align_pyramid
jerrywgz 7 years ago
parent d497bd9079
commit d1901f27bc

@ -328,6 +328,7 @@ paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varar
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691'))
paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0'))
paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '41ef443800fa2976299e73e788336cae'))
paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', '9808534c12c5e739a10f73ebb0b4eafd'))
paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'e0e95334fce92d16c2d9db6e7caffc47'))
paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '98a5050bee8522fcea81aa795adaba51'))

@ -2233,22 +2233,16 @@ def distribute_fpn_proposals(fpn_rois,
"""
Distribute all proposals into different fpn level, with respect to scale
of the proposals, the referring scale and the referring level. Besides, to
restore the order of proposals, we return an array which indicate the
restore the order of proposals, we return an array which indicates the
original index of rois in current proposals. To compute fpn level for each
roi, the formula is given as follows:
.. code-block:: text
roi_scale = sqrt(BBoxArea(fpn_roi));
level = floor(log2(roi_scale / refer_scale) + refer_level)
where BBoxArea is the function to compute the area of each roi:
roi_scale = \sqrt{BBoxArea(fpn_roi)};
level = \floor{\log \frac{roi_scale}{refer_scale} + refer_level}
.. code-block:: text
w = fpn_roi[2] - fpn_roi[0]
h = fpn_roi[3] - fpn_roi[1]
area = (w + 1) * (h + 1)
where BBoxArea is the area of each roi
Args:
fpn_rois(variable): The input fpn_rois, the last dimension is 4.
@ -2258,7 +2252,8 @@ def distribute_fpn_proposals(fpn_rois,
come from.
refer_level(int): The referring level of FPN layer with specified scale.
refer_scale(int): The referring scale of FPN layer with specified level.
name(str|None): The name of this operator.
Returns:
tuple:
A tuple(multi_rois, restore_ind) is returned. The multi_rois is

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