From cc783563a610922c0692c5ae9fd12b4cb8185721 Mon Sep 17 00:00:00 2001 From: zoooo0820 Date: Thu, 30 Jun 2022 08:06:08 +0000 Subject: [PATCH] im_info -> img_size --- .../test_generate_proposals_v2_op.py | 14 ++--- python/paddle/vision/ops.py | 56 +++++++++---------- 2 files changed, 34 insertions(+), 36 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_generate_proposals_v2_op.py b/python/paddle/fluid/tests/unittests/test_generate_proposals_v2_op.py index ab6d17c38289b..b1a4b45d7d257 100644 --- a/python/paddle/fluid/tests/unittests/test_generate_proposals_v2_op.py +++ b/python/paddle/fluid/tests/unittests/test_generate_proposals_v2_op.py @@ -260,7 +260,7 @@ 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') @@ -268,7 +268,7 @@ def setUp(self): 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, @@ -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, @@ -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', @@ -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, @@ -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, }, diff --git a/python/paddle/vision/ops.py b/python/paddle/vision/ops.py index 8d1a2cf13339f..cc5a0caf71f47 100644 --- a/python/paddle/vision/ops.py +++ b/python/paddle/vision/ops.py @@ -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, @@ -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 @@ -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 @@ -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 @@ -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) """ @@ -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 @@ -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') @@ -1656,7 +1654,7 @@ def generate_proposals(scores, inputs={ 'Scores': scores, 'BboxDeltas': bbox_deltas, - 'ImShape': im_shape, + 'ImShape': img_size, 'Anchors': anchors, 'Variances': variances },