detection

anchor_generator

paddle.fluid.layers.anchor_generator(input, anchor_sizes=None, aspect_ratios=None, variance=[0.1, 0.1, 0.2, 0.2], stride=None, offset=0.5, name=None)[source]

Anchor generator operator

Generate anchors for Faster RCNN algorithm. Each position of the input produce N anchors, N = size(anchor_sizes) * size(aspect_ratios). The order of generated anchors is firstly aspect_ratios loop then anchor_sizes loop.

Parameters:
  • input (Variable) – The input feature map, the format is NCHW.
  • anchor_sizes (list|tuple|float) – The anchor sizes of generated anchors, given in absolute pixels e.g. [64., 128., 256., 512.]. For instance, the anchor size of 64 means the area of this anchor equals to 64**2.
  • aspect_ratios (list|tuple|float) – The height / width ratios of generated anchors, e.g. [0.5, 1.0, 2.0].
  • variance (list|tuple) – The variances to be used in box regression deltas. Default:[0.1, 0.1, 0.2, 0.2].
  • stride (list|tuple) – The anchors stride across width and height,e.g. [16.0, 16.0]
  • offset (float) – Prior boxes center offset. Default: 0.5
  • name (str) – Name of the prior box op. Default: None.
Returns:

two variables:

  • Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4]. H is the height of input, W is the width of input, num_anchors is the box count of each position. Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
  • Variances(Variable): The expanded variances of anchors with a layout of [H, W, num_priors, 4]. H is the height of input, W is the width of input num_anchors is the box count of each position. Each variance is in (xcenter, ycenter, w, h) format.
Return type:

Anchors(Variable),Variances(Variable)

Examples

import paddle.fluid as fluid
conv1 = fluid.layers.data(name='conv1', shape=[48, 16, 16], dtype='float32')
anchor, var = fluid.layers.anchor_generator(
    input=conv1,
    anchor_sizes=[64, 128, 256, 512],
    aspect_ratios=[0.5, 1.0, 2.0],
    variance=[0.1, 0.1, 0.2, 0.2],
    stride=[16.0, 16.0],
    offset=0.5)

bipartite_match

paddle.fluid.layers.bipartite_match(dist_matrix, match_type=None, dist_threshold=None, name=None)[source]

This operator implements a greedy bipartite matching algorithm, which is used to obtain the matching with the maximum distance based on the input distance matrix. For input 2D matrix, the bipartite matching algorithm can find the matched column for each row (matched means the largest distance), also can find the matched row for each column. And this operator only calculate matched indices from column to row. For each instance, the number of matched indices is the column number of the input distance matrix.

There are two outputs, matched indices and distance. A simple description, this algorithm matched the best (maximum distance) row entity to the column entity and the matched indices are not duplicated in each row of ColToRowMatchIndices. If the column entity is not matched any row entity, set -1 in ColToRowMatchIndices.

NOTE: the input DistMat can be LoDTensor (with LoD) or Tensor. If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size. If Tensor, the height of ColToRowMatchIndices is 1.

NOTE: This API is a very low level API. It is used by ssd_loss layer. Please consider to use ssd_loss instead.

Parameters:
  • dist_matrix (Variable) –

    This input is a 2-D LoDTensor with shape [K, M]. It is pair-wise distance matrix between the entities represented by each row and each column. For example, assumed one entity is A with shape [K], another entity is B with shape [M]. The dist_matrix[i][j] is the distance between A[i] and B[j]. The bigger the distance is, the better matching the pairs are.

    NOTE: This tensor can contain LoD information to represent a batch of inputs. One instance of this batch can contain different numbers of entities.

  • match_type (string|None) – The type of matching method, should be ‘bipartite’ or ‘per_prediction’. [default ‘bipartite’].
  • dist_threshold (float|None) – If match_type is ‘per_prediction’, this threshold is to determine the extra matching bboxes based on the maximum distance, 0.5 by default.
Returns:

a tuple with two elements is returned. The first is matched_indices, the second is matched_distance.

The matched_indices is a 2-D Tensor with shape [N, M] in int type. N is the batch size. If match_indices[i][j] is -1, it means B[j] does not match any entity in i-th instance. Otherwise, it means B[j] is matched to row match_indices[i][j] in i-th instance. The row number of i-th instance is saved in match_indices[i][j].

The matched_distance is a 2-D Tensor with shape [N, M] in float type . N is batch size. If match_indices[i][j] is -1, match_distance[i][j] is also -1.0. Otherwise, assumed match_distance[i][j] = d, and the row offsets of each instance are called LoD. Then match_distance[i][j] = dist_matrix[d+LoD[i]][j].

Return type:

tuple

Examples

>>> import paddle.fluid as fluid
>>> x = fluid.layers.data(name='x', shape=[4], dtype='float32')
>>> y = fluid.layers.data(name='y', shape=[4], dtype='float32')
>>> iou = fluid.layers.iou_similarity(x=x, y=y)
>>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)

box_clip

paddle.fluid.layers.box_clip(input, im_info, name=None)[source]

Clip the box into the size given by im_info For each input box, The formula is given as follows:

xmin = max(min(xmin, im_w - 1), 0)
ymin = max(min(ymin, im_h - 1), 0)
xmax = max(min(xmax, im_w - 1), 0)
ymax = max(min(ymax, im_h - 1), 0)

where im_w and im_h are computed from im_info:

im_h = round(height / scale)
im_w = round(weight / scale)
Parameters:
  • input (variable) – The input box, the last dimension is 4.
  • im_info (variable) – The information of image with shape [N, 3] with layout (height, width, scale). height and width is the input size and scale is the ratio of input size and original size.
  • name (str) – The name of this layer. It is optional.
Returns:

The cliped tensor variable.

Return type:

Variable

Examples

import paddle.fluid as fluid
boxes = fluid.layers.data(
    name='boxes', shape=[8, 4], dtype='float32', lod_level=1)
im_info = fluid.layers.data(name='im_info', shape=[3])
out = fluid.layers.box_clip(
    input=boxes, im_info=im_info)

box_coder

paddle.fluid.layers.box_coder(prior_box, prior_box_var, target_box, code_type='encode_center_size', box_normalized=True, name=None, axis=0)[source]

Box Coder Layer

Encode/Decode the target bounding box with the priorbox information.

The Encoding schema described below:

\[ \begin{align}\begin{aligned}ox = (tx - px) / pw / pxv\\oy = (ty - py) / ph / pyv\\ow = \log(bs(tw / pw)) / pwv\\oh = \log(bs(th / ph)) / phv\end{aligned}\end{align} \]

The Decoding schema described below:

\[ \begin{align}\begin{aligned}ox = (pw * pxv * tx * + px) - tw / 2\\oy = (ph * pyv * ty * + py) - th / 2\\ow = \exp(pwv * tw) * pw + tw / 2\\oh = \exp(phv * th) * ph + th / 2\end{aligned}\end{align} \]

where tx, ty, tw, th denote the target box’s center coordinates, width and height respectively. Similarly, px, py, pw, ph denote the priorbox’s (anchor) center coordinates, width and height. pxv, pyv, pwv, phv denote the variance of the priorbox and ox, oy, ow, oh denote the encoded/decoded coordinates, width and height.

During Box Decoding, two modes for broadcast are supported. Say target box has shape [N, M, 4], and the shape of prior box can be [N, 4] or [M, 4]. Then prior box will broadcast to target box along the assigned axis.

Parameters:
  • prior_box (Variable) – Box list prior_box is a 2-D Tensor with shape [M, 4] holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate of the anchor box, if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the anchor box.
  • prior_box_var (Variable|list|None) – prior_box_var supports two types of input. One is variable with shape [M, 4] holds M group. The other one is list consist of 4 elements shared by all boxes.
  • target_box (Variable) – This input can be a 2-D LoDTensor with shape [N, 4] when code_type is ‘encode_center_size’. This input also can be a 3-D Tensor with shape [N, M, 4] when code_type is ‘decode_center_size’. Each box is represented as [xmin, ymin, xmax, ymax]. This tensor can contain LoD information to represent a batch of inputs.
  • code_type (string) – The code type used with the target box. It can be encode_center_size or decode_center_size
  • box_normalized (int) – Whether treat the priorbox as a noramlized box. Set true by default.
  • name (string) – The name of box coder.
  • axis (int) – Which axis in PriorBox to broadcast for box decode, for example, if axis is 0 and TargetBox has shape [N, M, 4] and PriorBox has shape [M, 4], then PriorBox will broadcast to [N, M, 4] for decoding. It is only valid when code type is decode_center_size. Set 0 by default.
Returns:

When code_type is ‘encode_center_size’, the

output tensor of box_coder_op with shape [N, M, 4] representing the result of N target boxes encoded with M Prior boxes and variances. When code_type is ‘decode_center_size’, N represents the batch size and M represents the number of deocded boxes.

Return type:

output_box(Variable)

Examples

import paddle.fluid as fluid
prior_box = fluid.layers.data(name='prior_box',
                              shape=[512, 4],
                              dtype='float32',
                              append_batch_size=False)
target_box = fluid.layers.data(name='target_box',
                               shape=[512,81,4],
                               dtype='float32',
                               append_batch_size=False)
output = fluid.layers.box_coder(prior_box=prior_box,
                                prior_box_var=[0.1,0.1,0.2,0.2],
                                target_box=target_box,
                                code_type="decode_center_size",
                                box_normalized=False,
                                axis=1)

box_decoder_and_assign

paddle.fluid.layers.box_decoder_and_assign(prior_box, prior_box_var, target_box, box_score, box_clip, name=None)[source]

Bounding Box Coder.

Decode the target bounding box with the prior_box information.

