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fix generate_proposal_labels in cascade-rcnn series model, test=develop (#27892)

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* fix generate_proposal_labels in cascade-rcnn series model, test=develop

* fix example code & unittest, test=develop

* update code from review comments, test=develop
swt-req
wangguanzhong 4 years ago committed by GitHub
parent afe68cb992
commit d1e1f17482
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5 changed files with 303 additions and 147 deletions
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15
paddle/fluid/operators/detection/bbox_util.h
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@ -149,5 +149,20 @@ void ClipTiledBoxes(const platform::DeviceContext& ctx,
}
}
// Calculate max IoU between each box and ground-truth and
// each row represents one box
template <typename T>
void MaxIoU(const framework::Tensor& iou, framework::Tensor* max_iou) {
const T* iou_data = iou.data<T>();
int row = iou.dims()[0];
int col = iou.dims()[1];
T* max_iou_data = max_iou->data<T>();
for (int i = 0; i < row; ++i) {
const T* v = iou_data + i * col;
T max_v = *std::max_element(v, v + col);
max_iou_data[i] = max_v;
}
}
} // namespace operators
} // namespace paddle

176
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
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43
python/paddle/fluid/layers/detection.py
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@ -2601,7 +2601,9 @@ def generate_proposal_labels(rpn_rois,
class_nums=None,
use_random=True,
is_cls_agnostic=False,
is_cascade_rcnn=False):
is_cascade_rcnn=False,
max_overlap=None,
return_max_overlap=False):
"""
**Generate Proposal Labels of Faster-RCNN**
@ -2638,25 +2640,29 @@ def generate_proposal_labels(rpn_rois,
use_random(bool): Use random sampling to choose foreground and background boxes.
is_cls_agnostic(bool): bbox regression use class agnostic simply which only represent fg and bg boxes.
is_cascade_rcnn(bool): it will filter some bbox crossing the image's boundary when setting True.
max_overlap(Variable): Maximum overlap between each proposal box and ground-truth.
return_max_overlap(bool): Whether return the maximum overlap between each sampled RoI and ground-truth.
Returns:
tuple:
A tuple with format``(rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights)``.
A tuple with format``(rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap)``.
- **rois**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4]``. The data type is the same as ``rpn_rois``.
- **labels_int32**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 1]``. The data type must be int32.
- **bbox_targets**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The regression targets of all RoIs. The data type is the same as ``rpn_rois``.
- **bbox_inside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of foreground boxes' regression loss. The data type is the same as ``rpn_rois``.
- **bbox_outside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of regression loss. The data type is the same as ``rpn_rois``.
- **max_overlap**: 1-D LoDTensor with shape ``[P]``. P is the number of output ``rois``. The maximum overlap between each sampled RoI and ground-truth.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
rpn_rois = fluid.data(name='rpn_rois', shape=[None, 4], dtype='float32')
gt_classes = fluid.data(name='gt_classes', shape=[None, 1], dtype='float32')
is_crowd = fluid.data(name='is_crowd', shape=[None, 1], dtype='float32')
gt_classes = fluid.data(name='gt_classes', shape=[None, 1], dtype='int32')
is_crowd = fluid.data(name='is_crowd', shape=[None, 1], dtype='int32')
gt_boxes = fluid.data(name='gt_boxes', shape=[None, 4], dtype='float32')
im_info = fluid.data(name='im_info', shape=[None, 3], dtype='float32')
rois, labels, bbox, inside_weights, outside_weights = fluid.layers.generate_proposal_labels(
@ -2673,6 +2679,8 @@ def generate_proposal_labels(rpn_rois,
'generate_proposal_labels')
check_variable_and_dtype(is_crowd, 'is_crowd', ['int32'],
'generate_proposal_labels')
if is_cascade_rcnn:
assert max_overlap is not None, "Input max_overlap of generate_proposal_labels should not be None if is_cascade_rcnn is True"
rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
labels_int32 = helper.create_variable_for_type_inference(
@ -2683,22 +2691,28 @@ def generate_proposal_labels(rpn_rois,
dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
max_overlap_with_gt = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
inputs = {
'RpnRois': rpn_rois,
'GtClasses': gt_classes,
'IsCrowd': is_crowd,
'GtBoxes': gt_boxes,
'ImInfo': im_info,
}
if max_overlap is not None:
inputs['MaxOverlap'] = max_overlap
helper.append_op(
type="generate_proposal_labels",
inputs={
'RpnRois': rpn_rois,
'GtClasses': gt_classes,
'IsCrowd': is_crowd,
'GtBoxes': gt_boxes,
'ImInfo': im_info
},
inputs=inputs,
outputs={
'Rois': rois,
'LabelsInt32': labels_int32,
'BboxTargets': bbox_targets,
'BboxInsideWeights': bbox_inside_weights,
'BboxOutsideWeights': bbox_outside_weights
'BboxOutsideWeights': bbox_outside_weights,
'MaxOverlapWithGT': max_overlap_with_gt
},
attrs={
'batch_size_per_im': batch_size_per_im,
@ -2718,7 +2732,10 @@ def generate_proposal_labels(rpn_rois,
bbox_targets.stop_gradient = True
bbox_inside_weights.stop_gradient = True
bbox_outside_weights.stop_gradient = True
max_overlap_with_gt.stop_gradient = True
if return_max_overlap:
return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap_with_gt
return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights

