Skip to content

Commit

Permalink
im_info -> img_size
Browse files Browse the repository at this point in the history
  • Loading branch information
@zoooo0820
zoooo0820 committed Jun 30, 2022
1 parent fa94248 commit cc78356
Show file tree
Hide file tree
Showing 2 changed files with 34 additions and 36 deletions.
14 changes: 7 additions & 7 deletions python/paddle/fluid/tests/unittests/test_generate_proposals_v2_op.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -260,15 +260,15 @@ def setUp(self):
np.random.seed(678)
self.scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
self.bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32')
self.im_shape_np = np.array([[8, 8], [6, 6]]).astype('float32')
self.img_size_np = np.array([[8, 8], [6, 6]]).astype('float32')
self.anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4),
[4, 4, 3, 4]).astype('float32')
self.variances_np = np.ones((4, 4, 3, 4)).astype('float32')

self.roi_expected, self.roi_probs_expected, self.rois_num_expected = generate_proposals_v2_in_python(
self.scores_np,
self.bbox_deltas_np,
self.im_shape_np,
self.img_size_np,
self.anchors_np,
self.variances_np,
pre_nms_topN=10,
Expand All @@ -285,14 +285,14 @@ def test_dynamic(self):
paddle.disable_static()
scores = paddle.to_tensor(self.scores_np)
bbox_deltas = paddle.to_tensor(self.bbox_deltas_np)
im_shape = paddle.to_tensor(self.im_shape_np)
img_size = paddle.to_tensor(self.img_size_np)
anchors = paddle.to_tensor(self.anchors_np)
variances = paddle.to_tensor(self.variances_np)

rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals(
scores,
bbox_deltas,
im_shape,
img_size,
anchors,
variances,
pre_nms_top_n=10,
Expand All @@ -310,7 +310,7 @@ def test_static(self):
bbox_deltas = paddle.static.data(name='bbox_deltas',
shape=[2, 12, 4, 4],
dtype='float32')
im_shape = paddle.static.data(name='im_shape',
img_size = paddle.static.data(name='img_size',
shape=[2, 2],
dtype='float32')
anchors = paddle.static.data(name='anchors',
Expand All @@ -322,7 +322,7 @@ def test_static(self):
rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals(
scores,
bbox_deltas,
im_shape,
img_size,
anchors,
variances,
pre_nms_top_n=10,
Expand All @@ -334,7 +334,7 @@ def test_static(self):
feed={
'scores': self.scores_np,
'bbox_deltas': self.bbox_deltas_np,
'im_shape': self.im_shape_np,
'img_size': self.img_size_np,
'anchors': self.anchors_np,
'variances': self.variances_np,
},
Expand Down
56 changes: 27 additions & 29 deletions python/paddle/vision/ops.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1534,7 +1534,7 @@ def _nms(boxes, iou_threshold):