The Decoding schema is described below:

$$ ox = (pw \times pxv \times tx + px) - \frac{tw}{2} $$ $$ oy = (ph \times pyv \times ty + py) - \frac{th}{2} $$ $$ ow = \exp (pwv \times tw) \times pw + \frac{tw}{2} $$ $$ oh = \exp (phv \times th) \times ph + \frac{th}{2} $$

where tx, ty, tw, th denote the target box’s center coordinates, width and height respectively. Similarly, px, py, pw, ph denote the prior_box’s (anchor) center coordinates, width and height. pxv, pyv, pwv, phv denote the variance of the prior_box and ox, oy, ow, oh denote the decoded coordinates, width and height in decode_box.

decode_box is obtained after box decode, then assigning schema is described below:

For each prior_box, use the best non-background class’s decoded values to update the prior_box locations and get output_assign_box. So, the shape of output_assign_box is the same as PriorBox.

Parameters:
  • prior_box (Variable) – (Tensor, default Tensor<float>) Box list PriorBox is a 2-D Tensor with shape [N, 4] which holds N boxes and each box is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate of the anchor box, if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the anchor box
  • prior_box_var (Variable) – (Tensor, default Tensor<float>, optional) PriorBoxVar is a 2-D Tensor with shape [N, 4] which holds N group of variance. PriorBoxVar will set all elements to 1 by default
  • target_box (Variable) – (LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape [N, classnum*4]. It holds N targets for N boxes
  • box_score (Variable) – (LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape [N, classnum], each box is represented as [classnum] which is the classification probabilities
  • box_clip (FLOAT) – (float, default 4.135, np.log(1000. / 16.)) clip box to prevent overflowing
  • name (str|None) – The name of this operator
Returns:

two variables:

  • decode_box(Variable): (LoDTensor or Tensor) the output tensor of op with shape [N, classnum * 4] representing the result of N target boxes decoded with M Prior boxes and variances for each class
  • output_assign_box(Variable): (LoDTensor or Tensor) the output tensor of op with shape [N, 4] representing the result of N target boxes decoded with M Prior boxes and variances with the best non-background class by BoxScore
Return type:

decode_box(Variable), output_assign_box(Variable)

Examples

import paddle.fluid as fluid
pb = fluid.layers.data(
    name='prior_box', shape=[4], dtype='float32')
pbv = fluid.layers.data(
    name='prior_box_var', shape=[4],
    dtype='float32', append_batch_size=False)
loc = fluid.layers.data(
    name='target_box', shape=[4*81], dtype='float32')
scores = fluid.layers.data(
    name='scores', shape=[81], dtype='float32')
decoded_box, output_assign_box = fluid.layers.box_decoder_and_assign(
    pb, pbv, loc, scores, 4.135)

collect_fpn_proposals

paddle.fluid.layers.collect_fpn_proposals(multi_rois, multi_scores, min_level, max_level, post_nms_top_n, name=None)[source]

Concat multi-level RoIs (Region of Interest) and select N RoIs with respect to multi_scores. This operation performs the following steps:

  1. Choose num_level RoIs and scores as input: num_level = max_level - min_level
  2. Concat multi-level RoIs and scores
  3. Sort scores and select post_nms_top_n scores
  4. Gather RoIs by selected indices from scores
  5. Re-sort RoIs by corresponding batch_id
Parameters:
  • multi_ros (list) – List of RoIs to collect
  • multi_scores (list) – List of scores
  • min_level (int) – The lowest level of FPN layer to collect
  • max_level (int) – The highest level of FPN layer to collect
  • post_nms_top_n (int) – The number of selected RoIs
  • name (str|None) – A name for this layer(optional)
Returns:

Output variable of selected RoIs.

Return type:

Variable

Examples

import paddle.fluid as fluid
multi_rois = []
multi_scores = []
for i in range(4):
    multi_rois.append(fluid.layers.data(
        name='roi_'+str(i), shape=[4], dtype='float32', lod_level=1))
for i in range(4):
    multi_scores.append(fluid.layers.data(
        name='score_'+str(i), shape=[1], dtype='float32', lod_level=1))

fpn_rois = fluid.layers.collect_fpn_proposals(
    multi_rois=multi_rois,
    multi_scores=multi_scores,
    min_level=2,
    max_level=5,
    post_nms_top_n=2000)

density_prior_box

paddle.fluid.layers.density_prior_box(input, image, densities=None, fixed_sizes=None, fixed_ratios=None, variance=[0.1, 0.1, 0.2, 0.2], clip=False, steps=[0.0, 0.0], offset=0.5, flatten_to_2d=False, name=None)[source]

Density Prior Box Operator

Generate density prior boxes for SSD(Single Shot MultiBox Detector) algorithm. Each position of the input produce N prior boxes, N is determined by the count of densities, fixed_sizes and fixed_ratios. Boxes center at grid points around each input position is generated by this operator, and the grid points is determined by densities and the count of density prior box is determined by fixed_sizes and fixed_ratios. Obviously, the number of fixed_sizes is equal to the number of densities. For densities_i in densities: N_density_prior_box =sum(N_fixed_ratios * densities_i^2),

Parameters:
  • input (Variable) – The Input Variables, the format is NCHW.
  • image (Variable) – The input image data of PriorBoxOp, the layout is NCHW.
  • densities (list|tuple|None) – the densities of generated density prior boxes, this attribute should be a list or tuple of integers. Default: None.
  • fixed_sizes (list|tuple|None) – the fixed sizes of generated density prior boxes, this attribute should a list or tuple of same length with densities. Default: None.
  • fixed_ratios (list|tuple|None) – the fixed ratios of generated density prior boxes, if this attribute is not set and densities and fix_sizes is set, aspect_ratios will be used to generate density prior boxes.
  • variance (list|tuple) – the variances to be encoded in density prior boxes. Default:[0.1, 0.1, 0.2, 0.2].
  • clip (bool) – Whether to clip out-of-boundary boxes. Default: False.
  • step (list|tuple) – Prior boxes step across width and height, If step[0] == 0.0/step[1] == 0.0, the density prior boxes step across height/weight of the input will be automatically calculated. Default: [0., 0.]
  • offset (float) – Prior boxes center offset. Default: 0.5
  • flatten_to_2d (bool) – Whether to flatten output prior boxes and variance to 2D shape, the second dim is 4. Default: False.
  • name (str) – Name of the density prior box op. Default: None.
Returns:

A tuple with two Variable (boxes, variances)

boxes: the output density prior boxes of PriorBox.

The layout is [H, W, num_priors, 4] when flatten_to_2d is False. The layout is [H * W * num_priors, 4] when flatten_to_2d is True. H is the height of input, W is the width of input, num_priors is the total box count of each position of input.

variances: the expanded variances of PriorBox.

The layout is [H, W, num_priors, 4] when flatten_to_2d is False. The layout is [H * W * num_priors, 4] when flatten_to_2d is True. H is the height of input, W is the width of input num_priors is the total box count of each position of input.

Return type:

tuple

Examples

import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9])
images = fluid.layers.data(name="images", shape=[3,9,12])
box, var = fluid.layers.density_prior_box(
    input=input,
    image=images,
    densities=[4, 2, 1],
    fixed_sizes=[32.0, 64.0, 128.0],
    fixed_ratios=[1.],
    clip=True,
    flatten_to_2d=True)

detection_map

paddle.fluid.layers.detection_map(detect_res, label, class_num, background_label=0, overlap_threshold=0.3, evaluate_difficult=True, has_state=None, input_states=None, out_states=None, ap_version='integral')[source]

Detection mAP evaluate operator. The general steps are as follows. First, calculate the true positive and false positive according to the input of detection and labels, then calculate the mAP evaluate value. Supporting ‘11 point’ and ‘integral’ mAP algorithm. Please get more information from the following articles: https://sanchom.wordpress.com/tag/average-precision/ https://arxiv.org/abs/1512.02325

Parameters:
  • detect_res – (LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the detections. Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax], M is the total number of detect results in this mini-batch. For each instance, the offsets in first dimension are called LoD, the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is no detected data
  • label – (LoDTensor) A 2-D LoDTensor represents theLabeled ground-truth data. Each row has 6 values: [label, xmin, ymin, xmax, ymax, is_difficult] or 5 values: [label, xmin, ymin, xmax, ymax], where N is the total number of ground-truth data in this mini-batch. For each instance, the offsets in first dimension are called LoD, the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is no ground-truth data
  • class_num – (int) The class number
  • background_label – (int, default: 0) The index of background label, the background label will be ignored. If set to -1, then all categories will be considered
  • overlap_threshold – (float) The lower bound jaccard overlap threshold of detection output and ground-truth data
  • evaluate_difficult – (bool, default true) Switch to control whether the difficult data is evaluated
  • has_state – (Tensor<int>) A tensor with shape [1], 0 means ignoring input states, which including PosCount, TruePos, FalsePos
  • input_states – If not None, It contains 3 elements: 1. pos_count (Tensor) A tensor with shape [Ncls, 1], store the input positive example count of each class, Ncls is the count of input classification. This input is used to pass the AccumPosCount generated by the previous mini-batch when the multi mini-batches cumulative calculation carried out. When the input(PosCount) is empty, the cumulative calculation is not carried out, and only the results of the current mini-batch are calculated. 2. true_pos (LoDTensor) A 2-D LoDTensor with shape [Ntp, 2], store the input true positive example of each class.This input is used to pass the AccumTruePos generated by the previous mini-batch when the multi mini-batches cumulative calculation carried out. . 3. false_pos (LoDTensor) A 2-D LoDTensor with shape [Nfp, 2], store the input false positive example of each class.This input is used to pass the AccumFalsePos generated by the previous mini-batch when the multi mini-batches cumulative calculation carried out. .
  • out_states – If not None, it contains 3 elements. 1. accum_pos_count (Tensor) A tensor with shape [Ncls, 1], store the positive example count of each class. It combines the input input(PosCount) and the positive example count computed from input(Detection) and input(Label). 2. accum_true_pos (LoDTensor) A LoDTensor with shape [Ntp’, 2], store the true positive example of each class. It combines the input(TruePos) and the true positive examples computed from input(Detection) and input(Label). 3. accum_false_pos (LoDTensor) A LoDTensor with shape [Nfp’, 2], store the false positive example of each class. It combines the input(FalsePos) and the false positive examples computed from input(Detection) and input(Label).
  • ap_version – (string, default ‘integral’) The AP algorithm type, ‘integral’ or ‘11point’
Returns:

(Tensor) A tensor with shape [1], store the mAP evaluate result of the detection

Examples

import paddle.fluid as fluid
from fluid.layers import detection
detect_res = fluid.layers.data(
    name='detect_res',
    shape=[10, 6],
    append_batch_size=False,
    dtype='float32')
label = fluid.layers.data(
    name='label',
    shape=[10, 6],
    append_batch_size=False,
    dtype='float32')

map_out = fluid.layers.detection_map(detect_res, label, 21)

detection_output

paddle.fluid.layers.detection_output(loc, scores, prior_box, prior_box_var, background_label=0, nms_threshold=0.3, nms_top_k=400, keep_top_k=200, score_threshold=0.01, nms_eta=1.0)[source]

Detection Output Layer for Single Shot Multibox Detector (SSD).

This operation is to get the detection results by performing following two steps:

  1. Decode input bounding box predictions according to the prior boxes.
  2. Get the final detection results by applying multi-class non maximum suppression (NMS).

Please note, this operation doesn’t clip the final output bounding boxes to the image window.

Parameters:
  • loc (Variable) – A 3-D Tensor with shape [N, M, 4] represents the predicted locations of M bounding bboxes. N is the batch size, and each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax].
  • scores (Variable) – A 3-D Tensor with shape [N, M, C] represents the predicted confidence predictions. N is the batch size, C is the class number, M is number of bounding boxes. For each category there are total M scores which corresponding M bounding boxes.
  • prior_box (Variable) – A 2-D Tensor with shape [M, 4] holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate of the anchor box, if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the anchor box.
  • prior_box_var (Variable) – A 2-D Tensor with shape [M, 4] holds M group of variance.
  • background_label (float) – The index of background label, the background label will be ignored. If set to -1, then all categories will be considered.
  • nms_threshold (float) – The threshold to be used in NMS.
  • nms_top_k (int) – Maximum number of detections to be kept according to the confidences aftern the filtering detections based on score_threshold.
  • keep_top_k (int) – Number of total bboxes to be kept per image after NMS step. -1 means keeping all bboxes after NMS step.
  • score_threshold (float) – Threshold to filter out bounding boxes with low confidence score. If not provided, consider all boxes.
  • nms_eta (float) – The parameter for adaptive NMS.
Returns:

The detection outputs is a LoDTensor with shape [No, 6]. Each row has six values: [label, confidence, xmin, ymin, xmax, ymax]. No is the total number of detections in this mini-batch. For each instance, the offsets in first dimension are called LoD, the offset number is N + 1, N is the batch size. The i-th image has LoD[i + 1] - LoD[i] detected results, if it is 0, the i-th image has no detected results. If all images have not detected results, LoD will be set to {1}, and output tensor only contains one value, which is -1. (After version 1.3, when no boxes detected, the lod is changed

from {0} to {1}.)

Return type:

Variable

Examples

import paddle.fluid as fluid

pb = fluid.layers.data(name='prior_box', shape=[10, 4],
             append_batch_size=False, dtype='float32')
pbv = fluid.layers.data(name='prior_box_var', shape=[10, 4],
              append_batch_size=False, dtype='float32')
loc = fluid.layers.data(name='target_box', shape=[2, 21, 4],
              append_batch_size=False, dtype='float32')
scores = fluid.layers.data(name='scores', shape=[2, 21, 10],
              append_batch_size=False, dtype='float32')
nmsed_outs = fluid.layers.detection_output(scores=scores,
                           loc=loc,
                           prior_box=pb,
                           prior_box_var=pbv)

distribute_fpn_proposals

paddle.fluid.layers.distribute_fpn_proposals(fpn_rois, min_level, max_level, refer_level, refer_scale, name=None)[source]

In Feature Pyramid Networks (FPN) models, it is needed to distribute all proposals into different FPN level, with respect to scale of the proposals, the referring scale and the referring level. Besides, to restore the order of proposals, we return an array which indicates the original index of rois in current proposals. To compute FPN level for each roi, the formula is given as follows:

\[ \begin{align}\begin{aligned}roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}\\level = floor(&\log(\frac{roi\_scale}{refer\_scale}) + refer\_level)\end{aligned}\end{align} \]

where BBoxArea is a function to compute the area of each roi.

Parameters:
  • fpn_rois (variable) – The input fpn_rois, the second dimension is 4.
  • min_level (int) – The lowest level of FPN layer where the proposals come from.
  • max_level (int) – The highest level of FPN layer where the proposals come from.
  • refer_level (int) – The referring level of FPN layer with specified scale.
  • refer_scale (int) – The referring scale of FPN layer with specified level.
  • name (str|None) – The name of this operator.
Returns:

A tuple(multi_rois, restore_ind) is returned. The multi_rois is a list of segmented tensor variables. The restore_ind is a 2D Tensor with shape [N, 1], N is the number of total rois. It is used to restore the order of fpn_rois.

Return type:

tuple

Examples

import paddle.fluid as fluid
fpn_rois = fluid.layers.data(
    name='data', shape=[4], dtype='float32', lod_level=1)
multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
    fpn_rois=fpn_rois,
    min_level=2,
    max_level=5,
    refer_level=4,
    refer_scale=224)

generate_mask_labels

paddle.fluid.layers.generate_mask_labels(im_info, gt_classes, is_crowd, gt_segms, rois, labels_int32, num_classes, resolution)[source]

** Generate Mask Labels for Mask-RCNN **

This operator can be, for given the RoIs and corresponding labels, to sample foreground RoIs. This mask branch also has a :math: K times M^{2} dimensional output targets for each foreground RoI, which encodes K binary masks of resolution M x M, one for each of the K classes. This mask targets are used to compute loss of mask branch.

Please note, the data format of groud-truth segmentation, assumed the segmentations are as follows. The first instance has two gt objects. The second instance has one gt object, this object has two gt segmentations.

#[
#  [[[229.14, 370.9, 229.14, 370.9, ...]],
#   [[343.7, 139.85, 349.01, 138.46, ...]]], # 0-th instance
#  [[[500.0, 390.62, ...],[115.48, 187.86, ...]]] # 1-th instance
#]

batch_masks = []
for semgs in batch_semgs:
    gt_masks = []
    for semg in semgs:
        gt_segm = []
        for polys in semg:
            gt_segm.append(np.array(polys).reshape(-1, 2))
        gt_masks.append(gt_segm)
    batch_masks.append(gt_masks)


place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=feeds)
feeder.feed(batch_masks)
Parameters:
  • im_info (Variable) – A 2-D Tensor with shape [N, 3]. N is the batch size, each element is [height, width, scale] of image. Image scale is target_size) / original_size.
  • gt_classes (Variable) – A 2-D LoDTensor with shape [M, 1]. M is the total number of ground-truth, each element is a class label.
  • is_crowd (Variable) – A 2-D LoDTensor with shape as gt_classes, each element is a flag indicating whether a groundtruth is crowd.
  • gt_segms (Variable) –

    This input is a 2D LoDTensor with shape [S, 2], it’s LoD level is 3. Usually users do not needs to understand LoD, The users should return correct data format in reader.

    The LoD[0] represents the gt objects number of each instance. LoD[1] represents the segmentation counts of each objects. LoD[2] represents the polygons number of each segmentation. S the total number of polygons coordinate points. Each element is (x, y) coordinate points.

  • rois (Variable) – A 2-D LoDTensor with shape [R, 4]. R is the total number of RoIs, each element is a bounding box with (xmin, ymin, xmax, ymax) format in the range of original image.
  • labels_int32 (Variable) – A 2-D LoDTensor in shape of [R, 1] with type of int32. R is the same as it in rois. Each element repersents a class label of a RoI.
  • num_classes (int) – Class number.
  • resolution (int) – Resolution of mask predictions.
Returns:

A 2D LoDTensor with shape [P, 4]. P is the total

number of sampled RoIs. Each element is a bounding box with [xmin, ymin, xmax, ymax] format in range of orignal image size.

mask_rois_has_mask_int32 (Variable): A 2D LoDTensor with shape [P, 1],

each element repersents the output mask RoI index with regard to to input RoIs.

mask_int32 (Variable): A 2D LoDTensor with shape [P, K * M * M],

K is the classes number and M is the resolution of mask predictions. Each element repersents the binary mask targets.