94
python/paddle/fluid/tests/test_detection.py
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@ -289,40 +289,39 @@ class TestAnchorGenerator(unittest.TestCase):
class TestGenerateProposalLabels(unittest.TestCase):
def check_out(self, outs):
rois = outs[0]
labels_int32 = outs[1]
bbox_targets = outs[2]
bbox_inside_weights = outs[3]
bbox_outside_weights = outs[4]
assert rois.shape[1] == 4
assert rois.shape[0] == labels_int32.shape[0]
assert rois.shape[0] == bbox_targets.shape[0]
assert rois.shape[0] == bbox_inside_weights.shape[0]
assert rois.shape[0] == bbox_outside_weights.shape[0]
assert bbox_targets.shape[1] == 4 * self.class_nums
assert bbox_inside_weights.shape[1] == 4 * self.class_nums
assert bbox_outside_weights.shape[1] == 4 * self.class_nums
if len(outs) == 6:
max_overlap_with_gt = outs[5]
assert max_overlap_with_gt.shape[0] == rois.shape[0]
def test_generate_proposal_labels(self):
program = Program()
with program_guard(program):
rpn_rois = layers.data(
name='rpn_rois',
shape=[4, 4],
dtype='float32',
lod_level=1,
append_batch_size=False)
gt_classes = layers.data(
name='gt_classes',
shape=[6],
dtype='int32',
lod_level=1,
append_batch_size=False)
is_crowd = layers.data(
name='is_crowd',
shape=[6],
dtype='int32',
lod_level=1,
append_batch_size=False)
gt_boxes = layers.data(
name='gt_boxes',
shape=[6, 4],
dtype='float32',
lod_level=1,
append_batch_size=False)
im_info = layers.data(
name='im_info',
shape=[1, 3],
dtype='float32',
lod_level=1,
append_batch_size=False)
class_nums = 5
rpn_rois = fluid.data(
name='rpn_rois', shape=[4, 4], dtype='float32', lod_level=1)
gt_classes = fluid.data(
name='gt_classes', shape=[6], dtype='int32', lod_level=1)
is_crowd = fluid.data(
name='is_crowd', shape=[6], dtype='int32', lod_level=1)
gt_boxes = fluid.data(
name='gt_boxes', shape=[6, 4], dtype='float32', lod_level=1)
im_info = fluid.data(name='im_info', shape=[1, 3], dtype='float32')
max_overlap = fluid.data(
name='max_overlap', shape=[4], dtype='float32', lod_level=1)
self.class_nums = 5
outs = fluid.layers.generate_proposal_labels(
rpn_rois=rpn_rois,
gt_classes=gt_classes,
@ -335,20 +334,27 @@ class TestGenerateProposalLabels(unittest.TestCase):
bg_thresh_hi=0.5,
bg_thresh_lo=0.0,
bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
class_nums=class_nums)
class_nums=self.class_nums)
outs_1 = fluid.layers.generate_proposal_labels(
rpn_rois=rpn_rois,
gt_classes=gt_classes,
is_crowd=is_crowd,
gt_boxes=gt_boxes,
im_info=im_info,
batch_size_per_im=2,
fg_fraction=0.5,
fg_thresh=0.5,
bg_thresh_hi=0.5,
bg_thresh_lo=0.0,
bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
class_nums=self.class_nums,
is_cascade_rcnn=True,
max_overlap=max_overlap,
return_max_overlap=True)
self.check_out(outs)
self.check_out(outs_1)
rois = outs[0]
labels_int32 = outs[1]
bbox_targets = outs[2]
bbox_inside_weights = outs[3]
bbox_outside_weights = outs[4]
assert rois.shape[1] == 4
assert rois.shape[0] == labels_int32.shape[0]
assert rois.shape[0] == bbox_targets.shape[0]
assert rois.shape[0] == bbox_inside_weights.shape[0]
assert rois.shape[0] == bbox_outside_weights.shape[0]
assert bbox_targets.shape[1] == 4 * class_nums
assert bbox_inside_weights.shape[1] == 4 * class_nums
assert bbox_outside_weights.shape[1] == 4 * class_nums
class TestGenerateMaskLabels(unittest.TestCase):

122
python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py
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