def generate_proposals(scores,
bbox_deltas,
im_shape,
img_size,
anchors,
variances,
pre_nms_top_n=6000,
Expand All @@ -1547,19 +1547,18 @@ def generate_proposals(scores,
name=None):
"""
This operation proposes RoIs according to each box with their
probability to be a foreground object and
the box can be calculated by anchors. Bbox_deltais and scores
to be an object are the output of RPN. Final proposals
probability to be a foreground object. And
the proposals of RPN output are calculated by anchors, bbox_deltas and scores. Final proposals
could be used to train detection net.
For generating proposals, this operation performs following steps:
1. Transposes and resizes scores and bbox_deltas in size of
(H*W*A, 1) and (H*W*A, 4)
1. Transpose and resize scores and bbox_deltas in size of
(H * W * A, 1) and (H * W * A, 4)
2. Calculate box locations as proposals candidates.
3. Clip boxes to image
4. Remove predicted boxes with small area.
5. Apply NMS to get final proposals as output.
5. Apply non-maximum suppression (NMS) to get final proposals as output.
Args:
scores (Tensor): A 4-D Tensor with shape [N, A, H, W] represents
Expand All @@ -1569,7 +1568,7 @@ def generate_proposals(scores,
bbox_deltas (Tensor): A 4-D Tensor with shape [N, 4*A, H, W]
represents the difference between predicted box location and
anchor location. The data type must be float32.
im_info (Tensor): A 2-D Tensor with shape [N, 2] represents origin
img_size (Tensor): A 2-D Tensor with shape [N, 2] represents origin
image shape information for N batch, including height and width of the input sizes.
The data type can be float32 or float64.
anchors (Tensor): A 4-D Tensor represents the anchors with a layout
Expand All @@ -1579,27 +1578,26 @@ def generate_proposals(scores,
variances (Tensor): A 4-D Tensor. The expanded variances of anchors with a layout of
[H, W, num_priors, 4]. Each variance is in
(xcenter, ycenter, w, h) format. The data type must be float32.
pre_nms_top_n (float): Number of total bboxes to be kept per
image before NMS. The data type must be float32. `6000` by default.
post_nms_top_n (float): Number of total bboxes to be kept per
image after NMS. The data type must be float32. `1000` by default.
nms_thresh (float): Threshold in NMS. The data type must be float32. `0.5` by default.
min_size (float): Remove predicted boxes with either height or
width < min_size. The data type must be float32. `0.1` by default.
eta(float): Apply in adaptive NMS, if adaptive `threshold > 0.5`,
`adaptive_threshold = adaptive_threshold * eta` in each iteration.
return_rois_num (bool): When setting True, it will return a 1D Tensor with shape [N, ] that includes Rois's
num of each image in one batch. The N is the image's num. For example, the tensor has values [4,5] that represents
the first image has 4 Rois, the second image has 5 Rois. It only used in rcnn model.
'False' by default.
pre_nms_top_n (float, optional): Number of total bboxes to be kept per
image before NMS. `6000` by default.
post_nms_top_n (float, optional): Number of total bboxes to be kept per
image after NMS. `1000` by default.
nms_thresh (float, optional): Threshold in NMS. The data type must be float32. `0.5` by default.
min_size (float, optional): Remove predicted boxes with either height or
width less than this value. `0.1` by default.
eta(float, optional): Apply in adaptive NMS, only works if adaptive `threshold > 0.5`,
`adaptive_threshold = adaptive_threshold * eta` in each iteration. 1.0 by default.
pixel_offset (bool, optional): Whether there is pixel offset. If True, the offset of `img_size` will be 1. 'False' by default.
return_rois_num (bool, optional): Whether to return `rpn_rois_num` . When setting True, it will return a 1D Tensor with shape [N, ] that includes Rois's
num of each image in one batch. 'False' by default.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
- **rpn_rois**: The generated RoIs. 2-D Tensor with shape ``[N, 4]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
- **rpn_roi_probs**: The scores of generated RoIs. 2-D Tensor with shape ``[N, 1]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
- **rpn_rois_num**: Rois's num of each image in one batch. 1-D Tensor with shape ``[B,]`` while ``B`` is the batch size. And its sum equals to RoIs number ``N`` .
- rpn_rois (Tensor): The generated RoIs. 2-D Tensor with shape ``[N, 4]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
- rpn_roi_probs (Tensor): The scores of generated RoIs. 2-D Tensor with shape ``[N, 1]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
- rpn_rois_num (Tensor): Rois's num of each image in one batch. 1-D Tensor with shape ``[B,]`` while ``B`` is the batch size. And its sum equals to RoIs number ``N`` .
Examples:
.. code-block:: python
Expand All @@ -1608,11 +1606,11 @@ def generate_proposals(scores,
scores = paddle.rand((2,4,5,5), dtype=paddle.float32)
bbox_deltas = paddle.rand((2, 16, 5, 5), dtype=paddle.float32)
im_shape = paddle.to_tensor([[224.0, 224.0], [224.0, 224.0]])
img_size = paddle.to_tensor([[224.0, 224.0], [224.0, 224.0]])
anchors = paddle.rand((2,5,4,4), dtype=paddle.float32)
variances = paddle.rand((2,5,10,4), dtype=paddle.float32)
rois, roi_probs, roi_nums = paddle.vision.ops.generate_proposals(scores, bbox_deltas,
im_shape, anchors, variances, return_rois_num=True)
img_size, anchors, variances, return_rois_num=True)
print(rois, roi_probs, roi_nums)
"""

Expand All @@ -1622,7 +1620,7 @@ def generate_proposals(scores,
'nms_thresh', nms_thresh, 'min_size', min_size, 'eta', eta,
'pixel_offset', pixel_offset)
rpn_rois, rpn_roi_probs, rpn_rois_num = _C_ops.generate_proposals_v2(
scores, bbox_deltas, im_shape, anchors, variances, *attrs)
scores, bbox_deltas, img_size, anchors, variances, *attrs)

return rpn_rois, rpn_roi_probs, rpn_rois_num

Expand All @@ -1632,7 +1630,7 @@ def generate_proposals(scores,
'generate_proposals_v2')
check_variable_and_dtype(bbox_deltas, 'bbox_deltas', ['float32'],
'generate_proposals_v2')
check_variable_and_dtype(im_shape, 'im_shape', ['float32', 'float64'],
check_variable_and_dtype(img_size, 'img_size', ['float32', 'float64'],
'generate_proposals_v2')
check_variable_and_dtype(anchors, 'anchors', ['float32'],
'generate_proposals_v2')
Expand All @@ -1656,7 +1654,7 @@ def generate_proposals(scores,
inputs={
'Scores': scores,
'BboxDeltas': bbox_deltas,
'ImShape': im_shape,
'ImShape': img_size,
'Anchors': anchors,
'Variances': variances
},
Expand Down

0 comments on commit cc78356

Please sign in to comment.