Return type:

mask_rois (Variable)

Examples

import paddle.fluid as fluid

im_info = fluid.layers.data(name="im_info", shape=[3],
    dtype="float32")
gt_classes = fluid.layers.data(name="gt_classes", shape=[1],
    dtype="float32", lod_level=1)
is_crowd = fluid.layers.data(name="is_crowd", shape=[1],
    dtype="float32", lod_level=1)
gt_masks = fluid.layers.data(name="gt_masks", shape=[2],
    dtype="float32", lod_level=3)
# rois, roi_labels can be the output of
# fluid.layers.generate_proposal_labels.
rois = fluid.layers.data(name="rois", shape=[4],
    dtype="float32", lod_level=1)
roi_labels = fluid.layers.data(name="roi_labels", shape=[1],
    dtype="int32", lod_level=1)
mask_rois, mask_index, mask_int32 = fluid.layers.generate_mask_labels(
    im_info=im_info,
    gt_classes=gt_classes,
    is_crowd=is_crowd,
    gt_segms=gt_masks,
    rois=rois,
    labels_int32=roi_labels,
    num_classes=81,
    resolution=14)

generate_proposal_labels

paddle.fluid.layers.generate_proposal_labels(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, batch_size_per_im=256, fg_fraction=0.25, fg_thresh=0.25, bg_thresh_hi=0.5, bg_thresh_lo=0.0, bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], class_nums=None, use_random=True, is_cls_agnostic=False, is_cascade_rcnn=False)[source]

** Generate Proposal Labels of Faster-RCNN **

This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth, to sample foreground boxes and background boxes, and compute loss target.

RpnRois is the output boxes of RPN and was processed by generate_proposal_op, these boxes were combined with groundtruth boxes and sampled according to batch_size_per_im and fg_fraction, If an instance with a groundtruth overlap greater than fg_thresh, then it was considered as a foreground sample. If an instance with a groundtruth overlap greater than bg_thresh_lo and lower than bg_thresh_hi, then it was considered as a background sample. After all foreground and background boxes are chosen (so called Rois), then we apply random sampling to make sure the number of foreground boxes is no more than batch_size_per_im * fg_fraction.

For each box in Rois, we assign the classification (class label) and regression targets (box label) to it. Finally BboxInsideWeights and BboxOutsideWeights are used to specify whether it would contribute to training loss.

Parameters:
  • rpn_rois (Variable) – A 2-D LoDTensor with shape [N, 4]. N is the number of the GenerateProposalOp’s output, each element is a bounding box with [xmin, ymin, xmax, ymax] format.
  • gt_classes (Variable) – A 2-D LoDTensor with shape [M, 1]. M is the number of groundtruth, each element is a class label of groundtruth.
  • is_crowd (Variable) – A 2-D LoDTensor with shape [M, 1]. M is the number of groundtruth, each element is a flag indicates whether a groundtruth is crowd.
  • gt_boxes (Variable) – A 2-D LoDTensor with shape [M, 4]. M is the number of groundtruth, each element is a bounding box with [xmin, ymin, xmax, ymax] format.
  • im_info (Variable) – A 2-D LoDTensor with shape [B, 3]. B is the number of input images, each element consists of im_height, im_width, im_scale.
  • batch_size_per_im (int) – Batch size of rois per images.
  • fg_fraction (float) – Foreground fraction in total batch_size_per_im.
  • fg_thresh (float) – Overlap threshold which is used to chose foreground sample.
  • bg_thresh_hi (float) – Overlap threshold upper bound which is used to chose background sample.
  • bg_thresh_lo (float) – Overlap threshold lower bound which is used to chose background sample.
  • bbox_reg_weights (list|tuple) – Box regression weights.
  • class_nums (int) – Class number.
  • use_random (bool) – Use random sampling to choose foreground and background boxes.
  • is_cls_agnostic (bool) – class agnostic bbox regression will only represent fg and bg boxes.
  • is_cascade_rcnn (bool) – cascade rcnn model will change sampling policy when settting True.

Examples

import paddle.fluid as fluid
rpn_rois = fluid.layers.data(name='rpn_rois', shape=[2, 4],
               append_batch_size=False, dtype='float32')
gt_classes = fluid.layers.data(name='gt_classes', shape=[8, 1],
               append_batch_size=False, dtype='float32')
is_crowd = fluid.layers.data(name='is_crowd', shape=[8, 1],
               append_batch_size=False, dtype='float32')
gt_boxes = fluid.layers.data(name='gt_boxes', shape=[8, 4],
               append_batch_size=False, dtype='float32')
im_info = fluid.layers.data(name='im_info', shape=[10, 3],
               append_batch_size=False, dtype='float32')
rois, labels_int32, bbox_targets, bbox_inside_weights,
bbox_outside_weights = fluid.layers.generate_proposal_labels(
               rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
               class_nums=10)

generate_proposals

paddle.fluid.layers.generate_proposals(scores, bbox_deltas, im_info, anchors, variances, pre_nms_top_n=6000, post_nms_top_n=1000, nms_thresh=0.5, min_size=0.1, eta=1.0, name=None)[source]

Generate proposal Faster-RCNN

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 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)
  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.
Parameters:
  • scores (Variable) – A 4-D Tensor with shape [N, A, H, W] represents the probability for each box to be an object. N is batch size, A is number of anchors, H and W are height and width of the feature map.
  • bbox_deltas (Variable) – A 4-D Tensor with shape [N, 4*A, H, W] represents the differece between predicted box locatoin and anchor location.
  • im_info (Variable) – A 2-D Tensor with shape [N, 3] represents origin image information for N batch. Info contains height, width and scale between origin image size and the size of feature map.
  • anchors (Variable) – A 4-D Tensor represents the anchors with a layout of [H, W, A, 4]. H and W are height and width of the feature map, num_anchors is the box count of each position. Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
  • variances (Variable) – The expanded variances of anchors with a layout of [H, W, num_priors, 4]. Each variance is in (xcenter, ycenter, w, h) format.
  • pre_nms_top_n (float) – Number of total bboxes to be kept per image before NMS. 6000 by default.
  • post_nms_top_n (float) – Number of total bboxes to be kept per image after NMS. 1000 by default.
  • nms_thresh (float) – Threshold in NMS, 0.5 by default.
  • min_size (float) – Remove predicted boxes with either height or width < min_size. 0.1 by default.
  • eta (float) – Apply in adaptive NMS, if adaptive threshold > 0.5, adaptive_threshold = adaptive_threshold * eta in each iteration.

Examples

import paddle.fluid as fluid
scores = fluid.layers.data(name='scores', shape=[2, 4, 5, 5],
             append_batch_size=False, dtype='float32')
bbox_deltas = fluid.layers.data(name='bbox_deltas', shape=[2, 16, 5, 5],
             append_batch_size=False, dtype='float32')
im_info = fluid.layers.data(name='im_info', shape=[2, 3],
             append_batch_size=False, dtype='float32')
anchors = fluid.layers.data(name='anchors', shape=[5, 5, 4, 4],
             append_batch_size=False, dtype='float32')
variances = fluid.layers.data(name='variances', shape=[5, 5, 10, 4],
             append_batch_size=False, dtype='float32')
rois, roi_probs = fluid.layers.generate_proposals(scores, bbox_deltas,
             im_info, anchors, variances)

iou_similarity

paddle.fluid.layers.iou_similarity(x, y, name=None)[source]

IOU Similarity Operator

Computes intersection-over-union (IOU) between two box lists. Box list ‘X’ should be a LoDTensor and ‘Y’ is a common Tensor, boxes in ‘Y’ are shared by all instance of the batched inputs of X. Given two boxes A and B, the calculation of IOU is as follows:

$$ IOU(A, B) = \frac{area(A\cap B)}{area(A)+area(B)-area(A\cap B)} $$

Parameters:
  • x (Variable) – (LoDTensor, default LoDTensor<float>) Box list X is a 2-D LoDTensor with shape [N, 4] holds N boxes, each box is represented as [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. [xmin, ymin] is the left top coordinate of the box if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the box. This tensor can contain LoD information to represent a batch of inputs. One instance of this batch can contain different numbers of entities
  • y (Variable) – (Tensor, default Tensor<float>) Box list Y holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. [xmin, ymin] is the left top coordinate of the box if the input is image feature map, and [xmax, ymax] is the right bottom coordinate of the box
Returns:

(LoDTensor, the lod is same as input X) The output of iou_similarity op, a tensor with shape [N, M] representing pairwise iou scores

Return type:

out(Variable)

Examples

import paddle.fluid as fluid

x = fluid.layers.data(name='x', shape=[4], dtype='float32')
y = fluid.layers.data(name='y', shape=[4], dtype='float32')
iou = fluid.layers.iou_similarity(x=x, y=y)

multi_box_head

paddle.fluid.layers.multi_box_head(inputs, image, base_size, num_classes, aspect_ratios, min_ratio=None, max_ratio=None, min_sizes=None, max_sizes=None, steps=None, step_w=None, step_h=None, offset=0.5, variance=[0.1, 0.1, 0.2, 0.2], flip=True, clip=False, kernel_size=1, pad=0, stride=1, name=None, min_max_aspect_ratios_order=False)[source]

Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm. The details of this algorithm, please refer the section 2.2 of SSD paper SSD: Single Shot MultiBox Detector .

