提交 3a0919ba 编写于 作者: Y yangyaming

Add test for configuration and add doc.

上级 b5ab4b69
......@@ -445,6 +445,11 @@ smooth_l1_cost
.. autoclass:: paddle.v2.layer.smooth_l1_cost
:noindex:
multibox_loss
--------------
.. autoclass:: paddle.v2.layer.multibox_loss
:noindex:
Check Layer
============
......@@ -468,3 +473,11 @@ prelu
--------
.. autoclass:: paddle.v2.layer.prelu
:noindex:
Detection output Layer
======================
detection_output
---
.. autoclass:: paddle.v2.layer.detection_output
:noindex:
......@@ -6,6 +6,6 @@ img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cos
test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv)
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer)
export whole_configs=(test_split_datasource)
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
input_loc = data_layer(name='input_loc', size=16, height=16, width=1)
input_conf = data_layer(name='input_conf', size=8, height=1, width=8)
priorbox = data_layer(name='priorbox', size=32, height=4, width=8)
detout = detection_output_layer(
input_loc=input_loc,
input_conf=input_conf,
priorbox=priorbox,
num_classes=21,
nms_threshold=0.45,
nms_top_k=400,
keep_top_k=200,
confidence_threshold=0.01,
background_id=0,
name='test_detection_output')
outputs(detout)
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
input_loc = data_layer(name='input_loc', size=16, height=16, width=1)
input_conf = data_layer(name='input_conf', size=8, height=1, width=8)
priorbox = data_layer(name='priorbox', size=32, height=4, width=8)
label = data_layer(name='label', size=24, height=4, width=6)
multibox_loss = multibox_loss_layer(
input_loc=input_loc,
input_conf=input_conf,
priorbox=priorbox,
label=label,
num_classes=21,
overlap_threshold=0.5,
neg_pos_ratio=3.0,
neg_overlap=0.5,
background_id=0,
name='test_multibox_loss')
outputs(multibox_loss)
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