提交 d1901f27 编写于 作者: J jerrywgz

refine doc

上级 d497bd90
...@@ -328,6 +328,7 @@ paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varar ...@@ -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.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.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.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.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.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')) 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, ...@@ -2233,22 +2233,16 @@ def distribute_fpn_proposals(fpn_rois,
""" """
Distribute all proposals into different fpn level, with respect to scale Distribute all proposals into different fpn level, with respect to scale
of the proposals, the referring scale and the referring level. Besides, to 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 original index of rois in current proposals. To compute fpn level for each
roi, the formula is given as follows: roi, the formula is given as follows:
.. code-block:: text .. code-block:: text
roi_scale = sqrt(BBoxArea(fpn_roi)); roi_scale = \sqrt{BBoxArea(fpn_roi)};
level = floor(log2(roi_scale / refer_scale) + refer_level) level = \floor{\log \frac{roi_scale}{refer_scale} + refer_level}
where BBoxArea is the function to compute the area of each roi: where BBoxArea is the area of each roi
.. code-block:: text
w = fpn_roi[2] - fpn_roi[0]
h = fpn_roi[3] - fpn_roi[1]
area = (w + 1) * (h + 1)
Args: Args:
fpn_rois(variable): The input fpn_rois, the last dimension is 4. fpn_rois(variable): The input fpn_rois, the last dimension is 4.
...@@ -2258,6 +2252,7 @@ def distribute_fpn_proposals(fpn_rois, ...@@ -2258,6 +2252,7 @@ def distribute_fpn_proposals(fpn_rois,
come from. come from.
refer_level(int): The referring level of FPN layer with specified scale. refer_level(int): The referring level of FPN layer with specified scale.
refer_scale(int): The referring scale of FPN layer with specified level. refer_scale(int): The referring scale of FPN layer with specified level.
name(str|None): The name of this operator.
Returns: Returns:
tuple: tuple:
......
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