 01:15:03,896 layers.py:1962] output for __conv_0__: c = 16, h = 48, w = 48, size = 36864
[INFO 2017-02-05 01:15:03,897 layers.py:4208] output for __maxout_layer_0__: c = 8, h = 48, w = 48, size = 18432
[INFO 2017-02-05 01:15:03,897 layers.py:2071] output for __pool_0__: c = 8, h = 24, w = 24, size = 4608
[INFO 2017-02-05 01:15:03,897 layers.py:1962] output for __conv_1__: c = 128, h = 24, w = 24, size = 73728
[INFO 2017-02-05 01:15:03,898 layers.py:4208] output for __maxout_layer_1__: c = 32, h = 24, w = 24, size = 18432
[INFO 2017-02-05 01:15:03,899 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:03,899 networks.py:1478] The output order is [__fc_layer_0__]
[INFO 2017-02-05 01:15:04,036 layers.py:1962] output for __conv_0__: c = 16, h = 48, w = 48, size = 36864
[INFO 2017-02-05 01:15:04,036 layers.py:4208] output for __maxout_layer_0__: c = 8, h = 48, w = 48, size = 18432
[INFO 2017-02-05 01:15:04,036 layers.py:2071] output for __pool_0__: c = 8, h = 24, w = 24, size = 4608
[INFO 2017-02-05 01:15:04,037 layers.py:1962] output for __conv_1__: c = 128, h = 24, w = 24, size = 73728
[INFO 2017-02-05 01:15:04,037 layers.py:4208] output for __maxout_layer_1__: c = 32, h = 24, w = 24, size = 18432
[INFO 2017-02-05 01:15:04,038 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:04,038 networks.py:1478] The output order is [__fc_layer_0__]
Generating  test_bi_grumemory
[WARNING 2017-02-05 01:15:04,175 layers.py:1254] NOTE: the gru memory layer's size is set by previous input layer, and should be input size / 3. Set size explicitly will be ignored.
[WARNING 2017-02-05 01:15:04,176 layers.py:1254] NOTE: the gru memory layer's size is set by previous input layer, and should be input size / 3. Set size explicitly will be ignored.
[INFO 2017-02-05 01:15:04,176 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:04,176 networks.py:1478] The output order is [__bidirectional_gru_0__]
[WARNING 2017-02-05 01:15:04,314 layers.py:1254] NOTE: the gru memory layer's size is set by previous input layer, and should be input size / 3. Set size explicitly will be ignored.
[WARNING 2017-02-05 01:15:04,314 layers.py:1254] NOTE: the gru memory layer's size is set by previous input layer, and should be input size / 3. Set size explicitly will be ignored.
[INFO 2017-02-05 01:15:04,315 networks.py:1472] The input order is [data]
[INFO 2017-02-05 01:15:04,315 networks.py:1478] The output order is [__bidirectional_gru_0__]
Generating  math_ops
[INFO 2017-02-05 01:15:04,459 networks.py:1472] The input order is [data_2, data]
[INFO 2017-02-05 01:15:04,459 networks.py:1478] The output order is [__mixed_3__]
[INFO 2017-02-05 01:15:04,603 networks.py:1472] The input order is [data_2, data]
[INFO 2017-02-05 01:15:04,603 networks.py:1478] The output order is [__mixed_3__]
Generating  test_split_datasource
[INFO 2017-02-05 01:15:04,740 networks.py:1472] The input order is [a]
[INFO 2017-02-05 01:15:04,740 networks.py:1478] The output order is [a]
[INFO 2017-02-05 01:15:04,874 networks.py:1472] The input order is [a]
[INFO 2017-02-05 01:15:04,874 networks.py:1478] The output order is [a]
--- /paddle/python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr	2017-01-13 21:24:57.699101326 +0000
+++ /paddle/python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr.unittest	2017-02-05 01:15:04.743770012 +0000
@@ -24,9 +24,6 @@
   load_data_module: "a"
   load_data_object: "c"
   load_data_args: ""
-  data_ratio: 1
-  is_main_data: true
-  usage_ratio: 1.0
 }
 opt_config {
   batch_size: 1000
@@ -63,9 +60,6 @@
   load_data_module: "b"
   load_data_object: "d"
   load_data_args: ""
-  data_ratio: 1
-  is_main_data: true
-  usage_ratio: 1.0
 }
 save_dir: "./output/model"
 start_pass: 0
@wangkuiyi wangkuiyi mentioned this issue Feb 5, 2017
Merged
@reyoung
Copy link
Collaborator

reyoung commented Feb 6, 2017

Well. It could be removed, because the data_ratio, is_main_data and usage_ratio is not used by PyDataProvider2. So we could just remove lines in python/paddle/trainer_config_helpers/tests/configs/protostr/test_split_datasource.protostr.

I will give a PR soon.

@Yancey1989
Copy link
Contributor

Close the inactive issue.

zhhsplendid pushed a commit to zhhsplendid/Paddle that referenced this issue Sep 25, 2019
@zhupengyang @hong19860320
fix argmax doc (PaddlePaddle#1261)
dfc3ab0 
* fix argmax doc
test=document_preview

* fix
test=document_preview
lizexu123 pushed a commit to lizexu123/Paddle that referenced this issue Feb 23, 2024
@ceci3
cancel opt_model in requirement.txt (PaddlePaddle#1261)
29eb61e
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees

@reyoung reyoung

@wangkuiyi wangkuiyi

Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
3 participants
@reyoung @Yancey1989 @wangkuiyi