Parameters:
  • inputs (list|tuple) – The list of input Variables, the format of all Variables is NCHW.
  • image (Variable) – The input image data of PriorBoxOp, the layout is NCHW.
  • base_size (int) – the base_size is used to get min_size and max_size according to min_ratio and max_ratio.
  • num_classes (int) – The number of classes.
  • aspect_ratios (list|tuple) – the aspect ratios of generated prior boxes. The length of input and aspect_ratios must be equal.
  • min_ratio (int) – the min ratio of generated prior boxes.
  • max_ratio (int) – the max ratio of generated prior boxes.
  • min_sizes (list|tuple|None) – If len(inputs) <=2, min_sizes must be set up, and the length of min_sizes should equal to the length of inputs. Default: None.
  • max_sizes (list|tuple|None) – If len(inputs) <=2, max_sizes must be set up, and the length of min_sizes should equal to the length of inputs. Default: None.
  • steps (list|tuple) – If step_w and step_h are the same, step_w and step_h can be replaced by steps.
  • step_w (list|tuple) – Prior boxes step across width. If step_w[i] == 0.0, the prior boxes step across width of the inputs[i] will be automatically calculated. Default: None.
  • step_h (list|tuple) – Prior boxes step across height, If step_h[i] == 0.0, the prior boxes step across height of the inputs[i] will be automatically calculated. Default: None.
  • offset (float) – Prior boxes center offset. Default: 0.5
  • variance (list|tuple) – the variances to be encoded in prior boxes. Default:[0.1, 0.1, 0.2, 0.2].
  • flip (bool) – Whether to flip aspect ratios. Default:False.
  • clip (bool) – Whether to clip out-of-boundary boxes. Default: False.
  • kernel_size (int) – The kernel size of conv2d. Default: 1.
  • pad (int|list|tuple) – The padding of conv2d. Default:0.
  • stride (int|list|tuple) – The stride of conv2d. Default:1,
  • name (str) – Name of the prior box layer. Default: None.
  • min_max_aspect_ratios_order (bool) – If set True, the output prior box is in order of [min, max, aspect_ratios], which is consistent with Caffe. Please note, this order affects the weights order of convolution layer followed by and does not affect the fininal detection results. Default: False.
Returns:

A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances)

mbox_loc: The predicted boxes’ location of the inputs. The layout is [N, H*W*Priors, 4]. where Priors is the number of predicted boxes each position of each input.

mbox_conf: The predicted boxes’ confidence of the inputs. The layout is [N, H*W*Priors, C]. where Priors is the number of predicted boxes each position of each input and C is the number of Classes.

boxes: the output prior boxes of PriorBox. The layout is [num_priors, 4]. num_priors is the total box count of each position of inputs.

variances: the expanded variances of PriorBox. The layout is [num_priors, 4]. num_priors is the total box count of each position of inputs

Return type:

tuple

Examples

import paddle.fluid as fluid

images = fluid.layers.data(name='data', shape=[3, 300, 300], dtype='float32')
conv1 = fluid.layers.data(name='conv1', shape=[512, 19, 19], dtype='float32')
conv2 = fluid.layers.data(name='conv2', shape=[1024, 10, 10], dtype='float32')
conv3 = fluid.layers.data(name='conv3', shape=[512, 5, 5], dtype='float32')
conv4 = fluid.layers.data(name='conv4', shape=[256, 3, 3], dtype='float32')
conv5 = fluid.layers.data(name='conv5', shape=[256, 2, 2], dtype='float32')
conv6 = fluid.layers.data(name='conv6', shape=[128, 1, 1], dtype='float32')

mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
  inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
  image=images,
  num_classes=21,
  min_ratio=20,
  max_ratio=90,
  aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
  base_size=300,
  offset=0.5,
  flip=True,
  clip=True)

multiclass_nms

paddle.fluid.layers.multiclass_nms(bboxes, scores, score_threshold, nms_top_k, keep_top_k, nms_threshold=0.3, normalized=True, nms_eta=1.0, background_label=0, name=None)[source]

Multiclass NMS

This operator is to do multi-class non maximum suppression (NMS) on boxes and scores.

In the NMS step, this operator greedily selects a subset of detection bounding boxes that have high scores larger than score_threshold, if providing this threshold, then selects the largest nms_top_k confidences scores if nms_top_k is larger than -1. Then this operator pruns away boxes that have high IOU (intersection over union) overlap with already selected boxes by adaptive threshold NMS based on parameters of nms_threshold and nms_eta.

Aftern NMS step, at most keep_top_k number of total bboxes are to be kept per image if keep_top_k is larger than -1.

Parameters:
  • bboxes (Variable) – Two types of bboxes are supported: 1. (Tensor) A 3-D Tensor with shape [N, M, 4 or 8 16 24 32] represents the predicted locations of M bounding bboxes, N is the batch size. Each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax], when box size equals to 4. 2. (LoDTensor) A 3-D Tensor with shape [M, C, 4] M is the number of bounding boxes, C is the class number
  • scores (Variable) – Two types of scores are supported: 1. (Tensor) A 3-D Tensor with shape [N, C, M] represents the predicted confidence predictions. N is the batch size, C is the class number, M is number of bounding boxes. For each category there are total M scores which corresponding M bounding boxes. Please note, M is equal to the 2nd dimension of BBoxes. 2. (LoDTensor) A 2-D LoDTensor with shape [M, C]. M is the number of bbox, C is the class number. In this case, input BBoxes should be the second case with shape [M, C, 4].
  • background_label (int) – The index of background label, the background label will be ignored. If set to -1, then all categories will be considered. Default: 0
  • score_threshold (float) – Threshold to filter out bounding boxes with low confidence score. If not provided, consider all boxes.
  • nms_top_k (int) – Maximum number of detections to be kept according to the confidences aftern the filtering detections based on score_threshold.
  • nms_threshold (float) – The threshold to be used in NMS. Default: 0.3
  • nms_eta (float) – The threshold to be used in NMS. Default: 1.0
  • keep_top_k (int) – Number of total bboxes to be kept per image after NMS step. -1 means keeping all bboxes after NMS step.
  • normalized (bool) – Whether detections are normalized. Default: True
  • name (str) – Name of the multiclass nms op. Default: None.
Returns:

A 2-D LoDTensor with shape [No, 6] represents the detections.

Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax] or A 2-D LoDTensor with shape [No, 10] represents the detections. Each row has 10 values: [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the total number of detections. If there is no detected boxes for all images, lod will be set to {1} and Out only contains one value which is -1. (After version 1.3, when no boxes detected, the lod is changed from {0} to {1})

Return type:

Out

Examples

import paddle.fluid as fluid
boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
                          dtype='float32', lod_level=1)
scores = fluid.layers.data(name='scores', shape=[81],
                          dtype='float32', lod_level=1)
out = fluid.layers.multiclass_nms(bboxes=boxes,
                                  scores=scores,
                                  background_label=0,
                                  score_threshold=0.5,
                                  nms_top_k=400,
                                  nms_threshold=0.3,
                                  keep_top_k=200,
                                  normalized=False)

polygon_box_transform

paddle.fluid.layers.polygon_box_transform(input, name=None)[source]

PolygonBoxTransform Operator.

PolygonBoxTransform Operator is used to transform the coordinate shift to the real coordinate.

The input is the final geometry output in detection network. We use 2*n numbers to denote the coordinate shift from n corner vertices of the polygon_box to the pixel location. As each distance offset contains two numbers (xi, yi), the geometry output contains 2*n channels.

Parameters:input (Variable) – The input with shape [batch_size, geometry_channels, height, width]
Returns:The output with the same shape as input
Return type:output(Variable)

Examples

import paddle.fluid as fluid
input = fluid.layers.data(name='input', shape=[4, 10, 5, 5],
                          append_batch_size=False, dtype='float32')
out = fluid.layers.polygon_box_transform(input)

prior_box

paddle.fluid.layers.prior_box(input, image, min_sizes, max_sizes=None, aspect_ratios=[1.0], variance=[0.1, 0.1, 0.2, 0.2], flip=False, clip=False, steps=[0.0, 0.0], offset=0.5, name=None, min_max_aspect_ratios_order=False)[source]

Prior Box Operator

Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm. Each position of the input produce N prior boxes, N is determined by the count of min_sizes, max_sizes and aspect_ratios, The size of the box is in range(min_size, max_size) interval, which is generated in sequence according to the aspect_ratios.

Parameters:
  • input (Variable) – The Input Variables, the format is NCHW.
  • image (Variable) – The input image data of PriorBoxOp, the layout is NCHW.
  • min_sizes (list|tuple|float value) – min sizes of generated prior boxes.
  • max_sizes (list|tuple|None) – max sizes of generated prior boxes. Default: None.
  • aspect_ratios (list|tuple|float value) – the aspect ratios of generated prior boxes. Default: [1.].
  • variance (list|tuple) – the variances to be encoded in prior boxes. Default:[0.1, 0.1, 0.2, 0.2].
  • flip (bool) – Whether to flip aspect ratios. Default:False.
  • clip (bool) – Whether to clip out-of-boundary boxes. Default: False.
  • step (list|tuple) – Prior boxes step across width and height, If step[0] == 0.0/step[1] == 0.0, the prior boxes step across height/weight of the input will be automatically calculated. Default: [0., 0.]
  • offset (float) – Prior boxes center offset. Default: 0.5
  • name (str) – Name of the prior box op. Default: None.
  • min_max_aspect_ratios_order (bool) – If set True, the output prior box is in order of [min, max, aspect_ratios], which is consistent with Caffe. Please note, this order affects the weights order of convolution layer followed by and does not affect the final detection results. Default: False.
Returns:

A tuple with two Variable (boxes, variances)

boxes: the output prior boxes of PriorBox. The layout is [H, W, num_priors, 4]. H is the height of input, W is the width of input, num_priors is the total box count of each position of input.

variances: the expanded variances of PriorBox. The layout is [H, W, num_priors, 4]. H is the height of input, W is the width of input num_priors is the total box count of each position of input

Return type:

tuple

Examples

import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9])
images = fluid.layers.data(name="images", shape=[3,9,12])
box, var = fluid.layers.prior_box(
    input=input,
    image=images,
    min_sizes=[100.],
    flip=True,
    clip=True)

retinanet_detection_output

paddle.fluid.layers.retinanet_detection_output(bboxes, scores, anchors, im_info, score_threshold=0.05, nms_top_k=1000, keep_top_k=100, nms_threshold=0.3, nms_eta=1.0)[source]

Detection Output Layer for Retinanet.

