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Enable the SSD loss to support normalization by the total number of output locations. #8630

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Merged
merged 5 commits into from
Feb 28, 2018
Merged

Enable the SSD loss to support normalization by the total number of output locations. #8630

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2591fc7
Register more data type for reshape operator.
qingqing01 Feb 27, 2018
a7a3c4f
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
qingqing01 Feb 28, 2018
c6304cb
Enable the SSD loss to support normalization by the total number of o…
qingqing01 Feb 28, 2018
bc4129c
Fix the doc format.
qingqing01 Feb 28, 2018
c7c8d93
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
qingqing01 Feb 28, 2018
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18 changes: 14 additions & 4 deletions python/paddle/fluid/layers/detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -328,6 +328,7 @@ def ssd_loss(location,
conf_loss_weight=1.0,
match_type='per_prediction',
mining_type='max_negative',
normalize=True,
sample_size=None):
"""
**Multi-box loss layer for object dection algorithm of SSD**
Expand Down Expand Up @@ -376,18 +377,20 @@ def ssd_loss(location,
`overlap_threshold` to determine the extra matching bboxes when
finding matched boxes. 0.5 by default.
neg_pos_ratio (float): The ratio of the negative boxes to the positive
boxes, used only when mining_type is max_negative, 3.0 by defalut.
boxes, used only when mining_type is 'max_negative', 3.0 by defalut.
neg_overlap (float): The negative overlap upper bound for the unmatched
predictions. Use only when mining_type is max_negative,
predictions. Use only when mining_type is 'max_negative',
0.5 by default.
sample_size (int): The max sample size of negative box, used only when
mining_type is hard_example.
loc_loss_weight (float): Weight for localization loss, 1.0 by default.
conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
match_type (str): The type of matching method during training, should
be 'bipartite' or 'per_prediction', 'per_prediction' by defalut.
mining_type (str): The hard example mining type, should be 'hard_example'
or 'max_negative', now only support `max_negative`.
normalize (bool): Whether to normalize the SSD loss by the total number
of output locations, True by defalut.
sample_size (int): The max sample size of negative box, used only when
mining_type is 'hard_example'.

Returns:
Variable: The weighted sum of the localization loss and confidence loss,
Expand Down Expand Up @@ -507,6 +510,13 @@ def __reshape_to_2d(var):

# 5.3 Compute overall weighted loss.
loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
# reshape to [N, Np], N is the batch size and Np is the prior box number.
loss = ops.reshape(x=loss, shape=[-1, num_prior])
loss = nn.reduce_sum(loss, dim=1, keep_dim=True)
if normalize:
normalizer = nn.reduce_sum(target_loc_weight)
loss = loss / normalizer

return loss


Expand Down