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1a88baae
编写于
8月 30, 2018
作者:
J
jerrywgz
提交者:
qingqing01
8月 30, 2018
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差异文件
add rpn_target_assign api test (#13013)
* Add unit test for rpn_target_assign API.
上级
03b1e4be
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
62 addition
and
7 deletion
+62
-7
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+8
-6
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+53
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
1a88baae
...
@@ -301,7 +301,7 @@ paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'neg
...
@@ -301,7 +301,7 @@ paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'neg
paddle.fluid.layers.detection_output ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0))
paddle.fluid.layers.detection_output ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0))
paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None))
paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None))
paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral'))
paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral'))
paddle.fluid.layers.rpn_target_assign ArgSpec(args=['loc', 'scores', 'anchor_box', 'gt_box', 'rpn_batch_size_per_im', 'fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap'], varargs=None, keywords=None, defaults=(256, 0.25, 0.7, 0.3))
paddle.fluid.layers.rpn_target_assign ArgSpec(args=['loc', 'scores', 'anchor_box', '
anchor_var', '
gt_box', 'rpn_batch_size_per_im', 'fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap'], varargs=None, keywords=None, defaults=(256, 0.25, 0.7, 0.3))
paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None))
paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None))
paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'gt_boxes', 'im_scales', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None))
paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'gt_boxes', 'im_scales', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None))
paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
1a88baae
...
@@ -58,6 +58,7 @@ for _OP in set(__auto__):
...
@@ -58,6 +58,7 @@ for _OP in set(__auto__):
def
rpn_target_assign
(
loc
,
def
rpn_target_assign
(
loc
,
scores
,
scores
,
anchor_box
,
anchor_box
,
anchor_var
,
gt_box
,
gt_box
,
rpn_batch_size_per_im
=
256
,
rpn_batch_size_per_im
=
256
,
fg_fraction
=
0.25
,
fg_fraction
=
0.25
,
...
@@ -96,6 +97,8 @@ def rpn_target_assign(loc,
...
@@ -96,6 +97,8 @@ def rpn_target_assign(loc,
if the input is image feature map, they are close to the origin
if the input is image feature map, they are close to the origin
of the coordinate system. [xmax, ymax] is the right bottom
of the coordinate system. [xmax, ymax] is the right bottom
coordinate of the anchor box.
coordinate of the anchor box.
anchor_var(Variable): A 2-D Tensor with shape [M,4] holds expanded
variances of anchors.
gt_box (Variable): The ground-truth boudding boxes (bboxes) are a 2D
gt_box (Variable): The ground-truth boudding boxes (bboxes) are a 2D
LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
bboxes of mini-batch input.
bboxes of mini-batch input.
...
@@ -145,30 +148,29 @@ def rpn_target_assign(loc,
...
@@ -145,30 +148,29 @@ def rpn_target_assign(loc,
# 1. Compute the regression target bboxes
# 1. Compute the regression target bboxes
target_bbox
=
box_coder
(
target_bbox
=
box_coder
(
prior_box
=
anchor_box
,
prior_box
=
anchor_box
,
prior_box_var
=
anchor_var
,
target_box
=
gt_box
,
target_box
=
gt_box
,
code_type
=
'encode_center_size'
,
code_type
=
'encode_center_size'
,
box_normalized
=
False
)
box_normalized
=
False
)
# 2. Compute overlaps between the prior boxes and the gt boxes overlaps
# 2. Compute overlaps between the prior boxes and the gt boxes overlaps
iou
=
iou_similarity
(
x
=
gt_box
,
y
=
anchor_box
)
iou
=
iou_similarity
(
x
=
gt_box
,
y
=
anchor_box
)
# 3. Assign target label to anchors
# 3. Assign target label to anchors
loc_index
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
loc_index
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
score_index
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
score_index
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
target_label
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
target_label
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
helper
.
append_op
(
helper
.