This operation is to get the detection results by performing following steps:

  1. Decode top-scoring bounding box predictions per FPN level according to the anchor boxes.
  2. Merge top predictions from all levels and apply multi-class non maximum suppression (NMS) on them to get the final detections.
Parameters:
  • bboxes (List) – A list of tensors from multiple FPN levels. Each element is a 3-D Tensor with shape [N, Mi, 4] representing the predicted locations of Mi bounding boxes. N is the batch size, Mi is the number of bounding boxes from i-th FPN level and each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax].
  • scores (List) – A list of tensors from multiple FPN levels. Each element is a 3-D Tensor with shape [N, Mi, C] representing the predicted confidence predictions. N is the batch size, C is the class number (excluding background), Mi is the number of bounding boxes from i-th FPN level. For each bounding box, there are total C scores.
  • anchors (List) – A 2-D Tensor with shape [Mi, 4] represents the locations of Mi anchor boxes from all FPN level. Each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax].
  • im_info (Variable) – A 2-D LoDTensor with shape [N, 3] represents the image information. N is the batch size, each image information includes height, width and scale.
  • score_threshold (float) – Threshold to filter out bounding boxes with a confidence score.
  • nms_top_k (int) – Maximum number of detections per FPN layer to be kept according to the confidences before NMS.
  • keep_top_k (int) – Number of total bounding boxes to be kept per image after NMS step. -1 means keeping all bounding boxes after NMS step.
  • nms_threshold (float) – The threshold to be used in NMS.
  • nms_eta (float) – The parameter for adaptive NMS.
Returns:

The detection output is a LoDTensor with shape [No, 6]. Each row has six values: [label, confidence, xmin, ymin, xmax, ymax]. No is the total number of detections in this mini-batch. For each instance, the offsets in first dimension are called LoD, the offset number is N + 1, N is the batch size. The i-th image has LoD[i + 1] - LoD[i] detected results, if it is 0, the i-th image has no detected results. If all images have no detected results, LoD will be set to 0, and the output tensor is empty (None).

Return type:

Variable

Examples

import paddle.fluid as fluid

bboxes = layers.data(name='bboxes', shape=[1, 21, 4],
    append_batch_size=False, dtype='float32')
scores = layers.data(name='scores', shape=[1, 21, 10],
    append_batch_size=False, dtype='float32')
anchors = layers.data(name='anchors', shape=[21, 4],
    append_batch_size=False, dtype='float32')
im_info = layers.data(name="im_info", shape=[1, 3],
    append_batch_size=False, dtype='float32')
nmsed_outs = fluid.layers.retinanet_detection_output(
                                        bboxes=[bboxes, bboxes],
                                        scores=[scores, scores],
                                        anchors=[anchors, anchors],
                                        im_info=im_info,
                                        score_threshold=0.05,
                                        nms_top_k=1000,
                                        keep_top_k=100,
                                        nms_threshold=0.3,
                                        nms_eta=1.)

retinanet_target_assign

paddle.fluid.layers.retinanet_target_assign(bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, gt_labels, is_crowd, im_info, num_classes=1, positive_overlap=0.5, negative_overlap=0.4)[source]

Target Assign Layer for Retinanet .

This layer can be, for given the Intersection-over-Union (IoU) overlap between anchors and ground truth boxes, to assign classification and regression targets to each anchor, these target labels are used for training retinanet. Every anchor is assigned with a length num_classes one-hot vector of classification targets, and a 4-vector of box regression targets. The assignment rules are as followed:

1. Anchors are assigned to ground-truth boxes when: (i) it has the highest IoU overlap with a ground-truth box, or (ii) it has an IoU overlap higher than positive_overlap(0.5) with any ground-truth box.

2. Anchors are assigned to background when its IoU ratio is lower than negative_overlap (0.4) for all ground-truth boxes.

When an anchor is assigned with a ground-truth box which is the i-th category, the i-th entry in its C vector of targets is set to 1 and all other entries are set to 0. When an anchor is assigned with background, all entries are set to 0. Anchors that are not assigned do not contribute to the training objective. The regression targets are the encoded ground-truth boxes associated with the assigned anchors.

Parameters:
  • bbox_pred (Variable) – A 3-D Tensor with shape [N, M, 4] represents the predicted locations of M bounding bboxes. N is the batch size, and each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax].
  • cls_logits (Variable) – A 3-D Tensor with shape [N, M, C] represents the predicted confidence predictions. N is the batch size, C is the number of classes (excluding background), M is number of bounding boxes.
  • anchor_box (Variable) – A 2-D Tensor with shape [M, 4] holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate of the anchor box, if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the anchor box.
  • anchor_var (Variable) – A 2-D Tensor with shape [M,4] holds expanded variances of anchors.
  • gt_boxes (Variable) – The ground-truth bounding boxes (bboxes) are a 2D LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth bboxes of mini-batch input.
  • gt_labels (variable) – The ground-truth labels are a 2D LoDTensor with shape [Ng, 1], Ng is the total number of ground-truth labels of mini-batch input.
  • is_crowd (Variable) – A 1-D LoDTensor which indicates ground-truth is crowd.
  • im_info (Variable) – A 2-D LoDTensor with shape [N, 3]. N is the batch size, 3 is the height, width and scale.
  • num_classes (int32) – The number of classes.
  • positive_overlap (float) – Minimum overlap required between an anchor and ground-truth box for the (anchor, gt box) pair to be a positive example.
  • negative_overlap (float) – Maximum overlap allowed between an anchor and ground-truth box for the (anchor, gt box) pair to be a negative examples.
Returns:

A tuple(predicted_scores, predicted_location, target_label, target_bbox, bbox_inside_weight, fg_num) is returned. The predicted_scores and predicted_location are the predicted result of the retinanet.The target_label and target_bbox are the ground truth, respectively. The predicted_location is a 2D Tensor with shape [F, 4], and the shape of target_bbox is same as the shape of the predicted_location, F is the number of the foreground anchors. The predicted_scores is a 2D Tensor with shape [F + B, C], and the shape of target_label is [F + B, 1], B is the number of the background anchors, the F and B is depends on the input of this operator. Bbox_inside_weight represents whether the predicted location is fake foreground or not and the shape is [F, 4]. Fg_num is the foreground number (including fake foreground) which is needed by focal loss.

Return type:

tuple

Examples

import paddle.fluid as fluid
bbox_pred = layers.data(name='bbox_pred', shape=[1, 100, 4],
                  append_batch_size=False, dtype='float32')
cls_logits = layers.data(name='cls_logits', shape=[1, 100, 10],
                  append_batch_size=False, dtype='float32')
anchor_box = layers.data(name='anchor_box', shape=[100, 4],
                  append_batch_size=False, dtype='float32')
anchor_var = layers.data(name='anchor_var', shape=[100, 4],
                  append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
                  append_batch_size=False, dtype='float32')
gt_labels = layers.data(name='gt_labels', shape=[10, 1],
                  append_batch_size=False, dtype='float32')
is_crowd = fluid.layers.data(name='is_crowd', shape=[1],
                  append_batch_size=False, dtype='float32')
im_info = fluid.layers.data(name='im_infoss', shape=[1, 3],
                  append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight, fg_num =
      fluid.layers.retinanet_target_assign(bbox_pred, cls_logits, anchor_box,
      anchor_var, gt_boxes, gt_labels, is_crowd, im_info, 10)

roi_perspective_transform

paddle.fluid.layers.roi_perspective_transform(input, rois, transformed_height, transformed_width, spatial_scale=1.0)[source]

ROI perspective transform op.

Parameters:
  • input (Variable) – The input of ROIPerspectiveTransformOp. The format of input tensor is NCHW. Where N is batch size, C is the number of input channels, H is the height of the feature, and W is the width of the feature.
  • rois (Variable) – ROIs (Regions of Interest) to be transformed. It should be a 2-D LoDTensor of shape (num_rois, 8). Given as [[x1, y1, x2, y2, x3, y3, x4, y4], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the top right coordinates, and (x3, y3) is the bottom right coordinates, and (x4, y4) is the bottom left coordinates.
  • transformed_height (integer) – The height of transformed output.
  • transformed_width (integer) – The width of transformed output.
  • spatial_scale (float) – Spatial scale factor to scale ROI coords. Default: 1.0
Returns:

The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape

(num_rois, channels, transformed_h, transformed_w).

Return type:

Variable

Examples

import paddle.fluid as fluid

x = fluid.layers.data(name='x', shape=[256, 28, 28], dtype='float32')
rois = fluid.layers.data(name='rois', shape=[8], lod_level=1, dtype='float32')
out = fluid.layers.roi_perspective_transform(x, rois, 7, 7, 1.0)

rpn_target_assign

paddle.fluid.layers.rpn_target_assign(bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, is_crowd, im_info, rpn_batch_size_per_im=256, rpn_straddle_thresh=0.0, rpn_fg_fraction=0.5, rpn_positive_overlap=0.7, rpn_negative_overlap=0.3, use_random=True)[source]

Target Assign Layer for region proposal network (RPN) in Faster-RCNN detection.

This layer can be, for given the Intersection-over-Union (IoU) overlap between anchors and ground truth boxes, to assign classification and regression targets to each each anchor, these target labels are used for train RPN. The classification targets is a binary class label (of being an object or not). Following the paper of Faster-RCNN, the positive labels are two kinds of anchors: (i) the anchor/anchors with the highest IoU overlap with a ground-truth box, or (ii) an anchor that has an IoU overlap higher than rpn_positive_overlap(0.7) with any ground-truth box. Note that a single ground-truth box may assign positive labels to multiple anchors. A non-positive anchor is when its IoU ratio is lower than rpn_negative_overlap (0.3) for all ground-truth boxes. Anchors that are neither positive nor negative do not contribute to the training objective. The regression targets are the encoded ground-truth boxes associated with the positive anchors.

