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体验新版 GitCode,发现更多精彩内容 >>
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cafdeb0a
编写于
6月 15, 2018
作者:
Y
Yibing Liu
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电子邮件补丁
差异文件
Fix docs for detection_output & target_assign
上级
67dc5c7f
变更
2
隐藏空白更改
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2 changed file
with
42 addition
and
24 deletion
+42
-24
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+35
-18
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+7
-6
未找到文件。
python/paddle/fluid/layers/detection.py
浏览文件 @
cafdeb0a
...
...
@@ -97,7 +97,9 @@ def detection_output(loc,
nms_eta(float): The parameter for adaptive NMS.
Returns:
Variable: The detection outputs is a LoDTensor with shape [No, 6].
Variable:
The detection outputs is a LoDTensor with shape [No, 6].
Each row has six values: [label, confidence, xmin, ymin, xmax, ymax].
`No` is the total number of detections in this mini-batch. For each
instance, the offsets in first dimension are called LoD, the offset
...
...
@@ -110,15 +112,15 @@ def detection_output(loc,
Examples:
.. code-block:: python
pb = layers.data(name='prior_box', shape=[10, 4],
pb = layers.data(name='prior_box', shape=[10, 4],
append_batch_size=False, dtype='float32')
pbv = layers.data(name='prior_box_var', shape=[10, 4],
pbv = layers.data(name='prior_box_var', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc = layers.data(name='target_box', shape=[2, 21, 4],
loc = layers.data(name='target_box', shape=[2, 21, 4],
append_batch_size=False, dtype='float32')
scores = layers.data(name='scores', shape=[2, 21, 10],
scores = layers.data(name='scores', shape=[2, 21, 10],
append_batch_size=False, dtype='float32')
nmsed_outs = fluid.layers.detection_output(scores=scores,
nmsed_outs = fluid.layers.detection_output(scores=scores,
loc=loc,
prior_box=pb,
prior_box_var=pbv)
...
...
@@ -296,8 +298,6 @@ def target_assign(input,
mismatch_value
=
None
,
name
=
None
):
"""
**Target assigner operator**
This operator can be, for given the target bounding boxes or labels,
to assign classification and regression targets to each prediction as well as
weights to prediction. The weights is used to specify which prediction would
...
...
@@ -311,20 +311,24 @@ def target_assign(input,
1. Assigning all outpts based on `match_indices`:
If id = match_indices[i][j] > 0,
.. code-block:: text
If id = match_indices[i][j] > 0,
out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
out_weight[i][j] = 1.
out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
out_weight[i][j] = 1.
Otherwise,
Otherwise,
out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][j] = 0.
out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][j] = 0.
2. Assigning out_weight based on `neg_indices` if `neg_indices` is provided:
Assumed that the row offset for each instance in `neg_indices` is called neg_lod,
for i-th instance and each `id` of neg_indices in this instance:
.. code-block:: text
out[i][id][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][id] = 1.0
...
...
@@ -341,10 +345,23 @@ def target_assign(input,
mismatch_value (float32): Fill this value to the mismatched location.
Returns:
out (Variable): The output is a 3D Tensor with shape [N, P, K],
N and P is the same as they are in `neg_indices`, K is the
same as it in input of X. If `match_indices[i][j]`.
out_weight (Variable): The weight for output with the shape of [N, P, 1].
tuple:
A tuple(out, out_weight) is returned. out is a 3D Tensor with
shape [N, P, K], N and P is the same as they are in
`neg_indices`, K is the same as it in input of X. If
`match_indices[i][j]`. out_weight is the weight for output with
the shape of [N, P, 1].
Examples:
.. code-block:: python
matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
gt = layers.data(
name='gt', shape=[1, 1], dtype='int32', lod_level=1)
trg, trg_weight = layers.target_assign(
gt, matched_indices, mismatch_value=0)
"""
helper
=
LayerHelper
(
'target_assign'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
cafdeb0a
...
...
@@ -3466,7 +3466,9 @@ def nce(input,
input (Variable): input variable.
label (Variable): label.
num_total_classes (int):${num_total_classes_comment}
sample_weight (int): ${sample_weight_comment}
sample_weight (Variable|None): A Variable of shape [batch_size, 1]
storing a weight for each sample. The default weight for each
sample is 1.0.
param_attr (ParamAttr|None): attributes for parameter
bias_attr (ParamAttr|None): attributes for bias
num_neg_samples (int): ${num_neg_samples_comment}
...
...
@@ -4638,10 +4640,6 @@ def random_crop(x, shape, seed=None):
"""
${comment}
Examples:
>>> img = fluid.layers.data("img", [3, 256, 256])
>>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
Args:
x(${x_type}): ${x_comment}
shape(${shape_type}): ${shape_comment}
...
...
@@ -4650,7 +4648,10 @@ def random_crop(x, shape, seed=None):
Returns:
${out_comment}
Examples:
>>> img = fluid.layers.data("img", [3, 256, 256])
>>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
"""
helper
=
LayerHelper
(
"random_crop"
,
**
locals
())
dtype
=
helper
.
input_dtype
()
...
...
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