append_op
(
type
=
"rpn_target_assign"
,
type
=
"rpn_target_assign"
,
inputs
=
{
'
Overlap'
:
iou
,
},
inputs
=
{
'
DistMat'
:
iou
},
outputs
=
{
outputs
=
{
'LocationIndex'
:
loc_index
,
'LocationIndex'
:
loc_index
,
'ScoreIndex'
:
score_index
,
'ScoreIndex'
:
score_index
,
'TargetLabel'
:
target_label
,
'TargetLabel'
:
target_label
},
},
attrs
=
{
attrs
=
{
'rpn_batch_size_per_im'
:
rpn_batch_size_per_im
,
'rpn_batch_size_per_im'
:
rpn_batch_size_per_im
,
'rpn_positive_overlap'
:
rpn_positive_overlap
,
'rpn_positive_overlap'
:
rpn_positive_overlap
,
'rpn_negative_overlap'
:
rpn_negative_overlap
,
'rpn_negative_overlap'
:
rpn_negative_overlap
,
'fg_fraction'
:
fg_fraction
,
'fg_fraction'
:
fg_fraction
})
})
# 4. Reshape and gather the target entry
# 4. Reshape and gather the target entry
...
@@ -181,7 +183,7 @@ def rpn_target_assign(loc,
...
@@ -181,7 +183,7 @@ def rpn_target_assign(loc,
predicted_location
=
nn
.
gather
(
loc
,
loc_index
)
predicted_location
=
nn
.
gather
(
loc
,
loc_index
)
target_label
=
nn
.
gather
(
target_label
,
score_index
)
target_label
=
nn
.
gather
(
target_label
,
score_index
)
target_bbox
=
nn
.
gather
(
target_bbox
,
loc_index
)
target_bbox
=
nn
.
gather
(
target_bbox
,
loc_index
)
return
predicted_scores
,
predicted_loc
,
target_label
,
target_bbox
return
predicted_scores
,
predicted_loc
ation
,
target_label
,
target_bbox
def
detection_output
(
loc
,
def
detection_output
(
loc
,
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
1a88baae
...
@@ -250,6 +250,59 @@ class TestDetectionMAP(unittest.TestCase):
...
@@ -250,6 +250,59 @@ class TestDetectionMAP(unittest.TestCase):
print
(
str
(
program
))
print
(
str
(
program
))
class
TestRpnTargetAssign
(
unittest
.
TestCase
):
def
test_rpn_target_assign
(
self
):
program
=
Program
()
with
program_guard
(
program
):
loc_shape
=
[
10
,
50
,
4
]
score_shape
=
[
10
,
50
,
2
]
anchor_shape
=
[
50
,
4
]
loc
=
layers
.
data
(
name
=
'loc'
,
shape
=
loc_shape
,
append_batch_size
=
False
,
dtype
=
'float32'
)
scores
=
layers
.
data
(
name
=
'scores'
,
shape
=
score_shape
,
append_batch_size
=
False
,
dtype
=
'float32'
)
anchor_box
=
layers
.
data
(
name
=
'anchor_box'
,
shape
=
anchor_shape
,
append_batch_size
=
False
,
dtype
=
'float32'
)
anchor_var
=
layers
.
data
(
name
=
'anchor_var'
,
shape
=
anchor_shape
,
append_batch_size
=
False
,
dtype
=
'float32'
)
gt_box
=
layers
.
data
(
name
=
'gt_box'
,
shape
=
[
4
],
lod_level
=
1
,
dtype
=
'float32'
)
predicted_scores
,
predicted_location
,
target_label
,
target_bbox
=
layers
.
rpn_target_assign
(
loc
=
loc
,
scores
=
scores
,
anchor_box
=
anchor_box
,
anchor_var
=
anchor_var
,
gt_box
=
gt_box
,
rpn_batch_size_per_im
=
256
,
fg_fraction
=
0.25
,
rpn_positive_overlap
=
0.7
,
rpn_negative_overlap
=
0.3
)
self
.
assertIsNotNone
(
predicted_scores
)
self
.
assertIsNotNone
(
predicted_location
)
self
.
assertIsNotNone
(
target_label
)
self
.
assertIsNotNone
(
target_bbox
)
assert
predicted_scores
.
shape
[
1
]
==
2
assert
predicted_location
.
shape
[
1
]
==
4
assert
predicted_location
.
shape
[
1
]
==
target_bbox
.
shape
[
1
]
print
(
str
(
program
))
class
TestGenerateProposals
(
unittest
.
TestCase
):
class
TestGenerateProposals
(
unittest
.
TestCase
):
def
test_generate_proposals
(
self
):
def
test_generate_proposals
(
self
):
data_shape
=
[
20
,
64
,
64
]
data_shape
=
[
20
,
64
,
64
]
...
...
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