Parameters:
  • bbox_pred (Variable) – A 3-D Tensor with shape [N, M, 4] represents the predicted locations of M bounding bboxes. N is the batch size, and each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax].
  • cls_logits (Variable) – A 3-D Tensor with shape [N, M, 1] represents the predicted confidence predictions. N is the batch size, 1 is the frontground and background sigmoid, M is number of bounding boxes.
  • anchor_box (Variable) – A 2-D Tensor with shape [M, 4] holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate of the anchor box, if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the anchor box.
  • anchor_var (Variable) – A 2-D Tensor with shape [M,4] holds expanded variances of anchors.
  • gt_boxes (Variable) – The ground-truth bounding boxes (bboxes) are a 2D LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth bboxes of mini-batch input.
  • is_crowd (Variable) – A 1-D LoDTensor which indicates groud-truth is crowd.
  • im_info (Variable) – A 2-D LoDTensor with shape [N, 3]. N is the batch size,
  • is the height, width and scale. (3) –
  • rpn_batch_size_per_im (int) – Total number of RPN examples per image.
  • rpn_straddle_thresh (float) – Remove RPN anchors that go outside the image by straddle_thresh pixels.
  • rpn_fg_fraction (float) – Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0), 0-th class is background.
  • rpn_positive_overlap (float) – Minimum overlap required between an anchor and ground-truth box for the (anchor, gt box) pair to be a positive example.
  • rpn_negative_overlap (float) – Maximum overlap allowed between an anchor and ground-truth box for the (anchor, gt box) pair to be a negative examples.
Returns:

A tuple(predicted_scores, predicted_location, target_label, target_bbox, bbox_inside_weight) is returned. The predicted_scores and predicted_location is the predicted result of the RPN. The target_label and target_bbox is the ground truth, respectively. The predicted_location is a 2D Tensor with shape [F, 4], and the shape of target_bbox is same as the shape of the predicted_location, F is the number of the foreground anchors. The predicted_scores is a 2D Tensor with shape [F + B, 1], and the shape of target_label is same as the shape of the predicted_scores, B is the number of the background anchors, the F and B is depends on the input of this operator. Bbox_inside_weight represents whether the predicted loc is fake_fg or not and the shape is [F, 4].

Return type:

tuple

Examples

import paddle.fluid as fluid
bbox_pred = fluid.layers.data(name='bbox_pred', shape=[100, 4],
                append_batch_size=False, dtype='float32')
cls_logits = fluid.layers.data(name='cls_logits', shape=[100, 1],
                append_batch_size=False, dtype='float32')
anchor_box = fluid.layers.data(name='anchor_box', shape=[20, 4],
                append_batch_size=False, dtype='float32')
anchor_var = fluid.layers.data(name='anchor_var', shape=[20, 4],
                append_batch_size=False, dtype='float32')
gt_boxes = fluid.layers.data(name='gt_boxes', shape=[10, 4],
                append_batch_size=False, dtype='float32')
is_crowd = fluid.layers.data(name='is_crowd', shape=[1],
                append_batch_size=False, dtype='float32')
im_info = fluid.layers.data(name='im_infoss', shape=[1, 3],
                append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight=
    fluid.layers.rpn_target_assign(bbox_pred, cls_logits,
    anchor_box, anchor_var, gt_boxes, is_crowd, im_info)

sigmoid_focal_loss

paddle.fluid.layers.sigmoid_focal_loss(x, label, fg_num, gamma=2, alpha=0.25)[source]

Sigmoid Focal Loss Operator.

Focal loss is used to address the foreground-background class imbalance existed on the training phase of one-stage detectors. This operator computes the sigmoid value for each element in the input tensor, after which focal loss is measured.

The focal loss is given as followed:

\[loss_j = (-label_j * alpha * {(1 - \sigma(x_j))}^{gamma} * \log(\sigma(x_j)) - (1 - labels_j) * (1 - alpha) * {(\sigma(x_j)}^{ gamma} * \log(1 - \sigma(x_j))) / fg\_num, j = 1,...,K\]

We know that

\[\sigma(x_j) = \frac{1}{1 + \exp(-x_j)}\]
Parameters:
  • x (Variable) – A 2-D tensor with shape [N, D], where N is the batch size and D is the number of classes (excluding background). This input is a tensor of logits computed by the previous operator.
  • label (Variable) – A 2-D tensor with shape [N, 1], which is the probabilistic labels.
  • fg_num (Variable) – A 1-D tensor with shape [1], which is the number of foreground.
  • gamma (float) – Hyper-parameter to balance the easy and hard examples. Default value is set to 2.0.
  • alpha (float) – Hyper-parameter to balance the positive and negative example. Default value is set to 0.25.
Returns:

A 2-D tensor with shape [N, D], which is the focal loss.

Return type:

out(Variable)

Examples

import paddle.fluid as fluid

input = fluid.layers.data(
    name='data', shape=[10,80], append_batch_size=False, dtype='float32')
label = fluid.layers.data(
    name='label', shape=[10,1], append_batch_size=False, dtype='int32')
fg_num = fluid.layers.data(
    name='fg_num', shape=[1], append_batch_size=False, dtype='int32')
loss = fluid.layers.sigmoid_focal_loss(x=input,
                                       label=label,
                                       fg_num=fg_num,
                                       gamma=2.,
                                       alpha=0.25)

ssd_loss

paddle.fluid.layers.ssd_loss(location, confidence, gt_box, gt_label, prior_box, prior_box_var=None, background_label=0, overlap_threshold=0.5, neg_pos_ratio=3.0, neg_overlap=0.5, loc_loss_weight=1.0, conf_loss_weight=1.0, match_type='per_prediction', mining_type='max_negative', normalize=True, sample_size=None)[source]

Multi-box loss layer for object detection algorithm of SSD

This layer is to compute detection loss for SSD given the location offset predictions, confidence predictions, prior boxes and ground-truth bounding boxes and labels, and the type of hard example mining. The returned loss is a weighted sum of the localization loss (or regression loss) and confidence loss (or classification loss) by performing the following steps:

  1. Find matched bounding box by bipartite matching algorithm.

1.1 Compute IOU similarity between ground-truth boxes and prior boxes.

1.2 Compute matched boundding box by bipartite matching algorithm.

  1. Compute confidence for mining hard examples

2.1. Get the target label based on matched indices.

2.2. Compute confidence loss.

  1. Apply hard example mining to get the negative example indices and update the matched indices.
  2. Assign classification and regression targets

4.1. Encoded bbox according to the prior boxes.

4.2. Assign regression targets.

4.3. Assign classification targets.

  1. Compute the overall objective loss.

5.1 Compute confidence loss.

5.1 Compute localization loss.

5.3 Compute the overall weighted loss.

Parameters:
  • location (Variable) – The location predictions are a 3D Tensor with shape [N, Np, 4], N is the batch size, Np is total number of predictions for each instance. 4 is the number of coordinate values, the layout is [xmin, ymin, xmax, ymax].
  • confidence (Variable) – The confidence predictions are a 3D Tensor with shape [N, Np, C], N and Np are the same as they are in location, C is the class number.
  • gt_box (Variable) – The ground-truth bounding boxes (bboxes) are a 2D LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth bboxes of mini-batch input.
  • gt_label (Variable) – The ground-truth labels are a 2D LoDTensor with shape [Ng, 1].
  • prior_box (Variable) – The prior boxes are a 2D Tensor with shape [Np, 4].
  • prior_box_var (Variable) – The variance of prior boxes are a 2D Tensor with shape [Np, 4].
  • background_label (int) – The index of background label, 0 by default.
  • overlap_threshold (float) –

    If match_type is ‘per_prediction’, use 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 default.
  • neg_overlap (float) – The negative overlap upper bound for the unmatched predictions. Use only when mining_type is ‘max_negative’, 0.5 by default.
  • 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 default.
  • 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 default.
  • sample_size (int) – The max sample size of negative box, used only when mining_type is ‘hard_example’.
Returns:

The weighted sum of the localization loss and confidence loss, with shape [N * Np, 1], N and Np are the same as they are in location.

Raises:

ValueError – If mining_type is ‘hard_example’, now only support mining type of max_negative.

Examples

>>> import paddle.fluid as fluid
>>> pb = fluid.layers.data(
>>>                   name='prior_box',
>>>                   shape=[10, 4],
>>>                   append_batch_size=False,
>>>                   dtype='float32')
>>> pbv = fluid.layers.data(
>>>                   name='prior_box_var',
>>>                   shape=[10, 4],
>>>                   append_batch_size=False,
>>>                   dtype='float32')
>>> loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
>>> scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
>>> gt_box = fluid.layers.data(
>>>         name='gt_box', shape=[4], lod_level=1, dtype='float32')
>>> gt_label = fluid.layers.data(
>>>         name='gt_label', shape=[1], lod_level=1, dtype='float32')
>>> loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)

target_assign

paddle.fluid.layers.target_assign(input, matched_indices, negative_indices=None, mismatch_value=None, name=None)[source]

This operator can be, for given the target bounding boxes or labels, to assign classification and regression targets to each prediction as well as weights to prediction. The weights is used to specify which prediction would not contribute to training loss.

For each instance, the output out and`out_weight` are assigned based on match_indices and negative_indices. Assumed that the row offset for each instance in input is called lod, this operator assigns classification/regression targets by performing the following steps:

  1. Assigning all outputs based on match_indices:
If id = match_indices[i][j] > 0,

    out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
    out_weight[i][j] = 1.

Otherwise,

    out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
    out_weight[i][j] = 0.
  1. Assigning out_weight based on neg_indices if neg_indices is provided:

Assumed that the row offset for each instance in neg_indices is called neg_lod, for i-th instance and each id of neg_indices in this instance:

out[i][id][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][id] = 1.0
Parameters:
  • inputs (Variable) – This input is a 3D LoDTensor with shape [M, P, K].
  • matched_indices (Variable) – Tensor<int>), The input matched indices is 2D Tenosr<int32> with shape [N, P], If MatchIndices[i][j] is -1, the j-th entity of column is not matched to any entity of row in i-th instance.
  • negative_indices (Variable) – The input negative example indices are an optional input with shape [Neg, 1] and int32 type, where Neg is the total number of negative example indices.
  • mismatch_value (float32) – Fill this value to the mismatched location.
Returns:

A tuple(out, out_weight) is returned. out is a 3D Tensor with shape [N, P, K], N and P is the same as they are in neg_indices, K is the same as it in input of X. If match_indices[i][j]. out_weight is the weight for output with the shape of [N, P, 1].

Return type:

tuple

Examples

import paddle.fluid as fluid
x = fluid.layers.data(
    name='x',
    shape=[4, 20, 4],
    dtype='float',
    lod_level=1,
    append_batch_size=False)
matched_id = fluid.layers.data(
    name='indices',
    shape=[8, 20],
    dtype='int32',
    append_batch_size=False)
trg, trg_weight = fluid.layers.target_assign(
    x,
    matched_id,
    mismatch_value=0)

yolo_box

paddle.fluid.layers.yolo_box(x, img_size, anchors, class_num, conf_thresh, downsample_ratio, name=None)[source]

This operator generates YOLO detection boxes from output of YOLOv3 network.

The output of previous network is in shape [N, C, H, W], while H and W should be the same, H and W specify the grid size, each grid point predict given number boxes, this given number, which following will be represented as S, is specified by the number of anchors. In the second dimension(the channel dimension), C should be equal to S * (5 + class_num), class_num is the object category number of source dataset(such as 80 in coco dataset), so the second(channel) dimension, apart from 4 box location coordinates x, y, w, h, also includes confidence score of the box and class one-hot key of each anchor box.

Assume the 4 location coordinates are \(t_x, t_y, t_w, t_h\), the box predictions should be as follows:

$$ b_x = \sigma(t_x) + c_x $$ $$ b_y = \sigma(t_y) + c_y $$ $$ b_w = p_w e^{t_w} $$ $$ b_h = p_h e^{t_h} $$

in the equation above, \(c_x, c_y\) is the left top corner of current grid and \(p_w, p_h\) is specified by anchors.

The logistic regression value of the 5th channel of each anchor prediction boxes represents the confidence score of each prediction box, and the logistic regression value of the last class_num channels of each anchor prediction boxes represents the classifcation scores. Boxes with confidence scores less than conf_thresh should be ignored, and box final scores is the product of confidence scores and classification scores.

$$ score_{pred} = score_{conf} * score_{class} $$

Parameters:
  • x (Variable) – The input tensor of YoloBox operator is a 4-D tensor with shape of [N, C, H, W]. The second dimension(C) stores box locations, confidence score and classification one-hot keys of each anchor box. Generally, X should be the output of YOLOv3 network
  • img_size (Variable) – The image size tensor of YoloBox operator, This is a 2-D tensor with shape of [N, 2]. This tensor holds height and width of each input image used for resizing output box in input image scale
  • anchors (list|tuple) – The anchor width and height, it will be parsed pair by pair
  • class_num (int) – The number of classes to predict
  • conf_thresh (float) – The confidence scores threshold of detection boxes. Boxes with confidence scores under threshold should be ignored
  • downsample_ratio (int) – The downsample ratio from network input to YoloBox operator input, so 32, 16, 8 should be set for the first, second, and thrid YoloBox operators
  • name (string) – the name of yolo box layer. Default None.
Returns:

A 3-D tensor with shape [N, M, 4], the coordinates of boxes, and a 3-D tensor with shape [N, M, class_num], the classification scores of boxes.

Return type:

Variable

Raises:
  • TypeError – Input x of yolov_box must be Variable
  • TypeError – Attr anchors of yolo box must be list or tuple
  • TypeError – Attr class_num of yolo box must be an integer
  • TypeError – Attr conf_thresh of yolo box must be a float number

Examples:

import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolo_box(x=x, img_size=608, class_num=80, anchors=anchors,
                                conf_thresh=0.01, downsample_ratio=32)

yolov3_loss

paddle.fluid.layers.yolov3_loss(x, gt_box, gt_label, anchors, anchor_mask, class_num, ignore_thresh, downsample_ratio, gt_score=None, use_label_smooth=True, name=None)[source]

This operator generates yolov3 loss based on given predict result and ground truth boxes.

The output of previous network is in shape [N, C, H, W], while H and W should be the same, H and W specify the grid size, each grid point predict given number bounding boxes, this given number, which following will be represented as S, is specified by the number of anchor clusters in each scale. In the second dimension(the channel dimension), C should be equal to S * (class_num + 5), class_num is the object category number of source dataset(such as 80 in coco dataset), so in the second(channel) dimension, apart from 4 box location coordinates x, y, w, h, also includes confidence score of the box and class one-hot key of each anchor box.

Assume the 4 location coordinates are \(t_x, t_y, t_w, t_h\), the box predictions should be as follows:

$$ b_x = \sigma(t_x) + c_x $$ $$ b_y = \sigma(t_y) + c_y $$ $$ b_w = p_w e^{t_w} $$ $$ b_h = p_h e^{t_h} $$

In the equation above, \(c_x, c_y\) is the left top corner of current grid and \(p_w, p_h\) is specified by anchors.

As for confidence score, it is the logistic regression value of IoU between anchor boxes and ground truth boxes, the score of the anchor box which has the max IoU should be 1, and if the anchor box has IoU bigger than ignore thresh, the confidence score loss of this anchor box will be ignored.

Therefore, the yolov3 loss consists of three major parts: box location loss, objectness loss and classification loss. The L1 loss is used for box coordinates (w, h), sigmoid cross entropy loss is used for box coordinates (x, y), objectness loss and classification loss.

Each groud truth box finds a best matching anchor box in all anchors. Prediction of this anchor box will incur all three parts of losses, and prediction of anchor boxes with no GT box matched will only incur objectness loss.

In order to trade off box coordinate losses between big boxes and small boxes, box coordinate losses will be mutiplied by scale weight, which is calculated as follows.

$$ weight_{box} = 2.0 - t_w * t_h $$

Final loss will be represented as follows.

$$ loss = (loss_{xy} + loss_{wh}) * weight_{box} + loss_{conf} + loss_{class} $$

While use_label_smooth is set to be True, the classification target will be smoothed when calculating classification loss, target of positive samples will be smoothed to \(1.0 - 1.0 / class\_num\) and target of negetive samples will be smoothed to \(1.0 / class\_num\).

While GTScore is given, which means the mixup score of ground truth boxes, all losses incured by a ground truth box will be multiplied by its mixup score.

Parameters:
  • x (Variable) – The input tensor of YOLOv3 loss operator, This is a 4-D tensor with shape of [N, C, H, W].H and W should be same, and the second dimention(C) storesbox locations, confidence score and classification one-hotkeys of each anchor box
  • gt_box (Variable) – groud truth boxes, should be in shape of [N, B, 4], in the third dimenstion, x, y, w, h should be stored. x,y is the center cordinate of boxes, w, h are the width and height, x, y, w, h should be divided by input image height to scale to [0, 1]. N is the batch number and B is the max box number in an image.
  • gt_label (Variable) – class id of ground truth boxes, shoud be in shape of [N, B].
  • anchors (list|tuple) – The anchor width and height, it will be parsed pair by pair
  • anchor_mask (list|tuple) – The mask index of anchors used in current YOLOv3 loss calculation
  • class_num (int) – The number of classes to predict
  • ignore_thresh (float) – The ignore threshold to ignore confidence loss
  • downsample_ratio (int) – The downsample ratio from network input to YOLOv3 loss input, so 32, 16, 8 should be set for the first, second, and thrid YOLOv3 loss operators
  • name (string) – the name of yolov3 loss. Default None.
  • gt_score (Variable) – mixup score of ground truth boxes, shoud be in shape of [N, B]. Default None.
  • use_label_smooth (bool) – Whether to use label smooth. Default True
Returns:

A 1-D tensor with shape [N], the value of yolov3 loss

Return type:

Variable

Raises:
  • TypeError – Input x of yolov3_loss must be Variable
  • TypeError – Input gtbox of yolov3_loss must be Variable
  • TypeError – Input gtlabel of yolov3_loss must be Variable
  • TypeError – Input gtscore of yolov3_loss must be None or Variable
  • TypeError – Attr anchors of yolov3_loss must be list or tuple
  • TypeError – Attr class_num of yolov3_loss must be an integer
  • TypeError – Attr ignore_thresh of yolov3_loss must be a float number
  • TypeError – Attr use_label_smooth of yolov3_loss must be a bool value

Examples

import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gt_box = fluid.layers.data(name='gt_box', shape=[6, 4], dtype='float32')
gt_label = fluid.layers.data(name='gt_label', shape=[6], dtype='int32')
gt_score = fluid.layers.data(name='gt_score', shape=[6], dtype='float32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label,
                                gt_score=gt_score, anchors=anchors,
                                anchor_mask=anchor_mask, class_num=80,
                                ignore_thresh=0.7, downsample_ratio=32)